Introduction

Ageing is a complex process that involves a progressive decline in organ function, disrupted homeostasis, reduced physiological reserve and changes in body composition.1, 2 Overall, these changes result in the development of multimorbidity and disability that synergistically impacts the health outcomes of older people.3, 5, 4 Unfavourable changes in body composition over time have pathological implications, such as obesity, osteoporosis and sarcopenia. In addition to the individual components of body composition, combinations of these components further suggest several at-risk conditions, such as sarcopenic obesity, osteosarcopenia or even osteosarcopenic obesity.6 These conceptual proposals originated from the double burden assumption that two or more unfavourable conditions that occur together generate more adverse impacts than either alone. However, the obesity paradox weakened the potential impacts of sarcopenic obesity, the most common combination of the above-mentioned conditions.7, 8 Previous studies have repeatedly confirmed the importance of functional ability over individual diseases or multimorbidity in older adults,3, 5, 4 and obesity increases the risk of cardiovascular diseases, immobility, falls and dementia.7, 9, 8 Therefore, the health risk of obesity in late life should be addressed based on the health characteristics of older adults. Moreover, cardiometabolic risk related to obesity also substantially increases the risk of sarcopenia, frailty and dementia. Malnutrition secondary to strict control of the cardiometabolic risk should be balanced to evaluate the risk of obesity later in life.

Sarcopenia is a disease defined by the age-related loss of skeletal muscle mass together with a loss of muscle strength and/or reduced physical performance.6 The adverse impacts of sarcopenia have been widely reported in the geriatric population and in patients with different clinical conditions, such as cancer, heart failure, chronic obstructive pulmonary disease, chronic kidney disease or liver disease.10, 11 However, older persons with sarcopenia may have two phenotypes, that is, lean or obese. Because of the potential survival benefits of the obesity paradox, the clinical impacts of sarcopenic obesity are still under debate.12 Moreover, the operational definition of obesity in late life is controversial, and neither body mass index nor waist circumference satisfies the pathological definition of obesity.

The definition and diagnosis of sarcopenic obesity are also confusing. Some studies have shown that sarcopenic obesity increases the risk of metabolic syndrome, cardiovascular disease and impairment in instrumental activities of daily living,13, 14 but the overall impact of sarcopenic obesity in older adults remains unclear. In particular, some new approaches and biomarkers are needed to identify older persons at risk for both functional disability and cardiovascular disease, which justifies the original concept of sarcopenic obesity.

The muscle-to-fat ratio (MFR) has been reported to be a biomarker for cardiometabolic conditions and chronic kidney disease in older adults.15, 16 However, the ratio of total body muscle mass to total body fat mass is not completely compatible with the concepts of sarcopenia that use appendicular muscle mass and the focus on mobility. Hence, this study aimed to compare the clinical characteristics, functional ability, cardiometabolic risks, and clinical outcomes of biomarkers of unfavourable body composition, that is, relative appendicular muscle mass (RASM), the ratio of appendicular muscle mass to total body fat mass (aMFR), and the ratio of total body muscle to total body fat (tMFR), to explore the feasibility of using potential biomarkers to better define sarcopenic obesity.

Methods Study design and participants

This study used the first-wave data of the Longitudinal Aging Study of Taipei, which recruited community-dwelling people aged 50 years and older living in the metropolitan area of Taipei, Taiwan.17 However, data of participants under 65 years of age were not included in the analysis. This study was approved by the Institutional Review Board of National Yang Ming University (YM104121F-5). All participants provided written informed consent after a thorough explanation of the study by the research staff before enrolment. The study was designed and conducted in accordance with the principles of the Declaration of Helsinki; the cross-sectional, observational design and reporting format follow the Strengthening the Reporting of Observational Studies in Epidemiology guidelines18 and the ethical guidelines of the Journal of Cachexia, Sarcopenia and Muscle.19

