Total bilirubin modified the association between diabetes and stroke: a cross-sectional study from NHANES 2011–2016
Bibliographic record
Abstract
Background Total bilirubin (TBIL) has antioxidant and anti-inflammatory properties. This study aimed to determine whether elevated TBIL could modify the association between diabetes and stroke. Method Data were obtained from the National Health and Nutrition Examination Survey 2011–2016. TBIL was stratified by median (10.3 µmol/L). The association between diabetes and stroke was quantified using multivariable logistic regression models. The cut-off concentration for the presence of TBIL modification effects was identified by Johnson-Neyman analyses. Mediation analyses were performed to determine the influence of TBIL on mediating factors that mediate the relationship between diabetes and stroke. Results This cross-sectional study included 16 130 participants, with the mean age of 46.8±0.4 years and 48.5% of men. Diabetes was associated with the presence of stroke at TBIL <10.3 µmol/L (OR=2.19, 95% CI 1.58 to 3.05) but not at TBIL ≥10.3 µmol/L (OR=1.27, 95% CI 0.85 to 1.88) after adjustment for confounders. Above associations were significantly different between the two TBIL concentrations (Pfor interaction=0.03). Moreover, the modification effect of TBIL specifically occurred in men (Pfor interaction=0.02) rather than in women (Pfor interaction=0.08). The cut-off concentration for the presence of TBIL modification effects was 17.05 µmol/L. Additionally, the TBIL of ≥10.3 µmol/L inhibited mediating effects of hypersensitive C reactive protein (mediating effect=0.03, 95% CI −0.15 to 0.22,P=0.72) and systemic immune-inflammation index (mediating effect=0.01, 95% CI −0.01 to 0.04,P=0.29) as compared with the TBIL of <10.3 µmol/L. Conclusions Elevated TBIL modified the association between diabetes and stroke through inhibiting mediating effects of inflammatory factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".