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Article

Prevalence and Clinical Associations of Osteosarcopenic Obesity and Frailty in Mexican Elderly Women: A Cross-Sectional Pilot Study

by
Ricardo García-Cabello
1,*,
Carlos Alberto Reyes-Torres
2,
Ana Cecilia Cepeda-Nieto
2 and
Itzel López-Topete
1
1
Departments of Endocrinology and Geriatrics, Hospital General de Zona con Medicina Familiar No. 2, Instituto Mexicano del Seguro Social, Saltillo 25240, Coahuila, Mexico
2
Facultad de Medicina Unidad Saltillo, Universidad Autónoma de Coahuila, Saltillo 25000, Coahuila, Mexico
*
Author to whom correspondence should be addressed.
J. Gerontol. Geriatr. 2026, 74(2), 15; https://doi.org/10.3390/jgg74020015
Submission received: 26 April 2026 / Revised: 30 May 2026 / Accepted: 2 June 2026 / Published: 5 June 2026
(This article belongs to the Topic Healthy, Safe and Active Aging, 3rd Edition)
Versions Notes

Abstract

The coexistence of obesity, osteoporosis, and sarcopenia has been associated with adverse outcomes such as risk of falls, fractures, immobility, disability and frailty, yet data from Latin American populations are scarce. This study aimed to determine the prevalence and associations of obesity, osteoporosis, and sarcopenia—individually and combined—with frailty in Mexican elderly women. We conducted a cross-sectional study in which patients with body mass index < 18.5 kg/m2, uncorrected sensory deficits, immobility, musculoskeletal diseases, or patients with implanted devices were excluded. Frailty was assessed using the FRAIL scale, obesity by body fat percentage, osteoporosis according to American Association of Clinical Endocrinology (AACE) guidelines and sarcopenia following the European Working Group on Sarcopenia in Older People-2 (EWGSOP2) recommendations. A total of 115 participants aged ≥60 years were assessed between January and June 2025. Frailty was present in 21.7% of the patients; 67.0% had obesity, 72.2% osteoporosis, 20.0% sarcopenia and 13.0% osteosarcopenic obesity. Sarcopenic phenotypes were associated with frailty: odds ratios (95% CI) were 3.05 (1.12–8.26) for sarcopenia, 4.23 (1.42–12.55) for sarcopenic obesity and 3.98 (1.28–12.40) for osteosarcopenic obesity. Sarcopenic phenotypes showed associations with frailty in Mexican elderly women.

1. Introduction

Frailty is a multidimensional geriatric syndrome characterized by a decline in physiological reserves and reduced capacity to respond to internal and external stressors [1,2]. This condition results in increased vulnerability to negative outcomes such as disability, hospitalization, and mortality [3]. Its prevalence increases with age, affecting a substantial proportion of older adults, particularly those over 80 years [2]. Although multiple operational definitions of frailty have been proposed, commonly used approaches include the frailty phenotype and screening tools such as the FRAIL scale, which allow for practical identification of at-risk individuals in clinical settings [4,5,6].
Obesity in older adults is associated with metabolic dysregulation, reduced mobility, and increased risk of geriatric syndromes [7,8,9]. Age-related changes in body composition include not only an increase in total fat mass but also a redistribution toward visceral and ectopic fat depots [10,11]. These changes are accompanied by hormonal alterations, chronic low-grade inflammation, and oxidative stress, all of which contribute to functional decline [12,13]. Interestingly, obesity in older adults presents a paradoxical relationship with mortality, with some studies suggesting protective effects under certain conditions [14,15].
Osteoporosis, defined by decreased bone mass and deterioration of bone microarchitecture, increases the risk of fragility fractures, which are a major cause of morbidity and mortality in older populations [16]. Bone loss occurs progressively with aging and is influenced by hormonal changes, nutritional deficiencies, and alterations in cellular processes [17]. The diagnosis is commonly established through bone mineral density assessment using dual-energy X-ray absorptiometry (DXA), although clinical criteria also include the presence of fragility fractures or high fracture risk [18].
Sarcopenia, characterized by the progressive loss of muscle mass, strength, and function, is another key contributor to frailty [19,20]. Its pathophysiology involves complex mechanisms including chronic inflammation, hormonal changes, mitochondrial dysfunction, and neuromuscular alterations [21,22]. Sarcopenia has been associated with impaired mobility, increased risk of falls, metabolic disorders, and mortality [23,24]. Current diagnostic criteria, such as those proposed by the European Working Group on Sarcopenia in Older People-2 (EWGSOP2), emphasize the importance of muscle strength as a primary parameter [25].
More recently, the coexistence of these three conditions has been conceptualized as osteosarcopenic obesity (OSO), a syndrome that reflects the interaction between bone, muscle, and adipose tissue [26]. This triad shares common pathophysiological pathways and may create a vicious cycle that amplifies functional decline and vulnerability [27,28]. Although research on this condition has grown, evidence remains heterogeneous, with wide variability in diagnostic criteria and reported prevalence [29].
Given the potential impact of OSO on frailty and adverse outcomes, it is essential to better characterize these relationships in Latin American populations. Therefore, this study aimed to evaluate the association between obesity, osteoporosis, and sarcopenia—individually and in combination—with frailty in Mexican elderly women, providing clinically relevant data to improve risk stratification and guide interventions in real-world settings.

