U-Shaped Relationship Between Proteinuria and High-Density Lipoprotein Cholesterol: Results of Cross-Sectional and Six Years Cohort Studies (KITCHEN-10)
Bibliographic record
Abstract
Background: Although a very high level of high-density lipoprotein cholesterol (HDL-C) may be a potential cardiovascular disease risk factor, the detail and underlying mechanism remain unclear. Therefore, we examined the associations of serum HDL-C with the incidence of proteinuria, a predictor for cardiovascular disease, in a community-based study. Methods: We investigated clinical parameters, including serum HDL-C and proteinuria, among 1,191,409 people aged 40 - 74 years who underwent a health checkup in a cross-sectional study. In the cohort study, the incidence of proteinuria after 6 years was investigated in 451,987 participants without proteinuria at baseline, who were simultaneously enrolled in the cross-sectional study. Results: The prevalence of proteinuria showed a U-shaped relationship with 10 HDL-C categories, with a minimum of 60 - 89 mg/dL in the cross-sectional study. Logistic regression analysis showed similar U-shaped relationships between odds ratios for proteinuria and HDL-C categories, with a minimum of 70 - 79 mg/dL. The associations between very high HDL-C (≥ 90 mg/dL) and proteinuria were strengthened after adjustment for body mass index (BMI). In the cohort study, a crude L-shaped relationship was observed between the incidence of proteinuria and baseline HDL-C, which turned into U-shaped relationship after adjustment for baseline BMI and HDL-C after 6 years. Conclusions: Low and very high levels of HDL-C may be associated with the incidence of proteinuria, and BMI may be a potent contributing factor to the underlying mechanism.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".