Carboxyhemoglobin and thiocyanate as biomarkers of exposure to carbon monoxide and hydrogen cyanide in tobacco smoke

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Experimental and Toxicologic Pathology 58 (2006) 101–124

EXPERIMENTAL ANDTOXICOLOGIC PATHOLOGY www.elsevier.de/etp

Carboxyhemoglobin and thiocyanate as biomarkers of exposure to carbon monoxide and hydrogen cyanide in tobacco smoke Gerhard Scherer ABF Analytisch-biologisches Forschungslabor GmbH, Goethestr. 20, 80336 Mu¨nchen, Germany Received 8 June 2006; accepted 26 July 2006

Abstract The determination of biomarkers in human body fluids is a useful tool, which allows the quantitative assessment of the exposure to chemicals or complex mixtures of chemicals and of early biological effects as a result of the exposure. Biomarkers require validation before their successful application in human studies. This review describes some general purposes of human biomonitoring and biomarkers including the requirements for validation. Risk assessment and harm reduction of smoking and tobacco products, respectively, is a very suitable field for the application of biomarkers. A brief historical review shows that the application of biomarkers of exposure and effect in human smoking goes back more than 150 years. Two ‘classical’ biomarkers of exposure to tobacco, namely carboxyhemoglobin (COHb and its equivalent carbon monoxide in exhalate, COex) and thiocyanate (SCN) in body fluids are discussed in terms of sources of exposure, metabolism, disposition kinetics and influencing host factors. Data on COHb/COex and SCN in nonsmokers and smokers as well as the power to discriminate between smokers and nonsmokers are presented. Both biomarkers are significantly correlated with the daily cigarette consumption. Smoking machine-derived yields of the precursors carbon monoxide and hydrogen cyanide were not correlated with COHb/ COex and SCN, respectively. It is concluded that, while COHb/COex is a useful biomarker for assessing the smoke inhalation, preferably in controlled studies, the application of SCN in body fluids as a biomarker for smoking is limited, mainly due to the abundance of other sources for SCN. r 2006 Elsevier GmbH. All rights reserved. Keywords: Biomarkers; Tobacco smoke; Carbon monoxide; Hydrogen cyanide; Carboxhemoglobin; Thiocyanate

Introduction Biomarkers and biomonitoring: general purposes and definitions Human biomonitoring is the acquisition of exposure and biological effect data through the analysis of cells, tissues, or body fluids (Suk et al., 1996). The biochemTel.: +49 89 535395; fax: +49 89 5328039.

E-mail address: [email protected]. 0940-2993/$ - see front matter r 2006 Elsevier GmbH. All rights reserved. doi:10.1016/j.etp.2006.07.001

ical or biological variable measured for the purpose of biomonitoring is designated as biomarker. Biomarkers can detect the exposure, effect of exposure or the individual susceptibility to an exposure (Ward Jr and Henderson, 1996). Biomarkers are, therefore, primarily of interest for the assessment of the exposure and early biological effects in epidemiology as well as occupational and environmental medicine. Biomarkers are observable endpoints in the continuum of events ranging from exposure to diseases (Ward Jr and Henderson, 1996). The chain of events includes:

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Table 1.

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Concept of biomonitoring and biomarkersa

Type of biomarker

Example: Smoking-related exposure to benzene

External dose


Benzene yield in mainstream smoke of cigarettes

Internal dose (absorbed dose)


Benzene concentration in blood, urine or exhaled air
trans,trans-Muconic acid in urine
S-phenylmercapturic acid in urine

Biologically effective dose (target dose)


DNA adducts of benzene in bone marrow cells
Surrogate marker: hemoglobin adduct of benzene

Early biological effects


Chromosomal aberrations (CA), micronuclei (MN), sister chromatid exchanges (SCE)

Health effects


Leukemia

a

The italicized text comprises the field of biomonitoring.

‘external dose’, ‘internal dose’ (absorbed dose), ‘biologically effective dose’ (target dose), ‘early biological effects’ and ‘health effects’ (Table 1) (Larsen, 1995). This chain of events is exemplified by the exposure agent benzene. A measure of the ‘external dose’ in this case is the benzene yield per cigarette as determined by standard machine smoking methods. The external dose could be assessed by filter retention methods which allow for individual puffing behavior (Shepherd and Mariner, 2001; Watson et al., 2004). The ‘internal dose’ is represented by the concentration of the agent or a metabolite in a suitable body fluid (blood, urine, saliva). Benzene in blood or urine and also in exhaled air (which is in equilibrium with the benzene blood concentration) is frequently used (Angerer et al., 1991; Riedel et al., 1996). The urinary metabolite trans,trans-muconic acid is also a suitable biomarker for the ‘internal dose’ ¼ (also termed ‘absorbed dose’) (Scherer et al., 1998). The ‘biologically effective dose’ (or ‘target dose’) is the amount of agent reaching the target organ and target cells. Since benzene can induce leukemia in humans, benzene or benzene reaction products (e.g. DNA adducts) in bone marrow cells would indicate the ‘target dose’. As conventional biomonitoring studies are performed with healthy individuals, cells from target organs and tissues are rarely available. A marker of ‘early biological effect’ in the case of benzene exposure could be cytogenetic changes in peripheral lymphocytes such as chromosomal aberrations, micronuclei and sister chromatid exchanges or somatic mutations (Nilsson et al., 1996). The ‘health effect’ (or disease) in the benzene example is leukemia (International Agency for Research on Cancer, 1987). The exposure biomarkers (internal dose, biologically effective dose) are specific for the chemical of exposure, while effect biomarkers are usually unspecific for the exposure agent (Ward Jr and Henderson, 1996).

Biomarkers of genetic susceptibility have gained increased interest in human biomonitoring studies (Wormhoudt et al., 1999). Particularly genetic polymorphisms of phase I and phase II enzymes involved in the metabolism of xenobiotics are considered as modifiers of the biomarker level (and probably also of the risk). Up to now, biomarkers are used primarily in epidemiology (IARC, 1997; Bonassi and Au, 2002), but have become increasingly important in occupational and environmental medicine. From the purposes described above it can be deduced that the development and validation of a suitable biomarker is an important, extensive and time-consuming task (Schulte and Talaska, 1995; Groopman and Kensler, 1999; Albertini, 1999) (Table 2). Particularly, the last step in the validation process of a biomarker (meeting the ‘gold standard’) is difficult to achieve and has been implemented for only a few biomarkers.

Biomarkers for smoking Purposes The concept of biomonitoring and biomarkers as outlined above has been widely applied to both smoking (International Agency for Research on Cancer, 1986; Hecht, 1997, 1999) and exposure to environmental tobacco smoke (ETS) (Scherer and Richter, 1997; Benowitz, 1999). Biomarkers for the internal dose of toxicologically relevant tobacco smoke constituents and biomarkers of (early) biological effects have been discussed elsewhere (Hecht, 2002; Phillips, 2002; Scherer, 2005; Gregg et al., 2006). In contrast to most other exposures, substance uses, and dietary and drinking habits, the habit of smoking

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Table 2. Steps for the development and validation of a biomarker Step

Description

1

Selection of chemical (sources, significant human exposure, adverse health effects) Selection of biomarker (a parameter suitable as a response to the exposure) Development of a method for measuring the biomarker Verification of the biomarker (e.g., in a suitable animal model) Adaptation of the method to field conditions (sensitivity, simplicity, throughput) Establish pharmacokinetics, persistence and doseresponse in humans Identify modifying factors (life-style, occupation, genetics) Validation of the biomarker in a ‘transitional epidemiological study’
Background concentrations and variability in a population
Inter- and intra-individual variation
Does the biomarker meet its intended purpose? (meet the gold standard): J The biomarker of (internal) dose should indicate the actual exposure J The biomarker of biological effect should strongly predict the risk for a disease J The biomarker of susceptibility should actually modify the risk

2 3 4 5 6 7 8

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determine the smoking dose (i.e., the amount of smoke uptake per day or per cigarette) by measuring one or a series of suitable biomarkers. This way, it would be possible to answer four important questions:









Does the ISO or FTC standard smoking regime provide cigarette yields for tar, nicotine and other smoke components that are adequate for the smokers’ dose assessment? Many studies have shown that low yield cigarettes are oversmoked (Scherer, 1999; Djordjevic and Stellman, 2000; US Department Health and Human Services, 2001; Hecht et al., 2005; Benowitz et al., 2005). Do smokers compensate when switching to cigarettes with lower yields? (for review see Scherer, 1999). Is a design change in the product (cigarette) intended for a harm reduction reflected in a decrease in suitable biomarkers? Is a lower level in one or several biomarkers linked with a reduced risk for smoking-related diseases?

An assessment of the health benefits of harm-reduced cigarettes can only be done through epidemiological studies of 10–20 years duration following the introduction of the modified cigarettes. Biomonitoring of smokers offers the opportunity of showing potential harm reduction in a much shorter time period and is proposed to be an element in evaluating new tobacco products (Institute of Medicine (IOM), 2001; Shields, 2002; Henningfield et al., 2005; Hatsukami et al., 2005).