2. Materials and Methods

2.1. Study Design and Participants

A cross-sectional analytical study was conducted between January and June 2025 in the outpatient Geriatrics and Endocrinology clinics of a secondary-level hospital in northern Mexico. Participants were ambulatory women aged ≥60 years, able to complete clinical evaluations independently or with caregiver assistance. Exclusion criteria were body mass index < 18.5 kg/m2, uncorrected sensory deficits, immobility, active malignancy, severe organ failure, musculoskeletal or neuromuscular disease, chronic edema, venous insufficiency, limb amputation or prosthesis, or implanted medical devices interfering with bioelectrical impedance analysis (BIA).

2.2. Clinical and Demographic Data

Age, sex, lifestyle habits, comorbidities, polypharmacy, and history of falls or fractures were recorded. The Charlson Comorbidity Index was used to estimate 10-year survival probability [30]. Polypharmacy was defined as the current use of 5 or more medications.

2.3. Frailty

Frailty was evaluated using the FRAIL scale [6,31]. Scores classify individuals as robust (0), pre-frail (1–2), or frail (3–5).

2.4. Body Composition

Height was measured with an ultrasonic stadiometer InBody Push® (InBody Co., Ltd., Seoul, Republic of Korea), and body composition via multifrequency segmental BIA InBody 120® (InBody Co., Ltd., Seoul, Republic of Korea). Body composition analysis was performed under standardized conditions. Participants were instructed to wear light clothing, maintain a fasting period of at least 4 h, avoid physical exercise during the previous 8 h, and empty their bladder immediately before the assessment. Prior to measurement, participants remained standing for at least 5 min. All metallic objects from the hands, wrists, feet, and legs were removed, and palms and soles were cleaned with disposable wet wipes before bioelectrical impedance analysis was conducted.

2.5. Obesity

Obesity was defined according to body fat percentage thresholds described in the European Society for Clinical Nutrition and Metabolism (ESPEN) and European Association for the Study of Obesity (EASO) Consensus Statement on Sarcopenic Obesity (>38% in women) [32]. These cutoffs were derived from a previous work that evaluated body composition phenotypes among Hispanic and non-Hispanic older adults in the New Mexico [33]. These thresholds served as reference values applied in later epidemiological studies that included non-Hispanic and Mexican American elder populations [34].

2.6. Sarcopenia

Sarcopenia was defined according to EWGSOP2 criteria. A confirmed case required the coexistence of both low muscle strength and low muscle quantity. Low muscle strength was defined as a grip strength < 16 kg. Low muscle quantity was defined as appendicular skeletal muscle mass (ASM) < 15 kg, or appendicular skeletal muscle mass index (ASMI) < 5.5 kg/m2 obtained from body composition analysis [25]. Handgrip strength was measured using a JAMAR hydraulic hand dynamometer (Performance Health, Warrenville, IL, USA) according to the Southampton protocol. Participants were seated with the forearm supported, wrist in neutral position, and measurements were obtained alternately in both hands. Three measurements were performed per hand, and the highest value recorded was used for analysis [35].

