Clinical Scorecard: Assessing the Role of Family History and Polygenic Risk Scores in the Risk of Cardiometabolic Disorders

At a Glance

Key Highlights

• Family history remains a strong predictor of cardiometabolic disease risk but is limited by recall bias and family size.
• Polygenic risk scores (PRSs) provide an objective, scalable measure of genetic susceptibility beyond family history.
• Combining quantitative family history scores with PRSs improves risk stratification for cardiometabolic diseases across diverse ancestries.

Guideline-Based Recommendations

Diagnosis

• Use electronic health records to define incident cardiometabolic disease cases with at least 6 months follow-up.
• Assess family history comprehensively including first- and second-degree relatives to quantify familial burden.
• Incorporate polygenic risk scores derived from genome-wide association studies for genetic risk assessment.

Management

• Consider both family history and polygenic risk scores when stratifying patients for cardiometabolic disease risk.
• Address modifiable lifestyle factors such as BMI and physical activity alongside genetic risk.

Monitoring & Follow-up

• Monitor individuals with high family history scores and/or high polygenic risk scores for early signs of cardiometabolic conditions.
• Use longitudinal electronic health record data to track incident disease development.

Risks

• Recall bias and incomplete family history can limit risk assessment accuracy.
• Polygenic risk scores may have reduced predictive power in non-European ancestry populations due to derivation from European cohorts.

Patient & Prescribing Data

Adults from diverse ancestries with available family history and genotypic data

Risk stratification incorporating family history and polygenic risk scores can guide personalized prevention and management strategies for cardiometabolic diseases.

Clinical Best Practices

• Collect detailed family history including number and degree of affected relatives to calculate a quantitative family history score.
• Utilize polygenic risk scores alongside family history to improve prediction of cardiometabolic disease risk.
• Apply ancestry-specific considerations when interpreting polygenic risk scores.
• Exclude prevalent cases and ensure sufficient follow-up duration when assessing incident disease risk.
• Integrate electronic health record data with genetic information for comprehensive risk assessment.

References

• All of Us Research Program
• Electronic Medical Records and Genomics (eMERGE) Network