Back

Environment-Wide Association Study of Chemical Biomarkers and Health Outcomes in NHANES 2017-2018: Discovery, Validation, and Dose-Response Analysis

Farquhar, H. L.

2026-02-09 occupational and environmental health
10.64898/2026.02.07.26345792 medRxiv
Show abstract

BackgroundEnvironment-wide association studies (ExWAS) offer a systematic approach to identifying chemical biomarker-health outcome associations, yet few have applied rigorous multi-stage validation. MethodsWe screened 92 chemical biomarkers against 48 health outcomes in NHANES 2017-2018 (2,796 tests across four screening rounds; not all chemicals were crossed with all outcomes). Associations passing an initial FDR screen were subjected to cross-cycle validation in NHANES 2015-2016--the primary inferential safeguard given the adaptive screening design--followed by dose-response analysis and multiple sensitivity specifications. Survey-weighted regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, BMI, and smoking. ResultsOf 26 associations passing FDR correction, 21 were testable in cross-cycle validation; of these, 15 (71%) replicated with concordant direction and p < 0.05 in a temporally independent NHANES 2015-2016 sample. Of these 15, 14 remained robust after analyte-specific sensitivity checks; urinary creatinine adjustment identified one association (iodine-BMI) as a dilution artifact. Two novel findings emerged: dimethylarsonic acid with uric acid ({beta} = 0.20 mg/dL per log-unit DMA, 95% CI: 0.15-0.26) and urinary perchlorate with BUN ({beta} = 1.21 mg/dL per log-unit perchlorate, 95% CI: 0.97-1.45); a third high-novelty association (methylmercury-waist circumference) is likely explained by fish consumption patterns. ConclusionsMulti-stage ExWAS with cross-cycle validation identified 14 robust chemical-health associations. Two novel findings--DMA-uric acid and perchlorate-BUN--survived all sensitivity checks and warrant prospective investigation.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.