Prevalence of Sarcopenia in Very Old Diabetic and Non-Diabetic Hospitalized Patients
by
, , , ,
Sandra De Breucker
1,*
,
Véronique Lachat
1, 
Emilia Frangos
2,
Andrea Trombetti
1,3,4,
Ulrich Vischer
1,†,
Aline Mendes
1,
François R. Herrmann
1 and
Christophe E. Graf
1 1
Division of Geriatrics and Rehabilitation, Department of Rehabilitation, and Geriatrics, Geneva University Hospitals, 1205 Geneva, Switzerland
2
Division of Internal Medicine and Rehabilitation Loëx-Joli-Mont, Department of Rehabilitation, and Geriatrics, Geneva University Hospitals, 1205 Geneva, Switzerland
3
Division of Bone Diseases, Geneva University Hospitals, 1205 Geneva, Switzerland
4
Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
†
Deceased Author.
Diabetology 2025, 6(9), 99; https://doi.org/10.3390/diabetology6090099
Submission received: 30 May 2025
/
Revised: 15 August 2025
/
Accepted: 25 August 2025
/
Published: 11 September 2025
Abstract
Background/Objectives: The prevalence of diabetes in very old people is rising sharply worldwide, due not only to obesity, nutritional and sedentary lifestyles, but also to aging per se. Diabetes is associated with a higher incidence of sarcopenia, malnutrition and physical disabilities. However, many age-specific issues in the clinical management of very old diabetic patients remain unstudied. Methods: This is a case–control prospective study including 162 very old hospitalized diabetic patients and 301 controls. We explored the impact of diabetes on the prevalence of sarcopenia according to the EWGSOP2 criteria, using Jamar handgrip to assess muscle strength, BIA-derived fat-free mass index to assess muscle mass, and the timed up and go test to assess physical performance. We also explored factors associated with sarcopenia in both groups in multiple logistic analysis. Results: Mean age was 84.8 ± 6.0 years. We found a prevalence of sarcopenia of 8.0% and 16.7% in the diabetic and the control groups, respectively (p = 0.010). BMI was independently associated with sarcopenia in both groups, explaining 25% of the model in the diabetic group and 33% of the model in the control group. Conclusions: Sarcopenia was less prevalent in diabetic hospitalized older patients than in other patients, indicating that old frail patients are not the same patients as those that are in epidemiological studies on sarcopenia in diabetes. These results should be confirmed in further studies.
1. Introduction
The prevalence of type 2 diabetes mellitus (T2DM) is expected to rise sharply worldwide, accounting for 6.2% of the population on average in EU countries in 2019 and could reach on average 16% in 2045 [1]. The so-called diabetes pandemic is often blamed on obesity and sedentary lifestyles, but also on the consumption of refined foods with a high glycemic load and sodas. However, a substantial part of the increase in T2DM prevalence can be attributed to aging per se, leading to 19.3 million people aged 60–79 having diabetes across EU countries, compared with 13 million people aged 20–59 [1].
T2DM in the elderly is associated with all-cause mortality and an increased incidence of most important geriatric syndromes, including functional disabilities, falls and fractures, depression and dementia, urinary incontinence and malnutrition [2,3,4].The association between diabetes and physical disabilities has been quite extensively documented in elderly subjects [4,5,6], T2DM predicting physical disabilities, even after adjusting for preexisting cardiovascular complications and obesity [5,6,7,8,9]. Patients who have both low muscle mass and T2DM are two times more likely to perform poorly on physical performance tests (e.g., gait speed test, grip strength, and Timed Up and Go test) compared with patients who have neither of these factors [10]. Moreover, diabetic patients with higher HbA1c levels showed a higher prevalence of frailty [11]. Above this, the age-related decrease in muscle mass and strength, also called sarcopenia, is associated with mortality and is a major determinant of physical disabilities. Among the associated factors of sarcopenia in community-dwelling older adults, T2DM was one independent disease-related risk factor (OR = 1.40, 95% CI: 1.18–1.66) [12]. On the other hand, sarcopenia may contribute to the onset and progression of T2DM through altered glucose disposal due to low muscle mass, and increased inflammation arising from inter- and intramuscular adipose tissue accumulation [13]. A recent bibliometric mapping on diabetes mellitus and sarcopenia research pointed out current research hotspots including the bidirectional interaction between the two conditions, sharing common pathological mechanisms like impaired insulin secretion, peripheral insulin resistance, chronic inflammation, and the influence of body composition [14].
The most recent European definition of sarcopenia from the European Working Group on Sarcopenia in Older People (EWGSOP2) defines sarcopenia as the combination of low muscle strength with low muscle mass, the severity coming from the impact of sarcopenia on physical performance [15]. In a meta-analysis including 17 studies providing 54,676 participants (mean age 65.4 ± 11.2 years), participants with T2DM had an increased prevalence of sarcopenia compared with controls (28.4% [95%CI 18.9–40.2] and 18.7% [95%CI 11.9–28.1] respectively), based on different consensus definitions, but not EWGSOP2’s [16]. Very few papers assessed the prevalence of sarcopenia in very old diabetic patients with the EWGSOP2 criteria: two studies comparing the prevalence of sarcopenia according to EWGSOP1 and EWGSOP2 found very different prevalences between the two definitions, and mean age was respectively 68.3 ± 5.6 years and 71.2 ± 8.2 years. [17,18]. Moreover, the concept of sarcopenic obesity, defined recently by European Society for Clinical Nutrition and Metabolism (ESPEN) and European Association for the Study of Obesity (EASO) consensus statements as the association of sarcopenia and obesity as defined by WHO, was scarcely studied in old diabetic patients, despite the double burden of the two diseases on health outcomes and quality of life [19,20]. It is unclear whether the EWGSOP2 criteria can be used in an obese population.
Considering these findings, we aimed to compare the prevalence of sarcopenia and its components according to the EWGSOP2 definition among very old, hospitalized patients with and without diabetes, and to examine the factors associated with sarcopenia in this population.
2. Materials and Methods
2.1. Design of the Study
This prospective study included diabetic and control patients consecutively admitted to the Geneva Geriatric Hospital (Hôpital des Trois-Chêne, Geneva University Hospitals) from 1 April 2011, to 30 September 2015. All patients were assessed during their hospital stay for anthropometric data, body composition, co-morbidities, cardiovascular drug prescriptions, nutritional status, functional status and blood sampling. Mortality data were obtained from the website of the Geneva’s population register (“Office Cantonal de la Population”). Informed consent was obtained for each participant.
2.2. Inclusion Criteria
Diabetic patients were defined by ongoing anti-diabetic treatment, a previous physician diagnosis, a fasting plasma glucose > 7.0 mmol/L and/or a random blood glucose > 11.0 mmol/L on more than two occasions.