Brief history of biomarkers of smoking Table 3.

Parameters for variation of the smoking dose

Number of cigarettes smoked per day Selection of brand according to nominal smoke yield Number of puffs per cigarette Puff interval (puff frequency) Puff volume Duration of puff Flow rate during puffing Amount of smoking expelled form the mouth (mouth spill) Depth of inhalation Duration of inhalation Butt length Blocking of filter vents

offers a wide range of parameters in order to vary the dose (Table 3). Suitable biomarkers of smoking should integrate over these dose-determining parameters and correctly indicate the uptake of a particular smoke component or, preferably, of a whole smoke fraction (i.e., gas phase or particulate phase). The ultimate goal is to precisely

The science of human biomonitoring is strongly dependent on the development of rapid, specific, and sensitive analytical methods, which evolved particularly over the past 50 years. Despite this, observations and studies with smoking-related biomarkers go back more than 100 years (Table 4) (for review see Larson et al., 1961). One of the first reports on a biomarker (actually a biomarker of very early effect, namely pulse rate) which changed in response to smoking was that by Riley (1856). In a self-experiment he observed an increase in pulse rate when smoking cigars compared to a nonsmoking period. A decrease of the peripheral skin temperature (another biomarker of a very acute effect) of the fingers and toes was reported by Maddock and Coller (1932). EEG changes immediately after smoking were first measured by Lambiase and Serra (1957). The first biomarker of (internal) smoking dose was thiocyanate (SCN), a detoxification product of cyanide, in urine, saliva and blood. Claude Bernard (1813–1878) discovered that smokers excrete higher amounts of SCN in their urine (and saliva?) than nonsmokers (Larson et al., 1961). Quantitative data of SCN in smokers and

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Table 4.

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Brief chronology of biomarkers for smoking

Year

Author(s) [Ref.]

Biomarker

Discovery or result

1856

Riley (1856)

Pulse rate

Self-experiment: Two 5-d nonsmoking periods: 85 and 86 min1 Two 5-d smoking periods (6 cigars/d): 92 and 90 min1

1870

Claude Bernard [cit. in (Larson et al., 1961)]

SCN (U)

Smokers excrete higher amounts than nonsmokers

1900

Mendel and Schneider (1900)

SCN (S)

Nonsmokers: 2.9 mg% Smokers: 13.4 mg%

1919/20

Hartridge (1919)

COHb

Two Smokers: 0% and 6%

1923

Noether (1923)

Nicotine (U determined by a leech-muscle bioassay)

2 h after a cigarette: Nicotine present 12 h abstinence: Nicotine absent 1.5 h after a cigar: Nicotine present 2.5 h after a cigar: Peak of response 12 h after a cigar: Nicotine absent

1925

Schreiber (1925)

SCN (B)

Nonsmokers: 0.025–0.04 mg% Smokers: 0.075–0.12 mg%

1928

Blum (1928)

SCN (B)

Nonsmokers: 0.03–0.06 mg% Smokers: 0.06–0.24 mg%

1932/33

Maddock and Coller (1932)

Peripheral skin temp.

Twenty smokers: Fingers: 0.7 to 6.0 1C Toes: 0.5 to 4.5 1C

1935

Bodna´r et al. (1935)

Nicotine (U)

After 10 cigarettes with inhalation: 1.31 mg After 10 cigarettes without inhalation: no nicotine found

1936

Eymer et al. (1936)

CO (B)

Twenty-one smokers (a.m., noninhalers): 0.12% Twenty-five smokers (a.m., inhalers): 0.1% Thirteen smokers (a.m., heavy inhalers): 0.26% Thirteen smokers (p.m., heavy inhalers): 0.52%

1945

Ramos and Visca [cit. in Larson et al., 1961)]

SCN (B)

Sixty-six nonsmokers: 0.4370.10 mg% Twenty-six smokers: 0.8470.20 mg%

1957

Lambiase and Serra (1957)

EEG changes

Twenty-five smokers before and after smoking 1 cigarette: Depression of voltage and acceleration in frequency of alpha rhythm

1959

Bowman et al. (1959)

Cotinine (U) Hydroxy-cotinine (U)

Identification of both major nicotine metabolites in urine of smokers as well as a nonsmoker given oral nicotine

1994

Osterman-Golkar et al. (1994)

Hb adducts of acrylonitrile

Four nonsmokers:o20 pmol/g globin Four smokers: 88 (75–106) pmol/g globin

Abbreviations: U: Urine; S: Saliva; B: Blood; EEC: Electro encephalogram; Hb: Hemoglobin.

nonsmokers were reported by Mendel and Schneider (1900); Schreiber (1925); Blum (1928) and Ramos and Visca (1945) (cited in Larson et al., 1961). The reported concentrations of SCN are in fair agreement with those found later with more sophisticated methods (Bliss and O’Connell, 1984). Hartridge (1919) reported concentrations of carboxyhemoglobin (COHb) of 0% and 6% in 2 smokers in 1919/20. The latter value is also consistent with the

smoking-related COHb concentrations determined with modern instruments. The level of 0% was probably caused by an insufficient limit of detection and by the fact that the subject was a noninhaler. Eymer et al. (1936) showed that the COHb level was dependent on the degree of inhalation and that COHb accumulated in smokers over the day. The quantitative analysis of nicotine and nicotine metabolites in body fluids is much more demanding than

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that of SCN and carbon monoxide (CO). Therefore, the first report on (semi-)quantitative nicotine determinations in urine of smokers by Noether (1923) was based on a leech-muscle bioassay. The results gave the first rough clue on the urinary half-life of nicotine in cigar smokers (about 12 h). The earliest publication on a quantitative chemical analysis of urinary nicotine after smoking was that by Bodna´r et al. (1935). These authors found an excretion of 1.31 mg nicotine after smoking 10 cigarettes with inhalation, whereas no nicotine was detectable when 10 cigarettes were smoked without inhalation. The urinary excretion of 1.31 mg nicotine after 10 cigarettes is amazingly close to concentrations found in today’s cigarette smokers with similar consumption (Tricker, 2003). Cotinine, the nicotine metabolite most widely used today as biomarker for tobacco smoke exposure, was discovered by the working group of McKennis (Bowman et al., 1959). The common use of nicotine and cotinine in body fluids as biomarkers for tobacco smoke exposure became possible with the development of methods based on gas chromatography (GC) (Beckett and Triggs, 1966), radioimmunoassay (RIA) (Langone et al., 1973) and high-performance liquid chromatography (HPLC) (Watson, 1977). The more recently developed biomarker for tobacco smoke exposure with some promising specificity is cyanoethylvaline, released from hemoglobin by a modified Edman degradation procedure (OstermanGolkar et al., 1994). This hemoglobin adduct indicates exposure to acrylonitrile, a constituent of tobacco smoke, but also present in automobile exhausts, resins, glues and at certain workplaces. Table 4 shows a brief chronology of biomarkers used for smoking. In this review, the very early (classic) smoking-related biomarkers COHb and SCN are discussed. Since CO and hydrogen cyanide (HCN) are gas phase constituents; COHb and SCN could be regarded as substitute markers for other gas phase components in tobacco smoke such as nitrogen oxides and aldehydes. However, due to differences in chemical reactivity and absorption in the respiratory tract, extrapolation from the uptake of CO or HCN to other gas phase constituents should be done with caution.

Carboxyhemoglobin and carbon monoxide in exhalate (COex) Occurrence and toxicology of carbon monoxide CO is a colorless, odorless, and nonirritant toxic gas, which is slightly lighter than air. Worldwide about 109 tons per year of CO are formed. Half of this amount is

