1H NMR spectroscopy of human blood plasma

Progress

ELSEVIER

in Nuclear

Magnetic Resonance 27 (1995) 475-554

Spectroscopy

‘H NMR spectroscopy of human blood plasma Mika Ala-Korpela’ Uniaersity of Oulu. Department qf Physical Sciences, NMR Research Group, P.O. Box 333 FIN-90571 Oulu, Finland Received

16 September

1994

Contents

476 476 477 478 478 481 481 481 485 486 489 490 491 493 495 498 499 501 504 504 505 505 506 508 508 510 510 514 525 527 527 528 530 531 532

1. Introduction 1.1 An overview 1.2 Physical basics of NMR spectroscopy 1.3 Biochemistry of blood plasma 1.4 Lipoprotein structure and metabolism 2. Biochemical aspects 2.1 General 2.2 Resonance assignments and interpretations 2.3 Ultracentrifugation, gel filtration chromatography ‘H NMR 3. Experimental technique and considerations 4. Mathematical data analysis methods 4.1 Time domain analysis 4.1.1 HLSVD algorithm 4.1.2 Removal of water and ‘pseudo-frequency selectivity’ 4.1.3 VARPRO: a non-linear least squares fitting algorithm 4.2 Frequency domain analysis 4.2.1 FITPLAC: a non-linear least squares fitting algorithm 4.3 Chemometric techniques 4.4 Neural networks 5. Metabolite quantification 5.1 Low molecular weight metabolites 5.1 .l Use of a Hahn spin-echo sequence 5.1.2 Use of deproteinised plasma 5.1.3 Use of WATR-CPMG 5.2 Plasma lipids 5.3 Lipoproteins and their lipids 5.3.1 A linear method; an experimental approach 5.3.2 A non-linear method; a mathematical approach 5.3.3 Neural network analysis 6. Cancer detection and research 6.1 The fossel index and related studies 6.1.1 Cases of specific medical interest 6.2 Malignancy associated lipoprotein 6.3 Metabolic modifications of plasma in cancer 6.4 Applications of chemometric techniques

‘Present address: The Robert U.K. E-mail address: alakorpe

Steiner NMR Unit, Hammersmith @ phoemix.oulu.fi.

Hospital,

0079-6565/95/$29.00 0 1995 Elsevier Science B.V. All rights reserved SSDI 0079-6565(95)01013-O

Du Cane Rd., London

W12 ONN,

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7. Other biomedical applications and related lipoprotein 7.1 Metabolic applications 7.2 Measurements at 750 MHz 7.3 Interaction and complexation studies 7.4 LDL and HDL lipid peroxidation 7.5 Lipoprotein phase transitions 7.6 Lipoprotein size distribution and chemical shifts 8. Concluding remarks Acknowledgements References

studies

533 533 538 539 542 544 545 547 548 549

1. Introduction

High resolution Fourier transform nuclear magnetic resonance (FT-NMR) spectroscopy is a relatively new technique in biochemical and biomedical research. Advanced commercial NMR instruments suitable for practical spectroscopy of biological samples have been available for over a decade or so. During that period many NMR facilities have been built up both at universities and in industry and at the moment NMR spectrometers and techniques are routinely used in many kinds of biomolecular and clinical research. It is becoming apparent that NMR measurements may provide an extensive amount of significant and useful information about various biological samples. Because of the increasingly common awareness about successful NMR applications and the possibilities that NMR can offer, even more widespread use of biomedical NMR can be expected

PI. One area that has attracted increased attention in recent years in ‘HNMR spectroscopy of human blood plasma [2-6] and lipoproteins [7]. Considerable progress has been achieved both in basic research and in clinically oriented applications. It has become clear that ‘H NMR enables one to obtain a wealth of biochemically and biophysically important data from these complex biomolecular systems. The developing experimental techniques and installations have also initiated, together with increasing basic knowledge and new mathematical data analysis algorithms, a bui!d up of potentially useful quantitative biomedical and clinical applications.

I.1 An overview

A brief introduction is given to the physical basics of NMR spectroscopy, to the biochemistry of blood plasma and to lipoprotein structure and metabolism. This is to clarify the various fields of expertise needed in this field. Properties of ‘H NMR spectroscopy are then discussed in relation to the biochemical and biomedical aspects of blood plasma and lipoprotein research. After that the experimental factors of ‘H NMR are considered. Treatment of selected mathematical NMR data analysis methods is then provided. Established methods are presented in detail and some clearly promising algorithms are also introduced. The many-sided biomedical applications of ‘H NMR of plasma are considered in the latter part of the article. Metabolite quantification is treated in detail and cancer research applications are widely discussed. Throughout the article the main attention is focussed on ‘H NMR studies of blood plasma, but in closely related cases or where understanding of the plasma application demands, ‘H NMR studies of lipoproteins are also discussed. Knowledge of both the basic principles of NMR and the biochemistry of the systems (discussed in Sections 1.2-1.4) are essential for a complete understanding of Sections 2, 3 and 5-7. Section 4 is a relatively independent part containing the basic mathematics of the most useful sophisticated data analysis methods used in the ‘H NMR spectroscopic studies of plasma and lipoproteins. It is mainly designed for readers who are particularly interested in data analysis. The paragraphs at the beginning of Section 4 together with Sections 4.1, 4.1.2 and the end of Section 4.2.1 (where the

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417

properties of the lineshape fitting analysis program FITPLAC are discussed) will be of interest to all readers, but the whole of Section 4 can be skipped without detracting from the understanding of the other Sections. Indeed, in Section 53.2, where the results of Section 4 are used extensively, an intuitive (much less mathematical) approach to the situation is also given. 1.2 Physical basics of NMR spectroscopy In NMR spectroscopy [S, 91 molecules which have nuclei with a non-zero intrinsic angular momentum, i.e. nuclear spin, can be studied. In ‘H NMR, hydrogen nuclei (protons) are probed. The nuclear spin for protons is S = :. During the experiments the sample is placed in a uniform and steady strong external magnetic field B,., to split the energy levels of the spin related magnetic dipole moment c = yhS; h is the Planck’s constant (h = h/2x), S is the spin angular momentum vector and y is the gyromagnetic ratio, a characteristic constant for every nucleus. The magnetic dipole moments of the protons interact with the external magnetic field. The potential energy of this so-called Zeeman interaction is directly proportional to the magnetic flux density: Enigma” = + &J&B&,, in which z refers to the laboratory frame z-direction. Thus, the energy difference between the states ‘spin down’ and ‘spin up’ increases as the external magnetic field increases. Transitions between the energy levels are induced by electromagnetic radiation in the radiofrequency region at the magnetic fields typically used. The orbital and spin magnetic moments of the individual electrons of the molecules will also interact with the external magnetic field. In a normal diamagnetic molecule (no electronic bulk magnetic moment) a slight modification of the molecular electronic ground state results. These induced electronic magnetic moments cause an addition to the Zeeman interaction and modify the magnetic flux density experienced by an individual nucleus i: Bi = (1 - Ci)B:.l. This chemical sh$t effect causes nuclei in different chemical environments to resonate at specific frequencies; ci is called a shielding constant of nucleus i. For instance, the methyl and methylene protons of the lipoprotein lipid fatty acid chains resonate at typical different frequencies. The Zeeman interaction can also be modified because of the interaction of the magnetic moments of nearby nuclei through the magnetic moments of their electrons. This indirect spin-spin interaction may split an individual resonance and can be used as additional information for identification purposes and structural determinations. When the intermolecular interactions are too strong to allow for isotropic tumbling of the resonating nuclei (e.g. in liquid crystalline phases), the shielding constant becomes ei = a,@ + afniso, i.e. an additional term cC”‘~~” results from the anisotropy. Furthermore, the direct (through space) spin-spin interactions of ;he magnetic moments of the nuclei are no longer averaged to zero and also an extra term from the anisotropy of the indirect spin-spin coupling results [lo]. Hence, in anisotropic cases the NMR spectra are much more complex than the spectra of isotropic liquids. However, the additional information and spectral changes caused by these interactions offer supplementary information which can be used for instance in phase transition studies (see Sections 7.5 and 7.6). In addition to being able to separate and identify different molecular groups the NMR spectrum also contains information about the number of various resonating nuclei. The relationship between the intensity of an NMR resonance r of metabolite m and the experimental and sample parameters can be written as

La'

v4B:r B:,,* T

n,s,

B:f refers to the additional external magnetic field which is applied to the sample to cause transitions between the nuclear spin states and hence to create the observable macroscopic magnetisation, the time dependence of which is then detected as the free induction decay (FID) in the receiver coils of the spectrometer. T stands for the temperature, n, is the total number of contributing nuclei, and s, is

478

M. Ala-Korpela 1Progress in Nuclear Magnetic Resonance Spectroscopy 27 (I 995) 475-554

the shape function of the resonance. The concentration as cm

=

Nm-

A,

A ref

crei

of the metabolite m can thus be expressed

(2)

in which the subscript ‘ref’stands for an internal or external reference standard of known concentration and A can be regarded as the amplitude of the measured sinusoid(s) in the time domain or as the integrated intensity (area) of the resonance in the frequency domain spectrum. The NMR spectrum is obtained by discrete fast Fourier transformation (DFT or FFT) of the measured FID. When the exact number, n,,,,, of nuclei in the molecular part giving rise to the resonance I is known, N, = nrer/nmr, in which nrer is the number of resonating nuclei in reference molecules. If nmnrare unknown the proportionality constants N, need to be determined separately for each metabolite resonance to be quantified (see Section 5). 1.3 Biochemistry of blood plasma Three major compartments of water exist in the human body; plasma, interstitial fluid and intracellular fluid. Blood consists of plasma and blood cells. Plasma is in close interaction with interstitial fluid in tissues through capillary walls, which act as physical filters and passively permeate low molecular weight metabolites. On the other hand, interstitial fluid interacts with intracellular water compartments by active metabolite transport mechanisms through cell membranes. Because plasma interacts with all tissues it contains a kind of physiological average information about the biochemical situation in the body. Plasma has many functions of vital importance. It gives the blood its fluid properties and thus enables the transport of nutrients to the tissues and waste materials away from them. In addition, the appropriate composition and function of plasma are essential for energy metabolism (see Section 1.4). Plasma contains humoral antibodies (immunoglobulins) that play an important role in the immune system and hormones and enzymes that influence many functions within the body. A normal arterial plasma pH of 7.40 f 0.05 is maintained by the buffer systems. Moreover, in physiological conditions the structure and chemical composition of plasma remain in a delicate balance which is a prerequisite for the continuous proper functioning of the body. Even though plasma is mainly solvent water (over 90%), it contains plenty of compounds. Various kinds of proteins (7 to 8%) are present. They can be grouped into three major categories; fibrinogens, albumins, and globulins. Fibrinogens are large proteins which have an essential role in blood clotting. If blood is allowed to clot and the clots are removed, serum without fibrinogens is obtained. Albumins, a group of simple and small proteins, are mainly responsible for the osmotic pressure of blood and the transport of many substances such as hormones, fatty acids, drugs, and calcium. The globulins include the immunoglobulins, many glycoproteins, and various transfer proteins such as transferring for iron and lipoproteins for lipid transport. The rest of plasma (1 to 2%) consists of electrolytes with various cations (mainly Na+ and smaller amounts of K+, Ca’+, and Mg’+) and anions (mainly Cl- and HCO; , and smaller amounts of HPOi- and SOf-). Plasma also contains glucose, amino acids, uric acid, lactic acid, creatinine, etc. In fact, all the metabolites present in the body are also present in plasma, at least in trace amounts [l l-131. 1.4 Lipoprotein structure and metabolism The lipoprotein particles function as transport vehicles for the water-insoluble lipids in the human body [ 11,12, 143. They have a spherical structure with a hydrophobic core of non-polar triglyceride and cholesterol ester molecules surrounded by an amphipathic surface of apolipoproteins and phospholipids [ 15-211. Cholesterol molecules are slightly polar and enter both the core and surface regions of the lipoprotein particles [22-241. They are in fast exchange between the core and the surface on a biological time scale and behave physiologically as a single pool. On the NMR time

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419

scale, however, these two microenvironments are in slow exchange and can thus be monitored separately by using ’%ZNMR [22-241. The lipoproteins are non-covalent associates and undergo continuous metabolic processing in the circulation. The protein components of the lipoproteins are called apolipoproteins [25-271 (or just apoproteins); there are over ten different apolipoprotein components present in the lipoproteins. Since the apolipoproteins have a high tendency to form amphipathic a-helices, they are able to float among the phospholipid monolayer in the lipoprotein surface. They may function as enzyme activators and can be components of specific receptormediated endocytosis of lipoproteins. In addition to lipids and proteins, lipoproteins contain a few fat-soluble antioxidant molecules such as a-tocopherol (vitamin E), p-carotene, and ubiquinol-10 [28, 291. Lipoproteins are a very diverse group of particles. They are usually divided into five main categories: chylomicrons and very low, intermediate, low, and high density lipoproteins (VLDL, IDL, LDL, and HDL, respectively). This classification is related to the physiological and physical characteristics of the lipoproteins and their isolation by ultracentrifugation based on their density [30, 311. The general lipoprotein structure is illustrated in Fig. 1 using an LDL particle as an example [l 1, 12, 32, 333 (see also Table 1 and Section 2). Dietary lipids are transported from the intestine to the bloodstream by chylomicron particles, which are assembled in the intestinal mucosa [34-381. The triglycerides are hydrolysed by an extracellular enzyme called lipoprotein lipase (LPL), which is present in the capillary walls of many tissues. LPL is activated by the apoC-II molecule of the chylomicron surface. The released fatty acids can then be taken up by the adipose tissue (for storage) or by skeletal muscle (as energy substrate). The fatty acids can also bind to serum albumin. The cholesterol enriched chylomicron remnants are removed from the circulation by specific receptors on the liver cells. In a blood sample taken from a healthy subject after an overnight fast there are no chylomicrons present. VLDL, IDL, and LDL are a group of interrelated particles which function in the transport of endogeneous triglycerides and cholesterol from the liver to other tissues [34-381. The triglycerides synthesised in the liver are secreted into the circulation in the VLDL particles, the core of which consists mainly of triglycerides with a small amount of cholesterol esters. The predominant apolipoproteins of the VLDL particles are apoB-100 and apoE. The VLDL triglycerides are hydrolysed in the capillaries of adipose tissue or skeletal muscle through the action of LPL as in the case of chylomicrons. The VLDL remnants are decreased in size, enriched in cholesterol esters and retain both apolipoprotein components are decreased in size, enriched in choleterol esters and retain both apolipoprotein components apoB-100 and apoE; they are now called IDL particles. In humans, about half of the circulating IDL particles are removed quickly, within 2-6 h, by LDL receptors in the liver cells, due to the high affinity of the IDL apoE molecules for these receptors. The LDL receptors are trans-membrane glycoproteins that bind specifically to apoB-100 and apoE. The other half of the IDL particles remain in the circulation much longer (about two and a half days) and develop into apoB-100 containing LDL particles, which play a central role in cholesterol transport; they deliver cholesterol to the tissues [33,39-431. The apoB-100 molecules of the LDL particles are recognised by the LDL receptors of tissue and liver cells, leading to endocytosis of whole LDL particles into the cells. This process is known as receptor-mediated endocytosis [44]. Newly formed HDL particles appear as spherical particles containing neutral lipids or as discoidal particles of apolipoproteins and phospholipids [45]. The mature spherical particles are secreted from the liver or intestinal cells, but the discoidal ones are probably formed extracellularly. HDL particles are the key components in the reverse cholesterol transport, the process in which cholesterol is transported from extrahepatic tissues to the liver [45-SO]. They function as a receptacle for excess phospholipids and cholesterol derived from the membranes of tissue cells or from the surfaces of other (degrading) lipoprotein particles. The HDL particles are also a reservoir for apoE and apoC. The flow of cholesterol to the HDL particles is strongly coupled to its esterification by the lecithin-cholesterol acyltransferase (LCAT) enzyme, the action of which transfers an acyl chain (often linoleic acid residue) from the C(2) of phosphatidylcholine to the cholesterol molecule with the concomitant formation of lysolecithin. The cholesterol esters enter the core of the HDL particles as apolar molecules (when cholesterol esters accumulate in discoidal particles, the latter adapt the

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Fig. 1. A schematic structure [ll, 12, 15-17,21-24, 32, 33,413 of a low density lipoprotein (LDL) particle. Dark greyish serpentine at the surface monolayer represents the apolipoprotein B-100 molecule (molecular weight x 550 kD). Open circles indicate the phospholipid and black circles the cholesterol molecules ( z 700 and 2 400 molecules, respectively) at the surface. In the core region of the particle the cholesterol molecules are depicted by black ellipses, the cholesterol ester molecules as open ellipses with a tail and the triglyceride molecules are the three-tail features ( x 200, 2 1500 and z 200 molecules, respectively).

Table 1 Characteristics

of the major

Lipoprotein

categories

Lipoprotein

of human

blood plasma

lipoproteins

[ll,

12, 333

category

property Chylomicrons Density (g cn- 3, Radius (A) Apolipoprotein content (wt. %) main components Lipids (wt. %) [33] triglycerides phospholipids cholesterol cholesterol esters apolipoprotein

< 0.95 400-2500 2-3 B48, CI-III, E

VLDL 0.95-1.006 150-400 5-10 B100, CI-III, E 39 17 7 13 17

IDL 1.00661.019 1255175 15-20 BlOO, CIII, E

27 26 8 23 16

LDL

HDL

1.019-1.063 90-140

1.063-1.210 25-60

2G-25 BlOO

4G55 AI, AH, E,

(ape(a))

(ape(a))

6 24 9 41 21

3 24 3 21 49

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spherical shape). Cholesterol esters accumulated in HDL particles are further transferred to apoB-containing lipoproteins by cholesterol ester transfer protein (CETP) and the apoB-containing particles are subsequently taken up by their receptors in the liver. There are also indications that specific HDL receptors might exist in the liver cells [Sl]. LDL and HDL particles are the key components of cholesterol transport. Disorders in their metabolism are critical to the development of atherosclerosis and coronary heart disease (CHD), the leading causes of death in the U.S.A. and many other western countries [32, 39, 52, 533. The development of CHD is in clear positive correlation with plasma LDL cholesterol level and in clear negative correlation with plasma HDL cholesterol level [54]. In the prevention of CHD knowledge of both the LDL and HDL cholesterol levels are therefore necessary in order to find persons at increased risk (see Sections 2.3 and 5.3). There is also evidence that certain modified forms of LDL, e.g. oxidatively modified LDL, play a crucial role in the development of atherosclerosis [28, 551 (see Section 7.4).

2. Biochemical aspects 2.1 General

Proper handling of the molecular complexity and heterogeneity of blood plasma in traditional biochemical analysis techniques normally means concentrating attention on special metabolites. In chromatographic methods, for example, an exact preselection of substances to be investigated is necessary for a proper choice of the instrumental specifications. After this physical separation of the desired metabolites further chemical assays, e.g. using enzymatic calorimetric methods, are often necessary in order to quantify the results. In biochemical analysis procedures the natural chemical equilibria of the samples are destroyed. This includes decomposition of macromolecular complexes such as lipoproteins. When ‘H NMR spectroscopy is used, the above mentioned problems are avoided. Only a little or no pretreatment of a sample is required before the ‘H NMR measurements. Typically, the venous blood sample is taken into a tube which contains ethylenediaminetetraacetic acid (EDTA) or heparin as an anticoagulant. The tube is centrifuged at z 1200 x g for 15-20 min to separate the plasma from the blood cells and platelets. Separated plasma can then be directly pipetted into an NMR sample tube and the measurements can be performed. A normal 1-D ‘H NMR measurement can be completed in a few minutes; no preselection of the metabolites of interest is necessary. One routine measurement can thus give quantitative information concerning both the low molecular weight metabolites and the macromolecules. Moreover, NMR is a non-destructive method, and it is possible to use the sample after NMR measurements in other biochemical assays if necessary. The ‘H NMR measurements also offer good possibilities to study induced metabolic processes in intact plasma or lipoproteins in vitro (see Sections 7.3 and 7.4). The amount of sample needed ranges from about 0.3 ml to several millilitre depending on the metabolite concentrations. 32 FIDs collected in about 3 min from a 5 mm outer diameter NMR sample tube containing 0.5 ml plasma will yield a good quality spectrum. 2.2 Resonance

assignments

and interpretations

Almost every molecule in plasma contains hydrogen. In principle this could mean that signals from all plasma metabolites would be observed in the ‘H NMR spectrum. Fortunately, this is not the case. There are dynamical limitations which reduce the complexity of the spectra. The electromagnetic interactions that cause the resonating nuclei to return to equilibrium after the excitation pulse are most effective for relatively immobile protons and as a consequence their resonances are broad, and can be lost in background noise. The protons of proteins are of this kind, that is they relax so fast that their contributions to the measured FID are often negligible. This means that many

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proteins, and also low molecular weight metabolites bound to proteins, give very broad weak signals which are often undetectable. The aliphatic regions (from 0.2-3.7 ppm from the external trimethylsilylpropionate, TSP, reference) of ‘H NMR spectra of plasma and VLDL, LDL and HDL fractions of a healthy adult volunteer are shown in Fig. 2 together with the resonance assignments [2, 4, 56-651. Different mobile portions of the lipoprotein lipids are seen to give rise to clear and intense signals which dominate the plasma spectrum. These signals in the plasma spectra are therefore complex composite resonances consisting of signals from all the abundant lipoprotein fractions. Also other clear signals in the aliphatic region are assigned in Fig. 2 (see below). In addition, the olefinic -HC = CH- protons resonate at 5.2 ppm and the water protons at 4.7 ppm. In the aromatic region (chemical shifts > 5.3 ppm) there are normally no clear signals. The lipid resonances (Ll to L6 in Fig. 2) of the lipoprotein fractions are clearly asymmetric. This indicates different chemical environments and/or coupling interactions of the lipoprotein lipid protons which are responsible for the proton NMR signals. Mathematical decompositions of the methyl CH3 and methylene ((CH,-), resonance regions in the ‘H NMR spectra of the VLDL, LDL and HDL fractions are shown in Fig. 3. For all spectra in each lipoprotein category the component structures of the methyl and methylene regions have been found to be similar, though not identical [66,67]. There is also a certain consistency (e.g. in the relative chemical shifts of the components) in all the component solutions. These findings, together with the different relative integrated intensities of the components in each lipoprotein category, have led to attempts to identify the different lipid molecules that might give rise to these ‘H NMR signals in the methylene region [66]. Because the (-CH,-), resonances arise from the protons in the fatty acid hydrocarbon chains of the lipoprotein lipids, the hypothesis to be tested was that each of the lipid classes having a hydrocarbon chain (i.e. phospholipid, triglyceride and cholesterol ester molecules) would contribute to the combined resonance and might be responsible for a specific component. The efforts to find a simple analogy between the biochemical analyses and the component structures of the NMR analyses were not successful, however [66]. In fact, this is not surprising since there may be many contributing factors which may hamper such efforts. These might include similarity of the chemical environments of the resonating protons in all fatty acid chains and overlapping resonances from subfractions of the major lipoprotein fractions. The resonances of a particular molecular species may also originate from chemically distinct sites (two or more) and there might be an intramolecular distribution of -CH2- chemical shifts. Hence the signals arising from different molecular species could overlap so heavily that the limited useful information available in the experimental spectra would not be adequate to allow separation of the molecular specific components; in this case the component structures of the lipoprotein fraction spectra would just represent a mathematical solution without any exact physical or chemical meaning. However, the lineshape models based on the component structures can of course be used efficiently in other applications such as lipoprotein quantification [67] (see Section 5.3.2). The situation is similar for the methyl -CH3 resonances. The intensity of the NMR signals also depends on the mobility of the resonating nuclei, which causes more difficulties for the characterisation of the resonances, especially in the LDL particles where the phase of the core cholesterol esters and triglycerides is strongly temperature dependent [68-721. The micellar structure of the lipoprotein particles also imposes mobility and organisation constraints especially for the smallest particles. Because of all the above problems, more information about the molecular structures of the lipoprotein particles in connection to NMR measurements would be needed in order to achieve more specific characterisations of the modelled resonances. Although several proteins are present in high concentrations in plasma, only a few protein-related resonances are observed in the ‘H NMR spectra. The albumin-bound immobile fatty acids and the ‘semi-mobile’ protons of albumin itself give rise to a broad resonance covering a large portion (from 0.5-2.5 ppm) of the aliphatic region [73]. Two narrow resonances (around 2.0 ppm) from the N-acetyl groups of mobile carbohydrate side-chains (largely N-acetylglucosamine and N-acetylneuraminic acid) of glycoproteins are clearly observed [63]. If is also possible that the resonance of the methylene protons of the C(3) carbon -CH2CH2COOC- (around 1.5 ppm) contains a contribution from proteins. Furthermore, on the high frequency side (around 0.9 ppm) of the methyl

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E6

3.5

3.0

2.5

210

1.5

1 .o

0:5

Fig. 2. The aliphatic region (from 0.2 to 3.7 ppm from the external TSP reference) of ‘H NMR spectra of plasma and the very low, low and high density lipoprotein fractions (VLDL, LDL and HDL, respectively) of a healthy adult. The spectra are measured at 37°C on a Jeol JNM-GX400 FT NMR spectrometer. A double tube system was used: a sealed external reference tube (outer diameter 5 mm) containing the reference and locking substances (TSP 4 mmoll- ‘, MnS04 0.3 mmoll-’ in 99.8% D,O) was placed coaxially into the NMR sample tube (outer diameter 10 mm) containing the actual sample (2.5 ml). In each experiment a binomial 1 - i pulse sequence was applied to suppress the water signal. The zero excitation was set on the water resonance frequency and the maximum excitations at f 3.8 ppm from that. 256 FID signals were accumulated using a spectral width of 5 kHz, 64k data points, 45” pulses of 28-33 ps and a pulse repetition time of 6.6 s. The spectra are scaled to the area of the reference peak and in addition, the VLDL, LDL and HDL fractions are multiplied by 2. The most distinct resonances are assigned as follows: [2,4, 56651 lipid hydrocarbon resonances; terminal methyl protons -CM, (Ll) (may contain a contribution from the cholesterol backbone -C(26)& and C(27)& methyl protons), methylene protons (C&). (L2) (also CH3<]H2-CH2- and CH3-CHZ-CH2-CH2-), methylene protons of the C(3) carbon -CH,CHzCOOC(L3) (protein contribution is possible), allylic methylene protons (-CH2-).CH2CH = (L4), methylene protons of the C(2) carbon -CH2-CH2-COOC(L5), methylene protons between the olefinic groups CH = CH-CH2-CH =CH- (L6), phospholipid choline headgroup resonances; methyl -N(Clj,), (Pl) and methylene -O-CH,-CIj-Nprotons (P2), cholesterol backbone resonances; -C(lS)FI, (Cl), C(21)& (C2) and -C(19)H, (C3) methyl protons, anticoagulant EDTA resonances; ethylenic protons -N-Clj-C&-Nof CaEDTA (El), MgEDTA (E2) and EDTA (E4) and acetate protons -CH,xO; of CaEDTA (E3), MgEDTA (E5) and EDTA (E6) (signals of the MgEDTA acetate protons, the ethylenic EDTA protons and the choline methyl protons overlap), resonances of N-a&y1 protons of mobile N-acetylated carbohydrate side-chains of glycoproteins; mainly N-acetylglucosamine (gl) and N-acetylneuraminic acid (sialic acid) (g2), other resonances;
resonance of the lipoprotein lipids there is a relatively broad signal assigned to serum proteins [64]. In the aromatic region (around 6.5-8.0 ppm) another broad unresolvable resonance appears. Some resonances of low molecular weight metabolites are absent from the ‘H NMR spectrum of plasma, even though their concentrations in plasma are known to exceed the detection limit of about 50-100 PM. In the case of the aromatic amino acids, tyrosine and phenylalanine, their binding to the

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Fig. 3. Mathematical lineshape models with the individual Lorentzian components for the lipoprotein lipid hydrocarbon methyl CHs and methylene (-CH,-). resonances in the ‘H NMR spectra of VLDL, LDL and HDL fractions. These components show only small variations in different experimental spectra and are calculated as an average of the results from the lineshape fitting analyses of several spectra of each lipoprotein fraction using the program FITPLAC [66, 671.

hydrophobic domain of plasma albumin causes the disappearance of their sharp resonances from the ‘H spectrum [74]. There is also evidence of similar behaviour with histidine molecules [74]. In the case of lactate a marked underestimation has been reported in spin-echo ‘H NMR assays when the results are compared to conventional chemical assays [3,75]. This underestimation has been attributed to the binding of lactate to transferrin, a-1-antitrypsin and possibly other plasma proteins. However, for alanine and valine the NMR measurements agree well with the biochemical amino acid quantification procedures [60]. The ‘H NMR-based quantification of low molecular weight metabolites of plasma is considered in Section 5.1. Because of the characteristic electromagnetic interactions of the hydrogen nuclei in different molecules or molecular groups (-CH, AX-, -CH3), identification of unknown metabolites or substances is possible, based on the chemical shift and spin-spin coupling patterns of the ‘H NMR resonances. This is certainly a great advantage allowing for unexpected and new findings, particularly since the NMR measurements can be performed with the sample in chemical equilibrium. This also allows examination of molecular mobilities and intermolecular interactions, which is an

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additional attractive advantage of NMR spectroscopy. Thus, in addition to the quantitative information obtainable from all kinds of metabolites, it is also possible to study phase transitions, lipid peroxidation, binding and interaction phenomena (see Sections 7.3-7.6). Moreover, 2-D chemical shift correlated NMR spectroscopic experiments (‘H-‘H or ‘H-‘3CCOSY) can produce spectra from which information about couplings between different nuclei can be obtained [61] (for details of the technique see, for example, Ref. [76]). Even though the ‘H NMR spectra of blood plasma and lipoproteins are complex and consist of many overlapping resonances, it should be kept in mind that the situation is much easier to handle than in the case of whole blood or other cell or tissue samples [4]. Utilisation of sophisticated mathematical data analysis methods has enabled specific quantifications and clarified the seemingly miscellaneous information in the ‘H NMR spectra of plasma [65-671. Recent developments have further increased the expectations that the vast NMR-based molecular information can be applied to study biophysical and metabolic processes in intact plasma and lipoproteins, and that this information could be used to provide additional understanding of the biochemistry of pathological metabolism (Sections 557). 2.3 Ultracentrijkgation,

gel jiltration chromatography and ‘HNMR

There are practical difficulties associated with the ultracentrifugational isolation of the lipoproteins. Even isolation of the main categories VLDL, IDL, LDL, and HDL from a plasma sample will take about one week requiring frequent handling of the sample including tube slicing and adjustment of the density by additions of salt solutions [30,31]. After this physical separation of the different particle categories, specific enzymatic assays are needed to estimate their compositions further [66,67]. This kind of technique is at present the most precise and versatile method for lipoprotein quantification. Even though laborious and expensive, it is in general use because various characteristics of different lipoprotein categories are important in disease diagnostics and epidemiological studies. A limited and less accurate, but relatively low-cost method to estimate the LDL cholesterol level is to apply the Friedewald approximation [77-813 in which the LDL cholesterol is calculated from the HDL cholesterol and plasma total cholesterol and total triglycerides as LDL CHO = PLASMA CHO - HDL CHO -

PLASMA TG 2.2

in which all concentrations are in millimol l- ‘. The HDL cholesterol is estimated from the selectively precipitated HDL fraction. As a challenging alternative, ‘H NMR-based lipoprotein quantification methods have been introduced and shown to have characteristics which are expected to be of considerable importance in this area [66, 67, 82, 831 (see Section 5.3). Isolation by ultracentrifugation is generally used to obtain compositional, structural and metabolic information from the lipoprotein particles both in medical and NMR spectroscopic studies. However, it is known that the lipoprotein particles are not clearly distinct groups of particles but rather form a heterogeneous continuum of particles differing in size and density as well as in lipid and protein composition and function [84]. Hence much benefit is expected from introducing gel filtration chromatographic isolation of the lipoprotein particles prior to the NMR spectroscopic studies [SS-901. By combining ultracentrifugation and gel filtration, a faster and more accurate and homogeneous separation of the lipoprotein particles is achieved than when ultracentrifugation is used alone. The full lipoprotein density range ( < 1.210 g cme3) can be isolated from plasma by a single ultracentrifugation step in 48 h. The lipoproteins can then be gel filtered to a few dozen size-specific samples in several hours. Moreover, structural modifications of the lipoproteins caused by the very high g-forces ( > 100000 x g) and the high salt concentrations used in ultracentrifugation are avoided if gel filtration is applied alone. This might be a preferable approach especially in some structural and dynamical NMR studies of lipoprofein particles.

