Association of Multimorbidity, Disease Clusters, and Modification by Genetic Factors With Risk of Dementia

Association between multimorbidity and the risk of dementia: A systematic review and meta-analysis

BACKGROUND

Multimorbidity has become increasingly prevalent and poses challenges in managing cognitive function. This study aimed to (1) systematically review and perform a meta-analysis to understand the relationship between multimorbidity and the risk of dementia and (2) examine the impact of different multimorbidity patterns on this relationship.

METHOD

A systematic review was conducted using PubMed, Embase, PsychINFO, CINAHL, and Cochrane Central to gather studies published up to December 16, 2023. For the meta-analysis, studies with consistent study designs, multimorbidity definitions, and stages of dementia were included. Heterogeneity was assessed using the I2 statistic, and Egger's and Begg's tests were used to evaluate publication bias.

RESULTS

Of the 12,074 studies identified, 11 were deemed suitable for systematic review, and eight were included in the meta-analysis. Meta-analysis of the longitudinal studies revealed that baseline multimorbidity was significantly associated with an increased risk of dementia compared with individuals without multimorbidity (HR: 1.34, 95 % CI: 1.08-1.68). Meta-analysis of the cross-sectional studies indicated that multimorbidity was significantly associated with a higher risk of being in the prodromal stages of dementia than in individuals without multimorbidity (OR: 1.32, 95 % CI: 1.16-1.51). The risk of dementia varied according to diverse multimorbidity patterns, and the cardiovascular-metabolic condition-related patterns were the most common and associated with high dementia risk.

CONCLUSIONS

Our findings provide quantitative evidence of a significant association between multimorbidity and the risk of dementia. To develop effective dementia prevention strategies, an in-depth understanding of specific multimorbidity patterns is invaluable for managing cognitive function.

Quadratic associations between sleep and multimorbidity among the older population in China: Evidence from CLHLS 2011 to 2018

OBJECTIVES

To investigate the relationship between sleep and multimorbidity, including the associations between sleep duration and multimorbidity, as well as between sleep quality and multimorbidity.

METHODS

Using data from the three latest waves of the Chinese Longitudinal Health Longevity Study (2011, 2014, and 2018), a binary panel regression was conducted to investigate the quadratic relationship between sleep duration and multimorbidity. Subsequently, quadratic fitting and robustness analysis were further utilized to strengthen the verification of this relationship.

RESULTS

From 2011 to 2018, the prevalence of multimorbidity increased, with average rates of 0.309, 0.345, and 0.367, respectively. Meanwhile, sleep duration was 7.45, 7.34, and 7.39, but sleep quality showed a declining trend with scores of 3.70, 3.63, and 3.47, respectively. Furthermore, the regression analysis revealed that the odds ratios (OR) for the relationship between sleep duration and multimorbidity, and between the square of sleep duration and multimorbidity were 0.734, with 95 % CI = [0.6272, 0.8582] and 1.016, with 95 % CI = [1.0058, 1.0262], respectively. From the quadratic relationship, it is evident that the multimorbidity among older Chinese adults initially decreases and then increases with long sleep durations.

CONCLUSIONS

The multimorbidity was significantly different among individuals with different sleep duration. A U-shaped relationship was observed between sleep duration and multimorbidity, whereby both short and excessive sleep durations were associated with higher rates of multimorbidity. Additionally, a negative association was found between sleep quality and multimorbidity, indicating that higher sleep quality was linked to lower rates of multimorbidity.

Ageing-related changes in the regulation of microglia and their interaction with neurons

Ageing is one of the most important risk factors for chronic health conditions, including neurodegenerative diseases. Inflammation is a feature of ageing, as well as a key pathophysiological mechanism for degenerative diseases. Microglia play multiple roles in the central nervous system; their states entail a complex assemblage of responses reflecting the multiplicity of functions they fulfil both under homeostatic basal conditions and in response to stimuli. Whereas glial cells can promote neuronal homeostasis and limit neurodegeneration, age-related inflammation (i.e. inflammaging) leads to the functional impairment of microglia and astrocytes, exacerbating their response to stimuli. Thus, microglia are key mediators for age-dependent changes of the nervous system, participating in the generation of a less supportive or even hostile environment for neurons. Whereas multiple changes of ageing microglia have been described, here we will focus on the neuron-microglia regulatory crosstalk through fractalkine (CX3CL1) and CD200, and the regulatory cytokine Transforming Growth Factor β1 (TGFβ1), which is involved in immunomodulation and neuroprotection. Ageing results in a dysregulated activation of microglia, affecting neuronal survival, and function. The apparent unresponsiveness of aged microglia to regulatory signals could reflect a restriction in the mechanisms underlying their homeostatic and reactive states. The spectrum of functions, required to respond to life-long needs for brain maintenance and in response to disease, would progressively narrow, preventing microglia from maintaining their protective functions. This article is part of the Special Issue on "Microglia".