Demographic data and functional assessment

Demographic characteristics, including age, sex, years of education, marital status, living status, smoking and drinking history, medical history and multimorbidity (evaluated using the Charlson Comorbidity Index), were collected. All participants underwent physical examinations, including blood pressure, body height and body weight. Muscle strength was measured by grip strength of the dominant hand, and the 6 m usual gait speed was used to evaluate physical performance. Moreover, the 6 min walking distance was used to evaluate muscle endurance, and the average energy expenditure of physical activity was evaluated using the International Physical Activity Questionnaire. Nutritional status was evaluated using the Mini-Nutritional Assessment (MNA). Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA), and depressive symptoms were evaluated by using the Center for Epidemiologic Studies - Depression Scale.

Body composition

Body composition, including the percentage of total body fat, lean body mass and estimated appendicular muscle mass, was evaluated using bioimpedance analysis (Inbody S10, Seoul, South Korea). Bone mineral density (BMD) was estimated by quantitative ultrasound at the calcaneus. Appendicular skeletal muscle mass was obtained by summing the lean tissue mass of all four limbs, and the RASM was calculated as appendicular skeletal muscle mass divided by the squared body height (in metres). In this study, low muscle mass was defined as the lowest quintile of sex-specific RASM measurements. The aMFR and tMFR were defined accordingly, and the sex-specific lowest quintile was used to define a low MFR.

Laboratory data

In this study, we used automated analysis (ADVIA Chemistry XPT, Siemens, Germany) to measure the serum levels of albumin, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triglycerides, glucose and high-sensitivity C-reactive protein. Whole-blood glycated haemoglobin (HbA1c) was measured using high-performance liquid chromatography (Bio-Rad D-100 System, Bio-Rad, USA). Serum levels of 25-hydroxyvitamin D were quantified by chemiluminescent immunoassay (LIAISON, DiaSorin, Saluggia VC. Italy).

Outcome measurements

All participants were clinically followed by the research staff every 3 months by telephone. Outcome events were defined as documented fractures and mortality during the follow-up period. Due to the low event rate during the study period, fractures and mortality were combined as the composite outcome for all participants. All participants were clinically followed for a mean of 32.6 months (range: 30–36 months).

Statistical analysis

In the present study, continuous variables are expressed as the mean ± standard deviation, and categorical variables are expressed as numbers or percentages. Comparisons of continuous variables were performed by independent t-tests, and χ2 analysis was used to compare categorical variables. Non-parametric methods were used for statistical analyses of nonnormally distributed variables. Multivariate linear regression was used to explore the independent associations of low RASM, low aMFR or low tMFR with the other variables (including demographic characteristics, functional assessment and laboratory data). In particular, the association between aMFR or tMFR and the composite outcome (fracture and mortality) was also assessed, adjusting for other variables. Only confounders reached statistical significance in univariate analyses before selection for multivariate analyses. In linear regression analyses, betas were standardized coefficients. A two-sided P value of <0.05 was considered indicative of statistical significance. All statistical analyses were carried out using SPSS 22.0 (SPSS Inc., Chicago, IL, USA).