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yk_-“-: PPM 3.5

3.0

2.5

2.0

1.5

1.0

0.5

Fig. 4. The aliphatic region (from 0.4 to 3.7 ppm from the external TSP reference) of 400 MHz ‘H NMR spectra of lipoprotein particles isolated using gel filtration. The experimental details were close to those given in Fig. 2. The spectra are scaled to the area of the reference peak. The resonance assignments are as in Fig. 2. The approximated radii of the lipoprotein particles are 24.7 nm (FR22), 19.9 nm (FR26), 10.6 nm (FR33), 6.4 nm

(FR37) and 3.5 nm (FR41). 10 ml of freshly obtained fasting plasma were added to a Sepharose 4B column with total bed volume of 180 ml and eluted with 0.15 M NaCl in water containing 1 mM EDTA, pH 7.4,at a flow rate of 27.4 ml h-l. Collection time of each 4.57 ml fraction was 10 min 1911.

Better resolution of all the lipoprotein lipid resonances is observed in the ‘H NMR spectra of gel filtered samples than in those of the ultracentrifuged ones [91]. As an illustration several resonances

from the aliphatic regions of the ‘H NMR spectra of lipoprotein samples isolated using gel filtration chromatography are shown in Fig. 4 (compare with Fig. 2). These procedures will enable more specific studies of molecular mobilities and phase transitions than with the conventional ultracentrifugation method. They will also allow for more specific ‘H NMR studies of lipoprotein related diseases. The total effort needed in these lipoprotein isolation protocols is also decreased, especially when gel filtration is used alone, but increased number and decreased concentrations of the samples cause a moderate increase in the duration of the NMR experiments. If gel filtration is used alone, some problems may also arise due to the elution of other plasma proteins, especially albumin, with the smallest lipoprotein particles.

3. Experimental techniques and considerations According to Eq. (1) there are some important experimental aspects to be considered. The gyromagnetic ratio of a proton, y“, is favourably large and its natural abundance is 99.985%. Also essentially all important metabolites contain protons. Thus, the proton is a nucleus that is inherently

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very sensitive to detection. The sensitivity of an NMR experiment can be increased instrumentally by increasing the magnetic flux density of the external magnetic field. This will also increase the chemical shift separation of the resonances making detection and quantification of different metabolites easier than in lower field. However, the inherent chemical shift range of protons is quite narrow ( Z 10 ppm) and the biological systems contain many compounds with similar functional groups. This leads, in practice, to inevitable overlap of different proton resonances. As discussed in Section 2.2, the situation is indeed very complex for the composite methyl and methylene resonances of the lipoprotein lipids in the ‘HNMR spectra of plasma. As illustrated in Figs. 2 and 3, these signals are at first composite overlapping resonances of the different lipoproteins VLDL, LDL and HDL and further, the individual lipoprotein signals are overlapping resonances of different methyl and methylene groups in the hydrocarbon chains of the constituent lipids. Biomolecules and biological processes occur naturally in a water environment; over 90% of plasma is solvent water. The concentration of water protons is thus about 100 M. Because the dynamic range of the analogue-to-digital converters (ACDs) of the spectrometers is limited, considerable problems arise when information about metabolites present in millimolar concentrations in water is to be obtained. Even though the new 16 bit ADCs offer a sixteenfold increase in the dynamic range compared to the standard 12 bit ADCs, application of a special water suppression technique is needed to properly detect the small metabolite resonances. As this is really a crucial aspect in ‘H NMR of biological samples, many different water suppression techniques have been developed [4,92]. Some standard experimental methods, which work sufficiently well in the case of plasma and lipoproteins, are available on commercial NMR spectrometers. The experimental elimination of the water resonance can be achieved, for instance, by using a special pulse sequence which either creates minimal detectable transverse magnetisation of the water protons or does not excite the water protons at all. The destruction of z-magnetisation by presaturation of the water resonance is an example of the first approach and the use of binomial pulse sequences an example of the latter. In the presaturation method a weak r.f. field at the water resonance frequency is applied to the sample to induce a uniform distribution of the spin states of the water protons. This considerably attenuates the transverse magnetisation of the water protons created by the actual detection pulse and thereby attenuates the observable water signal. The binomial pulse sequences consist of equally spaced pulses with lengths as ratios of binomial coefficients and phases alternating between 0 and 180”. If the zero-excitation of the binomial pulse sequence is set on the water resonance frequency, there is ideally no observable magnetisation from the water protons. The extent of the zero-excitation is different in different binomial pulse sequences as is the width of the uniform excitation area. The 1-i and 1-3-3-i pulse sequences for example are easy to use and can provide more than 103-fold suppression of the water resonance [93]. In modern high field NMR spectrometers equipped with superconducting magnets, the magnetic flux density ranges from 4.7 to 17.6 T corresponding to proton resonance frequencies from 200 to 750 MHz, respectively. Recently a study of human blood plasma applying a 750 MHz instrument has been published [94] (see Section 7.2). An additional advantage of high field instruments is that the water signal can be more easily attenuated since the water protons resonate further away from the metabolite resonances of interest. Although a remarkable increase in the sensitivity and chemical shift dispersion is achieved using higher field instruments, many applications can be properly carried out at lower frequencies (200~-300 MHz). The ‘H NMR spectra of plasma (and lipoproteins) are complex regardless of the field strength used (Section 2.2). Accurate NMR-based quantification requires (Eqs. (1) and (2)) precise knowledge of the amplitudes (in the time domain) or areas (in the frequency domain) of the metabolite resonances. A common procedure is to carry out the FFT of the collected FID and to integrate the metabolite resonances of interest by applying the standard integration routines. Especially for ‘HNMR spectra of plasma and lipoproteins, where the signal overlap is remarkable and protein and residual water resonances cause complicated backgrounds, this method is unreliable and inaccurate (and often unfeasible). For instance specification of the integration limits and baseline is very difficult and operator biased. Use of sophisticated mathematical data analysis methods can improve accuracy and allow reliable quantitative information to be obtained (Sections 4 and 5).

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Even after using an instrumental water suppression technique as described above, the residual water signal often markedly interferes with the metabolite quantification. The time domain SVD-based analysis for mathematical removal of the water resonance is an elegant solution to this problem (see Section 4.1.2). Using spin-echo methods, the baseline distortions caused by the immobile protons of the proteins and albumin-bound fatty acids could be reduced in order to reveal low molecular weight metabolites. This is based on much faster spin-spin relaxation for the immobile protons compared with the mobile protons. The two commonly applied spin-echo pulse sequences [3,4, S] are the Hahn echo 90;~t-180+FID and the Carr-PurcelllMeiboom-Gill 90$+180;-r),-FID

(4) (CPMG) echo (5)

The Hahn echo gives rise to spin-spin coupling related phase modulation of the resonances, which can be used to assist in signal assignments but on the other hand tends to complicate the spectrum and its quantification. The CPMG sequence eliminates spin-diffusion and therefore no phase modulations occur. This means that ‘normal’ type spectra with variably reduced immobile components can be recorded. It has been found that a spin-echo time of 120 ms is usually sufficient to attenuate the broad underlying resonances to such an extent that resonances from low molecular weight metabolites can be detected without interference [3, 43. These pulses will also slightly attenuate the water resonance, but normally specific elimination is still necessary, e.g. by applying continuous secondary irradiation or selective presaturation at the water frequency. Quantification of low molecular weight metabolites from serum using ‘H NMR and the Hahn spin-echo pulse sequence is dealt with in Section 5.1.1. A disadvantage of spin-echo sequences is that detection of ‘semi-mobile’ molecular groups is severely impeded. Hence a lot of important and possibly clinically relevant information is lost in the spin-echo experiments. This will be the situation for many resonances of the lipoprotein lipids, particularly in the smaller lipoprotein particles LDL and HDL, where the structural mobility constraints and liquid crystal phases can cause notable reduction in molecular mobility. In hypercholesterolaemic patients phase transitions have also been observed in VLDL particles [3]. Use of a single-pulse sequence and application of sophisticated data analysis methods would thus be a recommended combination in order to obtain detailed knowledge about lipoprotein lipids. During NMR experiments it is necessary to adjust the magnetic field homogeneity in the sample volume and also to maintain field-frequency stability. The deuterium signal is normally used to achieve these regulations in ‘H NMR spectroscopy of plasma and lipoproteins. Adequate signal intensity (natural abundance of deuterium is 0.015%) is often achieved by adding DzO ( z 5-10 ~01%) directly to the sample. Double tube systems can be used to avoid adding D,O and/or concentration standards such as formate or TSP directly to the sample [67,82,95]. Another motivation for the use of D20 is to avoid water suppression; if a sufficient number of Hz0 molecules can be exchanged for DzO molecules, the intensity of the water resonance will naturally be attenuated. The replacement of HZ0 with D20 can be achieved by using gel filtration chromatography, which at the same time can also be used to remove low and medium molecular weight metabolites from plasma [89]. Both the storage conditions and the measurement temperatures of plasma samples are worthy of attention. It has been noted that freezing and thawing of plasma lead to biochemical changes that cause broadening of the methylene and methyl resonance regions in the ‘HNMR spectra [96]. Storage at 4°C for longer than six days has also given rise to changes in these resonances notably for plasma with high triglyceride concentrations [82]. Hence it is recommended that the NMR measurements should be carried out without delay after blood collection. The samples should be stored at 4°C and freezing ought to be strictly avoided. It should be remembered that signal intensities might be temperature dependent. This is clearly so for LDL particles in which core cholesterol esters and triglycerides undergo phase transitions [68-72, 821. Hence, the relative

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ZL mm I

2.2

489

-

4%

’ I

2.0

’ I

1.8

- I

1.6

-,

1.4

.I

.I

1.2

.I.,

1.0

0.8

0.6

.

,

0.4

.-

Fig. 5. Part of the aliphatic region of the 400 MHz ‘H NMR spectrum of a plasma sample at different temperatures. The experimental details and resonance assignments are as in Fig. 2. The-spectra are scaled to the

area of reference peak. (See also Fig. 10.)

contributions of the lipoprotein fractions to the plasma spectra depend on the temperature; at temperatures around 20°C the LDL (and HDL) resonances are much weaker than at the physiological temperature of 37°C. This is illustrated in Fig. 5 (see also Sections 5.3 and 7.5 and Fig. 10). Recently the importance of knowing the exact internal temperature of a biofluid sample has also been emphasised by Lindon and co-workers [97,98]. A general method for temperature calibration of human blood plasma (and cerebrospinal fluid) samples inside a high resolution NMR spectrometer has been presented based on the temperature dependence of the chemical shift difference between the water signal and that from the H-l proton of endogenous a-glucose (or in some circumstances fi-glucose) [98].

4. Mathematical

data analysis methods

At the moment only a few studies have been published in which mathematical data analyses are applied to the results of ‘H NMR experiments of plasma or lipoproteins. These studies have been promising and indicate that incorporation of sophisticated data analysis algorithms considerably increases the obtainable quantitative biochemical information. Difficulties such as a dominating residual water signal and heavily overlapping broad resonances can be resolved by using proper mathematical analyses. In this Section established algorithms are discussed in detail and some clearly promising methods are introduced as well. According to Eqs. (1) and (2), the goal of quantitative NMR is reached if the amplitudes (in the time domain) or areas (in the frequency domain) can be estimated for the metabolite resonances of interest. Under ideal conditions this can be done in many ways. However, analyses of NMR spectra of biological samples involve some remarkable problems. In handling ‘H NMR spectra of plasma and lipoproteins two major problems are faced. Firstly, an intense residual water resonance causes a curved baseline at the chemical shift region of the metabolite resonances. Secondly, different metabolite signals overlap heavily. These problems can only be overcome with sophisticated data analysis methods [65-67,99-1081.

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The quantification can be carried out either in the time or in the frequency domain. In time domain algorithms (Section 4.1), the measured FID is analysed directly, whereas in frequency domain methods (Section 4.2) the FFT of the original FID, i.e. the NMR-spectrum, is processed. These two alternative analysis domains are inherently different and have their advantages and disadvantages [99]. If any preprocessing of the data is to be avoided before the model function is fitted, it is preferable to operate in the time domain. Especially in in vivo NMR spectroscopy problems may arise due to non-ideal experimental conditions which cause a non-ideal FID to be measured [lOS]. The FFT will then produce a distorted frequency domain spectrum [102, 1031. For instance, truncation of data points either at the beginning or at the end of the time domain data series will introduce sine-wiggles in to the spectrum. Also, rapidly decaying signals from immobile components give rise to broad baseline features in the FFT spectrum. It is clear that in these kinds of situations accurate estimation of the model parameters will be very difficult. Many of the problems mentioned above can be handled efficiently using time domain analyses [99, 1051. However, there are methods of reducing distortions caused by these problems in the frequency domain spectra. Also accurate parameter estimation algorithms that use frequency domain model functions are available. If the measured FID is free from the above mentioned problems, as is normally the case in in vitro NMR spectroscopy, and if the baseline and residual water resonance (when present) are properly separated from the actual metabolite resonance by applying a baseline correction or are eliminated by an SVD-based time domain analysis technique, then the use of a frequency domain analysis method becomes more attractive [99]. A clear advantage of frequency domain analysis methods is that frequency selective parameter estimation appears in a natural fashion [65]. Although frequency selective analysis is relatively intricate, it is sometimes also possible to use this approach in the time domain [lOS]. Moreover, the computer requirements for the frequency domain methods are much more favourable than those for the time domain methods. In fact, for complex spectra with many heavily overlapping resonances the frequency domain approach is the only possible choice at the moment [65-67,993. In the ‘HNMR studies of lipoproteins and plasma in which sophisticated data analysis techniques have been applied, the frequency domain applications have clearly been very effective and successful (See, e.g. Sections 5.3.2 and 7.4). No quantitative applications of time domain methods to ‘HNMR of plasma exist. However, recently van den Boogaart et al. [99] have carried out comparative analyses of ‘HNMR data of ultracentrifuged lipoprotein fractions using HLSVD (Section 4.1.1) and VARPRO (Section 4.1.3) as the time domain methods, and FITPLAC (Section 4.2.1) as the frequency domain method. It was found that both sophisticated parameter estimation methods, VARPRO and FITPLAC, are capable of arriving at the same consistent solutions in every case. The SVD-based removal of the residual water resonance was also shown to be very effective (Section 4.1.2). However, the possible capability of the VARPRO algorithm to analyse ‘H NMR spectra of plasma remains to be established. Chemometric techniques are a heterogeneous group of mathematical and statistical methods for analysing, interpreting and predicting experimental data [109-1131. Even though they are used extensively in many areas of research, e.g. in analytical chemistry and NMR spectroscopy in general, only a few applications to ‘H NMR of plasma have been published [114-l 171. An introduction to the chemometric methods is given since new applications are becoming available in this area (Section 4.3). Properties of neural networks are also briefly discussed (Section 4.4). 4.1 Time domain analysis The measured FID x, can be modelled as a sum of complex exponentially damped sinusoids, f,: K x, = j?, + r, = 1 ak eiWo+Ok)e(uk+ iwr)tn+ r,, k=i

n=O,l,...,N-1

(6)

in which ak, @& (ak < 0), wk and & (k = 1, . . , K) are the amplitude, damping factor, angular frequency and phase, respectively, of the kth sinusoid; vk is the frequency in hertz and is related to

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wk via wlr = 27~~. The discretely sampled time steps are t, = (n + 6)At, with to = 6Ar the begin, or dead, time of the spectrometer; 4. is an overall phase. The number of sinusoids is K and the number of complex data points is N. The frequency domain equivalent of an exponentially damped sinusoid is a pure Lorentzian signal [99]. In practice the measured data points, x,, contain the residual r, arising from the noise and the incompleteness of the mathematical model to reproduce the experimental situation. Especially in the case of complex ‘H NMR data of biological samples the exact experimental lineshapes are often unknown [65].

4. I. I HSL VD algorithm The HLSVD (Hankel-Lanczos SVD) method is a so-called Black Box method, i.e. it requires minimal user-interaction. The algorithm is linear and non-iterative and thus requires no starting values for the parameters to be estimated [99,100]. This is an advantage when the program is meant to be applied automatically to remove the (residual) water resonance from various ‘H spectra [lOO]. It can also be very fast compared to interactive minimization algorithms. The HLSVD program [ 1043 is based on the State Space algorithm, using matrix algebra. In the HLSVD program a faster SVD algorithm than in the conventional HSVD method [ 1181 is used. The acceleration of the SVD algorithm lies in the exploitation of the Hankel structure of the data matrix. Another great benefit of the Hankel-Lanczos SVD algorithm is that only a limited number of singular values and vectors needs to be calculated, rather than all singular values and vectors, as is the case in the conventional SVD-based algorithms [99, 100, 104, 1183. The theory of the HLSVD algorithm [lOO, 104,105] is briefly outlined starting from an ideal case where the data are noiseless and comprise K complex exponentially damped sinusoids. In this case x. equals & in Eq. (6). First, the data x, are arranged in an L x M data matrix X as follows:

It can be seen that all entries on any antidiagonal are identical; the data matrix is said to have Hankel symmetry with size L x M, where L + M = N + 1. L and M are normally chosen in the interval 0.5 < L/M < 2.0, but both should at least be larger than the number of sinusoids, K.This data matrix X can now be f;;torized in two ways. Firstly, by 3dermonde Decomposition, using the complex amplitudes cL = ak ei(bo+4r) and signal poles zk = e(nr+ior)At (k = 1, . . . , K): 1 z:

”

1 1 zf(

x= L-1 21

zz

.

4LK

L-l

ZK

\

,I 4

c

0

M-1 21

(8) M-l

ZK

SYK

cLKand cMKare the so-called Vandermonde matrices of size L x K and A4 x K, respectively. Both have full rank and contain the signal poles zk. The tilde denotes transposition. C is the K x K diagonal amplitude matrix (full rank) with diagonal elements c; = COZY’ (k = 1, . . , K).It is important to note that each rf=y of a Vandermonde matrix is equal to the product of the previous row and , zK). The desired parameters (writing out Eq. (8) gives Eq. the diagonal matrix 2 = diag(z, , z2, (6) in matrix form) can be directly drawn from the matrices 5LKand C. However, to date no method JPWS

27:5/6-D

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has been found to achieve a direct Vandermonde Decomposition of a data matrix X, so one has to resort to the second factorisation, the Singular Value Decomposition (SVD), of the data matrix. The State Space algorithm, by means of the SVD, will then produce a Vandermonde Decomposition in a least squares sense. The Singular Value Decomposition is the diagonalisation of a complex data matrix X as follows: x=

UZV’,

(9)

or, depicted symbolically:

(LxM)

(9’)

Z is the so-called singular value matrix (size L x M) with all entries equal to zero apart from the diagonal elements which represent the (real and non-negative) singular values s, (I = 1, , L), ordered according to decreasing magnitude. U and V are unitary matrices of size L x L and M x M, respectively. Their columns are called the left and right singular vectors, respectively. The dagger symbol indicates hermitian conjugation. If the data are noiseless and comprise exactly K sinusoids, only K singular values are non-zero, one per sinusoid. In this case the other L - K singular values and their corresponding singular vectors do not contribute to the matrix product in Eqs. (9) and (9’) any more. So from matrix Z, one only needs the K x K diagonal matrix &, which contains the first K non-zero singular values. Likewise, one can discard the last L - K columns from matrix U to give UK (L x K) and the last M - K rows from matrix Vt to give Vi (K x M). Rewriting Eq. (9) now yields the Truncated SVD:

x =

U,E,

vi

(10)

as denoted by the shaded areas in Eq. (9’). The previous theory is only exact for a noiseless signal. If noise is present, all singular values become non-zero and the matrix X has full rank. However, because the magnitude of a singular value is proportional to the amplitude of the corresponding sinusoid, the singular values belonging to the noise will be relatively small, and if the signal to noise ratio (SNR) is not too low, they will appear in matrix Z only after the signal-related singular values. When the signal-related and noise related singular values can be distinguished from each other the latter are put to zero to arrive at Eq. (10). If no clear distinction can be made, one either has to involve some of the larger noise-related components into the parameter estimation, or one will lose some information about the smaller resonances of interest. Next, it can be seen that the matrices in Eq. (10) have the same size and rank as their corresponding partners in Eq. (8). If a caret is used on the matrices to denote the contributions by the model function rather than the actual data, one can always transform the Vandermonde Decomposition of Eq. (8) into a matrix product of the form of Eq. (10) as follows:

(11) The structure of the Vandermonde matrices 5LKand CMKis then still present in UK and px, albeit in a more intricate way: each row is now equal to the product of the previous row and a non-singular matrix 2 of size K x K. The eigenvalues of % are then equal to the signal poles and will reveal the damping factors and frequencies of the K sinusoids. The Truncated SVD matrix UKwith elements

M. Ala-Korpela/Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554 ul,k-iS

493

used to find the matrix 2 via: (W+1,1 ~It1.z

... u,+1,d=

@4.1~~.2

'.-4.&

l=l,2

)...,

L-l.

(12)

For the noiseless case with K exponentially damped sinusoids, this solution is exact. In all other cases Eq. (12) needs to be solved in the linear least squares sense. Diagonalisingz then opens the way to finding all four parameters per sinusoid; the amplitudes and phases can be found in a linear least squares round, fitting Eq. (6) to the data with the damping factors and frequencies kept fixed at the values just found. This profitable way of parameter estimation arises from the properties of the Vandermonde matrices, the truncation of the SVD and the exponential damping in the model function x*. in Eq. (6) [lOO, 104, 1051.