Controls (non-diabetic patients) were selected applying the same inclusion and exclusion criteria. Two non-diabetic control patients were recruited for each diabetic patient on the same or the next day.
2.3. Exclusion Criteria
Patients with severe cognitive disorders, in end-of-life care, with transient (related to stress and/or acute disease) or secondary diabetes (steroid-induced or post-pancreatectomy), with disseminated cancer or myelodysplastic syndromes, admitted for post-operative care, undergoing hemodialysis or that had been readmitted <1 month after a previous admission were excluded.
2.4. Sampling Strategy
Patients were selected by the nursing staff according to a computer-generated random table. A signed written informed consent was obtained from each patient. Exclusion criteria and reasons for refusal were collected and the characteristics (age, sex, length of stay, diabetes status) of patients accepting inclusion were compared with those who did not.
2.5. Data Collection
The following information and data were collected during the initial evaluation within 7 days after admission:
Socio-demographic, clinical and geriatric assessment data: age, gender, length of stay, and in-hospital mortality were collected from the hospital’s electronic records. Diabetes duration, smoking habits, comorbidities burden according to the Cumulative Illness Rating Scale for Geriatrics (CIRS-G), previous cardio-vascular disorders, number of medical treatments on admission, cardiovascular and glucose-lowering medication, height (measured or calculated from knee height), body weight, body mass index (BMI), waist circumference (WC), nutritional status according to the Mini Nutritional Assessment (MNA), basic activities of daily living (ADL), instrumental activities of daily living (iADL), cognitive assessment according to the Mini Mental State Examination (MMSE) and symptoms of anxiety and depression assessed by the Hospital Anxiety and Depression Scale (HAD) for all patients were collected.
Sarcopenia assessment: The diagnosis of sarcopenia was based on criteria of the EWGSOP2 [15]. Probable sarcopenia was considered if the maximal grip value of the dominant hand was inferior to 27 kg in men and 16 kg in women. Handgrip strength (HGS) was assessed in two trials on the dominant hand using a standard handheld dynamometer (JAMAR; TEC; Clifton, NJ, USA), and the maximum value was retained. Sarcopenia was diagnosed as a low grip strength associated with a low muscle mass index. Muscle mass was measured by bioimpedance (BIA Nutriguard, Data Input, Pöcking, Germany). We calculated the fat-free mass (FFM) using the formula referenced for the Swiss population by Kyle et al. and validated against DXA: FFM = −4.104 + (0.518 × height2 [cm]/resistance [Ω]) + (0.231 × weight [kg]) + (0.130 × reactance [Ω]) + (4.229 × sex [1 for men and 0 for women]) [21]. Fat-free mass index (FFMI) was then calculated by dividing fat-free body mass by squared height. A low muscle mass was considered by using the lowest tertile of the FFMI of the sample: <13.3 kg/m2 for the whole group, <15.3 kg/m2 for men and <12.9 kg/m2 for women. Severe sarcopenia was defined by the presence of sarcopenia associated with a low physical performance, as defined by a timed up and go test (TUG) higher or equal to 20 s.
2.6. Statistical Analysis
Statistical analyses were performed by STATA SE, version 18.0 (Stata Corp, College Station, TX, USA, 2023). The normality of the distribution of continuous variables was checked with the Shapiro–Francia tests for normality. Normally distributed variables were expressed in means ± standard deviation, non-parametric continuous variables were expressed in medians [25–75 interquartile range], and logistic, ordinal and nominal variables were expressed in numbers (percentages).
The descriptive data of diabetic and control were compared using the Student’s t test for normally distributed continuous variables, the Mann–Whitney test for non-parametric continuous variables, and chi-square test or Fisher exact test for categorical and discrete variables.
The EWGSOP2 categories were dichotomized into two groups: the non-sarcopenic control group including patients with no sarcopenia and probable sarcopenia; and the sarcopenia group including confirmed sarcopenia and severe sarcopenia. Factors associated with sarcopenia were analyzed in the total group, the diabetic group and the control group using univariate followed by multiple logistic regression models adjusted for sex and age. All assumptions for multiple regression were verified. p-values < 0.05 were considered as significant.
3. Results
Characteristics of participants are summarized in Table 1. Four hundred sixty-three patients were included in the cohort, of whom 162 were diabetic patients, and 301 were controls. Age ranged from 70.2 to 101.8 years (mean ± SD, 84.8 ± 6.0). Diabetic patients were younger and had more comorbidities and more polypharmacy and took more preventive cardiovascular medications than controls. One third of diabetic patients were treated with insulin. The prevalence of geriatric syndromes was similar in both groups, even when including nutritional status. Diabetic patients had a higher prevalence of obesity and a higher waist circumference. In both genders, diabetic patients had similar handgrip strength and physical performance than non-diabetic patients and had a higher fat-free mass index, leading to a significantly lower pooled prevalence of confirmed sarcopenia (8.0% versus 16.7%) and severe sarcopenia (3.1% versus 6.0%) in diabetic patients compared with controls. Diabetic patients had a better lipid profile than non-diabetic patients as they were more frequently treated by statins than controls.
Table 2 shows factors associated with sarcopenia in univariate logistic regression.
In multiple logistic regression, after adjustment for sex and age, higher BMI was associated with lower risk of sarcopenia: ORs 0.68 (95% CI 0.62–0.76), <0.001 total group; 0.69 (95% CI 0.57–0.83), p < 0.001 in the diabetic group; and 0.68 (95% CI 0.60–0.77), p < 0.001 in the control group (Table 3).
4. Discussion
We aimed to determine the impact of diabetes on the prevalence of sarcopenia and its components according to EWGSOP2 definition and factors associated with sarcopenia in very old, hospitalized patients. We observed that diabetic patients have a lower prevalence compared with non-diabetic patients. A recent systematic review and meta-analysis reported a higher prevalence of sarcopenia and severe sarcopenia (respectively 23% and 12.1%) according to the Asian Working Group on Sarcopenia definition [22], which comprises composite criteria like EWGSOP2 [23]. We know that the prevalence of sarcopenia in T2DM is two to threefold higher in Asian adults compared with Europeans, due to higher abdominal obesity, lower muscle mass, and increased insulin resistance, all of which are known risk factors for diabetic sarcopenia [24,25,26]. A more recent study reported a prevalence of 8% of sarcopenia according to EWGSOP2 criteria in younger diabetic patients (mean age 68.3 ± 5.6 years), which is equal to our results. However, our patients with T2DM had a mean age of 83.3 ± 5.9 years and age is recognized as an independent risk factor for sarcopenia, both in the general population and in the population with T2DM [17,22]. One study reported a higher prevalence of sarcopenia in a cohort of 182 T2DM patients compared with 138 controls in the age category 50–74, but not in the age groups of 75–79, 80–84 and ≥ 85 years. This suggests that the influence of age on sarcopenia may be fading, particularly in favor of other parameters such as the duration of diabetes [27,28].