2.7. Osteoporosis

Bone mineral density was measured by DXA (Lunar Prodigy Advance, GE Healthcare, Madison, WI, USA) using Encore software version 17/18 (GE Healthcare, Madison, WI, USA) at lumbar spine and hip. According to the American Association of Clinical Endocrinology (AACE), osteoporosis was defined as T-score ≤ −2.5 at either the lumbar spine, femoral neck or total hip. Patients with osteopenia (T-score between −1.0 to −2.5) were assessed using FRAX® (University of Sheffield, Sheffield, UK) specific for Mexican population and those with elevated FRAX risk (≥20% major osteoporotic or ≥3% hip fracture) were also considered to have osteoporosis. Finally, patients who presented with fragility fractures in the absence of other metabolic bone disorders, even if they had a normal bone mineral density (T-score between 1 to −1), were also considered to have osteoporosis [36].

2.8. Physical Performance

Physical performance was assessed using the Short Physical Performance Battery (SPPB), including standing balance, gait speed, and chair stand tests. Balance evaluation included side-by-side, semi-tandem, and tandem positions held for up to 10 s each. Gait speed was assessed over a 4-m walking course, with the fastest of two trials recorded. Lower extremity strength was evaluated using the five-times chair stand test, timed from the initial seated position to completion of the fifth stand. Participants were allowed to use assistive devices during gait assessment if needed [37].

2.9. Statistical Analysis

Categorical variables were expressed as frequencies; continuous variables as mean ± SD or median (IQR). Group comparisons used χ2, Fisher’s, t-test, or Mann–Whitney U as appropriate. Odds ratios (OR) and 95% confidence intervals (CI) were calculated to evaluate associations between frailty and each condition or their combinations. Statistical significance was set at p < 0.05, all analyses were performed using SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA).

3. Results

A total of 115 elderly women were included in the study. The mean age was 72.48 ± 6.9 years. Regarding clinical characteristics, 9.6% of participants reported a history of smoking, 1.7% alcohol consumption, and only 7.8% reported regular physical activity. Falls in the previous year were reported by 37.4% of participants, while 6.1% experienced fractures. Polypharmacy was highly prevalent (72.2%), and the median 10-year survival estimated by the Charlson Comorbidity Index was 53.39% (21.36–77.48). When comparing participants with and without OSO, individuals with OSO were significantly older (78.53 vs. 71.57 years, p < 0.001), although no significant differences were observed in lifestyle factors, comorbidities, or history of falls and fractures (Table 1).
In terms of body composition and physical performance, important differences were observed between groups. Patients with OSO had significantly lower height, lower ASM and lower ASMI. Additionally, muscle strength was substantially lower in the OSO group. Physical performance was also significantly impaired, with poorer balance (Tandem balance), longer chair stand time, and lower scores in the Short Physical Performance Battery. No significant differences were observed in weight, body mass index, body fat, side-by-side balance, semi-tandem balance or gait speed (Table 2).
Regarding the prevalence of individual and combined conditions, 67.0% of participants had obesity, 72.2% osteoporosis, and 20.0% sarcopenia. Combined phenotypes were also common: 44.3% had osteoporotic obesity, 14.8% sarcopenic obesity, 18.3% sarcopenic osteoporosis, and 13.0% presented the full OSO phenotype. Only 5.2% of participants had none of these conditions. Frailty was identified in 21.7% of the study population.
The analysis of associations revealed that sarcopenia was the only individual component significantly associated with frailty. In contrast, obesity and osteoporosis alone were not significantly associated with frailty. Sarcopenic obesity and OSO were significantly associated with frailty. Sarcopenic osteoporosis showed a borderline significant association with frailty. Osteoporotic obesity was not significantly associated with frailty (Table 3).
A multivariate logistic regression analysis was performed. To avoid model overfitting due to the limited number of frailty events, only clinically relevant variables with significant univariate associations were included in the multivariate logistic regression model. Variables structurally related to osteosarcopenic obesity (obesity, osteoporosis, and sarcopenia individually) were not simultaneously included in the multivariate model to avoid collinearity. The model explained 27% of the variability in frailty (Nagelkerke R2 = 0.272, p < 0.001). Among the variables analyzed, low physical performance and age ≥ 75 years showed association with frailty (Table 4).
Due to recruitment constraints and strict eligibility criteria, the final sample size was smaller than initially projected (152 vs. 115). Therefore, a post hoc power analysis was conducted based on the observed association between osteosarcopenic obesity and frailty. Using the observed effect size (OR 3.98) and a significance level of 0.05, the estimated statistical power was approximately 71%, suggesting moderate sensitivity for detecting clinically relevant associations in this exploratory study.

