Abstract
Background/Aims
Sarcopenic obesity is variably characterized by low muscle mass and/or low muscle strength in co-existence with excess adiposity. We investigated, in young women of South Asian (Indian) descent in Mauritius, the relationships between low muscle strength or low muscle mass with adverse body fat distribution patterns and blood markers of cardiometabolic risks.
Methods
Data were collected after an overnight fast in healthy young women (n = 203) across a wide range of body mass index (14–42 kg/m2). After blood pressure measurements, blood was withdrawn and assays performed for glycemic profile (glucose, insulin, HbA1c), blood lipid profile (triglycerides, cholesterols), and the inflammation marker C-reactive protein. Body composition, appendicular lean mass (ALM) and fat distribution were determined by dual-energy X-ray absorptiometry, while handgrip strength (HGS) was measured using a digital dynamometer. Obesity was defined as body fat% exceeding 40, while low muscle mass and strength were determined as values below ESPEN/EASO cut-offs for diagnosing sarcopenic obesity in Asian women, namely: HGS < 18 kg and ALM relative to weight (ALM/W) < 23%.
Results
Among the women with obesity (60% of the cohort), 41% showed low HGS and 43% low ALM/W. Those with low HGS, though not differing from those with higher HGS in body fat% and ALM/W, had significantly higher visceral-to-peripheral adiposity ratio and higher blood lipids. Furthermore, linear regression analysis indicates a significant inverse relationship between HGS adjusted for arm lean mass and visceral-to-peripheral adiposity ratio. By contrast, no adverse body fat distribution pattern nor adverse cardiometabolic risk markers were observed in those with low ALM/W.
Conclusions
These results in young women suggest that an increase in the ratio of visceral-to-peripheral adiposity associated with low muscle strength (but not low muscle mass) may constitute early events in the complex interactions between adverse body fat distribution, cardiometabolic risks and proneness to sarcopenic obesity.
Subject terms: Physiology, Medical research
Introduction
The age-related decline in skeletal muscle mass, muscle strength and physical performance, which is encompassed in the concept of sarcopenia, are important causes of frailty, disability, morbidity, and mortality [1]. In this era of global obesity pandemic, sarcopenia often co-exists with excessive adiposity and its co-morbidities, in particular type 2 diabetes and cardiovascular diseases [2]. There is some evidence that a combination of adverse conditions due to sarcopenia and obesity may act synergistically to confer higher risks for physical disability and cardiometabolic diseases in people with sarcopenic obesity than in those with either condition alone [3–6]. The underlying mechanisms are ill-defined, but several postulations center upon roles for adipokines, myokines, subclinical inflammation and insulin resistance, amid the notion that sarcopenia and obesity may aggravate each other in a vicious cycle [7–12].
In fact, several longitudinal studies have indicated that low muscle mass or strength can predict future development of cardiometabolic diseases that could partly be attributed to total or abdominal adiposity [13–15], which in turn have been shown to predict future loss of muscle mass or an accelerated decline in muscle strength [16–22]. These studies have highlighted the importance of an adverse body fat distribution in the pathogenesis of sarcopenic obesity, although the impact of specific components of central adiposity (visceral vs. abdominal subcutaneous) and that of peripheral subcutaneous adiposity on muscle strength and muscle mass is yet to be clarified. Furthermore, the studies investigating the interrelationships between body fat distribution and risks for sarcopenic obesity have been conducted in populations of European origins and of East Asia, and there is scarce information about these relationships in other races/ethnicities.
South Asians (people living in or with origins from South Asian countries), by virtue of their often ‘thin-fat’ phenotype (excess fat in a thin frame) and increased cardiometabolic disease morbidity at a younger age and lower body mass index (BMI) [23, 24], are believed to be particularly prone to the development of sarcopenia and sarcopenic obesity [25–29]. For the same age, sex and BMI across the lifespan, they have less lean mass and more fat than many other ethnicities [30–36], and a few studies suggest they have lower muscle mass and muscle strength even after adjustments for body size [37–39].
In the study reported here, we have explored potential relationships between muscle strength, muscle mass, body fat distribution and blood markers of cardiometabolic risks in healthy young women of South Asian (Indian) descent living in Mauritius—an island population well characterized for its high predisposition to type 2 diabetes and cardiovascular disease [40–43]. More specifically, we have addressed the question of whether, as young adults without diabetes, lower muscle strength or lower muscle mass could be related specifically to a higher visceral adiposity, abdominal subcutaneous adiposity, or to peripheral subcutaneous adiposity, given the distinct metabolic and inflammatory profiles of these fat depots in the pathogenesis of cardiometabolic diseases [32, 44].
Subjects and methods
Participants and study design
The present study constitutes further analysis of data collected between 2017 to 2019 in Mauritius in the context of studies applying dual-energy X-ray absorptiometry (DXA) technology to study the relationships between regional body composition and cardiometabolic health in healthy young adults [45, 46]. Due to an insufficient number of participants among men and among adults in other ethnicities for the purpose of this study, the analysis reported here could be performed only in women of South Asian (Indian) ethnicity, i.e., whose ancestors originated from the Indian subcontinent. All participants (n = 203) were recruited from the general public, the staff populations of two major hospitals on the island and among students at the nursing school. Participants were eligible if they were women of 18–40 years of age, without diabetes, not on medication, with relatively stable body weight (defined as <3% variation during the past 3 months), and non-physically active as defined by the Sedentary Behaviour Research Network [47]. Smokers, those who regularly consume alcoholic drinks, and with menstrual irregularities, pregnant or breastfeeding women were excluded. The study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Ministry of Health and Wellness, Republic of Mauritius (ethical approval reference code: MHC/CT/NETH/RAME); written informed consent was obtained from all participants.
