Lifestyle trajectories in middle-aged adults and their relationship with health indicators

• Published in: Frontiers in public health Vol. 12; p. 1412547
• Main Authors: Roca-Ventura, Alba, Solana-Sánchez, Javier, Cattaneo, Gabriele, Tormos-Muñoz, Josep M, Pascual-Leone, Álvaro, Bartrés-Faz, David
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05.06.2024
• Subjects: Adult; Aged; clustering; Female; health; Health Behavior; Health Status Indicators; Humans; joint trajectories; k-means; Life Style; lifestyles; Male; Middle Aged; multiple health behaviors; Public Health; Risk Factors; Spain; Spain; ageing; joint trajectories; k-means; health; clustering; brain health; lifestyles; multiple health behaviors
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2024.1412547

Understanding the impact of different lifestyle trajectories on health preservation and disease risk is crucial for effective interventions. This study analyzed lifestyle engagement over five years in 3,013 healthy adults aged 40-70 from the Barcelona Brain Health Initiative using K-means clustering. Nine modifiable risk factors were considered, including cognitive, physical, and social activity, vital plan, diet, obesity, smoking, alcohol consumption, and sleep. Self-reported diagnoses of new diseases at different time-points after baseline allowed to explore the association between these five profiles and health outcomes. The data-driven analysis classified subjects into five lifestyle profiles, revealing associations with health behaviors and risk factors. Those exhibiting high scores in health-promoting behaviors and low-risk behaviors, demonstrate a reduced likelihood of developing diseases ( < 0.001). In contrast, profiles with risky habits showed distinct risks for psychiatric, neurological, and cardiovascular diseases. Participant's lifestyle trajectories remained relatively stable over time. Our findings have identified risk for distinct diseases associated to specific lifestyle patterns. These results could help in the personalization of interventions based on data-driven observation of behavioral patterns and policies that promote a healthy lifestyle and can lead to better health outcomes for people in an aging society.