4.1.2 Removal of water and ‘pseudo-frequency selectivity’ As van den Boogaart and co-workers [99,100, 1191 have demonstrated, the SVD-based method provides an efficient way of removing a dominating residual water peak, which hampers the parameter estimation by non-linear least squares fitting algorithms. The shape of the water resonance is often badly affected by water suppression pulse sequences. It is known, however, that any arbitrary lineshape can be fitted by the HLSVD program, as long as one uses a sufficient number of Lorentzian components to describe the non-Lorentzian shape. In SVD terms, we notice that a non-exponentially damped sinusoid gives rise to a number of non-zero singular values, rather than one as in the purely exponential case. In practice, it is found that the number of singular values necessary to represent the arbitrary lineshape of the water resonance is limited. As the water resonance dominates all peaks of interest, its constituent components will have amplitudes larger than those of the sinusoids belonging to the peaks of interest. The magnitude of a singular value is proportional to the amplitude of the corresponding sinusoid and therefore one can find the relevant singular values belonging to the water resonance as the first entries in matrix Cx, Eqs. (9’) and (10). From these singular values and their singular vectors, a number of exponentially damped sinusoids can then be determined, each with their parameterS vk, a,+, ak and &. Although these parameters do nor have a physical meaning any more, they serve to describe the water resonance, enabling its efficient removal. Thus the following protocol can be used to remove the residual water resonance from ‘H NMR data without affecting the metabolite resonances of interest [99, 1001: (1) A very fast fit of the time domain FID is made with HLSVD method; only a few singular values are used ( z 10). The number can be based on a singular value plot (plotting the magnitudes of the singular values by index k) by a relatively slow conventional HSVD method or on the judgement of the spectroscopic. This is not crucial as excess singular values only result in a slightly longer calculation time. (2) From the parameter list of reconstructed sinusoids (one for every non-zero singular value, i.e. the number used in (1) above), those with a frequency corresponding to the water region are used to constitute the reconstructed time domain water signal via Eq. (6). (3) The reconstructed water signal is subtracted from the original FID. (4) The resulting reduced FID is now free from the water signal, and can be subjected to a more sophisticated data analysis method. The more singular values that are used in step (l), the better the water resonance removal, but the longer the calculation will be. In practice it has been shown that only a relatively small number, such as three or four, Lorentzians are necessary in order to almost completely clear the water region from the ‘H NMR spectra of lipoprotein samples [99, 1001. Some spikes in the water region may still be present, but these no longer have a detrimental effect on the parameter estimation of the peaks of interest. Thus, there is no need to use conventional SVD techniques to find all the singular values. The major advantage of this protocol is that the water resonance is entirely removed, i.e. not only the water region, but also the long ‘tails’ are absent. The adjacent metabolite signals are not influenced since their central frequencies lie outside the water region. It should be noted that this protocol can be fully automated. Typical calculation times used to remove water resonances from the FIDs (4096

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complex data points) with the HLSVD method were several seconds of CPU time on a Convex C3820 minisuper computer [99]. An example of the water removal procedure in the case of a ‘HNMR spectrum of an LDL sample is shown in Fig. 6. The protocol can also be extended to remove other unwanted features from the spectra, such as the baseline. In the case of ‘HNMR spectra of lipoprotein fractions, it has been shown that the entire baseline can be reconstructed with one to three Lorentzians [99]. These ‘baseline-related sinusoids have abnormally large damping factors, which indicate abnormally broad lines in the spectrum. These sinusoids also have a fairly large amplitude and can thus be determined with a small number of singular values. In fact, any arbitrary region can be removed from the spectrum in this way, without affecting the peaks of interest (see Fig. 6). This kind of technique has been introduced as a ‘pseudo-frequency selective’ approach to time domain parameter estimation [99]. If the peaks to be removed do not have dominating amplitudes, many singular values are needed to find and represent them and longer calculation times will result. A Visual Basic program SELECT (0 University of Oulu, Department of Physical Sciences, NMR Research Group) has been developed around the HLSVD program to enable easy removal of the water and baseline components and make possible fully graphical interactive applications of the ‘pseudo-frequency selectivity’ to complicated data [120]. More recently, a Matlab (The Mathworks, Inc., Natick, MA, USA) graphical user interface has also been developed, allowing easy use of the HLSVD and VARPRO algorithms [121]. 4.1.3 VARPRO: a non-linear

least squares jitting algorithm

Non-interactive methods have disadvantages such as that prior knowledge of the signals at hand cannot be imposed on the parameter estimation, and that the model function is restricted to a sum of exponentially damped sinusoids as in Eq. (6). The VARPRO (VARiable PROjection) method [99, 105,122] is a non-linear least squares fitting method operating in the time domain, i.e. directly on the FID. Using VARPRO one can include prior knowledge of the metabolite signals in the parameter estimation procedure. Also different model functions (e.g. different forms of damping) can be used and for white and Gaussian noise r, the errors in the parameters will reach the Cramer Rao lower bounds. A drawback is the need for an increased user-interaction and also longer calculation times. The user-interaction required consists of supplying starting values for all non-linear parameters, and supplying prior knowledge (if present). Starting values for the non-linear parameters, (frequencies wk and damping factors Q) can be obtained either from estimates of a rapid black box method (HLSVD) or from the FFT spectrum of the signal. In the FFT spectrum frequencies of sinusoids correspond to the Lorentzian peak positions and the damping factors of the sinusoids are directly proportional to the half linewidths of the Lorentzians. Prior knowledge of amplitude ratios and linked damping factors and frequencies in multiplets reduces the number of parameters to be estimated and thus enhances the convergence behaviour, yields improved results and speeds up the algorithm. For a short review of the VARPRO algorithm [99, 105, 1223, 2” in Eq. (6) can be rewritten as: K n=O,l,...,N-1 (13) %= CkMYk,4,

c

k=l

Fig. 6. The SVD-based water removal and time domain ‘pseudo-frequency selectivity’ illustrated in the case of a ‘H NMR spectrum of an HDL sample. (a) To process the FID/spectrum more accurately, the very intense water resonance was first removed, utilising the rapid SVD method. The inset shows the TSP-based reference peak used to scale the peak areas of the small metabolite resonances of interest at the negative frequencies. (b) When scaling up the plot 40 times, it becomes apparent how much the tails of the water resonance affect the peaks of the metabolites of interest. (c) The entire water resonance, inclusive of its long tails, was efficiently removed by the time domain SVD method. (d) In this work only the methylene and methyl regions of the spectrum were studied and the removal process was thus carried even further, and all other main features in the FID were likewise removed. This is an example of the ‘pseudo-frequency selective’ approach to time domain fitting. (From Ref. [99].)

M. Ala-Korpela 1Progress in Nuclear Magnetic Resonance Spectroscopy

496

27 (1995) 475-554

where ck are the complex amplitudes as defined in the text to Eq. (8), and fk(yk,n) are independent functions of the non-linear parameters yk. It should be noted that the model function Eq. (13) is not necessarily restricted to a sum of exponentially damped sinusoids. However, for a model as in Eq. (6), fk(Yk, n) = exp(ak + i(l)k)t”, and yk stands for the pair &, w k. It is important to note that Eq. (13) can be divided into a linear part (the complex amplitudes ck) and a non-linear part (the functions fk). Concerning the prior knowledge, a multiplet as mentioned above might require various separate fk values with coupled frequencies and damping factors, and interrelated amplitudes. Next, we write Eq. (13) in matrix form:

f= ft(Yl>

N -

l)

fz(Y2,O)

..’

fi(YZt

...

f2b2,

h

1)

-

1)

.‘.

(14) fK(YK,

k

-

1)

where f is a column vector containing the N contributions from the model function to the time domain data points, c is a column vector containing the K complex amplitudes, and F is a full rank matrix containing the functions fk(Yk, n). The non-linear least squares fitting procedure now pertains to minimising the sum of squared residues: 11-x-PII

= (Ix - Fcj12

(15)

wherex is a column vector containing the N measured time domain data points. The right hand side of Eq. (15) is to be minimised as a function of C~and yk. Assuming temporarily that the yk are known and kept fixed, the least squares solution of the amplitudes is given analytically by: c z (F+F)-‘F’x

(16)

Combining Eqs. (15) and (16), we can eliminate the linear part of Eq. (13) (the complex amplitudes) from the minimisation criterion, yielding: IIX-fll2

% IIx - F(F+F)-‘F+x~/~

(17)

The right hand side of Eq. (17) is now to be minimised iteratively as a function of the non-linear parameters yk(frequencies ok and damping factors a&.),for which starting values need to be provided. For the iterative minimisation procedure the Levenberg-Marquardt algorithm is implemented (Section 4.2.1). Finally, keeping the frequencies and damping factors fixed to the values found, the complex amplitudes can be calculated from Eq. (16), which is a linear least squares solution and therefore does not need starting values. Recently van den Boogaart et al. [99] have applied the VARPRO algorithm to study ‘H NMR spectra of ultracentrifuged lipoprotein fractions VLDL, IDL, LDL, and HDL. Also comparison to the results obtained by the frequency domain fitting algorithm FITPLAC (Section 4.2) was done (see Fig. 7). The intense residual water peaks were removed using a rapid time domain SVD method before actual parameter estimation of the smaller metabolites of interest. Even though the water removal is not necessary for the frequency domain method FITPLAC, it increases the reliability of the metabolite quantification and makes it possible to analyse larger frequency regions at the same time. The comparison of the final results is summarised in Table 2. Peak areas of the individual components for all lipoprotein fractions are shown as determined by both the VARPRO and the FITPLAC programs, and relative to the peak area of the TSP-based reference. For VARPRO these values correspond to the estimated amplitudes of the exponentially damped sinusoids; for FITPLAC the values correspond to the integrated intensities of the Lorentzians. It was noticed that the time domain VARPRO program using the frequencies and damping factors found by the FITPLAC program, as starting values, resulted in an almost identical reconstructed spectrum, i.e. built up by the same Lorentzians (exponentially damped sinusoids in Eq. (6)) with similar amplitudes, frequencies and damping factors. The phases had to be fixed in the VARPRO runs to arrive at biochemically relevant solutions. Because of the low concentration in the IDL fraction and therefore the low signal

hf. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy

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497

Table 2 Integrated intensities* of the modelled singlet metabolite resonances in the methylene and methyl regions of the ‘H NMR spectra of the VLDL, IDL, LDL and HDL fractions (from Ref. [99]) Lipoprotein fraction and the individual componentsb

VARPRO

FITPLAC

VLDL

1 2 3 4 5 6 7 8 9 10

94 36 332 471 39 64 19 76 56 9

(2) (4) (11) (41) (4) (6) (3) (5) (4) (1)

94 32 342 468 33 86 17 79 51 7

(2) (5) (8) (4) (4) (7) (2) (4) (3) (2)

IDL

1+2 3+4

24 8

(4) (2)

25 8

(4) (2)

LDL

1 2 3 4 5 6 7 8 9 10

70 372 69 139 53 16 124 73 32 101

(13) (43) (25) (60) (43) (20) (48) (52) (38) (37)

78 374 70 134 50 15 120 73 45 71

(13) (45) (23) (52) (37) (18) (43) (45) (53) (28)

HDL

1 2 3 4 5 6 7 8

134 78 503 84 87 31 211 61

(3) (10) (9) (7) (9) (5) (2) (3)

124 101 481 88 86 31 213 43

(3) (10) (8) (6) (6) (3) (2) (3)

’The intensities are given in percent of the integrated intensity of the reference. peak (around - 1850 Hz, see inset in Fig. 6). b For the numbering of the resonances, see Fig. 7. ’The uncertainties in parentheses are one standard deviation in the last figure quoted.

to noise ratio in the spectrum only the summed peak area of the methylene or methyl group could be reliably analysed. The approximated standard deviations of the estimated peak areas are given in parentheses in Table 2. The equivalences in the peak areas were striking. This was true even when the errors were high because of substantial overlap of the individual components in the spectrum of the LDL fraction. It should be noted that the structures of the reconstructed signals were obtained with the maximum number of components, allowing both mathematically unequivocal solutions and biomolecular interpretations. The results summarised above [99] show that the VARPRO algorithm also has potential in analyses of ‘HNMR data of plasma. However, some practical problems may arise. The water removal is essential, even though it can be done using the HLSVD method. VARPRO also requires good initial values for the non-linear parameters which makes application of either HLSVD or DFT necessary. Application of a frequency domain analysis method might be needed to obtain initial

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M. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554

values in the case of extensive overlap as, for example, in the case of methyl and methylene regions in the ‘HNMR spectra of lipoproteins or plasma. If a limited frequency region is to be analysed by applying the VARPRO algorithm additional difficulties will arise and frequency selective VARPRO fitting [ 1081 or inclusion of the HLSVD based ‘pseudo-frequency selective’ approach [99] will be required. Moreover, incorporation of prior knowledge and assessment of the mathematical and physical unequivocality of solutions for ‘H NMR data of plasma would require more flexibility of the program. Because of large memory requirements, both the HLSVD and VARPRO programs can not be run on PCs (if a sufficient number of data points is to be used), which makes their applications more difficult than the FITPLAC algorithm. Therefore at the moment the FITPLAC program appears to be superior for the analysis of ‘H NMR spectra of plasma (see Sections 4.2 and 5.3.2). 4.2 Frequency domain analysis The conversion from the time domain to the frequency domain is carried out by the DFT; therefore the corresponding model for the frequency domain spectrum is obtained from the model FID .+Z,of Eq. (6) as follows [99, 1033 N-l

N-l

E(v)= At c )2ne-i2nv”A’= At c n=o

n=O

K

1 ake i(do+9rle(ar+iwr)(n+d)Are-i2nvnAt

(18)

k=l

in which the parameters are as described for Eq. (6). In the above equation v is a continuous variable, and hence produces a continuous spectrum. However, in practice only a finite number of frequency domain data points N are calculated by using a discrete frequency step-variable v, = m/NAt, (m = 0, 1, . , N - 1). The discrete spectrum is now given by akei(80+m~)e(ar+io*)(n+6)Ate-i2nmniN n=o

(19)

k=l

in which ok = 27~~. This leads to 1

=At

i

ei(~o+0k)e(u*+i2nvr)~Af

ak

_

1_

!i=l

e[i2n(vr-m/NAr)+ak]NAr e[i2n(v*-m/NAt)+nrlAt

for the exact expression of the DFT of a discretely sampled model FID. Generally, the frequency domain spectrum is then not a sum of pure Lorentzian signals, as predicted by a continuous FFT of a continuous FID. Deviations from the Lorentzian lineshape may occur due to truncation of the FID, slow sampling rate, and the dead time to = 6At of the spectrometer. However, even in experiments where such problems may arise, Eq. (20) can be fitted directly to the spectrum. A disadvantage is that the use of Eq. (20) is limited to a relatively small number of signals [102, 1031. In practice, experimental conditions in in vitro NMR spectroscopy are often ideal so that acquisition time is long, NAt 9 - l/z, for all k, and a sufficiently high sampling rate (At approaches zero) can be used together with a properly short initial delay (6 z 0). In these situations Eq. (20) reduces to K akei(do+dr) ak

1 - i2rr(vI, - m/NAt)/a, 1 + [2n(vk - m/NAt)/ak12

(21)

This is also the case for ‘H NMR of plasma and lipoproteins. Eq. (21) describes a sum of K complex Lorentzian lines. Although both the real and imaginary parts of the Lorentzians can be used for the parameter estimation, only the real part of Eq. (21) is normally used for display: Re{f(v,)}

= 2 k=l

[ABS,(v,)cos(4o

+

4k)

+

DISPkb’m)Sin($‘o

+

$k)l

M. Ala-Korpelal Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554

2hkzkb, - vk) sin@0 + h,Z+ 4(v, - vk)’

+

(Pk)

1

499

(22)

AI&(v) stands for the absorption and DISP,(v) for the dispersion part of the Lorentzian k. K is the total number of modelled Lorentzians, hk = -ak/rt is the half linewidth, Zk = -ak/uk the intensity, vk = wk/2?t the resonance frequency, and 4. + &_the phase angle of the kth Lorentzian. If the phase correction of the spectrum is exact, the real part of the spectrum is purely absorptive and the imaginary part purely dispersive. The phase correction of the ‘HNMR spectra of plasma and lipoproteins is in practice so difficult that inaccuracies of a few degrees easily occur and Eq. (21) has to be used for lineshape fitting analyses in its complete form. In the quadrature detected spectrum the imaginary part can also be described using Eq. (22), except that the sine term becomes a cosine term (and vice versa) due to a 90” phase shift. The area under a Lorentzian signal is calculated’as Ak = nh,Zk/2 = - CikZk/2= &/2. 4.2.1 FZTPLA’: a non-linear least squares$tting algorithm The FORTRAN version of program FITPLA was first developed for the analysis of ‘HNMR spectra of human blood plasma and lipoproteins [65,66,123,124], but the new C version FITPLAC is generalised to enable its use in general spectroscopic data analysis [67, 99, 1203. The current version of the program (0 University of Oulu, Department of Physical Sciences, NMR Research Group) will be distributed provided that a signed letter, in which confirmation is given that the program will be used for research purposes only, is sent to the author. The program uses the Levenberg-Marquardt algorithm [65, 1071 to solve the non-linear matrix equation, as concisely explained below: The maximum likelihood estimate of the P model parameters (/Ii, . . . , BP) = b of Eq. (21) is obtained by minimising the chi-square function N-l x2@)

=

I> 2

Y(vm) - E&b)

-!_

1

m=o 1 0,

[

(23)

in which (I,,, is the standard deviation (experimental error) of each data point, Y (v,) represents the data points in the spectrum and Y(v,, b) the frequency domain model data points as in Eq. (21). Expansion of the x2 function as a Taylor series leads to a non-linear matrix equation: DbA=d

(24)

in which bA is a vector containing changes of the model parameters in the iterative process, vector d contains the derivatives of the x2 function with respect to the model parameters Bi, (i = 1, . , P): d.

=

_’-= ax2(b) 2

Ybm)

-

f(vm,

b)

(25)

Vi

and D is a curvature matrix with elements N-1 (1 +

Dij = c

m=O

2

sijlt)

CJnl

#i @i1’

aP(V,,

~ [

b)

af(V,,

~

b)

i,j=

1, . . ..P

(26)

where 6ij is the Kronecker delta function and 1 is a positive convergence control parameter. The minimisation process is iterative: starting values b, are corrected with the tA obtained by solving Eq. (24); a new vector b is calculated as b = b, + bA; if the model spectrum Y (v,, b) deviates from the experimental spectrum Y (v,) too much, Eq. (24) is solved again with the corrected vector b as a new set of starting values b,. This procedure is repeated until convergence is achieved with desired accuracy. The parameter I is very important for optimal convergence behaviour. If the correction step bAobtained by solving Eq. (24) leads to divergence, 1 is increased to diminish the correction step in order to obtain convergence (again). On the other hand, once convergence is obtained, 1 is

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decreased to increase the rate of convergence. This procedure guarantees convergence to the minimum of the criterion (23) in almost all cases and accelerates the calculation near the minimum. The key facts for successful applications of the program FITPLAC are some special features developed in the algorithm [65-67, 99, 120, 123, 1241: (1) Frequency selective analyses are easily performed; any portion of the spectrum can undergo parameter estimation separately. When only certain resonances are of interest and need to be quantified this feature is crucial in economising both human and computer resources. Both real and imaginary parts of the complex frequency domain spectrum can be used in the analysis. (2) The real spectrum is purely absorptive and the imaginary spectrum purely dispersive only if phase correction is perfect. In practice the phase correction of the ‘H NMR spectra of plasma and lipoproteins is so difficult that inaccuracies of a few degrees are unavoidable. Therefore both absorption and dispersion parts of the Lorentzians, with the phase as one of the variable parameters, are used in the algorithm. This allows an automatic correction of the phase deviations. (3) Any model parameter can be fixed if proper prior knowledge is available. Prior knowledge of first order couplings (doublets and triplets) can be directly exploited. Additionally, model lineshapes can be built up as combinations of Lorentzian lines with special internal relations of intensities and chemical shifts. This is important as prior knowledge reduces the number of free parameters and improves the accuracy and reliability. Moreover, in many complex cases (see, e.g. Section 5.3.2) only accurate and sufficient prior knowledge makes physically and biochemically reasonable solutions possible. (4) In non-linear problems the solution might be affected by the starting values b, of the estimated parameters. An automatic guess-system has therefore been incorporated into the FITPLAC algorithm. The variable parameters are randomly chosen from a given appropriate interval (wide but reasonable). Final parameters are not constrained to these given initial value limits and therefore this technique is used only to effectively probe the multidimensional parameter space and to ensure reliable decisions about the unequivocality and physicality of the final solution. This technique can be recommended for non-linear problems. In practice it means that about 5-25 different solutions are calculated in each case (the number of solutions can be specified by the user) and at the end the solution with the lowest r.m.s. error of the residual is selected as the final one if it is both mathematically unequivocal and physically meaningful. If two (or more) different physical solutions are found with similar r.m.s. values, the chosen model is not appropriate; experimental information in the spectrum is insufficient to ensure a unique result within the model. In this case a model with fewer variable parameters has to be chosen, i.e. there will be need for (additional) prior knowledge of the system. The different solutions can be easily checked using the graphical capability of the program. (5) An arbitrary number of data points can be used in the analysis. Several thousand data points and up to a hundred parameters can be processed on a 486 PC. This is an advantage over the time domain HLSVD and VARPRO analysis algorithms, in which sizes of temporary matrices generally prohibit use of a normal PC with the current versions of the computer programs. Recently a modification of the HLSVD program was shown to run on PC with 512 complex data points and about 10 singular values [120]. (6) Usually baseline correction has to be added to the frequency domain model. For narrow regions a linear correction, B(v) = c0 + civ, serves as a good approximation, but for larger regions higher order polynomials or Fourier series might be needed. If HLSVD is used in the time domain to model and subtract the baseline and any possible dominating solvent components, the entire frequency domain spectrum can be parametrised without baseline difficulties. Especially when wide frequency regions need to be analysed in complex ‘HNMR spectra of plasma and lipoproteins this is the recommended technique. The actual signal manipulations, leading to the final spectrum for FITPLAC or (VARPRO), can be easily performed on a PC by applying the SELECT program [120] (see Section 4.1.2). It has to be emphasised that is a new situation the number of signals to be modelled is at first unknown. The analysis procedure begins with inclusion of the most prominent resonances represented by one (Lorentzian) component and proceeds with additions of further components to the model until the residual spectrum Y(v,) - Y (v,, b) no longer shows any clear features and there is no significant decrease in the r.m.s. error of the fit. If there is severe peak overlap, the mathematical

M. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554

501

unequivocality of the solutions must be carefully verified by calculating a few solutions, all with a different set of starting values b, chosen randomly from an appropriately large interval. The more complex the problems encountered, the more important will be inclusion of biochemical prior knowledge to achieve accurate results and confirm exclusion of possible non-physical minima. The Lorentzian component structures for the resonance regions ranging from 0.4-1.6 ppm in the ‘H spectra of VLDL, IDL, LDL and HDL fractions, identified using the program FITPLAC and the procedure described above, are shown in Fig. 7. Use of the SVD-based water removal and baseline flattening made it possible to analyse properly these large regions in the frequency domain (Section 4.1.2). An extreme overlap of different lipid resonances is observed in the spectra. However, the final component models are consistent and can be interpreted biochemically [99]. 4.3 Chemometric techniques ‘HNMR spectra of plasma consist of thousands of variables which reflect the abundance of hydrogen containing metabolites in plasma. A normal procedure in analysing a spectrum is to restrict it to a certain region or signals which can be identified and quantified. This is a straightforward choice as complete analyses and assignments of the resonances of plasma spectra would be extremely difficult. In cases where the biochemical background for the experimentally observed changes or differences is uncertain or unknown, e.g. in spectroscopic studies of cancer, this imposes an incomplete (and even arbitrary) preselection of the important metabolites together with a rejection of possibly biologically significant experimental data. In order to use the whole versatile multidimensional experimental information present in the ‘H NMR spectra of plasma to sample or make individual classification, a successive application of different chemometric techniques may be useful [109-1161. At first, the dimension of the problem can be reduced using principal component analysis (PCA). This is an effective mathematical technique, which requires no statistical model (no assumptions about the probability distribution of the original variables are needed) [109]. In the case of a set of N correlated variables, e.g. experimental intensity points in the spectrum i, UT = [Yi(vO), Yi(vi), . . . , Yi(vN-i)], a transformation to a new set of N uncorrelated variables, principal components, ZO, Zi, . . . , ZN_ i, is performed. These new variables are linear combinations Of the o@& variables; Zk = Cylai tS, Yi(V,) = fil Yi (fl: flk = I). They are derived in decreasing order of importance; the first principal component accounts for as much as possible of the variation in the original data. This leads to the matrix equation A =

pTcp

(27)

in which

A=

is

the

0 ...

a1

0 a0

0

..

covariance

I i \“N-10

:I

... .. .

0

...

#IN_1 0

(27’)

.

..’

matrix

of

the

. . UN-11

...

principal

component

vector

Zi = flTx

with

(27”) UN-lN-1

502

M. Ala-Kolpela / Progress in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

is the covariance matrix of the original variables Y, and /

B00

BfJ1

‘.’

BON-1 \ (27”)

\

h-10 Ll .

‘.’

PN-lN-1 . I

is the (N x N) matrix of the eigenvectors of E, i.e. matrix of the principal components. Particularly, the sum of the variances of the original variables equals that of the principal components. Therefore, it is appropriate to state that the first k components account for a proportion C:Z~ ni/C”Lo’ li of the total variation. The main aim of PCA is to reduce the dimension of the problem; in the case of highly correlated original variables it is possible (and desirable) that the first few principal components account for most of the variation in the original data. If this is the case, the effective dimension of the problem is much less than N and further analysis of the data can be carried out in many fewer dimensions than N. In some cases an intuitive meaning can be given to the first principal components, but generally interpretation of principal components is very difficult. Moreover, it is quite common to perform PCA on normalised data in which all the variables have unit variance; this means that a correlation matrix is used instead of the covariance matrix. After PCA pattern recognition techniques start to play an important role [ 1l&l 131. The pattern recognition methods work as a general tool in predicting an obscured property of an object on the basis of a set of indirect measurements. As far as ‘HNMR spectra of plasma samples from cancer patients and control subjects are concerned this would be a category of a sample (spectrum) [114-l 161. There are a number of different pattern recognition techniques operating at different levels. At the stage of analysis when no a priori assumptions about the type or class of the samples are made, but intrinsic patterns or clusters in the data set are searched for, unsupervised methods are used. If it turns out that after PCA, only two (or three) components are needed to describe the situation (such as ‘H NMR spectra), and a scatter diagram of the scores of the components, Zi = gTYi (Yi consists of mean referenced original variables), for all individuals i can be directly drawn and a visual cluster analysis worked out. However, for ‘H NMR spectra of plasma many more principal components would be needed, but PCA would still be useful since it is likely to be more sound and it is easier to apply algorithmic clustering methods on the scores of the first principal components than on the original data points. In order to be able to apply algorithmic clustering methods, a quantitative measure of dissimilarity dj, between individuals 1 and M is needed; the Euclidean distance, dl, = CfLJ (zli - zmi)‘, is often used. A common way of describing the dissimilarity structure of the individuals or objects is a hierarchical tree, a dendrogram. The dendrogram can be constructed using for instance single-link clustering; for any threshold dissimilarity dthrr all individuals are divided into p ( < number of all individuals) clusters in a way that individuals i and t are in the same cluster if it is possible to find a chain of individuals, for which the dissimilarities in the chain dij,djk, . , d,, are all < d,,,. To create mathematical discriminants to allow for distinction between different types of individuals (spectra) and thus classification of unknown samples, many supervised (learning) techniques are also available. These techniques involve a data set, the training set, with known types of individuals. The mathematical classification rule is developed based on the training set and validated using an additional data set, a so-called test-set, with known sample types. If the test set can be classified properly, the rule can be considered to be well developed and thus applied to classify unknown samples. One direct classification scheme is the K-nearest neighbour method in which an unknown sample is assigned to the class into which its nearest neighbours belong. A more advanced technique is the SIMCA method [110-l 12, 1141 in which the members of each class are separately modelled (by a multidimensional plane with confidence regions) in terms of the principal components. The unknown samples are fitted to the class models and the classifications are made according to the goodness of the fits. Each unknown sample is assigned a probability for each class; this also allows an unknown sample to be taken as an outlier, i.e. belonging to none of the original classes, if

M. Ala-Korpela /Progress

in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

503

IDL

VLDL

L2 I

R

1

’

17

I.

1

’

1

1.

1.).

I.

I

,

.

I

.I

1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.8

s

,‘l’I’,~,~I.,.~.,~,‘,.,’

1.7 1.8 1.5 1.4 1.3 12

pm

1.1 1.0 0.9 0.8 0.7 0.6

wm

HDL

Ll

L2n

l-l

I

.I,

I

.,.,.,.I.,.

I.

I

.,.I-

1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.6 0.7 0.6 fwm

I~I~I’I’I~I~,~,~I~I~I~I~

1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 pm

Fig. 7. Extended methyl and methylene resonance regions (from 0.55 to 1.70 ppm; 460 Hz) of the rH NMR spectra of the VLDL, IDL, LDL, and HDL fractions. The use of the SVD-based water and baseline removal made it possible to properly analyse these wide regions in the frequency domain. In all insets, the experimental spectrum is shown at the top and the reconstruction below it. The experimental details and assignments are as in Fig. 2. All the single Lorentzian components used to model the metabolite resonances are shown below the reconstructed spectra. Quantitative results for these components are given in Table 2. Note that individual Lorentzian lines have been plotted with phase zero. The difference spectra (experimental minus reconstruction) are shown at the bottom. The results presented in the figure were calculated using the FITPLAC program, though VARPRO gave nearly identical results (see Table 2). An extreme overlap of different lipoprotein lipid resonances is observed in the spectra. However, the final component models are consistent and can be interpreted biochemically. (Modified from Ref. [99].)

