Quality assurance in the pre-analytical phase of human urine samples by 1H NMR spectroscopy

Accepted Manuscript 1 Quality assurance in the pre-analytical phase of human urine samples by H-NMR spectroscopy Kathrin Budde, Ömer-Necmi Gök, Maik Pietzner, Christine Meisinger, Michael Leitzmann, Matthias Nauck, Anna Köttgen, Nele Friedrich PII:

S0003-9861(15)30019-9

DOI:

10.1016/j.abb.2015.07.016

Reference:

YABBI 7028

To appear in:

Archives of Biochemistry and Biophysics

Received Date: 8 May 2015 Revised Date:

6 July 2015

Accepted Date: 22 July 2015

Please cite this article as: K. Budde, Ö.-N. Gök, M. Pietzner, C. Meisinger, M. Leitzmann, M. Nauck, A. 1 Köttgen, N. Friedrich, Quality assurance in the pre-analytical phase of human urine samples by H-NMR spectroscopy, Archives of Biochemistry and Biophysics (2015), doi: 10.1016/j.abb.2015.07.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Quality assurance in the pre-analytical phase of human urine

RI PT

samples by 1H-NMR spectroscopy

Kathrin Budde1*, Ömer-Necmi Gök2*, Maik Pietzner1, Christine Meisinger3, Michael

SC

Leitzmann4, Matthias Nauck1,5, Anna Köttgen2, Nele Friedrich1,5

1

Institute of Clinical Chemistry and Laboratory Medicine, University of Greifswald,

M AN U

Ferdinand-Sauerbruch-Straße NK, 17475 Greifswald, Germany 2

Medical Center - University of Freiburg, Berliner Allee 29, 79110 Freiburg, Germany

3

Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany.

4

Department of Epidemiology and Preventive Medicine, University of Regensburg,

5

TE D

Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany DZHK (German Centre for Cardiovascular Research), Greifswald partner site,

EP

Greifswald, Germany

AC C

*equal contribution

Address for correspondence: Nele Friedrich

Institute for Clinical Chemistry and Laboratory Medicine University Medicine Greifswald Ferdinand-Sauerbruch-Straße NK D-17475 Greifswald, Germany Phone:

+49 - 3834 - 8619655

FAX:

+49 - 3834 - 865502

e-mail:

[email protected]

1

ACCEPTED MANUSCRIPT ABSTRACT

Metabolomic approaches investigate changes in metabolite profiles, which may reflect changes in metabolic pathways and provide information correlated with a specific biological 1

H-NMR spectroscopy is used to identify

RI PT

process or pathophysiology. High-resolution

metabolites in biofluids and tissue samples qualitatively and quantitatively. This pre-analytical study evaluated the effects of storage time and temperature on 1H-NMR spectra from human

SC

urine in two settings. Firstly, to evaluate short time effects probably due to acute delay in sample handling and secondly, the effect of prolonged storage up to one month to find

M AN U

markers of sample miss-handling. A number of statistical procedures were used to assess the differences between samples stored under different conditions, including Projection to Latent Structure Discriminant Analysis (PLS-DA), non-parametric testing as well as mixed effect linear regression analysis. The results indicate that human urine samples can be stored at 10°C for 24 hours or at -80°C for 1 month , as no relevant changes in 1H-NMR

TE D

fingerprints were observed during these time periods and temperature conditions. However, some metabolites most likely of microbial origin showed alterations during prolonged storage but without facilitating classification. In conclusion, the presented protocol for urine sample and

semi-automatic

metabolite

EP

handling

quantification

is

suitable

for

AC C

epidemiological studies.

Keywords: 1H-NMR spectroscopy, preanalytic, quality assurance, metabolome

2

large-scale

ACCEPTED MANUSCRIPT INTRODUCTION Metabolic profiling of biofluid specimens is an established method for investigating disease states in clinical studies. Changes in metabolites, which reflect changes in metabolic pathways, provide information concerning a disease state or other biological stressors of an

using high-resolution

RI PT

organism even in subclinical conditions [1, 2]. Metabolomic studies are frequently conducted 1

H-nuclear magnetic resonance (1H-NMR) spectroscopy or liquid

chromatography–mass spectrometry (LC/MS) [3] and gas chromatography-MS (GC/MS) [4]. 1

H-NMR spectroscopy is an established analytical technique for metabolic fingerprinting of

SC

biofluids and various tissues, allowing for differentiating both qualitative and quantitative profiles. For example, it has been used for elucidating metabolic effects of dietary factors in

M AN U

humans [5-8], animals [9-11], and cell cultures [12]. These studies demonstrated that NMRbased metabolomics is extremely efficient in detecting metabolic perturbations and enhances the ability to accurately profile the metabolic composition of different biological fluids qualitatively and quantitatively [13-15]. Urine is a preferentially used biofluid for many human

TE D

and animal studies due to its complex metabolic nature and the ability to collect multiple samples over a period of time by a non-invasive technique. Of note, human urine profiles offer a stable unique character predicting individual persons reliably [16, 17]. However,

EP

changes in metabolite composition caused by variation in specimen handling could generate serious biases for subsequent analysis [18].

AC C

Besides the application in clinical studies, there is an increasing use of 1H-NMR-based metabolomics

techniques

in large-scale population-based studies [19].

