Highlights
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The association of sarcopenia and obesity (sarcopenic obesity) shows differential effect on several outcomes in comparison with sarcopenia, depending upon both the definition of obesity and the outcome assessed.
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Differential associations may be found depending on obesity definition.
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Obesity amplifies sarcopenia’s association with frailty and hospitalization.
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Higher fat mass may be linked to reduced mortality in sarcopenic individuals.
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Clinicians should consider both muscle and fat metrics in geriatric assessments.
Keywords: sarcopenic obesity, frailty, disability, hospitalization, mortality
Abstract
Objectives
To assess the impact of different definitions of obesity on the association between sarcopenia and negative health outcomes in community-dwelling older adults.
Design
A longitudinal analysis using data from the Toledo Study of Healthy Ageing
Setting
Community-dwelling older adults.
Participants
1546 older adults (mean age 74.76 ± 5.75 years; 45.54% men).
Measurements
Sarcopenia was defined using population-standardized Foundation for the National Institutes of Health criteria. Obesity was assessed using several criteria previously prosed in the literature: Body Mass Index (BMI: >28, >30, >33 kg/m²), waist circumference (WC: 88♀; >102♂; and 98♀; >109♂), waist-to-hip ratio (WHR: >0.85♀; >0.90♂), fat mass percentage (%FM: >38%♀, >27%♂; and >40%♀, >30%♂; and >43%♀, >31%♂), and population-based quartiles for trunk and appendicular fat mass. Frailty and disability were evaluated at baseline and 2.99 years later; hospitalization and mortality were tracked at 3.63 and 6.28 years, respectively. Regression models (logistic and Cox) and ROC analyses were conducted, adjusting for age, sex, comorbidities, and malnutrition.
Results
350 (22.63%) met sarcopenia criteria. Obesity prevalence varied from 18.63% to 76.13%, depending on the definition. Obesity, regardless of the criterion, strengthened the associations between sarcopenia and frailty while only some of them did for hospitalization (BMI and WC), but not impacted disability. Sarcopenia was not significantly associated with death in the adjusted model, but the association became significant after adjustment for some obesity markers (WHR, truncal fat mass, and %FM. ROC curves results suggested that the capacity of sarcopenia to predict worsening frailty increased 2%, when the obesity markers were included.
Conclusion
Obesity—particularly defined by BMI and WC—strengthened the association between sarcopenia and adverse outcomes such as frailty and hospitalization. In contrast, higher fat mass was associated with lower mortality, suggesting a potential obesity paradox that warrants further research. These findings highlight the importance of assessing multiple obesity criteria alongside sarcopenia, while the potential protective role of obesity against mortality requires confirmation in further studies.
1. Introduction
Sarcopenia is a progressive muscle-based disease that captures the reduction in function and muscle mass that accompanies aging [1]. This condition is highly prevalent in older adults, with estimates indicating that one in five older adults suffer from sarcopenia [2]. Sarcopenia progression is associated with numerous negative outcomes, including decreased quality of life, falls, frailty, disability, institutionalization, hospitalization, and ultimately, death [3,4]. Consequently, health professionals are advised to actively monitor sarcopenia in older adults.
The presence of sarcopenia is associated with adherence to inadequate lifestyle habits (e.g., poor nutrition) that might contribute to the development of other chronic conditions [5,6]. Particularly, the simultaneous occurrence of sarcopenia and obesity, called sarcopenic obesity [7], has been commonly observed in older adults [8]. This scenario seems to have distinct clinical characteristics in comparison to both conditions individually, potentially amplifying the risk of negative events [9].
However, results remain inconclusive. For instance, a recent systematic review observed that while obesity increased the risk of physical impairment, it improved survival rates in older adults with sarcopenia [10]. A possible explanation is the criteria used to define obesity. In fact, authors found protective effects when obesity was operationalized according to body mass index (BMI) and body composition parameters (e.g., fat mass), whereas no significant results were observed with waist circumference [10]. A further criticism is that the World Health Organization’s definition of obesity—based on BMI values above 30—may not adequately capture the harmful effects attributed to the increase of fat tissue [[11], [12], [13]]. Nevertheless, there is no consensus on how to effectively assess this condition, leading to the proposal of multiple cut-off points, whose evidence in the presence of sarcopenia has been little explored [14,15]. In this sense, the 2022 ESPEN and EASO consensus proposed specific diagnostic criteria for sarcopenic obesity as a distinct entity, emphasizing separate assessment of sarcopenia and obesity phenotypes [15]. Finally, to the best of our knowledge, no previous studies have examined different operational definitions of obesity in the context of sarcopenia within the same population and provides comparative evidence that may inform future standardized approaches. Therefore, the aim of this study was to evaluate the impact of obesity (defined according to different operationalization methods) on the association between sarcopenia and its predictive ability for negative events (i.e., frailty, hospitalization, disability and mortality) in community-dwelling older adults.