To explain the prevalence found, we can assume that the measurement of FFMI (composed of trunk and limb muscle mass and in-extenso total body mass, except fat mass) in our study overestimates muscle mass when compared with other studies in which skeletal mass index (based on limb muscle mass only) was measured [29]. We also observed that, in the total group, loop diuretics increased the risk of sarcopenia, reducing the fluids that are accounted for in FFMI. In a recent review, Sbrignadello et al. explored the use of BIA to assess sarcopenia in T2DM: 7 out of 40 included studies used FFM or FFMI to define muscle mass, 4 of which had the same reservations about the use of FFMI [29,30,31,32,33]. Despite this, fat-free mass estimates from BIA shows a direct linear relationship with skeletal muscle mass and can be estimated as a relevant proxy for skeletal muscle mass assessment. Indeed, BIA is a simple, quick, and noninvasive tool, requiring only affordable and portable equipment [34].
Among these studies, one showed that muscle mass was similar between diabetic and non-diabetic old (69.4 ± 4.1 years) and young (57.3 ± 5.1 years) groups, whereas muscle strength was significantly lower in diabetic and non-diabetic older subjects compared with younger diabetic and non-diabetic subjects. The duration of diabetes was also not found to be associated with muscle mass or strength, and uncomplicated diabetes did not seem to accelerate aging-related muscle mass or strength loss [35].
We observed that the difference of prevalence of sarcopenia between groups was mainly due to a difference in muscle mass, but not in physical performance. However, due to missing data on the timed up and go test, we hypothesize that the prevalence of severe sarcopenia may have been underestimated similarly in both groups, and that the patients who were not able to get up and walk were probably those who did not undertake the performance test. In parallel, body mass index, waist circumference and fat mass were significantly higher in the diabetic group, and a high BMI was the only factor independently associated with sarcopenia in multiple logistic regression (in all groups), although it explained only 25 to 30% of the model, suggesting that other factors not explored could explain sarcopenia. A study including 2404 diabetic patients with an average age of 63.2 ± 12.9 years showed that the prevalence of both low muscle mass and sarcopenic obesity was the lowest in the highest BMI group (≥27 kg/m2) compared with all other classes of BMI, respectively 2.8% and 2.8% [31]. Donnini et al. showed that muscle mass assessment corrected by height is not relevant to the assessment of sarcopenic obesity. Muscle mass should be therefore corrected by weight [19].
Another possible explanation for the relatively preserved muscle mass in the older diabetic group is the characteristics of diabetes in our sample. In our study, the median duration of diabetes was 4 years for a mean age of 83 years old, which is short compared with the duration of diabetes in other studies where age was much lower (ranging from 50 to 75 y) and diabetes duration was higher (ranging from 8 to 15.5 y) [16,22,35]. Moreover, the median glycosylated hemoglobin was relatively low, indicating that older diabetic hospitalized patients have a profile of quite controlled diabetes. In cross-sectional studies, higher levels of HbA1C were related to relatively lower muscle mass and strength, and greater levels of hyperglycemia also predicted persistently lower muscle strength up to 7.5 years later, partly attributed to the presence of peripheral neuropathy [36]. Further studies could explore how longer durations of diabetes affect sarcopenia in oldest old adults.
Older diabetic patients are typically not admitted for decompensated diabetes, but for other acute medical situations associated with malnutrition and overtreated diabetes (hypoglycemia) due to weight loss and loss of appetite. In our study, the nutritional status according to the Mini Nutritional Assessment did not differ between groups and was relatively good across them all; as a result, it cannot be considered as a risk factor of sarcopenia and thus cannot explain the difference between diabetic and controls.
Antidiabetic treatment may also play a role in the preservation of muscle mass in oldest old diabetic patients. According to the current guidelines, glycemic goals and pharmacologic treatments in older diabetic patients need to be adjusted to minimize the occurrence of hypoglycemic events, especially in the frailest and oldest [36,37,38,39]. Antidiabetic agents may alter the balance between protein synthesis and degradation through various mechanisms of skeletal muscle mass regulation [10]. Insulin plays an anabolic role in preclinical models, but clinical models are inconclusive regarding muscle anabolism. Metformin improves muscle mass and probably muscle strength in preclinical and clinical models but might attenuate exercise-induced muscle benefits, causing myalgia and muscle weakness. Other more recent antidiabetic drugs, like sodium-glucose co-transporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-ra) were not yet on the market when our data were collected, although we know that weight loss induced by these therapies also affect muscle mass [40].
In any case, sarcopenia in old diabetic patients has a non-negligible impact on the individualization of physical and nutritional interventions in oldest old diabetic patients, and deserves to be taken into account. In fact, there are a number of factors which limit an optimal care plan, such as a genetic predisposition that influences insulin secretion, pharmacokinetics, functional limitations that restrict the possibility of intensive rehabilitation, nutritional needs that are not met due to a number of factors, and psychosocial factors that influence motivation and adherence to the proposed care plan. As this is a retrospective study, these considerations have not been explored and could be the subject of future studies.
Our study has several strengths. First, patients were randomly included, and all underwent a thorough geriatric assessment, in addition to anthropometric evaluations. To date, few studies have investigated hospitalized elderly diabetic patients, though they have a different clinical profile from patients usually included in studies assessing body composition in T2DM. Moreover, very few studies to date have used the European EWGSOP2 criteria to characterize sarcopenia in these patients, as most of the studies have been carried out in Asia. Secondly, the number of patients included is quite large, which reinforces the statistical power of our analyses.
Our study also has limitations. The main concerns the delay between the end of the study and the data analyses, which revealed several variables whose missing data could not be imputed, given their excessive number.
5. Conclusions
In a population of very old hospitalized diabetic patients, the prevalence of sarcopenia according to EWGSOP2 criteria was almost half that of the non-diabetic control population. Body mass index was predictive of sarcopenia in both groups and may be a protective factor despite the advanced age of the patients. Further studies are needed to confirm our results.
Author Contributions
Conceptualization, U.V.; methodology, U.V. and F.R.H.; formal analysis, S.D.B. and F.R.H.; investigation, A.T. and V.L.; resources, U.V.; data curation, F.R.H.; writing—original draft preparation, S.D.B.; writing—review and editing, U.V., A.M. and E.F.; visualization, S.D.B.; supervision, C.E.G.; project administration, F.R.H. Author Ulrich Vischer passed away prior to the publication of this manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the SWISS NATIONAL SCIENCE FOUNDATION, grant number 320030_134973.
