Evidence map›Paper›PMID 40638618›Full record

ArticlePloS one2025

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

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Corey L NagelCollege of Nursing, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.ORCID https://orcid.org/0000-0003-3725-500X
Siting ChenOHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon, United States of America.
Heather G AlloreDepartment of Internal Medicine, School of Medicine, Yale University, New Haven, Connecticut, United States of America.
Anda BotoseneanuDepartment of Health & Human Services, University of Michigan, Dearborn, Michigan, United States of America.
Jason T NewsomDepartment of Psychology, Portland State University, Portland, Oregon, United States of America.
Nick BishopNorton School of Human Ecology, University of Arizona, Tucson, Arizona, United States of America.
David A DorrDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America.ORCID https://orcid.org/0000-0003-2318-7261
Jeffrey KayeDepartment of Neurology, Oregon Health & Science University, Portland, Oregon, United States of America.
Ana R QuiñonesDepartment of Family Medicine, Oregon Health & Science University, Portland, Oregon, United States of America.ORCID https://orcid.org/0000-0001-6554-7734

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatterns 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.

methodsWe 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.

results11.8% 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).

conclusionsDevelopment 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.

Indexed as

Cardiovascular DiseasesDementiaMultimorbidityAgedAged, 80 and overCluster AnalysisFemaleHumansLongitudinal StudiesMale

Identifiers

PMID40638618
PMCPMC12244708

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