Hence,

a

standardized pre-analytical protocol of sample storage and preparation is an indispensable requirement for generating reliable data for such large cohorts. The aims of the present study were to investigate 1) the influence of different pre-analytical conditions at a single study centre (influence of delayed centrifugation and time to freezing) of The German National Cohort (GNC) on NMR measurements and 2) long-term effects with respect to storage time and temperature on the stability of the urine metabolome in an

3

ACCEPTED MANUSCRIPT experimental setting. In addition to healthy volunteers, persons with reduced kidney function were included to evaluate the protocol in a near clinical setting.

MATERIAL and METHODS

RI PT

The German National Cohort (GNC) is a large-scale, nationwide, long-term population study. The GNC aims to recruit up to 200,000 men and women aged 20-69 years sampled from the general population from 2014-2018. The aim of the study is to investigate the development of major chronic diseases and their corresponding subclinical and functional

SC

changes. Participants of the study will supply a diverse range of biomaterials, which will be stored in a biobank for subsequent research projects. A follow-up examination will be

planned every 2-3 years [20].

M AN U

performed after five years in all subjects, and active follow-up by postal questionnaires is

Short-term effects on urine specimen handling

TE D

Large-scale studies such as this require careful testing of all workflows, including the collection, processing and storage of the collected biomaterials. For this report, we aimed to evaluate the workflow for spot urine. Approximately 100 ml of spontaneous mid-stream urine

EP

were collected from participants of the pre-test phase of the GNC at three participating study centres (Augsburg, Regensburg, Freiburg). To examine the pre-analytical phase at the

AC C

regional GNC study centres, in a near clinical setting, urine samples from 11 participants with estimated GFR below 70 ml/min/1.73m2 were partitioned in aliquots of 0.8 ml and were run through the following workflow: (A) immediate centrifugation (B) 60 min delay at 4°C followed by centrifugation (C) centrifugation followed by 10 hours delay at 4°C (D) 60 min delay at 4°C followed by centrifugation followed by 10 hours delay at 4°C. Thereafter, all samples were immediately frozen at -80°C. The time frames of one and ten hours were chosen to reflect two possible occurring scenarios when immediate processing of samples is not possible. Firstly, when urine specimens could only be processed after another study module was finished (1h). Secondly, the maximum time frame (10h) when processing is only possible

4

ACCEPTED MANUSCRIPT after all participants for the day have been seen. Frozen samples were then shipped, and all subsequent analyses were performed centrally at the University Medicine of Greifswald laboratory.

RI PT

Preanalytic (PA) study at the Institute of Clinical Chemistry and Laboratory Medicine To examine the pre-analytical parameters in the NMR lab, non-fasting spontaneous urine samples were provided by eleven healthy volunteers between 08:00 and 10:00 a.m. on a single occasion. Analytical replicates (aliquots) of urine samples were measured several

SC

times [0, 2, 4, 8, 12, 24, 48 and 72h] after different storage conditions: 1) at room temperature (25°C) and 2) in a NMR cooling rack (10 °C) as well as under immediate

M AN U

phosphate buffer addition or as native biological fluid (buffer addition before measurement). The dense monitoring of sample quality in the first hours of storage allows for the determination of immediate effects possibly due to chemical reactions. In contrast, prolonged storage (up to 72h) would likely reflect alterations due to microbial activity. The need for

TE D

cooling of samples could be elucidated in comparison with samples stored at room temperature. Furthermore, the effect of prolonged storage at deep-freeze (-80°C) of up to one month was investigated, since usually a considerable delay occurs between sampling

AC C

and S2.

EP

and spectroscopic measurements. The workflows of both studies are displayed in Figure S1

Laboratory methods

Preparation of urine specimens for NMR analysis For spectroscopic analysis of all specimens, 450 µl of urine were mixed with 50 µl of phosphate buffer for stabilization of the urinary pH at 7.0 (+/-0.35). The buffer was prepared with D2O and contained sodium 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate (TSP) as internal chemical shift standard.

5

ACCEPTED MANUSCRIPT 1

H NMR spectroscopic analysis of urine specimens

Spectra were recorded at a Bruker DRX-400 NMR spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) operating at 400.13MHz

1

H frequency equipped with a 4mm

selective inverse flow probe (FISEI, 120 µl active volume) with z-gradient. Specimens were

RI PT

automatically delivered to the spectrometer via flow injection. The acquisition temperature was set to 300 K. A standard one-dimensional 1H NMR pulse sequence with suppression of the water peak (NOESYPRESAT) was used, i.e., RD − P(90o) − µs − P(90o) − tm − P(90o) − acquisition of the free induction decay (FID). The non-selective 90° hard pulse P(90 o) was

SC

adjusted to 9.4 µs. The relaxation delay (RD), mixing time tm, and acquisition time were set to 4 s, 100 ms and 3.96 s, respectively, resulting in a total recycle time of ~ 8.0 s. Low power

M AN U

continuous wave irradiation on the water resonance at an RF-field strength of ~ 25 Hz was applied during RD and tm for pre-saturation. After application of 4 dummy scans, 64 free induction decays (FIDs) were collected into 65536 (64K) complex data points using a spectral width of 20.689 ppm. FIDs were multiplied with an exponential function

TE D

corresponding to a line broadening of 0.3 Hz prior to Fourier transformation. Quality control of the spectroscopic data was carried out by analyzing the line width and signal-to-noise-ratio of the TSP signal, the standard error of the creatinine concentration and the positional

