Body mass index, waist circumference, insulin, and leptin plasma levels differentiate between clozapine-responsive and clozapine-resistant schizophrenia
Bibliographic record
Abstract
BACKGROUND: Between 25% and 50% of patients suffering from treatment-resistant schizophrenia fail to achieve a clinical response with clozapine. The rapid identification and treatment of this subgroup of patients represents a challenge for healthcare practice. AIMS: To evaluate the relationship between metabolic alterations and the clinical response to clozapine. METHODS: A multicenter, observational, case-control study was performed. Patients diagnosed with schizophrenia treated with clozapine were eligible (minimum dose 400 mg/d for at least 8 weeks and/or clozapine plasma levels ⩾ 350 µg/mL). According to the Positive and Negative Syndrome Scale (PANSS) total score, patients were classified as clozapine-responsive (CR) (<80 points) or clozapine non-responsive (CNR) (⩾80 points). Groups were compared based on demographic and treatment-related characteristics, together with body mass index (BMI), waist circumference, insulin, leptin, and C-reactive protein plasma levels. Plasma levels of clozapine and its main metabolite, nor-clozapine, were measured in all the participants. In addition, the potential relationship between PANSS scores and leptin or insulin plasma levels was assessed. RESULTS: A total of 46 patients were included: 25 CR and 21 CNR. BMI and waist circumference, fasting insulin and leptin plasma levels were lower in the CNR group, while C-reactive protein was not different. Moreover, significant negative correlations were observed between PANSS positive and general psychopathology subscores, on one hand, and insulin and leptin plasma levels, on the other hand, as well as between PANSS negative subscores and leptin plasma levels. CONCLUSIONS: Our results suggest that the lack of metabolic effect induced by clozapine is associated with the lack of clinical response.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".