Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels

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.

Highly Elevated Serum Concentrations of Perfluoroalkyl Substances in Fishery Employees from Tangxun Lake, China

“…20−22 Meanwhile, it is reported that serum PFOA in occupational exposed people was associated with total cholesterol (CHOL), total bilirubin, serum aspartate transaminase (AST), and thyroid parameters. 23,24 However, the findings of statistically significant associations between PFAS exposure and clinical biomarkers vary between different studies, 25,26 and so far the evidence for human health effects as a consequence of PFAS exposure remain largely inconclusive.…”

A data‐driven search for semen‐related phenotypes in conception delay

“…While the analyses conducted herein have been used to search for associations in chronic diseases and biomarkers such as serum lipids (Patel et al ., 2012), we are not aware of any published work focusing on human fecundity or related impairments such as conception delay or pregnancy loss. To this end, we cannot interpret our findings in the context of earlier work, but data‐driven techniques can be extended to include emerging biomarkers of male fecundity, such as sperm function tests or in vitro capacitation tests (Wang & Swerdloff, 2014).…”

“…Prioritization and/or hypothesis generation research aimed at delineating the prognostic role of semen phenotypes for male fecundity may benefit from the use of emerging methods capable of systematically analysing many factors to identify environmental exposures and phenotypes associated with disease (Patel & Ioannidis, 2014). Examples of analogous data‐driven investigations included searches of environmental or behavioral factors associated with type 2 diabetes (Patel et al ., 2010), serum lipids (Patel et al ., 2012), blood pressure (Tzoulaki et al ., 2012), endometrial cancer (Merritt et al ., 2015), all‐cause mortality (Patel et al ., 2014), family income (Patel et al ., 2015) and pre‐term birth (Patel et al ., 2013). …”

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

“…A few recent papers aim to examine the problem more generally as opposed to single exposure-effect associations and have had some success in generating novel hypotheses. Patel et al [26,27,28] used multiple regression models to screen several exposures for possible effects on a targeted health marker. This approach enabled ranking of the different exposures within and across NHANES cycles using a false discovery rate corrected p-value.…”

“…They also don't easily allow comparisons and ranking between different independent variable(s) unless an approach like Patel et al [26,27,28] is taken. In the case of missing or non-co-occurring variables it is not possible to estimate the common effects co-exposures may have.…”