among biomarkers
Erika Györffy, Lívia Anna, Péter Rudnai, Katalin Kovács and Bernadette Schoket National Institute of Environmental Health, József Fodor
National Center for Public Health, Budapest, Hungary
6.1. Introduction
Correlations among biomarkers, an important issue in biomarker research, provides enhanced insight and understanding of the complexity encountered on a molecular level during exposure to genotoxic agents. Exploration of correlations between biomarkers will contribute to the development of human biomonitoring to genotoxic exposures and will help in the selection of optimal biomarkers for more efficient monitoring of various human exposures.
Our present summary covers approximately 100 research papers published during the last two decades. From these we have collected the following information:
• publication details,
• study population, type of exposure, • biological sample/tissue,
• studied biomarkers, • methods,
• lack or presence of correlation (number of subjects, correlation coefficients and p values of statistical significance where available), and
• comments, discussion/main conclusions of the paper.
In this first report we give an overview of the main themes covered in the literature and provide an extract of the qualitative data on correlations. It should be noted that in only a fraction of the vast literature on human biomonitoring do authors analyse correlations between biomarker data. It should also be mentioned that we do not deal here with those studies whose primary purpose was DNA adduct standard or method validation.
6.2. Study populations and types of exposure
6.2.1. Occupational populationsOccupational populations were the most frequently studied populations. The occupa-tional groups included iron foundry workers [1–3], coke oven workers [4–13], aluminium plant workers [14–17], garage workers or car mechanics [18–21], bus drivers [22,23], policemen [24], US army soldiers [25], rubber industry workers [19,26], petrochemical
industry workers [27], plastic lamination workers [28,29] and incineration workers [30]. The nature of chemical exposure was mostly complex mixtures of polycyclic aromatic hydrocarbons (PAHs), diesel exhaust, and in some studies benzene, styrene and ethylene oxide.
6.2.2. General population/healthy volunteers
Environmental exposure to ambient air pollutants was investigated in Silesian [31,32] and Danish populations [33,34]. Several studies analysed the effects of active and involun-tary tobacco smoke exposure in the general population or in healthy volunteers [35–45].
6.2.3. Medicinal exposure
Typical medicinal exposures included treatment of psoriasis and eczema patients with tar products [9,46–49] and platinum therapy of cancer patients [50].
6.2.4. Cancer patients
Many studies investigated the genotoxic effects of smoking in tissue samples obtained at surgery and/or blood, from patients with cancer of the lung [51–62], the larynx [63], and the breast [64].
6.3. Most frequent biomarkers used in the studies evaluated here
The most frequent end points measured in the studies were:
• DNA adducts {large aromatic or bulky, PAHs, benzo[a]pyrene (B[a]P)-tetrol, O6
-alkyl-guanine, 7-methyl-alkyl-guanine, O4-ethylthymidine, 4-aminobiphenyl (4-ABP),
cis-dia-mminedichloro platinum (II) (cisplatin), malondialdehyde, 8-oxo-deoxyguanosine (8-oxo-dG)};
• protein adducts {4-ABP-haemoglobin, PAH-albumin, benzo[a]pyrene-diol-epoxide--(BPDE)-albumin, BPDE-globin, hydroxyethylvaline, N-terminal valine adduct of sty-rene};
• DNA strand breaks;
• 1-hydroxypyrene (1-OHPY), other urinary metabolites, and urinary mutagenicity; and
• cytogenetic end points, such as chromosomal aberrations (CAs), sister-chromatid exchange (SCE), micronuclei (MN), hypoxanthine-guanine phosphoribosyl transferase (HPRT) mutation frequency.
6.4. Correlation between levels of DNA adducts in human samples
from a methodological point of view
6.4.1. Common DNA adduct structures — different methods
DNA adduct levels determined by the two adduct enrichment versions of 32
P-postlabel-ling, nuclease P1 digestion and butanol extraction, were compared for bronchioalveolar lavage (BAL) cells and mononucleated white blood cells obtained from healthy volunteers [65]. There was a significant positive correlation between the two versions of sensitivity enhancement in BAL cells but not in white blood cells. Nielsen et al. [18] found a positive correlation between the two adduct enrichment methods for lymphocytes from garage workers exposed to diesel exhaust. Van Delft et al. [12] compared thin-layer chroma-tography (TLC) and high performance liquid chromachroma-tography (HPLC) adduct separation following 32P-postlabelling of lymphocyte DNA samples from coke oven workers and
found no correlation between the results obtained by the two chromatographic techniques.
