Chronic kidney disease stage affects small, dense low-density lipoprotein but not glycated low-density lipoprotein in younger chronic kidney disease patients: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Small, dense low-density lipoprotein (sd-LDL) and glycated LDL (g-LDL) have been associated with cardiovascular disease (CVD) in chronic kidney disease (CKD) in patients >60 years of age. Since young adult and paediatric patients have shorter exposure to Framingham-type risk factors, our study aims to determine whether younger CKD patients exhibit the same sd-LDL and g-LDL pattern. METHODS: After ethics board approval, this cross-sectional study was conducted at two universities with 44 patients (mean ± standard deviation age 12.6 ± 4.9, range 2-24 years) with CKD stage of 1-5. Laboratory parameters studied were Cystatin C (CysC), CysC estimated glomerular filtration rate (eGFR) (calculated from the Filler formula), sd-LDL, g-LDL and albumin. Lipid samples were measured for sd-LDL and g-LDL using ELISA. Non-linear correlation analysis was performed to determine the relationship between g-LDL, sd-LDL and eGFR. Clinical Trials Registration is at clinicaltrials.gov, NCT02126293, https://clinicaltrials.gov/ct2/show/NCT02126293. RESULTS: = -0.3895, P = 0.0189), but not with age. By contrast, there was no significant correlation between g-LDL and CKD stages or CysC eGFR (P = 0.9678). CONCLUSIONS: Our study demonstrates that only triglycerides and sd-LDL were associated with CKD stages in this young cohort without confounding Framingham-type CVD risk factors. While larger studies are needed, this study suggests that lowering sd-LDL levels may be a potential target to ameliorate the long-term CVD risks in paediatric CKD patients.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".