Individual and co-exposure to perchlorate, nitrate, and thiocyanate and insulin resistance in the U.S population: A dominant role of perchlorate

Insulin resistance significantly contributes to diabetes; it can be influenced not only by lifestyle but also by environmental contaminants. Emerging evidence suggests that perchlorate, nitrate, and thiocyanate-widely dispersed sodium/iodide symporter (NIS)-inhibiting pollutants- might influence insulin resistance; however, their direct associations remain insufficiently investigated. Using NHANES data, this study investigated whether urinary levels of these chemicals correlate with insulin resistance, addressing a critical gap in understanding environmental contributors to diabetes risk. We analyzed 2011-2020 NHANES data from 3047 U.S. adults (≥20 years). Insulin resistance was assessed via HOMA-IR, with values > 2.6 being used to define insulin resistance. Weighted multivariable regression models showed that the highest tertile of perchlorate exposure demonstrated positive associations with HOMA-IR (β=0.13, [95 % CI: 0.03, 0.23]) and insulin resistance (OR=1.96, [95 % CI: 1.20, 3.22]) in fully adjusted models. (RCS) Analyses revealed an inverted U-shaped nonlinear dose-response relationship between nitrate exposure and both HOMA-IR and insulin resistance. Stratified analyses identified pronounced effects among obese individuals (BMI≥30 kg/m²), with perchlorate showing the strongest association (HOMA-IR β=0.45, [95 % CI: 0.31, 0.59]; insulin resistance OR=3.47, [95 % CI: 1.93, 6.23]). Similar patterns emerged for those with large waist circumference (≥100 cm). Weighted quantile sum regression indicated a significant positive trend mixture effect (HOMA-IR β=0.06, [95 % CI: 0.02, 0.12]), with perchlorate contributing most substantially (weight=0.74). However, BKMR further captured the complex joint effects of the chemical mixture, demonstrating component-specific and nonlinear exposure-response relationships, with perchlorate emerging as the primary contributor to the mixture-related metabolic effects. In conclusion, this study identified a positive association between higher perchlorate exposure and insulin resistance, with obesity markedly potentiating this association, underscoring the need to consider environmental chemical exposures in diabetes prevention strategies.

Authors

Institutions

Publication Details

Journal
Ecotoxicology and Environmental Safety
Published
2026-02-23
DOI
https://doi.org/10.1016/j.ecoenv.2026.119909
Primary Topic
Chemical Analysis and Environmental Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Individual and co-exposure to perchlorate, nitrate, and thiocyanate and insulin resistance in the U.S population: A dominant role of perchlorate

Manthar Ali Mallah, Qiao Zhang, Xiaotong Jian, Nour El Houda Keddari et al.
Ecotoxicology and Environmental Safety
Chemical Analysis and Environmental Impact
article

Individual and co-exposure to perchlorate, nitrate, and thiocyanate and insulin resistance in the U.S population: A dominant role of perchlorate

Manthar Ali Mallah, Qiao Zhang, Xiaotong Jian, Nour El Houda Keddari, Xiaojun Ma
article en

Abstract

Insulin resistance significantly contributes to diabetes; it can be influenced not only by lifestyle but also by environmental contaminants. Emerging evidence suggests that perchlorate, nitrate, and thiocyanate-widely dispersed sodium/iodide symporter (NIS)-inhibiting pollutants- might influence insulin resistance; however, their direct associations remain insufficiently investigated. Using NHANES data, this study investigated whether urinary levels of these chemicals correlate with insulin resistance, addressing a critical gap in understanding environmental contributors to diabetes risk. We analyzed 2011-2020 NHANES data from 3047 U.S. adults (≥20 years). Insulin resistance was assessed via HOMA-IR, with values > 2.6 being used to define insulin resistance. Weighted multivariable regression models showed that the highest tertile of perchlorate exposure demonstrated positive associations with HOMA-IR (β=0.13, [95 % CI: 0.03, 0.23]) and insulin resistance (OR=1.96, [95 % CI: 1.20, 3.22]) in fully adjusted models. (RCS) Analyses revealed an inverted U-shaped nonlinear dose-response relationship between nitrate exposure and both HOMA-IR and insulin resistance. Stratified analyses identified pronounced effects among obese individuals (BMI≥30 kg/m²), with perchlorate showing the strongest association (HOMA-IR β=0.45, [95 % CI: 0.31, 0.59]; insulin resistance OR=3.47, [95 % CI: 1.93, 6.23]). Similar patterns emerged for those with large waist circumference (≥100 cm). Weighted quantile sum regression indicated a significant positive trend mixture effect (HOMA-IR β=0.06, [95 % CI: 0.02, 0.12]), with perchlorate contributing most substantially (weight=0.74). However, BKMR further captured the complex joint effects of the chemical mixture, demonstrating component-specific and nonlinear exposure-response relationships, with perchlorate emerging as the primary contributor to the mixture-related metabolic effects. In conclusion, this study identified a positive association between higher perchlorate exposure and insulin resistance, with obesity markedly potentiating this association, underscoring the need to consider environmental chemical exposures in diabetes prevention strategies.

Ecotoxicology and Environmental SafetyVol. 312
Zhengzhou University (CN), First Affiliated Hospital of Zhengzhou University (CN), Mississippi Valley State University (US)
Openalex Percentile: Top 51%
Chemical Analysis and Environmental Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.