Several studies have investigated the levels of bulky DNA adducts in human samples using 32P-postlabelling and determining the levels of PAH-DNA adducts by using
B[a]PdG-DNA immunoassays. The immunoassay methods used were enzyme-linked immunosorbent assay (ELISA) [19,52,66–69], USERIA (ultrasensitive radioimmunoassay) [4], dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA) [25] and chemiluminescence immunoassay (CIA) [51,62]. Hemminki et al. [66] found a positive correlation between the two assays in white blood cells from foundry workers using
32P-postlabelling and ELISA. Van Schooten et al. [52] also found a positive correlation
between the BPDE-DNA-like thin-layer chromatography (TLC) spot and the PAH-DNA adduct level determined by ELISA in normal lung tissue from lung cancer patients. One study observed a weak negative correlation comparing 32P-postlabelling with
ELISA in peripheral blood lymphocytes from aluminium plant workers [14]. In comparing
32P-postlabelling data with corresponding BPDE-DNA CIA values, Györffy et al.
[51] found a borderline-significant positive correlation for lung tumour tissue DNA; however, lack of correlation was found for normal lung tissue DNA. The other studies using 32P-postlabelling and PAH-DNA immunoassays did not show significant
correlations between the individual DNA adduct levels. Bulky DNA adducts deter-mined by 32P-postlabelling and aromatic amine-derived DNA adducts detected
by G-C8-4-ABP-DELFIA were analysed for correlation in peripheral blood lymphocytes of rubber industry workers but no association was observed between the two biomarkers [19]. Synchronous fluorescence spectroscopy (SFS) and 32P-postlabelling were compared by
Andreassen et al. [70] for the determination of BPDE-DNA or total diffuse radioactive zone (DRZ) for DNA samples from peripheral lung tissue from lung cancer patients. There was no correlation between DNA adduct levels obtained by immunoaffinity chromatography (IAC)-SFS or HPLC-SFS for BPDE-DNA and 32P-postlabelling either for
the BPDE-DNA-like spot or for the DRZ of the TLC maps. However, there was a strong correlation between IAC-SFS and HPLC-SFS. Shields et al. [55] found a strong correlation
between IAC-32P-postlabelling and IAC-HPLC-SFS specific for BPDE-DNA adducts
in human lung DNA samples. Wilson et al. [71] did not find a correlation between HPLC-SFS and USERIA in peripheral lung tissue samples taken at autopsy. Alexandrov
et al. [53] found a good correlation between B[a]P-tetrol determined by fluorometry
and 32P-postlabelling, but there was no correlation between B[a]P-tetrol fluorometry
and ELISA [15]. Beland et al. [62] compared BPDE-DNA adduct levels determined by HPLC-electrospray (ES)-tandem mass spectrometry (MS/MS) and bulky/PAH-DNA adduct levels determined by 32P-postlabelling and CIA, respectively. Samples in which
measurable levels were revealed by 32P-postlabelling and CIA did not show measurable
levels of adducts when the B[a]PdG-specific HPLC-ES-MS/MS method was used.
6.4.2. Different DNA adduct structures — common methods
A lack of correlation was observed by Szyfter et al. [72] between bulky DNA adduct and 7-alkylguanine DNA adduct levels determined by 32P-postlabelling methods either
in larynx tumour or normal larynx tissue from laryngeal cancer patients. Godschalk
et al. [61] found a positive correlation between smoking-related bulky DNA adduct levels
measured by 32P-postlabelling and O4-ethylthymidine level determined by IAC-32
P-post-labelling in normal lung tissue from lung cancer patients. Zhang et al. [13] observed a significant positive correlation between leucocyte 8-oxo-dG and leucocyte bulky DNA adducts in coke oven workers exposed to PAHs. Kadlubar et al. [73] investigated several oxidative stress-related adducts, namely, 1,N6-etheno(2’-deoxy)adenosine (εdA),
3,N4-etheno(2’-deoxy)cytidine (εdC), 8-oxo-2’-deoxyguanosine (8-oxo-dG) and
pyrimi-do[1,2-alpha]purin-10(3H)-one (M1G) in normal pancreatic tissue from smokers and non-smokers. Only for the pair 8-oxo-dG and M1G was a positive correlation detected.
