The Impact of Multimorbidity on Resting Metabolic Rate in Community-Dwelling Women over a Ten-Year Period: A Cross-Sectional and Longitudinal Study

Higher baseline resting metabolic rate is associated with 1-year frailty decline among older adults residing in an urban area

BACKGROUND

Dysregulated energy metabolism is one hypothesized mechanism underlying frailty. Resting energy expenditure, as reflected by resting metabolic rate (RMR), makes up the largest component of total energy expenditure. Prior work relating RMR to frailty has largely been done in cross section with mixed results. We investigated whether and how RMR related to 1-year frailty change while adjusting for body composition.

METHODS

N = 116 urban, predominantly African-American older adults were recruited between 2011 and 2019. One-year frailty phenotype (0-5) was regressed on baseline RMR, frailty phenotype, demographics and body composition (DEXA) in an ordinal logistic regression model. Multimorbidity (Charlson comorbidity scale, polypharmacy) and cognitive function (Montreal Cognitive Assessment) were separately added to the model to assess for change to the RMR-frailty relationship. The model was then stratified by baseline frailty status (non-frail, pre-frail) to explore differential RMR effects across frailty.

RESULTS

Higher baseline RMR was associated with worse 1-year frailty (odds ratio = 1.006 for each kcal/day, p = 0.001) independent of baseline frailty, demographics, and body composition. Lower fat-free mass (odds ratio = 0.88 per kg mass, p = 0.008) was independently associated with worse 1-year frailty scores. Neither multimorbidity nor cognitive function altered these relationships. The associations between worse 1-year frailty and higher baseline RMR (odds ratio = 1.009, p < 0.001) and lower baseline fat-free mass (odds ratio = 0.81, p = 0.006) were strongest among those who were pre-frail at baseline.

DISCUSSION

We are among the first to relate RMR to 1-year change in frailty scores. Those with higher baseline RMR and lower fat-free mass had worse 1-year frailty scores, but these relationships were strongest among adults who were pre-frail at baseline. These relationships were not explained by chronic disease or impaired cognition. These results provide new evidence suggesting higher resting energy expenditure is associated with accelerate frailty decline.

Greater Skeletal Muscle Oxidative Capacity Is Associated With Higher Resting Metabolic Rate: Results From the Baltimore Longitudinal Study of Aging

Resting metabolic rate (RMR) tends to decline with aging. The age-trajectory of decline in RMR is similar to changes that occur in muscle mass, muscle strength, and fitness, but while the decline in these phenotypes has been related to changes of mitochondrial function and oxidative capacity, whether lower RMR is associated with poorer mitochondrial oxidative capacity is unknown. In 619 participants of the Baltimore Longitudinal Study of Aging, we analyzed the cross-sectional association between RMR (kcal/day), assessed by indirect calorimetry, and skeletal muscle maximal oxidative phosphorylation capacity, assessed as postexercise phosphocreatine recovery time constant (τ PCr), by phosphorous magnetic resonance spectroscopy. Linear regression models were used to evaluate the relationship between τ PCr and RMR, adjusting for potential confounders. Independent of age, sex, lean body mass, muscle density, and fat mass, higher RMR was significantly associated with shorter τ PCr, indicating greater mitochondrial oxidative capacity. Higher RMR is associated with a higher mitochondrial oxidative capacity in skeletal muscle. This association may reflect a relationship between better muscle quality and greater mitochondrial health.

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Multimorbidity, the presence of 1+ chronic condition in an individual, remains one of the greatest challenges to health on a global scale. Although the prevalence of multimorbidity has been well-established, its incidence is not fully understood. This systematic review determined the incidence of multimorbidity across the lifespan; the order in which chronic conditions accumulate to result in multimorbidity; and cataloged methods used to determine and report accumulation of chronic conditions resulting in multimorbidity. Studies were identified by searching MEDLINE, Embase, CINAHL, and Cochrane electronic databases. Two independent reviewers evaluated studies for inclusion and performed quality assessments. Of 36 included studies, there was high heterogeneity in study design and operational definitions of multimorbidity. Studies reporting incidence (n = 32) reported a median incidence rate of 30.7 per 1,000 person-years (IQR 39.5 per 1,000 person-years) and a median cumulative incidence of 2.8% (IQR 28.7%). Incidence was notably higher for persons with older age and 1+ chronic conditions at baseline. Studies reporting patterns in accumulation of chronic conditions (n = 5) reported hypertensive and heart diseases, and diabetes, as among the common starting conditions resulting in later multimorbidity. Methods used to discern patterns were highly heterogenous, ranging from the use of latent growth trajectories to divisive cluster analyses, and presentation using alluvial plots to cluster trajectories. Studies reporting the incidence of multimorbidity and patterns in accumulation of chronic conditions vary greatly in study designs and definitions used. To allow for more accurate estimations and comparison, studies must be transparent and consistent in operational definitions of multimorbidity applied.