504

hf. Ala-Korpela J Progress in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

all the probabilities are sufficiently low. Furthermore, use of special techniques may allow for connection of the observed multidimensional spectral changes to the biochemistry of the system [ 1253. Neural networks (see below) have also been successfully used in supervised pattern recognition [ 1133. Some applications of the SIMCA method to detect malignant tumors by using ‘H NMR spectra of plasma are discussed in Section 6. 4.4 Neural networks A neural network consists of simple signal-processing units, called neurons. Each neuron can have multiple inputs, but has only a single output. Transfer functions for the input-output relationship can have various forms. A common feature is that inputs of a neuron are first multiplied by a weighting factor that determines the extent to which each input influences the output: the weighted inputs are summed together to form a pre-neuron sum, which is then processed and modified through the transfer function to produce an output to other neurons. To form a neural network the neurons are organised into a series of layers. The input data, for instance the NMR spectrum, is fed into neurons in the first layer. Usually each neuron gets an input from each neuron in the preceding layer and also gives an output to each neuron in the following layer: this kind of construction is called a feedforward fully connected neural network. The layers between the input and output layers are called hidden layers. The topology of a network (i.e. number of neurons in each layer and number of layers) depends on the complexity of the problem to be solved [126-1281. With a given topology and transfer functions, the desired behaviour of the neural network can be approximated by adjustment of the weights of the neuronal connections. This is called training of the network and is carried out by using a data set for which the output of the corresponding inputs is known. Various algorithms are available for finding the weights, but the fairly robust (and slow) back-propagation algorithm is the most popular [126]. An empirical model is obtained if the training process is able to reduce the errors in the outputs of the neural network for the training data set to negligible value. Thus, mimicking the human cognitive processes, the above schedule can lead to learning by the neural network. The idea and desire is that the trained network also models the underlying process(es) that generated the training data and that the network can be used to calculate the right output values for input data that were not used in the training. In practice, it has turned out that neural networks trained in a proper way do have this generalisation capability [126128]. Before any applications, the constructed and trained neural network needs to be validated to derive statistical confidence limits for the results that are obtained when the network is applied to unknown samples. The only way to do this is to apply the trained neural network to a test data set for which the output of the corresponding inputs is known, but which was not used in the training of the network [126128]. Use of a neural network requires no specific biochemical or biophysical model of the system. This is certainly a great benefit in complex situations as often encountered in biological spectroscopic applications. In this respect they are fundamentally different from conventional spectral analysis where the resonances are analysed peak-by-peak or from lineshape fitting analysis which requires a mathematical model to be able to properly handle heavily overlapping experimental information. It should also be emphasised that after the neural network has been trained it gives the result for each input instantly. No further concern about the unequivocality of the results is needed either since no more fitting occurs during the actual neural network analysis. In most applications neural networks have been used to successfully classify experimental data [113,126129]. However, recently a few applications have been published in which their quantitative properties have also been established [126,130,131]. The first application to quantify NMR spectroscopic data is presented in Section 5.3.3 [130,131]. 5. Metabolite quantification The major advantages of ‘H NMR-based quantification of plasma metabolites are easy sample preparation, fast measurements of samples in chemical equilibrium and perceivable information on

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505

both the low molecular weight metabolites and the macromolecules. Under the most favourable conditions quantitative data from all proton containing metabolites of interest can be recorded in one single-pulse experiment. However, in practice the intense and broad overlapping signals from lipoprotein lipids, an irregular background resonance of albumin and albumin-bound immobile fatty acids and the dominating residual water peak cause such problems that a special quantification approach needs to be adapted for both the low molecular weight metabolites and the lipoprotein lipids. Additional attention is also required to quantify some low molecular weight metabolites (e.g. tyrosine, phenylalanine, histidine and lactate) that bind to plasma proteins [74, 751. The experimental techniques and mathematical solutions for these special problems are discussed separately for the low molecular weight metabolites (Section 5.1) plasma lipids (Section 5.2) and lipoproteins and their lipids (Section 5.3).

5.1 Low molecular weight metabolites 5.1.1 Use of a Hahn spin-echo sequence

Spin-echo sequences are the basis of an efficient method for reducing the dominating lipoprotein lipid resonances and the intense albumin-related baseline in the proton spectra of plasma (see Section 3). They allow simultaneous detection and quantification of all mobile low molecular weight metabolites, which are present in plasma in high enough concentrations of about 0.1 mM and over. However, quantification of signals from some low molecular weight metabolites such as valine, lactate, alanine, acetone, glucose, alcohols (if present) and the N-acetyl groups of mobile carbohydrate side-chains of glycoproteins will be possible directly from the single-pulse spectrum (see Fig. 2). Use of sophisticated data analysis techniques (Section 4) can then be applied and a remarkable increase in quantitative information results since lipoproteins and their lipids can be quantified at the same time (Section 5.3.2). Formation of CaEDTA and MgEDTA complexes and their separate intense resonances in proton spectra (both spin-echo and single-pulse; see Fig. 2) make it possible to quantify the free (EDTAchelatable) calcium and magnesium in plasma when EDTA is used as an anticoagulant in the blood samples. It has been shown that less than 1% of the total Ca2+ and Mg2+ remains uncomplexed at physiological pH if EDTA is maintained at millimolar excess levels and that the intensities of the ethylenic -N-CH2-(X,-Nsinglet resonances of CaEDTA and MgEDTA (El and E2 in Fig. 2, respectively) in the Hahn spin-echo (r = 60 ms) spectra are generally within 5% of the values obtained by atomic absorption spectrophotometry [59]. Use of concentration standard is necessary if absolute concentrations of the metabolites from the ‘H NMR spectra of plasma are to be determined. TSP (sodium 3-trimethylsilyl[2,2,3,3-DJpropionate) is a commonly used reference in NMR spectroscopy. However, it is not a proper choice for an internal reference standard because of its interactions with the plasma proteins, e.g. it has a short aliphatic -CD2-CD2-COOchain which can bind to albumin [95]. It has been demonstrated that, unlike TSP, formate does not interact with serum macromolecules [95]. This has allowed for development of a methodology for quantitative analyses of low molecular weight metabolites in serum and plasma based on additions of a known quantity of formate to the samples [95]. Even though formate is an endogenous metabolite, it is normally present in human serum at low concentrations ( z 0.3 mM) and produces a unique singlet at 8.47 ppm. It can thus be used as a serum metabolite reference in both normal and pathological situations except in cases where disorders of endogenous formate metabolism occur. Kriat et al. [95] have studied 10 healthy subjects and collected the blood samples after a 48 h fast into vacutainer tubes containing no anticoagulant. Serum was immediately separated by centrifugation and stored at - 20°C until analysed with ‘H NMR. Formate (30 ~1, final concentration 15.4 mM) and D20 (20 $1) were added to each serum sample (400 ul) and the pH was adjusted to 7. The measurements were carried out at 22 + 1°C and 400 MHz using 5 mm tubes. 32 FIDs were collected for each Hahn spin-echo spectrum (r = 60 ms) and the water signal was attenuated by applying continuous secondary irradiation at the water frequency during the relaxation delay (6 s).

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Tl and T2 relaxation times were also estimated for selected metabolites to enable correction of the different relaxation behaviour of the metabolite resonances in the concentration calculations. The metabolite M concentration, CM, was calculated using the equation

CM = Fd

G&FMT,FYT~ AFNMFFT,FFT~

in which CF is the concentration of formate, AM is the area of the metabolite M and AF that of the formate resonance in the spectrum, Fd is the dilution factor due to the addition of D?O and formate (value, 1.125), FOIT,, FFTlr FMMT2and FFTl are the T1 and T2 correction factors for the metabolite M and formate, respectively, and NM is the number of metabolite M protons. The correction factors FT1 and FTI were calculated as FT1 = (1 - e-r~I~l)-~

and FT2 = e2Tl*z

(28’)

where TR is the repetition time and T the interpulse delay in the spin-echo sequence [95]. Also experiments in which ammonium chloride (final concentration 0.8 M) was added to the samples to release the protein-bound metabolites and to allow complete water signal suppression were performed [95]. This procedure increased intensities of lactate, /I-hydroxybutyrate and acetate resonances. No significant differences for the quantification of acetate, alanine, lactate and valine were observed between the NH&l sera and the extracts deproteinised by ultrafiltration. The authors [95] preferred addition of NH&l to the serum samples instead of time-consuming deproteinisation. Comparison of the ‘HNMR-based concentrations (C NMRin mM) with the values obtained as a result of specific biochemical assays (C,io) showed good correlations for alanine (r = 0.89, CNMR= 0.8 x Cbio + 0.04) glycine (r = 0.88, CNMs= 1.0 x Cbio - O.Ol), lactate (r = 0.96, CNm = 0.8 x Cbio + 0.10) and valine (r = 0.97, C NMs= 0.9 x Csia + 0.04). A very good correlation (I = 0.97) was obtained also for glucose despite the fact that the absolute NMR-based concentrations tended to be slightly underestimated (CNMR= 0.9 x Cbio - 0.75) [95]. It should be noted that if an external reference and locking substance is used for instance in a concentric tube, the interaction effects are completely avoided and any reference substance can be used. Moreover, relative areas of metabolite signals (e.g. percent of the area of the reference signal) can be calibrated using the biochemical assays; in the case of a linear relationship the absolute concentration of a metabolite can be estimated even in the case of a non-zero intercept in the correlation equation calculated using Eq. (28). If the samples are prepared using an identical protocol and the NMR experiments are performed under identical conditions, this procedure would take into account the relaxation behaviour of the metabolites, the effects of water suppression on the metabolite resonance and effects of protein binding [67]. This is based on assumptions that all interactions in the different samples are similar (this assumption is also partly behind Eq. (28) when the same average correction factors for the relaxation behaviour of the metabolites are used for individual samples). 5.1.2

Use of deproteinised

plasma

Recently Wevers et al. [132] have introduced ‘H NMR of deproteinised plasma as an alternative technique for the spin-echo sequence-based quantification of low molecular weight plasma metabolites. They presented a standardised ‘HNMR spectroscopic procedure which allows detection limits between 2 and 40 uM for various metabolites. To avoid additional EDTA signals either serum or heparinised plasma samples were preferred. The samples were stored at - 80°C until analysis and deproteinised by centrifuging the samples for 2 h over a filter with molecular weight cut-off of 10 kD. The ultrafiltrate was evaporated to dryness using an automatic concentrator, then dissolved in 0.5 ml HZ0 and adjusted to pH 2.50 + 0.10 at room temperature and evaporated again. Finally, the samples were dissolved in 0.5 ml DzO containing 0.812 mM TSP as an internal standard [132]. The ‘H NMR spectra of the deproteinised plasma samples (400 ~tl)were obtained at 25°C using a 60” pulse, 16k data points, a 15 s repetition time (132 FIDs) and a sweep width of 6605 Hz at 600 MHz. The residual water resonance was suppressed by low power presaturation during the relaxation

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,cose

Threonine Ereatinine

Glutamine

I

I

Va!ip

J 5.00

4.00

3.00 PPM

2.00

1 .oo

Fig. 8. A 600 MHz ‘H NMR single-pulse spectrum (at 25°C) of a deproteinised plasma sample from a healthy male. The sample was prepared as described in the text (4 x -concentrated in this case). (From Ref. [132] with permission.)

delay. An example of a ‘H NMR spectrum of a four times concentrated deproteinised plasma sample of a healthy adult volunteer is shown in Fig. 8. The NMR-1 software (New Methods Research, East Syracuse, NY, USA) was used to perform Lorentzian lineshape fitting analysis and the metabolite concentrations were calculated from the area of the corresponding resonance(s) with respect to the area of the TSP resonance. A library containing chemical shifts of 38 identified compounds (plus 14 unidentified resonances) was presented (49 plasma samples from patients who were clinically suspected to have an inborn error of metabolism were studied) [ 1321. It is seen from Fig. 8 that clear signals from the amino acids isoleucine, leucine, threonine, lysine, proline, glutamine, serine, tyrosine, phenylalanine and histidine are observed. This represents a clear advantage over the spin-echo spectra of serum or plasma where quantification of these compounds is either inaccurate or not possible at all. In contrast to the spin-echo spectra of serum at pH 7, the threonine and lactate methyl resonances are clearly separated at 1.33 ppm and 1.41 ppm, respectively. Also creatine and creatinine signals are resolved. The NMR analysis is in good agreement with conventional amino acid analysis of alanine, threonine and tyrosine [132]. Also lactate concentrations obtained by the NMR method correlated with those from an enzymatic assay (both with or without protein removal from plasma). Under their experimental conditions, Wevers et al. [132] found no evidence of NMR-invisible lactate or of a partial binding of lactate to serum proteins. This agrees with the spin-echo ‘H NMR data from low molecular weight ultrafiltrates of plasma by Bell et al. [75]. Wevers et al. [132] also demonstrated that detection and quantification of low molecular weight metabolites in plasma using ‘HNMR of deproteinised plasma samples can be applied to the detection and study of inborn errors of metabolism. As an example of the diagnostic power of the technique they presented a case of 5oxoprolinuria (pyroglutamic aciduria), an enzymatic defect in glutathione biosynthesis. Advantages of this technique are good sensitivity (achieved by concentrating the samples up to a factor of 4) and the absence of disturbing indirect spin-spin coupling effects present in the Hahn spin-echo spectra. Even though the technique requires replacement of the

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sample Hz0 with D20, which may cause some disturbing exchange effects, and results in removal of volatile compounds such as acetone and ethanol, the quality and usefulness of the single-pulse ‘H spectra obtained from the deproteinised plasma samples is much better than those from the commonly used spin-echo method [132].

5.1.3 Use of WATR-CPMG Fan et al. [133] have shown that the CPMG pulse sequence, when combined with the WATR method [4,134,135] (Water Attenuation by T2 Relaxation) for water suppression, can be used to quantify glucose in plasma (and in fruit juices). In the WATR method the T2 relaxation of water protons is enhanced by adding proton exchange reagents such as ammonium or guanidium chloride or urea to the sample. Application of a CPMG pulse sequence will then attenuate the broad water signal [4,133-1351. Fan et al. [133] made ‘H NMR measurements at 250 MHz from 0.45 ml (plus 0.05 ml D#) solutions in 5 mm tubes. The agreement between the plasma glucose concentrations determined using the WATR-CPMG technique and a glucose oxidase method was excellent with a deviation of less than 5% across a range of values from 90 to 500 mg per 100 ml: the correlation coefficient was 0.999. It was concluded that the WATR-CPMG technique is unable to compete with the glucose oxidase method on a routine basis, but potential research applications are considerable since the glucose oxidase method is /?-specific whereas ‘H NMR can provide structural as well as multicomponent quantitative information on both glucose anomers and other metabolites simultaneously [133].

5.2 Plasma lipids Analyses of plasma lipids by NMR spectroscopy (both ‘H and 31PNMR) have been introduced as an alternative method to the tedious and time-consuming conventional biochemical multistep procedures, which include separation and derivatisation and require a combination of several analytical techniques such as thin-layer chromatography, gas chromatography and high performance liquid chromatography. Kriat et al. [136] have applied ‘HNMR to study plasma lipid extracts of nine healthy subjects and nine cancer patients (the cancer-related aspects are discussed in Section 6). They collected the blood samples from fasted subjects into sterile EDTA-containing plastic vials. The plasma samples were immediately centrifuged and stored at - 20°C. Total plasma lipids were extracted with chloroform-methanol (2 : 1) from 2 ml of plasma. The lipid extracts were dissolved in 2.5 ml of CDCIJ-CDsOD (2 : 1). The proton spectra of the lipid extracts were recorded at 22 f 1°C at 400 MHz using 5 mm probes, 16k data points, a 90” pulse and a repetition time of 5.5 s. The total accumulation time was 3 min and the residual methanol signal at 4.65 ppm was suppressed by applying continuous selective secondary irradiation during the relaxation delay. Tetramethylsilane (TMS) was added to the samples as a chemical shift reference, but the volatility of TMS prevented its use as a reference for absolute quantification [136]. A typical ‘H NMR spectrum of a plasma lipid extract contains a number of resonances as shown in Fig. 9. The peak assignments are given in the caption. Resonances arising from different protons in the lipid hydrocarbon chains, glycerol backbone, free and esterified cholesterol and choline containing phospholipids are clearly observed and identified. Note that separate signals from the C(19)Ha protons of free and esterified cholesterol at (1.012 ppm and 1.04 ppm, respectively) and from the choline headgroup -N(CHs)s protons of sphingomyelin and phosphatidylcholine (at 3.21 ppm and 3.22 ppm, respectively) are resolved. Also the glycerol backbone C(3)Hz and C(1)H2 protons of phospholipids (at 4.01 ppm and 4.44 ppm, respectively) and triglycerides (at 4.34 ppm) resonate as distinct multiplets. Therefore this type of proton spectrum provides possibilities for versatile plasma lipid quantification. In the study of Kriat et al. [136] only relative quantification was possible; a very good correlation (r = 0.98) between the triglyceride/phospholipid ratio estimated by biochemical assays and by ‘H NMR was found. The NMR triglyceride concentration was based on the glycerol backbone C(l)Hz and C(3)Hz proton signals of the triglycerides and the

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27 (1995) 475-554

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1.50

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Fig. 9. A 400 MHz ‘H NMR spectrum of a plasma lipid extract sample obtained from a healthy subject. 32 FIDs were recorded at 22 f 1°C with 16k data points, a 90” pulse and a repetition time of 5.5 s. The water signal was attenuated by application of a continuous secondary irradiation at the water resonance frequency during the relaxation delay. The numbered resonances are assigned as follows [136]: C(18)Hs in total cholesterol (I), C(26)Hs (0.86 ppm) and -C(27)H, (0.88 ppm) in total cholesterol (2), CH, in fatty acyl chain (3), C(21)Hs in free cholesterol (4), C(19)H, in free cholesterol (5), C(19)Hs in esterified cholesterol (6), (CH,-), in fatty acyl chain (7), = CH-CH,(CH,). in fatty acyl chain (8), CO-CH,CJH,in fatty acyl chain (9), =CH-CHr- in fatty acyl chain (lo), COCH2in fatty acyl chain (ll), =CH-CH,-CH = in fatty acyl chain (12), -N+(CH,), in sphingomyelin head group (13), -N ’(CH,), in phosphatidylcholine head group (14), solvent methanol (15) -CH,-N’(CHs)s in phospatidylcholine or sphingomyelin head group (16), -C(3)H, in glycerol backbone of phosphatidylcholine (17), C(l)H, in glycerol backbone of phosphatidylcholine and triglycerides (18), CH2CH2-N+ (CH,), in phosphatidylcholine or sphingomyelin head group (19), C(1)H2 and -C(3)H, in glycerol backbone of triglycerides (20), C(l)H, in glycerol backbone of phosphatidylchohne (21), C(2)H in glycerol backbone of phosphatidylcholine and triglycerides (22), and -HC=CH- in fatty acyl chain (23).(From

Ref. [ 1361 with permission.)

phospholipid concentration on the choline headgroup -N(CH3)S proton signals of sphingomyelin and phosphatidylcholine [136]. It is obvious that use of a proper external or internal proton concentration standard would allow absolute quantification of many different plasma lipids; a practical advantage of the method is that after extraction of the lipids, no additional treatment is needed to perform the NMR analysis. Additional reasons for studying plasma lipid extracts, suggested by Cozzone’s group [ 1361, are as follows: “The analysis of the methylene and methyl proton signals of intact plasma cannot be accurate because these signals are extremely heterogeneous, consisting of overlapping resonances from protons of (i) different classes of lipids (triglycerides, cholesterol and phospholipids), (ii) proteins (albumin), and also (iii) low molecular mass metabolites (lactate, threonine, and valine).” However, it has been shown that prior knqwledge of lipoprotein model lineshapes enables mathematical separation of VLDL, LDL and HDL fractions from experimental ‘HNMR spectra of plasma C65-671. The possibility of identifying the resonances of the different lipid categories of the various lipoproteins seems rather unlikely (Sections 2.2, 4 and 5.3).

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5.3 Lipoproteins and their lipids

Observation that iH NMR can be used to quantify plasma lipoproteins is closely related to the original claim by Fossel et al. [137] that the average width of the methyl -CHJ and methylene (-CH,-). resonances in a proton spectrum of plasma could indicate cancerous disorder (‘H NMR studies of plasma which relate to cancer research are discussed in Section 6). Related investigations were set up after this finding in research laboratories all over the world. Unfortunately, the new studies could not reproduce the original results (Section 6). However, the studies revealed interesting new aspects which suggested that there is a relationship between the width of the methyl and methylene envelopes and the relative lipoprotein concentrations. It was subsequently shown that the methyl and methylene envelopes in the ‘HNMR spectrum of plasma arise from the lipoprotein lipids [66, 67, 82, 83, 1381. Alterations in lipoprotein metabolism are related to many pathological conditions such as coronary heart disease (CHD), diabetes mellitus, and cancer. Particularly LDL and HDL particles, and the cholesterol they carry, play a central role in the development of CHD (Section 1.4). Therefore, there is a practical need for a rapid quantification technique for lipoproteins and their lipids. This is important because the conventional methods require a physical separation of the different lipoprotein categories followed by specific biochemical assays of the protein and lipid constituents. This means time-consuming and laborious measurements and may also induce inaccuracies (Section 2.3). It should be emphasised that even though the two independently developed ‘H NMR-based lipoprotein quantification methods [65-67,82,83, 124, 1381 are, at least from the clinical point of view, quite preliminary, they have clearly shown the versatile possibilities that ‘H NMR can offer. Advantages of these NMR approaches are that quantification can be done directly from a ‘H NMR spectrum of a plasma sample on a time scale of minutes and no sample pretreatment nor any physical or chemical decomposition of the lipoproteins is needed. The samples can be used in further analyses after NMR measurements if required. There is evidence that the NMR estimates could be at least as accurate as the biochemical ones [67, 77, 78, 81-831. The other technique has already been compared to a wide variety of biochemical lipoprotein lipid assays in a double-blind fashion [67] and there is extensive evidence that both methods can be extended to offer even more relevant information than they do at present [65-67, 82, 83, 138, 1393. A growing body of experimental evidence has accumulated that the methyl-CH3 and methylene (-CH,-), resonances in the ‘HNMR spectra of different lipoprotein fractions (VLDL, LDL and HDL) show remarkably little variability (in the chemical shifts and total lineshapes) from person to person [66, 67, 82, 1381. This has been the situation for both the lipoprotein fraction samples obtained from plasma of healthy control subjects and from different types of cancer patients [ 1381. Otvos et al. [ 1381 have studied over 100 samples of each lipoprotein fraction at 500 MHz and 23°C. In fact, the invariability of the methyl and methylene envelopes in the proton spectra of the main lipoprotein categories is the key fact to enable their quantification directly from the ‘HNMR spectrum of plasma. This prior knowledge is needed because the methyl and methylene resonances arise from the fatty acid hydrocarbon chains of the lipoprotein lipids and in the ‘H NMR spectrum of plasma these resonance regions contain strongly overlapping contributions from all lipoprotein particles. Without any additional information the mathematical data analysis procedures would not be able to extract reliably the specific lipoprotein information from the combined experimental data [65-671. The experimental and mathematical bases and the present success of the existing lipoprotein quantification methods are discussed below.

5.3.1 A linear method; an experimental approach Many interesting aspects influencing the ‘H NMR characteristics of the lipoproteins and plasma have been studied by Otvos and co-workers [82,83,138,140]. After collecting blood into Vacutainer Tubes containing EDTA and storing the plasma samples at 4”C, they studied by means of ‘H NMR at 250 MHz a total of 48 cases of healthy fasted subjects and some postprandial and simulated plasma samples [82]. Also several chylomicron, VLDL, LDL, HDL and lipoprotein-free fraction

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511

(‘bottom’ fraction; density in the ultracentrifugational isolation > 1.21 gcme3) samples were studied. Total cholesterol and triglyceride concentrations were measured enzymatically. HDL cholesterol was measured with an Ektachem 700 analyser in the supernatant obtained after precipitation of a plasma aliquot with dextran sulphate, and LDL cholesterol was calculated using the Friedewald approximation (Section 2.3). Ultracentrifuged lipoprotein fractions from several subjects were combined to provide stock lipoprotein solutions for preparation of simulated plasma samples and generation of the reference spectra used in the curve fitting of the plasma lineshapes [SZ]. A systematic study of the effects of sample storage conditions on the lineshapes of the methyl and methylene resonances in the plasma spectra indicated that negligible changes occurred during the first six days at 4°C but occasionally apparent changes were observed (notably for plasma samples with high triglyceride concentrations) after longer storage. A sealed coaxial tube (2 mm outer diameter) containing an external intensity standard (TSP 8 mmol l- ‘, MnS04 0.6 mmol l- ’ and 99.8% DzO) was used with every sample (0.5 ml) placed in 5 mm outer diameter tubes. Measurements were carried out at several temperatures ranging from 15 to 45°C. The magnetic field homogeneity was optimised for each sample by shimming on the water signal and was controlled by requiring the linewidth of the sharp singlet CaEDTA resonance (El in Fig. 2) to be < 3.0 Hz. A single-pulse sequence, preceded by a 1.0 s selective presaturation pulse at the water frequency, was used. The spectral width was 2800 Hz and 120 FIDs were accumulated using 4k data points; zero-filling to 16k and apodisation by a 1 Hz exponential function preceded the Fourier transformations. The spectra were phased and chemical shift referenced to the CaEDTA signal before a linear baseline correction was applied to flatten the baseline between 1.8 and - 0.2 ppm [82]. The methyl lineshape in the ‘H NMR spectrum of plasma was described by Otvos et al. [82] with the equation

j=l

in which the superscripts R and I denote the real and imaginary parts of the spectrum, respectively, Pi is the experimental plasma spectrum (consisting in this case of 132 discrete data points i), Vji refer to the reference spectra of the n lipoprotein components, Vkiis the spectrum of the lipoprotein-free fraction and Cj, ck and cp are the unknown weighting factors whose values are determined by minimising the root mean square deviation between the experimental and the calculated plasma spectrum. n was 3 or 4 referring to the VLDL, LDL and HDL fractions plus the possible chylomicron fraction. In an ideal case of a perfectly phased spectrum and the absence of the underlying lipoprotein-free fraction components, only the first term in Eq. (29) needs to be used to approximate the experimental spectrum and the lipoprotein amounts. Inaccuracies of phase correction and the ‘bottom’ fraction resonances do exist, and thus the second term (to approximate the underlying signals) and the third term (to take into account phase differences between the plasma spectra and the reference spectra of the lipoprotein fractions) have been included [82]. The weighting factors, cj, derived by this method have no absolute meaning, but they can be scaled to lead to the concentrations of the lipoprotein components. Eq. (29) was solved by using a conventional multipleregression program (written in BASIC) in which matrix inversion is used for solving the set of linear equations [82]. Results from plasma samples from fasted subjects and samples known to contain chylomicrons were presented; if one (or more) of the components in Eq. (29) is absent from the plasma spectrum, application of the program will often lead to negative concentrations for these components. For example, inclusion of the chylomicron component in Eq. (29) in the analysis of plasma spectra of (healthy) fasted subjects frequently gave negative chylomicron concentrations, and hence incorrect concentrations for the other lipoprotein fractions [82]. A non-negative least squares program that introduces a physical constraint that the derived concentrations must be positive, should be tried in these cases (see below). The intensity and shape of the methyl resonance in the ‘HNMR spectrum of HDL and especially LDL fractions are temperature dependent (Section 7.5). Based on several measurements in the temperature range from 15 to 45”C, Otvos et al. [82] concluded that the relative amplitudes of the

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TG = 1135 mg/dl HDL-C

090

086

0.82 PPM

078

0.74

070

= 42 mg/dl

0.90

0.86

0.82

078

0.74

0.70

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Fig. 10. The methyl resonance region of a 250 MHz ‘H NMR spectrum of a plasma sample at 23 and 40°C. A single-pulse sequence, preceded by a 1.0 s selective presaturation pulse at the water frequency, was used. Spectral widths were 2800 Hz and 120 FIDs were accumulated using 4k data points; zero-filling to 16k and apodisation by a 1 Hz exponential function preceded the Fourier transformations. The spectra were phased and chemical shift referenced to the CaEDTA (El in Fig. 2) signal before a linear baseline correction was applied to flatten the baseline between 1.8 and - 0.2 ppm. Beneath the experimental (solid line) and calculated (dashed line) plasma spectra the amplitudes of the average experimental lineshapes of the lipoprotein and lipoprotein-free fractions obtained by applying a linear least squares curve fitting algorithm [82] are shown (Eq. (29)). Note the different shape of the plasma methyl envelope concurrent with the change in the shape and amplitude of the LDL component; see also Fig. 5. (Modified from Ref. [82] with permission.)

methyl resonances of the chylomicron and VLDL fractions do not significantly change, but they discovered that the amplitudes of the HDL and LDL methyl signals increase almost twofold and fivefold, respectively. The important consequence of this behaviour is that HDL and especially LDL make much greater contributions to the composite methyl lineshape of the plasma spectrum at high temperatures than they do at lower temperatures. This is clearly seen from Fig. 10 in which the experimental and calculated methyl resonances in the ‘H NMR spectrum of a plasma sample at 23 and 40°C are shown together with the experimental reference lineshapes of the lipoprotein fractions (used in Eq. (29)). After testing their method at both temperatures the authors concluded that analyses at 40°C were more accurate than at 23°C [82]. Finally, they applied the method (the NMR measurements were done at 45°C) to analyse 48 different plasma samples from fasted subjects. They showed that proton NMR provides information about lipoprotein concentrations that is comparable with that given by biochemical measurements; correlation coefficients between the NMR derived amounts of VLDL, LDL and HDL and the biochemically estimated plasma triglycerides, LDL cholesterol (approximated using the Friedewald formula) and HDL cholesterol were 0.914, 0.801 and 0.912, respectively [82]. By comparing the ‘HNMR-based VLDL, LDL and HDL quantification of plasma samples obtained from fasted subjects and from the same subjects after a high-fat meal (the chylomicron component was then included in Eq. (29)) the authors showed that plasma samples from non-fasted subjects could also be analysed using ‘H NMR [82]. This is illustrated in Fig. 11. From the practical point of view this finding is very interesting since, e.g. the widely used LDL cholesterol approximations based on the Friedewald formula require fasting plasma samples [77,82-J. To refine their lipoprotein quantification method Otvos et al. [83] also approximated the lipoprotein subspecies distributions in the reference spectra of the lipoprotein components ( Vii in Eq. (29)). Using pooled EDTA-plasma from two healthy fasted donors, they applied sequential ultracentrifugation to isolate the following lipoprotein components: VLDL (density < 1.006 gcme3), LDL (density 1.0061.063 gcme3), large LDL (density 1.0061.035 gcme3), small LDL (density 1.035-1.063 gcmW3), HDL (density 1.063-1.21 gcmm3), large HDL (density 1.063-1.125 gcmm3) and small HDL (density 1.125-1.21 gcme3). Subtle differences were observed in the methyl resonance region of the ‘H spectra (at 45°C and 250 MHz) between the LDL and HDL subspecies.

