Evidence map›Paper›PMID 41540139›Full record

ArticleScientific reports2026

Associations of metal mixtures with insulin resistance and glucose homeostasis in U.S. adults from the NHANES 2011-2018.

Qinting Wu, Ruijing Gan, Siying Chen, Mocan Qiu, Jing Chen, Daizheng Huang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qinting Wu *School of Life Sciences and Medical Engineering, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China.
Ruijing Gan *School of Basic Medical Sciences, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China.
Siying Chen *School of Basic Medical Sciences, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China.
Mocan QiuSchool of Life Sciences and Medical Engineering, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China.
Jing ChenSchool of Life Sciences and Medical Engineering, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China.
Daizheng HuangSchool of Life Sciences and Medical Engineering, Guangxi Medical University, No. 22 Shuangyong Road, Nanning City, 530021, Guangxi Province, China. huangdaizheng@gxmu.edu.cn.

Funding

National Natural Science Foundation of China 62341601Natural Science Foundation of Guangxi Zhuang Autonomous Region 2018GXNSFAA281133
6 · The paper itself

Abstract

Research on the association of metal mixtures with glucose-insulin homeostasis is limited, and previous studies have typically focused on single metals. This study utilized data from 3110 adult subjects in the NHANES survey (2011-2018). Generalized linear models (GLM), logistic regression (LR), and restricted cubic splines (RCS) were employed to assess the associations of blood and urine metals with insulin resistance (IR) and glucose-insulin homeostasis. The Bayesian kernel machine regression (BKMR) and Bayesian weighted quantile sum (BWQS) models were further used to explore the independent and combined effects of metal exposures. In single-metal analyses, manganese (Mn) was positively correlated with insulin resistance (IR); cadmium(Cd), lead(Pb), mercury(Hg), and arsenic (As) were negatively correlated with the homeostasis model assessment of beta-cell function (HOMA-β); manganese and selenium (Se) were positively correlated with fasting plasma insulin (FPI); Se and cobalt (Co )were positively correlated with fasting plasma glucose (FPG); molybdenum (Mo) was positively correlated with HbA1c. In addition, both BWQS and BKMR models consistently showed that overall metal co-exposure had a positive effect on insulin resistance in the general population. Manganese was the most heavily weighted metal across all subgroups, with this association being more pronounced in males and individuals over 60 years of age. A negative association of metal mixtures with HOMA-β was observed in BWQS models. Furthermore, the analysis of BKMR models revealed possible interactions between insulin resistance and some components of metal mixtures in glucose homeostasis. The RCS model also identified nonlinear relationships between urinary Mo and HOMA-β, as well as between Co and both FPG and HbA1c. Our results suggest that metal mixtures may have adverse individual or combined effects on insulin resistance and glucose homeostasis in different population subgroups. These findings highlight the need for targeted interventions to mitigate the adverse effects of metal exposure on insulin-glucose homeostasis, which may provide new ideas for preventing and controlling the risk of type 2 diabetes due to metal exposure.

Indexed as

Blood GlucoseGlucoseHomeostasisInsulin ResistanceMetalsAdultBayes TheoremEnvironmental ExposureFemaleHumansInsulinMaleMiddle AgedNutrition SurveysUnited StatesBlood GlucoseGlucoseInsulinMetalsBayesian kernel machine regressionBayesian weighted quantile sum regressionGlucose-insulin homeostasisMetal mixture

Identifiers

PMID41540139
PMCPMC12808658

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.