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  1. Home
  3. Polygenic And Clinical Risk Scores And Their Impact On Age At Onset And Prediction Of Cardiometabolic Diseases And Common Cancers.
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  3. Polygenic And Clinical Risk Scores And Their Impact On Age At Onset And Prediction Of Cardiometabolic Diseases And Common Cancers.

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Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and

Nina Mars1, Jukka T Koskela1, Pietari Ripatti1

  • 1Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.

Nature Medicine
|April 11, 2020

View abstract on PubMed

Summary
This summary is machine-generated.

Polygenic risk scores (PRS) enhance disease prediction. High PRS increases lifetime risk and advances disease onset by several years, showing added clinical value for common conditions.

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Area of Science:

  • Genetics
  • Epidemiology
  • Clinical Prediction

Background:

  • Polygenic risk scores (PRS) show potential for predicting common disease susceptibility.
  • Clinical risk prediction models often lack comprehensive genetic insights.

Purpose of the Study:

  • To evaluate the added value of genome-wide PRS in clinical risk prediction for five common diseases.
  • To assess the impact of PRS on lifetime risk, disease onset, and model performance.

Main Methods:

  • Utilized large-scale biobank data (FinnGen, n=135,300) and the FINRISK study.
  • Estimated PRS for coronary heart disease, type 2 diabetes, atrial fibrillation, breast cancer, and prostate cancer.
  • Compared PRS performance with and without clinical risk factors and demographic data.

Main Results:

  • High PRS correlated with 21-38% higher lifetime risk and 4-9 years earlier disease onset compared to average PRS.
  • PRS improved prediction models over age/sex for type 2 diabetes, atrial fibrillation, breast cancer, and prostate cancer.
  • PRS enhanced risk reclassification over clinical thresholds, particularly for early-onset coronary heart disease, atrial fibrillation, and prostate cancer.

Conclusions:

  • PRS provide significant added value to clinical risk prediction models for common diseases.
  • PRS can improve identification of individuals at high risk, potentially enabling stratified screening.
  • Further research is needed to define practical clinical applications of PRS for interventions.