Anthropometry
Body weight was measured on an electronic weighing scale (Tanita Corporation, Tokyo), height was measured using a portable stadiometer (Tanita Leicester Height Measure, Leicester, UK), and waist circumference (WC) was measured at navel level using a non-stretchable tape, and according to the Standardization Reference Manual of Lohman et al. [48].
Body composition and fat distribution
Whole-body composition was determined by DXA using a HologicTM Horizon® QDR® WI System (Hologic Inc., Bedford, MA, USA), and according to guidelines for DXA procedures [49]. Scans were also analyzed to estimate the regional fat mass using the standardized regions specified by the manufacturer for Trunk (region includes the neck, chest, abdominal and pelvic areas), Android (area overlying the abdomen between the ribs and the pelvis), Gynoid (hips and upper thigh; portion of the legs from the greater femoral trochanter, extending caudally to the mid-thigh), and Appendicular or Limb fat (the sum of fat mass of the arms and legs). The visceral adipose tissue (VAT) mass, also referred to as visceral fat, was assessed in the visceral regions that occupy a band crossing the subject’s abdominal cavity between the pelvis and the rib cage using the Hologic Visceral Fat software. Abdominal subcutaneous adipose tissue (ASAT) (i.e., subcutaneous fat in the android region) was calculated as the difference between android fat and visceral fat. Peripheral adiposity refers to gynoid fat or appendicular (limb) fat, and the ratio of visceral-to-peripheral adiposity (VPA-ratio) refers to the ratio of VAT/gynoid fat or VAT/limb fat.
Handgrip strength
Forearm grip strength was measured using a Jamar handgrip dynamometer with a digital display. It was performed with the subject seated in an upright position and with the arm of the measured hand unsupported and parallel to the body. For each individual, the first of four measurements was regarded as a practice, and the maximum force (kg) in the following three measurements performed at intervals of 30–60 s was recorded and the highest value from the dominant arm was used in the analysis.
Blood assays and blood pressure
Resting blood pressure (systolic and diastolic) was measured by oscillometry using an OMRON® M2 automatic blood pressure monitor (OMRON Healthcare Ltd., Milton Keynes, UK), after which a blood sample was collected. HbA1c was measured on the same day on whole blood by HPLC (TosohG8, Tosoh Bioscience Inc., Tokyo, Japan). The other blood parameters were measured from plasma or serum (obtained by centrifugation and stored at −20 °C until later assays) using automated clinical analyzers (Abbott Architect c8000 and i2000, Illinois, USA), namely plasma glucose and insulin, and serum concentrations of C-reactive protein (CRP), triglycerides (TG), total cholesterol (Total-C) and HDL cholesterol (HDL-C). The serum value for LDL-cholesterol (LDL-C) was calculated using the Friedewald formula [50]. The Homeostatic Model Assessment for insulin resistance (HOMA-IR) was used to determine the insulin resistance status [51].
Sarcopenic obesity cut-offs
Muscle strength and muscle mass were assessed as dominant handgrip strength (HGS) and as soft appendicular lean mass (ALM) relative to weight (ALM/W) or to height2 (ALMI), respectively. The categorization of subjects into those with low versus higher values for adiposity, HGS and ALM/W or ALMI was based on cut-offs from the ESPEN/EASO consensus for diagnosing sarcopenic obesity in women of Asian ethnicity, namely: ‘Obesity’ as DXA-derived Body fat% >40, ‘Low muscle strength’ as dominant HGS < 18 kg, and ‘Low muscle mass’ assessed as DXA-derived ALM/W < 23% or ALMI < 5.5 kg/m2 [2]; details are provided in Appendix/Supplementary tables in latter reference [2].
Data analysis and statistics
Data analyses were performed using statistical software (STATISTIX version 8.0; Analytical Software, St Paul, Minnesota, USA). The tabulated data are presented as Mean ± standard deviation (SD), and the Wilcoxon Rank Sum test was used to test the significance of differences between the two groups. Linear model procedures that were applied included Pearson’s product-moment correlations for determining linear associations between variables and stepwise regression analysis for identifying independent predictor variables. Because of their skewed distributions, the values of blood TG, insulin, HOMA-IR index and CRP were logarithmically transformed to normalize the distribution prior to the application of statistical analyses. For all tests, significance was set at p < 0.05.
