Evidence map›Paper›PMID 39763538›Full record

ArticlemedRxiv : the preprint server for health sciences2024

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 readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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, USA.
Konstantinos HatzikotoulasInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Raji BalasubramanianDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
Elizabeth Bertone-JohnsonDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
Na CaiHelmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany.ORCID 0000-0001-7496-2075
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, USA.
Margaret JaniczekDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
Chaoran MaDepartment of Nutrition, University of Massachusetts Amherst, Amherst, MA, USA.
Ravi MandlaDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Amanda PaluchDepartment of Kinesiology, Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA.
Nigel W RaynerInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Lorraine SouthamInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Susan R SturgeonDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
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, USA.
Nicole VanKimDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
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, USA.
Cassandra N SpracklenDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.ORCID 0000-0003-3590-7182

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
Type 2 diabetes risk variant effects on mitochondrial (patho)physiologyR01DK136671 · NIDDK · JACKSON LABORATORY · PI Scott Soleimanpour, Michael Lee Stitzel · 2023 to 2026
$3.0M
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
NIDDK NIH HHS K01 DK123193NIDDK NIH HHS R01 DK118011NIDDK NIH HHS R01 DK136671NIMH NIH HHS R34 MH118396
6 · The paper itself

Abstract

Background: Type 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied. We aimed to determine whether unhealthy modifiable health behaviors were associated with similar increases in the risk of incident T2D among individuals with different levels of genetic risk. Methods: We performed a genetic risk score (GRS) by lifestyle interaction analysis within 332,251 non-diabetic individuals at baseline from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 783 T2D-associated variants and ranked into tertiles. We used baseline self-reported data on smoking, BMI, physical activity level, and diet quality to categorize participants as having a healthy, intermediate, or unhealthy lifestyle. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) of T2D risk and associated 95% confidence intervals (CI). Results: During follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS ( Conclusions: High genetic risk and unhealthy lifestyle were the most significant contributors to the development of T2D. Individuals at all levels of genetic risk can greatly mitigate their risk for T2D through lifestyle modifications.

Identifiers

PMID39763538
PMCPMC11702748

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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 OpenQuestion graph.