EP

variability of selected signals. Spectra were processed within TOPSPIN 1.3 (Bruker BioSpin GmbH, Rheinstetten, Germany). Metabolite levels were semi-automatically quantified using

AC C

spectral pattern matching as implemented in Chenomx NMR suite 7.0 (Chenomx Inc., Edmonton, Canada). The reliability of the procedure was confirmed by spiking experiments with several known concentrations of hippuric acid (Figure S3). Bucketing of 1H NMR spectra Spectra were normalized to the NMR signal intensity of the CH3-group of creatinine (3.03 ppm) to compensate for the large variations in urine concentrations and then segmented into N = 500 consecutive integrated spectral regions (buckets) of fixed bucket width (0.018 parts per million (ppm)) covering the range from 0.5 ppm to 9.5 ppm (MATLAB 7.0; Mathwork,

6

ACCEPTED MANUSCRIPT Natich, MA, USA). The 4.5-5.1 ppm chemical shift region was left out of the analysis in order to remove effects of variations in the suppression of water resonance.

Statistical methods

RI PT

In a first step, statistical analyses were performed on the basis of the buckets. Separately in both studies, projections to latent structures discriminant analysis (PLS-DA) models using mean centering and unit variance scaling (scaling weight per column: 1/standard deviation) were applied for subject classification as well as classification with respect to the pre-

SC

analytical condition. PLS-DA is a regression method that finds the relation between predictor variables (X, here the buckets) and dependent variables (Y, here a class variable) by

M AN U

maximizing the explained variance. Additionally, PLS-DA involves reduction of dimensionality by construction of latent variables, presenting linear combinations of the initial variables. For validation purpose, 7-fold cross-validation was used. Therefore, all samples were split in seven equally sized groups. Each time leaving out one of the groups and building the model

TE D

on the remaining ones. The prediction performance on the left group (Q²) was than averaged across all seven splits [21]. Beside the total predictive ability (Q²Ycum) of the model, the total explained variance in Y (R²Ycum) was given and score plots were displayed.

EP

With respect to the GNC study, additional analyses were performed based on quantified metabolites. Metabolite levels over the four preanalytic conditions are given as median (25th

AC C

quartile, 75th quartile) in table 1. Wilcoxon signed rank sum tests were used to assess the differences between condition A (immediate centrifugation followed by freezing) and the three other conditions (B: 60 min delay at 4°C foll owed by centrifugation; C: centrifugation followed by 10 hours delay at 4°C; D: 60 min delay at 4°C followed by centrifugation followed by 10 hours delay at 4°C). Since this involves mult iple testing, 30 metabolites across the three groups, differences between groups were considered significant if they exhibited a pvalue <0.0005 (0.05/30*3), which corresponds to a Bonferroni correction. To detect even moderate changes in urine spectra due to storage temperature and/or duration in the PA study, we used mixed-effect linear regression models accounting for the

7

ACCEPTED MANUSCRIPT repeated measurement character. For this purpose the previously described buckets were log10-transformed and used as dependent variables. Storage time and temperature as well as an interaction term between both were modeled as fixed effects. The random effect in the model consisted of the proband. Buckets were declared as significantly affected if one of the

RI PT

fixed effect terms exhibited a p-value < 0.0001 (0.05/500), which corresponds to Bonferroni correction for 500 buckets. Since the creatinine peaks seems to be at least slightly affected during the experiment, we additionally performed probabilistic quotient normalization (PQN) [22] to account for a possible bias in the analysis. Therefore, we used the median bucket-

SC

spectra as reference and subsequently estimated a dilution factor. Not at least, alterations in urine spectra stored for up to one month at -80°C w ere assessed by means of a Friedman

M AN U

test. Statistical analyses were performed with SAS 9.3 (SAS Institute Inc., Cary, North Carolina, USA) and SIMPCA P+ 13.0 (Umetrics AM, Umeå, Sweden).

RESULTS

TE D

The study was performed to assess the stability of urine metabolic profiles based on two different settings investigating short-term (time to freezing, centrifugation) and long-term (temperature, storage time) pre-analytical effects.

EP

PLS-DA models based on buckets, performed to differentiate the metabolic patterns, revealed that the major source of variation was dependent on inter-subject differences rather

AC C

than on the investigated setting. The models (Figure1 left side) consisted of 12 latent variables with R²Ycum = 0.986 and Q²Ycum = 0.956 for the PA study and thirteen latent variables with R²Ycum = 0.994 and Q²Ycum = 0.954 for the GNC study. Considering one as the maximum value for each, indicates that both models had good prediction characteristics to differentiate between individuals.

Long-Term (The PA Study) Similar analyses were performed for pre-analytical storage conditions, with no model detection confirmed by cross-validation for the PA study. Plots of the first two components

8

ACCEPTED MANUSCRIPT confirm the missing separation between the pre-analytical steps (Figure 1A: right side). Despite no influence on classification analysis could be observed, linear regression models revealed a number of altered signals with increasing storage time and/or temperature (Figure 2, upper panel). In general, the affected peaks tend to increase with time, including singlets

RI PT

at ppm 1.925 and 2.41, most likely consisting of acetate and succinate as well as an unknown triplet at ppm 4.04 (Figure 2, lower panel). These alterations were significantly more pronounced when samples were stored at RT as compared to samples stored at 10°C indicated by a significant interaction term. Furthermore, an increase in creatine (ppm 3.92

SC

and 3.04) and ethanol (triplet at ppm 1.19, doublet at 2.6 as well as several singlets around 3.7) after at least 24h room temperature could be observed (Figure 3A). However, under

M AN U

normal pre-analytic conditions (storage temperature below 4°C), no effects on both metabolites were detected, as shown in the GNC study (Figure 3B). Since the increase of creatine is likely due to a conversion from creatinine we repeated the analysis using PQN instead of creatinine normalization to account for dilution. Interestingly, spectral alterations

TE D

were less pronounced and some completely disappeared (Figure 2, upper panel). However, the alteration in signals likely corresponding to ethanol, succinate, creatine and creatinine still persisted. With respect to the effect of prolonged sample storage at -80°C no significant

EP

effect became obvious (data not shown).