4. Discussion

This study evaluated the association between obesity, osteoporosis, and sarcopenia—individually and in combination—with frailty in Mexican elderly women. Our findings showed significant associations between sarcopenic phenotypes and frailty, particularly among individuals with sarcopenia, sarcopenic obesity and osteosarcopenic obesity.
The prevalence of OSO in our cohort (13%) falls within the wide range reported in the literature (0.8–66.3%), likely reflecting variability in diagnostic criteria, population characteristics, and measurement methods [29].
Obesity, defined by body fat percentage, was not independently associated with frailty. This finding may be explained by the “obesity paradox” described in older adults, where excess adiposity does not necessarily translate into worse outcomes [14]. However, the literature remains inconsistent, with some studies reporting increased frailty risk with central obesity or extreme body mass index values [38,39,40,41]. These discrepancies highlight the limitations of traditional anthropometric measures and the importance of assessing body composition more precisely. Additionally, the diagnosis of obesity based on body fat percentage remains heterogeneous, as no universal consensus exists regarding optimal cutoff values; thresholds may vary according to ethnicity, age, and body composition methodology [32]. This is particularly relevant for underrepresented populations such as Mexican and Latin American older adults.
Similarly, osteoporosis alone or combined with obesity was not independently associated with frailty. This association may be indirect, as osteoporosis contributes primarily to adverse outcomes through fractures, particularly hip fractures, which lead to functional decline and increased mortality [39,42]. The relatively low prevalence of fractures in our study population may partly explain this finding.
In contrast, sarcopenia alone was significantly associated with frailty. This is consistent with previous studies demonstrating that reduced muscle mass and strength are central to functional decline [39,43,44]. These findings reinforce the concept that muscle impairment is closely associated with frailty and may contribute to reduced physical function in older adults. Combined phenotypes showed variable associations that may reflect differences in body composition, inflammatory burden, hormonal dysregulation, metabolic reserve, and functional adaptation [27].
The heterogeneous associations observed across body composition phenotypes may reflect the complex interplay between adipose tissue, muscle, bone, and physical function [45]. Recent evidence suggests that these alterations and frailty should be viewed as interconnected manifestations along a spectrum of age-related physiological decline rather than as isolated conditions [46]. Within this framework, combined phenotypes may represent more advanced stages of body composition impairment and reduced functional reserve; phenotypes including sarcopenia may have a greater impact on strength and physical performance, potentially explaining their association with frailty in our cohort. However, these interpretations should be considered exploratory.
The observed association between OSO and frailty in univariate analysis is supported by international evidence, although its magnitude may vary depending on the assessment tool used [47,48,49]. In this study, frailty was evaluated using the FRAIL scale, a simple and practical screening tool. While it is feasible in clinical settings, it relies on self-reported data and may underestimate frailty compared with more objective measures [15,50,51]. Although OSO showed a significant univariate association with frailty, this association did not persist after multivariate adjustment, possibly due to the limited sample size and overlap with physical performance variables.
Body composition assessment methods also influence results. In this study, BIA was used to estimate fat and muscle mass, while DXA was used to assess bone mineral density. Body composition was assessed using BIA because it is a portable, low-cost, and radiation-free technique that is readily applicable in routine clinical practice. Although DXA remains the reference method for body composition assessment, previous studies have reported acceptable agreement between BIA and DXA when standardized measurement conditions are applied, supporting its use as a practical alternative in clinical and epidemiological settings [52,53,54]. Additionally, the lack of standardized diagnostic criteria for obesity, osteoporosis, and sarcopenia remains a major limitation in the field.
This study has several limitations. The cross-sectional design precludes causal inference between body composition phenotypes and frailty. Additionally, the relatively small and female-only sample limits external generalizability. Although statistically significant associations were identified, the relatively small sample size may have limited statistical power for subgroup analyses and contributed to wide confidence intervals. Additionally, the relatively small number of OSO cases may have limited precision in subgroup analyses. The results should therefore be interpreted as hypothesis-generating rather than definitive evidence of the relationship between osteosarcopenic obesity and frailty.
This study provides valuable insight into the burden of OSO and its association with frailty in the Mexican population, a group underrepresented in the literature. Identifying older adults with concurrent muscle, bone, and fat abnormalities could help guide targeted interventions. Further multicenter studies with larger, balanced samples and standardized criteria are needed to establish OSO as a clinical construct and to explore whether its treatment can reduce frailty risk and improve quality of life among older adults.