2. Methods
The present study examined data from the Toledo Study of Healthy Ageing (TSHA) database [16]. The TSHA is a longitudinal population-based cohort study that examined community-dwelling individuals older than 65 years who lived in the province of Toledo, Spain. This study aimed at investigating the determinants and consequences of frailty in older adults. Participants were selected through a two-stage random sampling stratified by sex, age, and town size, resulting in a representative sample comprising approximately 24% of the population aged 65 years and older residing in the province of Toledo, Spain. Comprehensive assessments, including anthropometry, dual-energy X-ray absorptiometry (DXA), physical performance tests, frailty evaluation, and disability measures, were conducted during home or clinical visits by trained healthcare professionals. In the present study, baseline assessments were conducted between 2011 and 2013. Follow-up duration was 2.99 years (range 2.0-5.4 years) for frailty and disability, and longer for hospitalization (mean 3.63 years, range 0.0164-5.24) and mortality (mean 6.28 years, range 0.59-7.47). The TSHA was conducted in concordance with ethical standards described in the Declaration of Helsinki and was approved by the Clinical Research Ethics Committee of the Toledo University Hospital Complex, Spain (ID:15072010.93). All participants voluntarily signed informed consent prior to enrolment. For this study, only participants who had no missing data for the variables examined were included (Supplementary Figure S1). A comparison of included vs excluded participants (n = 696 participants excluded due to missing frailty assessment, missing DXA, loss to follow-up) revealed that excluded participants were older, predominantly women, and malnutrition (Supplementary Table S1).
2.1. Primary exposure variables
2.1.1. Sarcopenia
Sarcopenia was operationalized according to the definition proposed by the Foundation for the National Institutes of Health (FNIH) [17,18], based on cutoff points standardized to our population (FNIHs) [19], as follows: i) low isometric handgrip strength (IHG) (<25.51 kg for men and <19.19 kg for women), ii) low muscle mass, based on appendicular lean soft mass (aLM) (< 0.65 in men and < 0.54 in women), and iii) low gait speed (<0.8 m/s). The FNIH criteria were selected over the more recent EWGSOP2 consensus because in our population, as they may offer more appropriate thresholds for detecting adverse outcomes in our population (particularly hospitalization and incident frailty, as measured by the Frailty Trait Scale-5) [4].
IHG was assessed using a hydraulic Jamar dynamometer (J. A. Preston Corporation, Clifton, NJ, USA), according to standard procedures. The highest value (in kilograms) of three trials was used for analysis. One minute rest interval was provided among attempts. aLM was measured through a whole-body dual-energy X-ray absorptiometry (DXA) on a Hologic scanner (Bedford, MA, USA). Body weight and height were determined using a stadiometer and an analogue medical scale, respectively. BMI was estimated as body weight in kilograms (adjusted to the nearest 0.1) divided by height (adjusted to the nearest cm) in meters squared. Then, aLM adjusted by BMI (aLM/BMI) was obtained. For the gait speed assessment, participants were instructed to walk 3 meters at their usual pace. The fastest of two trials (measured in meters per second) was analyzed.
2.1.2. Obesity
Obesity was defined according to BMI, body composition (i.e., total, trunk and appendicular fat mass), and body circumferences (i.e., waist and waist-to-hip ratio). Waist and hip perimeters (in cm) were evaluated using an anthropometric inelastic tape. Body fat tissue was measured by DEXA. The cut-off points utilized for each definition according to assessment methods are shown in Supplementary Table S2 [[20], [21], [22], [23], [24], [25], [26], [27]], selected based on established literature. This approach acknowledges recent advancements in obesity assessment frameworks, particularly those highlighted in the ESPEN/EASO consensus statement led by Donini and colleagues [15], which proposes specific cut-off points as markers of the obesity component in sarcopenic obesity.
2.2. Primary negative health outcomes
Negative outcomes included worsening in frailty status and dependence levels, hospitalization, and death. Frailty and disability status were determined after 2.99 years of follow- (2.0-5.4 years), while hospitalization and death were retrieved after mean follow-up time of 3.63 years (range 0.0164-5.24) years and 6.28 years (range 0.59-7.47), respectively.
Frailty status was evaluated according to the Frailty Trait Scale 5 (FTS5) [28]. FTS5 involves the evaluation of 5 health domains (i.e, gait speed, grip strength, physical activity, BMI and balance) scored from 0 (the lowest) to 10 (the highest), with a maximum of 50 points, and higher values reflecting increased frailty (Supplementary Table S3). An increase of 2.5 points was utilized as a marker of worsening in frailty status. The ability to perform basic activities of daily living (BADL) was determined by the Katz Index [28]. Disability was operationalized according to increases in the levels of dependence in any category of BADL. Hospitalization was obtained from the hospital record of the Toledo University Hospital Complex. Data on deaths were ascertained by phone contact, hospital records, and the Spanish national registry.