AC C

Short-Term (The GNC Study)

In line with results from PLS-DA analysis in the PA study, even in the GNC study intersubject variation clearly exceeded variation caused by pre-analytical conditions (Figure 1). Plots of the first two components confirmed the missing separation between the preanalytical steps (Figure 1B: right side). These results likely indicate only minor changes arising from differences in short-term treatment. Therefore, additional univariate analyses were performed. With respect to annotation, 57 metabolites could be quantified in the GNC study samples. Exemplary subject-specific concentrations of citric acid, hippuric acid, phosphorylcholine and methylamine over all four conditions are given in Figure 4. The figure

9

ACCEPTED MANUSCRIPT clearly showed no substantial variations in concentrations of highly abundant metabolites. For metabolites of low abundance or single 1H-NMR peaks, such as phosphorylcholine or methylamine, the analyses showed qualitatively similar spectra. However, the semiautomatic quantification approach relies on unambiguous assignment of peak patterns to

RI PT

metabolites. In combination with signals near to noise this leads to alternate decisions regarding the quantifiability of the metabolites. The concentrations of 30 metabolites which were detectable/quantifiable over all four pre-analytical conditions in at least 5 subjects are displayed in Table 1. No significant systematic alterations depending on the pre-analytical

M AN U

concentrations under condition B and D compared to A.

SC

condition became apparent. However, at least succinate showed slightly elevated

DISCUSSION

NMR spectroscopy of biofluids in combination with multivariate pattern recognition is a precise approach for metabolomic studies, suitable for high-throughput long-term

TE D

epidemiological studies [19]. There are numerous investigations dealing with quality assurance of 1H-NMR, such as investigations of baseline data variability [17, 23, 24], analytical reproducibility [18], metabolite identification [25, 26], and validated sample

EP

preparation and storage [27-31]. The stability of urine metabolites in large metabolomic investigations is essential for accurate, valid and reproducible studies. Before identification of

AC C

metabolites and the evaluation of subsequent changes in their concentrations, it is important to evaluate the origin of the observed changes through pre-analytical studies. The present study was carried out across repeated 1H-NMR based measurement of global profiles or single metabolites primarily to assess the effects of pre-analytical conditions on the composition of urine from healthy and diseased volunteers and to validate a sampling protocol. For this purpose, two settings were investigated to reflect short-term (GNC study) and long-term (PA study) pre-analytical effects as well as a population-based and a more clinical setting. While short-term effects can be introduced by differences in urine handling

10

ACCEPTED MANUSCRIPT including timing of centrifugation and freezing, long-term effects may indicate metabolite decay processes, which may be active even at storage temperatures below -80°C.

Long-term stability of the urine metabolome

RI PT

Multivariate methods used to identify possible changes in the metabolome revealed no differences in the variance observed between technical replicates measured at time point t=0 and the variance between samples stored at 10°C up to 24h before measurement as well as samples stored at -80°C for one month. Based on our results from the PA Study, it can

SC

unambiguously be observed that the variance in the metabolome of a single volunteer urine sample stored at 10°C, RT or -80°C for 24 h is rath er small compared with inter-individual

M AN U

variations of all 10 subjects (Figure 3). Much is known about phenotypic factors that influence the composition of the metabolome, such as diet, health, lifestyle, and diurnal and hormonal cycles [24, 32]. Many metabolomic studies have shown that the intra-individual changes in the urine metabolite profile after storage at 4°C f or 24 h are minimal when compared with

TE D

inter-individual changes [17, 28, 30]. Taken together, our observations in combination with previously presented evidence indicate at most minor influence of sample miss-handling on the potential biomarker profile contained in 1H-NMR spectra of urine specimens.

EP

However, detailed analysis by means of regression modelling offered a number of, in terms of p-values, highly significant alterations due to storage conditions despite not strong enough

AC C

to facilitate classification. The origin of most of these metabolites may be microbial rather than mammalian, which is in line with previous studies [30, 31, 33] on the origin of alterations in urine spectra/metabolites due to differing pre-analytical conditions. It is well known that bacteria produce a multitude of metabolites under appropriate conditions. Furthermore, the quantity of bacterial metabolites like ethanol increased significantly with increasing bacterial count, establishing their link with the bacteria [34]. The concentration of creatine in urine samples increased after 24h at room temperature (Figure 3A: left side), while the concentration of creatinine decreased (data not shown). Creatinine in the urine can be used by bacteria and will be converted by the enzyme creatinkinase to creatine [35, 36]. Since the

11

ACCEPTED MANUSCRIPT analysis of urine metabolomics highly relay on an appropriate normalization to account for diurnal variation this could have implications for practice. Within the PA study a comparison between creatinine and PQN revealed an overestimation of storage effects when using creatinine. In contrast, PQN tends to under estimate the effects, since a clear increase in

RI PT

acetate levels after prolonged storage was blurred (Figure 2). Similar explanations as for the increase in creatine could be given for elevated ethanol and acetate levels in urine samples stored over a prolonged period of time, resulting in the advice of the addition of preservatives like sodium fluoride [30, 37]. However, since the addition of

SC

further preservatives affects shift resonances of selected metabolites like citrate [30], the best practice in long-term sample storage would be to immediately freeze samples. This

M AN U

suggestion was further supported by our and previous observations [38] indicating no alterations in spectral intensities during storage at -80°C.

