Longitudinal sequencing of cardiometabolic multimorbidity among older adults and association with subsequent dementia onset. - Corey L Nagel; Siting Chen; Heather G Allore; Anda Botoseneanu; Jason T Newsom; Nick Bishop; David A Dorr; Jeffrey Kaye; Ana R Quiñones (PLoS ONE)
Background Patterns of development of cardiometabolic multimorbidity (CMM) and the impact of specific cardiometabolic disease combinations on cognitive function are not well understood. This study utilizes sequence analysis to describe the ordering and timing of cardiometabolic disease accumulation over a five-year period and to assess both sociodemographic predictors and cognitive outcomes of typical cardiometabolic disease sequences. Methods We analyzed data from the National Health and Aging Trends Study (2011-2022), including respondents aged ≥65 years without CMM or cognitive impairment at baseline (N = 4956). We used sequence analysis with optimal matching and hierarchical cluster analysis to describe temporal patterns of cardiometabolic disease accumulation and to construct a typology by clustering similar sequences. Sociodemographic predictors of CMM cluster membership were assessed using multinomial logistic regression and discrete time survival analysis was used to examine the association of CMM clusters with subsequent dementia development. Results 11.8% of respondents developed CMM within 5-years. From a total of 366 distinct cardiometabolic disease sequences, we identified eight cardiometabolic sequence clusters. The first five clusters, "No Cardiometabolic Disease" (N = 2283, 46.1%); "Diabetes Only" (N=642, 13.0%); Heart Disease Only" (N = 297, 6.0%); "MI Only" (N = 145, 2.9%); "Stroke Only" (N = 132, 2.7%), were composed of persons who did not develop CMM over the observation period. The sixth cluster, "Incident CVD with Multimorbidity" (N = 656, 13.2%), was largely composed of persons with no conditions at baseline who developed incident cardiometabolic disease and/or CMM during the observation period (N = 477, 72.7%) and the seventh cluster, "Diabetes Multimorbidity" (N = 333, 6.7%), primarily consisted of persons with diabetes who developed incident CMM. Finally, the eight cluster (N = 468, 9.4%) was characterized by mortality early in the observation period with minimal CMM development during the observation period. Black and Hispanic race/ethnicity, lower wealth, and obesity were associated with increased likelihood of membership in one or both of the clusters characterized by CMM development. We observed increased dementia risk among persons in the Incident CVD with Multimorbidity cluster (HR = 1.32, 95% CI = 1.04-1.67) and the Diabetes MM cluster (HR = 1.88, 95% CI = 1.44,2.44). Conclusions Development of cardiometabolic multimorbidity is more likely among minoritized and/or low-income older adults and is associated with increased risk of subsequent dementia. Targeted approaches to cardiometabolic disease prevention and risk reduction may be an effective means of slowing or preventing the onset of cognitive decline among these groups.