PDF(Pubmed)
Abstract：
UNASSIGNED: Understanding the impact of different lifestyle trajectories on health preservation and disease risk is crucial for effective interventions.
UNASSIGNED: 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.
UNASSIGNED: 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 (p < 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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 (p < 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.
UNASSIGNED: 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.
摘要：
■了解不同的生活方式轨迹对健康保护和疾病风险的影响对于有效的干预措施至关重要。
这项研究使用K-means聚类分析了来自巴塞罗那大脑健康倡议的3,013名40-70岁健康成年人在五年内的生活方式参与。考虑了九个可改变的风险因素，包括认知，物理，和社会活动，重要的计划,饮食，肥胖，吸烟,酒精消费,和睡眠。在基线后不同时间点自我报告的新疾病诊断允许探索这五个概况与健康结果之间的关联。
■数据驱动的分析将受试者分为五种生活方式，揭示与健康行为和危险因素的关联。那些在促进健康行为和低风险行为方面得分很高的人，显示降低发展疾病的可能性（p<0.001）。相比之下,有危险习惯的档案显示出明显的精神病风险，神经学,和心血管疾病。参与者的生活方式轨迹随着时间的推移保持相对稳定。
■我们的研究发现了与特定生活方式相关的不同疾病的风险。这些结果可能有助于基于对促进健康生活方式的行为模式和政策的数据驱动观察的干预措施的个性化，并可能为老龄化社会的人们带来更好的健康结果。
这项研究使用K-means聚类分析了来自巴塞罗那大脑健康倡议的3,013名40-70岁健康成年人在五年内的生活方式参与。考虑了九个可改变的风险因素，包括认知，物理，和社会活动，重要的计划,饮食，肥胖，吸烟,酒精消费,和睡眠。在基线后不同时间点自我报告的新疾病诊断允许探索这五个概况与健康结果之间的关联。
■数据驱动的分析将受试者分为五种生活方式，揭示与健康行为和危险因素的关联。那些在促进健康行为和低风险行为方面得分很高的人，显示降低发展疾病的可能性（p<0.001）。相比之下,有危险习惯的档案显示出明显的精神病风险，神经学,和心血管疾病。参与者的生活方式轨迹随着时间的推移保持相对稳定。
■我们的研究发现了与特定生活方式相关的不同疾病的风险。这些结果可能有助于基于对促进健康生活方式的行为模式和政策的数据驱动观察的干预措施的个性化，并可能为老龄化社会的人们带来更好的健康结果。
