Gut Microbiota, Antipsychotics, and Metabolic Alterations in Children and Adolescents: Protocol for a Longitudinal Observational Study
Bibliographic record
Abstract
Background: Over the past decade, numerous studies have emphasized the important role of gut microbiota (GM) in maintaining the body's homeostasis. Imbalances in GM have been linked to many dysfunctions, such as metabolic and neurodevelopmental disorders. GM can be influenced by many factors, among them the use of certain medications, such as second-generation antipsychotics (SGAs), and, in turn, act upon the endocrine, immune, and nervous systems. Despite the growing interest in the microbiota-gut-brain axis, significant gaps remain in our understanding of how SGAs affect GM and the host metabolic profile. Objective: This study aims to build on the current knowledge on the impact of SGAs on clinical parameters, microbial and metabolic profiles, and behavior of children and adolescents undergoing treatment with SGAs. Methods: This is a prospective longitudinal study, in which the effects of SGAs will be assessed before and 3 to 6 months after their introduction. An integrated approach will be used, encompassing clinical data (such as weight, lipid profile, and glucose levels); microbiome and metabolome analyses; emotional, behavioral, and sleep patterns (assessed through psychiatric scales); and dietary habits. Results: This project was funded in November 2023 and will start data collection in January 2026. It is expected to be completed in 2027. Conclusions: This study is expected to provide insights into the multidimensional effects of SGAs on children and adolescents, including clinical data, GM microbial profile, metabolism, and behavior. The findings may contribute to a better understanding of treatment impacts and provide information on more personalized therapeutic strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".