Management of dyslipidaemia in patients with comorbidities: facing the challenge
Bibliographic record
Abstract
Dyslipidaemia is a common chronic kidney disease (CKD) and contributes to excessively elevated cardiovascular mortality. The pathophysiology is complex and modified by comorbidities like the presence/absence of proteinuria, diabetes mellitus or drug treatment. This paper provides an overview of currently available treatment options. We focused on individuals with CKD and excluded those on renal replacement therapy (haemodialysis, peritoneal dialysis, or kidney transplantation). The use of statins is safe and recommended in most patients, but guidelines vary with respect to low-density lipoprotein (LDL) cholesterol goals. While no dedicated primary or secondary prevention studies are available for pro-protein convertase subtilisin/kexin type 9 inhibitors, secondary analyses of large outcome trials reveal no effect modification on endpoints by the presence of CKD. Similar data have been shown for bempedoic acid, but no definite conclusion can be drawn with respect to efficacy and safety. No outcome trials are available for inclisiran while the cholesterol lowering effects seem to be unaffected by CKD. Finally, the value of fibrates and icosapent ethyl in CKD is unclear. Lipid abnormalities contribute to the massive cardiovascular disease burden in CKD. Lowering of LDL cholesterol with statins (and most likely PCSK9 inhibitors) reduces the event rate and thus statin therapy should be initiated in almost all individuals. Other interventions (bempedoic acid, inclisiran, fibrates, or icosapent ethyl) currently need a case-by-case decision before prescription.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".