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评估老年人中多种疾病、非传染性疾病组合、衰弱表型与死亡风险之间的关系。

Assessing the relationship between multimorbidity, NCD configurations, frailty phenotypes, and mortality risk in older adults.

作者信息

Ogaz-González Rafael, Corpeleijn Eva, García-Chanes Rosa Estela, Gutierréz-Robledo Luis Miguel, Escamilla-Santiago Ricardo Antonio, López-Cervantes Malaquías

机构信息

Department of Public Health, Faculty of Medicine, National Autonomous University of México, Sixth Floor, Building B, 411A Circuito Escolar, Copilco Universidad, Mexico City, Coyoacán, 04360, Mexico.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

出版信息

BMC Geriatr. 2024 Apr 22;24(1):355. doi: 10.1186/s12877-024-04948-9.

DOI:10.1186/s12877-024-04948-9
PMID:38649809
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11034053/
Abstract

BACKGROUND

Older adults are increasingly susceptible to prolonged illness, multiple chronic diseases, and disabilities, which can lead to the coexistence of multimorbidity and frailty. Multimorbidity may result in various noncommunicable disease (NCD) patterns or configurations that could be associated with frailty and death. Mortality risk may vary depending on the presence of specific chronic diseases configurations or frailty.

METHODS

The aim was to examine the impact of NCD configurations on mortality risk among older adults with distinct frailty phenotypes. The population was analyzed from the Costa Rican Longevity and Healthy Aging Study Cohort (CRELES). A total of 2,662 adults aged 60 or older were included and followed for 5 years. Exploratory factor analysis and various clustering techniques were utilized to identify NCD configurations. The frequency of NCD accumulation was also assessed for a multimorbidity definition. Frailty phenotypes were set according to Fried et al. criteria. Kaplan‒Meier survival analyses, mortality rates, and Cox proportional hazards models were estimated.

RESULTS

Four different types of patterns were identified: 'Neuro-psychiatric', 'Metabolic', 'Cardiovascular', and 'Mixt' configurations. These configurations showed a higher mortality risk than the mere accumulation of NCDs [Cardiovascular HR:1.65 (1.07-2.57); 'Mixt' HR:1.49 (1.00-2.22); ≥3 NCDs HR:1.31 (1.09-1.58)]. Frailty exhibited a high and constant mortality risk, irrespective of the presence of any NCD configuration or multimorbidity definition. However, HRs decreased and lost statistical significance when phenotypes were considered in the Cox models [frailty + 'Cardiovascular' HR:1.56 (1.00-2.42); frailty + 'Mixt':1.42 (0.95-2.11); and frailty + ≥ 3 NCDs HR:1.23 (1.02-1.49)].

CONCLUSIONS

Frailty accompanying multimorbidity emerges as a more crucial indicator of mortality risk than multimorbidity alone. Therefore, studying NCD configurations is worthwhile as they may offer improved risk profiles for mortality as alternatives to straightforward counts.

摘要

背景

老年人越来越容易患长期疾病、多种慢性病和残疾,这可能导致多种疾病并存和身体虚弱。多种疾病并存可能导致各种非传染性疾病(NCD)模式或组合,这些模式或组合可能与身体虚弱和死亡有关。死亡风险可能因特定慢性病组合或身体虚弱的存在而有所不同。

方法

目的是研究非传染性疾病组合对具有不同身体虚弱表型的老年人死亡风险的影响。对哥斯达黎加长寿与健康老龄化研究队列(CRELES)的人群进行了分析。共纳入2662名60岁及以上的成年人,并随访5年。利用探索性因素分析和各种聚类技术来确定非传染性疾病组合。还评估了非传染性疾病累积频率以进行多种疾病并存的定义。根据弗里德等人的标准设定身体虚弱表型。估计了Kaplan-Meier生存分析、死亡率和Cox比例风险模型。

结果

确定了四种不同类型的模式:“神经精神型”、“代谢型”、“心血管型”和“混合型”组合。这些组合显示出比单纯非传染性疾病累积更高的死亡风险[心血管型风险比(HR):1.65(1.07 - 2.57);“混合型”HR:1.49(1.00 - 2.22);≥3种非传染性疾病HR:1.31(1.09 - 1.58)]。无论是否存在任何非传染性疾病组合或多种疾病并存的定义,身体虚弱都表现出高且持续的死亡风险。然而,在Cox模型中考虑表型时,HR值下降且失去统计学意义[身体虚弱 + “心血管型”HR:1.56(1.00 - 2.42);身体虚弱 + “混合型”:1.42(0.95 - 2.11);身体虚弱 + ≥3种非传染性疾病HR:1.23(1.02 - 1.49)]。

结论

与多种疾病并存相伴的身体虚弱比单纯的多种疾病并存更成为死亡风险的关键指标。因此,研究非传染性疾病组合是值得的,因为它们可能提供比简单计数更好的死亡风险概况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/09ca84835544/12877_2024_4948_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/098be20a363b/12877_2024_4948_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/df1c265b26e9/12877_2024_4948_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/09ca84835544/12877_2024_4948_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/098be20a363b/12877_2024_4948_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/df1c265b26e9/12877_2024_4948_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3357/11034053/09ca84835544/12877_2024_4948_Fig3_HTML.jpg

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