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4 h Postprandial

0 62 PPM

Fig. 11. The methyl resonance region of a 250 MHz ‘H NMR spectrum of a plasma sample at 45°C from the same individual after a 12-h fast (top) and 4 h after a fat-rich meal (bottom). The experimental and analytical details are as in Fig. 10. Note the appearance of the chylomicron component and its successful description using the experimental lineshape. (From Ref. [82] with permission.)

Although the chemical shift and lineshape differences between the subspecies are much smaller than those between the major lipoprotein classes, they were completely reproducible (the lipoprotein size-related chemical shifts of the ‘H resonances are discussed in Section 7.6). Because Otvos et al. [83] had not isolated a sufficient number of pure lipoprotein subspecies to serve as reference standards in the lineshape analysis (Eq. (29)), they used digitally shifted spectra of VLDL, LDL and HDL to approximate the methyl lineshapes in the ‘H spectra of the lipoprotein subspecies. They

used six, five and seven artificially shifted spectra for VLDL, LDL and HDL, respectively. This meant that the number of the lipoprotein components in the linear lineshape fits was expanded (from 3 or 4) to 18. Because of the increased number and close similarity of the spectral components used to deconvolute the plasma methyl lineshapes, Otvos et al. [83] made several. modifications to the least squares fitting program to address the greater mathematical difficulty of the linear problem. Singular value decomposition was used (rather than matrix inversion) to solve the set of linear equations and a non-negativity algorithm was incorporated to restrict the solutions to those giving positive lipoprotein concentrations. However, details of the algorithm were not presented [83]. The method was applied to analyse plasma samples from 30 healthy non-obese normolipidaemic men. The samples were selected to represent a range of plasma lipid values and LDL particle sizes and they were taken after an overnight fast and stored at - 70°C before NMR analysis. Nondenaturing gradient-gel electrophoresis was used to measure LDL and HDL subclass distributions.

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Strong correlations were observed between the ‘H NMR derived and the biochemical values for all the major lipoprotein fractions; r = 0.98 for VLDL triglycerides, r = 0.91 for LDL cholesterol and r = 0.93 for HDL cholesterol. Weaker correlations were observed when the same comparison was made with the NMR concentrations obtained by lineshape analysis with single VLDL, LDL and HDL reference components (r = 0.97 for VLDL, r = 0.84 for LDL and r = 0.52 for HDL). If the artificial lipoprotein subspecies reference spectra could be replaced by the actual subspecies reference spectra the results would be expected to improve as discussed by Otvos et al. [83]. However, on the basis of the discussion of the unequivocality of data analysis in Sections 4.2.1 and 53.2 and the analysis problems encountered [82, 831, it seems necessary to pay more attention to the prior knowledge, i.e. number and lineshapes of the lipoprotein reference signals used in the mathematical deconvolution of the plasma spectra. The use of an LDL cholesterol value based on the Friedewald approximation as a reference and comparison value for the NMR results should also be avoided (though Otvos and co-workers [82, 831 criticised use of the Friedewald approximation, they continued to use it). Moreover, a good correlation (r = 0.88) was obtained for a weighted-average HDL particle size (calculated from the areas under the different regions of the gradient-gel densitometry traces) and an NMR derived size variable HZ (calculated from the relative concentrations of the HDL subspecies). The LDL subspecies distributions derived by NMR also correlate well with those determined by gradient-gel electrophoresis and it was preliminarily suggested that the method could be used to determine whether an individual has predominantly small or large LDL particles. This might have diagnostic value since different lipoprotein subspecies may have remarkably different association with CHD [83,84]. It should also be noted that there is no additional cost associated with obtaining the lipoprotein size information from the ‘H NMR experiments, because the subspecies distributions are a byproduct of the analysis procedure [83].

5.3.2 A non-linear method; a mathematical approach Our group has published ‘HNMR spectroscopic studies on human blood plasma and lipoproteins in which particular attention has been paid to mathematical data analysis [65-67,99, 1231. The first lineshape fitting model [65] for the methylene (-CH,-), resonance region in the ‘H NMR spectra of plasma consisted of two Lorentzian components for VLDL and one each for LDL and HDL. For the methyl -CH3 resonance, three Lorentzians for VLDL and one each for LDL and HDL were established. The component model and the assignments were based on 400 MHz ‘H NMR experiments of plasma samples of a healthy subject and of VLDL, LDL and HDL samples and plasma samples to which the different lipoprotein fractions were separately added [65]. The measurements were carried out at 30°C using a 4 s presaturation pulse at the water resonance frequency to suppress the large water signal. The non-linear lineshape fitting analyses were performed using the FITPLA program written in FORTRAN (the current version of this lineshape fitting analysis program, FITPLAC, is written in C, see Section 4.2.1). This mathematical approach enabled detailed characteristics, i.e. chemical shifts, linewidths and relative intensities, of the individual lipoprotein components to be obtained directly from the plasma spectra [65]. Using a similar approach we have also been able to explain explicitly and to confirm mathematically the reasons for the failure of the Fossel cancer test; namely the varying relative concentrations of the different lipoprotein fractions [123] (see Section 6). In another study both ‘HNMR lineshape fitting analyses and extensive biochemical lipid analyses were applied to study plasma, VLDL, LDL and HDL samples from six subjects [66]. Two subjects were normolipidaemic while the others had hyperlipidaemias. Venous blood was taken into EDTA tubes after an overnight fast and the lipoprotein isolations were commenced on the same day. All the lipoprotein fractions were dialysed extensively overnight against 0.15 M NaCl - 1 mM EDTA adjusted to pH 7.4 with NaOH. The concentrations of total cholesterol, free cholesterol, triglycerides and phospholipids in plasma and lipoprotein fractions were determined by enzymatic calorimetric methods. The ‘H NMR spectra were recorded at 30°C and 400 MHz using 5 mm tubes with D20 as a locking substance added to the samples (about 5 wt.%). The water signal was suppressed in the case of the lipoprotein fraction samples by applying a binomial 1 - i pulse

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sequence and in the case of the plasma samples by a 4 s presaturation pulse at the water resonance frequency [66]. At first, attention was focused on the methylene (-CH,-), regions in the spectra of VLDL, LDL and HDL fractions [66]. All spectral profiles of these regions were found to be clearly asymmetric and were thus examined using the program FITPLA to gain specific information about their shapes and component structures (see Fig. 3). This strategy (Levenberg-Marquardt non-linear lineshape fitting in the frequency domain), which allows straightforward analysis of the spectrum in restricted parts and thus limits the number of model signals needed, was demonstrated to be efficient and practical [66]. The theoretical counterparts of the methylene resonance regions in the experimental ‘HNMR spectra of the lipoprotein fractions were described as (see also Eq. (22))

iP=“ F (V) = f: L,(v)’+ B(v)’ k=l

+c$+cfv

(30)

where hk is the half linewidth, Ik the intensity, vk the resonance frequency, and @kthe phase angle of the Lorentzian signal k. K is the total number of components, cg and CTdefine the linear baseline B(v)’ and F = VLDL, LDL or HDL. The characteristic parameters in Eq. (30) were determined to produce the calculated spectrum iUXH 2-)n(v) (caret refers to a model function) as close to the experimental ‘H NMR spectrum M’,‘Hz-)n(v) as possible in the chi-square sense (see Section 4.2.1). These analyses led to quantitative information of both the shape and the integrated intensity of the spectral regions [66]. The meaning of the component structures of the methylene (and methyl) regions in the lipoprotein fraction spectra are discussed in Section 2.2. Together with some additional properties (discussed below) this kind of strategy also enabled efficient and easy analyses of the methylene regions in the ‘H NMR spectra of plasma, and made lipoprotein quantifications possible

cw. The first part in the Lorentzian shape in Eq. (30) is the absorption part of the signal and the second is the dispersion part. If the phase correction of the spectrum is exact, the real spectrum is purely absorptive (and the imaginary spectrum purely dispersive). The phase correction of the ‘H NMR spectra of plasma and lipoproteins is in practice so difficult that inaccuracies of a few degrees easily result. Hence both absorption and dispersion parts of the Lorentzians were used in the lineshape fitting analyses with the phase as one of the variable parameters (to allow for an automatic correction of these deviations for every spectrum). Only one phase parameter was used for the whole methylene region and the lactate signals were described using a doublet Lorentzian line. Furthermore, when narrow spectral regions are analysed (100 Hz in this case), the linear baseline is a proper and practical way to account for the (unknown and varying) underlying broad resonances

cm Mathematical analysis of the methylene regions in the ‘HNMR spectra of plasma are always difficult since resonances from the LDL fraction overlap strongly with both the VLDL and HDL resonances. Therefore results from the lineshape fitting analyses of the lipoprotein fraction spectra (see Figs. 2 and 3) were used to construct average model signals for VLDL, LDL, and HDL to ensure more reliable and accurate analyses [66]. Relative intensity ratios and relative chemical shifts of the individual components in each model signal were fixed for plasma analyses. This meant that there were only two variable parameters; total intensity and chemical shift, for each lipoprotein fraction model signal in the lineshape fitting analysis procedure. Thus the theoretical expression of the methylene region in the proton spectrum of a plasma sample could be written as

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The analyses were fast (under a minute for one solution on PC) and stable (pertaining to the convergence of the non-linear problem). The fixing of the parameters was based on the similarity of the Lorentzian component structures of the methylene resonances in each lipoprotein category. Justification of these fixings was tested by doing the lineshape fitting analyses of the plasma spectra with the fixed values of the model function parameters taken from the lineshape analyses of the subject’s own lipoprotein fractions. Results of the lineshape fitting analyses of the plasma spectra showed no significant change in the correlation coefficients between the NMR derived values and the biochemical ones for VLDL and HDL, but for LDL some minor changes occurred (see Table 3) [66]. The average fixing approximation proved to be a reasonable choice. The experimental and calculated methylene regions of the ‘H NMR spectra of the six plasma samples are shown in Fig. 12 together with the lipoprotein model signals and the difference spectra. The relative integrated intensities of the lipoprotein model signals in each spectrum are also shown. These percentages were calculated using the equation VJP =

100% x-

(32)

FAk

in which AF is the integrated intensity of the lipoprotein F (F = VLDL, LDL, or HDL). Comparison with the biochemical lipid and protein analyses showed that these percentages can be considered as relative amounts of the different lipoproteins [66]. The relative molar percentages of the different lipid contents between the lipoprotein fractions were calculated based on the biochemical analyses using an equation-

in which mr is the concentration (mmol I-‘) of molecule(s) Xr in the lipoprotein fraction F (F = VLDL, LDL, or HDL). Correlations between these biochemically determined relative amounts and the NMR values from Eq. (32) are shown in Table 3 where the different molecular

Table 3 The correlation coefficients between the NMR amounts u, (Eq. (32)) and the biochemically determined Y,“”(Eq. (33)) (from Ref. [66]) k

1 2 3 4 5 6 7 8 9 10

&

TG PL ECHO FCHO CHO TG+PL TG + PL + ECHO TG + PL + FCHO TG+PL+CHO TLIPO

VLDL

LDL

HDL

a

b

a

b

a

b

0.97 0.96 0.92 0.93 0.94 0.99 0.97 0.98 0.96 0.97

0.94 0.96 0.94 0.95 0.95 0.98 0.97 0.98 0.97 0.98

0.39 0.94 0.87 0.61 0.90 0.82 0.88 0.76 0.85 0.95

0.54 0.91 0.78 0.68 0.88 0.88 0.87 0.84 0.86 0.91

0.89 0.98 0.97 0.96 0.97 0.99 0.98 0.98 0.98 0.99

0.85 0.98 0.96 0.96 0.97 0.98 0.98 0.98 0.97 0.98

a: analysis of the total plasma spectrum of a donor using the results of the lipoprotein fraction analyses of the same donor; b: analysis of the total plasma spectra of all donors using the mean values of the results from the lipoprotein fraction analyses of all donors. TG, triglycerides; PL, phospholipids; ECHO, cholesterol esters; FCHO, free cholesterol; CHO, total cholesterol; TLIPO, total lipoprotein amount.

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BI 10

20

10

0

-10

-30

-20

-40

-50

-60

-70

-00

8Z 30

20

10

0

-10

-20

-30

-10

-50

&iii&zz C.k.

X,62,

-60

-70

26(30,

S(9)

Diff.

Diff.

-

66 20

10

0

-10

J

-20

-30

-60

-80

12(20,

SOL

30

517

-50

-60

Lm

-70

-60

BI 30

20

10

0

-10

-20

-30

40

-50

-60

-70

-60

CdC.

56,&l, “Dr.

22f20) Dlft

--

.

Diff.

a* 30

20

10

0

-10

-20

-30

-60

-50

-60

-70

-00

II6 30

20

10

0

-10

-20

-30

-60

-50

-60

-,o

-00

Fig. 12. The experimental (Exp.) and calculated (Calc.) methylene regions of 400 MHz ‘H NMR spectra of plasma samples from six subjects. The spectra were recorded at 30°C using 5 mm tubes with D20 as a locking substance added to the samples (about 5 wt.%.). The water signal was suppressed by a 4 s presaturation pulse at the water resonance frequency. 100 FIDs were accumulated using a spectral width of 3 kHx, 16k data points, a 60”pulse and a pulse repetition time of 6.7 s. The FIDs were zero-filled and Fourier-transformed after applying an exponentially decaying apodisation function leading to a line broadening of 0.2 Hz. The lipoprotein model signals (Eq. (30), mean alternatives shown) used in the lineshape fitting analyses of these methylene regions of the plasma spectra (Eq. (31) using the program FITPLA) are drawn below the calculated spectra together with the relative percentages of the areas of the signals (Eq. (32)). The first value is calculated using the mean values for the fixed parameters in the lipoprotein model lineshapes and the values in parentheses correspond to the analyses with the fixed values of the model parameters taken from the lineshape analyses of subject’s own lipoprotein fractions. The lactate doublets were modelled in each case but are not shown for clarity. The difference spectra (Diff. = Exp. - Calc.) are shown at the bottom; see also Table 3. (From Ref. [66].)

combinations of Xk (k = l-10) are also given. Indeed, it was found that nearly all correlations are very good [66]. At first this may seem peculiar. The resonances in the methylene region arise most likely from all lipoprotein lipid molecules which have a fatty acid chain (i.e. triglycerides, phos-

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pholipids, and cholesterol esters). The correlations observed must hence arise from the fact that the amounts of the lipids, contributing to the ‘H NMR signal, correlate with each other and also with the amount of free and total cholesterol, i.e. the chemical composition of the lipoprotein particles is relatively constant. This was demonstrated in the study [66] and is the reason for the well-known strong autocorrelation in biochemical lipoprotein lipid analyses. Since it is mathematically possible to separate a signal arising from a given lipoprotein fraction from the plasma spectrum, the correlations become understandable after at least one good correlation between ur and r+ has been found. Thus the estimation of the amounts of free cholesterol and total cholesterol in the different lipoprotein fractions becomes intelligible. In fact, the observed ‘everything correlates with everything’ situation is promising from the application point of view, because experimental correlation lines could be constructed to determine the lipid contents in every major lipoprotein fraction directly from the three NMR values up. In addition, these preliminary results showed that using an internal or external proton reference for the integrated intensity in the NMR experiments, it should also be possible to obtain absolute concentrations of the lipoproteins and possibly those of their lipids [66]. Indeed, as discussed below, this soon proved to be the case [67]. The promising results described above [66] directed us to perform a study including subjects with a broad range of lipoprotein lipid values and subjects with various lipoprotein abnormalities. In this study a ‘H NMR-based method was developed for the absolute quantification of VLDL, LDL and HDL and their lipid and protein contents directly from plasma [67]. This method enables complete lipoprotein lipid profiles to be obtained in less than one hour. Absolute concentrations of phospholipids, total cholesterol, free cholesterol, esterified cholesterol, total protein and total mass can be estimated for VLDL, LDL and HDL fractions. Also VLDL and LDL triglycerides can be quantified [67]. The method applies the sophisticated lineshape fitting analysis program FITPLAC. The method was constructed on the basis of the analyses of the methyl regions in the ‘H NMR spectra of the lipoprotein and plasma samples and comparison of the NMR results with the results from extensive biochemical lipoprotein lipid analyses [67]. The methylene regions of the lipoprotein and plasma spectra were also analysed, but they did not give quantification as good as the methyl resonances. The study group consisted of 58 subjects (38 males and 20 females, age ranging from 20 to 86 years) with a broad range of lipoprotein lipid values [67]. 21 subjects had lipoprotein abnormalities such as hypercholesterolaemia, hypertriglyceridaemia and combined hyperlipidaemia (including one patient with heterozygotic familial hypercholesterolaemia). Five patients suffering from uraemia caused by chronic renal failure were studied. The study group was divided into two different sets [67]. A calibration set of 15 subjects with known lipoprotein lipid values that varied considerably among the subjects was set up to ensure a good preliminary calibration for the ‘HNMR-based quantification method. The second set, a double-blinded test set, consisted of 51 plasma samples from 43 different subjects (five identical samples from two subjects and one each from all the others). Quantification of the lipoprotein lipid values in the test plasma samples was carried out by both the ‘HNMR and the biochemical analysis methods in a double-blind fashion. The results of both methods were calculated before any comparisons were made and both analyses were completed without information on the clinical status of the subjects. The VLDL, IDL, LDL and HDL fractions were isolated from 20 subjects. The blood samples were drawn after an overnight fast of 12 h into EDTA-containing tubes and plasma was separated by centrifugation at 1200 x g for 15 min at 4°C. Plasma samples were stored at 4°C for less than 36 h before NMR measurements. The lipoproteins were isolated from plasma by sequential ultracentrifugation and they were dialysed against 0.15 M NaCl solution containing 0.01% EDTA, pH 7.4. Total cholesterol, free cholesterol, triglyceride and phospholipid concentrations were determined using enzymatic calorimetric methods. Protein, contents were measured with the Lowry method. ‘H NMR spectra of all samples were recorded at 37°C and 400 MHz. A double tube system was used: the sealed external reference tube (outer diameter 5 mm) containing the reference and locking substances (TSP 4 mmoll-‘, MnS04 0.3 mmoll-’ in 99.8% D20) was placed coaxially into the NMR sample tube (outer diameter 10 mm) containing the actual sample (2.5 ml). The water signal was suppressed by applying a binomial 1-i pulse sequence. In each experiment 256 FID signals (28 min) were accumulated using a spectral

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width of 5 kHz, 64k data points, 45” pulses of 28-33 us and a pulse repetition time of 6.6 s. For some plasma samples 32 FIDs (3.5 min) were accumulated. The FIDs were Fourier transformed without apodisation to produce frequency domain spectra, which were then transferred to a 486 IBM compatible PC for lineshape fitting analyses [67]. The lineshape fitting analyses of the methyl (and methylene) regions of the ‘H NMR spectra of VLDL, IDL, LDL, and HDL fractions from 20 subjects revealed that the individual Lorentzians were uniform for all the subjects in every lipoprotein category [67]. This is in accord with the previous qualitative experimental observations [82, 1381 (Section 53.1) and indicates that total intensity, i.e. concentration, is the main variable in the ‘H NMR spectra of a particular lipoprotein fraction. This permits construction of lineshape models for the methyl resonances of the lipoprotein fractions and their use in the analyses of ‘H NMR spectra of plasma. Three individual Lorentzians were needed for the VLDL and LDL fractions and one for the HDL fraction to give an accurate description of the methyl resonances as illustrated in Fig. 3. The uniform parameters were the linewidths, relative chemical shifts and relative intensity ratios of the individual Lorentzians. Thus, the following mathematical lineshape models X&:;;(v)

= &(v)VLDL+ &(v)VLDL+ &(V)VLDL

G$F’(v)

= L,(V)LD”+ L,(V)LDL+ t,(V)=

&$:3(v)

= &(“)“DL

(34)

could be constructed for the methyl resonances of the VLDL, LDL and HDL fractions (see also Eq. (30)). The uniform parameters of the individual Lorentzians in Eq. (34) were fixed to the values obtained as an average of the analyses [67]. The lineshape models for the lipoprotein methyl resonances were used in the analyses of the methyl lineshapes of the plasma spectra [67]. The theoretical description of the methyl resonance region in a plasma spectrum was expressed as G$&&)

= rci$&,

+ G$$(v,

+ &$$(v)

+ cc + c:v

(35)

in which cc + c;(v) takes into account the varying underlying background resonances related to the residual water, albumin and albumin-bound immobile fatty acids and proteins (see also Eq. (31)). For every plasma spectrum Eq. (35) was fitted to the experimental methyl envelope in a X2-sense using the Levenberg-Marquardt method for solving the non-linear matrix equation. The position (resonance frequency) and total intensity for every model signal was mathematically determined. The areas of the model signals were scaled in every spectrum using the area of the external reference signal (fitted by one Lorentzian) [67]. The methyl resonance in the ‘HNMR spectrum of plasma is a sum of heavily overlapping individual components of all lipoprotein fractions. In reconstructing the experimental ‘HNMR signal of plasma (or an isolated lipoprotein fraction), mathematical difficulties should be kept in mind (see Section 4.2.1) [65-67,99]. In the case of plasma spectra, a considerable amount of prior knowledge connecting the system biochemistry to the data analysis mathematics is needed to ensure mathematically unequivocal and physically consistent solutions. Sufficient prior knowledge can be obtained from the separate ‘HNMR experiments and lineshape fitting analyses of the ultracentrifuged lipoprotein fractions, i.e. the lipoprotein methyl model lineshapes &$Ez(v), J&$‘~(v) and &,$$r3(v) were necessary to ensure that the extremely overlapping information in the ‘HNMR spectra of plasma could be separated in an accurate and biochemically consistent way [67]. In non-linear problems the final solution might also depend on the initial values of the model function parameters [99]. Therefore an automatic guess-system has been incorporated into the FITPLAC program; the initial values of the variable parameters are randomly chosen from given wide (but reasonable) limits, but the final parameters are not constrained to these limits. This technique is therefore used only to effectively probe the multidimensional parameter space and to ensure reliable decisions about the unequivocality and physicality of the mathematical solutions. In practice, this

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means that about 5-25 different solutions are calculated for each spectrum; the solution with lowest r.m.s. error is selected as the final one if it is physically correct. The different solutions are easily checked with the graphical capability of the FITPLAC program [120]. Both the mathematical unequivocality and physical reasonableness of the presented solutions were carefully tested and found to be fulfilled for every spectrum. This meant that unique results for all the lipoprotein values could be obtained in each case [67]. The calculation time of one solution was about 2&30 s in a 486 PC operating at 33 MHz (normally 10 solutions were calculated for each spectrum). Hence, the mathematical analysis of one ‘H NMR spectrum of plasma could be completed in less than 10 min C671. The areas of the lipoprotein model signals were calibrated on the basis of the biochemical lipid and protein measurements (the calibration set). Linear correlations between the areas of the lipoprotein model signals scaled to the external reference and the biochemically estimated VLDL, LDL and HDL triglycerides, phospholipids, total cholesterol, free cholesterol, esterified cholesterol, total proteins and total masses were calculated. The correlations were very good, ranging from 0.84 to 0.99 in VLDL, from 0.88 to 0.95 in LDL and from 0.40 to 0.84 in HDL (see Table 4) [67]. The areas of the lipoprotein model signals scaled to the reference, estimated mathematically from the ‘H NMR spectrum of plasma, thus reflect the absolute concentrations of all lipids, total proteins and total masses of the lipoprotein fractions. Therefore these existing correlation equations were taken as conversion equations between the NMR and biochemical assays and used in further studies as a basis to estimate the lipoprotein lipid and protein values directly from the ‘H NMR spectrum of an unknown plasma sample. Especially for the clinically most essential lipid values, i.e. the VLDL triglycerides, LDL cholesterol and HDL cholesterol, the correlations were striking; the correlation coefficients were 0.99,. 0.93 and 0.84, respectively [67]. After the ‘HNMR method was calibrated, a double-blind test of 51 unknown plasma samples from 43 volunteers was performed using both biochemical lipid and protein assays and the developed ‘H NMR method utilising the specified conversion equations [67]. This served to assess the capabilities of the new method in true clinical situations. The methyl regions of the experimental ‘HNMR spectra from plasma of three subjects are shown as an example in Fig. 13 together with the calculated reconstructions, the lipoprotein lineshape models and the difference spectra. From Fig. 13 it is evident that different lipoprotein lipid profiles give different plasma methyl lineshapes. These ‘H NMR characteristics were shown to give lipoprotein lipid and protein values that are in good agreement with those estimated biochemically [67]. Correlation lines between the results of the NMR and biochemical assays of VLDL triglycerides, LDL cholesterol and HDL cholesterol are shown in Fig. 14 (for all 66 samples; see also Table 4) [67]. Differences between the NMR and the biochemical estimates of the same values are illustrated in Fig. 15. Moreover, the VLDL model signal area was found to correlate with the plasma triglycerides (r = 0.97, n = 66, plasma triglycerides (mmoll-‘) = 0.55385 + 0.00537 x VLDL area %) and the total area of the methyl resonance, i.e. the summed integrated intensity of the VLDL, LDL and HDL model signals, with the plasma cholesterol (r = 0.86, n = 66, plasma cholesterol (mmoll-‘) = 0.07988 + 0.00521 x (VLDL + LDL + HDL area %)) [67]. The reproducibility and accuracy of the ‘HNMR and biochemical analyses were assessed by analysing five identical plasma samples of two different subjects. The results were consistent and the ‘H NMR-based estimates are as accurate as the biochemical ones. The scatter of both methods was less than 0.2 mmoll-‘. Using these two sets of identical plasma samples the effect of NMR data collection time on the quantification results was also tested; 32 FIDs For each substance two equations are given; the first is calculated from the calibration set of 15 samples and the second from the whole study population. These conversion equations enable calculation of the lipoprotein lipid concentrations (mmol I- I), protein amounts (mg dl-‘) and total masses (mg dl- ‘) directly from the VLDL, LDL and HDL lineshape model areas obtained from the lineshape fitting analysis of a ‘HNMR spectrum of an unknown plasma sample (from Ref. [67]). ‘TG, triglycerides; PL, phospholipids; CHO, total cholesterol; FCHO, free cholesterok ECHO, cholesterol esters; PROT, total proteins; MASS, total mass (calculated as MASS (mg dl-‘) = 88.5 xTG (mm01 I-‘) + 77.5 x PL(nmt011-l)+ 38.7 x FCHO (mm01 1-t) + 64.9 x ECHO (mm01 1-l) + PROT (mg dl- ‘))

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coefficients (r) and conversion equations (BIO = A + B * NMR) between the reference scaled of the VLDL, LDL and HDL methyl lineshape models and the biochemical lipoprotein lipid and The NMR areas of the lipoproteins were obtained for each plasma sample from the lineshape of the methyl resonance region in the ‘H NMR spectrum [67]

Lipoprotein

r

BIO=A+BrNMR A ( x 103)

B [ x 1031

VLDL” TG (n = 15) TG (n = 66) PL(n= 15) PL (n = 63) CHO (n = 15) CHO (n = 66) FCHO (n = 15) FCHO (n = 66) ECHO (n = 15) ECHO (n = 66) PROT (n = 15) PROT (n = 66) MASS (n = 15) MASS (n = 63)

0.99 0.98 0.96 0.96 0.89 0.88 0.93 0.91 0.84 0.85 0.93 0.91 0.97 0.97

103.45 83.92 25.04 37.40 59.28 -7.10 - 5.78 - 7.47 65.07 0.37 4375.11 4883.34 19469.83 16666.98

LDL TG (n = 15) TG (n = 65) PL (n = 15) PL (n = 62) CHO (n = 15) CHO (n = 65) FCHO (n = 15) FCHO (n = 64) ECHO (n = 15) ECHO (n = 64) PROT (n = 15) PROT (n = 65) MASS (n = 15) MASS (n = 61)

0.94 0.63 0.94 0.90 0.93 0.88 0.88 0.82 0.93 0.89 0.95 0.89 0.95 0.92

-138.18 12.46 - 59.84 -7.33 72.80 10.03 58.75 60.84 14.06 -42.36 -22890.73 -12779.11 -36571.66 - 10045.92

0.68 2.92 2.79 8.10 8.28 2.02 2.02 6.08 6.23 244.31 227.10 1023.02 981.50

HDL PL (n = 15) PL (n = 64) CHO (n = 15) CHO (n = 66) FCHO (n = 15) FCHO (n = 66) ECHO (n = 15) ECHO (n = 66) PROT (n = 15) PROT (n = 66) MASS (n = 15) MASS (n = 64)

0.68 0.83 0.84 0.93 0.67 0.85 0.83 0.91 0.40 0.75 0.61 0.84

166.08 57.58 - 15.90 -67.04 -64.10 - 121.61 48.19 54.58 101973.49 57567.21 161220.59 80905.04

2.63 2.67 3.49 3.42 0.68 0.82 2.80 2.60 187.68 279.76 518.58 683.53

See footnote on opposite.

