Background: Anxiety and depression are common in patients with cancer. The aim of this study is to determine the prevalence of anxiety and depression symptoms in colorectal cancer (CRC) patients awaiting elective surgery and whether there is an association with their preoperative nutritional status and postoperative mortality. Methods: A prospective study was conducted on 215 patients with CRC proposed for surgery. Data about nutritional status were collected using the Global Leadership Initiative on Malnutrition (GLIM) criteria, while anxiety and depression symptoms data were collected using Hospital Anxiety and Depression Scale (HADS). Results: HADS detected possible anxiety in 41.9% of patients, probable anxiety in 25.6%, possible depression in 21.9%, and probable depression in 7.9%. GLIM criteria found 116 (53.9%) patients with malnutrition. The HADS score for depression subscale was significantly higher in malnourished patients than in well-nourished (5.61 ± 3.65 vs. 3.95 ± 2.68; p = 0.001). After controlling for potential confounders, malnourished patients were 10.19 times more likely to present probable depression (95% CI 1.13–92.24; p = 0.039). Mortality was 1.9%, 4,2%, and 5.6% during admission and after 6 and 12 months, respectively. Compared to patients without depressive symptomatology, in patients with probable depression, mortality risk was 14.67 times greater (95% CI 1.54–140.21; p = 0.02) during admission and 6.62 times greater (95% CI 1.34–32.61; p = 0.02) after 6 months. Conclusions: The presence of anxiety and depression symptoms in CRC patients awaiting elective surgery is high. There is an association between depression symptoms, preoperative nutritional status, and postoperative mortality.

(1) Background: The assessment of muscle mass is crucial in the nutritional evaluation of patients with colorectal cancer (CRC), as decreased muscle mass is linked to increased complications and poorer prognosis. This study aims to evaluate the utility of AI-assisted L3 CT for assessing body composition and determining low muscle mass using both the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition and the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria for sarcopenia in CRC patients prior to surgery. Additionally, we aim to establish cutoff points for muscle mass in men and women and propose their application in these diagnostic frameworks. (2) Methods: This retrospective observational study included CRC patients assessed by the Endocrinology and Nutrition services of the Regional University Hospitals of Malaga, Virgen de la Victoria of Malaga, and Vall d’Hebrón of Barcelona from October 2018 to July 2023. A morphofunctional assessment, including anthropometry, bioimpedance analysis (BIA), and handgrip strength, was conducted to apply the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia. Body composition evaluation was performed through AI-assisted analysis of CT images at the L3 level. ROC analysis was used to determine the predictive capacity of variables derived from the CT analysis regarding the diagnosis of low muscle mass and to describe cutoff points. (3) Results: A total of 586 patients were enrolled, with a mean age of 68.4 ± 10.2 years. Using the GLIM criteria, 245 patients (41.8%) were diagnosed with malnutrition. Applying the EWGSOP2 criteria, 56 patients (9.6%) were diagnosed with sarcopenia. ROC curve analysis for the skeletal muscle index (SMI) showed a strong discriminative capacity of muscle area to detect low fat-free mass index (FFMI) (AUC = 0.82, 95% CI 0.77–0.87, p < 0.001). The identified SMI cutoff for diagnosing low FFMI was 32.75 cm²/m² (Sn 77%, Sp 64.3%; AUC = 0.79, 95% CI 0.70–0.87, p < 0.001) in women, and 39.9 cm²/m² (Sn 77%, Sp 72.7%; AUC = 0.85, 95% CI 0.80–0.90, p < 0.001) in men. Additionally, skeletal muscle area (SMA) showed good discriminative capacity for detecting low appendicular skeletal muscle mass (ASMM) (AUC = 0.71, 95% CI 0.65–0.76, p < 0.001). The identified SMA cutoff points for diagnosing low ASMM were 83.2 cm² (Sn 76.7%, Sp 55.3%; AUC = 0.77, 95% CI 0.69–0.84, p < 0.001) in women and 112.6 cm² (Sn 82.3%, Sp 58.6%; AUC = 0.79, 95% CI 0.74–0.85, p < 0.001) in men. (4) Conclusions: AI-assisted body composition assessment using CT is a valuable tool in the morphofunctional evaluation of patients with colorectal cancer prior to surgery. CT provides quantitative data on muscle mass for the application of the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia, with specific cutoff points established for diagnostic use.

This study aims to assess the proficiency of supervised machine learning techniques in discriminating between normal and abnormal bone mineral density (BMD) by leveraging clinical features and texture analysis of spinal bone tissue in patients diagnosed with primary hyperparathyroidism (PHP). From a total of 219 patients diagnosed with PHP, the 58 who had undergone both DXA and abdominal CT scan were included in this study. BMD was assessed by quantifying the Hounsfield units (HU) and performing texture analysis on every CT scan. The first lumbar vertebral body texture features were extracted by using LifeX 7.3.0 software. Initial classification into normal or abnormal BMD was performed with different machine learning techniques by training a model with the variables obtained from the texture analysis. Differentiating osteopenia from osteoporosis was evaluated by creating two models, one including the variables obtained from the texture analysis and HU and another one which only included the HU. Their performance was evaluated in the validation and test groups by calculating the accuracy, precision, recall, F1 score, and AUC. Bayes demonstrated higher performance for discerning individuals with normal and abnormal BMD, with an AUC of 0.916. The results from the second analysis showed a better performance for the model including the variables obtained from the texture analysis compared to the model that was solely trained with the HU (AUC in the training group of 0.77 vs. 0.65 in the test groups, respectively). In conclusion, analysis of BMD obtained from abdominal CT scans including texture analysis provide a better classification of normal density, osteopenia and osteoporosis in patients with PHP.