Institutional Review Board Statement
This study was approved by the Institutional Review Board Statement with the approval number 09-285/Psy 09-035. The study was conducted in accordance with the Declaration of Helsinki and approved by the Swiss Ethics Committee (protocol code 09-285/Psy 09-035; date of approval: 14 January 2010).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.
Data Availability Statement
Additional data can be obtained upon request to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
| ACEI | Angiotensin-converting enzyme inhibitors |
| ADL | Basic activities of daily living |
| ARB | Aldosterone 2 receptor blockers |
| BIA | Bioimpedance analysis |
| BMI | Body mass index |
| CIRS-G | Comorbidity Index Rating Scale for Geriatrics |
| DXA | Dual X-ray absorptiometry |
| EASO | European Association for the Study of Obesity |
| ESPEN | European Society for Clinical Nutrition and Metabolism |
| EWGSOP | European Working Group on Sarcopenia in Older People |
| FFMI | Fat free mass index |
| HAD | Hamilton’s anxiety and depression scale |
| HGS | Handgrip strength |
| iADL | Instrumental activities of daily living |
| MMSE | Mini Mental State Examination |
| MNA | Mini Nutritional Assessment |
| SGLT-2i | Sodium–glucose transporter type 2 inhibitors |
| T2DM | Type 2 diabetes mellitus |
| TUG | Timed up and go test |
| WC | Waist circumference |
| WHO | World Health Organization |
References
- OECD/European Union. Diabetes Prevalence. In Health at a Glance: Europe 2020: State of Health in the EU Cycle; OECD Publishing: Paris, France, 2020. [Google Scholar] [CrossRef]
- Vischer, U.M.; Bauduceau, B.; Bourdel-Marchasson, I.; Blickle, J.F.; Constans, T.; Fagot-Campagna, A.; Kaloustian, E.; Lassman-Vague, V.; Lecomte, P.; Simon, D.; et al. A call to incorporate the prevention and treatment of geriatric disorders in the management of diabetes in the elderly. Diabetes Metab. 2009, 35, 168–177. [Google Scholar] [CrossRef]
- Zekry, D.; Frangos, E.; Graf, C.; Michel, J.P.; Gold, G.; Krause, K.H.; Herrmann, F.; Vischer, U.M. Diabetes, comorbidities and increased long-term mortality in older patients admitted for geriatric inpatient care. Diabetes Metab. 2012, 38, 149–155. [Google Scholar] [CrossRef]
- Wong, E.; Backholer, K.; Gearon, E.; Harding, J.; Freak-Poli, R.; Stevenson, C.; Peeters, A. Diabetes and Risk of Physical Disability in Adults: A Systematic Review and Meta-Analysis. Lancet Diabetes Endocrinol. 2013, 1, 106–114. [Google Scholar] [CrossRef] [PubMed]
- Kalyani, R.R.; Saudek, C.D.; Brancati, F.L.; Selvin, E. Association of diabetes, comorbidities, and A1C with functional disability in older adults: Results from the National Health and Nutrition Examination Survey (NHANES), 1999–2006. Diabetes Care 2010, 33, 1055–1060. [Google Scholar] [CrossRef] [PubMed]
- Gregg, E.W.; Mangione, C.M.; Cauley, J.A.; Thompson, T.J.; Schwartz, A.V.; Ensrud, K.E.; Nevitt, M.C. Diabetes and incidence of functional disability in older women. Diabetes Care 2002, 25, 61–67. [Google Scholar] [CrossRef]
- Blaum, C.S.; West, N.A.; Haan, M.N. Is the Metabolic Syndrome, With or Without Diabetes, Associated With Progressive Disability in Older Mexican Americans? J. Gerontol. A Biol. Sci. Med. Sci. 2007, 62, 766–773. [Google Scholar] [CrossRef]
- Bianchi, L.; Zuliani, G.; Volpato, S. Physical disability in the elderly with diabetes: Epidemiology and mechanisms. Curr. Diabetes Rep. 2013, 13, 824–830. [Google Scholar] [CrossRef]
- Sakurai, T.; Iimuro, S.; Sakamaki, K.; Umegaki, H.; Araki, A.; Ohashi, Y.; Ito, H.; Japanese Elderly Diabetes Intervention Trial Study Group. Risk factors for a 6-year decline in physical disability and functional limitations among elderly people with type 2 diabetes in the Japanese Elderly Diabetes Intervention Trial. Geriatr. Gerontol. Int. 2012, 12 (Suppl. S1), 117–126. [Google Scholar] [CrossRef]
- Wu, C.N.; Tien, K.J. The Impact of Antidiabetic Agents on Sarcopenia in Type 2 Diabetes: A Literature Review. J. Diabetes Res. 2020, 2020, 9368583. [Google Scholar] [CrossRef]
- Abdelhafiz, A.H.; Sinclair, A.J. Metabolic phenotypes explain the relationship between dysglycaemia and frailty in older people with type 2 diabetes. J. Diabetes Complicat. 2022, 36, 108144. [Google Scholar] [CrossRef]
- Gao, Q.; Hu, K.; Yan, C.; Zhao, B.; Mei, F.; Chen, F.; Zhao, L.; Shang, Y.; Ma, Y.; Ma, B. Associated Factors of Sarcopenia in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis. Nutrients 2021, 13, 4291. [Google Scholar] [CrossRef]
- Mesinovic, J.; Zengin, A.; De Courten, B.; Ebeling, P.R.; Scott, D. Sarcopenia and type 2 diabetes mellitus: A bidirectional relationship. Diabetes Metab. Syndr. Obes. 2019, 12, 1057–1072. [Google Scholar] [CrossRef] [PubMed]
- Wang, M.; Zhou, Q.; Guo, X.; Cui, X.; Tang, Z.; Ou, T.; Zhao, X.; Liang, C.; Xu, P.; Dai, X.; et al. Bibliometric mapping of diabetes mellitus and sarcopenia research: Hotspots and emerging trends. Front. Med. 2025, 12, 1586308. [Google Scholar] [CrossRef]
- Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [PubMed]
- Veronese, N.; Pizzol, D.; Demurtas, J.; Soysal, P.; Smith, L.; Sieber, C.; Strandberg, T.; Bourdel-Marchasson, I.; Sinclair, A.; Petrovic, M.; et al. Association between sarcopenia and diabetes: A systematic review and meta-analysis of observational studies. Eur. Geriatr. Med. 2019, 10, 685–696. [Google Scholar] [CrossRef]
- de Freitas, M.M.; de Oliveira, V.L.P.; Grassi, T.; Valduga, K.; Miller, M.E.P.; Schuchmann, R.A.; Souza, K.L.A.; de Azevedo, M.J.; Viana, L.V.; de Paula, T.P. Difference in sarcopenia prevalence and associated factors according to 2010 and 2018 European consensus (EWGSOP) in elderly patients with type 2 diabetes mellitus. Exp. Gerontol. 2020, 132, 110835. [Google Scholar] [CrossRef]
- Villani, A.; McClure, R.; Barrett, M.; Scott, D. Diagnostic differences and agreement between the original and revised European Working Group (EWGSOP) consensus definition for sarcopenia in community-dwelling older adults with type 2 diabetes mellitus. Arch. Gerontol. Geriatr. 2020, 89, 104081. [Google Scholar] [CrossRef]
- Donini, L.M.; Busetto, L.; Bischoff, S.C.; Cederholm, T.; Ballesteros-Pomar, M.D.; Batsis, J.A.; Bauer, J.M.; Boirie, Y.; Cruz-Jentoft, A.J.; Dicker, D.; et al. Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement. Obes. Facts 2022, 15, 321–335. [Google Scholar] [CrossRef]
- World Health Organization: Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=For%20adults%2C%20WHO%20defines%20overweight,than%20or%20equal%20to%2030 (accessed on 18 January 2025).