Trajectories of Body Composition during Advanced Aging in Consideration of Diet and Physical Activity: A 20-Year Longitudinal Study

This prospective study investigates age-dependent changes in anthropometric data and body composition over a period of two decades in consideration of physical activity and diet in community-dwelling subjects ≥60 years. Overall, 401 subjects with median follow-up time of 12 years were examined. Fat-free mass (FFM) and fat mass (FM) were analyzed using bioelectrical impedance analysis. Physical activity was assessed via a self-administered questionnaire. Dietary intake was examined by 3-day dietary records. Linear mixed-effects models were used to analyze the influence of age, sex, physical activity and energy/protein intake on anthropometric data and body composition by considering year of entry, use of diuretics and diagnosis of selected diseases. At baseline, median values for daily energy and protein intakes were 8.5 megajoule and 81 g and physical activity index was 1.7. After adjusting for covariates, advancing age was associated with parabolic changes indicating overall changes from age 60 to 90 years in women and men in body mass: −4.7 kg, −5.0 kg; body mass index: +0.04 kg/m², −0.33 kg/m²; absolute FFM: −2.8 kg, −3.5 kg; absolute FM: −1.8 kg, −1.2 kg and waist circumference: +16 cm, +12 cm, respectively. No age-dependent changes were found for upper arm circumference and relative (%) FFM. Dietary and lifestyle factors were not associated with changes in anthropometric or body composition parameters. In summary, the results indicate non-linear age-dependent changes in anthropometric data and body composition, which are largely unaffected by the degree of habitual physical activity and dietary protein intake in well-nourished community-dwelling subjects.

Longitudinal Changes in Resting Metabolic Rates with Aging Are Accelerated by Diseases

Resting metabolic rate (RMR) declines with aging and is related to changes in health status, but how specific health impairments impact basal metabolism over time has been largely unexplored. We analyzed the association of RMR with 15 common age-related chronic diseases for up to 13 years of follow-up in a population of 997 participants to the Baltimore Longitudinal Study of Aging. At each visit, participants underwent measurements of RMR by indirect calorimetry and body composition by DEXA. Linear regression models and linear mixed effect models were used to test cross-sectional and longitudinal associations of RMR and changes in disease status. Cancer and diabetes were associated with higher RMR at baseline. Independent of covariates, prevalent COPD and cancer, as well as incident diabetes, heart failure, and CKD were associated with a steeper decline in RMR over time. Chronic diseases seem to have a two-phase association with RMR. Initially, RMR may increase because of the high cost of resiliency homeostatic mechanisms. However, as the reserve capacity becomes exhausted, a catabolic cascade becomes unavoidable, resulting in loss of total and metabolically active mass and consequent RMR decline.

Clinical determinants of resting metabolic rate in geriatric outpatients

PURPOSE

Accurate estimation of the energy requirements including resting metabolic rate (RMR) is important for optimal nutritional care, yet its clinical determinants are unknown. This study examined the associations between clinical determinants of the Comprehensive Geriatric Assessment (CGA) domains with RMR among geriatric outpatients.

MATERIALS & METHODS

Data were retrieved from cohorts of community-dwelling older adults (n = 84, 54 female) referring to geriatrics outpatient mobility clinics in both Amsterdam, The Netherlands and Melbourne, Australia. Determinants within domains of the CGA included diseases (number, type and severity of diseases, polypharmacy), nutrition (body weight, body mass index, absolute and relative skeletal muscle mass, fat-free mass and fat mass, risk of malnutrition), physical function (handgrip strength, Short Physical Performance Battery, Timed Up & Go), cognition (Mini-Mental State Examination), psychological wellbeing (Geriatric Depression Scale) and blood pressure. RMR was objectively measured using indirect calorimetry with a canopy hood. Association between the clinical determinants with standardized RMR (country and sex-specific z-score) were analysed with linear regression adjusted for age, sex and body weight.

RESULTS

Determinants within the nutritional domain were associated with RMR; body weight showed the strongest association with RMR. Significant associations between determinants within the nutritional domain with RMR disappeared after further adjustment for body weight. None of the other domains were associated with RMR.

CONCLUSIONS

Body weight is the strongest clinical determinant of RMR and should be taken into account when estimating RMR in geriatric care.