Damiano Cecilia, Costanzo Simona, Marcozzi Benedetta, Panzera Teresa, Donfrancesco Chiara, Di Castelnuovo Augusto, Lo Noce Cinzia, Magnacca Sara, Triolo Federico, Zazzara Maria Beatrice, Palmieri Luigi, Iacoviello Licia, Onder Graziano, Vetrano Davide Liborio
Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Research Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Aging Clin Exp Res. 2025 Aug 20;37(1):250. doi: 10.1007/s40520-025-03150-0.
Different multimorbidity patterns can affect health trajectories and influence survival.
We investigated their association with mortality in two population-based cohorts of older adults.
Two Italian cohorts of randomly selected individuals (60–79 years old) from general population: CUORE (baseline 2008–2012) and Moli-sani (baseline 2005–2010). Latent Class Analysis used to identify homogeneous groups of multimorbid individuals (≥ 2 diseases) with similar underlying disease patterns. Cox regression models used to assess the association of multimorbidity patterns and all-cause mortality (end of follow-up 12/31/2019). Results pooled in a random-effects meta-analysis.
Total samples of 3,695 individuals in CUORE (48% male, mean age 68.8 years [SD 5.6]) and 7,801 in Moli-sani (51% male, mean age 68.2 years [SD 5.4]). In both cohorts, six multimorbidity patterns were identified and named after their overexpressed diseases: ,, (i.e., no diseases overexpressed). Overall mortality rates were 1.66 per 100 person/years in CUORE and 1.85 per 100 person/years in Moli-sani. Compared to the multimorbidity-free group (< 2 diseases), individuals displaying a pattern showed the highest mortality (pooled HR 2.62, 95% CI 2.15–3.10), followed by (pooled HR 1.45, 95% CI 1.21–1.68), (pooled HR 1.33, 95% CI 1.01–1.64) and , (pooled HR 1.33, 95% CI 1.06–1.60).
Multimorbidity patterns in older adults are differentially associated to shorter survival.
Their identification may help optimize clinical management by improving risk stratification, allowing for more targeted prevention and intervention strategies.
The online version contains supplementary material available at 10.1007/s40520-025-03150-0.
不同的共病模式会影响健康轨迹并影响生存。
我们在两个基于人群的老年队列中研究了它们与死亡率的关联。
从意大利普通人群中随机选取两个队列（60 - 79岁）：CUORE（基线时间为2008 - 2012年）和莫利 - 萨尼（基线时间为2005 - 2010年）。采用潜在类别分析来识别具有相似潜在疾病模式的共病个体（≥2种疾病）的同质组。使用Cox回归模型评估共病模式与全因死亡率（随访截止到2019年12月31日）之间的关联。结果汇总在随机效应荟萃分析中。
CUORE队列共有3695名个体（48%为男性，平均年龄68.8岁[标准差5.6]），莫利 - 萨尼队列有7801名个体（51%为男性，平均年龄68.2岁[标准差5.4]）。在两个队列中，识别出六种共病模式，并根据其过度表达的疾病进行命名：，，（即无疾病过度表达）。CUORE队列的总死亡率为每100人年1.66例，莫利 - 萨尼队列的总死亡率为每100人年1.85例。与无共病组（<2种疾病）相比，表现为模式的个体死亡率最高（合并风险比2.62，95%置信区间2.15 - 3.10），其次是（合并风险比1.45，95%置信区间1.21 - 1.68）、（合并风险比1.33，95%置信区间1.01 - 1.64）和，（合并风险比1.33，95%置信区间1.06 - 1.60）。
老年人的共病模式与较短的生存期存在不同程度的关联。
识别这些共病模式可能有助于通过改善风险分层来优化临床管理，从而制定更具针对性的预防和干预策略。
在线版本包含可在10.1007/s40520 - 025 - 03150 - 0获取的补充材料。
