Population ageing is among the defining demographic transitions of the twenty-first century; the number of people aged ≥60 years is projected to reach 2.1 billion globally by 2050, with low- and middle-income countries bearing the greatest burden.¹ India’s older population alone is expected to exceed 340 million by 2050.² This shift has moved geriatric syndromes to the forefront of clinical and public-health attention, with sarcopenia emerging as one of the most clinically impactful.

Derived from the Greek sarx (flesh) and penia (loss), sarcopenia was first conceptualised by Rosenberg in 1989 to describe the age-related decline in skeletal-muscle mass.³ The construct subsequently broadened to encompass muscle mass, strength, and physical performance, reflecting its impact on functional decline, frailty, disability, and mortality,⁴ and is now recognised as an independent disease entity (ICD-10-CM M62.84). Skeletal muscle, roughly 40% of body weight in young adults, declines 1–2% per year after age 50, with strength falling 1.5–3% annually.⁵

In hospitalised older adults sarcopenia is associated with prolonged length of stay, post-operative complications, higher 30-day readmission, and substantially greater healthcare costs,^6,7 and hospitalisation itself accelerates muscle wasting (an estimated 1–5% muscle-mass loss per day during acute illness).⁸ Whereas pooled community prevalence is approximately 10%, hospital prevalence is consistently two- to three-fold higher (27–65%).^9,10 In India, the Longitudinal Ageing Study in India (LASI) reported sarcopenia in 43.6% and severe sarcopenia in 19.4% of more than 31,000 community-dwelling adults aged ≥60 years,¹¹ yet published inpatient studies applying AWGS 2019 criteria within dedicated geriatric-medicine units are absent.^12,13,14,15

The AWGS 2019 consensus defines low muscle mass as ASMI <7.0 kg/m² (men) or <5.7 kg/m² (women) by BIA, low handgrip strength as <28 kg (men) or <18 kg (women), and low gait speed as <1.0 m/s. Probable sarcopenia requires low strength or low gait speed; confirmed sarcopenia adds low muscle mass; severe sarcopenia requires all three criteria.¹⁶ Sarcopenic obesity—the coexistence of low muscle mass and excess adiposity—is systematically missed by BMI and is better captured by BIA-derived percent body fat.¹⁷

Against this background, this study addresses four evidence gaps: (1) absence of AWGS 2019-based inpatient prevalence data from Indian dedicated geriatric units; (2) lack of systematic sarcopenic-obesity assessment using percent body fat in Indian inpatients; (3) absence of comprehensive multivariable determinant analyses; and (4) limited gender-stratified functional and biochemical comparisons in this setting.

Study design and population A hospital-based cross-sectional observational study was conducted in the Department of Geriatric Medicine of a tertiary-care teaching hospital over six months following Institutional Ethics Committee approval (September 2025). All patients aged ≥60 years admitted under the department who met eligibility criteria and provided written informed consent were enrolled consecutively (N=100). Eligibility criteria Inclusion criteria were age ≥60 years, admission to the geriatric ward through emergency or outpatient services, and voluntary written informed consent. Exclusion criteria were acute severe pain, acute cardiopulmonary decompensation, or significantly altered sensorium limiting participation; metal implants or pacemakers contraindicating BIA; significant musculoskeletal deformities (e.g., severe kyphoscoliosis, bilateral knee contractures) precluding anthropometric or performance testing; and hospital stay shorter than 24 hours. Measurements A structured proforma captured demographics, dietary pattern, comorbidities, and medications. Anthropometry (weight, height, BMI, mid-upper-arm circumference [MUAC], calf circumference) followed standardised protocols. Body composition was assessed by multi-frequency BIA (InBody S10, InBody Co., Seoul, Korea) in the supine position after a ≥2-hour fast. Handgrip strength was measured with a calibrated Jamar hydraulic dynamometer (best of three) and gait speed by the 6-metre walk test (mean of two trials). Comprehensive geriatric assessment comprised MNA-SF, SARC-F, Barthel ADL, Lawton IADL, HMSE, and GDS-15; comorbidity burden was quantified by the Charlson Comorbidity Index (CCI). Laboratory studies included complete blood count, serum albumin, total protein, creatinine, fasting glucose, HbA1c, thyroid function, and 25-hydroxy vitamin D. Diagnostic definitions (AWGS 2019) Case-finding used SARC-F ≥4 or low calf circumference (<34 cm men / <33 cm women). Probable sarcopenia was low grip strength or low gait speed; confirmed sarcopenia was low ASMI plus low grip strength or low gait speed; severe sarcopenia required all three criteria. Sarcopenic obesity was defined as low ASMI plus percent body fat ≥27% (men) / ≥38% (women).16,17 Statistical analysis Data were analysed in IBM SPSS v26. Continuous variables are presented as mean±SD or median (IQR); categorical variables as frequencies and percentages. Between-group comparisons used the independent t-test or Mann–Whitney U test for continuous variables and the chi-square or Fisher exact test for categorical variables. Binary logistic regression (univariate, then multivariable for variables significant at p<0.10) produced crude and adjusted odds ratios with 95% confidence intervals. Acute-phase reactants (haemoglobin, albumin), serum creatinine (reverse causality), and diabetes (BMI-mediated paradox) were pre-specified for exclusion from regression. Two-sided p<0.05 was considered significant.

