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. 2012 Jun;41(3):828-43.
doi: 10.1093/ije/dys003. Epub 2012 Mar 15.

Systematic Evaluation of Environmental Factors: Persistent Pollutants and Nutrients Correlated With Serum Lipid Levels

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Systematic Evaluation of Environmental Factors: Persistent Pollutants and Nutrients Correlated With Serum Lipid Levels

Chirag J Patel et al. Int J Epidemiol. .
Free PMC article

Abstract

Background: Both genetic and environmental factors contribute to triglyceride, low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C) levels. Although genome-wide association studies are currently testing the genetic factors systematically, testing and reporting one or a few factors at a time can lead to fragmented literature for environmental chemical factors. We screened for correlation between environmental factors and lipid levels, utilizing four independent surveys with information on 188 environmental factors from the Centers of Disease Control, National Health and Nutrition Examination Survey, collected between 1999 and 2006.

Methods: We used linear regression to correlate each environmental chemical factor to triglycerides, LDL-C and HDL-C adjusting for age, age(2), sex, ethnicity, socio-economic status and body mass index. Final estimates were adjusted for waist circumference, diabetes status, blood pressure and survey. Multiple comparisons were controlled for by estimating the false discovery rate and significant findings were tentatively validated in an independent survey.

Results: We identified and validated 29, 9 and 17 environmental factors correlated with triglycerides, LDL-C and HDL-C levels, respectively. Findings include hydrocarbons and nicotine associated with lower HDL-C and vitamin E (γ-tocopherol) associated with unfavourable lipid levels. Higher triglycerides and lower HDL-C were correlated with higher levels of fat-soluble contaminants (e.g. polychlorinated biphenyls and dibenzofurans). Nutrients and vitamin markers (e.g. vitamins B, D and carotenes), were associated with favourable triglyceride and HDL-C levels.

Conclusions: Our systematic association study has enabled us to postulate about broad environmental correlation to lipid levels. Although subject to confounding and reverse causality bias, these findings merit evaluation in additional cohorts.

Figures

Figure 1
Figure 1
Summary of environmental factors and analytic method. (A) Summary of the 26 factor classes and the number of factors within them for each NHANES test survey. (B) 100–7500 individuals had their HDL-C, LDL-C and triglyceride levels measured for each of these factors in each survey; these lipid levels were log transformed to assume normality for least squares regression. (C) Each of these 126, 157 and 65 factors was tested for association with the logarithm base 10 of HDL-C, LDL-C and triglyceride levels with a linear regression model adjusted for age, age2, sex, BMI, ethnicity and SES. (D) We estimated the FDR by permuting the lipid levels and re-computing the linear models; an FDR of 0.05 was considered significant. We deemed a factor to be tentatively validated if it was found to be significant in the validation survey with P ≤ 0.05 and an effect in the same direction. (E) We estimated a final coefficient for tentatively validated factors by combining all surveys and adjusting for age, age2, sex, ethnicity, SES, BMI, waist circumference, type 2 diabetes status (fasting blood glucose ≥ 126 mg/dl), blood pressure and survey. (F) We estimated the coefficient of determination (R2) for the final, combined models. (G) We recomputed our final models, adding 62 self-report variables one by one to attempt to check the validity of the environmental effect
Figure 2
Figure 2
Partial Pearson's correlation between environmental factors. Partial Pearson's correlation, adjusted by age and BMI (and creatinine for factors measured in urine) for each of the 188 factors were computed for each survey separately. We combined correlations between surveys using a meta-analytic random-effects estimate and displayed them in a heatmap (above), and ordered them by environmental ‘class’, coloured as in Figure 1A. Pairs of factors where correlations could not be computed are shown in grey
Figure 3
Figure 3
Significance of association [−log10(FDR)] for each of 188 factors by survey in association to (A) triglycerides, (B) LDL-C, (C) HDL-C. Y-axis indicates −log10(FDR) of the adjusted linear regression coefficient for each of the environmental factors. Colours represent different environmental classes as represented in Figure 1A. Red line corresponds to an FDR of 0.05. Findings validated in the 2003–04 survey are seen in the open markers
Figure 4
Figure 4
Forest plots for validated environmental factors associated with (A) triglycerides, (B) LDL-C, (C) HDL-C. Survey (labelled as 1999–2000, 2001–02, 2005–06, filled points) denotes the NHANES survey in which the specific factor was found to be significant (FDR < 0.05) in a model adjusting for age, age2, SES, ethnicity, sex, BMI. ‘Validation’ indicates the estimates found for the significant factor in the validation survey. Combined survey (unfilled points) denotes the estimate attained when combining all surveys available for exposure in a model adjusting for age, age-squared, SES, ethnicity, sex, BMI, waist circumference, type 2 diabetes status, blood pressure and survey. Percent change (x-axis) is the percent change of lipid level for a change in 1 SD of logged exposure value. Effect size (in mg/dl) attained when fitting the untransformed lipids to the model. Symbols proportional to sample size and colours represent different environmental classes as represented in Figure 1A. For triglycerides and HDL-C, only the top most significant factors for each factor class is shown; forest plots of all validated factors are seen in Supplementary Figure 1, available as Supplementary Data at IJE online

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