Physical fitness and frailty index in developing biological age prediction model

Abstract

The global increase in the older population has resulted in escalating healthcare costs and burdens on governments and families. Understanding biological age (BA) as distinct from chronological age (CA) holds significant potential in accurately assessing individuals' health status and susceptibility to diseases. During exercise, myokines like irisin and lactate are released from skeletal muscles, facilitating cross-talk with organs such as the brain and heart. This may improve physical fitness, reducing frailty and BA. This research aimed to develop a comprehensive BA prediction model integrating genetic and epigenetic factors. The study involved 59 healthy adults, comprising 31 males and 28 females, with average ages of 58.2 ± 7 years and 50.1 ± 8.5 years, respectively. Assessments of physical fitness and completion of the Frailty Index (FI34) questionnaire were conducted to capture genetic and epigenetic influences. Feature selection, principal component analysis (PCA), and multiple linear regression (MLR) were employed to tailor BA prediction models for each gender. We identified seven significant biomarkers for males, including FI34, percent of skeletal muscle mass (SM), handgrip strength (GS), flexibility via sit-and-reach test (SR), peak torque of quadriceps muscles (PTQ), cardiopulmonary fitness (VO2max), and basal metabolic rate (BMR). Conversely, females exhibited six key biomarkers: FI34, SM, GS, waist-to-hip ratio (WHR), peak torque of hamstring muscles (PTH), and percentage of body fat (PBF). We have successfully developed a comprehensive model for estimating BA by integrating key biomarkers representing epigenetic and genetic impacts. Estimating BA is crucial for precise health evaluations and disease risk assessments.

Keywords

• healthy aging
• biological age
• lifestyle
• exercise physiology
• epigenetic factors