Baseline characteristics

All 100 enrolled patients completed the full protocol. Fifty-five (55%) were male and 45 (45%) female; mean age was 71.3±6.6 years. Compared with women, men had significantly lower BMI (21.8±5.0 vs 23.5±5.3 kg/m²; p=0.028), higher underweight rates (23.6% vs 13.3%), higher handgrip strength (8.9±5.1 vs 5.4±4.1 kg; p<0.001), and higher ASMI (6.3±1.4 vs 5.6±1.3 kg/m²; p=0.012) (Table 1). Mean handgrip strength (7.33±4.93 kg) and gait speed (0.36±0.12 m/s) were profoundly below AWGS thresholds (Table 2).

Table 1: Baseline sociodemographic, anthropometric, and clinical characteristics by gender (N=100)

* p<0.05. MUAC, mid-upper-arm circumference; ASMI, appendicular skeletal muscle mass index; COPD, chronic obstructive pulmonary disease.

Table 2: Descriptive statistics of key anthropometric, functional, and body-composition variables (N=100)

SMI, skeletal muscle mass index; IQR, interquartile range.

Comorbidity profile

Hypertension was most prevalent (57%), followed by diabetes mellitus (45%), COPD (23%), and coronary artery disease (17%). Mean CCI was 4.2±1.8 and polypharmacy was present in 73% (Table 3).

Table 3: Prevalence of comorbidities (N=100)

Mean Charlson Comorbidity Index 4.2±1.8.

Prevalence and severity of sarcopenia

Overall sarcopenia prevalence was 62%; probable sarcopenia 99%; severe sarcopenia 55% (88.7% of confirmed cases). Low gait speed was universal (100%) and low handgrip strength near-universal (99%); low ASMI was present in 65% (Table 4). In a mutually exclusive classification, 1% had no sarcopenia, 37% probable-only, 7% confirmed non-severe (moderate), and 55% severe (Figure 1).

Table 4: Prevalence of sarcopenia and AWGS 2019 components (N=100)

HGS, handgrip strength; ASMI, appendicular skeletal muscle mass index.

Figure 1: Sarcopenia prevalence (A) and mutually exclusive severity distribution (B), N=100.

Categorical associations and BMI gradient

Male sex was significantly associated with sarcopenia (70.9% vs 51.1%; p=0.009; Figure 3). A highly significant BMI gradient was observed, from 89.5% in underweight to 21.4% in obese participants (p<0.001; Figure 2). Low MUAC was also significantly associated (p=0.041) (Table 5).

Table 5: Categorical variables and sarcopenia status (N=100)

* p<0.05 (chi-square test). MUAC, mid-upper-arm circumference.

Figure 2: Sarcopenia prevalence across BMI categories (p<0.001).

Figure 3: Gender-wise prevalence of sarcopenia (p=0.009).

Continuous variables

Sarcopenic participants had significantly lower weight, BMI, MUAC, calf circumference, SMI, ASMI, and skeletal muscle mass (all p<0.001), consistent across both sexes (Table 6).

Table 6: Continuous variables by sarcopenia status, with gender sub-analysis (N=100)

† Overall sarcopenic vs non-sarcopenic (independent t-test). * p<0.05. M, male; F, female; circ., circumference.

Comorbidity-wise prevalence

COPD carried the highest sarcopenia prevalence (78.3%). Diabetic patients showed a paradoxically lower prevalence (44.4% vs 74.5%; p=0.001), attributable to higher BMI and insulin-mediated anabolic effects in the hospitalised type-2 diabetes phenotype (Table 7, Figure 4).

Table 7: Sarcopenia prevalence by comorbidity status (N=100)

* p<0.05 (chi-square test). CAD, coronary artery disease; CKD, chronic kidney disease; CLD, chronic liver disease.

Figure 4: Sarcopenia prevalence by comorbidity status (*diabetes p=0.001).

