Evidence map›Paper›PMID 38166936›Full record

ArticleEnvironmental health : a global access science source2024

Exposure to cadmium and lead is associated with diabetic kidney disease in diabetic patients.

Yuan Zhang, Xiaoyu Gong, Runhong Li, Wenhui Gao, Daibao Hu, Xiaoting Yi, Yang Liu, Jiaxin Fang, Jinang Shao, Yanan Ma and 1 more

Open access · goldAbstract read
In one paragraph

Article in Environmental health : a global access science source, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
9.8field-weighted citation impact, top 1% 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

14 citing papers in PubMed, 31 citations in OpenAlex.

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  9. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
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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

11 authors at 3 institutions in 1 country.

Yuan Zhang *Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, P.R. China.
Xiaoyu Gong *Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, P.R. China.
Runhong LiDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, P.R. China.
Wenhui GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, P.R. China.
Daibao HuDepartment of Biostatistics and Epidemiology, School of Public Health, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, P.R. China.
Xiaoting YiDepartment of Public Health, Xinjiang Medical University, Urumqi, 830011, P.R. China.
Yang LiuDepartment of Biostatistics and Epidemiology, School of Public Health, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, P.R. China.
Jiaxin FangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, P.R. China.
Jinang ShaoDepartment of Biostatistics and Epidemiology, School of Public Health, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, P.R. China.
Yanan MaDepartment of Biostatistics and Epidemiology, School of Public Health, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, P.R. China. ynma@cmu.edu.cn.
Lina JinDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, P.R. China. jinln@jlu.edu.cn.
China Medical University · CNJilin University · CNXinjiang Medical University · CN

Funding

Scientific Research Project of Education Department of Jilin Province, China JJKH20231145KJ
6 · The paper itself

Abstract

backgroundCadmium (Cd) and lead (Pb) exhibit nephrotoxic activity and may accelerate kidney disease complications in diabetic patients, but studies investigating the relation to diabetic kidney disease (DKD) have been limited. We aimed to examine the associations of Cd and Pb with DKD in diabetic patients.

methods3763 adults with blood metal measurements and 1604 adults with urinary ones who were diabetic from National Health and Nutrition Examination Survey (NHANES) 2007-2016 were involved. Multivariate logistic regression models were used to analyze the associations of blood Cd (BCd), blood Pb (BPb), urinary Cd (UCd), and urinary Pb (UPb) with DKD.

resultsBPb, BCd, and UCd levels were higher among participants with DKD than diabetics without nephropathy, but UPb performed the opposite result. BPb and UCd were significantly associated with DKD in the adjusted models (aOR, 1.17 (1.06, 1.29);1.52 (1.06, 2.02)). Participants in the 2nd and 3rd tertiles of BPb and BCd levels had higher odds of DKD, with a significant trend across tertiles, respectively (all P-trend < 0.005). Multiplication interaction was also identified for BPb and BCd (P for interaction = 0.044).

conclusionBPb, BCd, and UCd were positively associated with the risk of DKD among diabetic patients. Furthermore, there were the dose-response relationship and multiplication interaction in the associations of BPb, BCd with DKD.

Indexed as

Diabetes MellitusDiabetic NephropathiesAdultCadmiumEnvironmental ExposureHumansLeadNutrition SurveysCadmiumLeadBlood cadmiumBlood leadDiabetic kidney diseaseUrinary cadmiumUrinary lead

Identifiers

PMID38166936
PMCPMC10763104
OpenAlexW4390542819

What OpenQuestion 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 OpenQuestion graph.