Short-term (<12 h) stability of the urine metabolome and metabolite annotation

TE D

As expected, in the short-term study, no effects in concentrations of ethanol or creatine were observed (Figure 3B), showing that 4 – 10°C storage of urine samples for just a few hours has no influence on these metabolites even when their concentrations differ from

EP

physiological levels due to reduced renal function. Moreover, none of the 30 reliably quantified metabolites displayed significant changes across the investigated sample handling

AC C

conditions. In particular, succinate showed slight alterations in condition B and D. As no alterations to group C could be detected this might indicate the need for immediate centrifugation at least for reliable succinate concentrations. Similar observations were reported by Bernini et al. [33] who linked elevated succinate concentrations with missing or less efficient centrifugation. As possible explanation they named the activity of microbial derived enzymes catalyzing the decomposition of urea [33]. Since urine urea levels showed no significant decline in the present study under condition B and D this effect might be only modest. A similar explanation could account for the increase of the succinate peak in the PA study. The general robust results are especially important since urine specimens from the

12

ACCEPTED MANUSCRIPT GNC cohort were obtained from participants with impaired renal function and various studies showed that impaired renal function significantly affects the urine metabolome (for review see [39]). For example, urine levels of trimethylamine-N-oxide and hippuric acid have been found to be altered in acute kidney disease or after kidney transplantation [39].

RI PT

Consequently, the quantification of metabolites from 1H-NMR spectra by the present study protocol is not necessarily sensitive to altered metabolite levels in impaired renal function, which may further be aggravated by suboptimal short-term sample handling conditions. Our data, however, do not indicate that varying pre-analytical conditions affect metabolite

SC

concentrations in individuals with impaired kidney function. In fact, reliable quantification was found to be more sensitive to low abundance metabolites with only a single dominant peak

M AN U

(Figure 4B).

Our study has strengths but also some potential limitations. We performed extensive experiments to assess the impact of several processing steps when urine metabolomics is

TE D

used in epidemiological studies even if not all possibilities could be tested. The robust and reproducible character of 1H-NMR spectroscopy enabled us to follow a great number of metabolites/peak intensities throughout the study period. Only a minor part was significantly

EP

affected with low impact on the whole spectra. However, we could not exclude that a more sensitive method like mass spectrometry would reveal additional affected metabolites. The

AC C

missing quantification of metabolites in the PA study is a further drawback of the present study. It has to be noted that even the fragmented spectra (buckets) contained relevant information for possible sample miss-handling and hence might enable quality control prior expensive metabolite quantification.

On the basis of the presented observations, we conclude that urinary metabolite profiles are stable under different pre-analytical conditions. Storage of the samples up to 24-hour in a NMR cooling rack (10°C) and centrifugation up to on e hour after sample collection did not lead to relevant spectrum alterations. The study clearly showed that inter-individual

13

ACCEPTED MANUSCRIPT differences represent the major source of variation in metabolic profiles. Taken together, the current protocol is well suited for healthy as well as clinical specimens and is highly recommended for large epidemiological studies such as the GNC, where many individuals

RI PT

are expected to have impaired kidney function.

ACKNOWLEDGMENTS

This project was conducted in the context of the pretest studies of the German National

SC

Cohort (www.nationale-kohorte.de). These were funded by the Federal Ministry of Education and Research (BMBF), Förderkennzeichen 01ER1001A-I and supported by the Helmholtz

M AN U

Association as well as by the participating universities and Institutes of the Leibniz Association.

The pre-analytical study conducted at the Institute of Clinical Chemistry and Laboratory Medicine or the University of Greifswald was supported in part by INSTAND e.V. (Gesellschaft zur Förderung der Qualitätssicherung in medizinischen Laboratorien e.V.;

TE D

project title: “Projekt „Qualitätssicherung von Metabolics Tandem - Massenspektrometrie

AC C

EP

(MS) und Kernspinresonanzspektroskopie (NMR) in Plasma und Urin“).

14

ACCEPTED MANUSCRIPT REFERENCES

6.

7.

8.

9.

10.

11.

12.

13.

14. 15.

16.

17.

RI PT

SC

5.

M AN U

4.

TE D

3.

EP

2.