6.5. Correlation between levels of the same DNA adduct
in various tissues
6.5.1. DNA adducts in normal target and surrogate tissues
Correlation between biomarkers of exposure in the target tissue for cancer induction and in other tissues considered to be surrogates provides valuable information for human biomonitoring. Such data help to assess genotoxic doses affecting the target tissue when only surrogate tissue is available in the usual environmental and occupational exposure situations in healthy populations.
Bulky DNA adduct levels were studied in normal solid tissues, such as lung or bronchial tissue, and in surrogate tissues such as peripheral blood lymphocytes and total white blood cells from lung cancer patients. Györffy et al. [51] found a significant correlation between bulky DNA adducts in normal peripheral lung tissue or bronchial tissue and peripheral blood lymphocytes in non-smokers, but lack of a cor-relation in smokers, which indicates an exposure dose-dependent corcor-relation. Wiencke
et al. [58] observed a significant correlation for bulky DNA adducts between lung and
blood mononuclear cells in a study population of smokers and non-smokers. On the contrary, van Schooten et al. [54] did not obtain a significant correlation between lung and white blood cells either in current smokers or in ex-smokers for DNA adducts deter-mined either by 32P-postlabelling or ELISA. Tang et al. [57] compared PAH-DNA adduct
levels in lung tissue and leucocytes by BPDE-DNA ELISA and observed positive correla-tion. Comparison of BAL cells and mononucleated white blood cells from healthy volun-teers showed no correlation for bulky adduct levels determined by 32P-postlabelling [65].
Bronchial mucosa, nasal mucosa and peripheral blood lymphocytes from patients undergoing bronchoscopy were compared by Peluso et al. [74]. The authors found a positive correlation between the levels of bulky DNA adducts determined by 32P-postlabelling in bronchial mucosa and lymphocyte samples, and bronchial mucosa
and nasal mucosa, respectively; however, no correlation was found between nasal mucosa and peripheral blood lymphocytes. Zhao et al. [39] found no correlation between nasal mucosa and total white blood cell bulky DNA adducts in tissue samples from healthy volunteers. Besaratinia et al. [42] compared PAH-DNA adducts in mouth-floor cells and buccal mucosa cells by immunochemical staining and found a positive correlation. There was also a positive correlation between the levels of bulky DNA adducts in induced sputum cells and peripheral blood lymphocytes from smokers, but there was no cor-relation in non-smokers [41]. Bulky DNA adduct levels were compared between non-tumorous larynx tissue and leucocytes by Szyfter et al. [63] and a positive correlation was found. Skin and white blood cell fractions were analysed for bulky DNA adducts in tar-treated eczema patients by Godschalk et al. [48] and the comparison resulted in a positive correlation.
7-Methylguanine (7-meGua)-DNA adduct levels were determined in normal bronchial tissue and peripheral blood lymphocytes from lung cancer patients by Mustonen et al. [56] and a significant positive correlation was found in smokers.
For comparison of various surrogates, 7-meGua-DNA adduct levels were determined in different white blood cell fractions from healthy volunteers and no correlation was detected among total white blood cells, granulocytes and lymphocytes [36]. Total white blood cell and peripheral blood lymphocyte samples obtained from coke oven workers were analysed for bulky DNA adducts by 32P-postlabelling by Binkova et al. [7] and the
two white blood cell fractions were positively correlated.
