Angela Cebria

Background/Objectives: Sarcopenia is common in patients with diabetes mellitus. The use of branched-chain amino acids may influence muscle mass. The aim of this study is to evaluate the effect of a diabetes-specific formula enriched with calcium hydroxy-methyl-butyrate (CaHMB) on muscle mass in patients with diabetes and high risk of malnutrition. Methods: A prospective observational study in 95 patients divided into two cohorts of patients with diabetes, treated with a tailored diet, dietary counseling, and diabetes-specific oral nutritional supplements (ONSs) administered between meals: one enriched with CaHMB (CaHMB Diabetes ONS) 44 (46.32%) patients; and another without CaHMB (Diabetes-Specific ONS) 51 (53.68%) patients. Anthropometric parameters, bioimpedance, artificial intelligence (AI)-assisted ultrasound of the rectus femoris muscle (PIIXMED^TM), and handgrip strength were assessed. Evaluations were conducted at baseline and after 3 months. Results: The mean age was 71.05 (10.67) years; 56.8% were male. After three months, both groups increased their nutritional intake with no differences in dietary protein content between groups. The CaHMB group showed a greater increase in muscle mass as measured by ultrasound, both in muscle area (CaHMB ONS: +5.84 (−3.3 ± 21.58)% vs. Diabetes-Specific ONS: −9.34% (−25.78 ± 12.02)%; p < 0.01) and muscle thickness (CaHMB ONS: +9.17 (−4.40 ± 21.05)% vs. Diabetes-Specific ONS −6.30 (−18.57 ± 12.56)%; p < 0.01). The CaHMB ONS group showed a higher likelihood of increased muscle mass compared to the Diabetes-Specific ONS, with an odds ratio (OR) of 9.31 (95%CI: 2.16–40.13) for thickness and 3.96 (95%CI: 1.11–14.13) for area, adjusted for gender, age, serum albumin, and baseline glycated hemoglobin. Conclusions: Supplementation with Ca-HMB in patients with diabetes and high risk of malnutrition showed significant improvements in muscle mass as assessed by AI-assisted ultrasound. Both groups increased nutritional intake, but only the CaHMB group showed specific benefits in muscle parameters.

Background: Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with inflammation. Methods: This observational, cross-sectional study included 502 malnourished patients, assessed through anthropometry, electrical bioimpedanciometry, and ultrasonography of the quadriceps rectus femoris (QRF). AI-assisted ultrasonography was used to segment regions of interest (ROI) from transversal QRF images to measure muscle thickness (RFMT) and area (RFMA), while a Multi-Otsu algorithm was used to extract biomarkers for muscle mass (MiT) and fat mass (FatiT). Inflammation was defined as C-reactive protein (CRP) levels above 3 mg/L. Results: The results showed a mean patient age of 63.72 (15.95) years, with malnutrition present in 82.3% and inflammation in 44.8%. Oncological diseases were prevalent (46.8%). The 44.8% of patients with inflammation (CRP > 3) exhibited reduced RFMA (2.91 (1.11) vs. 3.20 (1.19) cm², p < 0.01) and RFMT (0.94 (0.28) vs. 1.01 (0.30) cm, p < 0.01). Muscle quality was reduced, with lower MiT (45.32 (9.98%) vs. 49.10 (1.22%), p < 0.01) and higher FatiT (40.03 (6.72%) vs. 37.58 (5.63%), p < 0.01). Adjusted for age and sex, inflammation increased the risks of low muscle area (OR = 1.59, CI: 1.10–2.31), low MiT (OR = 1.49, CI: 1.04–2.15), and high FatiT (OR = 1.44, CI: 1.00–2.06). Conclusions: AI-assisted ultrasonography revealed that malnourished patients with inflammation had reduced muscle area, thickness, and quality (higher fat content and lower muscle percentage). Elevated inflammation levels were associated with increased risks of poor muscle metrics. Future research should focus on exploring the impact of inflammation on muscles across various patient groups and developing AI-driven biomarkers to enhance the diagnosis, monitoring, and treatment of malnutrition and sarcopenia.

Background: User-friendly tools for assessing nutrition status and interventions in malnourished patients are crucial. This study evaluated the effectiveness of a personalised nutrition intervention using a novel oral nutritional supplement and AI-supported morphofunctional assessment to monitor clinical outcomes in patients with disease-related malnutrition (DRM). Methods: This prospective observational study involved patients receiving concentrated high-protein, high-calorie ONS (cHPHC-ONS), per usual clinical practice. Comprehensive assessments were performed at baseline (B₀) and three months (M3) post-intervention. Results: 65 patients participated in the study. Significant decreases were observed in the percentage weight loss from B₀ (−6.75 ± 7.5%) to M3 (0.5 ± 3.48%) (p < 0.01), in the prevalence of malnutrition (B₀: 93.4%; M3: 78.9%; p < 0.01), severe malnutrition (B₀: 60.7%; M3: 40.3%; p < 0.01), and sarcopenia (B₀: 19.4%; M3: 15.5%; p < 0.04). Muscle area increased (p = 0.03), and there were changes in the echogenicity of the rectus femoris muscle (p = 0.03) from B₀ to M3. In patients aged ≥60, an increase in muscle thickness (p = 0.04), pennation angle (p = 0.02), and handgrip strength (p = 0.04) was observed. There was a significant reduction in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01) and severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01). Conclusions: In patients with DRM, a personalised intervention with cHPHC-ONS significantly reduces the prevalence of malnutrition, severe malnutrition, and sarcopenia and improves muscle mass and function.

(1) Background: The aim was to validate an AI-based system compared to the classic method of reading ultrasound images of the rectus femur (RF) muscle in a real cohort of patients with disease-related malnutrition. (2) Methods: One hundred adult patients with DRM aged 18 to 85 years were enrolled. The risk of DRM was assessed by the Global Leadership Initiative on Malnutrition (GLIM). The variation, reproducibility, and reliability of measurements for the RF subcutaneous fat thickness (SFT), muscle thickness (MT), and cross-sectional area (CSA), were measured conventionally with the incorporated tools of a portable ultrasound imaging device (method A) and compared with the automated quantification of the ultrasound imaging system (method B). (3) Results: Measurements obtained using method A (i.e., conventionally) and method B (i.e., raw images analyzed by AI), showed similar values with no significant differences in absolute values and coefficients of variation, 58.39–57.68% for SFT, 30.50–28.36% for MT, and 36.50–36.91% for CSA, respectively. The Intraclass Correlation Coefficient (ICC) for reliability and consistency analysis between methods A and B showed correlations of 0.912 and 95% CI [0.872–0.940] for SFT, 0.960 and 95% CI [0.941–0.973] for MT, and 0.995 and 95% CI [0.993–0.997] for CSA; the Bland–Altman Analysis shows that the spread of points is quite uniform around the bias lines with no evidence of strong bias for any variable. (4) Conclusions: The study demonstrated the consistency and reliability of this new automatic system based on machine learning and AI for the quantification of ultrasound imaging of the muscle architecture parameters of the rectus femoris muscle compared with the conventional method of measurement.