2.3. Covariables and adjusted variables
Adjusted variables were assessed at baseline and included comorbidity and malnutrition. Comorbidity was assessed using the Charlson Comorbidity Index [29], which assigns weights to 19 conditions from 1 (i.e., myocardial infarction) to 6 (i.e., metastatic cancer or AIDS) based on mortality risk, with higher scores indicating greater comorbidity burden. Nutritional status was evaluated by the Mini‐Nutritional Assessment (MNA) [30]. Participants were categorized as well-nourished (≥24), at risk of malnutrition (17–23.5), or undernourished (<17). Due to the small number of undernourished subjects, we grouped individuals with scores <24 in one category.
2.4. Statistical analysis
Data are presented as mean ± standard deviation (SD) for continuous variables, and frequency (percentages) for categorical variables. Differences between individuals according to sarcopenic status were tested using Mann-Whitney or Kruskal-Wallis, for continuous data, and Chi-square test, for categorical data. The influence of obesity markers on the associations between sarcopenia and negative events were texted using logistic regression models, for frailty or disability, and Cox proportional hazard models, for death and hospitalization. To this aim, analyses were first adjusted by the model 1, which included age, sex, nutritional status, and Charlson Index. Then, each definition of obesity was individually included, and the OR/HR of sarcopenia and its corresponding CI were examined. Interaction analysis was conducted in those models in which both sarcopenia and definition of obesity were significant. Receiver operating characteristic (ROC) curves were created to evaluate the diagnostic accuracy of sarcopenia and obesity definitions through adjusted models. Akaike's Information Criterion (AIC) was performed to evaluate the model fit and determine the best-fitting model. Statistical significance level was set at p-value <0.05. Analyses were performed using the Statistical Package R version 4.1.2 for Windows (Vienna, Austria).
3. Results
3.1. Sample characteristics
Data of 1546 individuals were analyzed. The main characteristics of the study participants according to sarcopenia status are shown in Table 1. Participants had a mean age of 74.7 ± 5.75 years, and most were women (54.5%). The prevalence of sarcopenia was 22.63%. When comparisons were conducted according to sarcopenic status, sarcopenic individuals were older, mostly women, had higher BMI, fat mass, and circumferences values, and had a higher prevalence of obesity and malnutrition. However, no differences in the prevalence of participants in the highest quartile of truncal and appendicular fat mass were observed.
Table 1.
Characteristics of participants.
| Variable | Whole sample | No sarcopenia | Sarcopenia | p-value |
|---|---|---|---|---|
| N | 1546 | 1196 (77.36%) | 350 (22.63%) | |
| Age, mean (SD) | 74.76 (5.75) | 73.92 (5.52) | 77.61 (5.63) | <0.0001 |
| Sex, men (%) | 704 (45.54) | 645 (53.93) | 59 (16.86) | <0.0001 |
| Charlson Index, mean (SD) | 1.19 (1.63) | 1.09 (1.54) | 1.51 (1.87) | <0.0001 |
| MNA<24, n (%) | 377 (25.42) | 234 (20.37) | 143 (42.81) | <0.0001 |
| BMI, mean (SD) | 29.17 (4.55) | 28.61 (4.30) | 31.10 (4.84) | <0.0001 |
| BMI>28, n (%) | 889 (57.50) | 629 (52.59) | 260 (74.29) | <0.0001 |
| BMI>30, n (%) | 598 (38.68) | 402 (33.61) | 196 (56.00) | <0.0001 |
| BMI > 33, n (%) | 288 (18.63) | 178 (14.88) | 110 (31.43) | <0.0001 |
| Waist perimeter, mean (SD) | 94.66 (12.11) | 94.07 (11.91) | 96.67 (12.58) | 0.0011 |
| WC >88♀; >102♂, n (%) | 714 (46.18) | 484 (40.47) | 230 (65.71) | <0.0001 |
| WC>98♀; >109♂, n (%) | 327 (21.15) | 196 (16.39) | 131 (37.43) | <0.0001 |
| Hip perimeter, mean (SD) | 105.22 (10.40) | 103.80 (9.65) | 110.05 (11.38) | <0.0001 |
| WHR, mean (SD) | 0.90 (0.09) | 0.91 (0.09) | 0.88 (0.08) | <0.0001 |
| WHR>0.85♀; >0.90♂, n (%) | 974 (63.00) | 775 (64.80) | 199 (56.86) | 0.0068 |
| Total FM, mean kg (SD) | 15.11 (4.02) | 14.43 (3.91) | 17.45 (3.48) | <0.0001 |
| % FM, mean (SD) | 36.34 (7.68) | 34.83 (7.62) | 41.47 (5.35) | <0.0001 |
| % FM >38♀; >27♂, n (%) | 1177 (76.13) | 875 (73.16) | 302 (86.29) | <0.0001 |
| % FM >40♀; >30♂, n (%) | 927 (59.96) | 654 (54.68) | 273 (78.00) | <0.0001 |
| % FM >43♀; >31♂, n (%) | 657 (42.50) | 466 (38.96) | 191 (54.57) | <0.0001 |
| TFM, mean (SD) | 8.25 (2.25) | 7.93 (2.19) | 9.35 (2.10) | <0.0001 |
| % FM > 75 percentile, n (%) | 386 (24.97) | 299 (25.00) | 87 (24.86) | 0.956 |
| AFM, mean (SD) | 6.34 (2.22) | 5.97 (2.12) | 7.60 (2.06) | <0.0001 |
| % AFM > 75 percentile, n (%) | 386 (24.97) | 299 (25.00) | 87 (24.86) | 0.956 |
| Incident negative events | ||||
| Worsening FTS5, n (%) | 456 (36.33) | 356 (35.96) | 100 (37.74) | 0.854 |
| Worsening disability, n (%) | 313 (23.95) | 221 (21.79) | 92 (31.40) | 0.0007 |
| Hospitalization, n (%) | 422 (27.30) | 282 (23.58) | 140 (40.00) | <0.0001 |
| Death, n (%) | 222 (14.36) | 152 (12.71) | 70 (20.00) | 0.0006 |
AFM: Appendicular Fat Mass.BMI: Body Mass Index. FTS5: Frailty Trait Scale-5. FM: Fat Mass. In bold: p-value <0.05. MNA: Mini-Nutritional Assessment. TFM: Trunk Fat Mass. WC: Waist Circumference. WHR: Waist-to-hip Ratio.
