Intestinal dysbiosis is key in the onset and development of Crohn’s disease (CD). We evaluated the microbiota changes in CD patients before and after a six-month anti-TNF treatment, comparing these changes with the microbiota of healthy subjects. This prospective multicenter observational study involved 27 CD patients initiating anti-TNF treatment and 16 healthy individuals. Inflammatory activity was determined at baseline, 3 and 6 months, classifying patients into responders and non-responders. Fecal microbiota was analyzed by massive genomic sequencing thought 16S rRNA amplicon sequencing before and after six months of anti-TNF treatment. The CD cohort showed a decrease in genera of the class Clostridia, short-chain fatty acid producers, and an increase in the phylum Proteobacteria (p < 0.01) versus the healthy cohort. After anti-TNF treatment, the phylum Proteobacteria also increased in non-responders versus responders (13/27) (p < 0.005), with the class Clostridia increasing. In addition, alpha diversity increased in responders versus non-responders (p < 0.01), tending towards eubiosis. An association was found (p < 0.001) in the F.prausnitzii/E.coli ratio between responders and non-responders. The F/E ratio was the most accurate biomarker of anti-TNF response (area under the curve 0.87). Thus, anti-TNF treatment allows partial restoration of intestinal microbiota in responders and the F.prausnitzii/E.coli ratio can provide a reliable indicator of response to anti-TNF in CD.

Small intestinal bacterial overgrowth (SIBO) arises from dysbiosis in the small intestine, manifesting with abdominal symptoms. This study aims to assess the efficacy of combined antibiotic therapy, herbal supplements, probiotics, and dietary modifications in SIBO management. A total of 179 SIBO-diagnosed patients underwent clinical evaluation and breath testing. Patients were categorized into hydrogen (H₂-SIBO) and methane (CH₄-SIBO) groups. The control group received standard antibiotic therapy and a low-FODMAP diet, while the intervention group received additional herbal antibiotics, probiotics, and prebiotics. After treatment, both groups exhibited reduced gas levels, particularly in CH₄-SIBO. Clinical remission rates were higher in the intervention group, especially in CH₄-SIBO cases. Logistic regression analysis showed gas concentrations at diagnosis as significant predictors of treatment success. In conclusion, adjunctive herbal supplements and probiotics did not significantly impact gas levels, but showed potential for clinical improvement, especially in CH₄-SIBO.

Obesity is caused by fat accumulation. BMI Z-score is used to classify the different degrees of weight status in children and adolescents. However, this parameter does not always express the true percentage of body fat. Our objective was to determine the degree of agreement between the fat mass percentage measured by DXA and the stratification of weight according to BMI Z-score in the pediatric age group. We designed a descriptive cross-sectional study. The patients were classified as underweight/normal weight with Z-scores between −2 and +0.99, overweight from 1 to 1.99, obese from 2 to 2.99, and very obese ≥3. We included 551 patients (47% girls), with a mean age of 11.5 ± 2.8 years (3.7–18 years). Higher BMI Z-scores were associated with a higher percentage of total fat (p < 0.001). However, there were important overlaps between both parameters, such that the BMI Z-score classified patients with the same percentage of total fat mass as having a different nutritional status classification. In conclusion, the stratification of weight status according to BMI Z-score revealed that 46.7% of patients had a fat percentage that did not correspond to their classification. For a more accurate weight assessment in clinical practice, we recommend combining anthropometric indices with diagnostic tools that better correlate with DXA, such as electrical bioimpedance.

A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model. The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study. The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units. In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature. An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

Introduction: Childhood obesity is a public health problem with repercussions in later life. As tissue formation peaks in childhood we determined how weight status influences bone mineral content. Material and methods: We studied 553 children aged 4–18 years over 10 years (46.8% girls). We measured age, weight, height and through bone densitometry (DXA), bone mineral content (BMC), bone mineral density (BMD), and waist, arm and hip circumferences. The patients were divided into groups using the body mass index z-score: underweight, normal weight, overweight, obese and very obese. Results: BMC and BMD values were highest in the normal-weight and overweight groups. Logistic regression showed bone mineralization was inversely associated with waist circumference, the association being positive for weight and age. No differences were found according to sex. Discussion: Studies of the relationship between weight and bone mineralization report contradictory results, often because of different study designs. Moreover, studies in children are either few or with small samples. Our findings in a large sample show the importance of weight status in bone mineralization given the risk of bone fractures or osteoporosis. Conclusions: Weight status influenced bone mineralization. BMC and BMD decreased in children with a higher degree of obesity. Waist circumference correlated negatively with bone mineralization.