Associations of modifiable dementia risk factors with dementia and cognitive decline: evidence from three prospective cohorts

Objective

This study aims to assess the relationship between modifiable dementia risk factors and both dementia and cognitive decline.

Methods

Data were obtained from the Health and Retirement Study (HRS) [2008-2020], the China Health and Retirement Longitudinal Study (CHARLS) [2011-2020], and the English Longitudinal Study of Ageing (ELSA) [2010-2020]. After adjusting for confounding factors, multivariable logistic regression was utilized to analyze the relationship between modifiable dementia risk factors and dementia, while multivariable linear regression was employed to examine the relationship between these risk factors and cognitive decline. Additionally, the Cox proportional hazards model was used to assess the relationship between the number of risk factor events, clusters, and dementia risk.

Results

A total of 30,113 participants from HRS, CHARLS, and ELSA were included (44.6% male, mean age 66.04 years), with an average follow-up period of 7.29 years. A low education level was significantly associated with an increased risk of dementia and accelerated cognitive decline (Overall, OR = 2.93, 95% CI: 2.70-3.18; Overall, β = -0.25, 95% CI: -0.60 to-0.55). The presence of multiple dementia risk factors correlated with a higher dementia risk; Specifically, compared with more than 5 risk factor events, both having no dementia risk factors and having only one dementia risk factor were associated with a significantly lower risk of dementia (Overall, HR = 0.15, 95% CI: 0.11-0.22, HR = 0.22, 95% CI: 0.18-0.25). Compared to the group with no coexistence of risk factors, the clusters of excessive alcohol, diabetes, vision loss, and hearing loss (HR = 4.11; 95% CI = 3.42-4.95; p < 0.001); excessive alcohol, vision loss, smoking, and hearing loss (HR = 5.18; 95% CI = 4.30-6.23; p < 0.001); and excessive alcohol, obesity, diabetes, and smoking (HR = 5.96; 95% CI = 5.11-6.95; p < 0.001) were most strongly associated with dementia risk.

Conclusion

Among the 11 risk factors, educational attainment has the greatest impact on dementia risk and cognitive decline. A dose-response relationship exists between the number of modifiable risk factor events and dementia risk. The coexistence of multiple risk factors is associated with dementia risk, and these associations vary by risk factor cluster.

Disease trajectories before dementia: evidence from a large-scale community-based prospective study

BACKGROUND

Systemic changes in multiple diseases may influence the onset of dementia. However, the specific temporality between exposure diseases and dementia remains uncertain.

AIMS

By characterising the full spectrum of temporal disease trajectories before dementia, this study aims to yield a global picture of precursor diseases to dementia and to provide detailed instructions for risk management and primary prevention of dementia.

METHOD

Using the multicentre, community-based prospective UK Biobank, we constructed disease trajectories before dementia utilising the phenome-wide association analysis, paired directional test and association quantification. Stratified disease trajectories were constructed by dementia subtypes, gender, age of diagnosis and Apolipoprotein E (ApoE) status, respectively.

RESULTS

Our study population comprised 434 266 participants without baseline dementia and 4638 individuals with all-cause dementia. In total, 1253 diseases were extracted as potential components of the disease trajectory before dementia. We identified three clusters of disease trajectories preceding all-cause dementia, initiated by circulatory, metabolic and respiratory diseases occurring approximately 5-15 years before dementia. Cerebral infarction or chronic renal failure following chronic ischaemic heart disease was the specific trajectory before vascular dementia. Apolipoprotein E (ApoE) ε4 non-carriers exhibited more complex trajectories compared with carriers. Lipid metabolism disorders remained in the trajectories regardless of dementia subtypes, gender, age of diagnosis and ApoE status.