3.2. Associations between sarcopenia, obesity and negative outcomes
Logistic and Cox regression analyses for the influence of obesity markers on the associations between sarcopenia and negative events are shown in Table 2. In the unadjusted analysis, sarcopenia increased the risk for all outcomes variables [HR/OR ranging from 1.47 for mortality to 2.96 for worsening disability, p-value <0.01 for all the events]. However, when the analysis was adjusted for the model 1, associations remained significant for frailty and hospitalizations, but not for disability (OR(95%CI): 1.33 (0.96, 1.84); p-value 0.086) and death (HR(95%CI): 1.33 (0.97, 1.82); p-value 0.078).
Table 2.
Associations between sarcopenia and several definitions of obesity with negative events: Frailty assessed through FTS5 and worsening disability (OR); hospitalization and mortality (HR).
| Outcome/ Variable | Model 1 | Model 2 |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BMI>28 | BMI>30 | BMI > 33 | WC >88♀; >102♂ | WC>98♀; >109♂ | WHR > 0.85♀; >0.90♂ | % FM >38♀; >27♂ | % FM >40♀; >30♂ | % FM >43♀; >31♂ | Q4 TFM | Q4 AFM | ||
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| FTS5 worsening | ||||||||||||
| Sarcopenia | 1.78 (1.23, 2.59) | 1.73 (1.19, 2.52) | 1.77 (1.22, 2.58) | 1.87 (1.28, 2.72) | 1.78 (1.23, 2.59) | 1.77 (1.22, 2.57) | 1.79 (1.23, 2.60) | 1.76 (1.21, 2.56) | 1.74 (1.19, 2.53) | 1.78 (1.22, 2.58) | 1.64 (1.13, 2.40) | 1.72 (1.18, 2.50) |
| Obesity Definition | 1.65 (1.27, 2.16) | 1.92 (1.45, 2.54) | 2.42 (1.69, 3.47) | 1.54 (1.18, 2.01) | 1.36 (0.98, 1.89) | 1.03 (0.78, 1.36) | 1.27 (0.94, 1.71) | 1.80 (1.38, 2.35) | 1.77 (1.35, 2.31) | 2.08 (1.50, 2.88) | 1.68 (1.20, 2.35) | |
| Worsening disability | ||||||||||||
| Sarcopenia | 1.33 (0.96, 1.84) | 1.24 (0.89, 1.73) | 1.26 (0.90, 1.76) | 1.27 (0.91, 1.77) | 1.26 (0.91, 1.75) | 1.31 (0.94, 1.82) | 1.33 (0.96, 1.85) | 1.36 (0.98, 1.88) | 1.32 (0.95, 1.83) | 1.26 (0.91, 1.76) | 1.34 (0.96, 1.88) | 1.28 (0.92, 1.79) |
| Obesity Definition | 1.32 (1.00, 1.74) | 1.24 (0.94, 1.63) | 1.30 (0.93, 1.80) | 1.24 (0.95, 1.62) | 1.04 (0.76, 1.44) | 0.91 (0.68, 1.21) | 0.85 (0.62, 1.16) | 1.02 (0.77, 1.33) | 1.21 (0.93, 1.58) | 0.95 (0.68, 1.33) | 1.21 (0.86, 1.70) | |
| HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | |
| Hospitalization | ||||||||||||
| Sarcopenia | 1.38 (1.09, 1.73) | 1.28 (1.01, 1.62) | 1.31 (1.04, 1.65) | 1.31 (1.04, 1.66) | 1.31 (1.04, 1.64) | 1.34 (1.06, 1.68) | 1.38 (1.10, 1.74) | 1.41 (1.12, 1.78) | 1.43 (1.13, 1.80) | 1.40 (1.11, 1.76) | 1.40 (1.11, 1.77) | 1.33 (1.05, 1.69) |
| Obesity Definition | 1.33 (1.09, 1.63) | 1.29 (1.05, 1.57) | 1.34 (1.05, 1.72) | 1.48 (1.21, 1.80) | 1.27 (1.01, 1.60) | 1.09 (0.87, 1.36) | 0.93 (0.74, 1.17) | 0.90 (0.74, 1.10) | 0.98 (0.81, 1.20) | 0.90 (0.69, 1.17) | 1.19 (0.90, 1.57) | |
| Mortality | ||||||||||||
| Sarcopenia | 1.33 (0.97, 1.82) | 1.39 (1.00, 1.92) | 1.36 (0.99, 1.88) | 1.32 (0.96, 1.82) | 1.36 (0.99, 1.86) | 1.36 (0.99, 1.87) | 1.39 (1.02, 1.91) | 1.52 (1.10, 2.10) | 1.51 (1.09, 2.08) | 1.41 (1.02, 1.95) | 1.42 (1.03, 1.96) | 1.32 (0.95, 1.82) |
| Obesity Definition | 0.86 (0.65, 1.13) | 0.89 (0.66, 1.19) | 1.02 (0.70, 1.48) | 1.18 (0.89, 1.55) | 1.19 (0.86, 1.66) | 1.03 (0.76, 1.41) | 0.62 (0.46, 0.82) | 0.77 (0.58, 1.01) | 0.96 (0.73, 1.27) | 0.61 (0.40, 0.95) | 1.04 (0.69, 1.59) | |