Results

Among 1060 community-dwelling adults aged 65 and older included in this study, 196 (34.2% male participants) were found to have a low RASM, but none of them were diagnosed with sarcopenia based on the 2019 consensus report of the Asian Working Group for Sarcopenia.7 Compared with those with high RASM, participants with a low RASM were older (71.9 ± 5.7 vs. 70.7 ± 4.6 years, P = 0.030) and had a lower body weight (52.5 ± 7.9 vs. 61.9 ± 10.2 kg P < 0.001), BMI (20.3 ± 1.9 vs. 24.4 ± 3.0 kg/m2, P < 0.001), percentage of body fat (27.0 ± 7.1 vs. 30.7 ± 7.1%, P < 0.001), and BMD (T score: −1.9 ± 1.1 vs. −1.6 ± 1.1, P = 0.001) (Table 1). In addition, participants with a low RASM had reduced handgrip strength (24.1 ± 7.1 vs. 26.3 ± 7.8 kg, P < 0.001), were less physically active (1620.9 ± 1301.7 vs. 2174.3 ± 1752.5 Kcal/week, P < 0.001) and had worse nutritional status (MNA: 25.7 ± 2.3 vs. 27.6 ± 1.8, P < 0.001); however, they had a better cardiometabolic risk profile (HbA1c: 5.7 ± 0.6 vs. 5.9 ± 0.7, P = 0.001; TG: 91.8 ± 38.4 vs. 115.5 ± 69.8 mg/dL, P < 0.001; HDL-C: 65.7 ± 16.9 vs. 57.6 ± 15.1 mg/dL, P < 0.001). The results of linear regression showed that older age (β coefficient: 0.145, P < 0.001), higher education years (β coefficient: 0.080, P = 0.003), lower BMI (β coefficient: −0.595, P < 0.001), higher percentage of total body fat (β coefficient: 0.292, P < 0.001), and higher serum levels of vitamin D (β coefficient: 0.068, P = 0.012) were independently associated with a low RASM. Sex-specific associations with a low RASM were identified and included age, medications used, BMD T score and serum levels of vitamin D (Table 2).