6.5.2. DNA adduct levels in tumour and normal tissues
Comparison of DNA adduct levels in tumour and non-tumour tissues may add knowledge on xenobiotic transport and metabolism, and DNA repair capacity of the tu-mour tissue. DNA adduct levels were investigated in lung tutu-mour and in macroscopically normal peripheral lung tissue samples by Györffy et al. [51]. A statistically significant correlation was found between DNA adduct levels measured in tumour, normal lung and bronchial tissue by both 32P-postlabelling and CIA. Szyfter et al. [63] compared larynx
tumour with surrounding normal tissues and a positive correlation was obtained between bulky DNA adduct levels in the smokers’ and non-smokers’ groups, respectively. There was also a positive correlation between 4-ABP-DNA adducts determined in breast tumour and normal breast tissues by Faraglia et al. [64]. A correlation was found between PAH-DNA adducts detected by the immunoperoxidase method in liver tumour and normal liver tissue samples from hepatocellular carcinoma patients [75]. However, the magnitudes of the DNA adduct levels varied in tumour and corresponding non-tumour tissues, suggesting organ specificity.
6.6. Correlation between different biomarkers
of human genotoxic exposure
6.6.1. DNA adducts and protein adducts
Exfoliated urothelial cells obtained from smokers and non-smokers were investigated by Talaska et al. [35]. The level of total bulky DNA adducts correlated significantly with the level of 4-ABP-haemoglobin adducts in the samples from smokers. Santella
et al. [47] studied PAH-DNA and PAH-albumin adducts, determined by immunoassay, in
blood samples from psoriasis patients treated with tar products. There was no correlation between the two biomarkers. Ruchirawat et al. [24] observed a positive correlation between bulky DNA adducts in peripheral blood lymphocytes and serum B[a]P-albumin adducts in police officers at a low level of exposure; however, no correlation existed at a high level of exposure. The BPDE-globin and BPDE-serum albumin adduct levels were positively correlated in a study population of garage workers [20]. Nielsen et al. [18] compared the hydroxyethylvaline adduct in haemoglobin and the bulky DNA adduct level in lymphocytes obtained from bus garage workers, and did not find a correlation. In a study population of plastic lamination workers exposed to styrene, there was a positive correlation between the O6-styrene guanine adduct in lymphocytes and
the N-terminal valine adduct of styrene in haemoglobin [29]. Boffetta et al. [50] found a positive correlation between cisplatin-DNA adduct levels and total protein-bound platinum in blood from testicular cancer patients.
6.6.2. Different urinary metabolites and urinary mutagenicity
A positive correlation was found by Wu et al. [10] between trans-anti-B[a]P-tetraol and 1-OHPY concentration in urine from coke oven workers. In a healthy study population, 1-OHPY and cotinine positively correlated in smokers and did not correlate in non-smo-kers [44]. 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNAL) and its glucuronide (NNAL-Gluc) were positively correlated with total cotinine in adult subjects exposed to environmental tobacco smoke (ETS) [38,40]. Similarly, a positive correlation was detected in newborns exposed to their mother’s smoking during pregnancy and in children exposed to ETS [76,77]. The PAH metabolite content detected by ELISA
and 1-OHPY determined by HPLC from urine of psoriasis patients treated with coal tar positively correlated in a study by Santella et al. [46]. In healthy volunteers, there was a borderline significant positive correlation between urinary 1-OHPY and urinary mutagenicity [46].
Boogaard et al. [27] investigated S-phenyl mercapturic acid (S-PMA) and
trans--trans-muconic acid (ttMA) as biomarkers for exposure to low concentrations of benzene,
and they observed positive correlation between the two benzene metabolites in end-shift urine samples from petrochemical industry workers. Similarly, a positive correlation was found by Kivisto et al. [6] between blood and/or urinary benzene, and urinary S-PMA and ttMA concentrations from benzene factory and cokery workers.