CONCLUSIONS

This study provides a comprehensive view of the longitudinal disease trajectories before dementia and highlights the potential targets of midlife cardiometabolic dysfunction for dementia screening and prevention.

Multimorbidity patterns in dementia and mild cognitive impairment

Design

This is a retrospective cohort study. Setting: The study was conducted at the Instituto de Neurociencias de la Junta de Beneficencia de Guayaquil, a primary neuroscience institute in Ecuador.

Participants

The study evaluated 425 participants diagnosed with Mild Cognitive Impairment (MCI) or dementia, out of which 272 individuals (mean age = 75 years; 164 female) presenting specific medical conditions were selected for analysis.

Measurements

Data were collected on demographics, medical history, and neuropsychological assessment using the Neuropsi scale. Conditions such as Type 2 Diabetes Mellitus, hypertension, obesity, and history of traumatic brain injury were specifically noted.

Results

Latent Class Analysis identified three distinct classes of patients: Unspecified Cognitive Deterioration, Dementia, and MCI. The three-class model provided the best fit, revealing varied morbidity patterns and highlighting the influence of vascular and metabolic conditions on cognitive decline. Notably, similarities in hypertension and diabetes prevalence between Dementia and MCI classes suggested shared risk factors. The study also found no significant age differences between the classes, indicating that age alone might not be the primary determinant in the progression of cognitive decline.

Conclusions

The study underscores the complexity of dementia and MCI in an ageing Ecuadorian population, with vascular health playing a crucial role in cognitive impairment. These findings advocate for a holistic approach in managing dementia and MCI, emphasising the importance of addressing cardiovascular and metabolic health alongside neurocognitive care. The distinct morbidity patterns identified offer insights into tailored intervention strategies, highlighting the need for comprehensive, multidisciplinary care in dementia management.

PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis

OBJECTIVES

To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies.

MATERIALS AND METHODS

PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies.

RESULTS

The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME.

DISCUSSION

PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records.

CONCLUSION

PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.

Life Events and Incident Dementia: A Prospective Study of 493,787 Individuals Over 16 Years

OBJECTIVES

Life events can be stressful and have a detrimental impact on health, but evidence is inconclusive regarding life events and dementia risk. The present study tests whether life events are associated with incident dementia, whether experiencing multiple events has cumulative effects, and whether the associations vary across age, sex, race/ethnicity, socioeconomic status, and genetic vulnerability.

METHODS

UK Biobank participants (N = 493,787) reported on 6 life events that occurred within the past 2 years: serious illness, injury, assault to yourself or close relative, death of a spouse/partner or close relative, marital separation/divorce, and financial problems. Incident all-cause dementia was ascertained through health records from the UK National Health Service over a 16-year follow-up.

RESULTS

Serious illness, injury, or assault to yourself, marital separation/divorce, and financial difficulties were associated with a higher risk of dementia; serious illness, injury, or assault of a close relative was associated with a lower risk of dementia. When combined, experiencing 3-4 events was associated with a more than 2-fold increase in dementia risk. The association for marital separation/divorce was stronger within the first 5 years of follow-up (consistent with reverse causality). Death of a spouse/partner or close relative was mostly unrelated to dementia risk. With few exceptions, the associations were similar across age, sex, race/ethnicity, socioeconomic status, and apolipoprotein E e4 status groups.

DISCUSSION

Severe illness, injury, or personal assault, marital separation or divorce, and financial hardships may raise risk of dementia, particularly when these events occur together.

Alzheimer disease blood biomarkers: considerations for population-level use

In the past 5 years, we have witnessed the first approved Alzheimer disease (AD) disease-modifying therapy and the development of blood-based biomarkers (BBMs) to aid the diagnosis of AD. For many reasons, including accessibility, invasiveness and cost, BBMs are more acceptable and feasible for patients than a lumbar puncture (for cerebrospinal fluid collection) or neuroimaging. However, many questions remain regarding how best to utilize BBMs at the population level. In this Review, we outline the factors that warrant consideration for the widespread implementation and interpretation of AD BBMs. To set the scene, we review the current use of biomarkers, including BBMs, in AD. We go on to describe the characteristics of typical patients with cognitive impairment in primary care, who often differ from the patient populations used in AD BBM research studies. We also consider factors that might affect the interpretation of BBM tests, such as comorbidities, sex and race or ethnicity. We conclude by discussing broader issues such as ethics, patient and provider preference, incidental findings and dealing with indeterminate results and imperfect accuracy in implementing BBMs at the population level.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions

INTRODUCTION

Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.