Weckwerth W and K Morgenthal. Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today 2005; 10:1551-1558. Lindon JC, E Holmes, ME Bollard, EG Stanley and JK Nicholson. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 2004; 9:1-31. Wilson ID, R Plumb, J Granger, H Major, R Williams and EM Lenz. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life Sci 2005; 817:67-76. Shellie RA, W Welthagen, J Zrostlikova, J Spranger, M Ristow, O Fiehn and R Zimmermann. Statistical methods for comparing comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry results: metabolomic analysis of mouse tissue extracts. J Chromatogr A 2005; 1086:83-90. Bertram HC, C Hoppe, BO Petersen, JO Duus, C Molgaard and KF Michaelsen. An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br J Nutr 2007; 97:758-763. Daykin CA, JP Van Duynhoven, A Groenewegen, M Dachtler, JM Van Amelsvoort and TP Mulder. Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J Agric Food Chem 2005; 53:14281434. Solanky KS, NJ Bailey, BM Beckwith-Hall, A Davis, S Bingham, E Holmes, JK Nicholson and A Cassidy. Application of biofluid 1H nuclear magnetic resonancebased metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal Biochem 2003; 323:197-204. Wang Y, H Tang, JK Nicholson, PJ Hylands, J Sampson and E Holmes. A metabonomic strategy for the detection of the metabolic effects of chamomile (Matricaria recutita L.) ingestion. J Agric Food Chem 2005; 53:191-196. Bertram HC, RH Andersen and HJ Andersen. Development in myofibrillar water distribution of two pork qualities during 10-month freezer storage. Meat Sci 2007; 75:128-133. Bertram HC, KE Bach Knudsen, A Serena, A Malmendal, NC Nielsen, XC Frette and HJ Andersen. NMR-based metabonomic studies reveal changes in the biochemical profile of plasma and urine from pigs fed high-fibre rye bread. Br J Nutr 2006; 95:955962. Solanky KS, NJ Bailey, E Holmes, JC Lindon, AL Davis, TP Mulder, JP Van Duynhoven and JK Nicholson. NMR-based metabonomic studies on the biochemical effects of epicatechin in the rat. J Agric Food Chem 2003; 51:4139-4145. Lamers RJ, EC Wessels, JJ van de Sandt, K Venema, G Schaafsma, J van der Greef and JH van Nesselrooij. A pilot study to investigate effects of inulin on Caco-2 cells through in vitro metabolic fingerprinting. J Nutr 2003; 133:3080-3084. Bollard ME, EG Stanley, JC Lindon, JK Nicholson and E Holmes. NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed 2005; 18:143-162. Griffiths L and AM Irving. Assay by nuclear magnetic resonance spectroscopy: quantification limits Analyst 1998; 123:1061-1068. Moolenaar SH, UF Engelke and RA Wevers. Proton nuclear magnetic resonance spectroscopy of body fluids in the field of inborn errors of metabolism. Ann Clin Biochem 2003; 40:16-24. Bernini P, I Bertini, C Luchinat, S Nepi, E Saccenti, H Schafer, B Schutz, M Spraul and L Tenori. Individual human phenotypes in metabolic space and time. J Proteome Res 2009; 8:4264-4271. Lenz EM, J Bright, ID Wilson, SR Morgan and AF Nash. A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. J Pharm Biomed Anal 2003; 33:1103-1115.

AC C

1.

15

ACCEPTED MANUSCRIPT

23.

24.

25.

26. 27.

28.

29.

30.

31. 32.

33.

34.

RI PT

SC

22.

M AN U

21.

TE D

20.

EP

19.

Dumas ME, EC Maibaum, C Teague, H Ueshima, B Zhou, JC Lindon, JK Nicholson, J Stamler, P Elliott, Q Chan and E Holmes. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 2006; 78:2199-2208. Bictash M, TM Ebbels, Q Chan, RL Loo, IK Yap, IJ Brown, M de Iorio, ML Daviglus, E Holmes, J Stamler, JK Nicholson and P Elliott. Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology. J Clin Epidemiol 2010; 63:970-979. German National Cohort C. The German National Cohort: aims, study design and organization. Eur J Epidemiol 2014; 29:371-382. Wiklund S, E Johansson, L Sjostrom, EJ Mellerowicz, U Edlund, JP Shockcor, J Gottfries, T Moritz and J Trygg. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry 2008; 80:115-122. Dieterle F, A Ross, G Schlotterbeck and H Senn. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem 2006; 78:4281-4290. Kochhar S, DM Jacobs, Z Ramadan, F Berruex, A Fuerholz and LB Fay. Probing gender-specific metabolism differences in humans by nuclear magnetic resonancebased metabonomics. Anal Biochem 2006; 352:274-281. Lenz EM, J Bright, ID Wilson, A Hughes, J Morrisson, H Lindberg and A Lockton. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. J Pharm Biomed Anal 2004; 36:841-849. Dieterle F, A Ross, G Schlotterbeck and H Senn. Metabolite projection analysis for fast identification of metabolites in metabonomics. Application in an amiodarone study. Anal Chem 2006; 78:3551-3561. Weljie AM, J Newton, P Mercier, E Carlson and CM Slupsky. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 2006; 78:4430-4442. Barton RH, JK Nicholson, P Elliott and E Holmes. High-throughput 1H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study. Int J Epidemiol 2008; 37 Suppl 1:i31-40. Dunn WB, D Broadhurst, DI Ellis, M Brown, A Halsall, S O'Hagan, I Spasic, A Tseng and DB Kell. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int J Epidemiol 2008; 37 Suppl 1:i23-30. Elliott P, TC Peakman and UK Biobank. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol 2008; 37:234-244. Lauridsen M, SH Hansen, JW Jaroszewski and C Cornett. Human urine as test material in 1H NMR-based metabonomics: recommendations for sample preparation and storage. Anal Chem 2007; 79:1181-1186. Saude EJ and BD Sykes. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics 2007; 3:19-27. Bollard ME, E Holmes, JC Lindon, SC Mitchell, D Branstetter, W Zhang and JK Nicholson. Investigations into biochemical changes due to diurnal variation and estrus cycle in female rats using high-resolution (1)H NMR spectroscopy of urine and pattern recognition. Anal Biochem 2001; 295:194-202. Bernini P, I Bertini, C Luchinat, P Nincheri, S Staderini and P Turano. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J Biomol NMR 2011; 49:231-243. Bala L, UC Ghoshal, U Ghoshal, P Tripathi, A Misra, GA Gowda and CL Khetrapal. Malabsorption syndrome with and without small intestinal bacterial overgrowth: a study on upper-gut aspirate using 1H NMR spectroscopy. Magn Reson Med 2006; 56:738-744.