Table 1. Comparisons between participants with different skeletal muscle mass status Variable Total Normal RASM Low RASM P value Normal aMFR Low aMFR P value Normal tMFR Low tMFR P value (n = 1060) (n = 864) (n = 196) (n = 864) (n = 196) (n = 864) (n = 196) Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Demographic characteristics Age (years) 71.0 ± 4.8 70.7 ± 4.6 71.9 ± 5.7 0.030 70.7 ± 4.6 72.0 ± 5.6 0.005 70.8 ± 4.7 71.7 ± 5.5 0.075 Male (n, %) 368 (34.7) 301 (34.8) 67 (34.2) 0.862 301 (34.8) 67 (34.2) 0.862 301 (34.8) 67 (34.2) 0.862 Education (years) 13.7 ± 3.7 13.6 ± 3.8 14.2 ± 3.3 0.075 13.8 ± 3.7 13.4 ± 3.9 0.367 13.8 ± 3.7 13.4 ± 4.0 0.444 Current smoker (n, %) 194 (18.3) 164 (19.0) 30 (15.3) 0.230 151 (17.5) 43 (21.9) 0.145 154 (17.8) 40 (20.4) 0.398 Current alcohol drinking (n, %) 760 (71.7) 636 (73.6) 124 (63.3) 0.004 622 (72.0) 138 (70.4) 0.657 624 (72.2) 136 (69.4) 0.426 Number of currently used medications 2.2 ± 2.7 2.3 ± 2.78 1.7 ± 2.0 0.012 2.1 ± 2.5 2.9 ± 3.3 0.002 2.1 ± 2.5 2.8 ± 3.3 0.006 Charlson Comorbidity Index 0.9 ± 1.1 0.8 ± 1.1 1.0 ± 1.2 0.219 0.9 ± 1.1 0.9 ± 1.1 0.874 0.86 ± 1.1 0.90 ± 1.2 0.844 Anthropometric measurements and body composition Height (cm) 159.2 ± 8.0 158.9 ± 7.9 160.5 ± 8.2 0.014 159.4 ± 7.9 158.3 ± 8.2 0.122 159.6 ± 7.9 157.6 ± 8.0 0.002 Weight (kg) 60.2 ± 10.5 61.9 ± 10.2 52.5 ± 7.9 <0.001 58.5 ± 9.7 67.5 ± 10.6 <0.001 58.6 ± 9.8 66.9 ± 10.8 <0.001 Body mass index (kg/m2) 23.7 ± 3.2 24.4 ± 3.0 20.3 ± 1.9 <0.001 23.0 ± 2.8 26.8 ± 2.9 <0.001 22.9 ± 2.8 26.9 ± 2.9 <0.001 Waist-to-hip ratio 0.87 ± 0.11 0.87 ± 0.07 0.86 ± 0.20 <0.001 0.87 ± 0.11 0.90 ± 0.07 <0.001 0.87 ± 0.11 0.90 ± 0.07 <0.001 Total body fat (%) 30.0 ± 7.3 30.7 ± 7.1 27.0 ± 7.1 <0.001 28 ± 6.4 38 ± 4.8 <0.001 28.0 ± 6.3 38.1 ± 4.7 <0.001 RASM (kg/m2) 6.5 ± 1.1 6.7 ± 1.0 5.8 ± 0.8 <0.001 6.5 ± 1.1 6.7 ± 1.0 0.001 6.5 ± 1.1 6.8 ± 1.0 <0.001 BMD T score −1.7 ± 1.1 −1.6 ± 1.1 −1.9 ± 1.1 0.001 −1.7 ± 1.1 −1.6 ± 1.1 0.054 −1.7 ± 1.1 −1.6 ± 1.1 0.128 Functional assessment Handgrip strength (kg) 25.9 ± 7.7 26.3 ± 7.8 24.1 ± 7.1 <0.001 26.1 ± 7.9 24.7 ± 6.7 0.047 26.2 ± 7.9 24.6 ± 6.7 0.017 5-time chair-rise test (s) 9.4 ± 3.1 9.4 ± 3.2 9.6 ± 3.1 0.204 9.3 ± 3.1 10.1 ± 3.3 <0.001 9.3 ± 3.1 10.1 ± 3.2 <0.001 6 min walking distance (m) 505.3 ± 77.7 503.8 ± 77.0 511.4 ± 81.1 0.127 511.5 ± 76.9 477.6 ± 75.7 <0.001 511.9 ± 76.8 475.6 ± 75.0 <0.001 6 m gait speed (m/s) 1.9 ± 0.6 1.9 ± 0.6 1.9 ± 0.6 0.633 1.9 ± 0.6 1.8 ± 0.6 <0.001 1.9 ± 0.6 1.8 ± 0.6 <0.001 IPAQ (Kcal/week) 2072.0 ± 1691.5 2174.3 ± 1752.5 1620.9 ± 1301.7 <0.001 2034.2 ± 1584.3 2238.6 ± 2096.3 0.848 2060 ± 1647.4 2124.8 ± 1876.9 0.619 MNA 27.2 ± 2.1 27.6 ± 1.8 25.7 ± 2.3 <0.001 27.1 ± 2.1 27.8 ± 1.8 <0.001 27.1 ± 2.1 27.7 ± 1.8 <0.001 MoCA 26.3 ± 3.3 26.4 ± 3.1 26.1 ± 3.7 0.428 26.4 ± 3.0 25.7 ± 4.2 0.143 26.5 ± 3.0 25.6 ± 4.2 0.056 CES-D 2.4 ± 4.9 2.3 ± 4.7 2.8 ± 5.5 0.109 2.5 ± 4.9 2.2 ± 4.7 0.351 2.4 ± 4.9 2.4 ± 4.7 0.824 