Model 1: Association between sarcopenia and each one of the four outcomes adjusted by age, sex, nutritional status, and Charlson Index. Model 2: Model 1 + adjustment by each one of the different operational definition of obesity addressed in the current study. Accordingly, column under the heading “Model 1” shows the association between sarcopenia and outcomes adjusted by the variables previously mentioned for the Model 1. Each column under the heading “Model 2” shows the OR/HR of the association between sarcopenia adjusted by the variables included in model 1 plus each one of the operational definitions of obesity and the risk of suffering the corresponding event. In the case of “Obesity Definition”, data show the association (OR/HR) between each criterion of obesity in the full model (Model 2).
AFM: Appendicular Fat Mass. BMI: Body Mass Index. HR: Hazard Ratio. FTS5: Frailty Trait Scale-5. FM: Fat Mass. In bold: p-value <0.05. OR: Odds Ratio. TFM: Trunk Fat Mass. WHR: Waist-to-hip Ratio. WC: Waist Circumference.
When the influence of different operational definitions of obesity was examined, BMI, waist circumference (>88♀; >102♂), and fat mass percentage (% FM >40♀; >30♂ and % FM >43♀; >31♂) were significantly associated with an increased risk of worsening frailty. After adjusting for BMI and waist circumference, the risk of hospitalization associated with obesity significantly increased, regardless of the cutoff point used. In this sense, sarcopenia was consistently associated with the risk of hospitalization. The associations between sarcopenia and death, which was non-significant in Model 1 [HR(95%CI): 1.33 (0.97, 1.82)], became statistically significant when the analysis were mutually adjusted for specific operational definitions of obesity (WHR > 0.85♀; >0.90♂, truncal fat mass Q4, and % FM at various cutoffs). Conversely, fat mass (% FM >38♀; >27♂) and truncal fat mass Q4 conferred a significant reduced risk of mortality.
3.3. Ability to discriminate individuals at higher risk of negative events
ROC curve results are shown in Table 3. When the capacity of sarcopenia plus the model 1 was examined, it had a fair to moderate ability to predict the different adverse events (AUC = 0.6316- 0.7685). When the obesity markers were included, the capacity of the model to predict worsening frailty increased up to 2%. However, no relevant changes were found for disability, hospitalization, and death.
Table 3.
Area Under the Curve (AUC) and incremental AUC results for the predictive capacity of sarcopenia and different obesity definitions on adverse negative outcomes.
| FTS5 worsening, AUC | Worsening disability, AUC | Hospitalization, IAUC | Death, IAUC | |
|---|---|---|---|---|
| Sarcopenia-Model 1 | 0.6958 | 0.6316 | 0.7624 | 0.7685 |
| BMI>28 | 0.7054 | 0.6388 | 0.7672 | 0.7678 |
| BMI>30 | 0.7103 | 0.6383 | 0.7652 | 0.7681 |
| BMI > 33 | 0.7140 | 0.6371 | 0.7649 | 0.7689 |
| WC >88♀; >102♂ | 0.7028 | 0.6384 | 0.7653 | 0.7693 |
| WC >98♀; >109♂ | 0.6983 | 0.6318 | 0.7635 | 0.7691 |
| WHR > 0.85♀; >0.90♂ | 0.6958 | 0.6320 | 0.7622 | 0.7686 |
| % FM >38♀; >27♂ | 0.6967 | 0.6303 | 0.7626 | 0.7699 |
| % FM >40♀; >30♂ | 0.7086 | 0.6320 | 0.7621 | 0.7682 |
| % FM >43♀; >31♂ | 0.7089 | 0.6374 | 0.7626 | 0.7686 |
| Q4 TFM | 0.7088 | 0.6317 | 0.7621 | 0.7702 |
| Q4 AFM | 0.7041 | 0.6351 | 0.7633 | 0.7683 |
Model 1 includes age, sex, nutritional status, Charlson Index score, in addition to sarcopenia. In subsequent models, each operational definition of obesity was added individually to Model 1.
