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.

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.

Background: Computed Tomography (CT) is considered a highly accurate tool for assessing body composition. The aim of this study is to assess the usefulness of chest CT for malnutrition diagnosis in people with cystic fibrosis (PwCF), compared with other body composition techniques, as well as to assess possible associations with nutritional and respiratory status. Methods: A cross-sectional study was carried out in clinically stable adult PwCF. Subjects who had undergone a CT including the twelfth thoracic vertebra (T12) during the 6 months prior to or after our assessment were included and body composition was assessed using FocusedON-BC. The results were compared with anthropometry, bioelectrical impedance analysis (BIA), muscle ultrasonography, and handgrip strength (HGS). Respiratory parameters were collected, and nutritional status was assessed using Global Leadership Initiative on Malnutrition (GLIM) criteria. Results: A total of 55 PwCF were included. Muscle area assessed by CT correlated significantly with fat-free mass determined by BIA (r = 0.725) and anthropometry (r = 0.645), muscle mass evaluated by ultrasonography (r = 0.657), HGS (r = 0.593), Bhalla score (r = 0.403), and FEV1 (r = 0.488). Differences were observed when comparing muscle area in CT based on the Bhalla score (94.6 ± 21.1 cm² in normal/mild involvement vs. 79.3 ± 20.9 cm² in moderate/severe involvement; p = 0.009) and on nutritional status (96.3 ± 17.9 cm² in normo-nourished vs. 75.9 ± 22.1 cm² in malnourished; p < 0.001). Conclusions: In adult PwCF, measurements obtained from CT image analysis correlate adequately with anthropometry, BIA, muscle ultrasound, and HGS. Muscle area in CT is related to nutritional and respiratory status.