Energy Metabolism and the Burden of Multimorbidity in Older Adults: Results From the Baltimore Longitudinal Study of Aging

Abstract

Excessively elevated resting metabolic rate (RMR) for persons of a certain age, sex, and body composition is a mortality risk factor. Whether elevated RMR constitutes an early marker of health deterioration in older adult has not been fully investigated. Using data from the Baltimore Longitudinal Study of Aging, we hypothesized that higher RMR (i) was cross-sectionally associated with higher multimorbidity and (ii) predicted higher multimorbidity in subsequent follow-ups. The analysis included 695 Baltimore Longitudinal Study of Aging participants, aged 60 or older at baseline, of whom 248 had follow-up data available 2 years later and 109 four years later. Multimorbidity was assessed as number of chronic diseases. RMR was measured by indirect calorimetry and was tested in regression analyses adjusted for covariates age, sex, and dual-energy x-ray absorptiometry-measured total body fat mass and lean mass. Baseline RMR and multimorbidity were positively associated, independent of covariates (p = .002). Moreover, in a three-wave bivariate autoregressive cross-lagged model adjusted for covariates, higher prior RMR predicted greater future multimorbidity above and beyond the cross-sectional and autoregressive associations (p = .034). RMR higher than expected, given age, sex, and body composition, predicts future higher multimorbidity in older adults and may be used as early biomarker of impending health deterioration. Replication and the development of normative data are required for clinical translation.

Keywords: Aging; Health status; Metabolism.; Multimorbidity; Resting metabolic rate.

Figure 1.

Three-wave bivariate autoregressive cross-legged model testing the longitudinal association between resting metabolic rate (RMR; kcal/d) and number of diseases. The model includes three time points: baseline visit (N = 695), 2-year follow-up visit (N = 248), and 4-year follow-up visit (N = 109). At each time point, the cross-lagged associations between RMR and number of diseases were simultaneously tested, adjusting for the autoregressive ones and baseline correlation. Baseline age, sex, and baseline dual-energy x-ray absorptiometry–measured total body fat mass and lean mass were also included in the model as covariates (not shown in the figure). Significant associations (p < .05) are represented using solid arrows, whereas not significant ones are represented using dashed arrows.