Oxidized low‐density lipoprotein and lipoprotein(a) levels in chronic kidney disease patients under hemodialysis: Influence of adiponectin and of a polymorphism in the apolipoprotein(a) gene
Bibliographic record
Abstract
Chronic kidney disease (CKD) has been associated with an abnormal lipid profile. Our aim was to study the interplay between oxidized low-density lipoprotein (ox-LDL), adiponectin, and blood lipids and lipoproteins in Portuguese patients with CKD under hemodialysis (HD); the influence of the pentanucleotide repeat polymorphism in the apolipoprotein(a) (apo [a]) gene upon lipoprotein(a) (Lp[a]) levels in these patients. We studied 187 HD patients and 25 healthy individuals. ox-LDL and adiponectin were measured using enzyme-linked immunoassays. Apo(a) genotyping was performed by polymerase chain reaction, followed by electrophoresis in polyacrylamide gel. Compared with controls, patients presented with significantly higher levels of adiponectin, Lp(a), and ox-LDL/low-density lipoprotein cholesterol (LDLc) ratio; significantly lower levels of total cholesterol (TC), LDLc, apo A-I, apo B, ox-LDL, and TC/high-density lipoprotein cholesterol (HDLc) ratio were also observed. Similar changes were observed for patients with or without statin therapy, as compared with controls, except for Lp(a). Multiple linear regression analysis showed that body mass index, HDLc, time on HD, and triglycerides (TG) were independent determinants of adiponectin levels, and that apo B, TG and LDLc were independent determinants of ox-LDL concentration. Concerning the apo(a) genotype, the homozygous (TTTTA)8/8 repeats was the most prevalent (50.8%). A raised proportion of LDL particles that are oxidized was observed. Adiponectin almost doubled its values in patients and seems to be an important determinant in HDLc and TG levels, improving the lipid profile in these patients. Apo(a) alleles with a lower number of repetitions are more frequent in patients with higher Lp(a).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".