High-resolution metabolomics of occupational exposure to trichloroethylene

Walker, Douglas I,Uppal, Karan,Zhang, Luoping,Vermeulen, Roel,Smith, Martyn,Hu, Wei,Purdue, Mark P,Tang, Xiaojiang,Reiss, Boris,Kim, Sungkyoon,Li, Laiyu,Huang, Hanlin,Pennell, Kurt D,Jones, Dean P,Rot Oxford University Press 2016 International journal of epidemiology Vol.45 No.5

<P><B>Background:</B> Occupational exposure to trichloroethylene (TCE) has been linked to adverse health outcomes including non-Hodgkin’s lymphoma and kidney and liver cancer; however, TCE’s mode of action for development of these diseases in humans is not well understood.</P><P><B>Methods:</B> Non-targeted metabolomics analysis of plasma obtained from 80 TCE-exposed workers [full shift exposure range of 0.4 to 230 parts-per-million of air (ppm<SUB>a</SUB>)] and 95 matched controls were completed by ultra-high resolution mass spectrometry. Biological response to TCE exposure was determined using a metabolome-wide association study (MWAS) framework, with metabolic changes and plasma TCE metabolites evaluated by dose-response and pathway enrichment. Biological perturbations were then linked to immunological, renal and exposure molecular markers measured in the same population.</P><P><B>Results:</B> Metabolic features associated with TCE exposure included known TCE metabolites, unidentifiable chlorinated compounds and endogenous metabolites. Exposure resulted in a systemic response in endogenous metabolism, including disruption in purine catabolism and decreases in sulphur amino acid and bile acid biosynthesis pathways. Metabolite associations with TCE exposure included uric acid (<I>β</I> = 0.13, <I>P</I>-value = 3.6 × 10<SUP>−5</SUP>), glutamine (<I>β</I> = 0.08, <I>P</I>-value = 0.0013), cystine (<I>β</I> = 0.75, <I>P</I>-value = 0.0022), methylthioadenosine (<I>β</I> = −1.6, <I>P</I>-value = 0.0043), taurine (<I>β</I> = −2.4, <I>P</I>-value = 0.0011) and chenodeoxycholic acid (<I>β</I> = −1.3, <I>P</I>-value = 0.0039), which are consistent with known toxic effects of TCE, including immunosuppression, hepatotoxicity and nephrotoxicity. Correlation with additional exposure markers and physiological endpoints supported known disease associations.</P><P><B>Conclusions:</B> High-resolution metabolomics correlates measured occupational exposure to internal dose and metabolic response, providing insight into molecular mechanisms of exposure-related disease aetiology.</P>

IL10 and TNF variants and risk of non-Hodgkin lymphoma among three Asian populations

Hosgood III, H. Dean,Au, Wing-Yan,Kim, Hee Nam,Liu, Jie,Hu, Wei,Tse, Jovic,Song, Bao,Wong, Kit-fai,Lee, Je-Jung,Chanock, Stephen J.,Siu, L. P.,Purdue, Mark P.,Shin, Min-ho,Yu, Jinming,Liang, Raymond,K Springer-Verlag 2013 International journal of hematology Vol.97 No.6

<P>Genetic variation in immune-related genes, such as IL10 and TNF, have been associated with the development of non-Hodgkin lymphoma (NHL) in Caucasian populations. To test the hypothesis that IL10 and TNF polymorphisms may be associated with NHL risk in Asian populations, we genotyped 20 single nucleotide polymorphisms (SNPs) within the IL10 and TNF/LTA loci in three independent case-control studies (2635 cases and 4234 controls). IL10 rs1800871, rs1800872, and rs1800896 were genotyped in all three studies, while 5 of the remaining SNPs were genotyped in two studies, and 12 in a single study. IL10 rs1800896 was associated with B cell lymphoma [per-allele odds ratio (OR)?=?1.25, 95?% confidence interval (CI)?1.08-1.45; p trend?=?0.003], specifically diffuse large B cell lymphoma (DLBCL) (per-allele OR?=?1.29, 95?% CI?1.08-1.53; p trend?=?0.004), as well as T cell lymphoma (per-allele OR?=?1.44, 95?% CI?1.13-1.82; p trend?=?0.003). TNF rs1800629, which was genotyped in only two of our studies, was also associated with B cell lymphoma (per-allele OR?=?0.77, 95?% CI?0.64-0.91; p trend?=?0.003), specifically DLBCL (per-allele OR?=?0.69, 95?% CI?0.55-0.86; p trend?=?0.001). Our findings suggest that genetic variation in IL10 and TNF may also play a role in lymphomagenesis in Asian populations.</P>

