Evidence map›Paper›PMID 36427371›Full record

ArticleEcotoxicology and environmental safety2022

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005-2012.

Yuqing Zhang, Xu Wang, Xu Yang, Qi Hu, Kuldeep Chawla, Bo Hang, Jian-Hua Mao, Antoine M Snijders, Hang Chang, Yankai Xia

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ecotoxicology and environmental safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
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  5. Article
  6. Environmental exposure to per- and polyfluoroalkyl substances mixture and asthma in adolescents.International archives of occupational and environmental health · 2024
    Article
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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

10 authors at 4 institutions in 3 countries.

Yuqing ZhangDepartment of Obstetrics and Gynecology, Women's Hospital of Nanjing Medical University，Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
Xu WangDepartment of endocrinology, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Xu YangState Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Qi HuState Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Kuldeep ChawlaScientific Computing Group, Information Technology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Bo HangBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Jian-Hua MaoBerkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Antoine M SnijdersBerkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Hang ChangBerkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. Electronic address: hchang@lbl.gov.
Yankai XiaState Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China. Electronic address: yankaixia@njmu.edu.cn.
Lawrence Berkeley National Laboratory · USMinistry of Education · KRNanjing Maternity and Child Health Care Hospital · CNSecond Affiliated Hospital of Nanjing Medical University · CN

Funding

Molecular and Therapeutic Basis of Morphometric Aberrations in Brain TumorsR01CA184476 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI CHANG, HANG · 2015 to 2019
$2.0M
Role of the gut microbiome in pesticide-induced effects on child neurodevelopmentR01ES031322 · NIEHS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI MAO, JIAN-HUA · 2020 to 2024
$1.4M
NCI NIH HHS R01 CA184476NIEHS NIH HHS R01 ES031322
6 · The paper itself

Abstract

backgroundThe effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated.

objectivesTo discover obesity-related mixed chemical exposure patterns in the general U.S. POPULATION:

methodsSparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR).

resultsEight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity.

conclusionOur study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

Indexed as

ObesityAdultBayes TheoremChromatography, GasHumansNutrition SurveysPrincipal Component AnalysisExposure mixturesExposure patternObesitySparse Decompositional Regression Model

Identifiers

PMID36427371
PMCPMC10012331
OpenAlexW4309524660

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.