METHODS

We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.

RESULTS

Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further.

DISCUSSION

Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments.

HIGHLIGHTS

Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.

Characteristics of Older Adults with Alzheimer's Disease Who Were Hospitalized during the COVID-19 Pandemic: A Secondary Data Analysis

We aim to investigate the relationships between the population characteristics of patients with Alzheimer's Disease (AD) and their Healthcare Utilization (HU) during the COVID-19 pandemic. Electronic health records (EHRs) were utilized. The study sample comprised those with ICD-10 codes G30.0, G30.1, G30.8, and G30.9 between 1 January 2020 and 31 December 2021. Pearson's correlation and multiple regression were used. The analysis utilized 1537 patient records with an average age of 82.20 years (SD = 7.71); 62.3% were female. Patients had an average of 1.64 hospitalizations (SD = 1.18) with an average length of stay (ALOS) of 7.45 days (SD = 9.13). Discharge dispositions were primarily home (55.1%) and nursing facilities (32.4%). Among patients with multiple hospitalizations, a negative correlation was observed between age and both ALOS (r = -0.1264, p = 0.0030) and number of hospitalizations (r = -0.1499, p = 0.0004). Predictors of longer ALOS included male gender (p = 0.0227), divorced or widowed (p = 0.0056), and the use of Medicare Advantage and other private insurance (p = 0.0178). Male gender (p = 0.0050) and Black race (p = 0.0069) were associated with a higher hospitalization frequency. We recommend future studies including the co-morbidities of AD patients, larger samples, and longitudinal data.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems

Background

Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research.

Methods

Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis.

Findings

Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights.

Interpretation

Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine.

Funding

VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Association between multimorbidity and cognitive decline in the elderly population of the Eastern Province, Saudi Arabia

BACKGROUND

Cognitive decline affects the quality of life, and dementia affects independence in daily life activities. Multimorbidity in older adults is associated with a higher risk of cognitive impairment. This research aims to study the relationship between cognitive decline and multimorbidity in the elderly population in the Eastern Province, Saudi Arabia.

MATERIALS AND METHODS

This cross-sectional research was conducted from July to October 2022 among adults over 60 years. All patients with two or more comorbidities were contacted for a face-to-face interview and cognitive testing to estimate cognitive function by trained family physicians using St. Louis University Mental State Examination. ANOVA and Chi-square test were used to test for statistical significance. Binary logistic regression was used to show the odds of having cognitive impairment and multimorbidity. All tests were performed at 5% level of significance.

RESULTS

The study involved 343 individuals; majority (74.1%) aged 60-75 years and were males (67.9%). Hypertension, diabetes, and chronic pain were reported by 56%, 48%, and 44% participants, respectively. Thirty percent participants had 3 or more comorbidities. About 36% had mild neurocognitive disorder and 31.2% had dementia. The results showed that age, gender (female), diabetes, stroke, chronic pain, and multimorbidity were significantly associated with cognitive impairment. In our study, hypertension, coronary artery diseases, depression, and anxiety were not significantly associated with risk of cognitive decline.

CONCLUSION

Our study found that multimorbidity is significantly associated with cognitive decline. Controlling comorbidities and preventing risk factors in midlife could help in delaying the progression of the disease.

Sex Differences in the Role of Multimorbidity on Poststroke Disability: The Taiwan Stroke Registry

BACKGROUND AND OBJECTIVES

Multimorbidity is common in patients who experience stroke. Less is known about the effect of specific multimorbidity patterns on long-term disability in patients with stroke. Furthermore, given the increased poststroke disability frequently seen in female vs male patients, it is unknown whether multimorbidity has a similar association with disability in both sexes. We assessed whether specific multimorbidity clusters were associated with greater long-term poststroke disability burden overall and by sex.