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formed by biological processes. The other half is due to human activities, mainly by incomplete combustion of organic materials. A major source of CO in ambient air is the exhaust from automotive traffic. The CO content in the exhaust of modern, catalyst-equipped combustion engines is about 1%. Outdoor CO concentrations in areas with low traffic density are usually at or below 1 ppm, whereas in urban areas with high traffic density CO levels show daily averages of 2–10 ppm or higher (Valerio et al., 1997; Englert, 1997). Indoor sources for CO are gas heaters and stoves, chimneys and tobacco smoke. Typical concentrations are 2–5 ppm (Englert, 1997; Scherer and Adlkofer, 1999). The ETS-related increase in indoor air CO levels is, on average, about 0.5 ppm (Turner et al., 1992; Scherer and Adlkofer, 1999). Since dichloromethane is metabolized to CO, exposure to this solvent (a component of paint removers) also increases the level of CO biomarkers (Benowitz, 1983). It is readily absorbed through the skin and lungs as a vapor and is metabolized to CO in the liver (Ernst and Zibrak, 1998). CO is endogenously formed from the catabolic degradation of heme. The COHb level from endogenous CO formation, on average, amounts to 0.7%, corresponding to about 4 ppm of CO in exhaled air (Coburn, 1970, 1979; Benowitz, 1983). Drug therapies, e.g. phenobarbital treatment, which can induce hepatic heme catabolism, may thus increase the COHb level (Frederiksen and Martin, 1979). Less than 1% of CO in the body is oxidized to carbon dioxide. It is predominantly eliminated unchanged through the lungs. CO competes with oxygen for binding to hemoglobin. Its affinity to hemoglobin is 200–250 times higher than that of oxygen. CO shifts the oxygen-hemoglobin dissociation curve to the left (Haldane effect) thus impairing the release of oxygen to the cells, resulting in hypoxia. In addition, CO is suggested to lead to typical re-perfusion injury coupled with the production of oxygen radicals and oxidative stress (Ernst and Zibrak, 1998). The neurotoxic effects of CO are probably related to its NO releasing properties resulting in peroxynitrite formation, which may lead to vascular, and perivascular neuro-histological lesions. To the same end, CO-induced oxidative stress and lipid peroxidation may be also relevant mechanisms of CO neurotoxicity. Acute symptoms of CO poisoning include headache, dizziness, weakness, nausea, shortness of breath, chest pain, loss of consciousness, abdominal pain, and muscle cramping (Ernst and Zibrak, 1998). Exposure to 2000 ppm CO for 3–4 h leads to 70% COHb and death within minutes. However, these symptoms are not very specific for CO poisoning and show rather wide inter-individual variation. Furthermore, COHb concentrations do not correlate well with the severity of the symptoms.

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Dissolved CO in plasma appears to be necessary to provoke severe symptoms (Ernst and Zibrak, 1998). Acute CO-related symptoms are unlikely to occur in cigarette smokers who commonly have COHb concentrations of 5–6%. Chronic exposure to CO with smoking is suggested to be involved in the induction of cardiovascular diseases (US Department Health and Human Services, 1983). In Germany, the threshold limit value for CO at work places (MAK-Wert) is 30 ppm CO ( ¼ 35 mg/m3) and the biological tolerance value (BAT-Wert) is 5% COHb (Deutsche Forschungsgemeinschaft, 1998). In the USA, the permissible exposure limit (PEL) is 50 ppm ( ¼ 55 mg/m3) (Occupational Safety and Health Administration, OSHA). The recommended exposure limit (REL) over 8 h is 35 ppm ( ¼ 40 mg/m3) with a ceiling of 200 ppm ( ¼ 229 mg/m3) (National Institute of Occupational Safety and Health, NIOSH). The biological exposure index (BEI) is 3.5% COHb (American Conference of Governmental Industrial Hygienists).

CO levels in cigarette mainstream smoke CO is a product of all incomplete combustions of organic materials and, hence, also present in tobacco smoke. During the smoking process, CO is produced in the high-temperature (400–800 1C) combustion region, where carbonized tobacco is oxidized, and in the lowtemperature (150–400 1C) pyrolysis region, where organic material decomposes to, among other compounds, carbon oxides (Baker and Kilburn, 1973). CO yields in mainstream smoke of cigarettes vary widely and are mainly dependent on the extent of filter ventilation and paper porosity as well as the moisture content of the cigarette (Robinson and Forbes, 1975; Benowitz, 1983). CO yields of modern cigarettes range from 0.5 to 13 mg/ cigarette corresponding to 0.2% to 4.5% of CO in undiluted mainstream smoke (Rickert, W.S., 1997). Mainly filter perforation but also the use of reconstituted and expanded tobacco has reduced the CO yield of cigarettes in the past four decades (Hoffmann and Hoffmann, 1997). The sales-weighted average of the CO yield (which takes into account the CO yield and the market share of representative cigarette brands) of UK cigarettes decreased from 17 to 13 mg/cigarette between 1972 and 1992 (Waller and Froggatt, 1996). The CO yield in US cigarettes decreased from 33 to 38 mg in nonfilter cigarettes in 1953 to 11 mg in filter cigarettes in 1994 (Hoffmann et al., 1997). The sales-weighted CO yield of the top 20 cigarettes in Germany in 1997 was 12 mg/cigarette (Rickert, B., 1997). The CO content per puff increases with the puff number during standard machine smoking due to the decrease of cigarette paper ventilation (Russell et al., 1975; Robinson and Forbes, 1975). In the Massachusetts Benchmark Study of 1999,

median CO yield of 18 US cigarettes was 22.5 (11.0–40.7) mg/cig when smoked according to the Massachusetts machine-smoking regime (puff volume: 45 ml, puff duration: 2 s, puff frequency: 2/min, butt length: 23 mm or overwrap+3 mm) (International Agency for Research on Cancer, 2004). The CO yield of sidestream smoke of cigarettes was reported to be 2.5–4.7 times higher an that of mainstream smoke (Klus and Kuhn, 1982). The Massachusetts Benchmark Study of 1999 revealed sidestream smoke yields of 31.5–54.1 mg/cig (International Agency for Research on Cancer, 2004). The recent Tobacco Directive of the European Union (EU) passed in 2001 sets a limit of CO in mainstream smoke of 10 mg/cig since January 1, 2004. The CO concentration of cigar smoke (9.7–12.7%) is generally higher than in cigarettes due to the less complete combustion in cigars (US Department Health and Human Services, 1998). The CO yield per gram of cigar tobacco smoked amounts to 39.1–64.5 mg (US Department Health and Human Services, 1998). The CO concentration per liter mainstream smoke was found to be highest for Canadian small cigars (90.1–119.0 mg/l), followed by hand-rolled cigarettes (39.2–74.4 mg/l), manufactured cigarettes (12.6– 64.7 mg/l) and large cigars (29.5–51.9 mg/l) (Rickert et al., 1985).

Metabolism, disposition kinetics and influence of host factors Inhaled CO is absorbed through the lung alveoli but not through the upper respiratory tract (Guyatt et al., 1981). COHb and COex are, therefore, good biomarkers for the intensity (depth, length) of inhalation during smoking (Benowitz, 1983). Absorption of CO depends on the alveolar ventilation and the CO-diffusing capacity including the partial pressure of alveolar CO. The absorption and elimination of CO can be described by the physiology-based Coburn–Foster–Kane model, which has as important variables, the CO concentration in inhaled air, duration of exposure, and alveolar ventilation (Coburn et al., 1965). The absorbed CO is rapidly bound to hemoglobin to form COHb. Affinity of CO to hemoglobin is about 200–250 times higher than that of oxygen (O2) (Coburn, 1970). Binding of CO to heme-containing proteins like myoglobin may also take place, but is of minor importance (Coburn, 1979). 10–15% of the absorbed CO binds to myoglobin. The affinity of CO to myoglobin is 8 times lower than that to hemoglobin. Less than 1% of the absorbed amount of CO is oxidized to carbon dioxide (CO2). CO is excreted almost entirely in the expired air. The half-life of COHb ranges from 1–4 h and is mainly dependent on the ventilation rate and the

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physical activity (Wald et al., 1975; Benowitz, 1983). For a person with a sedentary working activity, the half-life of COHb is about 3 hours. With light physical activity, the half-life of COHb is about 2 h. Sampling at the end of the day for CO biomarkers, therefore, gives a reasonable good indication of the daily CO exposure (Benowitz, 1983). However, when biomonitoring for smoking, the time interval to the last cigarette smoked is of importance and has to be considered. The CO concentration in the end-respiratory (tidal) air (COex) is in equilibrium with the COHb level in the circulating blood. An empirical relationship between both parameters has been reported (Cohen et al., 1971) COHbð%Þ ¼ 0:6 þ 0:3COexðppmÞ. The correlation between COHb and COex was reported to be linear and strong (r40.9) over the relevant range of 0–10% COHb in numerous studies (Cohen et al., 1971; Jarvis and Russell, 1980; Benowitz, 1983; Heinemann et al., 1984; Irving et al., 1988). The invasive CO biomarker COHb has, therefore, been frequently replaced by the noninvasive measuring of COex. Some divergence in the linear correlation between COHb and COex has been reported when measuring COex immediately after smoking a cigarette. Woodman et al. (1987) found a bi-phasic decline of COex after smoking a Table 5.

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cigarette. The half-life of CO in the body was significantly shorter in the first 5 min after smoking compared to the following 55 min. The authors speculate that residual CO from smoking in the lung and/or accumulation of COHbrich blood in the lung capillary bed might be responsible for this finding. In another study, Guyatt et al. (1988) observed that the COex level after smoking a cigarette was often lower than before smoking. The authors suggest that the gas exchange might be transiently lowered by the sampling procedure and/or by smoking. It can be concluded from both studies that COex values may be unreliable when samples are collected shortly after smoking a cigarette (o30 min). While there is some inter-individual variation in CO uptake and elimination, no biological basis has been found for this (Frederiksen and Martin, 1979). The role of constitutional factors in uptake and elimination of CO, therefore, needs further investigation.

Analytical methods for the determination of biomarkers for CO exposure Determination of COHb COHb can be determined by various methods (Table 5) (DFG-German Science Foundation, 1985).

