Cardiometabolic risk and the MTHFR C677T variant in children treated with second-generation antipsychotics
Bibliographic record
Abstract
Second-generation antipsychotics (SGAs) are increasingly being used to treat children with a variety of psychiatric illnesses. Metabolic syndrome (MetS), a risk factor for cardiovascular disease, is a side-effect of SGA-treatment. We conducted a cross-sectional study and assessed the association of the methylenetetrahydrofolate reductase (MTHFR) C677T variant with features of MetS in SGA-treated (n=105) and SGA-naïve (n=112) children. We targeted the MTHFR C677T variant, because it is associated with risk for cardiovascular disease, and features of MetS in adults without psychiatric illness. MetS in children is based on the presence of any three of the following: waist circumference ≥ 90th percentile for age and sex; plasma triglyceride ≥ 1.24 mmol l(-1); plasma high-density lipoprotein-cholesterol ≤ 1.03 mmol l(-1); systolic or diastolic blood pressure ≥ 90th percentile for age, sex, and height; and fasting glucose ≥ 5.6 mmol l(-1). We found that 15% of SGA-treated children had MetS compared with 2% of SGA-naïve children (OR 8.113, P<0.05). No effect of the MTHFR C677T variant on psychiatric diagnosis was observed. The MTHFR 677T allele was associated (P<0.05) with MetS (OR 5.75, 95% CI= 1.18-28.12) in SGA-treated children. Models adjusted for duration of SGA treatment, ethnicity, sex, age and use of other medications revealed a positive relationship between the MTHFR 677T allele and diastolic blood pressure Z-scores (P=0.001) and fasting plasma glucose (P<0.05) in SGA-treated children. These findings illustrate the high prevalence of MetS in SGA-treated children and suggest metabolic alterations associated with the MTHFR C677T variant may have a role in the development of MetS features in SGA-treated 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".