Results
General health characteristics
In this population sample of women with a large range of BMI (varying between 14 and 42 kg/m2), 67% had abdominal obesity defined as WC > 80 cm, and nearly 60% could be allocated to the obesity group with body fat% >40 (Table 1). Based upon fasting blood glucose and HbA1c, none of these young subjects presented diabetes, but more than a third (36.9%) could be characterized with pre-diabetes based upon values for HbA1c between 5.7 and 6.4%. Only a few subjects showed high systolic blood pressure, but diastolic blood pressure exceeding 80 mmHg was observed in about 25% of the subjects. Most of those characterized by pre-diabetes or high blood pressure are found in the ‘Obesity’ group. Table 1 also presents data on the proportion of subjects below ESPEN/EASO cut-offs for diagnosis of sarcopenic obesity, i.e., with low HGS, low ALM (relative to weight or height2) or both. Nearly 40% of subjects show low HGS, 27% of them (mostly among those with obesity) show low ALM/W, and 38% low ALMI. As a percentage of the total population sample (n = 203), 10% of subjects show obesity together with low values for both HGS and ALM/W, and 6% show obesity together with both low HGS and low ALMI.
Table 1.
Health characteristics of subjects.
| All n = 203 | No obesity n = 82 | With obesity n = 121 | |||||
|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | ||
| Excess fatness | |||||||
| BMI > 25 kg/m2 | 87 | 42.8 | 9 | 11.0 | 78 | 64.5 | |
| WC > 80 cm | 136 | 67.0 | 27 | 32.9 | 109 | 90.0 | |
| Body fat% >40 | 121 | 59.6 | 0 | 0 | 121 | 100 | |
| Glycemic profile | |||||||
| Glucose (>5.5 mmol/L) | 13 | 6.4 | 2 | 2.4 | 11 | 9.1 | |
| - Prediabetes (5.6–6.9) | 13 | 6.4 | 2 | 2.4 | 11 | 9.1 | |
| - Diabetes (≥7.0) | 0 | 0 | 0 | 0 | 0 | 0 | |
| HbA1c (>5.6%) | 75 | 36.9 | 20 | 24.4 | 55 | 45.5 | |
| - Prediabetes (5.7–6.4%) | 75 | 36.9 | 20 | 24.4 | 55 | 45.5 | |
| - Diabetes (≥6.5%) | 0 | 0 | 0 | 0 | 0 | 0 | |
| HOMA-IR index (>2) | 93 | 45.8 | 21 | 25.6 | 72 | 59.5 | |
| Lipid profile | |||||||
| Triglycerides (>2.0 mmol/L) | 4 | 2.0 | 2 | 2.4 | 2 | 1.6 | |
| Cholesterol (mmol/L) | |||||||
| Total (>5.2) | 33 | 16.2 | 6 | 7.3 | 27 | 22.3 | |
| HDL (<0.9) | 2 | 1 | 0 | 0 | 2 | 1.6 | |
| LDL (>4.1) | 11 | 5.4 | 0 | 0 | 11 | 9.1 | |
| Blood pressure (BP) | |||||||
| Systolic BP (mm Hg) | |||||||
| Stage 1 (130–139) | 3 | 1.5 | 1 | 1.2 | 2 | 1.6 | |
| Stage 2 (≥140) | 0 | 0 | 0 | 0 | 0 | 0 | |
| Diastolic BP (mm Hg) | |||||||
| Stage 1 (80–89) | 47 | 23.2 | 11 | 13.4 | 36 | 29.8 | |
| Stage 2 (≥90) | 10 | 4.9 | 1 | 1.2 | 9 | 7.4 | |
| Below ESPEN/EASO cutoffs for sarcopenia diagnosis | % total sample | ||||||
| Low HGS (<18 kg) | 80 | 39.4 | 30 | 36.6 | 50 | 41.3 | 24.6 |
| Low ALM/W (<23%) | 54 | 26.6 | 2 | 2.4 | 52 | 43.0 | 25.6 |
| HGS < 18 kg and ALM/W < 23% | 23 | 11.3 | 2 | 2.4 | 21 | 17.4 | 10.3 |
| Low ALMI (<5.5 kg/m2) | 78 | 38.4 | 54 | 65.8 | 24 | 19.8 | 11.8 |
| HGS < 18 kg and ALMI < 5.5 kg/m2 | 36 | 17.7 | 24 | 29.3 | 12 | 9.9 | 5.9 |
BMI body mass index, WC waist circumference, HGS handgrip strength, ALM/W Appendicular Lean Mass × 100/Weight, ALMI Appendicular Lean Mass Index (ALM/Height2).
Analysis by obesity categorization
The physical characteristics and body composition of the subjects categorized according to obesity status are presented in Table 2. As expected, the group with obesity presents higher values (p < 0.001) for body weight, BMI, WC, indices of total adiposity (as body fat% and fat mass index), and higher lean mass indices (as fat-free mass index and ALMI); by contrast, ALM/W is lower in those with obesity (p < 0.001). Table 2 also shows that there is no significant difference in absolute HGS between the two groups, despite higher lean mass in those with obesity. However, after adjusting HGS (by linear regression) for arm lean mass (the strongest among anthropometric and lean mass correlates of HGS; Supplementary Material Table S1A), the adjusted HGS is significantly lower by 1.4 kg in the group with obesity (p < 0.05).
Table 2.