Laboratory data White blood cell count (/mm3) 5.5 ± 1.5 5.5 ± 1.5 5.3 ± 1.4 0.093 5.4 ± 1.4 6.0 ± 1.6 <0.001 5.4 ± 1.4 6.0 ± 1.6 <0.001 Haemoglobin (g/dL) 14.0 ± 1.4 14.0 ± 1.4 13.9 ± 1.3 0.120 14 ± 1.3 14.1 ± 1.4 0.084 14.0 ± 1.3 14.1 ± 1.4 0.139 Vitamin D (ng/mL) 24 ± 7.3 23.7 ± 7.0 25.1 ± 8.2 0.009 24.4 ± 7.5 22.3 ± 5.6 <0.001 24.5 ± 7.5 22 ± 5.6 <0.001 Total cholesterol (mg/dL) 196.5 ± 34.6 195.5 ± 34.8 201.2 ± 33.5 0.028 196.5 ± 33.4 196.6 ± 39.6 0.403 196.2 ± 33.2 197.9 ± 40.4 0.835 Triglyceride (mg/dL) 111.1 ± 65.8 115.5 ± 69.8 91.8 ± 38.4 <0.001 108.6 ± 67.5 122.5 ± 56.9 <0.001 108.8 ± 67.8 121.4 ± 55.5 <0.001 HDL-C (mg/dL) 59.1 ± 15.8 57.6 ± 15.1 65.7 ± 16.9 <0.001 59.8 ± 16.0 56.2 ± 14.6 0.010 59.7 ± 15.9 56.4 ± 14.9 0.021 LDL-C (mg/dL) 114.2 ± 28.6 114.3 ± 28.9 114.0 ± 27.1 0.856 113.4 ± 27.7 117.8 ± 32.1 0.292 133.2 ± 27.5 118.5 ± 32.7 0.187 Albumin (mg/dL) 4.5 ± 0.2 4.5 ± 0.2 4.5 ± 0.2 0.985 4.5 ± 0.2 4.5 ± 0.2 0.061 4.5 ± 0.2 4.5 ± 0.2 0.143 Vitamin B12 (pg/mL) 743.9 ± 470.5 743.4 ± 489.1 746.4 ± 377.8 0.260 736.7 ± 453.6 776 ± 539 0.660 735.8 ± 452.7 779.7 ± 542.1 0.608 Fasting plasma glucose (mg/dL) 98.3 ± 20.9 98.9 ± 20.9 95.8 ± 20.7 0.001 96.8 ± 18.7 105 ± 27.5 <0.001 96.9 ± 18.7 104.8 ± 27.6 <0.001 HbA1c (%) 5.8 ± 0.7 5.9 ± 0.7 5.7 ± 0.6 0.001 5.8 ± 0.6 6.0 ± 0.8 <0.001 5.8 ± 0.6 6.1 ± 0.9 <0.001 Homocysteine (mmol/L) 13.6 ± 5.1 13.7 ± 4.5 13.2 ± 7.3 0.001 13.5 ± 5.3 14.3 ± 4.3 0.002 13.5 ± 5.3 14.2 ± 4.2 0.003 hs-CRP (mg/dL) 0.19 ± 0.47 0.19 ± 0.45 0.17 ± 0.53 0.005 0.17 ± 0.49 0.23 ± 0.35 <0.001 0.18 ± 0.49 0.23 ± 0.35 <0.001 Clinical outcome Mortality and fractures (n, %) 52 (4.9) 44 (5.1) 8 (4.1) 0.554 43 (4.9) 9 (5.1) 0.888 41 (4.7) 11 (5.6) 0.612 aMFR, appendicular muscle mass to total body fat mass; BMD, bone mineral density; CES-D, Center for Epidemiology Study-Depression; HbA1c, glycated haemoglobin; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; IPAQ, International Physical Activity Questionnaire; LDL-C, low-density lipoprotein cholesterol; MNA, Mini-Nutritional Assessment; MoCA, Montreal Cognitive Assessment; RASM, relative appendicular skeletal muscle; tMFR, total body muscle to total body fat. Table 2. Sex differences in independent factors associated with low relative appendicular skeletal muscle mass (RASM) among study participants Total (n = 1060) Men (n = 368) Women (n = 692) 95% CI β P value 95% CI β P value 95% CI β P value Demographic characteristics Age (years) 0.008, 0.017 0.145 <0.001 −0.004, 0.011 0.052 0.306 0.003, 0.015 0.100 0.003 Education (years) 0.003, 0.014 0.080 0.003 −0.004, 0.016 0.050 0.261 −0.001, 0.013 0.056 0.088 Number of currently used medications −0.019, −0.001 −0.066 0.023 −0.026, 0.001 −0.095 0.070 −0.025, −0.001 −0.075 0.027 Anthropometric measurements and body composition Body mass index (kg/m2) −0.087, −0.066 −0.595 <0.001 −0.111, −0.071 −0.689 <0.001 −0.100, −0.074 −0.678 <0.001 Total body fat (%) 0.011, 0.021 0.292