Introduction
Aging is characterized by a progressive decline in skeletal muscle mass, muscle strength, and bone mass, which contributes to recurrent falls, bone fragility, and increased morbidity and mortality [1]. The progressive loss of muscle mass is referred to as sarcopenia, while the loss of bone strength and deterioration of bone microarchitecture is termed osteoporosis [2]. The bone‐muscle unit interacts with each other via paracrine and endocrine signaling.
Consequently, osteoporosis and sarcopenia share the common pathway, including reduced anabolic activity, increased inflammatory signals, and decreased physical activity, all of which negatively impact health [3, 4].
With aging, particularly following menopause in women, changes in body composition result in an absolute or relative increase in fat mass, which exacerbates the negative effects of sarcopenia on bone health. Furthermore, in individuals with obesity, adipose tissue‐dependent metabolic derangements further predispose to sarcopenia [5]. The term sarcopenic obesity (SO) describes the concurrent presence of obesity in individuals with low muscle mass or sarcopenia [6].
Studies indicate that SO contributes to an increased risk of osteoporosis, bone fragility, disability, metabolic syndrome, and ultimately higher mortality. The risk of these complications is greater in individuals with SO compared to those with sarcopenia or obesity alone [7, 8, 9, 10, 11, 12]. The reported prevalence of SO ranges from 0.3% to 30% [13, 14, 15] across studies, largely due to the absence of standardized diagnostic criteria.
Despite the increased prevalence and associated morbidities of these conditions, data from the Indian subcontinent remain limited. Existing studies from India provide insufficient representation of postmenopausal women [13, 16] and rural populations [17]. Postmenopausal women are at heightened risk for bone fragility, and the co‐existence of sarcopenia and SO may further increase skeletal morbidity.
Therefore, this study assessed the prevalence and predictors of sarcopenia and SO among community‐dwelling ambulant postmenopausal women from rural India, as well as their association with bone fragility.
Methods
Study Participants
This observational cross‐sectional study included ambulant postmenopausal women ≥ 50 years of age from a rural community (Tirupattur district of Tamil Nadu—Southern India) over a period of 2 years. Consecutive ambulant postmenopausal women ≥ 50 years of age were recruited by simple random sampling. Those participants who had disorders that could affect bone health, like untreated hyperthyroidism, chronic liver disease, chronic kidney disease, rheumatoid arthritis, and those on medication that affect bone health, like glucocorticoids, alternative medications, and those already on treatment for osteoporosis were excluded from the study.
Ethics Consideration
The study was approved by the Institutional Review Board, Christian Medical College, Vellore, Tamil Nadu, India (IRB no. 11853/19 (OBSERVE) dated 08.04.2019). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.
Written informed consent was obtained from all the study participants prior to enrolment.
Data Collection
Demographic details and medical histories were obtained from all participants. After an overnight fast, blood was collected for assessment of blood bone biochemistry, including serum calcium, inorganic phosphate, alkaline phosphatase, creatinine, 25‐hydroxy vitamin D (25OHD) and intact parathyroid hormone (iPTH). Anthropometric data, including height, weight and body mass index (BMI), were assessed.
Height was measured using a wall‐mounted stadiometer to the nearest centimeter (cm). Weight was measured using a digital weighing machine to the nearest 0.1 kg. BMI was calculated using the formula—weight (in kg)/height2 (in meters).
The standard cut‐off of 23–24.9 kg/m2 and ≥ 25.0 kg/m2 was used to define overweight and obesity, respectively, as recommended for the Asian‐Indian population [18].
Serum calcium, phosphate, and albumin were analyzed using a calorimetric method with the Roche C702 system, while 25OHD levels were assessed using an electrochemiluminescence assay (Roche Cobas E801‐Immunoassay system). iPTH levels were measured using a chemiluminescence assay (Advia Centaur XPT Immunoassay system). A 25OHD level of < 20 ng/mL was defined as vitamin D deficiency [19].
All participants underwent assessment of bone mineral density (BMD) at the lumbar spine (L1–L4) and the neck of femur (NOF), as well as body composition, using a Hologic Discovery A‐QDR 4500 DXA (dual‐energy X‐ray absorptiometry) scanner by the same DXA technician with 16 years of experience. The NHANES database was used to calculate T‐scores. Fat mass (FM%) and relative appendicular skeletal mass (RASM) were obtained from the body composition data.
Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) criteria [20, 21]. Hand grip was measured using a hand‐held dynamometer (Camry, model EH101, Hong Kong). Muscle strength < 18 kg was defined as low.
Muscle mass was obtained from the body composition (measured using a DXA scanner), and a RASM of < 5.4 kg/m2 was defined as low muscle mass. Physical performance was assessed by the 6 m walk test, and a gait speed < 1 m/s was defined as low physical performance. Sarcopenia was defined as the presence of low RASM and either low muscle strength or low physical performance.
