Dataset Information

Metabolite of the pesticide DDT and incident type 2 diabetes in urban India.

ABSTRACT:

Background

Previous epidemiological studies, largely conducted in high-income countries and cross-sectional, have suggested a relatively strong association between exposure to dichlorodiphenyldichloroethylene (DDE), a metabolite of the pesticide dichlorodiphenyltrichloroethane (DDT), and type 2 diabetes. DDT is widely used in India and the prevalence of type 2 diabetes there is increasing, but the association between these factors has not been explored to date.

Objective

The objective was to estimate the association of the p,p' isomer of DDE with incident type 2 diabetes in India.

Methods

A nested case-control study was conducted in a representative prospective cohort of adults from two cities in India. Participants were enrolled in 2010-11 (n = 12,271) and followed for annual assessment of chronic diseases including type 2 diabetes. Baseline plasma samples from incident cases of diabetes (n = 193) and sex-city-matched controls (n = 323) were selected for analysis of p,p-DDE. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using conditional logistic regression.

Results

At baseline, cases had higher p,p-DDE concentrations: geometric mean (95% CI) 330 (273-399) ng/g lipid compared to 223 (189-262) ng/g lipid among controls. Delhi participants had higher p,p-DDE concentrations: 579 (521-643) ng/g lipid compared to 122 (102-145) ng/g lipid in Chennai. In unadjusted models, being in the highest versus lowest quartile of p,p-DDE was associated with a more than doubling of the odds of diabetes: unadjusted OR (95% CI), 2.30 (1.19, 4.43). However, this effect was no longer significant after adjustment for age: adjusted (95% CI), 0.97 (0.46, 2.06).

Discussion

Results suggest that levels of p,p'-DDE in Delhi are exceptionally high, but we did not observe a significant association between p,p-DDE and incident type 2 diabetes. As this is the first study to evaluate this association in India, more studies are needed to inform our understanding of the association in this context, including potential routes of exposure.

SUBMITTER: Jaacks LM

PROVIDER: S-EPMC6860016 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Metabolite of the pesticide DDT and incident type 2 diabetes in urban India.

Jaacks Lindsay M LM Yadav Sudesh S Panuwet Parinya P Kumar Sushil S Rajacharya Girish H GH Johnson Cierra C Rawal Ishita I Mohan Deepa D Mohan Viswanathan V Tandon Nikhil N Barr Dana Boyd DB Narayan K M Venkat KMV Prabhakaran Dorairaj D

Environment international 20191022 Pt A

<h4>Background</h4>Previous epidemiological studies, largely conducted in high-income countries and cross-sectional, have suggested a relatively strong association between exposure to dichlorodiphenyldichloroethylene (DDE), a metabolite of the pesticide dichlorodiphenyltrichloroethane (DDT), and type 2 diabetes. DDT is widely used in India and the prevalence of type 2 diabetes there is increasing, but the association between these factors has not been explored to date.<h4>Objective</h4>The objec ...[more]

PMID: 31654984

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Project description:Aims/hypothesisThe identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.MethodsWe developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.ResultsAmong single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.Conclusions/interpretationOmic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Project description:BackgroundThough evidence suggests that some pesticides may have thyroid-disrupting properties, prospective studies of associations between specific pesticides and incident thyroid disease are limited.ObjectiveWe evaluated associations between use of specific pesticides and incident hypothyroidism in private pesticide applicators in the Agricultural Health Study (AHS).MethodsSelf-reported incident hypothyroidism ([Formula: see text] cases) was studied in relation to ever-use and intensity-weighted cumulative days of pesticide use at study enrollment. We estimated adjusted hazard ratios (HRs) and 95% confidence intervals (CI) using Cox proportional hazards models applied to 35,150 male and female applicators followed over 20 y. All models were stratified by state and education to meet proportional hazards assumptions ([Formula: see text] for age x covariate interactions). Models of pesticides that did not meet proportional hazards assumptions were stratified by median attained age (62 y).ResultsHypothyroidism risk was significantly increased with ever- vs. never-use of four organochlorine insecticides (aldrin, heptachlor, and lindane among participants with attained age [Formula: see text]; chlordane in all participants), four organophosphate insecticides (coumaphos in those [Formula: see text]; diazinon, dichlorvos, and malathion in all participants) and three herbicides (dicamba, glyphosate, and 2,4-D in all participants). HRs ranged from 1.21; 95% CI: 1.04, 1.41 (chlordane) to 1.54; 95% CI: 1.23, 19.4 (lindane in those [Formula: see text]). Hypothyroidism risk was greatest among those with higher intensity-weighted lifetime days of using chlordane, lindane, coumaphos (over age 62), diazinon, permethrin, and 2,4-D.ConclusionsOur findings support associations between exposure to several pesticides and increased hypothyroidism risk. These findings are generally consistent with prior analyses of prevalent hypothyroidism in the AHS. https://doi.org/10.1289/EHP3194.

Dataset Information

Metabolite of the pesticide DDT and incident type 2 diabetes in urban India.

Background

Objective

Methods

Results

Discussion

Publications

Metabolite of the pesticide DDT and incident type 2 diabetes in urban India.

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