Methods for measuring biomarkers of exposure to carbon monoxide (CO)

Biomarker

Principle of method

Method characteristics

Reference

COHb

Photospectrometry (578 nm) after reduction of blood sample with sodium dithionite

P: 5% R: 98% LOD: 0.78% COHb

DFG-German Science Foundation (1985)

COHb

Infrared (IR) spectrometry after release of CO from COHb in blood with K3 Fe(CN)6

P: 5% R: 99% LOD: 0.22% COHb

DFG-German Science Foundation (1985)

COHb

Gas chromatography (GC) after release of CO from COHb in blood with K3 Fe(CN)6 , reduction to methane and determination with flame ionization detector (FID)

P: 2.5% R: 101% LOD: 0.17% COHb

DFG-German Science Foundation (1985)

COHb COex

CO-Oximeter Infrared (IR) measurement of CO in exhaled air sampled in a plastic bag Electrochemical measurement of CO in exhaled air sampled in a plastic bag

P: 2% P: 0.6%

Heinemann et al. (1984) Heinemann et al. (1984)

Intra-participant (N ¼ 2947) differences in COex: 0 ppm: 49.5% 1 ppm: 34.4% 2 ppm: 10.7% 3 ppm: 3.1% 4 ppm: 1.6% 44 ppm: 0.6%

Johnson and Townsend (1986)

COex

P: Precision (intra-day); R: Recovery; LOD: Limit of detection.

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The photometric, one-wavelength method takes advantage of the fact that the peak absorption of oxyhemoglobin (O2Hb) at 578 nm is shifted to shorter wavelengths after treatment of the blood sample with sodium dithionite, whereas the absorption maximum of COHb remains unchanged. The recovery rate was 98% and the intra-day precision was 5%. The limit of detection (LOD) was reported to be 0.78%. An infrared (IR) spectroscopic method measures the CO gas released from COHb by potassium hexacyanoferrate (K3Fe(CN)6). The intra-day precision is 1.4% and the recovery rate is 99%. The LOD is reported to be 0.22%. For lower COHb concentrations (o5%), a gas chromatographic method was recommended. CO was released from COHb by potassium hexacyanoferrate, separated by gas chromatography, catalytically reduced to methane and subsequently determined by a flame ionization detector (FID). The intra-day precision was 2.5% and the recovery rate was 101%. The LOD was reported to be 0.17%. Today, COHb is commonly measured by semi- or fully automatic devices, which use multi-wavelength techniques to photometrically measure COHb in fresh blood samples. Determination of carbon monoxide in exhaled air (COex) The alveolar CO pressure is proportional to the concentration of COHb in blood. Therefore, the endexpired CO tension accurately reflects blood COHb (Cohen et al., 1971; Jarvis and Russell, 1980; Heinemann et al., 1984; Irving et al., 1988). COex can easily be measured by commercially available instruments, which measure the CO concentration either electrochemically, or by IR spectrometry (Table 5). The latter instruments show higher sensitivity and specificity but are much more expensive than the former. Also, a gas chromatographic method for measuring CO in 4 ml exhaled air after catalytic reduction to methane and subsequent determination with a flame-ionization detector was described (Grieder and Buser, 1971). Since the middle of the 1980s, portable, inexpensive devices are available (e.g., Bedfont EC50, Draeger Ltd, Hemel Hempstead, Hertfordshire, UK). The Bedfont EC50 instrument samples CO by diffusion from expired air trapped over the sensor in a one-way valve. These instruments show acceptable accuracy when compared with conventional COex measurements or determinations of COHb (Jarvis et al., 1986; Irving et al., 1988). Various protocols have been described to collect exhaled air for ‘conventional’ COex measurements. In the protocol of Jarvis and Russell (1980), the subjects were asked to first exhale completely, then take a deep breath and hold it for 20 s before breathing out rapidly into a disposable mouthpiece connected to a sampling bag. Heinemann et al. (1984) described a procedure

comprising 6 steps: (1) deeply inhale, (2) hold breath for 10 s, (3) exhale normally, (4) maximally exhale to the end-expiratory volume into a plastic bag, (5) breathe normally for 10 s, (6) repeat step 1–5 until the bag (9.4 l) is filled. Prue et al. (1983) used a similar procedure. Nil and Ba¨ttig (1989) described a method for measuring mixed expiratory tidal air CO concentrations. According to their protocol, the respiratory air was collected in a Teflon bag during normal breathing. Simultaneously, the air was analyzed with an IR CO analyzer until a stable reading was obtained (3–5 min). It is worth mentioning that the different methods of COex determination may measure different things—ideally, end expiratory CO should be measured (e.g. by using a Haldane tube) as this provides an indication of alveolar CO, which should be in equilibrium with COHb. However, some methods measure mixed expiratory CO levels—others convert mixed expiratory to alveolar CO using the Bohr equation (Rawbone et al., 1978).

Observed concentrations in smokers and nonsmokers Dependent on a series of variables (Frederiksen and Martin, 1979) which are discussed in more detail in the following sections, smokers show significantly elevated COHb and COex concentrations compared to nonsmokers (Table 6). The mean COHb concentrations for smokers were reported to be about 4–7% and those for nonsmokers about 1–2%. The corresponding values for COex were about 20–30 ppm for smokers and about 4–7 ppm for nonsmokers. In heavy smokers, COHb concentrations 412% and COex concentrations 450 ppm can occur. No differences in the CO biomarker concentrations between nonsmokers and passive smokers exposed to ETS under real-life conditions have been reported (for review see International Agency for Research on Cancer, 1986; Scherer and Richter, 1997; Scherer and Adlkofer, 1999). A small but significant increase in COex levels of real-life ETS exposed nonsmokers was reported in two studies (Svendsen et al., 1987; Laranjeira et al., 2000). Nonsmokers exposed to ETS under extreme (experimental) conditions showed significantly elevated COHb and COex concentrations (Hugod, 1984; Scherer et al., 1992). Since both biomarkers have been used to differentiate smokers from nonsmokers, cut-off levels have been defined for this purpose. Cut-off levels are a compromise between optimal (close to 100%) levels of sensitivity (identifying smokers correctly as smokers, i.e., no ‘false negatives’) and specificity (identifying nonsmokers correctly as nonsmokers, i.e., no ‘false positives’). In Table 7, cut-off levels for COHb and COex are summarized together with the observed sensitivity and specificity. Acceptable cut-off levels for COHb and COex would be 1.6% and 8–10 ppm, respectively. Both

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Table 6.

Average concentrations of the CO biomarkers COHb and COex in nonsmokers, passive smokers and smokers

Authors [Reference] Carboxyhaemoglobin (COHb) (%) Cole (1975) Szadkowski et al. (1976) Saloojee et al. (1982) Jarvis et al. (1984b) Pojer et al. (1984) Heinemann et al. (1984) Wald and Watt (1997)

Nonsmokers (N)

Passive smokers (N)

0.97 (120) 1.12 (100) 0.72 (37) 0.63 (118) 0.73 (79) 0.9 (46) 0.8 (54)a 0.93 (181) 2.2 (171) Pipe/cigar smokers (never cigarettes) Pipe/cigar smokers (switched from cigarettes) Current cigarette smokers

Carbon monoxide (CO) in exhaled air (ppm) Frederiksen and Martin (1979) 4.93 (15) Jarvis et al. (1984b) 5.7 (46) Fortmann et al. (1984) 4.64 (742) Heinemann et al. (1984) 6.5 (87) 2.7 (79) Irving et al. (1988)b Lando et al. (1991) Never smokers: 4.2 (2328) Quitters: 4.6 (1148)

a

109

5.5 (54)a

Smokers (N) 5.5 (100) 3.37 (115) 7.09 (360) 3.9 (94) 4.36 (187) 6.9 (472) 1.0 (1309) 1.2 (522) 4.6 (4184) 34.38 (53) 20.8 (94) 27.3 (400) 29.5 (261) 24.5 (59) Occasional smokers: 7.6 (178) Light smokers: 14.4 (238) Moderate smokers: 24.7 (351) Heavy smokers: 33.3 (273)

Weighted mean of ETS exposed nonsmokers reporting, ‘a little’, ‘some’ and ‘a lot’ of passive smoke exposure (Jarvis et al., 1984a). Data were obtained with portable analyzer (Bedfont EC50). The CO background of 1.3 ppm was subtracted.