- Kyle, U.G.; Genton, L.; Lukaski, H.C.; Dupertuis, Y.M.; Slosman, D.O.; Hans, D.; Pichard, C. Comparison of fat-free mass and body fat in Swiss and American adults. Nutrition 2005, 21, 161–169. [Google Scholar] [CrossRef]
- Yogesh, M.; Patel, M.; Gandhi, R.; Patel, A.; Kidecha, K.N. Sarcopenia in type 2 Diabetes mellitus among Asian populations: Prevalence and risk factors based on AWGS- 2019: A systematic review and meta-analysis. BMC Endocr. Disord. 2025, 25, 101. [Google Scholar] [CrossRef]
- Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307.e2. [Google Scholar] [CrossRef] [PubMed]
- Kim, T.N.; Park, M.S.; Yang, S.J.; Yoo, H.J.; Kang, H.J.; Song, W.; Seo, J.A.; Kim, S.G.; Kim, N.H.; Baik, S.H.; et al. Prevalence and determinant factors of sarcopenia in patients with type 2 diabetes: The Korean Sarcopenic Obesity Study (KSOS). Diabetes Care 2010, 33, 1497–1499. [Google Scholar] [CrossRef]
- Murata, Y.; Kadoya, Y.; Yamada, S.; Sanke, T. Sarcopenia in elderly patients with type 2 diabetes mellitus: Prevalence and related clinical factors. Diabetol. Int. 2017, 9, 136–142. [Google Scholar] [CrossRef]
- Wang, T.; Feng, X.; Zhou, J.; Gong, H.; Xia, S.; Wei, Q.; Hu, X.; Tao, R.; Li, L.; Qian, F.; et al. Type 2 diabetes mellitus is associated with increased risks of sarcopenia and pre-sarcopenia in Chinese elderly. Sci. Rep. 2016, 6, 38937. [Google Scholar] [CrossRef]
- Tamura, Y.; Ishikawa, J.; Fujiwara, Y.; Tanaka, M.; Kanazawa, N.; Chiba, Y.; Iizuka, A.; Kaito, S.; Tanaka, J.; Sugie, M.; et al. Prevalence of frailty, cognitive impairment, and sarcopenia in outpatients with cardiometabolic disease in a frailty clinic. BMC Geriatr. 2018, 18, 264. [Google Scholar] [CrossRef]
- Koo, B.K.; Roh, E.; Yang, Y.S.; Moon, M.K. Difference between old and young adults in contribution of β-cell function and sarcopenia in developing diabetes mellitus. J. Diabetes Investig. 2016, 7, 233–240. [Google Scholar] [CrossRef]
- Sbrignadello, S.; Göbl, C.; Tura, A. Bioelectrical Impedance Analysis for the Assessment of Body Composition in Sarcopenia and Type 2 Diabetes. Nutrients 2022, 14, 1864. [Google Scholar] [CrossRef]
- Lee, C.; Kim, H.J.; Chang, T.I.; Kang, E.W.; Joo, Y.S.; Kim, H.W.; Park, J.T.; Yoo, T.H.; Kang, S.W.; Han, S.H. Synergic association of diabetes mellitus and chronic kidney disease with muscle loss and cachexia: Results of a 16-year longitudinal follow-up of a community-based prospective cohort study. Aging 2021, 13, 21941–21961. [Google Scholar] [CrossRef]
- Lin, C.L.; Yu, N.C.; Wu, H.C.; Lee, Y.Y.; Lin, W.C.; Chiu, I.Y.; Chien, W.C.; Liu, Y.C. Association of Body Composition with Type 2 Diabetes: A Retrospective Chart Review Study. Int. J. Environ. Res. Public Health 2021, 18, 4421. [Google Scholar] [CrossRef] [PubMed]
- Akpinar, T.S.; Tayfur, M.; Tufan, F.; Şahinkaya, T.; Köse, M.; Özşenel, E.B.; Karan, M.A. Uncomplicated diabetes does not accelerate age-related sarcopenia. Aging Male 2014, 17, 205–210. [Google Scholar] [CrossRef]
- LeCroy, M.N.; Hua, S.; Kaplan, R.C.; Sotres-Alvarez, D.; Qi, Q.; Thyagarajan, B.; Gallo, L.C.; Pirzada, A.; Daviglus, M.L.; Schneiderman, N.; et al. Associations of Changes in Fat Free Mass with Risk for Type 2 Diabetes: Hispanic Community Health Study/Study of Latinos. Diabetes Res. Clin. Pract. 2021, 171, 108557. [Google Scholar] [CrossRef]
- Jensen, B.; Braun, W.; Geisler, C.; Both, M.; Klückmann, K.; Müller, M.J.; Bosy-Westphal, A. Limitations of Fat-Free Mass for the Assessment of Muscle Mass in Obesity. Obes. Facts 2019, 12, 307–315. [Google Scholar] [CrossRef]
- Al-Sofiani, M.E.; Ganji, S.S.; Kalyani, R.R. Body composition changes in diabetes and aging. J. Diabetes Complicat. 2019, 33, 451–459. [Google Scholar] [CrossRef] [PubMed]
- Anagnostis, P.; Gkekas, N.K.; Achilla, C.; Pananastasiou, G.; Taouxidou, P.; Mitsiou, M.; Kenanidis, E.; Potoupnis, M.; Tsiridis, E.; Goulis, D.G. Type 2 Diabetes Mellitus is Associated with Increased Risk of Sarcopenia: A Systematic Review and Meta-analysis. Calcif. Tissue Int. 2020, 107, 453–463. [Google Scholar] [CrossRef] [PubMed]
- Bourdel-Marchasson, I.; Maggi, S.; Abdelhafiz, A.; Bellary, S.; Demurtas, J.; Forbes, A.; Ivory, P.; Rodríguez-Mañas, L.; Sieber, C.; Strandberg, T.; et al. Essential steps in primary care management of older people with Type 2 diabetes: An executive summary on behalf of the European geriatric medicine society (EuGMS) and the European diabetes working party for older people (EDWPOP) collaboration. Aging Clin. Exp. Res. 2023, 35, 2279–2291. [Google Scholar] [CrossRef]
- American Diabetes Association Professional Practice Committee. 13. Older Adults: Standards of Care in Diabetes—2025. Diabetes Care 2025, 48 (Suppl. S1), S266–S282. [Google Scholar] [CrossRef] [PubMed]
- Bolt, J.; Carvalho, V.; Lin, K.; Lee, S.J.; Inglis, C. Systematic review of guideline recommendations for older and frail adults with type 2 diabetes mellitus. Age Ageing 2024, 53, afae259. [Google Scholar] [CrossRef]
- Satoshi, I.; Kaneko, R.; Imataka, K.; Okubo, K.; Shirakura, Y.; Azuma, K.; Fujiwara, R.; Murata, K. Effects of Antidiabetic Drugs on Muscle Mass in Type 2 Diabetes Mellitus. Curr. Diabetes Rev. 2021, 17, 293–303. [Google Scholar] [CrossRef]
Table 1.
