Sex differences in cholesterol and triglyceride levels among hospitalized adolescents and young adults with eating disorders
Bibliographic record
Abstract
Abstract Objective To determine sex differences in cholesterol and triglyceride levels among adolescents and young adults hospitalized for medical complications of eating disorders. Methods A retrospective electronic medical record review of patients aged 9–25 years admitted to the University of California, San Francisco Eating Disorders Program for medical stabilization, between 2012 and 2020, was conducted. Non‐fasting total cholesterol and triglycerides were collected; however, LDL and HDL levels were not available. Results Among 83 males and 441 females, mean ± SD age was 15.5 ± 2.8 years, 64.1% had anorexia nervosa, and admission percent median body mass index was 87.3 ± 13.9. The proportion of males and females with high total cholesterol (13.3% vs. 18.1%, Cramer's V = 0.05, p = .28) and high triglyceride levels (9.6% vs. 8.1%, Cramer's V = 0.02, p = .63) did not differ. Mean total cholesterol levels were higher in females compared to males (F 169.6 ± 41.1 mg/dL vs. M 154.5 ± 45.1 mg/dL, Cohen's d = 0.36, p = .003), although a majority were within the normal range. In adjusted linear regression models, male (compared to female) sex (B = −14.40, 95% CI −24.54, −4.27) and higher percent median body mass index (B = −0.33, 95% CI −0.60, −0.06) were associated with lower total cholesterol levels in adjusted models (R2 = 0.04). Discussion Building on prior work showing equally severe complications of eating disorders in males compared to females, we did not find sex differences in those presenting with high total cholesterol or triglycerides. Future research is needed to understand the pathophysiology and role of dyslipidemia in acute malnutrition, and the impact of nutritional rehabilitation and weight restoration. Public Significance We found that the proportion of male and female adolescents and young adults hospitalized for medical complications of an eating disorder with high total cholesterol did not significantly differ. Although average total cholesterol levels were higher in female compared to male patients with eating disorders, a majority of these levels remained within the normal range. Patients with more severe malnutrition had a higher risk of elevated total cholesterol levels. Clinicians should consider monitoring cholesterol levels in young people hospitalized for restrictive eating disorders.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".