b

cut-off levels apply for an urban environment. Low et al. (2004) pointed out that COex sampling should be within 5 h of the last cigarette, in order to be reliable. Berlin et al. reported that false negative rates were twice as high at 10 than at 8 ppm COex (Berlin et al., 2001). According to an epidemiologic study in the North of England, the COex cut-off level varies with ethnicity (Pearce and Hayes, 2005). Airway diseases such as asthma and COPD also influence the COex cut-off level for distinguishing between smokers and nonsmokers (Sato et al., 2003). Frederiksen and Martin (1979) listed 4 groups of smoking related variables affecting the CO exposure levels: ‘Substance smoked’ (type of tobacco product, tobacco moisture, paper, filter, size/shape of cigarette, packing of tobacco, brand of tobacco), ‘smoking occurrence’ (number smoked, temporal distribution), ‘smoking topography’ (puff volume, number of puffs, puff duration, puff interval, cigarette duration, amount of tobacco used, puff intensity, puff distribution), ‘passive (involuntary) smoking’ (ambient CO level, type of ETS, proximity to a smoker, room ventilation). Except for the last group of variables (passive smoking), these factors will be discussed on the basis of available data in the next sections. Relation to the number of cigarettes smoked The relation between cigarette consumption (either expressed as the average number of cigarettes smoked

per day or as the number of cigarettes smoked before taking the sample) has been investigated in many studies (typical data are shown in Table 8). The coefficients of correlation are in the range of 0.3–0.8. Thus, the number of cigarettes smoked explains about 10–60% of the variability in the COHb or COex concentrations. Benowitz (1983) estimated that cigarette consumption explains about 25% of the variance in COHb in smokers. The coefficients of correlation are usually slightly higher when the number of cigarettes smoked prior to sampling was assessed rather than using the average daily cigarette consumption figures. Henningfield et al. (1980) determined COex in two smokers (smoking 5 and 10 cigarettes with intervals of 30 min). They reported that each cigarette produced a peak in the COex level but there was also a gradual accumulation in the COex level during the smoking period. Law et al. (1997) found that COHb increased linearly with the daily cigarette consumption up to 20 cig/d and leveled off at higher consumption rates. Type and yield of cigarettes A number of studies have investigated the relation between CO burden (as measured by COHb or COex) and the smoke yield (including tar, nicotine, and CO) of the cigarette smoked. The general finding was that the CO yield of the cigarette does not predict the CO biomarker level when the number of cigarettes smoked is taken into account (Wald et al., 1975; Russell et al.,

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Table 7. CO Biomarker cut-off levels and predictive power (sensitivity and specificity) for the differentiation of smokers and nonsmokers Authors [Reference]

COHb

COex Cut-off level (ppm)

Predictive power (%)

8

Sensitivity: 97.4 Specificity: 95.8

8.4

Sensitivity: 99 Specificity: 89

Irving et al. (1988)a

4 8

Sensitivity: 93.2 Specificity: 88.0 Sensitivity: 86.3 Specificity: 98.6

Lando et al. (1991)

8 10

Sensitivity: 91.1 Specificity: 95.9 Sensitivity: 87.6 Specificity: 98.6

Low et al. (2004)

5

Sensitivity: 96 Specificity: 98

Murray et al. (1993)

8 10

Sensitivity: 96.2 Specificity: 77.8 Sensitivity: 93.7 Specificity: 87.2

Javors et al. (2005)

3 8

Sensitivity: 71.5 Specificity: 84.8 Sensitivity: 40.6 Specificity: 98.2

Hung et al. (2006)

6

Sensitivity: 84 Specificity: 85

Saloojee et al. (1982)

Cut-off level (%)

Predictive power (%)

1.6

Sensitivity: 96.39 Specificity: 97.47 Sensitivity: 95.83 Specificity: 98.73

2.0 Fortmann et al. (1984) Pojer et al. (1984)

2.0

Sensitivity: 87.7 Specificity: 97.8

Heinemann et al. (1984)

1.6

Sensitivity: 96 Specificity: 97

a

Data were obtained with portable analyzer (Bedfont EC50). The CO background of 1.3 ppm was subtracted.

1980; Rickert and Robinson, 1981; Jaffe´ et al., 1981; Ba¨ttig et al., 1982; Kanzler et al., 1983; Gori and Lynch, 1985; Jacober et al., 1994). In a few studies a decrease in the COHb, COex level or the CO boost (which is the difference between the COex post- and pre-smoking a cigarette) was observed when switching to a cigarette with lower CO yield (Turner et al., 1974; Dunn and Freiesleben, 1978; Martin et al., 1981; Ossip-Klein et al., 1983; Stitzer et al., 1992). In one study (Weinhold and Stitzer, 1989), COboost was found to be equivalent when smoking high yield (14.3 mg CO) and low yield cigarettes (5.9 mg CO), while the number of puffs increased by 50% (from 8 to 12 puffs) when smoking the low yield cigarette. COboost from ultra-low yield cigarettes (1.6 mg CO) was still

marginally lower than exposure form high yield cigarettes after a 4-fold increase in puff number (8–32 puffs). In general, reduction in the CO uptake was usually lower than the corresponding reduction in the CO yield of the cigarettes, indicating compensation. Wald et al. (1977) reported 30% higher COHb levels in smokers of unventilated cigarettes compared to plain cigarettes, consistent with the difference in the CO yield between the two types of cigarettes. However, COHb levels in smokers of ventilated cigarettes were slightly higher (7%) than that in plain cigarette smokers, although the CO yield of ventilated cigarettes were by 21% lower than that of plain cigarettes. Smokers smoking a potentially reduced exposure product (PREP) with a new filter technology showed

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Table 8.

111

Correlation between the CO biomarkers COHb and COex and the number of cigarettes smoked

Authors [Reference]

N

Type of consumption

Coefficient of correlationa

Carboxyhaemoglobin (COHb) Hawkins et al. (1976)

374

Average daily Cigarettes before test

0.871*** 0.785***

Rickert and Robinson (1981)

130

Average daily

0.43**

139 72 (men) 128 (women)

Average daily Average daily Cigarettes before test Average daily Cigarettes before test

0.476*** 0.267* 0.307* 0.496*** 0.563***

Rickert and Robinson (1981)

62

Average daily

0.63***

Ba¨ttig et al. (1982)

110

Average daily Cigarettes before test

0.47*** 0.55***

Kanzler et al. (1983)

96

Average daily

0.299**

Secker-Walker et al. (1997)

397

Average daily

0.65***

Carbon monoxide in expired air (COex) Vogt et al. (1977) Jaffe et al. (1981)

a

Level of significance: *po0.05,

**

po0.01,***po0.001.

small but consistently lower COex levels compared to smoking their usual brand with similar tar and nicotine yields (Breland et al., 2003). Smokers who switched from their usual brand to carbon-heated or electrically heated cigarettes were found to have higher or lower CO boosts, respectively (Breland et al., 2002). Roethig et al. (2005) reported that COex and COHb levels in smokers switching from conventional to electrically heated cigarettes decreased by about 80% and 90%, respectively. Increased CO uptake when switching to carbon-heated cigarettes was also observed in other studies (Rennard et al., 2002; Fagerstrom et al., 2002; Lee et al., 2004). In two studies, which investigated the CO uptake in smokers who switched to a PREP using palladium as a catalyst in tobacco, no change in COex (Hatsukami et al., 2004) or an (unexpected) increase in the COboost was observed (Hughes et al., 2004). An ‘inhaling index’ (I) on the basis of COex boosts or COHb boosts and the CO yield of the cigarette smoked has been defined as follows (Wald et al., 1980, 1981, 1984; Nil et al., 1981; Stepney, 1982b): I¼

COboost . COyield

By multiplying ‘I’ with the yield of a smoke constituent, relative intakes can be calculated (Wald et al., 1984). Smoking topography Smoking topography describes the puffing pattern of a smoker including puff volume, puff duration, puff profile, puff frequency, puff interval, and puff number.

Together with the type of cigarette, the butt length and the degree of filter vent blocking (in highly filter ventilated cigarettes), these parameters determine mouth level exposure. The real uptake of smoke constituents by a smoker is decisively influenced by the amount of mouth spill and by the depth and duration of inhalation (Scherer, 1999). Since CO absorption is limited to the lung alveoli, the CO biomarkers should be particularly suitable to monitor the inhalation behavior. It is interesting to note that the self-reported inhalation pattern is not or only marginally related to the COex or the COex boost levels (Wald et al., 1975; Stepney, 1982a). Wald et al. (1978) reported mean COHb levels of 4.0%, 5.2%, 5.3%, and 5.6% in 520 cigarette smokers who stated that they did not inhale, inhale slightly, moderately, or deeply, respectively. In a more recent study of this working group, a similar pattern was found for cigarette smokers, there was, however, a steeper increase in COHb with the selfreported degree of inhaling in pipe and cigar smokers (Wald and Watt, 1997). Gust and Pickens (Gust and Pickens, 1982) found a significant correlation between COex boost and the total puff volume per cigarette (r ¼ 0.62) and total puff duration per cigarette (r ¼ 0.65), whereas Woodman et al. (1986) found no relationship between these parameters. Ho¨fer et al. (1992) observed that the variance in COex was less explainable (15–27%) than the variance in plasma nicotine (19–41%) in terms of cigarette smoke yield, consumption and self-reported inhalation. Nil et al. (1986a) reported a discrepancy between mouth level intake (determined by multiplying the