0346 Occupational Exposure to Lead and Cancer in Two Cohort Studies of Men and Women in Shanghai, China

Liao, Linda M,Friesen, Melissa C,Xiang, Yong-Bing,Cai, Hui,Koh, Dong-Hee,Ji, Bu-Tian,Yang, Gong,Li, Hong-Lan,Locke, Sarah J,Rothman, Nathaniel,Zheng, Wei,Gao, Yu-Tang,Shu, Xiao-Ou,Purdue, Mark P BMJ Publishing Group Ltd 2014 Occupational and environmental medicine Vol.71 No.suppl1

<P><B>Objectives</B></P><P>Epidemiologic studies of occupational lead exposure have suggested increased risks of cancers of the brain, kidney, lung, meninges, and stomach; however, the totality of the evidence is inconsistent. To clarify whether lead is a carcinogen, we investigated the relationship between occupational lead exposure and risks of these five cancer sites in two prospective cohort studies in Shanghai, China.</P><P><B>Method</B></P><P>Annual job/industry-specific estimates of lead fume and lead dust exposure were derived from a statistical model that combined expert ratings of lead intensity with inspection measurements collected by the Shanghai Centre for Disease Control and Prevention. The job/industry estimates were applied to the lifetime work histories of subjects from the Shanghai Women’s Health Study (73 363 participants) and the Shanghai Men’s Health Study (61 379 participants) to estimate cumulative exposure to lead dust and lead fume. Cohort-specific relative hazard rate ratios (RRs) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression models and then pooled using a random effects meta-analysis model.</P><P><B>Results</B></P><P>We observed a statistically significant increased risk of meningioma among individuals with estimated occupational exposure to lead dust or fumes (RR=2.4, 95% CI:1.1–5.0), and in particular among those with an above-median cumulative exposure to dust or fumes (RR=3.1, 95% CI:1.3–7.4). We observed suggestive associations with lead exposure for cancers of the kidney (RR=1.4, 95% CI:0.9–2.3) and brain (RR=1.8, 95% CI:0.7–4.8), and null findings for cancers of the lung and stomach.</P><P><B>Conclusions</B></P><P>Our findings provide additional evidence that occupational lead exposure increases risk of meningioma.</P>

0199 Using machine learning to efficiently use multiple experts to assign occupational lead exposure estimates in a case-control study

Friesen, Melissa C,Locke, Sarah J,Zaebst, Dennis,Viet, Susan,Shortreed, Susan,Chen, Yu-Cheng,Koh, Dong-Hee,Pardo, Larissa,Schwartz, Kendra L,Davis, Faith G,Stewart, Patricia A,Colt, Joanne S,Purdue, M BMJ Publishing Group Ltd 2014 Occupational and environmental medicine Vol.71 No.suppl1

<P><B>Objectives</B></P><P>We applied machine learning approaches to efficiently assist multiple experts to transparently estimate occupational lead exposure in a case-control study of renal cell carcinoma.</P><P><B>Method</B></P><P>We used hierarchical cluster models to classify the 7154 study jobs with occupational history and job/industry questionnaires into 360 groups with similar responses. Each group was reviewed independently by two or three experts and was assigned probabilities of lead exposure (<5%, ≥5– <50%, ≥50%) for three time periods (<1980, 1980–1994, ≥1995). When the group’s mean response pattern suggested within-group exposure variability, experts identified programmable conditions that defined the rating differences where possible or flagged the group for further review. After splitting jobs that overlapped time periods at the calendar cut point, the 9992 job/time periods were assigned their relevant expert/group/time period estimate. Classification and regression tree (CART) models were developed to predict each expert’s expected assignment, based on previous decisions, to assign estimates for jobs in groups that expert had not assessed and for jobs requiring further review.</P><P><B>Results</B></P><P>In preliminary analyses, CART models predicted 91–96% of the experts’ pre-1995 estimates and 77–96% of ≥1995 estimates. CART estimates were assigned to 3–48% of the job/time periods, varying by expert. Overall, 92% of the job/time periods were assigned the same estimate by at least two experts.</P><P><B>Conclusions</B></P><P>Our framework reduced the number of exposure decisions needed from each expert compared to job-by-job assessment. Future work will use CART models to identify differences between experts to be resolved and incorporate frequency and intensity of lead exposure estimates.</P>