Characteristics of participants.
| n | Total Group | n | Diabetes | n | Controls | p | |
|---|---|---|---|---|---|---|---|
| Socio-demographic, clinical and geriatric data | |||||||
| Age (years) | 463 | 84.8 ± 6.0 | 162 | 83.3 ± 5.9 | 301 | 85.7 ± 5.9 | <0.001 |
| Sex (F) | 463 | 314 (67.8) | 162 | 70 (43.2) | 301 | 222 (73.8) | <0.001 |
| Length of stay (days) | 448 | 25 [17–34] | 155 | 24 [16–31] | 293 | 25 [17–37] | 0.416 |
| In-hospital mortality | 463 | 7 (1.5) | 162 | 2 (1.2) | 301 | 5 (1.7) | 0.720 |
| Smoking habits | 318 | 113 | 205 | 0.078 | |||
| Never | 207 (65.1) | 68 (60.2) | 139 (67.8) | ||||
| Current | 31 (9.8) | 7 (6.2) | 24 (11.7) | ||||
| Former | 80 (25.1) | 38 (33.6) | 42 (20.5) | ||||
| Diabetes duration (years) | - | - | 159 | 4 [4–5] | - | - | - |
| Glycosylated hemoglobin (%) | 162 | 6.8 [6.2–7.5] | |||||
| Comorbidity | 450 | 158 | 292 | ||||
| CIRS-G (points) | 16.8 ± 4.8 | 18.4 ± 4.8 | 15.9 ± 4.7 | <0.001 | |||
| Cardiovascular comorbidities | 463 | 162 | 301 | ||||
| Hypertension | 299 (64.6) | 117 (72.2) | 182 (60.5) | 0.012 | |||
| Myocardial infarct | 89 (19.2) | 45 (27.8) | 44 (14.6) | 0.001 | |||
| Heart failure | 35 (7.6) | 16 (9.9) | 19 (6.3) | 0.166 | |||
| Atrial fibrillation | 99 (21.3) | 37 (25.8) | 62 (20.6) | 0.575 | |||
| Stroke | 75 (16.2) | 34 (21.0) | 41 (13.6) | 0.040 | |||
| Arteritis obliterans | 37 (8.0) | 17 (10.5) | 20 (6.6) | 0.145 | |||
| Visual impairment | 78 (16.9) | 34 (21.0) | 44 (14.6) | 0.081 | |||
| Wounds | 27 (5.8) | 21 (13.0) | 6 (2.0) | <0.001 | |||
| Number of meds | 460 | 5 [4–7] | 160 | 6 [4–8] | 300 | 5 [3–7] | <0.001 |
| Anti-diabetic treatments | - | - | 162 | - | - | - | |
| Oral antidiabetics | 101 (62.4) | ||||||
| Insulin | 56 (34.6) | ||||||
| Both | 21 (13.0) | ||||||
| Cardiovascular treatments | 463 | 162 | 301 | ||||
| Aspirin | 191 (41.3) | 91 (56.2) | 100 (33.2) | <0.001 | |||
| Loop diuretics | 137 (29.6) | 59 (36.4) | 78 (25.9) | 0.018 | |||
| Thiazides | 63 (13.6) | 21 (13.0) | 42 (14.0) | 0.767 | |||
| ACEI/ARB | 206 (44.5) | 86 (53.1) | 120 (39.9) | 0.006 | |||
| Beta blockers | 134 (28.9) | 55 (34.0) | 79 (26.3) | 0.081 | |||
| Calcium channel inhibitors | 91 (19.7) | 43 (26.5) | 48 (16.0) | 0.006 | |||
| Statins | 127 (27.4) | 68 (42.0) | 59 (19.6) | <0.001 | |||
| Nutritional assessment | 463 | 162 | 301 | ||||
| MNA | 23 [20–26] | 23 [19.5–25.5] | 23.5 [20.5–26.5] | 0.164 | |||
| Normal | 207 (44.7) | 68 (42.0) | 139 (46.2) | 0.657 | |||
| At risk | 204 (44.1) | 74 (45.7) | 130 (43.2) | ||||
| Malnourished | 52 (11.2) | 20 (12.3) | 32 (10.6) | ||||
| Functional assessment | 463 | 162 | 301 | ||||
| ADL (points) | 5 [4–5] | 5 [4–5] | 5 [4.5–5] | 0.227 | |||
| IADL (points) | 7 [6–8] | 7 [6–8] | 7 [6–8] | 0.062 | |||
| Cognitive assessment | 384 | 132 | 252 | 0.095 | |||
| MMSE | 26 [24–28] | 26 [24–28] | 26 [24–28] | ||||
| Psychiatric assessment (HAD) | 448 | 156 | 292 | ||||
| Anxiety score | 6 [4–9] | 6 [3–9] | 6.5 [4–9] | 0.582 | |||
| Depression score | 5 [3–8] | 5 [3–8] | 5 [3–8] | 0.155 | |||
| Anthropometric and sarcopenia data | |||||||
| BMI (kg/m2) | 463 | 25.7 [22.5–29.1] | 162 | 27.5 [24.3–31.6] | 301 | 24.6 [21.6–28.2] | <0.001 |
| Obesity (BMI > 30) | 99 (21.4) | 54 (32.4) | 45 (15.0) | <0.001 | |||
| Waist circumference (cm) | 449 | 94 [83–103] | 159 | 98 [91–111] | 290 | 91 [79–100] | <0.001 |
| Fat-free mass index (kg/m2) | 463 | 15.0 [13.6–16.5] | 162 | 15.7 [14.3–17.6] | 301 | 14.7 [13.4–15.9] | <0.001 |
| Lowest tertile of FFMI (kg/m2) | 463 | <13.3 | 162 | <13.5 | 301 | <13.3 | <0.001 |
| Maximum handgrip strength | 463 | 19 [16–24] | 162 | 20 [16–28] | 301 | 18 [15–24] | 0.040 |
| Low handgrip | 463 | 162 (35.0) | 162 | 54 (33.3) | 301 | 108 (35.9) | 0.584 |
| Timed up and go test (s) | 258 | 18.7 [13.9–26.4] | 98 | 19.5 [13.7–25.7] | 160 | 18.6 [14.0–27.0] | 0.887 |