AC C

18.

16

ACCEPTED MANUSCRIPT

RI PT

SC

39.

M AN U

38.

TE D

37.

EP

36.

Cattell WR, JM Sardeson, E Hutchinson and F O'Grady. Creatinine content of urine and bacterial growth. Br Med J 1969; 3:175-176. Jones JD and PC Burnett. Implication of creatinine and gut flora in the uremic syndrome: induction of "creatininase" in colon contents of the rat by dietary creatinine. Clin Chem 1972; 18:280-284. Jones AW, L Hylen, E Svensson and A Helander. Storage of specimens at 4 degrees C or addition of sodium fluoride (1%) prevents formation of ethanol in urine inoculated with Candida albicans. J Anal Toxicol 1999; 23:333-336. Rist MJ, C Muhle-Goll, B Görling, A Bub, S Heissler, B Watzl and B Luy. Influence of freezing and storage procedure on human urine samples in NMR-based metabolomics. Metabolites 2013; 3:243-258. Atzler D, E Schwedhelm and T Zeller. Integrated genomics and metabolomics in nephrology. Nephrol Dial Transplant 2014; 29:1467-1474.

AC C

35.

17

ACCEPTED MANUSCRIPT FIGURES LEGENDS

Figure 1. PLS-DA score plot for urine stability showing the first two principal components in two different settings: A) long-term effect of pre-analytic storage time and temperature (PA

RI PT

study) and B) short-term effect of pre-analytic time to freezing and centrifugation (GNC study). Left side: Each colour represents urine samples from one volunteer under different conditions. Right side: Each colour represents a different pre-analytic condition [PA study: 1) 0h, 2) 10°C 24h, 3) room temperature (RT) 24h, 4) R T 24h wB (buffer addition immediately

SC

before measurement), 5) RT 48h, 6) -80°C 1 month; G NC study: 1) centrifugation (centr.), 2) 4°C 60min –> centrifugation, 3) centrifugation –> 4 °C 10h, 4) 4°C 60min -> centrifugation –>

M AN U

4°C 10h].

Figure 2. Upper panel: Results from mixed-effect linear regression models presented as pseudo p-value spectra using storage time (red) and temperature (green) as well as an

TE D

interaction term between both (blue) as fixed effects. Fragmented spectra intensities (buckets) were used as outcome. The graphic is further divided by the normalization procedure used for urine specimens (creatinine: upper part; probabilistic quotient

EP

normalization (PQN): lower part). The dashed lines indicate the Bonferroni correction to account for multiple testing (0.05/500 = 0.0001). Lower panel: Representative raw 1H-NMR

AC C

spectra of selected significantly affected spectral regions from regression analysis. If possible, annotations of metabolites were added.

Figure 3. Increase in creatine (left) and ethanol (right) concentration entailed by different preanalytic conditions in spontaneous urine samples among two different settings: A) long-term effect of pre-analytic storage time and temperature (PA study) and B) short-term effect of pre-analytic time to freezing and centrifugation (GNC study). RT = room temperature; centr = centrifugation.

18

ACCEPTED MANUSCRIPT Figure4. A) Concentrations of citric acid, hippuric acid, O-Phosphocholine and methylamine based on four different preanalytic storage conditions (A-D). B) 1H NMR spectra of the

AC C

EP

TE D

M AN U

SC

RI PT

metabolites displayed in A) presented in physiological proportion. NQ = not quantifiable.

19

ACCEPTED MANUSCRIPT

Table 1. Quantified metabolite levels across four different preanalytic conditions* A-D in the GNC study. Group A

Group B

Group C

Group D

p(AvsB)**

p(AvsC)**

p(AvsD)**

9 11 11 11 10 10 11 11 11 10 10 11 11 11 9 10 10 9 7 7 7 5 8 11 5 9 11 10 10 5

28 (15; 90) 21 (15; 23) 42 (31; 205) 44 (25; 205) 84 (42; 208) 17 (8; 36) 627 (182; 2574) 2917 (1539; 5481) 124 (60; 229) 95 (59; 163) 54 (35; 203) 193 (112; 719) 83 (67; 392) 658 (249; 2771) 99 (92; 143) 59 (20; 104) 58 (50; 66) 15 (7; 36) 142 (70; 224) 90 (52; 233) 10 (6; 58) 234 (58; 238) 23 (18; 54) 110 (72; 189) 62 (47; 120) 199 (129; 808) 11490 (5554; 22419) 14 (10; 22) 13 (10; 51) 114 (79; 132)