AFM: Appendicular Fat Mass. AUC: Area Under the receiver operating characteristic Curve. BMI: Body Mass Index. IAUC: Incremental Area Under the receiver operating characteristic Curve. FTS5: Frailty Trait Scale-5. FM: Fat Mass. In bold: p-value <0.05. TFM: Trunk Fat Mass. WHR: Waist-to-hip Ratio. WC: Waist Circumference.
3.4. Comparisons among significant associations
AICs for comparisons among sarcopenia and different outcomes depending upon the obesity definitions are shown in Table 4. When models were compared, models that included BMI > 33 (1487.80), WC >88♀; >102♂ (1552.54) and % FM >38♀; >27♂ (1089.20) presented the lowest AICs for both frailty, hospitalization and death, respectively.
Table 4.
Akaike's Information Criterion (AIC) of sarcopenia and different obesity definitions adjusted for covariates toward different negative outcomes.
| FTS5 worsening | Hospitalization | Death | |
|---|---|---|---|
| Sarcopenia-Model 1 | 1508.82 | 1566.79 | - |
| BMI>28 | 1496.93 | 1556.18 | - |
| BMI>30 | 1489.35 | 1560.05 | - |
| BMI > 33 | 1487.80 | 1562.92 | - |
| WC >88♀; >102♂ | 1500.70 | 1552.54 | - |
| WC >98♀; >109♂ | 1507.58 | 1563.97 | - |
| WHR > 0.85♀; >0.90♂ | 1510.78 | 1568.37 | 1096.41 |
| % FM >38♀; >27♂ | 1508.06 | 1568.66 | 1089.20 |
| % FM >40♀; >30♂ | 1491.45 | 1568.74 | 1094.00 |
| % FM >43♀; >31♂ | 1492.56 | 1568.34 | 1096.46 |
| Q4 TFM | 1491.31 | 1568.42 | 1091.90 |
| Q4 AFM | 1501.48 | 1566.92 | - |
Model 1 includes age, sex, nutritional status, Charlson Index score, in addition to sarcopenia. In subsequent models, each operational definition of obesity was added individually to Model 1.
AFM: Appendicular Fat Mass. BMI: Body Mass Index. FTS5: Frailty Trait Scale-5. FM: Fat Mass. In bold: p-value <0.05. TFM: Trunk Fat Mass. WHR: Waist-to-hip Ratio. WC:
3.5. Interaction between sarcopenia, obesity and negative outcomes
No significant interactions between sarcopenia and obesity were found (Table 5).
Table 5.
Results of the interaction term between sarcopenia and different obesity parameters adjusted for covariates toward different negative outcomes.
| Variable | Interaction term | Obesity parameter | Sarcopenia |
|---|---|---|---|
| FTS5 worsening | OR(95%CI) | OR(95%CI) | OR(95%CI) |
| BMI>28 | 1.88 (0.93, 3.78) | 1.50 (1.12, 2.00) | 1.10 (0.59, 2.08) |
| BMI>30 | 1.49 (0.80, 2.79) | 1.76 (1.29, 2.40) | 1.43 (0.86, 2.37) |
| BMI > 33 | 1.13 (0.56, 2.26) | 2.33 (1.52, 3.56) | 1.81 (1.18, 2.75) |
| WC >88♀; >102♂ | 0.93 (0.49, 1.76) | 1.56 (1.16, 2.10) | 1.85 (1.07, 3.23) |
| % FM >40♀; >30♂ | 1.46 (0.69, 3.07) | 1.72 (1.29, 2.30) | 1.28 (0.64, 2.58) |
| % FM >43♀; >31♂ | 1.60 (0.86, 2.96) | 1.61 (1.20, 2.17) | 1.37 (0.83, 2.26) |
| Q4 TFM | 1.38 (0.72, 2.65) | 1.88 (1.27, 2.77) | 1.41 (0.86, 2.31) |
| Q4 AFM | 0.67 (0.35, 1.29) | 1.92 (1.29, 2.87) | 2.03 (1.28, 3.22) |
| Hospitalization | HR(95%CI) | HR(95%CI) | HR(95%CI) |
| BMI>28 | 1.37 (0.85, 2.19) | 1.23 (0.97, 1.55) | 1.02 (0.67, 1.55) |
| BMI>30 | 1.15 (0.76, 1.74) | 1.23 (0.96, 1.57) | 1.22 (0.90, 1.67) |
| BMI > 33 | 0.83 (0.51, 1.34) | 1.46 (1.06, 2.02) | 1.37 (1.06, 1.78) |
| WC >88♀; >102♂ | 1.14 (0.75, 1.74) | 1.42 (1.12, 1.80) | 1.19 (0.84, 1.69) |
| WC >98♀; >109♂ | 0.89 (0.56, 1.41) | 1.33 (0.99, 1.80) | 1.37 (1.05, 1.79) |
The Model was adjusted by age, sex, nutritional status, Charlson Index, sarcopenia, each operational definition of obesity (one at a time) and the interaction term between sarcopenia and obesity.