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nominal smoke yield with the ratio of the subject’s total puff volume/machine total puff volume) and the pre- to post-smoking COboost. This discrepancy could not be explained by the volumes or durations of the post-puff respiratory cycle, which describes the inhalation pattern. The authors state that the dynamics of smoke inhalation are still poorly understood and that self-assessments of inhalation are quite unreliable. According to these authors, COboost or COboost/COyield (inhalation efficiency) are suitable measures to determine the smokers’ extent of smoke inhalation. In another study (Nil et al., 1986b), smokers were classified as ‘high CO absorbers’ (COboost43.5 ppm) and ‘low CO absorbers’ (COboosto1 ppm). It was found that high CO absorbers differed from low CO absorbers, among other things, by more intensive patterns of puffing and respiratory inhalation, by higher daily cigarette consumption and by shorter latencies to the first cigarette in the morning. Strength of cigarette in terms of tar, nicotine and CO yield was not different between the two groups. After smoking deprivation, high CO absorbers were found to have a slightly larger increase in puff volume and duration than the low CO absorbers (Nil et al., 1987). In a controlled study, Weinhold and Stitzer (1989) found that the number of puffs taken from a cigarette, but not the puff interval, clearly affected the COboost. In another controlled study, Woodson and Griffiths (1992) could show that when taking 8 puffs from the butt end (i.e., 8 last puffs of 8 cigarettes ) of a cigarette, COboost was significantly lower than when smoking the whole cigarette or taking the puffs from a full length cigarette (i.e., 8 first puffs). In the same study, experiments with a proximal and distal filter attached to the cigarette indicated that the inhalation pattern as measured by the COboost is determined by the smoke concentration rather than the draw resistance, although the latter can influence puff volume. Zacny et al. (1986) found a clear increase in COboost when smoking ultra low yield cigarettes with the filter vents unblocked (0.83 ppm COboost), partly (50%) blocked (2.87 ppm) and completely blocked (7.07 ppm) under controlled conditions (puff number, puff intervals, puff volume, inhalation volume, and breathhold duration held constant). When smoking these cigarettes under ad libidum conditions, COboost levels were 4.32, 6.44, and 8.96 ppm, respectively. The authors interpret these data together with the data for the puffing and inhalation patterns as indicative for changes in the physical characteristics of the cigarette (particularly the draw resistance) rather than the changed smoke concentration being responsible for the smokers’ response. Although these experiments might not be conclusive, they show that COboost is a suitable biomarker for the actual CO exposure.

Simple pharmacokinetic model: smoking rate and level of biomarker in body fluid Wald et al. (1975) described a simple method for calculating the COHb level of a smoker during the day. The smoking history during the last 24 h, the average COHbboost per cigarette and background COHb concentration of a comparable nonsmoker with the same atmospheric CO exposure has to be known. With this information the contribution of each cigarette to the COHb level can be estimated and the daily COHb calculated. This model takes into account the decay of COHb as a function of the physical activity. The authors presented an example with two hypothetical smokers who both smoked 20 cigarettes, had a COHbboost of 1% and eliminate COHb with a half-life of 2 h while awake. The two smokers differed in the time pattern when smoking. The smoker with the more even distribution of smoking between 8 a.m. and midnight reached a peak COHb level of 5%, while the smoker primarily smoking during the evening had a peak COHb level of 8%. Jourmard et al. (1981) used mathematical models for the uptake of CO by hemoglobin at low CO levels. The models are solutions of Coburn’s differential equation for COHb concentrations as a function of time. In their example, a smoker smoked one cigarette every hour (103 cigarettes per week). The authors assume that each cigarette was smoked with 10 puffs of 30 ml at 4% CO. COHb concentrations oscillated between 1.7% and 8.7%, while the range for a comparable nonsmoker was 0.2–2.2%.

Summary and validation Table 9 summarizes the characteristic properties as well as the strengths and limitations of COHb and COex as biomarkers for smoking. COHb and COex are biomarkers of exposure and biological effect. The latter because the saturation of hemoglobin with CO is a direct measure for the biological effect of CO, namely impairment of the oxygen supply of organs. The most prominent strengths are the simplicity and rapidness of the measurements, particularly of COex by means of hand-held devices and the suitability of both CO biomarkers as indicators of tobacco smoke inhalation. Limitations are the unspecificity of CO for tobacco smoke and the relatively short half-life of 2–3 h. The latter property makes these biomarkers very dependent on the time interval to the last cigarette smoked. It has to be kept in mind that there is a nonsteady-state exposure pattern for COHb in smokers. Due to physiological reasons, COex measurements taken directly after smoking a cigarette may not accurately reflect COHb concentrations.

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Table 9.

113

Characteristic properties and validation of COHb and COex as biomarkers for smoking

Type of biomarker

1. Biomarker of exposure (internal dose) 2. Biomarker of biologically effective dose

Possible role of CO in smoking-related diseases and adverse effects

Impeding of oxygen supply of the heart and other tissues: Coronary heart diseases?, myocardial infarction?, growth retardation of fetus during pregnancy

Other sources

Traffic exhausts, incomplete combustions, endogenous formation

Typical ranges in relation to tobacco smoke exposure

Cut-off levels (smokers vs. nonsmokers), sensitivity, specificity

Genetic polymorphisms as modifiers

Nonsmokers Passive smokers Smokers

COHb (%) 0.8–1.5 0.8–1.5 4.0–8.0

COex (ppm) 3–7 3–7 20–40

Cut-off Sensitivity (%) Specificity (%)

COHb 1.6–2.0% 490 495

COex 4–10 ppm 485 480

Unknown

Strengths


Easy and rapid measurements (particularly COex)
Indicates inhalation (exclusively absorbed through alveoli)

Limitations


Unspecific (important other sources)
Short half-life (2–3 h), therefore strong dependence on interval to last cigarette and time of day


COex measurements directly after smoking may be problematic Recommended application

In controlled (experimental) settings to determine an inhalation-index

Taken together, COHb and COex are useful biomarkers in controlled (experimental) settings to measure the inhalation intensity of smokers, which can be expressed as inhalation index (as described above).

Thiocyanate in body fluids Occurrence and toxicology of hydrogen cyanide and thiocyanate Cyanides occur, mostly as glycosides, in various food products such as almonds, nuts, pulses, bamboo sprouts, beans and linseeds (Baumeister et al., 1975). SCN, the main metabolite of cyanide, is present in Brassica vegetables such as cabbage, broccoli and cauliflower (Bliss and O’Connell, 1984) and also in mustard and cow’s milk (Borgers and Junge, 1979). SCN is also formed endogenously by bacteria in the colon. HCN is highly toxic. For man, an oral dose of 0.5–3.5 mg/kg leads immediately to death as does inhaled HCN at a concentration of about 270 ppm for a few minutes. The LD50 of inhaled HCN for rats is 200 ppm for 30 min exposure. HCN exerts its effect by

inhibiting the cytochrome oxidase in the respiratory chain. HCN is also ciliatoxic, thus contributing to the development of acute inflammatory and chronic obstructive lung disease. However, cilia damage by irritants in tobacco smoke such as acrolein is probably a more relevant mechanism of smoking-related ciliatoxicity. The threshold limit value for HCN at the workplace was set at 10 ppm ( ¼ 11 mg/m3) (German MAK, US OSHA). The short-term exposure level according to NIOSH is 4.7 ppm. The lethal dose of HCN is 1 mg/kg (oral or inhaled). Chronic exposure to low levels of HCN is usually tolerated, since cyanide is effectively detoxified through the formation of SCN. However, if this metabolic pathway is disturbed, chronic HCN exposure can lead to tobacco-related amblyopy, retrobulbar neuritis and sterility. The latter has been occasionally observed in female heavy smokers (Eyer, 1994a, b). A possible explanation is that tobacco smoke (both the particulate and gas phase) is able to inhibit the key enzyme of HCN metabolism (rhodanese), probably by the action of cyanidic compounds in smoke (Schievelbein et al., 1969). SCN can catalyze the endogenous nitrosation of amines leading to potentially carcinogenic N-nitrosamines in saliva and in the stomach (Prue and Martin, 1980; Ladd et al., 1984; Tsuda and Kurashima, 1991).

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A reduced iodide/SCN ratio can increase the risk to develop goitre (Olea and Parras, 1992).

Mainstream smoke levels of hydrogen cyanide Reported HCN yields in mainstream smoke of cigarettes vary between 150 and 500 mg/cigarette. Artho and Koch (cited in Baumeister et al., 1975) reported a HCN level of 150–300 mg/cigarette for nonfilter cigarettes and 150 mg/cigarette for filter cigarettes. Hoffmann and Hoffmann (1997) reported an HCN range of 400–500 mg/cigarette for nonfilter cigarettes. For 115 cigarette brands on the Canadian market, an average yield of 133 (range: 2233) mg/cigarette was found (Rickert et al., 1983). In the 1999 Massachusetts Benchmark Study, the median HCN yield was 380.8 (98.7–567.5) mg/cig (International Agency for Research on Cancer, 2004). HCN is formed in the burning zone mainly from proteins and nitrate at temperatures 4700 1C oxygendeficient conditions. This is why HCN yields in sidestream smoke (lower combustion temperature, better oxygen supply) are lower than in mainstream smoke (14–134 mg/cigarette, International Agency for Research on Cancer, 1986). Sidestream smoke yields of cigarettes in the Massachusetts Benchmark Study were 190–350 mg/cig (International Agency for Research on Cancer, 2004). Filter ventilation and charcoal filters are effective in reducing the HCN yield of mainstream smoke whereas acetate filters reduce HCN on average by about 14% (Rickert et al., 1983). Different smoking regimes have a significant effect on the HCN yield of mainstream smoke. The HCN yield of a single brand ranged from 5–241 mg/cigarette when the smoking conditions were varied (under standard machine smoking conditions, the HCN yield was 39 mg/ cigarette) (Rickert et al., 1983). This may explain the lack of correlation between HCN yields of cigarettes and SCN concentrations in body fluids (Rickert and Robinson, 1981). The HCN yield of mainstream smoke of cigars was reported to be 1035 mg/g (of tobacco) and that of little cigars 510–780 mg/g (US Department Health and Human Services, 1998).