METHODS

In the Taiwan Stroke Registry, an ongoing nationwide prospective registry, patients with first-ever ischemic stroke were enrolled; this analysis is restricted to those individuals surviving to at least 6 months poststroke. Using a hierarchical clustering approach, clusters of prestroke multimorbidity were generated based on 16 risk factors; the algorithm identified 5 distinct clusters. The association between clusters and 12-month poststroke disability, defined using the modified Rankin Scale (mRS), was determined using logistic regression models, with additional models stratified by sex. The longitudinal association between multimorbidity and functional status change was assessed using mixed-effects models.

RESULTS

Nine-thousand eight hundred eighteen patients with first-ever ischemic stroke were included. The cluster with no risk factors was the reference, "healthier" risk group (N = 1,373). Patients with a cluster profile of diabetes, peripheral artery disease (PAD), and chronic kidney disease (CKD) (N = 1882) had significantly greater disability (mRS ≥ 3) at 1 month (OR [95% CI] = 1.36 [1.13-1.63]), 3 months (OR [95% CI] = 1.27 [1.04-1.55]), and 6 months (OR [95% CI] = 1.30 [1.06-1.59]) but not at 12 months (OR [95% CI] = 1.16 [0.95-1.42]) than patients with a healthier risk factor profile. In the sex-stratified analysis, the associations with this risk cluster remained consistent in male patients (OR [95% CI] = 1.42 [1.06-1.89]) at 12 months, who also had a higher comorbidity burden, but not in female patients (OR [95% CI] = 0.95 [0.71-1.26]), who had higher proportions of severe strokes and severe disability (p-interaction = 0.04).

DISCUSSION

Taiwanese patients with multimorbidity, specifically the concurrent presence of diabetes, PAD, and CKD, had higher odds of a worse functional outcome in the first 6 months poststroke. Clusters of multimorbidity may be less informative for long-term disability in female patients. Further studies should evaluate other mechanisms for worse disability in female patients poststroke.

Multimorbidity, cognitive phenotypes, and Alzheimer's disease plasma biomarkers in older adults: A population-based study

INTRODUCTION

To examine the burden and clusters of multimorbidity in association with mild cognitive impairment (MCI), dementia, and Alzheimer's disease (AD)-related plasma biomarkers among older adults.

METHODS

This population-based study included 5432 participants (age ≥60 years); of these, plasma amyloid beta (Aβ), total tau, and neurofilament light chain (NfL) were measured in a subsample (n = 1412). We used hierarchical clustering to generate five multimorbidity clusters from 23 chronic diseases. We diagnosed dementia and MCI following international criteria. Data were analyzed using logistic and linear regression models.

RESULTS

The number of chronic diseases was associated with dementia (multivariable-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). Metabolic cluster was associated with VaD and non-amnestic MCI, whereas degenerative ocular cluster was associated with AD (p < 0.05). The number of chronic diseases was associated with increased plasma Aβ and NfL (p < 0.05).

DISCUSSION

Multimorbidity burden and clusters are differentially associated with subtypes of dementia and MCI and AD-related plasma biomarkers in older adults.

HIGHLIGHTS

We used hierarchical clustering to generate five clusters of multimorbidity. The presence and load of multimorbidity were associated with dementia and mild cognitive impairment. Multimorbidity clusters were differentially associated with subtypes of dementia and Alzheimer's disease plasma biomarkers.

Association of mild and complex multimorbidity with structural brain changes in older adults: A population-based study

INTRODUCTION

We quantified the association of mild (ie, involving one or two body systems) and complex (ie, involving ≥3 systems) multimorbidity with structural brain changes in older adults.

METHODS

We included 390 dementia-free participants aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen who underwent brain magnetic resonance imaging at baseline and after 3 and/or 6 years. Using linear mixed models, we estimated the association between multimorbidity and changes in total brain tissue, ventricular, hippocampal, and white matter hyperintensities volumes.

RESULTS

Compared to non-multimorbid participants, those with complex multimorbidity showed the steepest reduction in total brain (β*time -0.03, 95% CI -0.05, -0.01) and hippocampal (β*time -0.05, 95% CI -0.08, -0.03) volumes, the greatest ventricular enlargement (β*time 0.03, 95% CI 0.01, 0.05), and the fastest white matter hyperintensities accumulation (β*time 0.04, 95% CI 0.01, 0.07).

