BACKGROUND: Research examining the role of obesity in cardiovascular disease (CVD) often fails to adequately consider heterogeneity in obesity severity, distribution, and duration. METHODS AND RESULTS: We here use multivariate latent class mixed models in the biracial Atherosclerosis Risk in Communities study (N=14 514; mean age=54 years; 55% female) to associate obesity subclasses (derived from body mass index, waist circumference, self-reported weight at age 25, tricep skinfold, and calf circumference across up to four triennial visits) with total mortality, incident CVD, and CVD risk factors. We identified four obesity subclasses, summarized by their body mass index and waist circumference slope as decline (4.1%), stable/slow decline (67.8%), moderate increase (24.6%), and rapid increase (3.6%) subclasses. Compared with participants in the stable/slow decline subclass, the decline subclass was associated with elevated mortality (hazard ratio [HR] 1.45, 95% CI 1.31, 1.60, P<0.0001) and with heart failure (HR 1.41, 95% CI 1.22, 1.63, P<0.0001), stroke (HR 1.53, 95% CI 1.22, 1.92, P=0.0002), and coronary heart disease (HR 1.36, 95% CI 1.14, 1.63, P=0.0008), adjusting for baseline body mass index and CVD risk factor profile. The moderate increase latent class was not associated with any significant differences in CVD risk as compared to the stable/slow decline latent class and was associated with a lower overall risk of mortality (HR 0.85, 95% CI 0.80, 0.90, P<0.0001), despite higher body mass index at baseline. The rapid increase latent class was associated with a higher risk of heart failure versus the stable/slow decline latent class (HR 1.34, 95% CI 1.10, 1.62, P=0.004). CONCLUSIONS: Consideration of heterogeneity and longitudinal changes in obesity measures is needed in clinical care for a more precision-oriented view of CVD risk.
Bibliographical noteFunding Information:
The authors thank the staff and participants of the ARIC study for their important contributions. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I). Studies on cancer in ARIC are also supported by the National Cancer Institute (U01 CA164975) and the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I). The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Cancer incidence data have been provided by the Maryland Cancer Registry, Center for Cancer Surveillance and Control, Maryland Department of Health, 201W. Preston Street, Room 400, Baltimore, MD 21201. We acknowledge the State of Maryland, the Maryland Cigarette Restitution Fund, and the National Program of Cancer Registries (NPCR of the Centers for Disease Control and Prevention [CDC]) for the funds that helped support the availability of the cancer registry data. We are grateful to the Carolina Population Center grant P2C HD050924. This analysis was funded by the National Heart, Lung, and Blood Institute through R01HL143885. Raffield was supported by National Heart, Lung, and Blood Institute T32 HL129982. The project described was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490 (Raffield). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
© 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.
- Cardiovascular disease
- Latent class models