6.6.3. DNA adducts, urinary PAH metabolites and urinary mutagenicity
Urinary PAH metabolite, 1-OHPY and bulky DNA adducts determined by 32
P-post-labelling or PAH-DNA adducts determined by ELISA or DELFIA were investigated together in numerous studies. A positive correlation was found between the 1-OHPY concentration in urine and white blood cell DNA adducts from smoking aluminium workers by van Schooten et al. [16]; 1-OHPY and leucocyte DNA adducts from coke oven workers (after stratification for the CYP1A1 genetic polymorphism) by Pan et al. [8]; 1-OHPY and peripheral blood lymphocytes from aluminium plant workers (after stratification for the GSTM1 homozygous deletion genotype) by Schoket et al. [78]; and 1-OHPY and white blood cells from incineration workers, specifically for the GSTM1 null genotype, by Lee et al. [79]. A positive correlation was also found between 1-OHPY and white blood cell PAH-DNA adducts from coke oven workers in the high-exposure group with the GSTM1 null genotype by Brescia et al. [80]. A lack of correlation was observed between urinary 1-OHPY or 1-OHPY-glucuronide and white blood cell bulky/PAH-DNA adducts in iron foundry workers, peripheral blood lymphocytes in bus/garage workers [18], white blood cells from US Army soldiers [25], and peripheral blood lymphocytes from garage mechanics and vulcanizing plant workers [19]. Additional studies that did not find correlation between white blood cell DNA adduct level and 1-OHPY were conducted in aluminium plant workers by Carstensen et al. [17], in traffic officers by Ruchirawat et al. [24], and in shipyard workers by Lee et al. [81]. By using BPDE-DNA specific adduct determination in white blood cells from various study populations, such as psoriatic patients, coke oven workers, chimney sweeps and aluminium plant workers, Pavanello et al. [9] did not find correlation with urinary 1-OHPY level. No correlation was found between 1-OHPY and bulky DNA adduct level in skin biopsy samples from eczema patients treated with coal tar [48]; however, with urinary 3-hydroxy-B[a]P content the correlation was significant.
Talaska et al. [35] found a positive correlation between bulky DNA adducts in exfoliated urothelial cells and the mutagenicity of urine in healthy volunteers.
There was no correlation between urinary 1-OHPY and the white blood cell 8-oxo-dG level in coke oven workers and graphite electrode producing plant workers [11,13].
6.6.4. Protein adducts, urinary 1-hydroxypyrene and urinary mutagenicity
The PAH-albumin adducts detected in maternal serum and umbilical cord blood showed a positive correlation [82].
Urinary 1-OHPY and erythrocyte BPDE-globin adducts positively correlated in one study on garage mechanics [20]; however, in another study there was a lack of correlation between the two biomarkers in office employees occupationally unexposed to PAHs [83]. Ruchirawat et al. [24] did not find a correlation between 1-OHPY and serum PAH-albumin level in traffic police officers.
Nielsen et al. [18] investigated the association between 1-OHPY and hydroxyethyl-valine adducts in haemoglobin from bus/garage workers and found a positive correlation between the two biomarkers.
Talaska et al. [35] found a positive correlation between 4-ABP-haemoglobin adduct levels in exfoliated urothelial cells and mutagenicity in urine from healthy volunteers.
6.6.5. DNA adducts, protein adducts and urinary cotinine
Levels of bulky DNA adducts in T-lymphocytes and in granulocytes from smokers positi-vely correlated with urinary cotinine [84]. There was also a positive correlation between lymphocyte DNA adducts and cotinine in an occupational study population of coke oven workers and controls investigated by Binkova et al. [7]. Scherer et al. [44] obtained a posi-tive correlation between the B[a]P-albumin adduct and cotinine level in smokers; however, there was no correlation for B[a]P-haemoglobin adduct and cotinine level.
6.7. Correlations among multiple biomarkers monitoring
genotoxic exposure and effect
Perera et al. [85] determined PAH-DNA adducts by ELISA in peripheral blood leuco-cytes and SCE in peripheral blood lymphocytes from lung cancer patients. There was no correlation between the two biomarkers. Perera et al. [86] conducted a complex study of biomarkers, including PAH-DNA adducts determined by ELISA in white blood cells, 4-ABP-haemoglobin adducts determined in erythrocytes, SCE detected in lymphocytes and plasma cotinine levels measured in smokers and non-smokers. A positive correlation was found between 4-ABP-haemoglobin and cotinine levels, 4-ABP-haemoglobin and SCE, and 4-ABP-haemoglobin and PAH-DNA adducts. When stratified for smoking status, it was only the smokers’ group in which the last correlation existed.
In a Polish study population, SCE and bulky DNA adducts determined by 32P-postlabelling were correlated. However, the DNA adduct levels determined
by ELISA did not correlate with SCE, or with the results of the postlabelling [67]. Van Delft et al. [12] conducted a biomonitoring study in coke-oven workers exposed to PAHs. Several biomarkers were determined such as urinary 1-OHPY, bulky DNA
adduct, DNA strand breaks, SCE, lymphocytes with a high frequency of SCE, and MN in exfoliated urothelial cells. No significant correlations were observed for any pair of biomarkers.