Participants with low muscle strength, low muscle mass and low physical performance were categorized to have severe sarcopenia. SO was defined as the presence of low muscle mass (RASM < 5.4 kg/m2) and increased FM% (> 40%). The threshold for FM% was derived from the study by Marwaha et al., which included 2347 healthy Indian adults, which showed that a BMI of 25 kg/m2 corresponded to a FM% of 40% in women [22].
A DXA T‐score ≤ −2.5 at the hip or lumbar spine was defined as osteoporosis [23]. All participants also underwent assessment of trabecular bone score (TBS), body composition. TBS (L1–L4) is an indirect measure of bone microarchitecture [24], and was performed using TBS iNsight software version 3 (MedImaps, Bordeaux, France).
A TBS value of ≥ 1.31 was defined as normal, 1.23–1.31 was considered to be partially degraded bone microarchitecture, and a value ≤ 1.23 was considered completely degraded bone [25]. Additionally, vertebral fractures (VF) were quantified using vertebral fracture assessment (VFA) and classified as mild, moderate, or severe based on DXA images, according to Genant's semiquantitative method. Only those with either moderate or severe vertebral fractures were categorized to have VF (presence or absence of fractures).
The sample size was calculated based on the study by Rahman et al. [26], where they found a prevalence of sarcopenia to be 23% among healthy women over 60 years of age attending the geriatric outpatient clinic. With a precision of 5% and for a desired confidence interval, we had to recruit at least 273 postmenopausal women.
Statistical Analysis
The data were analyzed using SPSS version 21.0 (IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp) and R software version 4.x.x (R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics such as age, BMI, and BMD, RASM etc. were reported using mean ± SD.
Categorical variables, such as the prevalence of osteoporosis, VF, sarcopenia, and SO, were reported as frequencies and percentages. Univariate and multivariate logistic regression analyses were used to examine predictors of sarcopenia and SO. Violin plots were used to visualize the distribution of FM% and BMI across sarcopenic and non‐sarcopenic groups.
The ROC curves were plotted to assess predictors of sarcopenia, and the area under the curve (AUC) values, along with 95% confidence intervals, were annotated directly on the curves. A two‐tailed p value of ≤ 0.05 was considered significant.
Results
We screened 350 postmenopausal women from a rural community and recruited 300 ambulant postmenopausal women. Among those excluded, 35 participants declined consent, 14 were non‐ambulant, and one had chronic kidney disease. The mean (SD) age of the study population was 61.9 (6.8) years, and the mean age at menopause was 46.2 (5.2) years.
The mean (SD) total serum calcium level of the study cohort was 9.2 (0.6) mg/dL, and the mean (SD) iPTH level was 66 (31) pg/mL. 69% of study participants were obese or overweight, and 29% had vitamin D deficiency (25OHD < 20 ng/mL).
Forty‐eight percent (144/300) of study participants had osteoporosis at any site (NOF/total hip/lumbar spine). 27.3% (82/300) of participants had at least one moderate or severe VF episode, and 51.7% (155/300) had completely degraded bone microarchitecture (low TBS) as per the cut‐off described above. 43.7% (131/300) of participants had sarcopenia, 29% (87/300) had severe sarcopenia, and 27% (81/300) had SO.
37% (111/300) of participants had Type 2 diabetes mellitus, among whom 19.7% (59/300) were newly diagnosed with diabetes.
Upon comparing the clinical, biochemical, and bone health parameters among participants with and without sarcopenia, participants with sarcopenia were found to be older, have a lower BMI, experience early menopause, have a lower FM%, and have lower BMD at the lumbar spine, NOF, and total hip, as well as a lower proportion of participants with diabetes (Table 1). As shown in Figure 1, violin plots comparing BMI and FM% parameters between participants with and without sarcopenia revealed that FM% and BMI were significantly lower in those with sarcopenia (p < 0.001). On multivariate analysis, only higher FM% (aOR 1.715, 95% CI 1.461–2.013, p < 0.001) and lower BMI (aOR 0.279, 95% CI 0.205–0.381, p < 0.001) were significant predictors of sarcopenia.
Univariate ROC curves were generated for BMI and FM% as predictors of sarcopenia. The AUC for BMI was 0.894 (95% CI: 0.857–0.931, p < 0.001) and for FM% was 0.652 (95% CI: 0.588–0.715, p < 0.001), indicating good discriminatory ability. A combined logistic regression model using both variables improved the prediction, with an AUC of 0.946 (95% CI: 0.920–0.971, p < 0.001) (Figure 2).
Participants with SO were older, had lower BMI, and had a higher prevalence of osteoporosis compared to those without SO (Table 2). On multivariate analysis, only BMI (aOR 0.913, 95% CI 0.852–0.978, p = 0.010) and age (aOR 1.053, 95% CI 1.01–1.1023, p = 0.010) emerged as significant predictors of SO.
Osteoporosis at any site (lumbar spine/NOF/total hip) was more prevalent in both participants with sarcopenia and SO. Prevalent vertebral fractures and low TBS were comparable among participants in both cohorts.