Anthropometry and body composition according to obesity status.
| No obesity | With obesity | p value | |
|---|---|---|---|
| Age (yr) | 26.4 | 26.5 | ns |
| ± 5.7 | ± 5.3 | ||
| Height (m) | 1.60 | 1.59 | ns |
| ± 0.06 | ± 0.07 | ||
| Weight (kg) | 51.4 | 69.2 | *** |
| ± 9.2 | ± 14.6 | ||
| BMI (kg/m2) | 20.1 | 27.5 | *** |
| ± 3.4 | ± 5.0 | ||
| WC (cm) | 77.1 | 92.7 | *** |
| ± 8.0 | ± 11.4 | ||
| Fat mass (%) | 35.1 | 44.7 | *** |
| ± 4.0 | ± 3.4 | ||
| Fat Mass Index (kg/m2) | 7.18 | 12.4 | *** |
| ± 1.82 | ± 3.0 | ||
| Fat-free Mass Index (kg/m2) | 13.0 | 15.1 | *** |
| ± 1.7 | ± 2.1 | ||
| ALMI (kg/m2) | 5.32 | 6.42 | *** |
| ± 0.86 | ± 1.08 | ||
| ALM/W (%) | 26.5 | 23.4 | *** |
| ± 2.0 | ± 1.4 | ||
| Handgrip strength (HGS) | |||
| HGS (kg) | 19.8 | 19.5 | ns |
| ± 4.8 | ± 4.6 | ||
| Arm lean mass (kg) | 1.70 | 1.94 | *** |
| ± 0.34 | ± 0.45 | ||
| Adjusted HGS (kg) | 20.3 | 18.9 | * |
| ± 4.2 | ± 4.4 |
Values are Mean ± SD.
BMI body mass index, WC waist circumference, HGS handgrip strength, ALM/W Appendicular Lean Mass × 100/Weight, ALMI Appendicular Lean Mass Index (ALM/Height2).
*p < 0.05; ***p < 0.001; ns not significant.
The data pertaining to body fat distribution between these two groups are presented in Table 3. Both VAT and ASAT are higher by nearly two-folds in the obesity group than in the no-obesity group (p < 0.001), but the ratio of these two measures of central adiposity (VAT/ASAT) is not different between these two groups (0.266 vs. 0.264). The indices of peripheral adiposity (Gynoid fat and limb fat), as well as indices of central-to-peripheral adiposity (VAT/gynoid, ASAT/Gynoid, VAT/limb and ASAT/limb) are all higher in the group with obesity (p < 0.001).
Table 3.
Body fat distribution and blood markers of cardiometabolic health according to obesity status.
| No obesity | With obesity | p value | |
|---|---|---|---|
| Body fat distribution | |||
| Central adiposity | |||
| VAT (kg) | 0.256 | 0.486 | *** |
| ± 0.111 | ± 0.170 | ||
| ASAT (kg) | 0.964 | 1.90 | *** |
| ± 0.360 | ± 0.68 | ||
| VAT/ASAT | 0.266 | 0.264 | ns |
| ± 0.071 | ± 0.075 | ||
| Peripheral adiposity | |||
| Gynoid fat (kg) | 3.64 | 5.60 | *** |
| ± 0.78 | ± 1.41 | ||
| Limb (appendicular) fat (kg) | 9.74 | 16.1 | *** |
| ± 2.25 | ± 4.4 | ||
| Central-to-peripheral adiposity ratio | |||
| VAT/Gynoid | 0.069 | 0.087 | *** |
| ± 0.024 | ± 0.026 | ||
| ASAT/Gynoid | 0.260 | 0.333 | *** |
| ± 0.060 | ± 0.061 | ||
| VAT/Limb | 0.026 | 0.031 | *** |
| ± 0.009 | ± 0.009 | ||
| ASAT/Limb | 0.097 | 0.118 | *** |
| ± 0.022 | ± 0.024 | ||
| Blood cardiometabolic profile | |||
| Glucose (mmol/l) | 4.92 | 4.98 | ns |
| ± 0.34 | ± 0.41 | ||
| HbA1c (%) | 5.43 | 5.62 | *** |
| ± 0.27 | ± 0.37 | ||
| Insulin (uU/ml) | 7.65 | 11.8 | *** |
| ± 3.5 | ± 5.8 | ||
| HOMA-IR Index | 1.68 | 2.65 | *** |
| ± 0.80 | ± 1.47 | ||
| Triglycerides (mmol/l) | 0.85 | 1.02 | * |
| ± 0.35 | ± 0.48 | ||
| Total cholesterol (mmol/l) | 4.37 | 4.74 | ** |
| ± 0.65 | ± 0.88 | ||
| HDL-cholesterol (mmol/l) | 1.35 | 1.29 | ns |
| ± 0.27 | ± 0.25 | ||
| LDL-cholesterol (mmol/l) | 2.68 | 3.04 | ** |
| ± 0.56 | ± 0.78 | ||
| Systolic blood pressure (mm Hg) | 102 | 106 | *** |
| ± 10 | ± 9 | ||
| Diastolic blood pressure (mm Hg) | 71 | 77 | *** |
| ± 7 | ± 8 | ||
| C-reactive protein (mg/L) | 1.69 | 5.11 | *** |
| ± 1.84 | ± 4.54 |
Values are Mean ± SD.
* p <0.05; **p < 0.01; ***p < 0.001; ns = not significant.