23 (18; 94) 26 (15; 32) 54 (31; 182) 46 (21; 212) 83 (56; 226) 11 (7; 32) 506 (217; 2594) 2932 (2114; 5529) 137 (71; 222) 95 (63; 159) 51 (41; 169) 188 (155; 713) 103 (75; 376) 472 (259; 2565) 117 (93; 122) 53 (16; 107) 68 (44; 75) 14 (8; 39) 142 (60; 236) 76 (53; 263) 17 (10; 68) 223 (68; 276) 24 (20; 55) 107 (75; 197) 61 (56; 112) 224 (130; 836) 12418 (7878; 23395) 14 (9; 24) 14 (10; 56) 114 (68; 146)

20 (19; 88) 16 (12; 31) 52 (32; 194) 45 (22; 216) 74 (59; 186) 10 (9; 39) 488 (199; 2360) 2932 (2097; 5503) 121 (69; 205) 101 (72; 149) 51 (37; 193) 182 (148; 685) 104 (76; 363) 521 (237; 2447) 97 (71; 101) 43 (19; 117) 56 (48; 70) 14 (10; 37) 149 (49; 211) 91 (48; 253) 10 (6; 55) 223 (50; 322) 23 (18; 55) 108 (73; 180) 58 (52; 100) 205 (134; 752) 11592 (7227; 21223) 12 (8; 21) 14 (11; 48) 112 (66; 136)

23 (17; 93) 20 (14; 44) 53 (33; 217) 49 (19; 209) 77 (35; 240) 10 (8; 35) 534 (182; 2715) 3019 (2157; 5563) 129 (73; 228) 105 (68; 145) 48 (38; 153) 184 (153; 755) 105 (69; 402) 530 (249; 2627) 101 (74; 140) 48 (22; 104) 59 (48; 78) 13 (8; 37) 129 (52; 215) 81 (55; 261) 18 (11; 73) 248 (51; 277) 33 (16; 59) 98 (73; 193) 57 (54; 110) 188 (132; 860) 12575 (8977; 22842) 13 (9; 22) 14 (10; 54) 96 (81; 145)

0.16 0.05 0.43 0.71 0.23 0.39 0.90 0.17 0.59 0.70 0.38 0.97 0.52 0.58 0.65 0.85 0.63 0.30 0.69 0.30 0.02 0.31 0.31 0.41 0.81 0.03 0.52 0.79 0.51 0.38

0.80 0.52 0.58 0.70 0.56 0.87 0.17 0.46 0.41 1.00 0.56 0.46 0.24 0.32 0.16 0.63 0.85 0.38 0.30 0.38 0.69 0.81 0.31 0.97 0.44 0.91 0.70 0.19 0.94 0.63

0.30 0.12 0.13 0.58 0.70 0.49 1.00 0.21 0.17 0.32 0.63 0.97 0.04 0.76 0.36 0.43 0.85 1.00 0.38 0.47 0.02 0.19 0.15 0.76 1.00 1.00 0.46 0.89 0.43 1.00

TE D

M AN U

SC

RI PT

# subjects†

EP

Acetate Acetone Alanine Betaine cis-Aconitate Carnitine Citric acid Creatinine Dimethylamine Ethanolamine Formate Glycine Glycolate Hippuric acid Histidine Lactate Methanol N,N-Dimethylglycine pi-Methylhistidine Pyroglutamate Succinate Taurine Threonine Trimethylamine N-oxide tau-Methylhistidine Trigonelline Urea 2-Hydroxyisobutyrate 3-Hydroxyisovalerate 4-Hydroxyphenylacetate

AC C

Metabolite

*A) immediate centrifugation; B) 60 min delay at 4°C followed by centrifugation; C) centrifugation followed by 10 hours delay at 4°C; D) 60 min delay at 4°C followed by centrifugation followed by 10 hours delay at 4°C. *Wilcoxon signed rank sum test for group A versus group B, C and D. †Only metabolites which were quantifiable over all four conditions in at least 5 subjects.

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT Research Highlights: •

We investigated effects of pre-analytical settings on urinary 1H-NMR measurements.

•

Short-term conditions (temp, centrifugation) had no effects on metabolic profiles.

•

Spectra showed long-term stability for 24h at room temp and over a freeze-thaw

RI PT

cycle. Inter-individual differences represent the major source of variation.

•

The protocol is suitable for global metabolic profiling in large-scale studies.

AC C

EP

TE D

M AN U

SC

•

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

SUPPLEMENT

TE D

Figure S1 The analytic workflow of the German National Cohort (GNC) study to investigate the influence of different pre-analytical conditions on metabolic profiling using NMR

AC C

EP

spectroscopy.

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure S2 The analytic workflow of the pre-analytic (PA) study to investigate the influence of different pre-analytical conditions on metabolic profiling using NMR spectroscopy.

RI PT

ACCEPTED MANUSCRIPT

SC

Figure S3 Left side: 1H NMR spectral regions corresponding to hippuric acid signals of different urinary concentrations. Right side: Comparison between spiked and measured

M AN U

concentrations of hippuric acid. Scatter plot with the regression line added in red (left box). Bland-Altman plot for the same data (right box). For this purpose, 1.8g hippurate were solved in 80ml double-distilled water given a concentration of 12.5 nM in the resulting solvent.

AC C

EP

TE D

Afterwards a series of five dilutions was prepared each time halving the concentration.