Evidence map›Paper›PMID 35836215›Full record

ArticleCardiovascular diabetology2022

Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study.

Jae-Seung Yun, Sang-Hyuk Jung, Manu Shivakumar, Brenda Xiao, Amit V Khera, Hong-Hee Won, Dokyoon Kim

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
10.5field-weighted citation impact, top 1% 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

31 citing papers in PubMed, 55 citations in OpenAlex.

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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

7 authors at 4 institutions in 2 countries.

Jae-Seung Yun *Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA.
Sang-Hyuk Jung *Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA.
Manu ShivakumarDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA.
Brenda XiaoDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA.
Amit V KheraCenter for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Hong-Hee Won *Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. wonhh@skku.edu.
Dokyoon Kim *Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA. dokyoon.kim@pennmedicine.upenn.edu.
University of Pennsylvania · USMassachusetts General Hospital · USSamsung Medical Center · KRSungkyunkwan University · KR

Funding

Unravelling genetic basis of comorbidity using EHR-linked biobank dataR01GM138597 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON · 2020 to 2023
$2.2M
Polygenic prediction of common, complex diseasesK08HG010155 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI KHERA, AMIT VIKRAM · 2018 to 2021
$1.1M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS K08 HG010155NIGMS NIH HHS R01 GM138597
6 · The paper itself

Abstract

backgroundFew studies have examined associations between genetic risk for type 2 diabetes (T2D), lifestyle, clinical risk factors, and cardiovascular disease (CVD). We aimed to investigate the association of and potential interactions among genetic risk for T2D, lifestyle behavior, and metabolic risk factors with CVD.

methodsA total of 345,217 unrelated participants of white British descent were included in analyses. Genetic risk for T2D was estimated as a genome-wide polygenic risk score constructed from > 6 million genetic variants. A favorable lifestyle was defined in terms of four modifiable lifestyle components, and metabolic health status was determined according to the presence of metabolic syndrome components.

resultsDuring a median follow-up of 8.9 years, 21,865 CVD cases (6.3%) were identified. Compared with the low genetic risk group, participants at high genetic risk for T2D had higher rates of overall CVD events, CVD subtypes (coronary artery disease, peripheral artery disease, heart failure, and atrial fibrillation/flutter), and CVD mortality. Individuals at very high genetic risk for T2D had a 35% higher risk of CVD than those with low genetic risk (HR 1.35 [95% CI 1.19 to 1.53]). A significant gradient of increased CVD risk was observed across genetic risk, lifestyle, and metabolic health status (P for trend > 0.001). Those with favorable lifestyle and metabolically healthy status had significantly reduced risk of CVD events regardless of T2D genetic risk. This risk reduction was more apparent in young participants (≤ 50 years).

conclusionsGenetic risk for T2D was associated with increased risks of overall CVD, various CVD subtypes, and fatal CVD. Engaging in a healthy lifestyle and maintaining metabolic health may reduce subsequent risk of CVD regardless of genetic risk for T2D.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Biological Specimen BanksHumansLife StyleProspective StudiesRisk FactorsUnited KingdomCardiovascular diseaseLifestyleMetabolic healthPolygenic risk scoreType 2 diabetes

Identifiers

PMID35836215
PMCPMC9284808
OpenAlexW4285391315

What OpenQuestion holds

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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.