The data comparing cardiometabolic health in these two groups are also presented in Table 3. There is no significant between-group difference in fasting blood glucose, but the group with obesity presents significantly higher values for HbA1c, insulin and HOMA-IR, a more adverse blood lipid profile, namely higher triglycerides and cholesterols (total and LDL), as well as higher blood pressure and C-reactive protein. Overall, the group with obesity shows a more adverse fat distribution and cardiometabolic risk profile, but lower ALM/W and lower HGS adjusted for arm lean mass.
Analysis by HGS categorization
The results comparing body fat distribution and cardiometabolic health in subjects with obesity and categorized as having low HGS vs. higher HGS are presented in Table 4. These two groups show no significant differences in age, ALM/W, total body fat%, VAT or ASAT. However, the group with low HGS is found to have significantly higher VAT/ASAT ratio, lower peripheral adiposity indices (gynoid and limb fat) and higher VPA-ratio (VAT/Gynoid, VAT/Limb). This contrasts with no between-group differences in the ratio of ASAT-to-peripheral adiposity (ASAT/Gynoid and ASAT/Limb). Furthermore, no significant between-group differences are observed in comparing blood markers of cardiometabolic health (Table 4), but the mean values for TG, total and LDL-cholesterol tended to be higher in the low HGS group than in the higher HGS group; the p values being close to reaching statistical significance (p < 0.1) for total- and LDL-cholesterol.
Table 4.
Muscle strength (Low vs. Higher HGS) and muscle mass (Low vs. Higher ALM/W) in women with obesity (n = 121).
| Handgrip strength (HGS) | Appendicular lean mass/weight (ALM/W) | |||||
|---|---|---|---|---|---|---|
| Higher (n = 71) | Low (n = 50) | p value | Higher (n = 69) | Low (n = 52) | p value | |
| Age (yr) | 26.6 | 26.3 | ns | 26.5 | 26.3 | ns |
| ±4.9 | ±5.9 | ±5.7 | ±4.9 | |||
| HGS (kg) | 22.1 | 15.3 | *** | 19.5 | 19.5 | ns |
| ±3.4 | ±2.3 | ±4.4 | ±4.9 | |||
| ALM/W (%) | 23.5 | 23.2 | ns | 24.4 | 22.0 | *** |
| ±1.4 | ±1.5 | ±0.8 | ±0.8 | |||
| Total body fat (%) | 44.7 | 44.8 | ns | 42.7 | 47.4 | *** |
| ±3.6 | ±3.1 | ±1.9 | ±3.0 | |||
| Body fat distribution | ||||||
| Central adiposity | ||||||
| VAT (kg) | 0.481 | 0.493 | ns | 0.434 | 0.555 | *** |
| ±0.172 | ±0.168 | ±0.140 | ±0.181 | |||
| ASAT (kg) | 1.97 | 1.81 | ns | 1.68 | 2.20 | *** |
| ±0.72 | ±0.62 | ±0.50 | ±0.77 | |||
| VAT/ASAT | 0.251 | 0.283 | * | 0.264 | 0.264 | ns |
| ±0.067 | ±0.083 | ±0.075 | ±0.078 | |||
| Peripheral adiposity | ||||||
| Gynoid fat (kg) | 5.87 | 5.32 | * | 5.11 | 6.35 | *** |
| ±1.50 | ±1.21 | ±0.86 | ±1.68 | |||
| Limb (appendicular) fat (kg) | 16.7 | 15.2 | * | 14.3 | 18.5 | *** |
| ±4.5 | ±4.1 | ±2.5 | ±5.1 | |||
| Central-to-peripheral adiposity ratio | ||||||
| VAT/Gynoid | 0.082 | 0.094 | * | 0.086 | 0.089 | ns |
| ±0.022 | ±0.030 | ±0.028 | ±0.024 | |||
| ASAT/Gynoid | 0.331 | 0.335 | ns | 0.326 | 0.342 | ns |
| ±0.057 | ±0.065 | ±0.064 | ±0.054 | |||
| VAT/Limb fat | 0.029 | 0.033 | * | 0.031 | 0.030 | ns |
| ±0.008 | ±0.011 | ±0.010 | ±0.008 | |||
| ASAT/Limb fat | 0.117 | 0.119 | ns | 0.118 | 0.118 | ns |
| ±0.023 | ±0.026 | ±0.026 | ±0.020 | |||
| Blood cardiometabolic profile | ||||||
| HOMA-IR Index | 2.69 | 2.57 | ns | 2.60 | 2.71 | ns |
| ±1.44 | ±1.53 | ±1.45 | ±1.52 | |||
| Triglycerides (mmol/l) | 0.96 | 1.10 | ns | 0.942 | 1.13 | ns |
| ±0.42 | ±0.54 | ±0.417 | ±0.53 | |||
| Total cholesterol (mmol/l) | 4.62 | 4.91 | ns (p = 0.06) | 4.76 | 4.71 | ns |
| ±0.85 | ±0.91 | ±0.99 | ±0.73 | |||
| HDL-cholesterol (mmol/l) | 1.29 | 1.29 | ns | 1.29 | 1.29 | ns |
| ±0.24 | ±0.27 | ±0.25 | ±0.26 | |||
| LDL-cholesterol (mmol/l) | 2.95 | 3.18 | ns (p = 0.08) | 3.10 | 2.97 | ns |
| ±0.80 | ±0.75 | ±0.90 | ±0.60 | |||
| Systolic blood pressure (mm Hg) | 106 | 107 | ns | 107 | 106 | ns |
| ±9 | ±9 | ±10 | ±9 | |||
| Diastolic blood pressure (mm Hg) | 77 | 78 | ns | 77 | 77 | ns |
| ±7 | ±9 | ±8 | ±8 | |||
| C-reactive protein (mg/L) | 5.28 | 4.87 | ns | 4.13 | 6.42 | ** |
| ±4.5 | ±4.7 | ±4.0 | ±4.9 | |||
Values are Mean ± SD.
