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Objectives: The aim of this investigation was to evaluate the discrepancies between bioelectrical impedance analysis (BIA) and computed tomography (CT) in assessing skeletal muscle mass and identifying low muscle mass in patients with colorectal cancer. Methods: This study recruited 137 patients with colorectal cancer from February 2028 to December 2023. CT scans were analyzed at the Lumbar 3 vertebral level to determine the area of skeletal muscle, which was then utilized to estimate whole-body skeletal muscle mass. [BIA] was also employed to measure skeletal muscle. Both skeletal muscle mass values [kg] were divided by height2 [m2] to calculate the skeletal muscle index [SMI, kg/m2], denoted as SMI-CT and SMI-BIA, respectively. Results: The median age was 69.8 + 9.5 years, with the sex ratio being 88/49 [male/female]. Whereas more than one-third of the patients were classified as malnourished based on the Global Leadership Initiative on Malnutrition GLIM-CT criteria using L3-SMI [n = 36.5%], fewer patients were classified as malnourished based on GLIM-BIA using SMI-BIA [n = 19.0%]. According to the CT analysis [low SMI-L3], 52 [38.0%] patients were diagnosed as having poor muscle mass, whereas only 18 [13.1%] patients were identified as having low muscle mass using BIA [low SMIBIA]. The measured SMI showed a positive association with SMI-CT in all patients [r = 0.63, p < 0.001]. Using Bland–Altman evaluation, a significant mean bias of 0.45 + 1.41 kg/m2 [95% CI 0.21–0.70; p < 0.001] between SMI-BIA and SMI-CT was reported. Receiver operating characteristic (ROC) curves were generated to detect poor muscle mass using SMI-BIA with CT as the gold standard. The area under the curve (AUC) for SMI-BIA in identifying poor muscle mass was 0.714 (95% CI: 0.624–0.824), with a good cut-off value of 8.1 kg/m2, yielding a sensitivity of 68.3% and a specificity of 66.9%. Conclusions: BIA generally overestimates skeletal muscle mass in colorectal cancer patients when contrasted to CT. As a result, BIA may underestimate the prevalence of poor muscle mass and malnutrition according to the GLIM criteria in this patient population. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

Background and aims: some studies have reported links between 25-hydroxyvitamin D levels and the presence of metabolic syndrome. The aim of the present study was to evaluate whether an association exists among 25-hydroxyvitamin D, rs2282679 of the GC gene and metabolic syndrome (MS). Methods: the study involved a population of 134 postmenopausal obese females. Measurements of anthropometric parameters, blood pressure, bone turnover markers, fasting blood glucose, insulin resistance (HOMA-IR), lipid profile, C-reactive protein and prevalence of MS were recorded. Genotype of CG gene polymorphism (rs2282679) was evaluated. Results: insulin (delta: 4.6 ± 0.9 mUI/l; p = 0.02), triglycerides (delta: 21.6 ± 2.9 mg/dl; p = 0.04) and HOMA-IR (delta: 1.1 ± 0.9 unit; p = 0.02) were lower in TT subjects than TG + GG patients. The percentages of individuals who had MS (OR = 2.80, 95 % CI = 1.39-5.65; p = 0.02), hypertriglyceridemia (OR = 2.39, 95 % CI = 1.44-5.96; p = 0.01), and hyperglycemia (OR = 2.72, 95 % CI = 1.23-6.00; p = 0.43) were higher in G allele carriers. Logistic regression analysis showed an increased risk of MS in G allele carriers (OR = 2.36, 95 % CI = 1.11-5.91, p = 0.02) and an increased risk of 25-hydroxyvitamin D deficiency (< 20 ng/ml) (OR = 2.43, 95 % CI = 1.13-6.69, p = 0.02), too. Conclusions: a negative association among G allele and insulin resistance, hypertriglyceridemia, deficiency of 25 hydroxyvitamin D levels and MS was reported in this population. [ABSTRACT FROM AUTHOR]