LAUSR

INTRODUCTION While some conditions clusters represent the chance co-occurrence of common individual conditions, others may represent shared causal factors. The aims of this study were to identify multimorbidity patterns in older adults, and to explore the relationship between social variables, lifestyle behaviors, and the multimorbitidity patterns identified. METHODS This was a cross-sectional design. Data came from 3273 individuals aged ≥65 from the Seniors-ENRICA-2 cohort; information on 60 chronic diseases categories, categorized according to the 2nd edition of the International Classification of Primary Care and the 10th edition of the International Classification of Diseases, was obtained from clinical records linkage. To identify multimorbidity patterns, an exploratory factor analysis was conducted over chronic disease categories with prevalence >5%, using Oblimin rotation and Kaiser's eigenvalues-greater-than-one rule. The association between multimorbidity patterns and their potential determinants was assessed with multivariable linear regression. RESULTS The three-factor solution (Musculoskeletal diseases and mental disorders, Cardiometabolic diseases, and Cardiopulmonary diseases) explained 64.5% of the total variance. Being older, lower occupational category, higher levels of loneliness, lower levels of physical activity, and higher body mass index were associated with higher scores in the multimorbidity patterns identified. Female sex was linked to the Musculoskeletal diseases and mental disorders pattern, while being male was revealed to the two remaining multimorbidity patterns. A high diet quality was inversely related to Cardiometabolic diseases, while optimal sleep duration was inversely related to Cardiopulmonary diseases. DISCUSSION/CONCLUSION Three multimorbidity patterns were identified in older adults. Multimorbidity patterns were differently associated with social variables and lifestyles behavioral factors.