Evidence map›Paper›PMID 38478982›Full record

ArticleEnvironmental science & technology2024

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach.

Rin Wada, Feng-Jiao Peng, Chia-An Lin, Roel Vermeulen, Alba Iglesias-González, Paul Palazzi, Barbara Bodinier, Sylvie Streel, Michèle Guillaume, Dragana Vuckovic and 4 more

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

Article in Environmental science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.2field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

14 authors at 6 institutions in 5 countries.

Rin WadaDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.ORCID 0009-0008-6598-2822
Feng-Jiao PengHuman Biomonitoring Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen L-1445, Luxembourg.ORCID 0000-0002-9913-7087
Chia-An LinDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.
Roel VermeulenDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.ORCID 0000-0003-4082-8163
Alba Iglesias-GonzálezHuman Biomonitoring Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen L-1445, Luxembourg.
Paul PalazziHuman Biomonitoring Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen L-1445, Luxembourg.
Barbara BodinierDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.
Sylvie StreelDepartment of Public Health Sciences, University of Liege, Liege 4000, Belgium.
Michèle GuillaumeDepartment of Public Health Sciences, University of Liege, Liege 4000, Belgium.
Dragana VuckovicDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.
Sonia DagninoDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.
Julien ChiquetUniversité Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, Palaiseau 91120, France.
Brice M R AppenzellerHuman Biomonitoring Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen L-1445, Luxembourg.ORCID 0000-0001-9021-929X
Marc Chadeau-HyamDepartment of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.ORCID 0000-0001-8341-5436
Imperial College London · GBLuxembourg Institute of Health · LUUniversity of Liège · BEAgroParisTech · FRUniversité Côte d'Azur · FRUtrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (

Indexed as

DyslipidemiasExposomeHypertensionBayes TheoremEnvironmental ExposureHairHumansObesitycardiometabolic healthenvironmental epidemiologyexposomehair analysismultitrait analysispollutants

Identifiers

PMID38478982
PMCPMC10976885
OpenAlexW4392764124

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.