Evidence map›Paper›PMID 42008691›Full record

ArticleDiabetes2026

Associations of Combined Genetic and Lifestyle Risks With Incident Type 2 Diabetes in the UK Biobank.

Chi Zhao, Konstantinos Hatzikotoulas, Raji Balasubramanian, Elizabeth Bertone-Johnson, Na Cai, Lianyun Huang, Alicia Huerta-Chagoya, Margaret Janiczek, Chaoran Ma, Ravi Mandla and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Chi ZhaoDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Konstantinos HatzikotoulasInstitute of Translational Genomics, German Research Center for Environmental Health, Helmholtz Munich, Neuherberg, Germany.
Raji BalasubramanianDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Elizabeth Bertone-JohnsonDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Na CaiHelmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany.
Lianyun HuangHelmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany.
Alicia Huerta-ChagoyaPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA.
Margaret JaniczekDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Chaoran MaDepartment of Nutrition, University of Massachusetts Amherst, Amherst, MA.
Ravi MandlaDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA.
Amanda PaluchDepartment of Kinesiology and Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA.
Nigel W RaynerInstitute of Translational Genomics, German Research Center for Environmental Health, Helmholtz Munich, Neuherberg, Germany.
Lorraine SouthamInstitute of Translational Genomics, German Research Center for Environmental Health, Helmholtz Munich, Neuherberg, Germany.
Susan R SturgeonDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Ken SuzukiDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Henry J TaylorCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Nicole VankimDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA.
Xianyong YinDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.
Chi Hyun LeeDepartment of Applied Statistics, Yonsei University, Seoul, South Korea.
Francis CollinsNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Cassandra N Spracklen

Funding

Genetic programming of human islet metabolic and endoplasmic reticulum (ER) stress responses in diabetesR01DK118011 · NIDDK · JACKSON LABORATORY · PI Michael Lee Stitzel · 2021 to 2026
$4.9M
Reducing stress, anxiety, and depressive symptoms via a family-centered preventative intervention for immigrants: A randomized controlled feasibility trialR34MH118396 · NIMH · UNIVERSITY OF MASSACHUSETTS AMHERST · PI POUDEL-TANDUKAR, KALPANA · 2020 to 2022
$732k
Physiologic stress and sexual orientation disparities in risk for type 2 diabetes among womenK01DK123193 · NIDDK · UNIVERSITY OF MASSACHUSETTS AMHERST · PI VANKIM, NICOLE A · 2020 to 2023
$612k
American Diabetes Association 11-23-PDF-35Commonwealth of MassachsuttsNIH HHS K01DK123193NIH HHS R01DK118011NIH HHS R34MH118396UK Biobank Resource 81009
6 · The paper itself

Abstract

Type 2 diabetes (T2D) results from the interplay of genetic susceptibility and an unhealthy lifestyle, but their combined effects are not well studied. We examined whether unhealthy modifiable behaviors were associated with similar increases in the risk of incident T2D in individuals with different levels of genetic risk. Among 332,251 UK Biobank participants without diabetes, we constructed a multiancestry genetic risk score (GRS) based on 783 T2D-associated variants, categorized into tertiles. Lifestyle was classified as healthy, intermediate, or unhealthy based on baseline self-reported smoking status, BMI, physical activity level, and diet quality. Cox proportional hazards regression models were used to generate adjusted hazard ratios (HRs) for T2D and associated 95% CIs. During follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS (P < 0.001) and lifestyle classification (P < 0.001) were independently associated with increased risk of T2D. Compared with a healthy lifestyle, an unhealthy lifestyle was associated with increased risk in all genetic risk strata, with adjusted HRs ranging from 7.11 to 16.33. High genetic risk and an unhealthy lifestyle were the most significant contributors to T2D development. Individuals at all levels of genetic risk can substantially mitigate their T2D risk through lifestyle modifications. ARTICLE HIGHLIGHTS: Both genetic susceptibility and an unhealthy lifestyle are known to be associated with elevated type 2 diabetes (T2D) risk. However, their combined effects on T2D risk are not well studied. In this large prospective cohort study of more than 332,000 individuals, unhealthy lifestyle factors were associated with risk of incident T2D within and across different levels of genetic risk. These findings suggest individuals at all levels of genetic risk can greatly mitigate their risk of T2D by adhering to a healthy lifestyle.

Indexed as

Diabetes Mellitus, Type 2Genetic Predisposition to DiseaseLife StyleAdultAgedBiological Specimen BanksExerciseFemaleGenetic Risk ScoreHumansIncidenceMaleMiddle AgedProportional Hazards ModelsRisk FactorsUK Biobank

Identifiers

PMID42008691
PMCPMC13097205

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

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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.