Discussion
The present study examined the prevalence and predictors of sarcopenia and SO and their association with bone fragility. Sarcopenia and SO were present in 44% and 27% of the participants, respectively. Lower FM% and lower BMI were significant predictors of sarcopenia, while older age and lower BMI were identified as significant predictors of SO.
In this cohort, 43% of the participants had sarcopenia, and 29% had severe sarcopenia. Previous studies from India have reported sarcopenia prevalence ranging from 3.2% to 43.6% [16, 17, 26, 27, 28]. The differences are largely attributable to variability in the age distribution of the study population.
For example, the study that reported a prevalence of 3.2%, participants over 60 years represented only 20% of the study population [16].
Reported prevalence of SO in the existing literature from India ranges from 6.3% to 27% [13, 17]. The low prevalence observed in the SO‐CUBES study (6.3%) could be explained by the fact that over 60 years constituted < 20% of the study cohort [13]. The secondary data analysis from the Longitudinal Aging Study in India (LASI), which included 72,250 older adults, reported a SO prevalence of 8.7%, despite a sarcopenia prevalence of 42% [17].
The discrepancy could be attributed to the use of BMI as the criterion for defining obesity. In our cohort, individuals with SO had significantly lower BMI than those without SO, and higher BMI was protective against SO in the multivariate analysis. These findings suggest that BMI may not be an ideal measure of adiposity in the context of SO.
Furthermore, while large population‐based datasets such as the LASI study [17] and SO‐CUBES [13] have provided valuable epidemiological insights, a detailed evaluation of sarcopenia and SO in rural postmenopausal women remains limited. Most available Indian studies are urban‐centric and do not adequately address the combined burden of muscle loss and adiposity on skeletal fragility.
Although participants with sarcopenia exhibited lower FM% compared to those without sarcopenia, multivariable logistic regression, after adjusting for age, BMI, and bone density parameters, identified higher FM% and lower BMI as independent risk factors for sarcopenia. This apparent contradiction suggests that, among individuals with sarcopenia, excess adiposity relative to muscle mass may drive the risk of sarcopenia.
These findings highlight an important conceptual distinction between the diagnostic identification of sarcopenia and the assessment of adiposity in sarcopenic obesity. In our cohort, BMI demonstrated a strong discriminatory ability for sarcopenia (AUC 0.894), likely because women with sarcopenia had significantly lower body weight and overall mass. Thus, lower BMI performed well as a marker of sarcopenia in this sample.
However, BMI is a limited surrogate for adiposity when defining SO. When adiposity was defined using BMI ≥ 25 kg/m2, the prevalence of sarcopenic obesity decreased substantially from 27% to 6.3%, suggesting potential underestimation. This likely reflects misclassification of women with relatively low or normal BMI but elevated fat mass percentage.
Therefore, while BMI may adequately identify individuals at risk of sarcopenia, it did not reliably capture excess adiposity in our cohort.
The significantly higher prevalence of osteoporosis among participants with sarcopenia (66% vs. 34%) and SO (59% vs. 44%) highlights the need for greater recognition of these entities in clinical practice.
The associated morbidity and mortality also warrant further investigation. Additional studies are needed to evaluate the impact of dietary interventions and structured exercise programs on these parameters.
This is the first study from Southern India to study the predictors of sarcopenia and SO among postmenopausal women and their association with bone health, including BMD, TBS, and prevalent VF, in the community. The study has the following limitations. Due to its cross‐sectional design, the clinical outcomes of sarcopenia and SO could not be assessed.
Participants aged 70 or older were underrepresented in this study, likely because only ambulant participants were included. Additionally, as the study was conducted in a rural population, the findings may not be generalizable to the urban population.
Conclusion
Sarcopenia and sarcopenic obesity were highly prevalent among community‐dwelling postmenopausal women, with lower body mass index identified as a significant predictor of both. The prevalence of osteoporosis is significantly higher among participants with sarcopenia and SO, indicating compromised bone health. Body mass index may not be an optimal measure of adiposity for assessing sarcopenic obesity.
Author Contributions
Remya Rajan contributed to the study design, conduct, data collection, data analysis, interpretation, and drafted the manuscript. Kripa Cherian contributed to study design, data analysis, and manuscript review and revision. Thomas Paul contributed to the study design, oversight of the study conduct, and critical revision of the manuscript.
Hesarghatta Shyamasunder Asha, Nihal Thomas contributed to the study design and revision of the manuscript. Arun Jose contributed to the laboratory analysis, data interpretation, and the critical revision of the manuscript. Grace Rebekah contributed to the data analysis and revision of the manuscript.
Nitin Kapoor contributed to study design, data analysis, manuscript review, and revision, and had primary responsibility for the final content. All authors read and approved the final manuscript. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by Institutional Fluid Research Grant (Christian Medical College Vellore), 11853.
Ethics Statement
The studies involving human participants were reviewed and approved by the ethics committee of Christian Medical College Vellore, India (IRB no. 11853/19 (OBSERVE) dated 08.04.2019). The patients/participants provided their written informed consent to participate in this study.
Conflicts of Interest
The authors declare no conflicts of interest.