Predictive health index: integrating body composition and heart rate variability metrics with artificial intelligence to predict chronic disease risk and specific chronic non-communicable diseases

Abstract Purpose/objective The field of predictive medicine focuses on assessing disease risk and implementing preventive strategies with a view to either preventing disease onset entirely or significantly minimizing its impact on affected individuals. An emerging subfield, predictive health, extends this approach by targeting healthy individuals, emphasizing proactive lifestyle modifications to reduce the risk or to potentially reverse the progression of chronic non-communicable diseases (NCDs). Predictive health employs a diverse array of tools to forecast health and disease, thereby transitioning from a reactive to a proactive healthcare model. By extending the duration of good health and reducing the incidence, prevalence, and costs of NCDs, it has the potential to revolutionize medical practice. This approach redirects the focus of medicine from treating NCDs to preventing them through lifestyle modifications, marking a fundamental shift toward disease prevention and long-term well-being. Methods This study developed and validated a Predictive Health Index (PHI) in the context of cardiovascular, metabolic, psychological, neoplastic, and chronic inflammatory diseases to assess health status and predict the risk of the disease contextually. A variety of metrics were obtained from non-invasive instrumental diagnostic tests, including body composition analysis using advanced bioimpedance techniques (BIA-ACC ® ) and heart rate variability (HRV) analysis employing a photoplethysmography (PPG) system. The data were obtained from 35,405 clinically monitored individuals over about 5 years. Artificial intelligence and a random forest machine learning algorithm were trained and tested to create the PHI. Results The results demonstrated highly significant (p-value < 0.0001) predictive performance concerning the PHI, successfully distinguishing healthy subjects from those at high risk of disease. PHI can thus be a fast, non-invasive, easy-to-use, highly accurate tool for assessing health and predicting risk of developing NCDs. Conclusion The acquired information could be extremely helpful for strengthening lifestyle measures and intervening early to prevent or reverse disease development.