Multimorbidity In Patients With Cognitive Impairment And Dementia
Overview
The study aimed to investigate the burden and clusters of multimorbidity in relation to mild cognitive impairment (MCI), dementia, and Alzheimer’s disease (AD)-related plasma biomarkers among older adults. A population-based sample of 5432 participants aged 60 years and above was included, with a subsample of 1412 individuals having plasma amyloid beta (Aβ), total tau, and neurofilament light chain (NfL) measurements. Hierarchical clustering was employed to identify five multimorbidity clusters based on 23 chronic diseases, and diagnoses of dementia and MCI were made following international criteria. Logistic and linear regression models were used for data analysis.
The results revealed that the number of chronic diseases was linked to an increased risk of dementia (adjusted odds ratio = 1.22; 95% confidence interval [CI] = 1.11 to 1.33), AD (1.13; 1.01 to 1.26), vascular dementia (VaD) (1.44; 1.25 to 1.64), and non-amnestic MCI (1.25; 1.13 to 1.37). Specific multimorbidity clusters, such as the metabolic cluster, were associated with VaD and non-amnestic MCI, while the degenerative ocular cluster was linked to AD (p < 0.05). Furthermore, a higher number of chronic diseases was correlated with elevated levels of plasma Aβ and NfL (p < 0.05).
In summary, the study demonstrates that multimorbidity burden and clusters are distinctively connected to different subtypes of dementia and MCI, as well as AD-related plasma biomarkers in older adults. This information contributes to our understanding of the complex interplay between multimorbidity and cognitive health in aging individuals.
Introduction
The study addresses the impact of multimorbidity, defined as the coexistence of two or more chronic health conditions, on cognitive aging phenotypes and plasma biomarkers associated with Alzheimer’s disease (AD) in a population of rural-dwelling older adults in China. While previous research has predominantly focused on urban populations in high-income countries, this study recognizes the need to investigate the associations in diverse settings, especially in rural areas of low- and middle-income countries, where the epidemiological features of chronic conditions and dementia disorders differ.
Multimorbidity, characterized by the simultaneous presence of multiple chronic health conditions, has been linked to accelerated cognitive decline, mild cognitive impairment (MCI), and dementia in various studies conducted in high-income countries. However, understanding these associations in rural populations, particularly in low- and middle-income countries like China, is essential due to significant differences in the prevalence, distribution, and risk factors of chronic conditions.
Recognizing that certain chronic conditions often co-occur in older adults due to shared risk factors or common underlying mechanisms, the study emphasizes the importance of identifying specific clusters of multimorbidity when investigating its relationship with cognitive outcomes. The hypothesis posits that a higher burden of multimorbidity, particularly in specific clusters like the metabolic cluster, will be associated with an increased likelihood of MCI and dementia, as well as alterations in plasma biomarkers related to AD.
In summary, the research aims to extend our understanding of the impact of multimorbidity on cognitive aging and AD-related biomarkers in a rural Chinese population, contributing valuable insights that go beyond the existing knowledge predominantly derived from urban populations in high-income countries.
Method
This population-based study, part of the World-Wide FINGERS Network, utilizes data from the Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND-China) project. The project, conducted in rural communities of Western Shandong Province, China, aimed to explore the association between multimorbidity and cognitive aging phenotypes, including dementia and mild cognitive impairment (MCI), in individuals aged 60 years and above.
A total of 5765 participants underwent a baseline examination, with 5432 subjects included in the analysis for multimorbidity and dementia, and 4968 subjects for multimorbidity and MCI. Plasma AD-related biomarkers data were available for 1412 individuals. The MIND-China protocol received ethical approval, and written informed consent was obtained from participants.
Data collection involved face-to-face interviews, clinical examinations, neuropsychological testing, and laboratory tests, covering sociodemographic factors, lifestyle choices, health conditions, and medication use. Chronic conditions were assessed based on various criteria, and multimorbidity was defined as the concurrent presence of two or more of these conditions. Plasma AD-related biomarkers, including Aβ42, Aβ40, total tau, and neurofilament light chain (NfL), were measured using the Simoa platform.
Neuropsychological assessments included the Chinese version of the Mini-Mental State Examination (MMSE) and the Chinese version of activities of daily living (C-ADLs). Dementia and MCI were diagnosed following established criteria, with dementia further classified into Alzheimer’s disease (AD) and vascular dementia (VaD). MCI was categorized into amnestic (aMCI) and non-amnestic (naMCI) types.
The study found associations between multimorbidity, especially metabolic and cardiac-musculoskeletal clusters, with dementia, VaD, and naMCI. Specific clusters like degenerative ocular were associated with AD. Additionally, certain plasma biomarkers (Aβ42 and NfL) showed correlations with multimorbidity. The findings suggest potential links between multimorbidity and cognitive outcomes, emphasizing the need for further prospective studies to explore these associations longitudinally and investigate underlying neuropathological mechanisms.
