Ferritin is a potential marker of cardiometabolic risk in adolescents and young adults with sleep-disordered breathing
Bibliographic record
Abstract
Abstract Objective To explore markers that reflect sleep-disordered breathing (SDB) severity and investigate their associations with cardiometabolic risk factors in adolescents and young adults. Methods Participants were recruited from our SDB epidemiological cohort. They underwent overnight polysomnography and ambulatory blood pressure (BP) monitoring. Complete blood count, ferritin, high-sensitivity C-reactive protein (hs-CRP), fasting blood glucose, and lipid profile were measured. Multiple linear regression was used to examine the association between red cell indices (RCIs), ferritin, and obstructive apnea-hypopnea index (OAHI). Subgroup analyses on participants with SDB were performed for the association of RCIs and ferritin with lipid profile, hs-CRP, and BP. Results There were 88 participants with SDB and 155 healthy controls aged 16–25 years. Hemoglobin (Hb; p < .001), hematocrit (HCT; p < .001), and ferritin (p < .001) were elevated with increasing SDB severity and were independently associated with OAHI (β=1.06, p < .001; β=40.2, p < .001; β=4.89 × 10−3, p = .024, respectively). In participants with SDB, after adjusting for age, sex, and BMI, significant associations were found between ferritin with low-density lipoprotein (LDL; β=0.936 × 10−3, p = .008) and triglyceride (TG; β =1.08 × 10−3, p < .001), as well as between Hb (β=1.40, p = .007), HCT (β=51.5, p = .010) and mean arterial pressure (MAP). Ferritin (β=0.091, p = .002), Hb (β=0.975, p = .005), and HCT (β=38.8, p = .004) were associated with hs-CRP independent of age, sex, BMI, plasma LDL, and MAP. OAHI was not associated with LDL and TG in the multivariable models. Conclusions Serum ferritin, but not OAHI, was associated with LDL and TG in participants with SDB, suggesting it is a potential marker of cardiometabolic risk in patients with SDB.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".