*p < 0.05; **p < 0.01; ***p < 0.001; ns = not significant.
In the group without obesity, no significant differences are observed in the various indices of body fat distribution, in blood lipid profile or HOMA-IR, but those with low HGS are found to have a significantly lower blood pressure (Supplementary Material Table S2).
Analysis by ALM/W categorization
The results of data analysis of those with obesity and categorized according to ALM/W status are also presented in Table 4. The group with low ALM/W shows similar HGS but significantly higher body fat% than the group with higher ALM/W; this is reflected in significantly greater values for central adiposity indices (VAT, ASAT) and peripheral adiposity (Gynoid fat and Limb fat) (all p < 0.001). However, unlike for HGS categorization analysis, there is no disproportionately greater visceral adiposity in those with low ALM/W, as indicated by no significant difference in the ratio of VAT/ASAT nor in any of the indices of central-to-peripheral adiposity. Similarly, no differences are observed in subjects with low ALM/W compared to those with higher ALM/W for the markers of cardiometabolic health, except for a higher CRP in those with low ALM/W, reflecting their higher total body fat%.
Analysis by HGS as a continuous variable by linear regression
From the above analyses based upon category comparison, the main findings are that among subjects with obesity, those with low HGS, but not those with low ALM/W, show an adverse fat distribution pattern characterized by a disproportionately higher visceral fat at the central (abdominal) level (i.e., higher VAT/ASAT ratio) and a higher VPA-ratio (i.e. higher VAT/Gynoid or VAT/Limb), as well as a tendency for a more adverse blood lipid profile. These associations based upon category analysis are also observed in simple correlation analysis (Table 5), with the application of stepwise regression analysis indicating that only the VPA-ratio (VAT/Gynoid or VAT/Limb) is retained as a statistically significant independent predictor variable for HGS adjusted for arm lean mass; i.e. all the other anthropometric, body fat distribution and cardiometabolic variables being dropped from the model. Plots showing a significant inverse association between the HGS (adjusted for arm lean mass) and VPA-ratio (VAT/Gynoid or VAT/Limb) in those with obesity, but not in those without obesity, are presented in Fig. 1.
Table 5.
Simple and multivariate regression analysis of handgrip strength (HGS) against indices of body fat distribution in subjects with obesity; r = correlation coefficient; ns: not statistically significant.
| Predictors | Handgrip strength (Adjusted for lean mass) |
|---|---|
| Whole-body adiposity | r |
| Total body fat% | ns |
| Total fat mass | ns |
| Fat mass index | ns |
| Central adiposity | |
| VAT | −0.18 (p < 0.05) |
| ASAT | ns |
| VAT/ASAT | −0.13 (ns; p = 0.1) |
| Peripheral adiposity | |
| Gynoid | ns |
| Appendicular (Limb) | ns |
| Central-to-peripheral adiposity | |
| VAT/Gynoid | −0.22 (p < 0.05) |
| ASAT/Gynoid | ns |
| VAT/Limb | −0.23 (p < 0.05) |
| ASAT/Limb | ns |
| Blood cardiovascular health markers | |
| HOMA-IR | ns |
| Triglycerides | −0.21 (p < 0.05) |
| Total-cholesterol | −0.17 (p = 0.06) |
| HDL-cholesterol | ns |
| LDL-cholesterol | −0.14 (p = 0.1) |
| Systolic blood pressure | ns |
| Diastolic blood pressure | ns |
| C-reactive protein | −0.18 (p = 0.05) |
| Stepwise regression VAT/Limba | −0.22 (p < 0.05) |
aThe omission of the VAT/Limb ratio in the stepwise regression analysis resulted in VAT/Gynoid ratio being retained in the model, while the omission of both VAT/Gy and VAT/limb resulted in no other fat distribution indices or other variables being retained in the model; thus indicating that once adjusted for arm lean mass, part of the variability in HGS can be explained by the visceral-peripheral fat ratio but not by any other adiposity or cardiometabolic parameters.
Fig. 1.
Plots of handgrip strength (adjusted for arm lean mass) versus indices of visceral-to-peripheral adiposity ratio; namely, versus visceral-to-Limb adiposity ratio (upper panel) and versus visceral-to-Gynoid adiposity ratio (lower panel).
