Cardiovascular and Metabolic Risk Profile and Acylation-Stimulating Protein Levels in Children with Prader-Willi Syndrome and Effects of Growth Hormone Treatment
Bibliographic record
Abstract
CONTEXT: Reports on the cardiovascular and metabolic risk profile in children with Prader-Willi syndrome (PWS) and the effects of GH treatment are scarce. Acylation-stimulating protein (ASP) stimulates glucose uptake and triglyceride storage in adipose tissue. OBJECTIVES: The aim was to study the metabolic and cardiovascular risk profile and ASP levels and to investigate the effects of GH treatment. DESIGN: We conducted a randomized controlled GH trial. Infants and prepubertal children were assigned to receive GH (1 mg/m(2) . d) or to serve as controls for 12 and 24 months, respectively. PATIENTS: Eighty-five children with PWS (mean +/- sd age of 4.9 +/- 3.0 yr) participated in the study. MAIN OUTCOME MEASURES: We measured fat percentage (fat%) with dual-energy x-ray absorptiometry, blood pressure, fasting insulin and glucose levels, serum lipids, and ASP levels. RESULTS: Mean +/- SD fat% was 28.4 +/- 6.2 in infants and 36.9 +/- 8.5 in prepubertal children. Fat% sd score (SDS) was above 2 SDS in 95% of prepubertal children. In addition, 63% of infants and 73% of prepubertal children demonstrated at least one cardiovascular risk factor, defined as hypertension or dyslipidemia. The metabolic syndrome was demonstrated in 5% of all children. Mean +/- sd baseline ASP was 107 +/- 45 nmol/liter (normal < 58 nmol/liter) and correlated with fat mass and TG levels. GH improved fat%SDS and the HDLc/LDLc ratio (P < 0.0001 and P = 0.04). GH had no effect on mean ASP levels in this population. CONCLUSIONS: Many children with PWS had dyslipidemia and high ASP levels. GH improved fat% and high-density lipoprotein cholesterol/low-density lipoprotein cholesterol, but not ASP. High ASP levels may prevent complete normalization of fat%SDS during GH treatment but may contribute in keeping glucose and insulin levels within normal range.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".