4.34 4.30 1.64 1.54 1.99 2.50 0.95 1.03 1.03 1.47 64.41 71.76 679.82 702.69 0.90

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VT =4.34(4.32) LC = 2.99(3.32) VT = 1.27(0.99)

HC =0.87(0.90)

LC = 5.24l5.51) HC=1.50(1.19)

VT =0.50(0.53) LC = 1.68(1.88) HC = 1.44l1.50)

0.84

0.80 ppm

0.76

0.64

0.80 ppm

0.78

0.84

0.80 ppm

0.76

Fig. 13. The experimental (exp) and calculated (talc) methyl regions of 400 MHz ‘H NMR spectra of three plasma samples. The experimental details are as in Fig. 2. The lipoprotein model lineshapes (Eq. (34)) used in the lineshape fitting analyses of the methyl resonance regions in the plasma spectra (Eq. (35) using the program FITPLAC) are shown and also the differencespectra (diff = exp - talc) are given at the bottom. The values given above the spectra are the VLDL triglyceride (VT), LDL cholesterol (LC) and HDL cholesterol (HC) concentrations from the ‘HNMR analyses and from the biochemical lipoprotein lipid analyses (in parentheses) in mmoll-‘. Note that different lipoprotein lipid profiles give different plasma methyl lineshapes and that each of these shapes is properly represented using the lipoprotein model lineshapes. The areas of the latter lead to lipoprotein lipid values which are in excellent agreement with those estimated biochemically; see also Table 4 and Figs. 14 and 15. (From Ref. [67].)

(accumulated in 3.5 min) and 256 FIDs produced identical results. This observation makes it possible to use only 32 FIDs for this application [67]. Although the NMR results were considered as preliminary, they clearly showed the capabilities and benefits of the ‘H NMR approach [67]. The method can be directly implemented in many areas of NMR spectroscopic research [67]. Many biomedical NMR studies, which are not necessarily aimed at studying lipoproteins, would inevitably gain considerable benefit from the additional biochemically important quantitative lipoprotein information, the measurement of which by conventional biochemical methods is usually too laborious and expensive to be adapted into study protocols. Fig. 13 gives a clear picture of the effects of the different lipoprotein lipid levels on the shape of the plasma methyl envelope and also demonstrates that visually apparently different situations can be analysed correctly by the lipoprotein model lineshapes and the data analysis technique [67]. It is emphasised that proper attention to the data analysis mathematics and the biochemical prior knowledge is necessary to achieve reliable and consistent results. In the FITPLAC algorithm, all essential aspects have been taken into account and also some special features facilitating the practical use of the program have been incorporated. FITPLAC estimates a linear baseline from the experimental spectrum and thus allows for automatic correction of the broad underlying hump from albumin, albumin-bound immobile fatty acids, some other protein and residual water resonances. The width of the analysed methyl resonance regions was 50 Hz, which allowed the described

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0.88

6. 5. 4. 3. 2. l-

NMR VLDL triglycerides

fmmollll

oNMR LDL cholesterol

2

3

NMR HDL cholesterol

[mmol/lJ

0

Immollll

1

Fig. 14. The correlation lines between the ‘HNMR and the biochemical assay estimates of the VLDL triglyceride, LDL cholesterol and HDL cholesterol concentrations. The numbers refer to the samples used in Ref. [67]; see text, Table 4 and Figs. 13 and 15 for details. (From Ref. [67].)

procedure to be applied correctly as all the underlying signals in these narrow regions are closely linear [67]. The baseline was fitted into the experimental spectrum simultaneously with the actual lipoprotein model signals. Thus varying underlying background resonances were mathematically taken into account in every spectrum and were therefore separated from the actual lipoprotein lipid methyl resonances described by the model lineshapes. As in the previous application (Eq. (30)), automatic phase correction was incorporated into the analysis algorithm; every spectrum was initially phased at the spectrometer and in the mathematical analysis of the methyl region a refinement to the phase correction was allowed. The FITPLAC algorithm also makes it possible to include all resonances present in the spectral region under study, for instance the lactate doublet in the methylene region and valine doublets in an extended methyl region. Therefore the problems encountered in the previously described ‘H NMR-based lipoprotein quantification approach [82, 83, 1381 (Section 53.1) can be avoided completely. The study population served as a good illustration of wide-ranging lipid values, e.g. VLDL triglycerides from 0.15 to 4.32 mmol l-‘, LDL cholesterol from 1.21 to 6.48 mmoll-’ and HDL cholesterol from 0.73 to 2.59 mmoll-’ [67]. Subjects with various lipoprotein abnormalities, familial hypercholesterolaemia and uraemia were also studied. The most common lipid anomalies such as hypercholesterolaemia, hypertriglyceridaemia and combined hyperlipidaemia were correctly identified by the ‘H NMR-based method [67]. This is the most important property for a method to be used in an automatic fashion to identify those persons at increased risk of CHD, i.e. to identify high LDL cholesterol and low HDL cholesterol levels. Good results in many different subjects inspires confidence in the potential of the method to be developed for a routine clinical assay for quantifying lipoprotein lipids and total protein and total mass of the lipoproteins [67]. More comparative experiments and analyses of plasma samples from subjects with distinct pathological conditions are needed to assess the validity of the method and to examine its possibilities in rare clinical conditions. As a first assessment of the limitations of this method and as a preliminary test towards a more specific classification of a person’s lipoproteins and an estimation of their lipid concentrations, several volunteers with elevated IDL levels were studied [67]. IDL particles exist in very low concentrations in normal plasma giving rise to insignificant contributions to the methyl lineshapes. Therefore IDL fractions have been excluded from the normal plasma analysis model to ensure reliable quantification of the abundant VLDL, LDL and HDL fractions. In 10 studied subjects the IDL cholesterol value was > 0.3 mmoll-’ because subjects assumed to have elevated IDL levels were specifically included in the study group [67]. The biochemical methods gave an LDL cholesterol concentration of 3.59 mmol l- ’ and an IDL cholesterol concentration of 1.08 mmoll-’ for one patient. In the NMR analysis, based on the VLDL, LDL and HDL model lineshapes, an LDL cholesterol concentration of 6.1 mmoll- ’ was estimated for that same patient (the greatest deviation in Fig. 15) but the VLDL triglyceride and HDL cholesterol estimates were still correct. In other samples with slightly elevated IDL levels the NMR JPNW

27:5/6-F

524

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Progress in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

-2.0 -

- -2.0

I . . . . I. 0 10

I a.

1.

20

a.

* I.

30

* .n

1..

40

. * I.

50

*.

* I . . .

60

Plasma sample no. Fig. 15. Differences (NMR-BIO) between the ‘HNMR (NMR) and the biochemical (BIO) estimates of the VLDL triglyceride (black circles), LDL cholesterol (crosses)and HDL cholesterol concentrations (open circles); see text and Figs. 13 and 14 for details. (From Ref. [67].)

estimates of LDL cholesterol tended to be slightly higher than the biochemical ones. Also in these subjects VLDL triglyceride and HDL cholesterol estimates agreed well with the biochemical results. Firstly, this finding indicates that elevated IDL levels will exaggerate the LDL levels in the NMR analysis if only three lipoprotein model lineshapes are used. As elevated IDL levels are uncommon this tendency is not a serious problem in lipoprotein quantification. Secondly, it proposes additional possibilities for the ‘HNMR approach: if the plasma analysis model could be expanded, i.e. including IDL in the model by describing it by its own model lineshape, more specific quantification of the lipoproteins could result [67] (see also the next Section). Based on ‘H NMR measurements of 20 IDL fractions and lineshape fitting analyses of the methyl resonances, we developed an IDL lineshape model as a combination of three individual Lorentzian components [67]. This IDL lineshape model was then used as an additional component in the analysis of the plasma spectrum of the above mentioned subject, i.e.

+ c$ + c:v

(36)

in.which GgLH3(v) = t,(v)IDL + t,(v)IDL + &(v)IDL. As a promising indication of further capabilities of the NMR method the mathematical solution shown in Fig. 16 was found to be unequivocal and the ‘new’ LDL cholesterol concentration approximated by using the earlier defined conversion equation was 4.71 mmoll-’ instead of 6.01 mmoll- ‘. It should also be noted that other methods, such as the commonly used Friedewald approximation, include the IDL cholesterol in the LDL cholesterol estimate as well [77, 813. For this patient the Friedewald approximation gave an LDL cholesterol value of 5.57 mmoll-’ [67]. As has been emphasised before, mathematical analysis of heavily overlapping information is a difficult task requiring sufficient prior knowledge and an appropriate analysis protocol to achieve biochemically meaningful and reliable results. If more lipoprotein model lineshapes are to be added to the plasma analysis model, extreme care has to be taken in the interpretation of the results. To ensure unique results for every spectrum, the necessary amount of prior knowledge in each model needs to be carefully tested to avoid over extraction of information from the experimental spectra. For example, if the IDL model lineshape is included in the same way as the other fractions in the case of normal low IDL concentration, consistent and reliable solutions are not automatically obtained.

M. Ala-Kovela /Progress in Nuclear Magnetic Resonance Spectroscopy 2 7 (I 995) 4 75-554

525

diff

I”~‘,..“,‘..‘,~~“,“‘.,.’

ppm 0.88

0.84

0.82

0.80

0.78

0.78

Fig. 16. The experimental (exp) and calculated (talc) methyl region of a 400 MHz ‘H NMR spectrum of a plasma sample from a subject with abnormally high IDL level (IDL cholesterol 1.08 mmol l- ‘). The experimental details are as in Fig. 2. The lipoprotein model lineshapes (Eqs. (34) and (36)) used in the lineshape fitting analysis of the methyl resonance region in the plasma spectrum (Eq. (36) using the program FITPLAC) are shown and the difference spectrum (diff = exp - talc) is given at the bottom [67]; see text for details.

This is because the model contains too many free parameters to be estimated from the experimental spectrum, in which information from the IDL particles is insignificant, and thus mathematical unequivocality is not reached. Nevertheless, as the preliminary IDL example shows, it might be possible to include IDL in the plasma analysis model, and in fact, addition of even more lipoprotein model lineshapes (chylomicrons and subfractions of VLDL, LDL and HDL) could be realised (see Sections 53.1, 5.3.3 and 7.6). This would considerably increase the information available from the ‘H NMR-based techniques because similar calibration of the different lipoprotein lipid concentrations applied to VLDL, LDL and HDL would be possible for all included lipoprotein model lineshapes [67]. The main practical difficulty is to find the most representative set of lipoproteins which can be included in the plasma analysis model in such a way that an unequivocal solution for every spectrum is automatically obtained. In order to resolve this problem, different lipoprotein model sets have to be tested. Also a special mathematical algorithm will be necessary for this application to ensure consistent results for all possible concentrations of the lipoproteins. Especially if one (or more) (sub)fractions have very low concentrations there have to be particular means to cope with this non-existing experimental information in a biochemically sensible way. A decisive factor for the development will be the amount of sound prior knowledge available for the lipoprotein model lineshapes (Section 7.6). Also application of neural network analysis seems particularly useful as discussed in the next Section. 5.3.3 Neural network analysis As is evident from the above discussion, non-linear lineshape fitting analysis of complex overlapping information is mathematically a very difficult task and requires prior knowledge of the number and shape of the underlying (lipoprotein) components. Also routine analysis of only occasionally observable quantities, e.g. lipids in the IQL fraction, has not yet been resolved. The neural network analysis has several important advantages (see Section 4.4). As an elevated IDL fraction is an independent CHD risk factor it is desirable to include it in the lipoprotein quantification procedure [ 1411. As we have recently shown, this can be automatically accomplished by adapting the neural network analysis to ‘H NMR spectroscopic data of plasma samples [130, 1313. Unlike lineshape fitting analysis the neural networks need neither a mathematical nor physico-chemical model to describe the data. Thus they offer a completely different way of data analysis, an approach that

526

M. Ala-Korpela / Progressin Nuclear Magnetic Resonance Spectroscopy

LD

TSP

27 (1995) 475-554

.j

.___......... -’ -.*i . . . . . .._____.~ ‘:.

0 Lo ,i

_iJ+, 5

:: ..-,’

../-

. . .. . . ... --....

!

z

8. 9

\

(-CH,-I,

/...” .. _.: . . .. ..-. 0 c::: . . .. . 5: ‘z;:... ...a.:... ‘..* P-

nssays

INPUT ‘H NMR DATA

Fig. 17. Topology of the neural network used to from the ‘H NMR data of plasma. A ‘H NMR between the absolute VLDL triglyceride, IDL, biochemical lipid assays and the neural network

quantify VLDL triglycerides, IDL, LDL and HDL cholesterol input for one subject is illustrated on the left. Correlations LDL and HDL cholesterol concentrations estimated by the analysis of ‘H NMR data from plasma samples are shown for

the training and test samples on the right. (Modified from Ref. [131].)

seems to be very efficient and particularly suitable for analysing and quantifying complex NMR data [126131-J. Hiltunen et al. [131] used a study group of 57 subjects (65 plasma samples) with a broad range of plasma lipoprotein lipid values. Twenty subjects had lipoprotein abnormalities such as hypercholesterolaemia, hypertriglyceridaemia and combined hyperlipidaemia. The details of the plasma samples, the biochemical lipid analyses and the ‘H NMR experiments were as explained in Section 53.2 and in Ref. [67]. In this application the study group was divided into two different sets: 28 plasma samples to form a training set and 37 samples to form a test set for the neural network analysis. The study group included a wide range of normal and abnormal lipoprotein values and the training set was selected to cover the whole range of studied lipoprotein lipid concentrations. The neural networks were trained to relate the real part of the ‘H NMR spectrum of a plasma sample (input) to its lipoprotein lipid concentrations obtained by the biochemical assays (output). A commercial DynaMindTM program was used [ 131, 1421. To quantify the clinically most important lipid values, e.g. VLDL triglycerides, IDL, LDL and HDL cholesterol, a feedforward fully connected neural network with an input layer of 962 neurons, one hidden layer of 100 neurons and an output layer of 4 neurons was used. The structure of the applied neural network with the input and output data is illustrated in Fig. 17. The first two layers were connected by sigmoidal transfer functions and the last two layers with linear ones. Two regions of the ‘H NMR spectra were offered to the neural network as the input: the singlet resonance of the TSP reference (to enable absolute quantification) and the methyl -CH3 and methylene (-CH,-), resonances of the lipoprotein lipids. The input regions consisted of 101 and 861 points and were 15 Hz and 250 Hz wide, respectively. The input values were scaled so that the top of the reference signal in all spectra was set to 1 and the spectral baseline was set to - 1. Also the values of the target outputs were scaled to have values between - 1 and 1. If the -CH2-CH2-COOCand the -CH2-CHz-COOCmethylene resonances of the lipoprotein lipids were also incorporated in the input data there were no significant improvements. Slightly better results were obtained using 100 rather than 50 neurons in the hidden layer and applying the Madaline III algorithm instead of the back-propagation one in the learning stage [131, 1421.

M. Ala-KotpelalProgress

in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

521

The training process with the ‘HNMR and biochemical lipoprotein data from the 28 plasma samples led to complete learning of the neural network. The correlation coefficient between the VLDL triglyceride, IDL, LDL and HDL cholesterol concentrations estimated by the biochemical lipid assays and the corresponding values from the neural network analysis of the ‘H NMR data was 1.00 with an r.m.s. error of 0.10 mmoll- ’(Fig. 17). The correlation coefficients for each lipid value were 1.00,0.98,0.99 and 0.97, respectively. The VLDL triglyceride, IDL, LDL and HDL cholesterol concentrations obtained from the neural network analysis of the ‘HNMR data of the 37 test set plasma samples were also in excellent agreement with the biochemical assay values: the correlation coefficient was 0.98 with an r.m.s error of 0.22 mmoll-’ (Fig. 17). The correlations for each lipid value were 0.99,0.74,0.87 and 0.84, respectively. In interpreting the separate correlations it should be remembered that in the test set the ranges of the lipid values are narrower than in the training set and thus the correlation coefficients get smaller although the absolute differences remain slight. This difference between test and training sets is largest for IDL cholesterol since the range in the training set was from 0.02 to 1.08 mmol l- ’ and in the test set only from 0.04 to 0.46 mmol l- ’ [ 1311. The complete lipid profiles plus total masses and total proteins for VLDL, LDL and HDL fractions have been shown to be available from the ‘H NMR data (Section 53.2) [67]. This was also demonstrated in the neural network analysis by constructing a neural network with the lipoprotein phospholipid concentrations as outputs: for example in the training and test sets the correlation coefficients between the biochemical VLDL, IDL, LDL and HDL phospholipid estimates and those derived from the constructed neural network were 1.00 and 0.97, respectively [131]. These preliminary results suggest that the ‘H NMR spectrum of a plasma sample does form an adequate basis for complete lipoprotein quantification. All the lipoprotein lipid concentrations can be automatically obtained as an output from the neural network analysis if a ‘H NMR spectrum of a plasma sample is provided as input. The NMR spectroscopic measurements give the pertinent lipoprotein data in one single measurement but in a very complex form of heavily overlapping lipoprotein lipid signals. However, the neural networks are able to establish the relation between the ‘H NMR spectrum and the desired quantitative lipoprotein information. Both essential steps are also very fast: a ‘H NMR measurement can be performed in about 20 min and the neural network analysis is instant [131]. It has been established that heterogeneity inside LDL and HDL fractions is related to the CHD risk [83, 84, 1431. Comparative biochemical and ‘H NMR spectroscopic measurements are underway in our laboratory to assess the possibility of using neural network analysis together with data preprocessing [99] and sophisticated lineshape fitting analysis incorporating biophysical prior knowledge [ 1393 to quantify also lipoprotein subfractions and estimate lipoprotein size distribution in plasma.

6. Cancer detection and research 6. I The Fossel index and related studies

“A potentially valuable approach to the detection of cancer and the monitoring of therapy*’ was the original conclusion in the work of Fossel et al. in 1986 [137]. Based on the measurements of ‘HNMR spectra of plasma at either 360 or 400 MHz at 2&22”C from 331 subjects including normal controls, patients with malignant and benign tumors, patients without tumors, and pregnant women, and examination of the spectra by applying a new parameter, the Fossel Index, FI, which is calculated as a mean of the approximate widths at half-height of the methylene and methyl resonance envelopes, it appeared possible to clearly and reliably distinguish (P < 0.0001) between normal controls (FI = 39.5 + 1.6 Hz) and patients with malignant tumors (FI = 29.9 + 2.5 Hz). Also patients with diseases unrelated to tumors and patients with benign tumors seemed to differ from the patients with malignant tumors (FI = 36.1 k 2.6 Hz, P < 0.0001 and FI = 36.7 + 2.0 Hz, P < 0.0001, respectively). Only pregnant women were found to give Indexes consistent with the presence of malignant tumors. These promising results led the authors to suggest that “such measurements may be applicable to many - perhaps all - types of cancer” [137].

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M. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554

As a general blood test to detect cancer would be of very great clinical value, the seemingly excellent promise of Fossel’s results led to a large number of subsequent investigations. Within a few years a number of papers reporting studies of the evaluations of the usefulness of the Fossel Index had been published [S, 96, 114, 117, 123, 124, 138, 140, 14441711. Many research laboratories were able to reproduce the difference in the Fossel Index between the cancer patients and controls, but unfortunately in other works a remarkable overlap between the different groups was established. The conclusion of an intensive interlaboratory collaboration [152] states very well the general results: “the test proposed by Fossel and coworkers on human blood plasma is precise (reproducible) but does not have predictive values high enough to be used as a general screening test for cancer.” With hindsight it is easy to state that the above conclusion should be the expected one. Since cancer includes about 150 reasonably distinct clinical conditions with great variability in invasiveness, metastatic potential and individual tumors, it would be very surprising to find a single test that could reliably detect all or even a large number of them [ 1521. Furthermore, a reduced predictive value for a screening test results even in the case of good sensitivity (the probability of correctly identifying an individual with the condition of interest) and specificity (the probability of correctly identifying an individual without the condition) if applied in a general case of low disease prevalence. However, in high risk populations or in cancer therapy management, ‘H NMR-based methods may have some value (see Sections 6.1.1 and 6.4). The biochemical basis for the observed differences in the Fossel Index between different groups of subjects started to evolve together with the maturation of the realistic picture of the impracticality of the ‘test’. Studies on the effects of plasma triglyceride levels and hypertriglyceridaemia [ 153,155,157, 1651, diet (postprandial lipaemia caused by chylomicrons) [162] and pregnancy [168, 1691 on the Fossel Index showed that the VLDL (triglyceride) level of plasma is a dominating factor. Along with the deeper understanding that the methyl and methylene resonances in the proton spectra of plasma can be assigned to originate from the different lipoprotein categories [62, 64-67, 82, 83, 123, 138, 1691, the substantial effect of plasma HDL levels also became evident. In fact, these observations are in complete accord with the known occurrence of lipoprotein lipid abnormalities such as increased VLDL (hypertriglyceridaemia) and (markedly) decreased HDL levels in cancer patients [136, 172-1751. They gave a natural explanation for the noticed behaviour of the Fossel Index: elevated VLDL levels cause narrowing of especially the plasma methylene resonance, an effect which is even accentuated by the weakening of the low frequency shoulder of the envelope(s) as a result of low HDL levels. It was also found that the chemical shifts and total lineshapes of the methyl and methylene resonances in the proton spectra of the individual lipoprotein fractions differ little in different subjects despite the presumed heterogeneity in fatty acid and lipoprotein subspecies compositions [66,67,82, 138, 1571. These findings were the basis for the mathematical deconvolutions of the plasma methyl and methylene envelopes (see Figs. 2, 3, l&l3 and 16) which finally clearly demonstrated that these envelopes are composite resonances of the different lipoprotein categories and that the linewidths, and consequently the Fossel Index, depend solely on the relative concentrations of the lipoprotein fractions [65-67,82,83, 123,124, 1383. For example, as illustrated in Fig. 18, the plasma methylene linewidths were demonstrated to be linearly dependent on the relative VLDL and HDL amounts (see Eq. (32)) in a study group of healthy adults, adults with small primary tumors and adults with large metastases [ 1231. The correlation coefficients were - 0.89 and 0.88, respectively. These studies also contributed to the development of the ‘HNMR-based lipoprotein quantification methods (Section 5.3).

6.1. I Cases of speci$c medical interest Malignant lung carcinoma is difficult to distinguish from infectious or inflammatory lung diseases by means of X-ray. Hence a complementary method for their differentiation would be helpful in practical diagnostics. However, as is obvious from the preceding conclusion, plasma methyl and/or methylene parameters have been found unable to distinguish between these different disease states [167]. Schuhmacher et al. [167] also measured the spin-lattice (T,) and spin-spin relaxation times (Tz) of the plasma water protons at 20°C at 20 MHz. Neither relaxation measurements were able to

M. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy

27 (1995) 475-554

?? kx

??

??

x

529

0

??

m

0

I

I

I

,

I

1

1

10

20

30

40

50

60

70

A(VLDL)

1

(%)

?? ??

0

10

40

A(I-I:L) m

Group A 0

50

60

(‘i; Group B

i

Group C

x Group D

Fig. 18. The relations Between the linewidth of the methylene envelope of the ‘H NMR spectrum of a plasma sample and the relative amounts of VLDL and HDL calculated using Eq. (32). The correlation coefficients are - 0.89 and 0.88, respectively. Group A is neonates (they were not taken into account in the correlation equation calculations since their lipid profiles were totally different from those of the adults), group B is healthy adults, group C adults with small primary tumors and group D adults with large metastases. (From Ref. [123].)

distinguish between the patient groups nor could the T1 values separate between the patients and the healthy controls [ 1671. Mean Tz was significantly shorter ( z 15%) in any kind of lung disease than in the controls. A combination of T2 and apolipoprotein-A levels led to a specificity,of 96.5% and sensitivity

of 80% in the separation

of tumor

patients

and healthy

controls.

However,

as

correlates inversely with plasma fibrinogen levels and the blood sedimentation rate, it appears to monitor a general inflammatory status of tumor patients rather than the presence or absence of cancer [ 1671. Pancreatic cancer is a diagnostic challenge to all investigation methods [170, 1713. Only about 10% are diagnosed early enough for resection, and the 5-year prognosis of these patients with resectable tumor is x 4%. Sometimes diagnosis can be made only after exploratory surgical Tz

530

hf. Ala-Korpela / Progress in Nuclear Magnetic Resonance Spectroscopy

27 (I 995) 475-554

procedures. Therefore, better methods to screen symptomless people and to diagnose pancreatic cancer early enough for curative treatment would be of great value [170, 1711. Among patients with jaundice and/or cholestasis the prevalence of pancreatic cancer is relatively high. To assess the complementary use of ‘HNMR to detect malignant disease in these high-risk patients with hepatopancreatobiliary disorders a study of 51 serum samples (25 malignant and 26 benign) has been performed [ 1701. Also several immunological tumor markers were studied in combination with ‘HNMR [171]. Pasanen et al. [170, 1711 performed NMR measurements at 400 MHz and 23°C from frozen ( -20°C) samples to which D20 (10%) and TSP (1 mM) were added. No useful discrimination between benign and malignant causes of jaundice and/or cholestasis was possible using ‘HNMR measurements and spectral analyses focused on the methyl and methylene resonances of the serum samples [170]. When the immunological serum tumor markers and the ‘HNMR spectroscopic parameters were evaluated as combinations, an increased specificity in comparison to that of the tumor markers alone was achieved. This could be seen as an advantage since the lack of specificity is a particular problem for immunological tumor markers [171].

