Describing the Profile of Individuals at Heightened Risk for Cardiometabolic Multimorbidity: A Secondary Analysis of the Canadian Longitudinal Study on Aging Data

Nicole I Ketter; Mary Jung; Suzanne Huot; Brodie M Sakakibara

doi:10.1177/08901171251374738

Describing the Profile of Individuals at Heightened Risk for Cardiometabolic Multimorbidity: A Secondary Analysis of the Canadian Longitudinal Study on Aging Data

Am J Health Promot. 2025 Aug 29:8901171251374738. doi: 10.1177/08901171251374738. Online ahead of print.

Authors

Nicole I Ketter^{1

2}, Mary Jung³, Suzanne Huot⁴, Brodie M Sakakibara^{2

4}

Affiliations

¹ Rehabilitation Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
² Centre for Chronic Disease Prevention and Management, Faculty of Medicine, University of British Columbia, Okanagan, BC, Canada.
³ School of Health and Exercise Sciences, University of British Columbia, Okanagan, BC, Canada.
⁴ Department of Occupational Science and Occupational Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.

PMID: 40882996
DOI: 10.1177/08901171251374738

Abstract

PurposeDevelop a parsimonious model of individuals at heightened-risk for 3-year cardiometabolic multimorbidity (CM) onset.DesignAn observational, secondary analysis of Canadian Longitudinal Study on Aging (CLSA) data.SettingCLSA is a national cohort study in Canada. Baseline data were collected between 2010-2015, and follow-up data were collected between 2015-2018.SubjectsCLSA included community-dwelling adults aged 45-85 at recruitment from across Canada.MeasuresHealth conditions: stroke, heart disease or heart attack and diabetes. Personal factors: age, sex, marital status, household income, education, and ethnicity. Environmental factors: social support, personal assistance, and location of residence. CM cases: at least two of stroke, heart disease and diabetes at follow-up assessment.AnalysisHierarchical logistic regression analyses with backwards elimination procedures were used to develop a parsimonious prediction model.ResultsThe sample consisted of 41 841 individuals, representing a weighted population of 13 741 119. The population had a mean age of 62.3 years (SD = 10.1), was 53% female, predominantly married or in common-law relationships (77%), post-secondary graduates (61%), white (95%), and lived in an urban area (81%). Males (OR:1.93, 95%CI:1.65-2.25, P < 0.001), ≥65 years (OR:1.51, 95%CI:1.29-1.76), P < 0.001), who had stroke (OR:20.09, 95%CI:12.88-30.35, P < 0.001), heart disease (OR:15.55, 95%CI:12.60-19.26, P < 0.001), or diabetes (OR:12.57, 95%CI:10.37-15.31, P < 0.001), not completed post-secondary (OR:1.30, 95%CI:1.04-1.61, P = 0.017), income of <50k (OR:1.29, 95%CI:1.10-1.52, P = 0.002), and received home care (OR:1.56, 95%CI:1.17-2.04, P = 0.002) were at heightened risk of CM.ConclusionsDeveloping a profile of high-risk individuals may enhance the efficiency of CM prevention and reduce disease onset. Critical limitations include the CLSA exclusion criteria, and the small proportion of minoritized individuals that restrict generalizability in these populations.

Keywords: CLSA; diabetes mellitus; heart disease; multimorbidity; stroke.