Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

Area of Science:

• Cardiovascular Disease Research
• Genetics and Precision Medicine
• Biomarker Discovery

Background:

• Hypertension prediction benefits from omics data, but comparisons across multiomics are lacking.
• The added value of multiomics over clinical risk factors for hypertension prediction remains unclear.

Purpose of the Study:

• To compare the predictive ability of various multiomics data in hypertension prediction.
• To determine if multiomics data provide additional predictive value beyond clinical risk factors.

Main Methods:

• Assessed clinical data with multiomics (polygenic risk score, metabolomics, gut microbiota) in 2573 nonhypertensive individuals.
• Utilized cross-validated machine learning models to predict incident hypertension.
• Evaluated model performance using the area under the curve (AUC).

Main Results:

• The best model combined clinical data with a polygenic risk score, achieving the highest AUC (0.735).
• A polygenic risk score significantly increased hypertension odds (29%) after adjusting for clinical factors.
• Metabolome and microbiota data did not improve risk prediction when added to clinical factors.

Conclusions:

• Clinical models augmented with polygenic risk scores offer the best prediction for incident hypertension.
• Polygenic risk scores provide limited incremental value over established clinical risk factors for hypertension risk assessment.