6.2 Malignancy

associated

lipoprotein

A proteolipid complex was present in sera of 94 of 96 patients with malignancies but not in sera of any of 58 patients with non-malignant disorders or 46 healthy subjects [ 176). In the ultracentrifugation procedure (KBr gradients) the complex could be observed in the distinct opalescent band between LDL and HDL fractions. Even with abnormal lipoprotein metabolism, no band interfering with this area was detected. The chemical composition of the proteolipid complex was found to be different from LDL and HDL compositions and RNA and glycosphingolipids were detected. Wieczorek et al. [ 1761 favoured a view that the complex represents a specific secretory product of the tumor cells, which may mediate host-tumor interactions. The proteolipid complex, so-called malignancy associated lipoprotein (MAL) (or Band-X), has also been detected and studied by ‘HNMR [153, 177-1821. The MAL fraction of a patient with ovarian tumor has been found to give rise to a characteristic cross peak (at 1.3; 4.2 ppm) in 2-D ‘H-‘H COSY spectra [178,179]. This resonance has been assigned to a methyl-methine coupling of fucose and the methyl part has been shown to have a high spin-spin relaxation value T2 of the order of 850-970 ms which is much longer than that in LDL (111 ms) or in HDL (154 ms) [179]. Nine months after resection of the entire tumor, a visible proteolipid band, possibly lipoprotein (a) (Lp(a)), persisted in the plasma but the originally found long T2 value was no longer observed [179]. In the 2-D ‘H-‘HCOSY spectrum of the Lp(a) fraction from a pregnant woman no cross peak was detected unlike in the MAL fraction isolated from the serum of a patient with prostate cancer [ 1821. The above studies together with several NMR studies of cancer cells have led Mountford and Wright [ 1821 to introduce a new model of lipid organisation in the cell membranes of malignant and stimulated cells. According to this model neutral lipid (isotropically tumbling triglyceride) is intercalated with phospholipid in the bilayer configuration. Moreover, it was also suggested that cancer cells could shed MAL particles and that they are the only serum lipoproteins which carry the fucosylated species in a form discernible by NMR [182]. The assignment of the cross peak (at 1.3; 4.2 ppm) in the 2-D ‘H-‘HCOSY spectra of the MAL fraction to surface fucosyl residues has been criticised by Phillips and Herring [183]. In fact, they offered a reinterpretation of studies by Mountford and co-workers [153, 177-179, 181, 1821. They used the SUPERCOSY pulse sequence, instead of COSY, to edit out peaks with short T2 values and thus attained better dynamic range in the case of plasma [183]. The decreased acquisition time afforded by the SUPERCOSY method allowed Phillips and Herring [ 1831 to screen a larger number ofsamples than had been attempted by Mountford and co-workers [153,177-179,181,182]. A cross peak similar to that found earlier in the COSY spectra of the MAL fractions was observed in samples of plasma and various lipoprotein fractions from both cancer patients and healthy subjects. The peak was most prominent in HDL and in Band-X from cancer patients [ 1831. Such widespread occurrence of this cross peak, at concentrations detectable by NMR, raised questions about the

M. Ala-Kotpela 1Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554

531

validity of earlier assignment of the peak to a fucolipid, which is expected to be a very minor component of plasma (less than 1% of the total lipoprotein population) [153, 1831. Indeed, Phillips and Herring [183] gave evidence that the cross peak could arise from lactate. They do not completely rule out the possibility of observing fucose however, but they emphasise that its clear separation from other similar materials will take a great deal of effort. They conclude that the results of previous workers appear to be due to a molecule exhibiting an NMR spectrum that is indistinguishable from lactate, a molecule which can bind tightly to HDL and serum albumin (undergoing a small chemical shift change) and can be elevated in plasma of cancer patients [183].

6.3 Metabolic modifications of plasma in cancer In a study in which Kriat et al. [136] applied ‘HNMR &ectroscopy to quantify plasma lipid extracts they also studied plasma and plasma lipid extracts from several patients with different types of cancers (see Section 5.2 and Fig. 9). The analyses of the ‘HNMR spectra of the lipid extracts indicated that the presence of cancer induces a significant increase in the triglyceride/phospholipid ratio. Using 31PNMR to quantify phospholipids from the plasma lipid extracts they also showed that concentrations of the two main phospholipids, phosphatidylcholine and sphingomyelin, are significantly reduced in the cancer patients [136]. These findings are in accordance with the high incidence of hyperlipidaemia in cancer patients, characterised by the decrease of HDL and concomitant increase of VLDL levels [172-1741. In addition to the 1-D spectra of plasma and plasma lipid extracts, Kriat et al. [136] performed 2-D ‘H-‘H COSY measurements at 400 MHz. They used seven standard lipids (triglyceride, phosphatidylcholine, lysophosphatidylcholine, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine and sphingomyelin) to identify lipid class specific cross peaks and recorded the COSY spectra from plasma and plasma lipid extracts of six healthy subjects and six cancer patients. The signals arising from the acyl chains of all lipids were very similar but distinctive features characterising the lipid classes were obtained by examining the glycerol backbone, phospholipid head group and sphingosine backbone resonances. In the 2-D ‘H-‘H COSY spectra of the plasma lipid extracts of the cancer patients the cross peak arising from the spin-spin coupling between geminal protons in phospholipid glycerol backbone C (1) was often undetectable due to the decrease in the phospholipid content. In one case of ovarian cancer with residual metastasis, Kriat et al. [136] detected a new cross peak (at 1.35; 4.14 ppm), which they tentatively assigned to the spin-spin coupling between the methyl and methine protons of fucose. In agreement with the results of Phillips and Herring [183] they found no evidence for the presence of a resonance from the fucosylated lipids in the 2-D ‘H-‘H COSY spectra of the plasma samples of cancer patients. They concluded that the incidence of hyperlipidaemia in patients with cancer is a more general observation than the presence of particular glycolipids [136]. In another recent study Vion-Dury et al. Cl753 have developed a special (and eccentric) graphicaided approach to study metabolic modifications of plasma in cancer. A notable property of NMR is the ability to detect many metabolic compounds originating from different metabolic pathways in a non-selective manner from a single spectrum of plasma. Using their ‘star plots’ they have been able to distinguish three types of metabolic patterns: (i) the ‘inflammatory’ pattern characterised by an increase of glycosylated moieties of glycoproteins, (ii) a ‘lipid modified’ pattern characterised by various modifications occurring mainly in the lipid moieties detected by NMR and (iii) a pattern which is often observed in sarcomas and mainly characterised by an alteration in the N-acetyl glucosamine/N-acetyl neuraminic acid ratio [ 1751. They studied plasma samples from 87 patients having evolving cancers (including sarcomas, gliomas, blood, breast, skin, mouth and neck cancers), from 23 patients with partially or totally regressive sarcomas or blood cancers, from 51 patients having non-tumoral miscellaneous pathologies (including mainly traumas, cerebral strokes and disc herniations), from 21 patients with inflammatory (including rheumatological and infectious) diseases and from 49 control subjects. The plasma samples were stored at - 20°C for up to 15 days. Both single-pulse and Hahn spin-echo spectra (a spin-echo time of 120 ms) were measured at

532

M. Ala-Koyela /Progress in Nuclear Magnetic Resonance Spectroscopy 27 (1995) 475-554 CH2iCH3

alehI

ela/val

Fig. 19. Illustration of the metabolic ‘star plots’ of Vion-Dury et al. [175]. The figure presents the evolution of the metabolic profile of a patient suffering from lung adenocarcinoma: the left plot is at the time of diagnosis and the right plot after chemotherapy inducing a very important regression of tumoral mass. Each metabolic NMR parameter is presented on one axis of the star. The shaded area represents the control values with the mean ( + standard deviation). The hatched areas reflect values of parameters which deviate from the mean values of the controls by more than one standard deviation. The CHr and CHs signal areas were approximated from the normal single-pulse ‘H spectra of plasma as well as the GRt (for ‘total’glycosylated residues). Other signal areas were estimated from the Hahn spin-echo spectra: alanine (ala), valine (val), N-acetylglucosamine (NAG), N-acetylneuraminic acid (NANA) and the ‘most mobile’ parts of lipids (CH*m and CH,m). (From Ref. [ 1753 with permission.)

(6 s) of the water resonance in both pulse sequences [175-j. Based on several approximated peak areas from both types of spectra Vion-Dury et al. [175] developed a relative quantification scheme in the form of the ‘star plots’ illustrated in Fig. 19 (the signal ratios used are explained in the caption). They found that the haematologic cancers which include numerous liquid tumors such as leukaemias, have the most drastic effect on plasma metabolic homeostasis, probably because the tumor cells are directly in contact with plasma. In contrast, local tumors (such as breast cancers) at the locoregional stages of their evolution, or those which are isolated in a parenchyma (such as gliomas) have little, if any, influence on plasma composition [175]. Since the metabolic, as well as clinical, variations associated with each cancer are very wide, the ‘star plots’ cannot identify the subjects a priori (even though they are often characteristic) and thus the ‘metabolic graphics’ is not a new diagnostic tool. However, the value of the NMR approach lies in the possibility to quantify with ease a wide range of plasma metabolites and thus monitor the progression or degradation of cancer in a patient [175]. 21 _+ 1°C and 400 MHz using presaturation

6.4 Applications of chemometric techniques As discussed in Section 4.3 and above, the metabolic information in ‘H NMR spectra of plasma is usually very complex and may be partly lost in conventional data analyses. The studies of Kruse and co-workers [114-l 161 have demonstrated that several advantages can be gained from the use of chemometric techniques. They have applied a PCA-based SIMCA method to distinguish between malignant (29 patients) and control serum samples (55 subjects) [115]. As an alternative to the Fossel Index they used 71 and 76 data points to describe the methylene and methyl peak profiles, respectively. In the control group six principal components were found to describe 99% of the variance in the data. Only two patients and one control were not correctly classified; compared to the original classification based on the Fossel Index in which about one third of the controls would have been incorrectly assigned as cancer patients, this is an interesting result [l IS]. However, a larger control group of patients who have non-malignant diseases needs to be studied to assess the

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multivariate analysis approach further. Kruse et al. [116] have also applied SIMCA analyses of the methylene and methyl envelopes of the ‘HNMR spectra of serum from rabbits to monitor progressive growth of implanted VX-2 carcinoma (in the thigh or in the kidney). The results showed that the progressive growth of the tumors’could be monitored. Applications of chemometric techniques to analyse ‘H NMR spectra of tumor and normal tissue extracts of rats have also shown promising results and established interesting possibilities for these chemometric analyses [113, 1841.

7. Other biomedical applications and related lipoprotein studies 7.1 Metabolic

applications

A remarkable number of papers dealing with applications of ‘H NMR to metabolic investigations of plasma metabolites has been published particularly by the groups of Nicholson, Sadler and Bell [2-4, 59,60,62,63,74,75,94,96, 185-1881. ‘HNMR of plasma has been applied at least to study diabetic [60], uraemic [94, 187, 1891, and malaric [190] patients. It has also been used to detect metabolic changes in self-poisoning episodes [ 1851, to identify organic anions [ 1911 and to quantify exogenous and endogenous glucose [192] in plasma. Moreover, ‘H NMR spectra of maternal and cord plasma have been compared [ 1861, ‘H NMR spectra of plasma have been used to detect heart graft rejection [193, 1941, vesicular lipoproteins in LCAT-deficient plasma [195, 1961 and to study w-3 fatty acid metabolism [188]. ‘H NMR spectra of deproteinised plasma samples have also been introduced as a method for detecting and studying inborn errors of metabolism [132] (Section 5.1.2). Moreover, comparative evaluation of the metabolic profiles of synovial fluid and corresponding serum samples from normal subjects and patients with rheumatoid arthritis have been made applying ‘H Hahn spin-echo NMR measurements [197, 1981. Using the Hahn spin-echo pulse sequence (t = 60 ms) at 25°C and 400 MHz (usually 0.5 ml of sample in a 5 mm tube) Nicholson et al. [60] were able to detect and quantify ketone bodies, 3-D-hydroxybutyrate, acetone and acetoacetate from plasma samples of diabetic patients. At the point of insulin withdrawal from insulin-dependent diabetic subjects the ‘H spectra of plasma were similar to those of normal subjects. Subsequent spectra after withdrawal showed an elevation of the above ketone bodies and glucose, e.g. the concentration of 3-D-hydroxybutyrate increased from near zero to approximately 5 mM in 12 h after insulin withdrawal [60]. In uraemic patients many low molecular weight metabolites, including nitrogen-containing waste products such as urea and creatinine, accumulate in plasma because they are not normally excreted by the kidneys. However, there is lack of specific knowledge of the molecules which cause the diverse clinical symptoms (e.g. nausea, vomiting, headache, dizziness, dimness of vision, coma, convulsions) of uraemia [94,199]. ‘H NMR has been applied to identify and study potential uraemic toxins [94, 187, 1891. Using the Hahn spin-echo pulse sequence (7 = 80 ms) at 25°C and 300 MHz (0.8 ml of plasma in a 5 mm tube) Grasdalen et al. [189] showed that ‘H NMR can be used as a swift method to quantify the effects of haemodialysis on plasma composition in chronic renal failure (CRF) patients. Bell et al. [187] applied the Hahn spin-echo pulse sequence (7 = 60 ms) at 25°C and 500 MHz to study plasma samples from 16 patients with CRF (0.45 ml of plasma plus 0.05 ml D20 in a 5 mm tube). Venous blood was collected in lithium heparin tubes before dialysis therapy was initiated and plasma was stored at - 20°C after centrifugation. They were able to assign new resonances in the ‘HNMR spectra of plasma from the CRF subjects: trimethylamine-N-oxide (TMAO) and dimethylamine (DMA)give rise to singlets at 3.27 ppm and 2.74 ppm, respectively. The amount of plasma TMAO correlated with plasma urea (r = 0.55) and creatinine (r = 0.74) levels in the CRF patients, which suggests that the presence of TMAO is closely related to the degree of renal failure [187]. In addition, the authors [187] studied plasma of two normal subjects after a TMAOcontaining fish meal: TMAO appeared rapidly in the plasma (and urine) indicating an efficient filtration of TMAO by the healthy kidneys. A 60 mM addition of urea (a typical level in the CRF patients studied) to a plasma sample of a normal subject caused marked increases in the intensities of

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the lactate -CH and -CH3 resonances [187]. This parallels the observations made when NH4Cl was added to normal plasma [75]. When NH&l was added to five different samples of CRF plasma there was little change in the intensities of the lactate signals relative to those of alanine and valine. These observations indicate that uraemia can impair transport roles of plasma proteins. Such binding effects may arise from structural alterations to plasma proteins induced by urea or from competitive binding by other molecules produced during uraemia [187]; see also Section 7.2. Malaria is an infectious disease caused by the presence of protozoan parasites within red blood cells. Nishina et al. [190] have applied ‘HNMR spectroscopy of serum to parasitology by measuring lactate concentrations and lipoprotein lipid methyl and methylene resonance linewidths from the spectra of patients with Plasmodium. The infected sera were collected from persons native to the epidemic region of malaria in Nigeria (20 persons with serum giving positive and 13 persons with serum giving negative results in the antibody titre test to Plnsmodium jdciparum). Sera from six apparently healthy Japanese persons were collected for a control group. The authors Cl903 performed the single-pulse measurements at room temperature and 270 MHz using 5 mm tubes (0.1 ml serum plus 0.3 ml DzO plus 1 mM TSP); the water signal was suppressed using gated irradiation. The amount of NMR-visible lactate was found to be greater in the sera of malaria positive patients than in the two other groups. This could indicate higher glucose utilisation in malarial red blood cells than in normal red blood cells because glycolysis in erythrocytes terminates in lactate [190]. Bales et al. Cl851 have applied ‘H NMR measurements of plasma to study subjects who had taken paracetamol (acetaminophen) either at a therapeutic dose or at an overdose in self-poisoning episodes with suicidal intent. Paracetamol is one of the most common drugs involved in selfpoisoning episodes in the United Kingdom with more than 5000 overdose cases a year (of which 5&100 die). In clinical toxicological investigations metabolic information about the patient must be obtained quickly to allow efficient therapeutic intervention. As the results of Bales et al. [lS5] demonstrate, rapid diagnostic scanning of body fluids (plasma and urine in this case) by ‘H NMR leads to convenient metabolic profiles which can readily reflect abnormal patterns of endogenous metabolites in liver dysfunction for example. The authors [185] studied plasma samples from subjects admitted into hospital so&r after overdose had occurred and from subjects who presented at the clinic 2-3 days after the overdosage. Heparinised plasma samples were frozen immediately after collection and stored at - 30°C prior to analysis. 5 mm sample tubes were used (0.3-0.5 ml of plasma plus 10 vol.% DzO) and the ‘H spectra were recorded at 25°C and 400 MHz or 500 MHz using either Hahn spin-echo (T = 60 ms) or 2-D ‘H-‘H COSY NMR measurements. For the COSY experiments, the samples were concentrated threefold by freeze-drying and redissolving in D20. A 500 MHz ‘H Hahn spin-echo spectrum from a normal plasma sample and from a plasma sample of a patient suffering from acute liver failure (two days after paracetamol overdose) are shown in Fig. 20. Unlike in the spectrum of normal plasma, where only the cc-anomeric proton resonance of glucose at 5.25 ppm is seen in the spectral region to high frequency of water, strong signals from phenylalanine, tyrosine and histidine are observed in the spectrum of plasma from the overdose patient. Furthermore, elevation of at least lactate, alanine, glutamine, valine, methionine, lysine and 3-D-hydroxybutyrate are detected in the aliphatic region. The abnormally intense amino acid resonances indicate severe liver failure and disruption of normal deamination and transamination processes. No signals for paracetamol or its metabolites were detected due to low concentrations and protein binding of the drug molecules in plasma [ 1851. An interesting demonstration of the ability of ‘H NMR spectroscopic measurements to identify organic anions in serum was presented by Traube et al. [191] in the case of a 40-year-old male acidosis after jejunoileal bypass. Serum L-lactate level was normal, but patient with D-h& single-pulse ‘H NMR measurements at 500 MHz (0.4 ml serum plus 0.1 ml D20 plus a small amount of TSP, presaturation to suppress the water signal) showed a high concentration of lactate in the patient’s serum. The diagnosis of D-lactic acidosis was confirmed by a specific enzymatic assay for D-lactate. Since acidosis with increased anion gap occurs in several pathologies and frequently the anion involved remains unidentified, ‘HNMR spectroscopic measurements seem useful in identifying organic acids in sera of patients with unexplained acidosis [191].

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Fig. 20. A 500 MHz Hahn spin-echo (T = 60 ms) ‘H NMR spectrum of plasma for: (a) a normal subject; (b) a subject with acute liver failure two days after paracetamol overdose. Assignments are His, histidine; Phe, phenylalanine; Tyr, tyrosine; 0: and /?, G(and B anomers of glucose; Glc, glucose; Lac, lactate; Cit, citrate; Met, methionine; Gln, glutamine; NAc, N-acetyls of glycan side-chains of glycoproteins; Lys, lysine; Ala, alanine; HBut, 3-D-hydroxybutyrate; Val. valine and Pt,r, Ps, Pb and NMe: for lipoprotein -CHa, (
Biesemans et al. [ 1921 have developed a spin-echo based ‘H homonuclear pulse sequence which enables selective editing of homonuclear first order J-multiple& The authors [192] have demonstrated its ability (at 500 MHz) to distinguish, edit and quantify exogenous 2-deuterated D-glUCose and endogenous natural D-glucose in lyophilised plasma samples dissolved in 99.8% D20. The approach is of potential interest in several biological investigations, such as studies of hexose metabolism, conducted in vitro [192]. Single-pulse and Hahn spin-echo (r = 60 ms) ‘H NMR spectra of maternal and cord plasma have been compared by Bell et al. [186]. They used 21 pairs of plasma from mother and cord taken at the time of delivery and performed the ‘H NMR measurements at 500 MHz. The blood samples were collected into lithium heparin tubes and the plasma samples were stored at - 20°C after centrifugation. Five mm NMR sample tubes were used with 0.45 ml plasma plus 0.05 ml DzO and continuous irradiation was used to suppress the water signal. No peaks were found in the aromatic region (from 6 to 9 ppm) of single-pulse or Hahn spin-echo spectra of either maternal or cord plasma. The protein envelope and the prominent lipoprotein lipid resonances were significantly diminished in the single-pulse spectra of cord plasma in comparison to the maternal samples [186]. This is in accord with our ‘H NMR observations [123, 1631 and known lipoprotein biochemistry in neonates. In almost all samples the levels of NMR-visible lactate in cord plasma exceeded those of paired maternal plasma. Also in both maternal and cord plasma the NMR-estimated lactate levels appear to increase with the length of the second stage of labour. Because recognition and assessment of foetal distress are of paramount importance in modern obstetrics but are difficult to realise in

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practice, ‘H NMR may be valuable in such diagnosis (cord blood lactate levels are related to foetal hypoxia) [186]. Also the NMR-measured levels of alanine and valine were found to be higher in the cord plasma than in the maternal plasma. Moreover, intensities of the N-acetyl glycoprotein signals were significantly greater in the spin-echo spectra of maternal plasma compared to those of cord plasma in all the paired plasma samples (mean ratio + SD, 2.1 _+0.8). This agrees with increased levels of circulating acute-phase glycoproteins during pregnancy [ 1861. Despite major improvements in immunosuppressive therapy, non-invasive detection of heart graft rejection remains a challenge. As lipoproteins are involved in several immunomodulation mechanisms, Eugene et al. Cl933 have studied ‘H NMR spectra of plasma from patients after heart transplantation and compared the NMR data to clinical and functional evaluation of the heart graft rejection process. They performed a total of 410 measurements on 46 patients who successfully underwent orthotopic cardiac transplantation. Blood samples were collected in EDTA from fasted subjects and plasma samples were stored at - 20°C after centrifugation. Five mm NMR tubes were used with 0.45 ml plasma plus 0.05 ml D20 and the water signal was suppressed by a presaturation of 2 s in the single-pulse ‘H experiments at 24°C and 400 MHz. Samples for NMR analysis were taken before transplantation, 8 days after surgery and each time any transplanted patient underwent a rejection detection study, i.e. weekly for 5 to 6 weeks, then every 2 weeks for 2 months and monthly thereafter during the first year including clinical evaluation, pulsed Doppler echography and right endomyocardial biopsy. Control NMR measurements were obtained for a group of 32 subjects without evidence of any pathology. The authors Cl933 used an approximated sum of the lipoprotein lipid methyl and methylene linewidths as a total linewidth (TLW) parameter derived from the ‘H NMR spectra of the plasma samples (TLW is twice the Fossel Index, see Section 6.1). The TLW values were significantly lower for patients without a rejection than for patients with evidence of a rejection process. The TLW f SD values for the control group, for the patients before transplantation, 8 days after transplantation, with no rejection process and with rejection process were, 69.2 f 5.8 Hz, 68.2 f 8.9 Hz, 53.6 f 5.1 Hz, 52.3 + 8.3 Hz, and 65.6 f 7.9 Hz, respectively. When the TLW values were referenced to the TLW value obtained 8 days after transplantation (TLW D8) to minimise the effects of the differences in the TLW values between subjects, a sensitivity of 80% and specificity of 95% were achieved. The positive and negative predictive values of the ‘H NMR heart graft rejection test were then 90% and 91%, respectively (TLW/TLW D8 cut-off of 1.15). Measurements of triglycerides and cholesterol for 66 samples from 10 patients showed no correlation between these lipid values and the TLW values [193]. This is well understood on the basis of the current knowledge of the lipoprotein lipid resonances in the ‘H NMR spectra of plasma (Sections 2.2 and 5.3). The observations that lipoproteins can affect lymphocyte proliferation, a process which on the other hand is associated with immunosuppression, support the hypothesis of Eugene et al. [193] that there are lipoprotein-related specific processes affecting their results. They have also observed similar alterations in the TLW values in a preliminary study of renal and liver transplantation patients [200]. Pont et al. Cl943 have also noticed that levels of glycoprotein N-acetyl resonances in the Hahn spin-echo (7 = 60 ms) ‘H NMR spectra of plasma (at 21°C and 400 MHz) from heart transplantation patients quite often correlate with isovolumic left-ventricular relaxation time estimates from Doppler echocardiography. They have studied plasma samples of 13 heart recipients over a period of 2 years in conjunction with biopsy and Doppler studies. In all cases, NMR findings were more closely related to Doppler indices than to histological results [194]. Lipoprotein X (LP-X) is an abnormal lipoprotein that accumulates in plasma of LCAT-deficient and cholestatic subjects [195, 1961. These conditions are associated with elevated plasma levels of unesterified cholesterol which accumulates predominantly in the LP-X particles. The LP-X particles consist of 65% phospholipids, 25% cholesterol and 10°/Oprotein. They have a density similar to that of VLDL and LDL and a radius of about 150-400 A. Unlike all other lipoproteins the LP-X particles have an enclosed bilayer surrounding an aqueous core [195, 1961. This bilayer structure enables their specific detection from a mixture of plasma lipoproteins by ‘H NMR: use of paramagnetic manganous ions Mn’+ quenches all of the ‘HNMR signal from the phospholipid choline headgroup methyl -N(CH& protons of normal lipoproteins but only the outer monolayer asso-

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ciated signal of the LP-X particles. Parmar et al. [196] showed that the residual -N(CH& signal can be used to detect and quantify vesicular phospholipids. Applying this technique they also estimated that approximately 42% of phospholipids in LCAT-deficient plasma lipoproteins are in the vesicular LP-X particles. They isolated the total lipoprotein fraction by ultracentrifugation (at 40 000 rev min- ’ for 24 h), used DrO exchanges to suppress the water signal and performed the single-pulse ‘H NMR measurements at 30°C and 200 MHz on 0.4 ml samples in 5 mm NMR tubes [196-J. Fish oil w-3 fatty acids (e.g. eicosapentaenoic acid, EPA, 20:5;w-3 and docosahexaenoic acid, DHA, 22:6;w-3) are thought to have an important protective effect against CHD mortality. However, the biological mechanisms from which this may originate are unclear. Interesting ‘HNMR applications, in which changes in the physico-chemical characteristics of .lipoprotein particles (especially LDL) have been observed due to fish oil diets have been published by Bell and co-workers [188, 2013. Healthy volunteers, who excluded alcohol and fish from their diets from 7 days before and during the study, were supplemented with 50 ml per day fish oil for 7 days. Gas chromatography showed that significant levels of EPA (1.36 f 0.22% vs. 11.78 f 2.52%) and DHA (4.34 f 0.84% vs. 7.48 + 2.02%) were incorporated into plasma lipoprotein lipids. Furthermore, standard biochemical assays showed that the serum triglycerides were significantly reduced in all subjects (0.77 f 0.22 mmoll-’ vs. 0.54 f 0.17 mmoll-‘) but no significant changes in plasma cholesterol concentration were observed. ‘H NMR measurements of plasma and ultracentrifuged LDL samples were performed at 500 MHz. Expansions of Hahn spin-echo spectra of plasma are shown in Fig. 21. Clear changes in both the methyl -CH3 and methylene (-CH,-). resonance regions are observed resulting from the fish oil supplementation. The observations of Bell and co-workers [188, 2011 effectively demonstrate the ability of NMR spectroscopic investigations to reveal molecular level characteristics of even complex macromolecules such as lipoproteins, but a lot of further work will be. needed to explain the observed changes and their possible importance in lipoprotein and cell membrane interactions for example.

