Evidence mapPaperPMID 42411037Full record

ReviewMolecular genetics & genomic medicine2026

The Use of Population Isolates to Identify Metabolic Syndrome's Genetic Aetiology.

Jacob W I Meyjes-Brown, Heidi G Sutherland, Rodney A Lea, Lyn R Griffiths

Abstract readReview
In one paragraph

Review in Molecular genetics & genomic medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing 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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jacob W I Meyjes-BrownGenomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-1335-361X
Heidi G SutherlandGenomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-8512-1498
Rodney A LeaGenomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-1148-5862
Lyn R GriffithsGenomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-6774-5475

Funding

Queensland University of Technology
6 · The paper itself

Abstract

backgroundGenome-wide association studies continue to recruit larger samples, increase resolution, and improve their statistical foundations. These investigations often remain restricted by design to large, outbred populations. This can exclude genetically distinct populations, to whom the genetic insights gained may not apply, and forgoes the benefits that isolated populations can offer to biomedical research. Metabolic Syndrome (MetS) is a collection of highly correlated risk factors that predisposes individuals to type 2 diabetes, cardiovascular disease, and chronic kidney disease. The environmental factors that lead to MetS are well understood, but each person's response to these influences is modulated by their genetics.

methodsIn this mini-review, we discuss the features of population isolates for mapping disease genes, briefly consider some of the clinical aspects of MetS, and compare the recent contributions to understanding the genetic causes of MetS by population isolates and by large open-population approaches.

resultsStudies in isolated populations have revealed novel genetic associations, including determining causal variants. Isolated populations have also validated and refined associated loci discovered in large, open populations.

conclusionMuch progress has been made uncovering the genetic basis of MetS and related conditions. However, while the field progresses, the definition of the syndrome remains contested, and a comprehensive understanding of MetS genetics remains elusive.

Indexed as

Genome-Wide Association StudyMetabolic SyndromeGenetic Predisposition to DiseaseHumansfounder effectgeneticsGWASheritabilityisolated populationsmetabolic syndrome

Identifiers

PMID42411037
PMCPMC13338578

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.