Geriatric assessment and laboratory parameters

Sarcopenic participants had significantly lower MNA-SF, Barthel ADL, Lawton IADL, and HMSE scores, higher GDS-15 and CCI (Table 8), and significantly lower haemoglobin, albumin, total protein, vitamin D, and creatinine (Table 9). The lower creatinine reflects reduced muscle mass (reverse causality) and was excluded from regression.

Table 8: Geriatric assessment scores: sarcopenic vs non-sarcopenic (N=100)

* p<0.05 (independent t-test). MNA-SF, Mini Nutritional Assessment–Short Form; HMSE, Hindi Mental State Examination; GDS, Geriatric Depression Scale.

Table 9: Laboratory parameters: sarcopenic vs non-sarcopenic (N=100)

* p<0.05. † Lower creatinine reflects reduced muscle mass (reverse causality); excluded from regression.

Predictors of sarcopenia

On univariate analysis, male sex, lower BMI, lower MUAC, lower MNA-SF, lower Barthel ADL, lower Lawton IADL, lower HMSE, and higher GDS were significant predictors (Table 10). On multivariable adjustment, lower BMI was the sole independent determinant (adjusted OR 0.82, 95% CI 0.75–0.90; p<0.001), corresponding to an 18% reduction in odds per unit BMI increase. Model fit was good (Nagelkerke R²=0.41; Hosmer–Lemeshow p=0.612) (Table 11).

Table 10: Univariate logistic regression: predictors of sarcopenia (N=100)

* p<0.05.

Table 11: Multivariable logistic regression: independent predictors of sarcopenia (N=100)

* p<0.001. Nagelkerke R²=0.41; Hosmer–Lemeshow p=0.612. BMI was the sole independent determinant.

Sarcopenic obesity

Sarcopenic obesity (low ASMI + high percent body fat) was present in 38% of participants, with similar rates in women (40.0%) and men (36.4%; p=0.714). All 38 affected patients would have been classified as nutritionally adequate or overweight by BMI alone (Table 12, Figure 5).

Table 12: Sarcopenic obesity — combined and gender-wise distribution (N=100)

‡ Chi-square (female vs male). PBF, percent body fat. All 38 sarcopenic-obesity patients were missed by BMI alone.

Figure 5: Gender-wise prevalence of sarcopenic obesity (p=0.714).