Coke oven and graphite electrode producing plant workers exposed to PAHs and controls were biomonitored by Marczynski et al. [11]. No correlation was found between pairs of biomarkers including white blood cell 8-oxo-dG, lymphocyte tail extent moment (DNA strand breaks and alkali-labile sites), urinary 1-OHPY and the sum of urinary five hydroxyphenanthrenes. A correlation was obtained, however, between 8-oxo-dG and tail extent moment for the whole study population.
Exposure of bus drivers and postal workers to ambient air pollutants was bio-monitored by Autrup et al. [23]. Biomarkers investigated were bulky carcinogen-DNA adduct, 2-amino-apidic semialdehyde (AAS) in plasma proteins, malondialdehyde (MDA) in plasma, PAH-albumin adduct, gamma-glutamyl semialdehyde (GGS) in haemoglobin, 8-oxo-dG in urine, as well as urinary mutagenic activity. A significant negative correlation was observed between bulky carcinogen-DNA adduct and PAH-albumin adduct levels, and between bulky DNA adducts and GGS. Highly significant correlations were found between PAH-albumin adducts and AAS and GGS. Significant correlations were also observed between urinary 8-oxo-dG and AAS, and PAH-albumin adducts. Furthermore, in the GSTM1 null genotype subgroup of the study population, a significant negative correlation was observed between bulky DNA adducts and PAH-albumin adducts, and between DNA adducts and urinary mutagenic activity.
Knudsen et al. [87] investigated the genotoxic exposure of stainless steel welders and found no correlation between SCE and CA. In a large Italian study population, SCE and MN frequency was determined in human lymphocytes from 1650 subjects and no correlation was found between the two cytogenetic biomarkers [88].
In shipyard workers exposed to PAHs, Lee et al. [81] found no correlation between urinary 1-OHPY-glucuronide/urinary 1-OHPY, peripheral white blood cell bulky DNA adduct level, and glycophorin A (GPA) variant frequency in red blood cells. There was a good correlation between free and glucuronidated 1-OHPY.
Biomarkers of oxidative DNA damage were investigated in blood mononuclear cells and urine from bus drivers by Loft et al. [22]. There was a positive correlation between 8-oxo-dG excretion and CYP1A2 activity on workdays, and no correlation was detected with unscheduled DNA synthesis (UDS).
Personal exposure to ambient PM2.5 was biomonitored in a study population of healthy volunteers by detecting 8-oxo-dG in lymphocytes and urine, DNA strand breaks by the comet assay, bulky DNA adducts in lymphocytes and 1-OHPY in urine. There was no correlation between any pair of the studied biomarkers [33]. Benzene exposure of urban inhabitants was biomonitored in Copenhagen. Sorensen
et al. [34] investigated 8-oxo-dG in lymphocytes and urine, DNA strand breaks
in lymphocytes by the comet assay, urinary ttMA and S-PMA. A significant correlation was found between ttMA and S-PMA, as well as between S-PMA and 8-oxo-dG in lymphocytes.
Besaratinia et al. [43] determined the level of 8-oxo-dG and oxidised pyrimidine bases in lymphocytes, urinary 8-oxo-dG excretion, overall DNA repair capacity in blood leuco-cytes, and plasma antioxidative capacity in healthy smokers and non-smokers. In general, there was no correlation among the biomarkers, except a significant negative correlation between lymphocytic and urinary 8-oxo-dG in smokers. There was a significant correla-tion between plasma antioxidative capacity and lymphocytic 8-oxo-dG after adjustment for gender and age.
Vodicka et al. [29] conducted a study among styrene-exposed lamination workers, and investigated urinary mandelic acid, O6-guanine DNA adducts of styrene in lymphocytes and granulocytes, N-terminal valine adducts of styrene in haemoglobin, DNA single strand breaks, HPRT mutation frequency in T-lymphocytes, and styrene level in blood. The HPRT mutation frequency correlated with urinary mandelic acid and haemoglobin adducts. No significant correlation was found between single strand break parameters and haemoglobin adducts or HPRT mutation frequency. Also in styrene-exposed workers, a significant correlation was seen between styrene-haemoglobin adduct levels and MN, and between MN and single strand breaks by Perera et al. [67].
