Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.

Kenneth E Westerman, Julie E Gervis, Luke J O'Connor, Miriam S Udler, Alisa K Manning

Abstract read

In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing 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

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

4 citing papers in PubMed.

Genetic basis of Asian diabetes.Journal of diabetes investigation · 2026
Review
Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.HGG advances · 2026
Article
Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026
Review
Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.medRxiv : the preprint server for health sciences · 2025
Article

4 · The record

Corrections and comments

Update of
Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.2025

5 · Who and what money

Authors and funding

5 authors.

Kenneth E WestermanClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: kewesterman@mgb.org.

Julie E GervisDepartment of Medicine, Harvard Medical School, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

Luke J O'ConnorHarvard Medical School, Department of Biomedical Informatics, Boston, MA, USA; Broad Institute, Program in Medical and Population Genetics, Cambridge, MA, USA.

Miriam S UdlerDepartment of Medicine, Harvard Medical School, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

Alisa K ManningClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Funding

Improved detection of gene-diet interactions via longitudinal data, metabolomic proxies, and polygenic scoresK01DK133637 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Kenneth E Westerman · 2022 to 2026

$749k

Doris Duke Charitable Foundation 2022063NIDDK NIH HHS K01 DK133637

6 · The paper itself

Abstract

Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.

Indexed as

AdiposityCardiometabolic Risk FactorsCardiovascular DiseasesMultifactorial InheritanceBody Mass IndexFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedRisk Factorsadipositycardiometabolicgene-environment interactionspolygenic scores

Identifiers

PMID41297543

DOI10.1016/j.xgen.2025.101075

PMCPMC12985365

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.