Evidence map›Paper›PMID 33433237›Full record

ArticleCirculation. Genomic and precision medicine2021

Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels.

Yixuan Ye, Xi Chen, James Han, Wei Jiang, Pradeep Natarajan, Hongyu Zhao

Open access · hybridAbstract read
In one paragraph

Article in Circulation. Genomic and precision medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 1 pooled it
15.9field-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

72 citing papers in PubMed, 1 synthesis or guideline pooled it, 119 citations in OpenAlex.

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12 more citing papers are in PubMed but not listed here.

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

6 authors at 3 institutions in 3 countries.

Yixuan YeProgram of Computational Biology and Bioinformatics (Y.Y., H.Z.), Yale University.
Xi ChenDepartment of Statistics and Data Science (X.C., J.H.), Yale University.
James HanDepartment of Statistics and Data Science (X.C., J.H.), Yale University.
Wei JiangDepartment of Biostatistics, Yale School of Public Health, New Haven, CT (W.J., H.Z.).
Pradeep NatarajanCenter for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston (P.N.).
Hongyu ZhaoProgram of Computational Biology and Bioinformatics (Y.Y., H.Z.), Yale University.
Yale University · USBroad Institute · USYo San University · US

Funding

Whole genome sequences in individuals to comprehensively characterize the genetic mechanisms of dyslipidemiasR01HL142711 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Satoshi Koyama, Gina Marie Peloso · 2019 to 2026
$4.7M
Statistical Models for Genetic Studies, Using Network and Integrative AnalysisR01GM122078 · NIGMS · OHIO STATE UNIVERSITY · PI CHUNG, DONGJUN · 2016 to 2020
$1.9M
Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing dataR01GM134005 · NIGMS · YALE UNIVERSITY · PI WU, BAOLIN, ZHAO, HONGYU · 2020 to 2023
$1.6M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS R01 HL142711NIGMS NIH HHS R01 GM122078NIGMS NIH HHS R01 GM134005
6 · The paper itself

Abstract

backgroundBoth lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol.

methodsOur study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models.

resultsWe observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%-14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%-3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level.

conclusionsBy leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence.

Indexed as

Life StyleAdultAgedArea Under CurveAtrial FibrillationBiological Specimen BanksCardiovascular DiseasesCholesterol, LDLDiabetes Mellitus, Type 2FemaleGenome-Wide Association StudyHumansMaleMiddle AgedRisk FactorsROC CurveCholesterol, LDLTriglyceridesatrial fibrillationcardiovascular diseasecoronary artery diseasehealthy lifestyletriglycerides

Identifiers

PMID33433237
PMCPMC7887077
OpenAlexW3119797572

What OpenQuestion holds

Textmetadata
LicenceTDM
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.