Key result
Polygenic risk scores for cardiovascular disease demonstrated predictive performance with AUROC scores ranging from 0.72 to 0.83, though limited external validation remains a key challenge.
Why the study?
To provide a comprehensive evaluation of genetic cardiovascular disease risk prediction models for the general population by assessing their methodological quality, predictive performance, and validation approaches.
Do genetic cardiovascular disease risk prediction models demonstrate reliable predictive performance and validity for the general population?
Systematic Review
Do genetic cardiovascular disease risk prediction models demonstrate reliable predictive performance and validity for the general population?
Answers are grounded in this paper and cite their sources.
Polygenic risk scores show promise for cardiovascular risk prediction with AUROCs of 0.72-0.83, but widespread clinical implementation is hindered by limited external validation and methodological inconsistencies.
This review provides a comprehensive evaluation of genetic cardiovascular disease (CVD) risk prediction models developed or validated for the general population. The study aims to assess these models’ methodological quality, predictive performance, and validation approaches. A systematic search of electronic databases was conducted to identify studies published between 1990 and 2024. Data on study design, participant characteristics, predictor variables, statistical modeling approaches, validation methods, and performance metrics were extracted and analyzed. A total of nine studies met the inclusion criteria. Among them, five studies (56%) focused on model development, two studies (22%) performed internal validation, and two studies (22%) were externally validated. The most frequently included predictors were age (n = 9; 100%), sex (n = 7; 78%), LDL cholesterol (n = 6; 67%), and diabetes (n = 5; 56%). The genetic variants most commonly integrated into the models included APOE (n = 4), LPA (n = 5), and PCSK9 (n = 4). The AUROC scores ranged from 0.72 to 0.83, with Hosmer–Lemeshow calibration reported in five studies (56%). Cross-validation was the predominant validation method utilized in 56% of studies, whereas external validation was applied in only 22%. Although polygenic risk scores (PRS) have improved cardiovascular risk prediction, methodological inconsistencies, limited external validation, and lack of standardization remain key challenges. Future research should prioritize large-scale external validation studies, incorporate broader population-based datasets, and establish standardized performance evaluation frameworks to enhance model reliability and clinical applicability.
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Khan et al. (2025) conducted a systematic review in Cardiovascular disease. Polygenic risk scores (PRS) was evaluated on Predictive performance (AUROC). Polygenic risk scores for cardiovascular disease demonstrated predictive performance with AUROC scores ranging from 0.72 to 0.83, though limited external validation remains a key challenge.
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