BACKGROUND

The prevalence of multimorbidity is increasing in aging populations globally. Multimorbidity involves various noncommunicable disease (NCD) combinations that extend beyond individual conditions. Identifying how multimorbidity patterns (MPs) configure is crucial for understanding the role of NCD patterns in health prognosis.

METHODS

This study identified MPs and examined their associations with sociodemographic and economic factors in 23,452 participants aged ≥ 60 years from the Lifelines cohort in northern Netherlands (baseline: 2007-2013; follow-up: 2011-2019). Complete data on 14 NCDs at two time points were analyzed, with multimorbidity defined as ≥ 2 NCDs. Latent class and factor analyses identified clusters of NCDs, stratified into MPs based on multimorbidity presence. Multinomial logistic regression assessed the relationships between MPs and sociodemographic and economic traits.

RESULTS

Multimorbidity prevalence was 55% at baseline. Five MPs, consistent across assessments, were identified. The 'Vascular' MP included the fewest NCDs (2-4), while the 'Complex-Treatment Spectrum' had the most (5-11). Adjusted analyses revealed that lower education, not having a partner, and lower income significantly increased the relative-risk of belonging to high-risk MPs, such as 'Metabolic Risk,' 'Major CVD-Vascular Conditions,' and 'Complex-Treatment Spectrum', compared to participants without multimorbidity. These MPs reflect profiles with distinct risk factors and prognoses.

CONCLUSIONS

Multimorbidity manifests as stable patterns in this population. MPs derived from latent class analysis were more interpretable and consistent over time compared to correlation-based approaches. Income disparities influence MP profiles, highlighting the need for tailored interventions. Longitudinal studies are recommended to explore NCD contributions to MP dynamics and inform strategies addressing health and social inequities.