(1) Background: Accurate body composition assessment in CCR patients is crucial due to the high prevalence of malnutrition, sarcopenia, and cachexia affecting survival. This study evaluates the correlation between body composition assessed by CT imaging as a reference technique, BIVA, nutritional ultrasound, and handgrip strength in CCR patients. (2) Methods: This retrospective study included CCR patients assessed by the Endocrinology and Nutrition Services of Virgen de la Victoria in Malaga and Vall d’Hebron in Barcelona from October 2018 to July 2023. Assessments included anthropometry, BIVA, NU, HGS, and AI-assisted CT analysis at the L3 level for body composition. Pearson’s analysis determined the correlation of CT-derived variables with BIVA, NU, and HGS. (3) Results: A total of 267 CCR patients (mean age 68.2 ± 10.9 years, 61.8% men) were studied. Significant gender differences were found in body composition and strength. CT-SMI showed strong correlations with body cell mass (r = 0.65), rectus femoris cross-sectional area (r = 0.56), and handgrip strength (r = 0.55), with a Cronbach’s alpha of 0.789. CT-based adipose tissue measurements showed significant correlations with fat mass (r = 0.56), BMI (r = 0.78), A-SAT (r = 0.49), and L-SAT (r = 0.66). Regression analysis indicated a high predictive power for CT-SMI, explaining approximately 80% of its variance (R² = 0.796). (4) Conclusions: Comprehensive screening of colorectal cancer patients through BIVA, NU, HGS, and CT optimizes the results of the evaluation. These methods complement each other in assessing muscle mass, fat distribution, and nutritional status in CCR. When CT is unavailable or bedside assessment is needed, HGS, BIVA, and NU provide an accurate assessment of body composition.

Parenteral nutrition (PN) composition could play a role in the management of systemic inflammatory response and intestinal barrier disruption. We aimed to evaluate changes in biomarkers of inflammation, oxidative status and intestinal permeability in patients with type 2 diabetes mellitus (T2DM) who received different PN lipid formulas. This was a prospective study, including 94 patients with T2DM who received omega (n)-3 polyunsaturated fatty acids (PUFA)-enriched PN, a mixture of medium and long chain triglycerides (MCT/LCT) PN, or an olive oil-based PN. Serum levels of biomarkers of oxidative status, intestinal permeability and inflammation biomarkers were determined at day 1 and day 5 after PN initiation. Registered under ClinicalTrials.gov Identifier no. NCT02706119. At day 5 after the onset of PN, the MCT/LCT group had a significant reduction of 2 proinflammatory cytokines [interleukin (IL)-15, IL-17A], elevation of the anti-inflammatory cytokine IL-13 and increase of zonulin and indoxylsulfate. The olive oil group showed a statistically significant reduction of 5 proinflammatory cytokines [IL-1β, IL-17A, IL-6, cytokine-leukemia inhibitory factor (LIF) and tumor necrosis factor alpha (TNF-α)] and reduced concentrations of the anti-inflammatory cytokine IL-1RA, while the n-3 PUFA-enriched group presented a statistically significant reduction of 8 proinflammatory cytokines (interferon-gamma, IL-1β, IL-15, IL-17A, IL-6, LIF, monocyte chemoattractant protein 1, and TNF-α). In the between-group comparisons, indoxylsulfate significantly increased in the MCT/LCT group compared to the n-3 PUFA-enriched group, while 8-isoprostane and indoxylsulfate significantly increased in the MCT/LCT group compared to the other groups and superoxide dismutase significantly decreased in the MCT/LCT group compared to the other groups. In patients with T2DM, PN lipid composition exerts a profound impact on proinflammatory, prooxidative and intestinal permeability biomarkers.

Background: Metabolic dysfunction–associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide and is strongly linked to obesity, type 2 diabetes, and cardiovascular disease. Growing evidence highlights the role of the gut–liver axis, particularly microbial metabolites such as the short-chain fatty acid (SCFA) butyrate, in MASLD pathophysiology. However, clinical data on butyrate levels and the abundance of butyrate-producing bacteria in MASLD patients remain inconsistent. Objectives: To systematically synthesize human evidence evaluating the associations between butyrate levels and butyrate-producing gut bacteria with MASLD presence and severity, as well as related metabolic comorbidities. Methods: A systematic search was conducted in PubMed and Embase from inception to April 7, 2025, following PRISMA 2020 guidelines (PROSPERO registration CRD420251162439). Eligible studies included observational human research assessing fecal or plasma SCFA concentrations and/or the abundance of butyrate-producing taxa in adults with MASLD and related metabolic disorders. Study quality was appraised using the Newcastle–Ottawa Scale, and results were narratively synthesized due to heterogeneity across methods and outcomes. Results: From 233 records, seven studies met inclusion criteria (2020–2025; n = 1,185). Most were cross-sectional or case–control designs of moderate to high quality (NOS 6–8/9). Individuals with MASLD generally exhibited lower fecal or serum butyrate concentrations and reduced abundance of Faecalibacterium prausnitzii , Eubacterium , and other butyrate-producing bacteria versus controls. These alterations were associated with hepatic steatosis, fibrosis, inflammation, and adverse metabolic profiles - higher BMI, insulin resistance, and dyslipidemia. Geographic and sex-related differences were also reported. Conclusions: This systematic review suggests that reduced butyrate availability and alterations in butyrate-producing gut taxa are associated with MASLD presence and severity and with adverse metabolic traits. However, substantial methodological heterogeneity and the observational design of available studies preclude causal inference. Larger, well-phenotyped, multicentre studies using standardized SCFA quantification, dietary and medication ascertainment, and functional microbiome profiling are needed to validate these findings and clarify their diagnostic and therapeutic implications.