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Abstract: Background: Probable sarcopenia, indicated by low handgrip strength, is a prevalent condition among hospitalized older adults and may reflect broader functional and nutritional decline. Methods: We examined differences in nutritional, functional, and cognitive status between Alzheimer’s clinical syndrome (ACS) patients with probable sarcopenia and those without sarcopenia. A cross-sectional analysis was conducted on 194 hospitalized older adults with ACS. Probable sarcopenia was defined using European Working Group on Sarcopenia in Older People (EWGSOP2) handgrip strength thresholds. Results: Patients with probable sarcopenia (n = 137) had significantly lower Mini-Mental State Examination (MMSE) scores, Geriatric Nutritional Risk Index (GNRI), albumin, hemoglobin, and gait speed compared to those without. After age and sex adjustment, MMSE (p = 0.023), GNRI (p = 0.002), hemoglobin (p = 0.022), albumin (p = 0.003), and gait speed (p < 0.001) remained significantly different. In the sex- and age-adjusted multivariable model (adjusted R2 = 0.442), higher nutritional risk (β = 0.26, p = < 0.001), lower MMSE scores (β = 0.17, p = 0.029), polypharmacy (β = −4.20, p = 0.002), and slower gait speed (β = 4.12, p = 0.010) were associated with reduced handgrip strength. In the multivariable binary logistic regression model (adjusted for age and sex), moderate or high nutritional risk and slow gait speed emerged as independent predictors of probable sarcopenia, with OR 5.14 (95% CI 1.34–19.75; p = 0.017) and OR 3.13 (95% CI 1.30–7.52; p = 0.011), respectively. Conclusions: Probable sarcopenia in hospitalized older adults with ACS is highly prevalent and is associated with higher nutritional risk, poorer cognitive and physical function, and polypharmacy; its early recognition may help to guide more targeted nutritional and functional interventions.
Keywords: sarcopenia, muscle strength, Alzheimer’s disease, cognitive function, gait speed, nutrition risk
Published in RUP: 25.01.2026; Views: 469; Downloads: 2
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Abstract: Purpose This study aims to identify key risk factors for sarcopenia using machine learning models, leveraging anthropomet- ric, biochemical, functional, nutritional, and genetic data. By developing predictive models, the research seeks to improve early detection, enhance diagnostic accuracy, and facilitate personalized interventions for individuals at risk of sarcopenia. Methods We analysed multimodal data from 484 older adults. Two scenarios: Set-a (including SARC–CalF, excluding SARC-F) and Set-b (including SARC-F, excluding SARC–CalF) were applied in a three-stage modeling process with progressively reduced features and optimized predictive performance using machine learning models. Key predictors were ranked using SHAP values, and model performance was evaluated using AUC, accuracy, sensitivity, and specificity. Internal validation and DeLong’s test were applied to assess robustness and statistical differences. Results The most predictive risk factors included functional measures (chair stand, gait speed), nutritional indicators (pro- tein, folate, copper, vitamin B7), clinical conditions (diabetes, comorbidities, low-density lipoprotein (LDL)), and anthro- pometric markers (body mass index (BMI), calf circumference). Genetic features also contributed to risk stratification. The best-performing model Set-b (with screening test SARC-F) achieved an AUC of 0.951 and an accuracy of 93.62%. While SARC–CalF showed higher individual feature importance, the model achieved an AUC of 0.945 and accuracy of 92.2%. Conclusions This study highlights that traditional sarcopenia screening can be enhanced by capturing complex interplay of functional, nutritional, clinical, and genetic factors, offering clinicians a more accurate and tailored tool for early detec- tion and risk stratification. Future research should focus on validating these models in larger, independent, and longitudinal cohorts to assess their predictive utility across diverse populations and over time.
Keywords: genetics, nutrition, risk factors, sarcopenia
Published in RUP: 23.07.2025; Views: 970; Downloads: 15
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