DISCUSSION

Multimorbidity, particularly when involving multiple body systems, is associated with accelerated structural brain changes, involving both neurodegeneration and vascular pathology.

HIGHLIGHTS

Multimorbidity accelerates structural brain changes in cognitively intact older adults These brain changes encompass both neurodegeneration and cerebrovascular pathology The complexity of multimorbidity is associated with the rate of brain changes' progression.

Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study

Background

The coexistence of coronary artery disease (CAD) and cognitive impairment has become a common clinical phenomenon. However, there is currently limited research on the etiology of this disease cluster, discovery of biomarkers, and identification of precise intervention targets.

Methods

We explored the causal connections between gut microbiota, blood metabolites, and the disease cluster of CAD combined with cognitive impairment through two-sample Mendelian randomization (TSMR). Additionally, we determine the gut microbiota and blood metabolites with the strongest causal associations using Bayesian model averaging multivariate Mendelian randomization (MR-BMA) analysis. Furthermore, we will investigate the mediating role of blood metabolites through a two-step Mendelian randomization design.

Results

We identified gut microbiota that had significant causal associations with cognitive impairment. Additionally, we also discovered blood metabolites that exhibited significant causal associations with both CAD and cognitive impairment. According to the MR-BMA results, the free cholesterol to total lipids ratio in large very low density lipoprotein (VLDL) was identified as the key blood metabolite significantly associated with CAD. Similarly, the cholesteryl esters to total lipids ratio in small VLDL emerged as the primary blood metabolite with a significant causal association with dementia with lewy bodies (DLB). For the two-step Mendelian randomization analysis, we identified blood metabolites that could potentially mediate the association between genus Butyricicoccus and CAD in the potential causal links.

Conclusion

Our study utilized Mendelian randomization (MR) to identify the gut microbiota features and blood metabolites characteristics associated with the disease cluster of CAD combined with cognitive impairment. These findings will provide a meaningful reference for the identification of biomarkers for the disease cluster of CAD combined with cognitive impairment as well as the discovery of targets for intervention to address the problems in the clinic.

Multimorbidity, healthy lifestyle, and the risk of cognitive impairment in Chinese older adults: a longitudinal cohort study

BACKGROUND

Evidence on the association between multimorbidity and cognitive impairment in Chinese older population is limited. In addition, whether a healthy lifestyle can protect cognitive function in multimorbid older population remains unknown.

METHODS

A total of 6116 participants aged ≥ 65 years from the Chinese Longitudinal Healthy Longevity Survey were followed up repeatedly. The number of coexisting chronic diseases was used for assessing multimorbidity and cardiometabolic multimorbidity. Three lifestyle statuses (unhealthy, intermediate, and healthy) were defined based on a lifestyle score covering smoking, alcohol drinking, body mass index, outdoor activities, and dietary pattern. Cognitive impairment was defined as the Mini-Mental State Examination score < 24. A modified Poisson regression model with robust error variance was used to assess the associations between multimorbidity, healthy lifestyle, and cognitive impairment.

RESULTS

During a median follow-up period of 5.8 years, 1621 incident cases of cognitive impairment were identified. The relative risk (RR) of cognitive impairment associated with heavy multimorbidity burden (≥ 3 conditions) was 1.39 (95% confidence interval: 1.22-1.59). This association declined with age, with RRs being 3.08 (1.78-5.31), 1.40 (1.04-1.87), and 1.19 (1.01-1.40) in subjects aged < 70 years, ≥ 70 and < 80 years, and ≥ 80 years, respectively (P for interaction = 0.001). Compared to unhealthy lifestyle, a healthy lifestyle was related to an approximately 40% reduced risk of cognitive impairment regardless of multimorbidity burden. Among the 5 lifestyle factors assessed, daily outdoor activities and a healthy dietary pattern showed convincing protective effects on cognitive function.

CONCLUSIONS

The relationship between multimorbidity and cognitive impairment is age-dependent but remains significant in the population aged 80 years or older. A healthy lifestyle may protect cognitive function regardless of the multimorbidity burden. These findings highlight the importance of targeting individuals with heavy multimorbidity burden and promoting a heathy lifestyle to prevent cognitive impairment in Chinese older population.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Artificial intelligence for dementia prevention

INTRODUCTION

A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS

ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS

Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION

ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS

Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis

Motivation

Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations.

