The current state and future of cardiometabolic polygenic risk scores in European and non-European population cohorts
Notice bibliographique
Résumé
Abstract Funding Acknowledgements Type of funding sources: None. Background A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Purpose To provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion In conclusion, this scoping review is the first of its kind and reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,077 | 0,128 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,007 | 0,011 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».