5. Conclusions

In this cohort of Mexican elderly women, sarcopenic phenotypes showed significant associations with frailty, whereas osteosarcopenic obesity did not remain independently associated after multivariable adjustment. Because of the cross-sectional design and the limited number of osteosarcopenic obesity cases, these findings should be interpreted cautiously and cannot establish causal relationships. Larger prospective studies are needed to clarify the clinical relevance of osteosarcopenic obesity and its relationship with frailty.

Author Contributions

Conceptualization, R.G.-C.; methodology, R.G.-C., C.A.R.-T. and A.C.C.-N.; project administration, A.C.C.-N. and I.L.-T.; investigation R.G.-C. and I.L.-T.; formal analysis, R.G.-C. and C.A.R.-T.; writing—original draft preparation, R.G.-C.; writing—review and editing, C.A.R.-T., A.C.C.-N. and I.L.-T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the National Scientific Research Committee of the Instituto Mexicano del Seguro Social (protocol code R-2024-785-045; approved on 28 August 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data is contained within the article. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AACEAmerican Association of Clinical Endocrinology
ASMAppendicular skeletal muscle mass
ASMIAppendicular skeletal muscle mass index
BIABioelectrical impedance analysis
BMIBody mass index
DXADual-energy X-ray absorptiometry
EASOEuropean Association for the Study of Obesity
ESPENEuropean Society for Clinical Nutrition and Metabolism
EWGSOP2European Working Group on Sarcopenia in Older People-2
OSOOsteosarcopenic obesity
SPPBShort Physical Performance Battery