Statistical Analysis
The hierarchical clustering method, implemented through the VARCLUS procedure in SAS version 9.4 software, was employed to create clusters of multimorbidity in this study. Binary and multinomial logistic regression models were utilized to investigate the relationships between the number of chronic conditions, multimorbidity, different multimorbidity clusters, and dementia, mild cognitive impairment (MCI), and their respective subtypes.
Plasma biomarker data were processed following a previously outlined method. Specifically, non-normally distributed biomarkers underwent log-transformation using the natural logarithm to mitigate the impact of outliers. Subsequently, all plasma biomarkers were standardized into z-scores, enabling a consistent comparison of effect sizes across different biomarkers. General linear regression models were then applied to explore the associations between multimorbidity measures and plasma Alzheimer’s disease (AD)-related biomarkers.
To ensure robust analyses, adjustments were made for various factors, including age, sex, education, current smoking, alcohol consumption, and physical inactivity. Statistical analyses were conducted using SAS version 9.4, with a significance level set at two-tailed p < 0.05. To account for multiple comparisons, Bonferroni correction was implemented. These methodological approaches enhance the validity of the study’s findings, contributing to a comprehensive understanding of the intricate associations between multimorbidity, cognitive outcomes, and plasma biomarkers.
Result
The study encompassed 5,432 participants aged 60 years or older, residing in rural China. The mean age was 70.71 years, with 57.46% women, 39.73% having no formal education, and 83.32% engaged in farming. Various health conditions exhibited gender disparities, with women more prone to certain conditions like hypertension, diabetes, and thyroid disease. The overall prevalence of concurrent chronic health conditions revealed a noteworthy distribution
Multimorbidity, defined as having two or more chronic conditions, was prevalent in 60.03% of participants. Dementia was diagnosed in 255 individuals, with 168 having Alzheimer’s disease (AD), 78 vascular dementia (VaD), and nine other types. Mild cognitive impairment (MCI) affected 1,313 participants, including 1,106 with amnestic MCI and 207 with non-amnestic MCI. The number of chronic conditions demonstrated significant associations with dementia, AD, and VaD, revealing increased likelihoods. Multimorbidity was linked to a higher likelihood of dementia and VaD but not AD.
Hierarchical clustering of multimorbidity unveiled five clusters, each associated with specific health conditions. The metabolic cluster exhibited a significant association with VaD, while the degenerative ocular cluster was linked to dementia, particularly AD. The cardiac-musculoskeletal cluster correlated with VaD. The respiratory and mixed clusters showed no significant associations.
In participants without dementia, multimorbidity, a higher number of chronic conditions, and the metabolic and cardiac-musculoskeletal clusters were associated with an elevated likelihood of non-amnestic MCI. However, after Bonferroni correction, some associations became statistically non-significant.
Exploring plasma AD-related biomarkers, an increased number of chronic diseases was linked to elevated levels of Aβ40, Aβ42, and NfL. Multimorbidity presence correlated with increased Aβ42 and NfL. Among the clusters, metabolic and degenerative ocular clusters were associated with elevated NfL, while the cardiac-musculoskeletal cluster correlated with increased Aβ42. Post Bonferroni correction, some associations became statistically non-significant.
These findings contribute valuable insights into the intricate relationships between multimorbidity, cognitive outcomes, and plasma biomarkers, shedding light on potential mechanisms underlying these associations.
Conclusion
In this comprehensive cross-sectional study involving rural Chinese older adults, several key associations were established. The presence and extent of multimorbidity were linked to increased probabilities of dementia, vascular dementia (VaD), and non-amnestic mild cognitive impairment (naMCI), along with elevated levels of plasma Aβ42 and NfL. Additionally, the number of chronic conditions was associated with Alzheimer’s disease (AD) and plasma Aβ40. Multimorbidity clusters, specifically the metabolic and cardiac-musculoskeletal (MSK) clusters, correlated with VaD and naMCI, while the degenerative ocular cluster was linked to AD. Furthermore, these clusters were associated with heightened plasma AD-related biomarkers.
The study, unique in its focus on rural-dwelling older adults in China, adds valuable insights to the existing literature. It underscores the nuanced associations of various multimorbidity clusters with cognitive aging phenotypes, shedding light on potential neuropathological mechanisms. Notably, the findings align with prior research on the impact of specific diseases, such as metabolic and cardiovascular conditions, on VaD and naMCI. The study also emphasizes the significance of considering multimorbidity clusters in understanding diverse disease patterns. The associations found in this study contribute to the understanding of cognitive aging phenotypes and provide further insights into potential neuropathological mechanisms. However, the cross-sectional nature of the study and the need for replication in diverse populations are acknowledged as study limitations.
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