| TUG test > 20 s | 280 | 138 (49.3) | 103 | 53 (51.5) | 177 | 85 (48.0) | 0.579 |
| Sarcopenia status EWGSOP2 | 463 | 162 | 301 | 0.054 | |||
| No sarcopenia | 301 (65.0) | 108 (66.7) | 193 (64.1) | ||||
| Probable sarcopenia | 99 (21.4) | 41 (25.3) | 58 (19.3) | ||||
| Sarcopenia | 40 (8.6) | 8 (4.9) | 32 (10.6) | ||||
| Severe sarcopenia | 23 (5.0) | 5 (3.1) | 18 (6.0) | ||||
| Sarcopenia (Y/N) | 463 | 162 | 301 | 0.010 | |||
| Yes | 63 (13.6) | 13 (8.0) | 50 (16.7) | ||||
| No | 4001 (86.4) | 149 (92.0) | 251 (83.4) | ||||
Data are expressed in mean ± SD, median [IQR] and n (%). Abbreviations: CIRS-G = Comorbidity Index Rating Scale for Geriatrics, ACEI = angiotensin-converting enzyme inhibitor, ARB = aldosterone 2 receptor blockers, BMI = body mass index, MNA = Mini Nutritional Assessment, ADL = basic activities of daily living, IADL = instrumental activities of daily living, MMSE = Mini Mental State Examination, HAD: Hamilton’s Anxiety and Depression scale, M = male, FFMI = fat-free mass index, TUG = timed up and go test, EWGSOP2 = European Working Group on Sarcopenia in Older People 2.
Table 2.
Univariate logistic regression analysis of factors associated with sarcopenia.
| Total Group | Diabetes | Controls | |||||||
|---|---|---|---|---|---|---|---|---|---|
| OR [95% CI] | p | n | OR [95% CI] | p | n | OR [95% CI] | p | ||
| Socio-demographic, clinical and geriatric data | |||||||||
| Age (years) | 463 | 1.10 [1.05–1.15] | <0.001 | 162 | 1.02 [0.94–1.10] | 0.621 | 301 | 1.12 [1.07–1.18] | <0.001 |
| Sex (F) | 463 | 1.56 [0.97–2.49] | 0.067 | 162 | 1.10 [0.44–2.71] | 0.841 | 301 | 2.16 [1.22–3.83] | 0.008 |
| Length of stay (days) | 448 | 1.00 [0.99–1.01] | 0.235 | 155 | 1.00 [0.99–1.02] | 0.145 | 293 | 1.00 [0.99–1.01] | 0.536 |
| In-hospital mortality | 463 | 10.60 [2.02–55.57] | 0.005 | 162 | No event | omitted | 301 | 5.13 [0.84–31.3] | 0.077 |
| Smoking habits | 318 | 0.65 [0.24–1.90] | 0.411 | 113 | 0.86 [0.48–1.55] | 0.613 | 205 | 0.98 [0.67–1.45] | 0.938 |
| Diabetes duration | - | - | - | 159 | 0.76 [0.43–1.34] | 0.342 | - | - | - |
| HbA1C [%] | - | - | - | 162 | 0.92 [0.65–1.29] | 0.626 | - | - | - |
| Comorbidity | |||||||||
| CIRS-G (points) | 450 | 1.02 [0.97–1.07] | 0.490 | 158 | 0.97 [0.88–1.07] | 0.586 | 292 | 1.06 [0.99–1.12] | 0.054 |
| Cardiovascular comorbidities | 463 | 162 | 301 | ||||||
| Hypertension | 0.69 [0.43–1.10] | 0.120 | 0.63 [0.24–1.62] | 0.336 | 0.77 [0.45–1.33] | 0.354 | |||
| Myocardial infarct | 1.21 [0.69–2.13] | 0.494 | 1.59 [0.62–4.10] | 0.336 | 1.28 [0.62–2.66] | 0.496 | |||
| Heart failure | 2.28 [0.82–6.35] | 0.113 | 1.54 [0.40–5.91] | 0.528 | 2.03 [0.77–5.37] | 0.155 | |||
| Atrial fibrillation | 1.29 [0.76–2.21] | 0.345 | 0.72 [0.23–2.28] | 0.577 | 1.63 [0.87–3.03] | 0.124 | |||
| Stroke | 1.34 [0.74–2.41] | 0.329 | 1.12 [0.38–3.31] | 0.829 | 1.65 [0.80–3.40] | 0.171 | |||
| Arteritis obliterans | 1.33 [0.60–2.92] | 0.480 | 3.13 [0.94–10.00] | 0.053 | 0.81 [0.26–2.52] | 0.722 | |||
| Visual impairment | 0.69 [0.36–1.34] | 0.278 | 0.55 [0.15–1.99] | 0.368 | 0.83 [0.38–1.81] | 0.634 | |||
| Wounds | 0.49 [0.14–1.66] | 0.249 | 0.29 [0.04–2.24] | 0.234 | 1.67 [0.30–9.31] | 0.559 | |||
| Number of meds | 460 | 0.93 [0.86–1.01] | 0.094 | 160 | 0.93 [0.79–1.10] | 0.402 | 300 | 0.95 [0.87–1.05] | 0.334 |
| Anti-diabetic treatments | - | - | - | 162 | 0.85 [0.34–2.13] | 0.735 | - | - | - |
| Cardiovascular treatments | 463 | 162 | 301 | ||||||
| Aspirin | 1.18 [0.75–1.88] | 0.471 | 1.43 [0.56–3.63] | 0.450 | 1.36 [0.78–2.37] | 0.279 | |||
| Loop diuretics | 0.70 [0.41–1.19] | 0.184 | 0.79 [0.30–2.06] | 0.630 | 0.73 [0.38–1.38] | 0.330 | |||
| Thiazides | 0.73 [0.36–1.50] | 0.394 | 1.07 [0.29–3.98] | 0.919 | 0.62 [0.26–1.47] | 0.279 | |||
| ACEI/ARB | 0.68 [0.42–1.09] | 0.105 | 0.70 [0.28–1.73] | 0.442 | 0.73 [0.42–1.28] | 0.277 | |||
