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随着全球人口老龄化趋势的加剧,老年人肥胖问题日益严重,且与多种疾病的风险和死亡率的增加密切相关.然而目前仍面临诸多挑战.首先,广泛用于体重评估的体质指数、腰围和腰臀比等指标存在一定的局限性.其次,除了关注体重相关并发症的筛查和管理,建立以改善身体功能和健康结局为目标的管理理念以外,还需重视身体成分的管理,特别是对老年人常见的肌少症和肌少症性肥胖进行监测和管理.在管理老年人群肥胖时,需要综合考虑患者的整体健康状况、生活习惯等制定符合其生理特点的生活方式干预措施.如生活方式干预效果不佳,药物治疗方案应考虑到对心血管、肾脏和代谢综合征的综合影响.随着人工智能技术的不断进步,老年体重管理有望通过机器学习建立风险预测模型实现精准个体化治疗,最终改善健康结果.这将为老年肥胖问题的解决提供新的策略和方法.

With the intensification of the global population aging trend,the problem of obesity in the elderly has become increasingly prominent,which is closely related to the increase of the risk of various diseases and mortality.However,there are still many challenges in dealing with this problem.First,body mass index,waist circumference and waist-to-hip ratio,which are widely used for weight assessment,have certain limitations.Secondly,the screening and management of weight-related complications should be strengthened,and management concepts should be established to improve physical function and enhance health outcomes.At the same time,attention should be paid to the management of body composition,especially the monitoring and management of sarcopenia and sarcopenic obesity,which are common in the elderly.In the management of obesity in the elderly,it is necessary to consider the overall health status of patients,living habits and other factors,and formulate lifestyle intervention measures in line with their physiological characteristics.If lifestyle interventions do not work well,the drug regimen should consider the combined effects of cardiovascular,renal,and metabolic syndrome.With the continuous advancement of artificial intelligence technology,weight management in the elderly is expected to improve health outcomes by building risk prediction models through machine learning and enabling precise personalized treatment.This will provide a new strategy and method for solving the problem of obesity in the elderly.