Metabolism of HCN, disposition kinetics and influence of host factors HCN can be absorbed through the alveolar region of the lung, the buccal mucosa, the stomach and the skin (Baumeister et al., 1975). Absorbed HCN binds to Fe(III)-hemoglobin, which is physiologically present at a level of 0.5–1%. This effect is use therapeutically to mitigate HCN intoxications by Fe(III)-hemoglobin

forming agents such as 4-dimethylaminophenol. The binding of HCN to plasma proteins is for the most part (60%) reversible. A small part of the absorbed HCN is excreted (unchanged) via the lung and kidney. HCN is mainly metabolized to SCN. The sulfur transfer is mainly mediated by the enzyme rhodanese using thiosulfate (S2 O2 3 ) as a sulfur donor (Baumeister et al., 1975; Eyer, 1994a, b). SCN is mainly (480%) excreted in the urine. The half-life of SCN in plasma or saliva is reported to range from 10–14 d (Bliss and O’Connell, 1984). Junge (1985) found a half-life of SCN of about 6 d. The investigations on the half-life were performed with smokers who stopped smoking. A slightly negative correlation was observed between body mass index (BMI) and SCN in serum (r ¼ 0.154) (Bridges et al., 1990b). This corresponds with the finding that females, on average, have higher salivary SCN concentrations than males (Foss and Lund-Larsen, 1986). After adjustment for the distribution volume, SCN concentrations are similar in both sexes (Foss and Lund-Larsen, 1986).

Analytical methods for the determination of thiocyanate in body fluids Four principle methods are used for the determination of SCN in body fluids: (i) Ko¨nig reaction: dye formation by reaction of SCN with an aromatic amine, bromocyan (BrCN) and pyridine; (ii) dye formation of SCN with Fe(III) nitrate; (iii) ion exchange chromatography and UV detection (Walters and Sawhney, 1987); (iv) gas chromatography with mass spectrometry (GCMS) after derivatization (Torano and van Kan, 2003). In Table 10, a selection of methods together with some method characteristics is shown. Due to the relatively high salivary SCN concentrations (about 20 times higher than in plasma) and the noninvasiveness of saliva collection, SCN is frequently determined in saliva. However, a number of factors may influence the determination of SCN in saliva, thus increasing the variability of this biomarker compared to SCN in plasma or urine (Prue et al., 1981b). Salivary SCN was reported to be dependent on the salivary flow. It was recommended to stimulate the flow and discard the initial saliva (Prue et al., 1981b). Gimenez and Adame (2003), however, observed no influence of salivary activity (as determined by the weight gain per time of the dental cotton used for saliva collection) on the concentration of SCN in saliva. In another study, SCN in saliva was found to be more stable than SCN in plasma or urine (Prignot, 1987). The time interval to the last cigarette was found to influence the SCN level, particularly in saliva and urine (Barylko-Pikielna and Pangborn, 1968; Prignot, 1987). This is somewhat surprising in view of the long half-life

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Table 10.

115

Methods for determining thiocyanate as a biomarker of exposure to hydrogen cyanide (HCN)

Principle of method

Characteristics of method

References

Photometry (460 nm) after complexation with Fe(III) nitrate

Precision: 0.8–1.8% (Autoanalyzer) Recovery: 97–98% Limit of detection (LOD): n.a.

Butts et al. (1974)

Photometry (532 nm) after reaction with bromine to form BrCN and subsequent reaction with amines

Precision: ca. 5% Recovery: ca. 95% LOD: n.a.

Altridge, 1945 (cited in (Bhide and Jayant, 1987))

Ion chromatography with UV detection (210 nm)

Precision: 0.01% at 1 mg/ml Recovery: 96.2–101.3% (urine) 95.3–100.1% (saliva) LOD: 0.02 mg/ml

Michigami et al. (1992)

Photometry (460 nm, multiphotometer) after complexation with Fe(III) nitrate; subtraction of blank value after removal of color with Hg(II) chloride

Precision: 5.1–12.8% (plasma) 1.2–2.7% (saliva) Recovery: 98.4–114.8% (plasma) 100.0–104.5% (urine) LOD: 13.1 mmol/l

DFG-German Science Foundation (1985), according to Degiampietro et al. (1987)

GC-MS after derivatizaton with pentafluorobenzyl bromide

Validated for urine: Pecision: 7.4–11.9% Recovery: 92–107% LOD: 0.06 ng/ml

Torano and van Kan (2003)

of SCN of 1–2 weeks. It was recommended to standardize the time interval between the last cigarette and sampling (Prignot, 1987).

Observed concentrations of thiocyanate in body fluids of smokers and nonsmokers In Table 11, average SCN concentrations in plasma or serum, saliva and urine of nonsmokers and smokers are listed. Concentrations in smokers were about 2–3 times higher than in nonsmokers. Average concentrations of SCN in plasma or serum are about 50 and 150 mmol/l for nonsmokers and smokers, respectively. Concentrations in saliva are about 20 times higher than in blood and amount to 1200 and 3000 mmol/l for nonsmokers and smokers, respectively. Urinary concentrations of SCN are similar to those in blood. There is a large overlap between SCN concentrations in smokers and nonsmokers, which is mainly due to the numerous other sources for cyanide, and SCN in the body (see above). As a result, the sensitivity and specificity of SCN in body fluids to discriminate between smokers and nonsmokers is usually poor. Cut-off levels for SCN in plasma between 66 and 83 mmol/l have been reported (Pojer et al., 1984; Jarvis et al., 1987; Nikitin et al., 1990; Ruth and Neaton, 1991). Sensitivities and specificities, which coincide with these cut-off levels, are

about 85% and 90%, respectively. Cut-off levels for SCN in saliva range from 800–1600 mmol/l (Jarvis et al., 1987; Murray et al., 1991). The corresponding sensitivity and specificity are about 80% and 70%, respectively. Jarvis et al. (1987) reported a cut-off level for SCN in urine of 118 mmol/l with a corresponding sensitivity of 59% and a specificity of 89%. Assaf et al. (2002) reported a sensitivity for men of 89.3% at a cut-off level of 91 mmol/l in serum and a sensitivity for women of 88.0% at 85 mmol/l. Specificities were 85.2% and 91.5% for men and women, respectively. SCN concentrations in body fluids do not suitably differentiate between moderate to light smokers and nonsmokers (Borgers and Junge, 1979; Vesey et al., 1982; Fortmann et al., 1984). For instance, a cut-off level of 73 mmol/l SCN in plasma was found to falsely classify 71% of the light smokers (1–5 cigarettes/d), 26% of the moderate smokers (6–15 cigarettes/d) and 4% of the stronger smokers (16+cigarettes/d) as nonsmokers (Vesey et al., 1982). Relation to number of cigarettes smoked In quite a large number of studies, the correlation between SCN in body fluids (particularly in serum or plasma and saliva) and the number of cigarettes smoked per day has been investigated. In a review article on SCN as a biochemical index for smoke exposure, Prue and Martin (1980) concluded that SCN concentrations

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Table 11.