In this cross-sectional study of 100 consecutive elderly inpatients at a dedicated tertiary-care geriatric unit, sarcopenia prevalence by AWGS 2019 criteria was 62%, with severe sarcopenia in 55% and near-universal probable sarcopenia (99%). To our knowledge, this is the first AWGS 2019-based prevalence study from a dedicated Indian geriatric-medicine inpatient unit. Prevalence in context The 62% prevalence lies at the higher end of rates reported from dedicated geriatric inpatient units worldwide (27–65%),6,9,10 and is comparable to the 60.2% reported in the acutely hospitalised Italian CRIME cohort.7 It represents a roughly 42% relative increase over the 43.6% Indian community baseline from LASI,11 consistent with inpatient amplification driven by acute-illness-mediated muscle catabolism of 1–5% per day.8 Earlier Indian inpatient studies using less sensitive criteria or non-geriatric settings reported 23–37%,12,13,14 all lower than the present rate. Severity and functional impairment Severe sarcopenia dominated, comprising 88.7% of confirmed cases—consistent with the concept of acute sarcopenia, in which acute illness superimposed on chronic sarcopenia rapidly exhausts functional reserve.8 Universal low gait speed (mean 0.36±0.12 m/s) confirms profound functional impairment; the AWGS 2025 consensus frames such near-universal hospital impairment as the downstream consequence of undetected midlife muscle loss, supporting the WHO ICOPE call for screening from age 50.18,19 SARC-F identified 99% of patients, validating its high-sensitivity case-finding role while underscoring the need for confirmatory BIA.20,21 The prognostic implications are substantial, given the three-fold 12-month mortality reported for severe sarcopenia7 and the pooled mortality hazard of 1.71 in sarcopenic frail oldest-old.22 BMI as the sole independent determinant Lower BMI was the only independent determinant (adjusted OR 0.82, 95% CI 0.75–0.90), with a striking gradient from 89.5% (underweight) to 21.4% (obese). Chronic protein-energy malnutrition depletes anabolic reserve and impairs muscle protein synthesis through reduced IGF-1 and mTORC1 signalling,5,23 mechanisms accentuated in the cereal-based, frequently vegetarian Indian diet with low leucine intake.24 Nutritional risk has previously been linked to a three-fold higher sarcopenia odds in hospitalised elderly.25 BMI <18.5 kg/m² thus serves as a simple, zero-cost bedside trigger for nutritional evaluation. The male predominance on univariate analysis (OR 2.17) attenuated after adjustment (adjusted OR 1.45; p=0.312), indicating mediation through lower BMI and nutritional status rather than an independent sex effect,14,15 partly reflecting the higher absolute ASMI cut-off applied to men.16 Multidimensional correlates Sarcopenic patients showed worse nutritional (MNA-SF), functional (Barthel ADL, Lawton IADL), cognitive (HMSE), and depressive (GDS) profiles—the sarcopenic means falling within malnourished, dependent, cognitively impaired, and depressed ranges respectively. These overlaps reflect shared substrates of systemic inflammation, vascular disease, nutritional deficiency, and deconditioning,26 and validate comprehensive geriatric assessment over isolated muscle measurement. Biochemically, lower haemoglobin, albumin, total protein, and vitamin D corroborate protein-energy malnutrition and endemic vitamin D deficiency; anaemia independently predicts muscle-mass loss and gait-speed decline through shared inflammatory and nutritional pathways,27 while vitamin D is essential for type II fibre function. Targeted correction of anaemia and vitamin D deficiency offers immediately actionable, low-cost dual benefit. Comorbidity patterns and sarcopenic obesity COPD carried the highest comorbidity-wise prevalence (78.3%), consistent with systemic inflammation, hypoxaemia, deconditioning, and corticosteroid-induced catabolism;28 the paradoxically lower prevalence in diabetes (44.4%) is a recognised hospital-specific, BMI-mediated phenomenon that contrasts with the higher community odds reported for diabetic men.29 Sarcopenic obesity in 38%—at the upper end of pooled Asian hospital estimates (10–42%)30—would have been entirely missed by BMI, reinforcing BIA-based body composition as the preferred bedside metric for elderly admissions. Clinical recommendations 1. Universal SARC-F plus calf-circumference screening on admission for all patients ≥60 years. 2. Handgrip dynamometry and 6-metre gait-speed testing within 24 hours for screen-positive patients, enabling Day-1 AWGS 2019 severity classification. 3. BIA-based body-composition assessment replacing BMI as the primary metric, to detect sarcopenic obesity. 4. BMI <18.5 kg/m² as an immediate trigger for nutritional intervention targeting ≥1.2–1.6 g protein/kg/day (≥2.0 g/kg/day during acute catabolism), with leucine-enriched supplementation. 5. Systematic measurement and correction of haemoglobin, albumin, and vitamin D in sarcopenic patients. 6. Early mobilisation and progressive resistance exercise within 24 hours of admission. 7. Comprehensive geriatric assessment as a standard admission protocol, with priority sarcopenia evaluation and pulmonary rehabilitation for COPD inpatients. Limitations The cross-sectional design precludes causal inference, and the single-centre tertiary geriatric setting limits generalisability to community or general-medicine populations. The sample of 100, adequate for proportion estimation, limits power for less prevalent comorbidity subgroups. BIA estimates may be affected by abnormal hydration common in inpatients, although multi-frequency BIA mitigates this; DXA, the reference standard, was unavailable but BIA shows high concordance with DXA in Asian populations (ICC >0.87). Exclusion of the most severely impaired patients may have led to underestimation, and dietary assessment was qualitative only. Absence of follow-up precludes evaluation of intervention response—a priority for future longitudinal work.

Sarcopenia affected 62% of elderly geriatric inpatients, with severe sarcopenia in 55% and near-universal functional impairment. Lower BMI was the sole independent determinant, and sarcopenic patients demonstrated significantly worse nutritional, functional, cognitive, and depressive profiles alongside lower haemoglobin, albumin, and vitamin D. Sarcopenic obesity affected 38%, all undetectable by BMI alone. Universal admission screening using AWGS 2019-aligned tools, BIA-based body-composition assessment, and a comprehensive multidisciplinary approach are recommended for all elderly hospital admissions, providing a replicable framework for sarcopenia identification and management in Indian geriatric-medicine settings.

Declarations

Ethics approval and consent to participate. The study was approved by the Institutional Ethics Committee (September 2025). Written informed consent was obtained from all participants.

Consent for publication. Not applicable; no individually identifiable data are presented.

Availability of data. The datasets generated and analysed during the study are available from the corresponding author on reasonable request.

Competing interests. The author declares no competing interests.

Funding. No external funding was received.

Acknowledgements. The author thanks the faculty and staff of the Department of Geriatric Medicine, the participating patients, and the ward nursing and paramedical staff.

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