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7.2 Measurements

in Nuclear Magnetic Resonance Spectroscopy

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at 750 MHz

The most expensive single item in an NMR spectrometer is the superconducting magnet. A lot of effort has been expended on the creation of new ultrahigh field magnets with properties enabling routine use in modern NMR spectrometers; even 750 MHz instruments (magnetic flux density of 17.6T) are now commercially available. According to the basic principles of NMR, these new ultrahigh field NMR instruments will lead to increased chemical shift separation of the resonances and also increased sensitivity (Eq. (1)). The concentration limit attainable with 750 MHz spectrometers approaches about 1 PM for ‘HNMR [94]. Foxall et al. [94] have recently shown that ‘H NMR measurements performed at 750 MHz can be very valuable in studies of low molecular weight metabolites of plasma. Especially applications of homonuclear 2-D J-resolved (2-D JRES) ‘H NMR spectroscopy were shown to be very efficient in low molecular weight metabolite investigations [94, 2021. Single-pulse, CPMG and 2-D JRES ‘H NMR measurements at 600 and 750 MHz were applied to study plasma obtained from a healthy volunteer and a CRF patient [94]. The blood was collected by venepuncture into lithium heparinised vacutainers and the plasma was frozen after centrifugation. The 0.7 ml samples were diluted (10 vol.%) with D,O and all spectra were measured at ambient probe temperature. At 750 MHz, 128 FIDs were collected using 64k data points and a spectral width of 10 kHz. In single-pulse experiments 90” pulses were used with an acquisition time of 3.28 s and a total pulse recycle time of 5.28 s. The water signal was suppressed by using a 1-D nuclear Overhauser effect spectroscopy (NOESY) presaturation pulse sequence applied at the water resonance frequency for 2 s in the delay between the 90” pulses; an attenuation of the order of lo5 or more for the water resonance was achieved in this way [94]. In the CPMG pulse sequence the total spin-spin relaxation delay 2zn was 87.8 ms. The 2-D JRES ‘H NMR spectra of the plasma samples were obtained by the pulse sequence 90: - ti - 180; - t1 - FID(t,)

(37)

with an FID detection time tz = 0.507 s and a delay of 2 s between the sequences when a secondary irradiation field was applied at the water resonance frequency. In the chemical shift domain (FZ) 8k data points with a spectral width of 8064 Hz were collected and the width of the J-coupling domain (F,) covered 63 Hz with 64 increments of ti (typically eight FIDs were summed for each tl) [94]. According to Foxall et al. [94] the single-pulse ‘H NMR measurements of plasma at 750 MHz do not give helpful data with respect to most of the low molecular weight components due to the extensive overlap with broad protein and lipid resonances. As in lower fields the protein and lipid signals are mostly absent from the CPMG spectra, except resonances from the N-acetyl protons of mobile N-acetylated carbohydrate side-chains of glycoproteins and the most mobile lipoprotein lipids. In contrast to earlier measurements (at 400 and 500 MHz) of normal plasma [74, 1851, signals 3-methylhistidine and p-hyfor free phenylalanine, tyrosine, histidine, I-methylhistidine, droxyphenyl-lactate (and certain other unassigned metabolites) were clearly detected in the CPMG ‘H spectra (at 600 and 750 MHz) of uraemic plasma at neutral pH [94]. This is illustrated in Fig. 22. The resonances of these aromatic metabolites in plasma of the CRF patient indicate an accumulation of motionally unconstrained, i.e. non-protein bound, substances due to renal failure. Histidine signals were also well resolved in the CPMG spectra of normal plasma, whereas tyrosine signals were weak and phenylalanine was not detected (see also Section 7.1). This observation may be consistent with a change in the chemical exchange rate of the aromatic molecules between the protein-bound and free-solution states from intermediate to slow on the NMR time-scale on going from 400 MHz to 600 or 750 MHz. The signal to noise ratio in the aromatic region of the CPMG ‘H spectrum of control plasma was still low because the protein-bound fraction is largely nonobservable due to reduced mobility. The authors [94] concluded that advent of new ultrahigh field NMR spectrometers will do much to benefit the area of dynamic studies of molecular interaction between small and large molecules in biological fluids. Moreover, they also emphasised the advantages of the measurements at 750 MHz over those at 600 MHz. In Fig. 23 two regions of the 750 MHz 2-D JRES ‘H NMR spectrum of normal plasma are shown with signal assignments [94]. As the 2-D JRES experiment is a two-dimensional application of

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Fig. 22. An aromatic region of the 750 MHz CPMG spin-echo (2n7 = 87.8 ms) ‘H NMR spectrum of: (a) normal plasma;(b) uraemic plasma. Assignments are For, formate; His, histidine; I-MeHis, l-methyl histidine; 3-MeHis, 3-methyl histidine; Phe, phenylalanine; Tyr, tyrosine and PHPL, p-hydroxyphenyl-lactate. (From Ref. [94] with permission.)

a spin-echo pulse sequence the protein and lipid resonances are attenuated and importantly, the skyline projection through the 2-D JRES map results in a greatly simplified spectral profile of the effectively fully ‘H-decoupled ‘H NMR spectrum of the motionally unconstrained low molecular weight metabolites in plasma. The simplification caused by the 2-D JRES experiments to the complex spectral region from 3 to 4 ppm has enabled Foxall and co-workers [94, 2023 to assign many biochemically important metabolite signals in this region (see Fig. 23 and the caption). Obviously 2-D JRES ‘H NMR measurements (at ultrahigh fields) can assist in the search and study of abnormal low molecular weight metabolites, e.g. uraemic toxins, in plasma [94]. 7.3 Interaction and complexation studies A few applications of ‘H NMR spectroscopy to the study of metal complexes and their kinetics in plasma have been published by Sadler and co-workers [73, 203-2061. Several plasma proteins are involved in binding functions (Section 1.3), but details of many of these biologically important processes, including the mechanisms of metal ion and small molecule uptake and release by plasma proteins, are currently poorly understood. The NMR results show that this approach can lead to relevant information about the molecular pharmacology of different metal complexes in biofluids [73, 203-2061. Using ‘H (and ‘IP) NMR Berners-Price and Sadler [203] have studied interactions of an antitumor Au(I) complex [Au((C,H,),P(CH,),P(C,H,)2)23C1 with human blood plasma and lipoproteins (and red cells). This highly lipophilic gold complex, which is soluble in water only in micromolar concentrations, exhibits antitumor activity in several animal models. The authors [203] used single-pulse or Hahn spin-echo (7 = 60 ms) measurements at 500 MHz from 5 mm tubes and suppressed the water signal by continuous irradiation. The studies showed that the complex is soluble at millimolar levels in plasma. This can be attributed to its partition into the hydrophobic compartments in plasma. Because the ‘H NMR resonances from the mobile parts of the lipoprotein lipids were decreased in intensity after addition of the complex to plasma, it seems that the complex interacts at least with the lipoprotein lipids affecting their mobility. No direct signals from the complex were observed either in the single-pulse or the spin-echo spectrum of the complex treated plasma sample (it was evident from the 31PNMR spectrum that the compound was present in plasma at millimolar levels). In the ‘H NMR spectrum of isolated lipoproteins (a mixture JPlwlS 27:5/W

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Fig. 23. Contour plots of the 750 MHz 2-D JRES ‘HNMR spectrum of normal plasma with skyline F2 projections above; from the study of Foxall et al. [94] with permission. Assignments are 3-OHB, 3-Dhydroxybutyric acid; Lac, lactate; Cn, creatinine; Asp, aspartate; Ala, alanine; Gol, glycerol; Ins, myo-inositol; TMAO, trimethylamine N-oxide; Cho, choline methyl groups; DMG, dimethylglycine; DMA, dimethylamine; MA, methylamine; Gln, glutamine; Pyr, pyruvate; Glu, glutamate; Acac, acetoacetate; N-ac, N-acetyls of glycan side-chains of glycoproteins; AC, acetate; ISB, isobutyric acid and Val, valine. Signals marked a and /I refer to the various proton signals of the two glucose anomers.

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containing HDL: VLDL: DzO in ratio 1: 2: 1 v/v) a very broad signal appears in the aromatic region as expected from the phenyl (C&H=,)protons of the complex. When the complex was added to plasma or isolated lipoprotein samples it caused some denaturation of lipoprotein structures. The 31PNMR measurements also suggested that some of the gold complex was transferred from plasma (lipoproteins) to red cells [203]. Reactions of the copper complexes Cu(II)C12, [Cu(II)(EDTA)]‘-, CU(II)~(DIPS)~ and [Cu(I)(DMP),]+ (where DIPS is 3,5diisopropylsalicylate and DMP is 2,9_dimethylphenanthroline) with human blood plasma (and urine) have been studied by Bligh and co-workers [73,204] at 500 MHz. In plasma most of the endogenous copper ions are strongly bound to caeruloplasmin and appear to be kinetically inert under normal conditions. Another strong protein binding site for Cu(I1) is thought to be at the N-terminus of albumin. Cu(I1) in this site is likely to be more reactive and may also form ternary complexes with small ligands such as histidine. Endogenous ligands in plasma may thus play a role in controlling the kinetics of copper uptake and release by albumin. Binding of transition metals to plasma proteins also prevents free radical reactions in plasma and can be considered as an important preventive antioxidant effect from, e.g. the point of view of LDL particles. Since albumin is not readily detectable in plasma by ‘H NMR under normal conditions, the authors [73, 2041 used circular dichroism (CD) spectroscopy as a complementary technique to monitor the transfer of Cu(I1) onto albumin in plasma. They performed single-pulse or Hahn spin-echo (T = 60 ms) ‘H NMR measurements from 5 mm tubes at 25°C and several field strengths (270, 400 and 500 MHz) and used gated irradiation to suppress the water signal. TSP (1.9 mM in DzO) in a concentric capillary way used as an external concentration standard, chemical shift reference and lock signal in quantitative work. Venous blood from healthy volunteers was collected in vials containing lithium heparin. After centrifugation, the plasma was always used immediately for spectroscopic experiments without further storage [73]. The effect of copper on NMR peaks depends on its oxidation state. Cu(I1) complexes are usually paramagnetic and Cu(1) complexes diamagnetic. Thus copper(bound ligands are expected to give rise to broadened ‘H NMR resonances compared to the free and Cu(I)-bound ligands. Additions of all the Cu(II)-complexes to plasma led to decreases in the signal intensities (broadening of the peaks) of valine, alanine, glutamine and citrate in the ‘HNMR spectra [73]. These broadenings are likely to arise from paramagnetic effects due to formation of Cu(IIkamino acid and Cu(II)-citrate complexes. In contrast, the peaks observed for small molecules all remained sharp when [Cu(I)(DMP),]+ was added to plasma. In the case of CU(II)~(DIPS).+ addition to plasma, the peaks for lactate in the spin-echo spectrum of plasma significantly increased in intensity. This was considered to be caused by amino acid and citrate assisted displacement of the ligand DIPS from CU(II)~(DIPS)~ complex in plasma by albumin, leading to release of protein-bound lactate, presumably through direct displacement of lactate by the lipophilic DIPS. No peaks for free DIPS (or DMP) were seen suggesting that it is indeed relatively immobile and bound to proteins or lipoproteins [73]. 500 MHz Hahn spin-echo spectra illustrating the above findings are shown in Fig. 24. The transfer of Cu(I1) from [Cu(II)(EDTA)]‘(0.5 mM) to albumin in plasma was slow enough to be followed as a function of time by monitoring the Cu-albumin band in the CD spectra [73]. This produced a half-time of 26.4 min at 21°C which can be compared with the half-time of 20.8 min for appearance of [Ca(II)(EDTA)12- resonances in the ‘H NMR spectra at 25°C. Also the rates of Cu(I1) binding to amino acids and citrate were similar. Reactions of Cu(II)C12 and CU(II)~(DIPS)~ in plasma followed a similar course, but were more rapid. The [Cu(I)(DMP),] + complex appeared to be relatively stable in plasma. The results of Bligh and co-workers [73,204] show that reactions of copper complexes in plasma involve a range of low molecular weight ligands as well as albumin, and the ligands play a major role in determining the kinetics of the reactions. Aluminium(II1) binding to citrate in human blood plasma has been detected by Bell et al. [205] using ‘H NMR spectroscopy. Either single-pulse of Hahn spin-echo (r = 60 ms) spectra were measured at ambient temperature and 500 MHz from 0.55 ml solution in a 5 mm tube. The use of combined exponential and sine-bell functions for resolution enhancement of single-pulse ‘H NMR spectra was also studied and shown to lead to an over-all appearance of the enhanced spectrum

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I

3

I

iwm

Lac

2

Fig. 24. Aliphatic regions of 500 MHz Hahn spin-echo (T = 60 ms) ‘H NMR spectra of heparinised plasma in the presence oh (a) 0.75 mM Cu(II)C12; (b) 0.5 mM Cu(II),(DIPS),; (c) 1 mM [Cu(I)(DMP),]NO,. DIPS is 3,5-diisopropylsalicylateand DMP is 2,9_dimethylphenanthroline. Assignments are Glc, glucose; NMe, choline headgroup methyl groups of HDL phospholipids; Cr, creatine; dmso, dimethyl sulphoxide; Gln, glutamine; NAc, N-acetyls of gfycan side-chains of glycoproteins; AC, acetate; Ala, alanine; Lac, lactate; Val, valine and Chyl, chylomicrons. (From Ref. [73] with permission.)

similar to that of the Hahn spin-echo spectrum without phase modulations. This resulted, for example, in the glutamine peaks being more clearly interpretable from the enhanced spectra than from the spin-echo spectra where they are severely distorted [205]. Addition of 50 uM A13+ to plasma caused the citrate (present at a concentration of 0.11 mM in this sample of plasma, i.e. within the normal range) quartet around 2.6 ppm in the ‘H NMR spectra markedly and selectively to decrease in intensity. The citrate peaks were found to completely disappear from the spectra after addition of 200 pM A13+ to plasma. Addition of desferrioxamine (a compound known to bind A13+ more strongly than citrate or transferrin and which is used clinically to decrease plasma aluminium levels after exposure to toxic levels) to the plasma sample containing 50 uM Al’+ led to a rapid reappearance of the citrate peaks, Similar results to those for the A13+ containing plasma samples were obtained from single-pulse ‘H NMR experiments of A13+ added to low molecular weight ( < 5 kDa) ultrafiltrates of plasma. The ‘HNMR spectra of model systems containing A13+ and citrate were also studied and shown to consist of about 20 quartets covering the range from 2.2 to 3.5 ppm; evidently there are a large number of different coordination modes for citrate in A13+citrate complexes which appears to explain why new peaks for an aluminium-citrate complex are not readily observable in spectra of plasma or its low molecular weight ultrafiltrate. Based on their ‘H NMR data, Bell et al. [205] suggested that Al 3+ added to heparinised plasma (or low molecular weight ultrafiltrate of plasma) in vitro initially binds to citrate. Recently Pate1 et al. [206] have shown that direct detection of albumin in resolution-enhanced single pulse and in 2-D ‘H NMR spectra (at 500 MHz) of plasma is possible. Also specific complexation sites for Ni’ + on albumin were detected and competitive binding of the nickel ions to free histidine could be studied [206]. 7.4 LDL and HDL lipid peroxidation

A substantial body of evidence has accumulated that oxidatively modified LDL (OX-LDL) plays a central role in the pathogenesis of atherosclerosis [55,207,208]. Through the specific unregulated

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macrophage scavenger receptors OX-LDL particles can deliver large amounts of cholesterol to macrophages and thus accelerate formation of foam cells in the atherosclerotic lesions. It is well known that properties of the LDL particles change during peroxidation, e.g. there is a complete loss of antioxidants, more or less complete loss of polyunsaturated fatty acids (PUFAs), increased content of conjugated dienes and aldehydes, partial loss of free amino groups in apoB, fragmentation of apoB to smaller peptides, increase in negative charge and density and changes in lipid composition [28, 208, 2091. However, knowledge of the chemical and physical properties of OX-LDL particles is rather limited (much more is known about their biological properties) and there is an apparent need for specific information about molecular level biochemistry of free radical induced lipid peroxidation [28, 2101. In order to be able to study oxidation processes, some problems and even pitfalls concerning the measurements and their interpretation have to be resolved. Indeed, there is no explicit method for measuring lipid peroxidation [210, 2111. A few recent studies have shown that in certain situations NMR can offer some valuable advantages [212-2191. It should be noted that formation of OX-LDL in vivo probably occurs in the intima and not in plasma because of its powerful antioxidant defence mechanisms [28, 55, 2201. Thus the influence of lipoprotein lipid peroxidation on the ‘HNMR measurements of plasma (and lipoproteins) should normally be negligible, especially when they are collected and prepared in the presence of metal complexing agents such as EDTA. Therefore the results from ‘H NMR studies of lipoprotein lipid peroxidation [212, 215, 216, 218, 2191 are only briefly discussed here. Bradamante and co-workers [212,215] have used ‘H (and “C and “P) NMR spectroscopy to study in vitro oxidatively modified LDL and HDL3 (density 1.120-1.210 gem-3) particles and their lipid extracts. Single-pulse ‘H spectra were recorded at 300 MHz (at 21, 37 or 45°C). Clear qualitative changes were observed in the ‘H spectra of the oxidatively modified lipoprotein samples compared to the control spectra. Particularly the bis-allylic -CH = CH-C&CH = CH- and allylic (-CH2-)nCI12-CH= methylene proton resonances which were present in the spectra of the control samples were (almost) completely absent. Other lipid and the cholesterol backbone -C(18)HJ resonances were also remarkably decreased in intensity in the ‘H NMR spectra of the OX-LDL and OX-HDLJ samples. Broadening of the remaining lipid resonances was observed. This was attributed to many concurrent physico-chemical changes, e.g. increased structural rigidity caused by bonds between aldehydes and protein and lipids in the OX-LDL and OX-HDLJ particles. Moreover, ‘H NMR spectra of OX-LDL and OX-HDL3 lipid extracts showed the presence of conjugated dienes and epoxide systems in the fatty acid chains and several oxidised cholesterol derivatives [212, 2151. Recently we have presented a quantitative application of ‘H NMR spectroscopy to study in vitro Cuz+ -oxidised LDL particles [218,219]. Comparative biochemical measurements of thiobarbituric acid-reactive substances (TBARS), malondialdehyde by HPLC and apoB free amino groups were also performed. LDL particles were isolated from plasma samples of healthy normolipidaemic volunteers by repeated ultracentrifugations and dialysed overnight against phosphate buffered saline and oxidised by exposure to 10 nM C&O4 at 37°C for various lengths of time (up to 20 h). The single-pulse ‘HNMR spectra were recorded at 37°C and 400 MHz; and example of a measurement series is shown in Fig. 25. A double tube system was used (see Section 5.3.2 and caption of Fig. 2) and the water signal was suppressed by applying a binomial 1 - 7 pulse sequence [67, 2191. Quantification of specific lipid resonances (e.g. arising from the CH=CH-CH2 -CH=CHmethylene protons) was attained by lineshape fitting analysis applying the program FITPLAC (Section 4.2.1). We showed that the area of this methylene resonance in the ‘H NMR spectra of oxidised LDL samples can be used as a quantitative measure of LDL lipid peroxidation: the correlation coefficients (from several series of measurements) between the amount of free amino groups in LDL apoB or the TBARS and the integrated intensity of the -CH =CHC&CH =CH- resonance were 0.97 and - 0.97, respectively [219]. Furthermore, an especially important advantage of NMR is the possibility to study the lipoprotein particles in their natural organisation state and thus probe the internal interactions and assess possible structural modifications in oxidised LDL particles [219]. Lodge et al. [216] have shown that aldehydic products from lipid peroxidation reactions can be detected in lipid extracts of copper-oxidised LDL using ‘H NMR measurements at 400 MHz. They

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0.6

F)

Cl NAT

PPM

1

3.5

3.0

w

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2.0

1.5

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Fig. 25. At the bottom the aliphatic region of a single-pulse 400 MHz ‘HNMR spectrum of a native LDL sample is shown. The resonance assignments are as in Fig. 2 and R stands for the reference signal of TSP. The experimental details were close to those given in Fig. 2. In the insets the boxed resonance regions are shown for the various in vitro degrees of Cu2+ -oxidised LDL samples. The chemical shift scale is from the external TSPbased reference and the intensities in each spectrum are normalised both to the reference and protein concentration. See text and Refs. [218] and [219] for details.

out that NMR also provides information about other intermediates and estimated that by applying new ultrahigh field NMR spectrometers detection limits of x 50 uM will be attainable [216].

pointed

7.5 Lipoprotein

phase

transitions

The lipoprotein particles are in continuous interaction with cells and with each other exchanging lipid and protein molecules. The specific lipid-lipid and lipid-protein interactions of these particles are of fundamental importance in explaining lipoprotein metabolism at the molecular level [15-24, 221-2321. There are many observations indicating that order-disorder transitions occur in human LDL particles at physiological temperatures [19, 20, 68-721. ‘H NMR spectroscopy is well suited for carrying out lipoprotein phase transition studies since resonances from both the core and the surface regions of the particles can be identified and quantified from the spectra [67,70-72,82,219, 233, 2341. In a novel study, Kroon [70] showed that the phase transition temperatures determined from the variation of the intensity of the lipid methylene (-CH,-), resonance in the ‘HNMR spectra (at 300 MHz) of LDL samples were almost identical with those determined calorimetrically. He also proposed that the core cholesterol esters of the LDL particles are in a disordered smectic state below the phase transition temperature and in a liquid state above the phase transition temperature. The disorder of the smectic state was attributed to the geometrical constraints imposed upon the packing of the core cholesterol esters by the high curvature of the LDL core and by the presence of a fluid phospholipid monolayer surrounding the molecules in the core. The disorder of the (radial) smectic state accounts for the smaller transition enthalpies observed for core cholesterol esters compared to

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extracted cholesterol esters [19]. Kroon and Krieger [71] have also applied ‘H NMR spectroscopy (at 300 MHz) to measure the thermal transitions of native LDL particles together with LDL particles from which apolar core lipids had first been extracted and then reconstituted with exogenous cholesterol esters (cholesterol oleate, cholesterol linoleate and cholesterol arachidonate) or a mixture of lipids extracted from the core of native LDL particles. The intensity changes of the lipid methylene (
7.6 Lipoprotein size distribution and chemical shift Many researchers have noticed and discussed [62, 65, 82, 83, 1881 that the methyl -CHB and methylene (-CH,-). signals of the lipoprotein lipids in the ‘H NMR spectra shift systematically to lower frequencies with decreasing lipoprotein particle size. Even though Otvos et al. [83] made a hypothesis that if the lipids in the lipoprotein particle core and surface shell have different magnetic susceptibilities, the size-related frequency shifts may arise because the ratio of core to surface lipids

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I

.

10.0

I

.

I

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20.0

.

I

40.0

T(‘C)

I

50.0

10.0

20.0

30.0 T (‘Cl

40.0

50.0

Fig. 26. Demonstration of lipoprotein phase transitions as observed by ‘H NMR. In the insets the intensity behaviour of the lipoprotein lipid fatty acid methylene (CH,-). and phospholipid choline headgroup methyl -N(CHs), resonances along with the temperature are shown as obtained from lineshape fitting analyses of the 400 MHz ‘H spectra applying the program FITPLAC. Corresponding derivative curves are shown enlarged to reflect the surface phospholipid monolayer transition in both LDL and HDL particles and the core phase transition in the LDL particles. See text and Ref. [234] for details.

varies with the particle diameter, the biophysical basis for this effect remained unknown, until Lounila et al. Cl393 recently gave an exact physical explanation for it. It was shown that isotropic magnetic susceptibilities of the lipids are not capable of explaining the observed behaviour. A simple physical model for the lipoprotein particles, involving the anisotropy of the magnetic susceptibility, was presented. The lipoprotein particles were modelled by a spherically symmetric micelle of radius R2 consisting of a core of radius RI and a spherical surface shell of thickness A = R2-RI as illustrated in Fig. 27. The hydrophobic core (region 1) and the medium surrounding the micelle (region 3) were taken to be in an isotropic liquid state, while the molecules in the shell (region 2) were taken to be radially oriented (water molecules hydrating the surface lipids are included in the shell). This means that the magnetic susceptibility of the shell material is anisotropic: Ax = x,, - x1 # 0, where xi, and x1 are the volume susceptibilities parallel and perpendicular to the radius vector r of the micelle, respectively. The frequency of the ith ‘H NMR line originating from the core region of the micelle was shown (by solving the magnetic field equations) to follow the equation Cl393 vi(R2) =

~0 +

5

R2

v,Ax~ In R2-A

(38)

in which vo is the frequency of the line in the absence of the contribution due to Ax2 and v,, is the operating frequency of the spectrometer. As vi is an explicit function of the radius R2, the model leads in a natural way to a systematic dependence of the frequency on the size of the micelle. It should be noted that the dependence is absent if Ax2 = 0, i.e. there is no orientational order in the micelle. The predicted sign, magnitude and functional form of the frequency shifts were verified by ‘HNMR data from both ultracentrifuged and gel filtered lipoprotein samples (the radii of the lipoprotein particles eluted in different fractions from the Sepharose 4B column were determined by calibrating the column with a series of protein standards of known size). The ‘H spectra were

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I

Bo

R

Fig. 27. Schematic representation of a physical model for the lipoprotein particles. Compare to the biochemical counterpart in Fig. 1 and see text and Ref. [139] for details.

measured at 37°C at v. = 399.8 MHz. As predicted by the theory, not only were the methyl -CHJ

and methylene (-CH&. .resonances of the lipoprotein particles observed to have a size-related chemical shift behaviour but all the lipid resonances of the particles [139]. The mathematical formulation of the lipoprotein particle size-related chemical shift behaviour, Eq. (38), can give important additional prior knowledge to overcome the problem of extensive lipoprotein signal overlap in the analysis of ‘H NMR spectra of plasma (Section 5.3). At the same time as enabling an extensive quantification of the lipoproteins, use of this formulation would naturally lead to a simultaneous estimate of the total lipoprotein particle size distribution [139].

8. coIteluding remarks A considerable number of research papers have been published in which ‘HNMR spectroscopy has been used to study biochemical and biophysical characteristics of human blood plasma and lipoproteins. Advantages of ‘HNMR over conventional biochemical assays have been underlined by many researches: no, or only little, pretreatment of the sample is needed, samples in chemical equilibria can be studied, no preselection of the metabolites of interest is necessary and thus knowledge of both low and high molecular weight metabolites is available even from a single experiment. The inherent property of NMR to produce quantitative data has been an essential part of the majority of the published works. However, in the quantification of the experimental data considerable approximations have been used. This has been understandable since most of the ‘H spectra of lipoproteins and especially plasma are very complex and contain unresolved heavily overlapping resonances. Moreover, programs offering enough capacity and tlexibility to be applied to these complex cases have not been generally available. Recent develop ment show, however, that this deficiency no longer exists since a few sophisticated analysis algorithms and programs have been introduced and are now becoming widely applied and accepted research tools. In recent years, mainly after, and in consequence of, the famous results of Fossel and co-workers, there has also been an extensive increase in the knowledge of the biochemical basis for the observed

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‘HNMR resonances and phenomena. Although the hope of a simple cancer detection test has naturally not materialised, much potential use of ‘HNMR of plasma has been found from its quantitative applications, which indeed can be applied to study metabolic changes in cancer and metabolic effects of drug treatments. From several separate studies it is evident that an application of a few experimental ‘H NMR techniques (single-pulse, spin-echo, 2-D JRES and COSY) for a few samples (plasma, deproteinised plasma and plasma lipid extracts) produces a large amount of biochemically important data. A real advantage would be achieved if some of these different experiments could be combined into a single ‘H NMR spectroscopic quantification (and identification) protocol. It is quite easy to see that an immediate use for such a rapid and versatile (and automated) quantification technique would be established in many biomedical research applications. Even though ‘H NMR measurements require expensive instrumentation, the quantification obtained by applying a ‘H NMR protocol could in many situations be less expensive than use of conventional biochemical assays. This is particularly evident in complete plasma lipoprotein lipid analysis where a cost-saving of up to 80% is estimated to be achieved compared to complete enzymatic determinations. The sophisticated data analysis algorithms (and combinations of them) have a central role in the development of reliable and automated quantification protocols. Recent results from plasma lipoprotein quantification by ‘HNMR offer illustrative examples, the main findings of which can be applied directly to other quantification procedures. In general, balanced combinations of different analysis methods seem very profitable. In particular, the time domain HLSVD method has been shown to be very efficient in removing dominating residual water signals from ‘H FIDs and thus make the experimental data much more amenable to accurate analysis by lineshape fitting analysis in the time or in the frequency domain or by chemometric or neural network analysis. In possible clinical applications, use of neural network analysis might be useful also in the sense that it permits easy utilisation of other biochemical assay results and also medical background knowledge in combination with the ‘HNMR data. I believe that in the future a crucial step will be application of the established ‘HNMR characteristics of plasma and lipoproteins to answer biophysical and biochemical questions and to really assist in biomedical analyses. Every NMR spectroscopist should bear the responsibility for clearly introducing the possibilities of modern NMR into biomedical research (even though with some clinicians this might be quite a painful job): maybe in the near future we will see examination requests, for instance, of the form: complete plasma lipid and metabolite analysis by ‘H NMR.

Acknowledgements

It is my pleasure to acknowledge helpful discussions with my colleagues Professor Jukka Jokisaari, Professor Antero Kedniemi, Professor Kalevi Kiviniitty, Professor John Griffiths, Dot. Yrjii Hiltunen, Dot. Juhani Lounila, Dot. Markku Savolainen, Dot. Sinikka Eskelinen, Dr. Aad van den Boogaart, Dr. Petri Ingman, Dr. Sohvi HGrkki5, Mr. Ari Korhonen, Mr. Jyrki Keisala, Mr. Joni Oja and Mr. Petri Korpi. My special thanks go to Aad van den Boogaart for his kind permission to use his contribution about the time domain analysis methods from our preliminary manuscript as a basis for Section 4.1 in this review. I also thank Professor Jukka Jokisaari, Dot. Yrjii Hiltunen, Dr. Aad van den Boogaart, Dr. Minna Hannuksela, Professor Risto Kauppinen and Professor Reino Laatikainen for carefully reading (parts of) the manuscript and for their many valuable comments. Moreover, I am indebted to Professor Jukka Jokisaari for all his support, advice and encouragement over the years. This work has been supported by grants from the University of Oulu, the Satakunta Foundation of the Finnish Cultural Foundation, the Finnish Academy of Sciences, the Cancer Society of Northern Finland, the Oulu University Scholarship Foundation, the Emil Aaltonen Foundation, the Jenny and Antti Wihuri Foundation, the Leo and Regina Wainstein Foundation and the British Council. I thank also the Academy of Finland for a research position.

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[44] [45] [46] [47] [48] [49] [SO] [Sl] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] [SO] [81] [82] [83] [SS] [SS] [86] [87] [88] [89] [90] [91] [92] [93]

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