Clinical and Genetic Determinants of Heart Failure: Optimized by Machine Learning and Mendelian Randomization

Abstract

Background: Identifying unrecognized, potentially modifiable risk factors is essential for heart failure (HF) management.Methods: The Atherosclerosis Risk in Communities (ARIC) study was used for machine learning (ML) to establish the top 20 important variables as potential risk factors for HF. Multivariable Cox regression analysis was performed in an explorative manner to find independent factors for HF and Mendelian randomization (MR) analysis to address causality.Results: Of the 14,842 participants included in the ARIC analysis, 20.4% of participants (3,028) were identified as HF. The 20 variables with the highest importance selected by ML were creatinine, glucose, age, previous coronary artery disease (CAD), systolic blood pressure, fibrinogen, albumin, income, diabetes, magnesium, insulin, white blood cell, hemoglobin, sodium, education, phosphorus, diastolic blood pressure, protein-c, heart rate and body mass index (BMI). Cox regression analysis demonstrated 19 independently associated variables except sodium. MR analysis provided evidence supporting that genetically determined BMI, CAD, diabetes and education was causally associated with HF.Conclusions: The ML plus MR framework was useful in identifying important causal factors of HF. BMI, CAD, diabetes, and education not only served as excellent prognostic factors for HF, but therapeutics targeted at these factors were likely to prevent HF effectively.