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. 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. 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. 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.

The accumulation of non-communicable chronic diseases is recognized to play a crucial role in the deterioration of health in older adults, partly because it may impact frailty states. This study assessed the influence of multimorbidity on the phenotype transitions in frailty over time and frailty-related mortality risk. Data from the Costa Rican Longevity and Healthy Aging Study Cohort (2005-2010) was used. A total of 2660 individuals aged 60 and above were included. Multimorbidity was defined as having three or more non-communicable diseases from 13 chronic conditions. Fried criteria were employed to assess frailty states (robust, pre-frail, frail). Markov-based multistate models were used to examine the impact of multimorbidity on frailty transitions and mortality over 5 years. Multimorbidity accelerates the transition to frailty. Compared to those with fewer than three non-communicable diseases, individuals with both multimorbidity and a prefrail state had a lower chance of recovering to robustness (HR:0.73 [95% CI: 0.54-0.99]), and a higher risk of becoming frail (HR:1.83 [95% CI: 1.43-2.34]). This translates into a 30-month earlier onset of frailty compared to individuals without chronic diseases. Multimorbidity increased the risk of all-cause mortality in those who were pre-frail (HR:1.83 [95% CI: 1.43-2.34]) but did not affect mortality risk in those who were already frail (HR:0.88 [95% CI: 0.62-1.23]). Multimorbidity is a predictor for dynamic transitions to frailty or death. It favors the transition to "Frailty" more than to death and impacts mortality mostly in people with a "Pre-frail" state, encouraging them to explore opportunities for intervention.

Background: Lifestyle factors often interact in complex ways when influencing chronic disease risk. We aimed to examine the prospective associations between empirically derived real-life lifestyle patterns (LPs) and the incidence of major chronic diseases, and to explore the linearity of the relationships between lifestyle summation scores and disease risk. Methods: We included adults free of cardiovascular diseases (CVDs), cancer, chronic respiratory diseases (CRDs), or type 2 diabetes (T2D) at baseline (2006–2013) from the Dutch Lifelines cohort. LPs and lifestyle summation scores were derived from baseline self-reported data on diet, physical activity, substance use, sleep, stress, and social connectedness, each categorised as healthy, moderately healthy, or unhealthy. Fine–Gray sub-hazard regression models assessed associations between LPs and disease incidence, with natural spline functions used to evaluate linearity in summation scores. Results: Among 114,919 T2D-free, 131,248 cancer-free, 91,777 CRD-free, and 77,645 CVD-free participants, we observed 3114 T2D, 4685 cancer, 4133 CRDs, and 2850 CVD incident cases (median follow-up time: 8 years). Compared to the “Unhealthy” pattern, both the “Healthy-in-a-balanced-way” and “Healthy-but-physically-inactive” patterns were broadly significantly protective. The “Unhealthy-but-no-substance-use” pattern was associated with increased T2D risk (Sub-Hazard Ratio (SHR) = 1.27, 95% Confidence Interval (CI): 1.11–1.47) but reduced cancer risk (SHR = 0.85, 95%CI: 0.74–0.97). The “Unhealthy-but-light-drinking-and-never-smoked” pattern was protective for T2D (SHR = 0.89, 95%CI: 0.79–0.99). Linear associations were observed between lifestyle summation scores and disease risk, except for “healthy lifestyle” scores with T2D and “unhealthy lifestyle” scores with CRDs (non-linear p-value < 0.05). Conclusions: There are potential protective effects of healthy lifestyles on T2D, cancer, CRDs, and CVDs. However, the “Unhealthy but no substance use” demonstrated increased risk on T2D, protective effect on cancer and no significant effect on CRDs or CVDs. The relationship between combined lifestyle factors and NCD risk is complex and partly non-linear, showing diminishing benefits beyond certain thresholds, especially T2D and CRDs.