Discussion
The present study indicates that an adverse body fat distribution characterized by a higher visceral relative to peripheral adiposity (i.e., the VPA-ratio) is associated with low muscle strength, but not with low muscle mass. This inverse association between HGS and VPA-ratio is demonstrated here in young women of Indian ethnicity with obesity but in the absence of diabetes, and independently of pre-diabetes or HOMA-IR status. That HGS is associated specifically with VPA-ratio, rather than with VAT, ASAT or peripheral adiposity per se, underscores a link between muscle strength and a body fat distribution pattern that integrates both hazardous (visceral) adiposity and protective (peripheral subcutaneous) adiposity.
Disproportionately low muscle mass and low muscle strength among South Asians
It is well documented that South Asians are at higher risks for cardiometabolic diseases at a younger age and with a lower BMI than many other races/ethnicities [23, 24], and that for the same age, sex and BMI, they have less lean mass and more fat [30–36]—a ‘thin-fat’ body composition phenotype. Furthermore, recent studies conducted in healthy young adults living in India have reported that, even after adjusting for body size, they have a much lower muscle mass (assessed as DXA-derived ALM) and a much lower muscle strength (assessed by dominant HGS) than generally observed for other races/ethnicities [38, 39]. In the study reported here in young Mauritian women of Indian ancestry, more than a third had low HGS and more than a quarter had low ALM when examined relative to cut-off values advocated for Asians, albeit derived primarily from East Asian populations (China, Taiwan, Japan, South Korean). It is nonetheless remarkable that such substantial proportions of apparently healthy young South Asians in India and in Mauritius are below these East Asian-based cut-offs. In our exploration of potential differences in fat distribution pattern and cardiometabolic health in Mauritian Indian women who are below compared to those above these cut-offs for HGS and ALM, several issues are addressed below pertaining to their categorization as people with obesity, low muscle strength or low muscle mass.
Obesity categorization
First, their categorization into subjects without or with Obesity is based upon a cut-off value of 40% for body fat as a percentage of body weight. This value corresponds to a BMI of 30 kg/m2 that defines obesity in white European women, but to a BMI of 25–27.5 kg/m2 that defines obesity in Asians [36]. In fact, our own data here indicate that a BMI of 25 kg/m2 in young women of Indian ethnicity in Mauritius corresponds to body fat% of 42%, (Supplementary Fig. S1), which is close to the value of body fat% of 40 for a BMI of 25 kg/m2 found in women of Indian ancestry living in India [52, 53], Singapore [30] and New Zealand [31]. The ‘thin-fat’ phenotype of Asian Indians, characterized by a much lower BMI relative to body fat% than in white Europeans, is particularly evident from our data indicating that among the young Mauritian Indian women with BMI lower than 18.5 kg/m2 (about 15% of our study population), none had body fat% below 20%.
Low muscle strength categorization
Second, in the absence of large-scale data to determine HGS cut-off for people of South Asian origins, we have defined low HGS as below the cut-off value of 18 kg, which is recommended by ESPEN-EASO consensus for diagnosis of sarcopenia and sarcopenic obesity in Asians [2], and advocated by several Asian [54–56] and South Asian [26, 29] consensus panels. The fact that such a large proportion of young women in our Mauritian Indian cohort is below this cut-off value is remarkable, albeit consistent with the findings of the larger-scale study of Zengin et al. [39], who reported comparable low values for HGS (about 20 kg on average) in healthy young women living in India.
Low muscle mass categorization
Third, our data indicate that a large proportion of the young women in our cohort have low values for ALM/W (or ALMI) relative to cut-offs for Caucasian and Asian women populations, but nonetheless comparable to values reported for healthy young women living in India [39, 57]. These latter findings have led to the proposal of a lower limit of ALMI of between 4.4 and 4.6 kg/m2 for defining low muscle mass in South Asian women rather an ALMI cut-off value of about 5.4–5.5 kg/m2 that is currently advocated by ESPEN/EASO and various Asian consensus panels. However, the threshold for defining low muscle mass adjusted for height2 (e.g., ALMI) may not be appropriate in individuals with obesity, owing to the potential masking effect of high adiposity on absolute muscle mass, which generally increases with obesity development. Consequently, body weight or BMI should be considered alongside muscle mass when diagnosing sarcopenic obesity [9, 12, 58]. In fact, in our present study, the correlation between ALM and weight is high (r = 0.94) and much stronger than that between ALM and Height2 (r = 0.39) (Supplementary Material Table S1B). Furthermore, our emphasis on ALM/W rather than ALMI in our analysis pertaining to proneness to sarcopenic obesity is in line with ESPEN/EASO recommendation that ALM measurements are normalized to body weight in order to account for the impact of higher muscle workload required for daily activity in obesity [58].
Adverse fat distribution patterns and sarcopenic obesity
Despite postulations centered upon a role for visceral fat in the pathogenesis of sarcopenic obesity, there is scarce evidence of a link between visceral fat per se (independently of other regional fat depots) and its sarcopenic components. A longitudinal study among South Koreans has indicated that VAT mass at baseline predicted future loss of skeletal muscle mass [18], and more recently, it has been reported that middle-aged Italians classified as having sarcopenic obesity displayed a significantly higher VAT mass than those without sarcopenic obesity, and that VAT was associated with low ALM/W [59]. In these latter studies, however, it is not known whether the reported associations with muscle mass are specific to VAT or the reflection of an association with overall adiposity. In our study here, a lower ALM/W was also found to be associated with a higher VAT, but it was also associated with a higher ASAT, peripheral adiposity and even more strongly with total adiposity (Table 4). In fact, in a stepwise regression analysis of these data, only total adiposity was retained as a predictor of ALM/W, thereby suggesting that a lower ALM/W is associated with obesity development rather than specifically with a higher VAT or any other adverse fat distribution pattern. This is therefore in sharp contrast to the inverse associations observed here between HGS and the ratio of VAT/ASAT or the VPA-ratio, with the latter being the sole regional adiposity predictor in multiple (stepwise) regression analysis.