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Table 1. Baseline characteristics of the study population (N = 115).
Table 1. Baseline characteristics of the study population (N = 115).
Overall
(n = 115)		Without OSO
(n = 100)		With OSO
(n = 15)		p Value
Age (years), mean ± SD72.48 ± 6.971.57 ± 6.5978.53 ± 6.11<0.001
Smoking, n (%)11 (9.6)9 (9.0)2 (13.3)0.436
Alcohol use, n (%)2 (1.7)2 (2.0)0 (0.0)0.755
Physical activity, n (%)9 (7.8)9 (9.0)0 (0.0)0.271
Falls (past year), n (%)43 (37.4)37 (37.0)6 (40.0)0.823
Fractures (past year), n (%)7 (6.1)6 (6.0)1 (6.7)0.635
Polypharmacy, n (%)83 (72.2)71 (71.0)12 (80.0)0.350
Charlson index (10-year survival %),
median (IQR)		53.39 (21.36–77.48)65.43 (21.36–77.48)53.39 (53.39–77.48)0.441
Frailty, n (%)25 (21.7)18 (18)7 (46.7)0.012
OSO: osteosarcopenic obesity.
Table 2. Body composition and physical performance (N = 115).
Table 2. Body composition and physical performance (N = 115).
Overall
(n = 115)		Without OSO
(n = 100)		With OSO
(n = 15)		p Value
Weight (kg), mean ± SD65.72 ± 14.2066.52 ± 14.5260.42 ± 10.870.122
Height (m), mean ± SD1.50 ± 0.061.50 ± 0.061.43 ± 0.05<0.001
BMI (kg/m2), mean ± SD29.12 ± 5.6429.11 ± 5.7929.19 ± 4.720.961
Body fat (%), mean ± SD40.91 ± 7.5040.52 ± 7.8443.48 ± 3.880.155
ASM (kg), mean ± SD13.84 ± 2.9714.23 ± 2.9211.25 ± 1.80<0.001
ASMI (kg/m2), mean ± SD6.10 ± 0.956.20 ± 0.945.43 ± 0.770.003
Handgrip strength (kg), median (IQR)20.00 (16.00–22.00)20.00 (18.00–23.75)14.00 (10.00–14.00)<0.001
Side-by-side balance ≥ 10 s, n (%)115 (100)100 (100)15 (100)
Semi-tandem balance ≥ 10 s, n (%)109 (94.8)95 (95.0)14 (93.3)0.576
Tandem balance ≥ 10 s, n (%)51 (44.3)49 (49.0)2 (13.3)0.008
Gait speed (sec), median (IQR)5.93 (4.84–8.03)5.88 (4.78–7.68)6.65 (5.25–11.62)0.065
Chair stand test (sec), median (IQR)14.19 (11.66–17.63)13.72 (11.41–16.35)17.63 (16.59–23.90)0.001
SPPB score, median (IQR)8.00 (6.00–10.00)8.00 (7.00–10.00)6.00 (4.00–8.00)0.001
ASM: appendicular skeletal muscle mass; ASMI: appendicular skeletal muscle mass index, BMI: body mass index; OSO: osteosarcopenic obesity; SPPB: Short Physical Performance Battery.
Table 3. Association between body composition phenotypes and frailty.
Table 3. Association between body composition phenotypes and frailty.
OR95% CIp Value
Obesity0.840.33–2.130.722
Osteoporosis0.770.29–2.020.599
Sarcopenia3.051.12–8.260.024
Osteoporotic obesity0.790.32–1.960.621
Sarcopenic obesity4.231.42–12.550.006
Sarcopenic osteoporosis2.781.00–7.770.044
Osteosarcopenic obesity3.981.28–12.400.012
Table 4. Multivariate logistic regression for frailty.
Table 4. Multivariate logistic regression for frailty.
OR95% CIp Value
Age ≥ 75 years3.241.18–8.660.022
Low physical performance (SPPB ≤ 8)6.431.73–23.950.005
Osteosarcopenic obesity1.910.54–6.690.310
SPPB: Short Physical Performance Battery.
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García-Cabello, R.; Reyes-Torres, C.A.; Cepeda-Nieto, A.C.; López-Topete, I. Prevalence and Clinical Associations of Osteosarcopenic Obesity and Frailty in Mexican Elderly Women: A Cross-Sectional Pilot Study. J. Gerontol. Geriatr. 2026, 74, 15. https://doi.org/10.3390/jgg74020015

AMA Style

García-Cabello R, Reyes-Torres CA, Cepeda-Nieto AC, López-Topete I. Prevalence and Clinical Associations of Osteosarcopenic Obesity and Frailty in Mexican Elderly Women: A Cross-Sectional Pilot Study. Journal of Gerontology and Geriatrics. 2026; 74(2):15. https://doi.org/10.3390/jgg74020015

Chicago/Turabian Style

García-Cabello, Ricardo, Carlos Alberto Reyes-Torres, Ana Cecilia Cepeda-Nieto, and Itzel López-Topete. 2026. "Prevalence and Clinical Associations of Osteosarcopenic Obesity and Frailty in Mexican Elderly Women: A Cross-Sectional Pilot Study" Journal of Gerontology and Geriatrics 74, no. 2: 15. https://doi.org/10.3390/jgg74020015

APA Style

García-Cabello, R., Reyes-Torres, C. A., Cepeda-Nieto, A. C., & López-Topete, I. (2026). Prevalence and Clinical Associations of Osteosarcopenic Obesity and Frailty in Mexican Elderly Women: A Cross-Sectional Pilot Study. Journal of Gerontology and Geriatrics, 74(2), 15. https://doi.org/10.3390/jgg74020015

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