Evidence map›Paper›PMID 38090261›Full record

ArticleCurrent research in toxicology2023

Association between metal mixture exposure and abnormal glucose metabolism in multiple mixture exposure models: Evidence from NHANES 2015-2016.

Jiamin Zhu, Shiman Hu, Shanshan Wang, Yuting Zhang, Qingyi Zhu, Mingzhi Zhang, Zhonghua Shi

Abstract read
In one paragraph

Article in Current research in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed.

  1. Article
  2. An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026
    Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

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

7 authors.

Jiamin ZhuState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.
Shiman HuState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.
Shanshan WangState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.
Yuting ZhangState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.
Qingyi ZhuState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.
Mingzhi ZhangState Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Zhonghua ShiState Key Laboratory of Reproductive Medicine, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies primarily focused on the single metal exposure and one-sided glucose metabolism disordered states, leading to conflicting results. Herein, we combined diabetes and prediabetes as abnormal glucose metabolism (AGM) to describe the effect of metal mixture exposure on it. Eligible data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2015-2016. In the generalized linear model (GLM), Cd (OR: 1.060, 95 %CI: 1.032-1.089,

Indexed as

Abnormal glucose metabolismCo-exposureDiabetesMachine learningMetalsPrediabetes

Identifiers

PMID38090261
PMCPMC10714339

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.