AFM: Appendicular Fat Mass. BMI: Body Mass Index. FTS5: Frailty Trait Scale-5. FM: Fat Mass. In bold: p-value <0.05. TFM: Trunk Fat Mass. WC: Waist Circumference.
4. Discussion
The main findings of the present study indicate that presence of obesity strengthened the association between sarcopenia and both frailty and hospitalization. Stronger associations were found when obesity was operationalized according to BMI and WC. Moreover, we observed that sarcopenia alone was not associated with mortality. However, this association became significant after adjusting for WHR, truncal fat mass, and % FM, which suggests that obesity might also modulate the risk of death in older adults with sarcopenia. When ROC curve results were examined, the combination with obesity and covariates increased up to 2% the capacity of sarcopenia to predict adverse outcomes, particularly worsening frailty.
To the best of our knowledge, this is the first study that examined the influence of different criteria of obesity, on the association between sarcopenia and negative health events in the same cohort of community-dwelling older adults. Our findings align with other observational studies. Hirani et al. [31] found that the presence of low muscle mass and high fat mass increased the risk of frailty over 5 years in community-dwelling older adults. Molina-Baena et al. [32] expanded these results by observing older adults with sarcopenic obesity, characterized according to the FNIH plus BMI values ≥30 kg/m2 or WC >88♀ >102♂, had increased risk of frailty. In contrast, no significant results were observed when sarcopenia was operationalized according to other definitions. These findings are confirmed by the recent meta-analysis of Eitmann and colleagues [10], in which the presence of obesity was associated with detriments in physical health.
There are some mechanisms by which obesity might increase the risk of frailty in people with sarcopenia. Weight gain leads to metabolic changes in the adipose tissue, increasing the generation of inflammatory molecules by cytokines and disrupted adipokynes [33]. These mediators are able to induce insulin resistance [34], promote muscle loss, and contribute to physical decline, which influences the development of frailty [[35], [36], [37]]. Indeed, many studies have found that older people with sarcopenic obesity have significant changes in the expression of microRNAs related to protein homeostasis, determination of muscle fibre type, and insulin resistance [38], and higher systemic levels of pro-inflammatory cytokines [39]. Furthermore, sedentary behavior is commonly observed in people with sarcopenic obesity [40]. As this condition progresses, more weight gain combined with locomotory problems, may cause further reductions in physical activity levels, promoting a vicious cycle [9].
Obesity enhanced the association between sarcopenia and hospitalization. Studies have shown that people with sarcopenic obesity have increased risk for many parameters potentially associated with hospitalization, including falls [41] and cardiometabolic morbidity and diseases [42]. Moreover, hospitalization is a common consequence of sarcopenia and frailty [43]. As obesity influenced the associations among both parameters, our findings might reflect the progression of frailty. These assumptions are exciting and deserve to be better explored in future studies.
Despite the results indicating increased risk of frailty and hospitalization in older adults with sarcopenia and obesity, the presence of elevated adipose tissue markers (i.e., FM) reduced mortality in those sarcopenic. Similar results were reported by a recent meta-analysis [10], in which individuals with sarcopenia and obesity had reduced risk of death when obesity was operationalized according to body composition measurements, but not with waist circumference [10].
These findings are likely explained according to the obesity paradox, which describes that the simultaneous presence of obesity and another disease may confer a protective effect against certain negative outcomes in older adults [44,45]. Increased adipose tissue might reflect better nutritional status and muscle reserves [44] and be associated with the synthesis of cardioprotective molecules, such as N-terminal pro-B-type natriuretic peptide (NT-proBNP) [45].
Our findings align with previous studies (Supplementary Table S2) [[20], [21], [22], [23], [24], [25], [26], [27]], but should be interpreted in light of contemporary frameworks. The 2022 ESPEN/EASO consensus highlights the need for unified criteria for SO, recognizing that traditional definitions such as those used here (BMI, WC, %FM) may not fully capture the distribution of visceral or ectopic fat in older adults. Future studies should validate our results with these new criteria. Regarding sarcopenia, it should be noted that we use the FNIH cut-off points standardized for our population, as these may be more appropriate for detecting certain adverse events such as frailty [4]. Therefore, extrapolation to other cut-off points (such as those proposed by the EWGSOP2) or other populations should be done with caution.
