Comorbidity

BackgroundThere is limited evidence on the long-term associations between cardiometabolic multimorbidity (CMM) and multiple domains of functioning, and the potential role of social activity in these associations. We aimed to examine the associations between CMM and joint trajectories of physical disability, depressive symptom, and cognitive function, and whether social activity modifies these trajectories.MethodsThis multicohort study used pooled data of four prospective cohorts of adults from China, the UK, the USA, and Europe. Participants with complete information on cardiometabolic diseases (CMDs, including heart diseases, stroke, and diabetes) and social activity at baseline were included. Physical disability, depressive symptom, and cognitive function were measured one to five times. Longitudinal modelling was used to describe post-baseline trajectories of the three domains, stratified by CMM status and social activity.ResultsAmong 73,778 participants (age 63.4 ± 9.1 years, 56.0% female), 20.0% had single CMD and 4.3% had CMM at baseline. Participants with single CMD or CMM had persistently worse physical disability, depressive symptom, and cognitive function compared to those without CMD. CMM was associated with faster worsening of physical disability (βlinear change*CMM = - 0.017 [95% confidence interval = - 0.030 to - 0.004]) and cognitive function (- 0.035 [- 0.050 to - 0.019]), particularly among those with social inactivity. Among 40,883 participants (age 62.9 ± 8.9 years, 55.6% female) in joint trajectory analysis, four trajectories were identified: 'favourable trajectories of physical disability, depressive symptom, and cognitive function' (48.1%); 'worsening cognitive function' (32.6%); 'worsening depressive symptom and cognitive function' (14.6%); and 'rapidly-worsening physical disability and worsening depressive symptom and cognitive function' (4.7%). CMD or CMM were associated with all the three worsening joint trajectories. The highest odds were observed for concurrent worsening across all three functional domains in participants with CMM (odds ratio 3.43 [2.80-4.20]), with more unfavourable trajectories in those with social inactivity (7.26 [5.49-9.59], P for additive interaction ConclusionsCMM was associated with worse levels, faster progression, and concurrent deterioration in physical disability, depressive symptom, and cognitive function, with the poorest trajectories among those who were socially inactive. This underscores the importance of implementing prevention strategies that integrate physical, psychological, cognitive, and social activities.

BackgroundPopulation aging has led to a rising prevalence of multimorbidity among older adults, posing a significant global public health challenge. This study aims to comprehensively integrate the evidence regarding the prevalence of multimorbidity among the older adults and the influencing factors through a systematic review and meta-analysis.MethodsWe conducted a systematic review and meta-analysis in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The research question was structured using the PICOS framework: Population: adults aged ≥60 or ≥65; Intervention/Phenomenon of Interest: multimorbidity (≥2 chronic conditions); Comparator: not applicable (prevalence synthesis) or various demographic groups (risk factor analysis); Outcomes: prevalence of multimorbidity and pooled odds ratios (OR) of risk factors; Study design: cross-sectional studies. We conducted a comprehensive search of multiple databases up to July 2025 to identify cross-sectional studies that reported the prevalence and/or risk factors of multimorbidity (defined as ≥2 chronic conditions) in adults aged ≥60 or ≥65. The pooled prevalence was estimated using a random-effects model, and heterogeneity was explored through subgroup analysis and meta-regression.ResultsForty-nine studies involving 729,043 older adults were included. The global pooled prevalence of multimorbidity was 46.0% (95% CI: 38.0%-55.0%), with substantial heterogeneity (I 2 = 99.98%). Meta-regression identified survey location as a key source of heterogeneity. Subgroup analyses revealed a higher prevalence in females (48%) than males (41%) and a lower prevalence in studies from China (40%) compared to other countries (53%). Significant risk factors included age ≥ 70 years (OR = 1.43), female sex (OR = 1.59), BMI ≥ 28 kg/m2 (OR = 1.97), low education (OR = 1.44), single status (OR = 1.32), and low income (OR = 1.46).ConclusionMultimorbidity is highly prevalent among the global older adults, with significant geographical and demographic variations. Modifiable risk factors such as obesity, low education, and low income are crucial targets for intervention to alleviate the burden of multimorbidity.

Aging is accompanied by the progressive accumulation of biological deficits, which increases susceptibility to developing multiple chronic diseases (that is, multimorbidity). The biological underpinnings of multimorbidity remain poorly understood. Here we analyzed 54 blood biomarkers reflecting inflammatory, vascular, metabolic and neurodegenerative processes in 2,247 individuals aged 60 and over from the Swedish National Study on Aging and Care in Kungsholmen. Multimorbidity was assessed using three measures: baseline total disease count, baseline multimorbidity patterns identified through latent class analysis and 15-year rate of disease accumulation. Associations between baseline biomarkers and multimorbidity measures were examined using least absolute shrinkage and selection operator regression. Growth differentiation factor 15, hemoglobin A1c, cystatin C, leptin and insulin were consistently and positively associated with all multimorbidity measures. Additional biomarkers demonstrated specific associations with distinct multimorbidity patterns. Moreover, faster disease accumulation was directly associated with gamma-glutamyl transferase and inversely with albumin. Longitudinal results were externally validated in 522 participants from the Baltimore Longitudinal Study of Aging, with comparable predictive accuracy. Our findings suggest that multiple biological processes contribute to multimorbidity through shared and distinct mechanisms. Metabolic disturbances emerged as a key driver of multimorbidity. If confirmed, these processes could represent targets for interventions to mitigate disease accumulation.