High-density lipoprotein cholesterol, particles and subspecies and the risk of chronic kidney disease: The PREVEND prospective study
Bibliographic record
Abstract
BACKGROUND: The relationships between high-density lipoprotein cholesterol (HDL-C), HDL particle concentration (HDL-P), and HDL subspecies with the development of chronic kidney disease (CKD) have not been well characterized. This study aimed to examine these associations and evaluate the role of alcohol consumption as a potential confounder or effect modifier. METHODS: Data was analyzed from 4,179 individuals (mean age: 52 years; 47.6% male) participating in the PREVEND cohort. Baseline measurements included HDL-P and its subfractions (small, medium, and large), quantified by nuclear magnetic resonance spectroscopy, and self-reported alcohol intake. Incident CKD was defined using criteria from the KDIGO guidelines. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each HDL metric per 1 standard deviation (SD) increment. RESULTS: Over a median follow-up of 8.3 years, 565 participants developed CKD. After adjusting for multiple confounders, including alcohol use, HDL-P, medium HDL, and H3P showed modest inverse associations with CKD risk, with adjusted HRs (95% CIs) of 0.90 (0.83-0.98), 0.91 (0.83-1.00), and 0.90 (0.82-0.99), respectively. Conversely, H7P was positively associated with CKD risk (HR 1.11, 95% CI: 1.00-1.22). Significant interactions with sex were observed for medium HDL, small HDL, and H1P. Alcohol intake neither significantly modified the associations nor showed a direct relationship with CKD risk. CONCLUSIONS: This study suggests distinct associations of HDL parameters with CKD risk as well as sex differences in the associations of these parameters with CKD risk. The findings underscore the heterogeneity of HDL subspecies and the need to consider sex-specific differences in future studies. Alcohol consumption had no impact on these associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".