Evidence map›Paper›PMID 35987817›Full record

ArticleClinical research in cardiology : official journal of the German Cardiac Society2023

Genetic and modifiable risk factors combine multiplicatively in common disease.

Shichao Pang, Loic Yengo, Christopher P Nelson, Felix Bourier, Lingyao Zeng, Ling Li, Thorsten Kessler, Jeanette Erdmann, Reedik Mägi, Kristi Läll and 5 more

Abstract read
In one paragraph

Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Clinical utility of polygenic risk scores in cardiovascular disorders.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
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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

15 authors.

Shichao Pang *Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.
Loic Yengo *Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia.
Christopher P NelsonDepartment of Cardiovascular Sciences, University of Leicester, Leicester, UK.
Felix BourierDepartment of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.
Lingyao ZengDepartment of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.
Ling LiDepartment of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.
Thorsten KesslerDepartment of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.
Jeanette ErdmannInstitute for Cardiogenetics, and University Heart Center, University of Lübeck, Lübeck, Germany.
Reedik MägiEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Kristi LällEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Andres MetspaluEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Bertram Mueller-MyhsokStatistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
Nilesh J SamaniDepartment of Cardiovascular Sciences, University of Leicester, Leicester, UK.
Peter M Visscher *Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia.
Heribert Schunkert *Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany. schunkert@dhm.mhn.de.ORCID http://orcid.org/0000-0001-6428-3001

Funding

German Federal Ministry of Education and Research 01KL1802German Federal Ministry of Education and Research 01ZX1706CGerman Federal Ministry of Education and Research 16GW0198KGerman Federal Ministry of Education and Research ZF4590201BA8Medical Research Council MC_PC_17228Medical Research Council MC_QA137853the Australian National Health 1113400the Australian National Health DE200100425the Australian National Health FL180100072
6 · The paper itself

Abstract

backgroundThe joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized.

objectivesWe modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors.

methodsWe analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions.

resultsIn UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p < 0.01). Irrespective of the affected gene, a single risk allele multiplied the effects of all others carried by a person, resulting in a 2.9-fold stronger effect size in the top versus the bottom decile (p < 0.01) and an exponential increase in risk (R > 0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer.

conclusionsAlleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed.

Indexed as

Coronary Artery DiseaseGene-Environment InteractionGenetic Predisposition to DiseaseAllelesGenome-Wide Association StudyHumansPolymorphism, Single NucleotidePrevalenceRisk FactorsUnited KingdomCoronary artery diseaseGenome-wide association studiesLiability thresholdRisk predictionRisk score

Identifiers

PMID35987817
PMCPMC9898372

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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 OpenQuestion graph.