Background: Obesity is a major global public health issue with no fully satisfactory solutions. Most nutritional interventions rely on caloric restriction, with varying degrees of success. Very low-calorie ketogenic diets (VLCKD) have demonstrated rapid and sustained weight loss by inducing ketone bodies through lipolysis, reducing appetite, and preserving lean mass while maintaining metabolic health. Methods: A prospective clinical study analyzed sociodemographic, anthropometric, and adherence data from 7775 patients undergoing a multidisciplinary nutritional single-arm intervention based on a commercial weight-loss program. This method, using protein preparations with a specific balanced nutritional profile, aimed to identify key predictors of weight-loss success and classify population phenotypes with shared baseline characteristics and weight-loss patterns to optimize treatment personalization. Results: Statistical and machine learning analyses revealed that male gender (−9.2 kg vs. −5.9 kg) and higher initial body weight (−8.9 kg vs. −4.0 kg) strongly predict greater weight loss on a VLCKD, while age has a lesser impact. Two distinct population clusters emerged, differing in age, sex, follow-up duration, and medical visits, demonstrating unique weight-loss success patterns. These clusters help define individualized strategies for optimizing outcomes. Conclusions: These findings translationally support associations with the efficacy of a multidisciplinary VLCK weight-loss program and highlight predictors of success. Recognizing variables such as sex, age, and initial weight enhances the potential for a precision-based approach in obesity management, enabling more tailored and effective treatments for diverse patient profiles and prescribe weight loss personalized recommendations.

A critical issue in microbiome research is the selection of reliable laboratory and bioinformatics pipelines. In the absence of generally accepted technical benchmarks and evaluation standards, comparing data generated by different studies becomes challenging. In this work, we carried out the most comprehensive study to date on this topic. We encompassed every stage of processing, from DNA extraction to computational assessment. We adopted four procedures for DNA purification, six for library construction, three for sequencing, and five for bioinformatics. Additionally, we used datasets published by others to corroborate our results. We introduced a software tool that distinctively delivers consistent results, irrespective of sample or dataset origins. This study underscores the importance of methodological optimization at the outset of research projects to ensure the reliability of results and their comparability with findings from other studies. Additionally, this study provides an optimized robust pipeline for gut microbiome analysis.

Background: Life course factors play an important role in the multifactorial etiology of obesity, yet quantitative analysis of patient-originated, unstructured weight gain cause narratives remains a challenge. This study automated the thematic labeling of such narratives with a large language model to assess the clinical relevance of patient-reported weight gain cause data in weight loss prediction and patient phenotyping. Subjects and methods: A total of 2,463 patients with overweight or obesity shared open weight gain cause narratives prior to starting a multidisciplinary medical-nutritional weight loss treatment, followed until reaching a pre-defined weight loss target or dropout. Narratives were labeled using 12 thematic categories in a GPT4.1 large language model. Associations of reported causal themes with age, sex, BMI class and treatment outcomes were evaluated using group-wise statistical comparisons and a Random Forest classifier. Weight gain cause co-occurrence patterns were modeled with a direct association network and pairwise risk ratio analyses. A partitional unsupervised clustering model integrating age, sex, baseline BMI and weight gain cause themes was designed to elucidate patient phenotypes defined by reported weight gain trajectories. Cluster-specific outcomes were compared using descriptive tests and linear mixed models. Results: Mean weight loss was 9.2 ± 6.8% over 108.6 ± 111.6 days. Automated weight gain narrative categorization achieved precision and recall of 0.906 and 0.897 against a reference sample. Reported weight gain causes were associated with age and sex but not BMI class. Associations between attributed causes and treatment outcomes were moderate, while between individual causes, strong associations were found. Disrupted schedules, mental health and external circumstances increased the risk ratio of unhealthy eating habits [3.65 (2.63–5.65), 2.16 (1.89–2.48), 1.51 (1.25–1.81) respectively], while medical issues and external circumstances increased physical inactivity risk [1.58 (1.31–1.90), 1.49 (1.23–1.82)]. Based on weight gain cause reports, age, sex and BMI class, seven clusters were identified with different demographic, clinical, treatment outcome and adherence characteristics. Conclusion: Patient-reported weight gain narrative analysis can be accurately automated using large language models, providing clinically relevant insights into obesity heterogeneity. While individual causes show modest associations with weight loss, their combined patterns allow the identification of distinct behavioral phenotypes with differential treatment responses. Integrating patient narratives into data-driven frameworks supports a more precise, person-centered obesity management.