Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, China.
Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, China.
Arch Gerontol Geriatr. 2024 Feb;117:105219. doi: 10.1016/j.archger.2023.105219. Epub 2023 Oct 4.
The study aimed to observe the trajectory of quality of life (QoL) and cognition, and to a analyze the bidirectional association between cognition and QoL for diverse multimorbidity patterns.
In total, 16,153 older participants age ≥50 years were included from the Survey of Health, Ageing and Retirement in Europe (SHARE). We used latent class analysis (LCA) to identify multimorbidity patterns in the baseline population. We used linear mixed models (LMM) to examine the trajectory of cognition and QoL in different multimorbidity patterns. A cross-lagged model was employed to analyze the bidirectional association between cognition and QoL in diverse multimorbidity patterns.
Latent class analysis identified four multimorbidity patterns: high and low comorbidity burden (HC and LC), cardiometabolic (CA), and osteoarthrosis (OS). The HC group had the poorest cognitive function and QoL (p for trend < 0.001). Delayed and immediate episodic memory in the OS group declined at a highest rate (p for trend < 0.001). Additionally, a bidirectional association between cognition and QoL was observed. The effect of cognitive function on QoL was relatively stronger than the reverse in the CA and LC groups.
The rate of decline in cognition and QoL over the time differs in diverse multimorbidity patterns, and patients with four or more chronic diseases should be specially considered. Notably, early monitoring of cognitive function and can help break the vicious cycle between cognitive deterioration and poor QoL in patients with OS or CA diseases.
本研究旨在观察生活质量（QoL）和认知的轨迹，并分析不同多种合并症模式下认知与 QoL 之间的双向关联。
共纳入来自欧洲健康、衰老和退休调查（SHARE）的 16153 名年龄≥50 岁的老年参与者。我们使用潜在类别分析（LCA）来确定基线人群中的多种合并症模式。我们使用线性混合模型（LMM）来检查不同多种合并症模式下认知和 QoL 的轨迹。采用交叉滞后模型分析不同多种合并症模式下认知与 QoL 之间的双向关联。
潜在类别分析确定了四种多种合并症模式：高合并症负担和低合并症负担（HC 和 LC）、心血管代谢疾病（CA）和骨关节炎（OS）。HC 组认知功能和 QoL 最差（趋势检验 p<0.001）。OS 组的即时和延迟情景记忆呈最高速度下降（趋势检验 p<0.001）。此外，还观察到认知与 QoL 之间存在双向关联。在 CA 和 LC 组中，认知功能对 QoL 的影响相对更强于相反方向的影响。
不同多种合并症模式下认知和 QoL 的下降速度不同，应特别关注患有四种或更多种慢性病的患者。值得注意的是，早期监测认知功能有助于打破 OS 或 CA 疾病患者认知恶化和 QoL 下降之间的恶性循环。