Our findings have important clinical implications. In fact, results of the present study emphasize the need for monitoring both sarcopenia and obesity parameters in older adults, to reduce the risk of negative events, particularly frailty and hospitalization. According to the present study, BMI and WC are the most valuable obesity markers to be combined with sarcopenia. Notably, the use of any of these parameters enhances the associations observed with sarcopenia, likely indicating that any of them might be used in the identification of individuals at increased risk. In this sense, routine screening for both sarcopenia and obesity may be required for improving risk stratification and early identification of vulnerable individuals. Future studies are needed to confirm and expand our data by proposing international representative cutoff points. On the other hand, increased fat mass might protect individuals from death. These findings support the existence of an obesity-paradox in community dwelling older adults where adipose reserves may provide metabolic protection in advanced age, and deserve to be better explored in future investigations that examine the specific causes of death, other related parameters (e.g., biomarkers, lifestyle habits and long-term trajectories of body composition), and differences among obesity markers.
Furthermore, these data emphasize that clinical strategies should prioritize interventions that might improve both sarcopenia and obesity. For instance, exercise training and protein supplementation [46,47] might improve sarcopenic aspects and might contribute to weight loss [48,49], if adequately prescribed. In our cohort, sarcopenic individuals exhibited a higher prevalence of malnutrition risk (Table S1), illustrating the potential coexistence of obesity, sarcopenia, and malnutrition. This may be driven by shared mechanisms previously discussed, including chronic low-grade inflammation, sedentary behavior, and insulin resistance, which exacerbates muscle catabolism and metabolic dysregulation. In our population, insulin resistance increased the risk of worsening in frailty status, but decreased the risk of mortality [36]. This underscores the need for comprehensive nutritional status in older adults with sarcopenic obesity, prioritizing high-quality protein intake and resistance exercise to mitigate functional decline.
Some limitations of the present study should be acknowledged to allow a better interpretation of our results. First, the generalizability of our results may be limited to community-dwelling older adults and may not apply to institutionalized, hospitalized, or acutely ill populations. Second, the causes of hospitalization were not recorded. Third, the occurrence of other negative events commonly related to the progression of sarcopenia were not evaluated (e.g., falls). Fourth, the observational nature of this study precludes definitive conclusions about causality, although its longitudinal nature provides support to some suggestions. We should mention that in this study, sarcopenia has shown associations close to significance with events such as disability or mortality. Given that in previous studies performed with other adjustment variables in this cohort [4], this association was significant, future studies should confirm whether our findings may be due to a lack of power. This assumption is reinforced taken into account the lower limit of the ranges of the HRs, many of them tightly close to the value of 1. The exclusion of a substantial number of participants (n = 696) due to missing data may introduce potential selection bias, as excluded individuals were older, predominantly women, and had higher levels of comorbidity, and malnutrition. This may have resulted in a healthier final sample, potentially attenuating the observed associations between sarcopenia, obesity, and adverse events. Finally, despite the data on hospitalizations were obtained from Toledo University Hospital Complex, some events occurring outside this complex could have been missed. However, the universal coverage and continuity of the Spanish National Health System, combined with the predominantly local care-seeking behavior of mixed rural-urban cohort of older adults in this inland area, make it unlikely that there has been substantial underreporting.
5. Conclusions
Our results indicate that obesity, particularly operationalized according to BMI and WC, strengthened the associations between sarcopenia and both frailty and hospitalization. These findings indicate that both obesity markers might be used to monitor sarcopenic obesity in older adults. On the other hand, older adults with sarcopenia and increased fat mass had reduced mortality, supporting the existence of an obesity-paradox, and encouraging the conduction of more investigations to provide a deeper knowledge among these parameters.
Authorship / CRediT authorship contribution statement
Conceptualization: AAB, HJCJ, LRM; Methodology: AAB, HJCJ, JAC, LRM; Formal analysis and investigation: AAB, HJCJ, JAC, WSL, LRM; Project administration: FJGG, LRM; Writing - original draft preparation: AAB, HJCJ, JAC, WSL, LRM; Writing - review and editing: AAB, HJCJ, LRM. All authors have read and agreed to the published version of the manuscript.
Declaration of Generative AI and AI-assisted technologies in the writing process
No Generative AI or AI -assisted technologies were used in the writing process.
Funding
TSHA research was funded by grants from the Spanish Ministry of Economy, Industry and Competitiveness, cofinanced by the European Regional Development Funds (Instituto de Salud Carlos III, PI20/00977 and PI23/00877) and the Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CB16/10/00464), MITOFUN, Fundación Francisco Soria Melguizo (Section 2/2020), and Innovative Medicines Initiative Joint Undertaking under grant agreement n◦115621.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data statement
Raw data files are available from Prof. Garcia-Garcia and Prof. Rodríguez-Mañas upon reasonable request.
Acknowledgments
We would like to thank the participants, cohort members, and research team members.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2026.100803.
Contributor Information
Alejandro Álvarez-Bustos, Email: a.alvarezbu@gmail.com.
Leocadio Rodriguez-Mañas, Email: leocadio.rodriguez@salud.madrid.org.
Appendix A. Supplementary data
The following are Supplementary data to this article:
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