<i>COMT</i> rs4680 variant and cardiometabolic side‐effects in children treated with second‐generation antipsychotics
Bibliographic record
Abstract
Second‐generation antipsychotics (SGA) are used to treat children for a wide‐range of mental health conditions but are associated with side effects such as rapid weight gain; increased waist circumference; and elevated fasting plasma glucose, blood pressure, and metabolic syndrome (MetS). The aim of this study is to investigate the interaction of the rs4680 variant in the catechol‐ O‐methyltransferase gene ( COMT ) and SGA treatment on cardiometabolic side‐effects in a cross‐sectional population of SGA‐treated (n=99) and SGA–naïve (n=114) children. SGA‐treated children had higher ( P <0.05) BMI z‐scores, systolic blood pressure (SBP) z‐scores, and fasting plasma glucose and total and LDL‐cholesterol concentrations than SGA‐naïve children. Fifteen percent of SGA‐treated children had MetS compared to 2% of SGA‐naïve children ( P =0.001). The COMT rs4680 variant genotype frequencies were not different between SGA‐treated (AA 24.2%, AG 50.5%, GG 25.3%) and SGA‐naïve (AA 24.6%, GA 45.6%, GG 29.8%) children. An interaction between SGA‐treatment and COMT rs4680 genotype was observed such that SGA‐treated children with the AA genotype had higher SBP, fasting plasma insulin, and HOMA‐IR than those with the GG genotype. The opposite effect was observed in SGA‐naïve children. These findings suggest that the COMT rs4680 variant may interact with SGAs and contribute to cardiometabolic side‐effects in children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".