G. Scherer / Experimental and Toxicologic Pathology 58 (2006) 101–124

Mean SCN concentrations in body fluids of smokers and nonsmokers

Study Bliss and O’Connell (1984) (Review: Weighted average of 19 studies) Cohen and Bartsch (1980) Jarvis et al. (1984b) Fortmann et al. (1984) Ockene et al. (1987)

Nonsmokers (N)

Smokers (N)

SCN in serum or plasma (mmol/l) 59.70741.10 (6815)

156.46758.62 (10377)

73.5748.3 (191) 50.8 (100) 53.1725.6 (970) 54.4730.1 (1356)

180.2755.7 (426) 122.9 (94) 163.7749.6 (543) Cigarette smokers: 173.8755.4 (5090) Primary cigar/pipe smokers: 90.2751.4 (414) Secondary cigar/pipe smokers: 111.6766.3 (497)

Nikitin et al. (1990)

Males: 42.8722.4 (706) Females: 41.0720.9 (1591)

Males: 116.6743.3 (1025) Females: 122.9749.0 (55)

Ruth and Neaton (1991) Assaf et al. (2002)

53.0727.3 (3274) Men: 60.6 (493) Women: 43.0 (850)

172.8752.2 (4553) Men: 137.9 (496) Women: 141.3 (507)

Korpilahde et al. (2004)

Never smoked: 5.376.2 (3776)

Ex-smokers: 7.479.6 (1417)

Only pipe or cigar: 29.3720.2 (85) Cigarettes and pipe/cigars: 31.1719.9 (63) 1–9 cig/d: 23.0716.1 (351) 10–19 cig/d: 31.4717.6 (577) 20+ cig/d: 39.0719.2 (612)

SCN in saliva (mmol/l) 12197757 (242)

272471112 (287)

12937652 (18) 1300 (100) Median: 1670 (207)

333971117 (12) 2450 (94) Median: 2920 (117)

SCN in urine (mmol/l) 285730 (6) 74.8 (1 0 0) (mmol/g creatinine) 77.0720.6 (42)

560735 (13) 154.9 (94) (mmol/g creatinine) 132.9752.0 (67)

Bliss and O’Connell (1984) (Review: Weighted average of 11 studies) Haley et al. (1983) Jarvis et al. (1984b) Degiampietro et al. (1987) Induced mixed saliva, parotid saliva: ca. 40% higher concentrations Densen et al. (1967) Jarvis et al. (1984b) Muranaka et al. (1988)

reliably reflect the rate of cigarette smoking. In Table 12, coefficients of correlation between SCN in serum, plasma or saliva and the daily cigarette consumption from a selection of studies are compiled. Although almost all correlations between SCN concentrations in body fluids and daily cigarette consumption are statistically significant, there is a large range for the coefficient of correlation (r ¼ 0.12–0.75). In a multiple regression model for serum SCN, which included 17 variables for smoking behavior, daily cigarette consumption was the only variable with a significant influence (Vogt et al., 1979). Type and yield of cigarettes The relationship between type or yield of cigarettes and the SCN level in body fluids has been investigated in several studies. In general, the results show that there is

either no or only a weak relationship between the yield of the cigarettes and the SCN level in body fluids of smokers. Several studies showed that the HCN yield of a cigarette is highly dependent on the way a cigarette is smoked (Rickert and Robinson, 1981; Robinson et al., 1984; Foss and Lund-Larsen, 1986). In one study, smokers changed to cigarettes with similar nicotine yields but reduced (51%) HCN yields. Only slight reductions in SCN concentrations in saliva (15%) and plasma (5%) were observed (Robinson et al., 1984). In another study (Rickert and Robinson, 1981), the HCN yields of cigarettes did not correlate with SCN in plasma (r ¼ 0, N ¼ 154) and saliva (r ¼ 0.04, N ¼ 51). In a number of studies, the relationship between the SCN concentrations in body fluids and tar, nicotine or

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Table 12.

Coefficient of correlation between SCN concentrations and daily cigarette consumption

Study

N

Coefficient of correlationa

SCN in serum/saliva Butts et al. (1974) Vogt et al. (1977) Cohen and Bartsch (1980) Rickert and Robinson (1981) Vesey et al. (1982) Hill et al. (1983) Gardner et al. (1984) Pojer et al. (1984) Bridges et al. (1990b)

38 94 426 125 360 459 60 187 161

Consumption, general: 0.46*** Consumption before test: 0.479*** Consumption, general: 0.25*** Consumption, general: 0.23*** Consumption, general: 0.412*** Consumption, general: 0.37*** Consumption, general: 0.748*** Consumption, general: 0.19** Consumption, general: 0.119 (ns)

SCN in saliva Rickert and Robinson (1981) Sexton et al. (1986)

125 524

Consumption, general: 0.27*** Consumption, general: 0.42***

a

117

Statistical significance: ns: not significant; *po0.05;

**

po0.01;

***

po0.001.

CO yields was investigated. Folsom et al. (1984) found only weak correlations between serum SCN and yields for tar (r ¼ 0.12), nicotine (r ¼ 0.11) and CO (r ¼ 0.15). Jaffe´ et al. (1980) found no change in salivary SCN when switching from high to very low tar cigarettes. Maron and Fortmann (1987) found a slight increase in plasma SCN with increasing tar yields, when the data were adjusted for daily cigarette consumption: Ultra low tar: 129.3, low: 146.0, medium tar: 155.3, high tar: 160 mmol/l. Similar results from a brand-fading study were reported by Prue et al. (1981a). A large study by Woodward and Tunstall-Pedoe (1993) with 1133 males and 1621 females showed no association between cigarette tar yield and serum SCN concentrations. Similar results were obtained in several smaller studies (Robinson et al., 1983; Sepkovic et al., 1984; Tuomisto et al., 1986). In a long-term switching study (12 weeks), Russell observed no significant change in plasma SCN when lower tar cigarettes were smoked (Russell et al., 1982). In a multiple regression model for serum SCN, no significant influence of tar or nicotine yield was found (Vogt et al., 1979). Gori (1990) calculated that the nicotine yield of a cigarette could only explain 1.5% of the variation in the SCN serum level of smokers. Puffing and inhalation profile The influence of the individual smoking profile (i.e., puffing and inhalation profile) on the SCN level in body fluids has been investigated in only a few studies. In a stepwise multiple regression analysis for serum SCN, the inhalation frequency and the depth of inhalation did not significantly contribute to the model (Vogt et al., 1979). Borgers and Junge (1979) found no relation between serum SCN and self-reported inhalation depth. Vesey et al. (1982) found a significant correlation (r ¼ 0.48) between the degree of inhalation (measured as COHb

level) and plasma SCN. By this model, about 23% of the variation in SCN could be explained by the inhalation pattern. Gardner et al. (1984) reported no significant difference in serum SCN of self-reported ‘deep’ (7.59 mg/l) and ‘light’ inhalers (8.56 mg/l). However, self-reported ‘non’inhalers showed significantly lower serum SCN concentrations (2.64 mg/l). Dolcini et al. (2003) found a significant influence of the self-reported degree of inhalation on salivary SCN in adolescents. Bridges et al. (1990a) observed no clear relation between serum SCN and smoking topography. Quite unexpectedly, they found a negative correlation between serum SCN and puff volume or total puff volume.

Simple pharmacokinetic model: smoking rate and level of biomarker in body fluid Gardner et al. (1984) proposed the following general equation for describing the stead-state serum SCN concentration: C SS ¼

DF , ðCLÞt

where CSS is the steady-state concentration of SCN in serum, D is the dose of HCN, F is the fraction of the dose that reaches the systemic circulation (this factor also considers the conversion rate of HCN to SCN), CL is the total body clearance and t is the dosing interval. The value for CL can be approximated using the subjects’ creatinine clearance.

Summary and validation Table 13 summarizes the characteristic properties as well as the strengths and limitations of SCN in serum/ plasma, saliva and urine as a biomarker for smoking.

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Table 13.

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Characteristic properties and validation of thiocyanate (SCN) in body fluids as a biomarker for smoking

Type of biomarker

Biomarker of exposure (internal dose for HCN exposure)

Possible role of HCN in smoking-related diseases and adverse effects

HCN is a strong cell toxicant. Inhaled HCN may exert cilia-toxic effects thus inhibiting the pulmonary clearance system which may lead to acute and chronic lung diseases

Other sources

Diet (HCN and SCN)

Typical ranges in relation to tobacco smoke exposure Cut-off levels (smokers versus nonsmokers), sensitivity, specificity

Genetic polymorphisms as modifiers

Nonsmokers Smokers

SCN in plasma 50–100 100–200

SCN in saliva (mmol/l) 1000–2000 1500–4000

Cut-off Sensitivity (%) Specificity (%)

SCN in plasma 65–85 mmol/l 80 90

SCN in saliva 800–1600 mmol/l 70 80

Probable (but no data for smoking-related exposures available)

Strengths


Easy and rapid measurements
Long half-life (1–2 weeks)

Limitations


Unspecific (important other sources)
Fluctuation in saliva due to salivary flow
Ratio of HCN to other smoke constituents in mainstream smoke variable (dependent on smoking profile)

Recommended application

Of limited use as a biomarker for smoking (of some historic value)

SCN is the oldest biomarker used as an indicator for tobacco smoke exposure (Table 4). The precursor in smoke is the gas phase constituent and cilia-toxicant HCN. SCN can be easily and rapidly measured by photometry, HPLC/UV or GC-MS. Due to numerous sources for both HCN and SCN in the diet, SCN shows a low specificity for tobacco smoke exposure. As a consequence, concentrations in smokers and nonsmokers broadly overlap. For the same reason, exposure to environmental tobacco smoke cannot be assessed by SCN in body fluids. Due to its long half-life, SCN is a long-term biomarker, which integrates the exposure to HCN over a time period of 1–2 weeks. SCN levels in body fluids are relatively independent of time points for sample collection. Although, the chronic exposure to HCN by smoking is of toxicological relevance, SCN as a biomarker of exposure to HCN in tobacco smoke is of only limited value.

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