In a project conducted among styrene-exposed workers by Somorovska et al. [28], the biomarkers determined were styrene in exhaled air and in blood, DNA strand breaks, oxidised bases in mononuclear leucocytes, CA in lymphocytes, immune parameters and haematological parameters. A statistically significant correlation was observed between DNA strand breaks and frequency of CA. The analysis evaluating the relation-ships between the immune and genotoxicological parameters revealed a significant correlation between the quantities of a series of CD antigens and DNA strand breaks and CA, respectively. In a study among rubber industry workers who were exposed to a complex mixture of PAHs, alkenes and 1,3-butadiene, Somorovska et al. [26] investi-gated DNA strand breaks, chromatid/chromosome breaks, MN and various immuno-toxicological end points. The strongest correlations were found between strand breaks and MN and chromatid/chromosome breaks and MN, respectively.
In hospital workers exposed to ethylene oxide, ethylene oxide-haemoglobin adduct levels were correlated with SCE [67].
6.8. Preliminary conclusions of the literature review
It is known from the large number of research papers in the field of human biomoni-toring of genotoxic exposure, that the various exposure and early effect biomarkers are suitable for recognising exposure/dose-related differences among exposure groups. In general, the various biomarker assays will probably provide the same qualitative answer in a comparison of different exposure groups. There may, though, be some differences in the sensitivity and ‘resolution’ of the different biomar-kers/methods. One method may recognise substantial differences among the exposure groups, whereas another one may also recognise smaller differences. Correlation between
individual pairs of biomarkers is a more complicated issue as can be seen from the present literature review. From a genotoxicological point of view, a positive correlation between two biomarkers may reflect events of molecular mechanisms running in parallel, or also a chain of consecutive events. From a methodological point of view, there are differences in the chemical structure specificity of the different biomarker methods, for example the difference between 32P-postlabelling and HPLC-ES-MS/MS.
The following preliminary conclusions emerge from the literature on correlation of biomarkers:
• Predominantly there were no correlations between DNA adduct levels determined by different DNA adduct methods. Most of the 32P-postlabelling and immunoassay
studies did not result in a significant correlation between the individual pairs of DNA adducts. This indicates partial to small overlapping of the substrate specificity of the different DNA adduct methods.
• Controversial results have been found regarding correlations between a group of related DNA adduct structures and a chemically specific single DNA adduct structure by using the same type of DNA adduct methodology. This controversy suggests the co-existence of closely linked as well as independent pathways within the metabolic activation processes of a complex mixture of xenobiotics, and may also reflect differences in the kinetics of DNA adduct formation and elimination.
• Considering the correlation between DNA adduct levels in target and surrogate tissues we have found a larger number of studies that concluded a positive correlation than other studies that did not find a correlation. The existence/lack of correlation may be exposure dose dependent with regard to the metabolic capacity of the corresponding tissues for the xenobiotics of interest.
• In the majority of the studies there was a positive correlation between DNA adduct levels in tumour and normal tissues, suggesting similarities in the xenobiotic activa-tion/elimination processes of the tumour and normal tissues. However, the magnitu-des of the DNA adduct levels varied in tumour and corresponding non-tumour tissues, which may suggest organ specificity.
• There was a positive correlation for different urinary metabolites and urinary mutagenicity in most of the studies.
• Predominantly, there was no correlation between DNA adducts and urinary PAH metabolites, but after stratification to a particular, mostly GSTM1 null genotype, correlation may have emerged between the two exposure markers.
• Correlation was more probable between structurally specified protein adducts and 1-OHPY than with less specific xenobiotic-protein structures.
• Stratification of the study population for confounding factors, such as smoking status, may reveal hidden correlations.
• Results of the studies including cytogenetic biomarkers were as complex as with the exposure markers. We have found examples of both their positive correlation and lack of correlation with exposure markers.
We are preparing a more detailed review of the literature that will also present the statistical details of the correlation analyses of the individual publications. Work is also in progress to provide more insight into the reasons for the differences in correlation found.
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