The higher VPA-ratio in those with lower HGS, demonstrated here in the absence of diabetes and independently of pre-diabetes, but in the presence of higher values for blood lipids (triglycerides, total and LDL-cholesterols), is in accord with an atherogenic lipid profile generally associated with a higher visceral adiposity [60–62] or higher VPA-ratio [45]. They are also in line with the findings of a negative association between preclinical atherosclerosis and HGS in non-hypertensive populations in India [63]. The fact that in our study here these blood lipid values are nonetheless well below thresholds for hypertriglyceridemia or hypercholesteremia, together with the observation of no difference in HOMA-IR index between those with low vs. higher HGS, may underscore an early link between the VPA-ratio and low muscle strength prior to the onset of overt adverse cardiovascular risk profile and insulin resistance in this population at high risk for type 2 diabetes [40–43].
In this context, the VPA-ratio may be underscoring a body fat distribution pattern that integrates the partitioning of excess adiposity towards more hazardous (visceral) adiposity at the expense of protective (peripheral subcutaneous) adiposity in its relationship with muscle strength. These fat depots are distinct in their morphological, developmental and functional characteristics. An expansion of VAT is believed to exert its hazardous effects through the release of FFAs and pro-inflammatory cytokines, which could impair skeletal muscle metabolism, insulin sensitivity and functionality [8–12]. This may occur through ectopic fat deposition, characterized by adipocytes and lipid infiltration into skeletal muscle and myocytes and which have been shown to compromise their structural integrity and force-generating capacity [11, 64]. It may also occur independently of muscle lipotoxicity as suggested by the findings that it was insulin resistance, and not muscle mass nor myosteatosis per se, that was associated with muscle weakness in middle-aged women with obesity [65]. In contrast to VAT (or deep ASAT), an expansion of the superficial subcutaneous adipose tissue, mostly located in the lower body and limbs (i.e. peripheral adiposity), is believed to offer protection against cardiometabolic diseases by acting as a ‘metabolic sink’ that helps buffer excess circulating free fatty acids through storage and metabolism, while also releasing mainly anti-inflammatory cytokines [44, 66–68]. Taken together, the VPA-ratio may serve as a strong indicator of overall risk for chronic metabolic diseases, reflecting the net outcome of the harmful effects of visceral fat and the protective effects of superficial subcutaneous fat.
Study limitations and strengths
This exploratory research is the first study that has applied DXA technology, together with measurement of muscle strength and cardiometabolic health markers, to investigate the interrelationship between regional fat distribution and proneness to sarcopenic obesity in South Asians. However, the fact that our analysis was performed on data only from women limits the ability to generalize the study outcome to South Asians in general or to other ethnicities. Another limitation is that the cross-sectional design of the study precludes causal inferences linking VAT, and more specifically the VPA-ratio, with muscle strength. It cannot determine which one among low HGS and high VPA-ratio might be an initiating factor, although the observed inverse association between the VPA-ratio and HGS in these young adults without diabetes may constitute early events in proneness to sarcopenic obesity, and early stages of the vicious cycle through which the development and progression to obesity and sarcopenia may aggravate each other.
Conclusions
This study in young Mauritian women of South Asian (Indian) ancestry suggests that a disproportionately higher visceral relative to peripheral adiposity is associated with low muscle strength, though not with low muscle mass, among those with obesity. The VPA-ratio may possibly reflect an integrated net outcome of hazardous (visceral) adiposity and protective (peripheral) superficial subcutaneous adiposity in the relationship between adiposity and muscle strength. Further studies are warranted to validate this ratio of visceral-to-peripheral adiposity as a critical body fat distribution pattern in proneness to early onset sarcopenic obesity among South Asians and other ethnicities in Mauritius and populations worldwide.
Supplementary information
Acknowledgements
We are grateful to the staff of the Nuclear Medicine Department of the Jawaharlal Nehru Hospital for accommodating our subjects for the DXA scans.
Author contributions
NJ, VR, and AGD were involved in the study planning and design. VR, BNR, NJ, and SH contributed to data collection and sample analysis. AGD, VR, J-PM, and YS contributed to data analysis and interpretation. AGD and VR wrote the initial draft of the manuscript, and J-PM, YS, NJ, and SH contributed towards its final version. All authors read and approved the final version.
Funding
This research study was funded by the International Atomic Energy Agency (IAEA) (project MAR 6012), the Mauritian Ministry of Health & Wellness, and the Faculty of Science & Medicine, University of Fribourg, Switzerland.
Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The online version contains supplementary material available at 10.1038/s41366-025-01934-y.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets analyzed during the current study are available from the corresponding author on reasonable request.