Evidence map›Paper›PMID 35458113›Full record

ArticleNutrients2022

Identifying Effects of Urinary Metals on Type 2 Diabetes in U.S. Adults: Cross-Sectional Analysis of National Health and Nutrition Examination Survey 2011-2016.

Jingli Yang, Kayue Chan, Cheukling Choi, Aimin Yang, Kenneth Lo

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.3field-weighted citation impact, top 13% of its field
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

12 citing papers in PubMed, 23 citations in OpenAlex.

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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

5 authors at 3 institutions in 2 countries.

Jingli YangCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.ORCID 0000-0003-0773-494X
Kayue ChanDepartment of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Cheukling ChoiDepartment of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Aimin YangDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID 0000-0003-2968-1762
Kenneth LoDepartment of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China.ORCID 0000-0003-4624-2737
Hong Kong Polytechnic University · HKChinese University of Hong Kong · HKLanzhou University · CN

Funding

Funding for Research Institutes CD69Start-up Fund for RAPs under the Strategic Hiring Scheme BD8H
6 · The paper itself

Abstract

Growing evidence supports the associations of metal exposures with risk of type 2 diabetes (T2D), but the methodological limitations overlook the complexity of relationships within the metal mixtures. We identified and estimated the single and combined effects of urinary metals and their interactions with prevalence of T2D among 3078 participants in the NHANES 2011-2016. We analyzed 15 urinary metals and identified eight metals by elastic-net regression model for further analysis of the prevalence of T2D. Bayesian kernel machine regression and the weighted quantile sum (WQS) regression models identified four metals that had greater importance in T2D, namely cobalt (Co), tin (Sn), uranium (U) and strontium (Sr). The overall OR of T2D was 1.05 (95% CI: 1.01-1.08) for the positive effects and 1.00 (95% CI: 0.98-1.02) for the negative effect in the WQS models. We observed positive (

Indexed as

Diabetes Mellitus, Type 2UraniumAdultBayes TheoremCross-Sectional StudiesHumansNutrition SurveysUraniumBayesian kernel machine regressioncardiometabolic diseasediabetesurinary metals

Identifiers

PMID35458113
PMCPMC9031490
OpenAlexW4224220159

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.