| Beta-blockers | 1.08 [0.61–1.90] | 0.788 | 0.69 [0.26–1.89] | 0.478 | 0.96 [0.52–1.78] | 0.908 | |||
| Calcium channel inhibitors | 0.84 [0.50–1.40] | 0.500 | 0.57 [0.18–1.81] | 0.344 | 1.64 [0.83–3.23] | 0.155 | |||
| Digoxin | 0.33 [0.04–2.56] | 0.288 | 1 | - | 0.32 [0.04–2.55] | 0.282 | |||
| Nitrous agents | 1.44 [0.59–3.52] | 0.419 | 1.17 [0.24–5.69] | 0.843 | 1.89 [0.61–5.86] | 0.266 | |||
| Statins | 0.92 [0.55–1.54] | 0.747 | 1.17 [0.48–2.91] | 0.722 | 1.03 [0.53–2.02] | 0.924 | |||
| Nutritional assessment | 463 | 162 | 301 | ||||||
| MNA | 0.90 [0.85–0.95] | <0.001 | 0.91 [0.81–1.01] | 0.072 | 0.89 [0.84–0.95] | <0.001 | |||
| Functional assessment | 463 | 162 | 301 | ||||||
| ADL | 0.76 [0.63–0.92] | 0.005 | 0.90 [0.62–1.30] | 0.575 | 0.69 [0.54–0.87] | 0.002 | |||
| IADL | 0.81 [0.71–0.92] | 0.001 | 0.87 [0.70–1.08] | 0.194 | 0.73 [0.62–0.87] | <0.001 | |||
| Cognitive assessment (MMSE) | 384 | 1.00 [0.99–1.01] | 0.478 | 132 | 1.00(0.99–1.00) | 0.989 | 252 | 0.99 [0.99–1.00] | 0.229 |
| Psychiatric assessment (HAD) | 448 | 156 | 292 | ||||||
| Anxiety score | 0.97 [0.91–1.03] | 0.371 | 0.97 [0.86–1.10] | 0.664 | 0.97 [0.90–1.04] | 0.405 | |||
| Depression score | 1.07 [1.02–1.14] | 0.013 | 1.06 [0.95–1.18] | 0.295 | 1.10 [1.02–1.17] | 0.010 | |||
| Anthropometric and sarcopenia data | |||||||||
| BMI (kg/m2) | 463 | 0.79 [0.74–0.85] | <0.001 | 162 | 0.73 [0.63–0.85] | <0.001 | 301 | 0.82 [0.76–0.88] | <0.001 |
| Waist circumference (cm) | 449 | 0.95 [0.93–0.97] | <0.001 | 159 | 0.94 [0.91–0.98] | 0.002 | 290 | 0.96 [0.93–0.98] | <0.001 |
| Fat-free mass index (kg/m2) | 463 | 0.63 [0.56–0.72] | <0.001 | 162 | 0.53 [0.39–0.71] | <0.001 | 301 | 0.68 [0.58–0.79] | <0.001 |
| Lowest tertile of FFMI (kg/m2) | 463 | 6.18 [3.77–10.14] | <0.001 | 162 | 5.26 [2.05–13.48] | 0.001 | 301 | 6.11 [3.39–11.05] | <0.001 |
| Maximum handgrip strength (kg) | 463 | 0.82 [0.77–0.86] | <0.001 | 162 | 0.81 [0.72–0.90] | <0.001 | 301 | 0.82 [0.77–0.87] | <0.001 |
| Timed up and go test (s) | 258 | 1.02 [1.00–1.04] | 0.048 | 98 | 1.05 [0.99–1.10] | 0.065 | 160 | 1.01 [0.99–1.04] | 0.206 |
| TUG > 20 s | 280 | 2.62 [1.48–4.86] | 0.001 | 103 | 3.23 [0.91–11.49] | 0.069 | 177 | 2.56 [1.30–5.04] | 0.007 |
Table 3.
Multiple logistic regression for sarcopenia (Y/N) adjusted for age and sex.
| OR (95 CI%) | p | R2 | ||
|---|---|---|---|---|
| Total group | BMI | 0.68 [0.62–0.76] | <0.001 | 0.317 |
| Diabetic group | BMI | 0.69 [0.57–0.83] | <0.001 | 0.253 |
| Control group | BMI | 0.68 [0.60–0.77] | <0.001 | 0.327 |
OR = odds ratio, CI = confidence interval, BMI = body mass index.
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De Breucker, S.; Lachat, V.; Frangos, E.; Trombetti, A.; Vischer, U.; Mendes, A.; Herrmann, F.R.; Graf, C.E. Prevalence of Sarcopenia in Very Old Diabetic and Non-Diabetic Hospitalized Patients. Diabetology 2025, 6, 99. https://doi.org/10.3390/diabetology6090099
AMA Style
De Breucker S, Lachat V, Frangos E, Trombetti A, Vischer U, Mendes A, Herrmann FR, Graf CE. Prevalence of Sarcopenia in Very Old Diabetic and Non-Diabetic Hospitalized Patients. Diabetology. 2025; 6(9):99. https://doi.org/10.3390/diabetology6090099
Chicago/Turabian StyleDe Breucker, Sandra, Véronique Lachat, Emilia Frangos, Andrea Trombetti, Ulrich Vischer, Aline Mendes, François R. Herrmann, and Christophe E. Graf. 2025. "Prevalence of Sarcopenia in Very Old Diabetic and Non-Diabetic Hospitalized Patients" Diabetology 6, no. 9: 99. https://doi.org/10.3390/diabetology6090099
APA StyleDe Breucker, S., Lachat, V., Frangos, E., Trombetti, A., Vischer, U., Mendes, A., Herrmann, F. R., & Graf, C. E. (2025). Prevalence of Sarcopenia in Very Old Diabetic and Non-Diabetic Hospitalized Patients. Diabetology, 6(9), 99. https://doi.org/10.3390/diabetology6090099