Results

PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia.

Availability and implementation

The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME.

Data availability statement

The data underlying this article are available in the article and in its online web application or supplementary material.

Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank

Previous hypothesis-driven research has identified many risk factors linked to dementia. However, the multiplicity and co-occurrence of risk factors have been underestimated. Here we analysed data of 344,324 participants from the UK Biobank with 15 yr of follow-up data for 210 modifiable risk factors. We first conducted an exposure-wide association study and then combined factors associated with dementia to generate composite scores for different domains. We then evaluated their joint associations with dementia in a multivariate Cox model. We estimated the potential impact of eliminating the unfavourable profiles of risk domains on dementia using population attributable fraction. The associations varied by domain, with lifestyle (16.6%), medical history (14.0%) and socioeconomic status (13.5%) contributing to the majority of dementia cases. Overall, we estimated that up to 47.0%-72.6% of dementia cases could be prevented.

Multimorbidity pattern and risk of dementia in later life: an 11-year follow-up study using a large community cohort and linked electronic health records

BACKGROUND

Several long-term chronic illnesses are known to be associated with an increased risk of dementia independently, but little is known how combinations or clusters of potentially interacting chronic conditions may influence the risk of developing dementia.

METHODS

447 888 dementia-free participants of the UK Biobank cohort at baseline (2006-2010) were followed-up until 31 May 2020 with a median follow-up duration of 11.3 years to identify incident cases of dementia. Latent class analysis (LCA) was used to identify multimorbidity patterns at baseline and covariate adjusted Cox regression was used to investigate their predictive effects on the risk of developing dementia. Potential effect moderations by C reactive protein (CRP) and Apolipoprotein E (APOE) genotype were assessed via statistical interaction.

RESULTS

LCA identified four multimorbidity clusters representing Mental health, Cardiometabolic, Inflammatory/autoimmune and Cancer-related pathophysiology, respectively. Estimated HRs suggest that multimorbidity clusters dominated by Mental health (HR=2.12, p<0.001, 95% CI 1.88 to 2.39) and Cardiometabolic conditions (2.02, p<0.001, 1.87 to 2.19) have the highest risk of developing dementia. Risk level for the Inflammatory/autoimmune cluster was intermediate (1.56, p<0.001, 1.37 to 1.78) and that for the Cancer cluster was least pronounced (1.36, p<0.001, 1.17 to 1.57). Contrary to expectation, neither CRP nor APOE genotype was found to moderate the effects of multimorbidity clusters on the risk of dementia.

CONCLUSIONS

Early identification of older adults at higher risk of accumulating multimorbidity of specific pathophysiology and tailored interventions to prevent or delay the onset of such multimorbidity may help prevention of dementia.

Diabetes and cognitive function: An evidence-based current perspective

Several epidemiological studies have clearly identified diabetes mellitus (DM) as a major risk factor for cognitive dysfunction, and it is going to be a major public health issue in the coming years because of the alarming rise in diabetes prevalence across the world. Brain and neural tissues predominantly depend on glucose as energy substrate and hence, any alterations in carbohydrate meta-bolism can directly impact on cerebral functional output including cognition, executive capacity, and memory. DM affects neuronal function and mental capacity in several ways, some of which include hypoperfusion of the brain tissues from cerebrovascular disease, diabetes-related alterations of glucose transporters causing abnormalities in neuronal glucose uptake and metabolism, local hyper- and hypometabolism of brain areas from insulin resistance, and recurrent hypoglycemic episodes inherent to pharmacotherapy of diabetes resulting in neuronal damage. Cognitive decline can further worsen diabetes care as DM is a disease largely self-managed by patients. Therefore, it is crucial to understand the pathobiology of cognitive dysfunction in relation to DM and its management for optimal long-term care plan for patients. A thorough appraisal of normal metabolic characteristics of the brain, how alterations in neural metabolism affects cognition, the diagnostic algorithm for patients with diabetes and dementia, and the management and prognosis of patients when they have this dangerous combination of illnesses is imperative in this context. This evidence-based narrative with the back-up of latest clinical trial reviews elaborates the current understanding on diabetes and cognitive function to empower physicians to manage their patients in day-to-day clinical practice.