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Record W2158042027 · doi:10.1177/089686080602600503

Statins for Treatment of Dyslipidemia in Chronic Kidney Disease

2006· article· en· W2158042027 on OpenAlexaff
Sabin Shurraw, Marcello Tonelli

Bibliographic record

VenuePeritoneal Dialysis International · 2006
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsMedicineDyslipidemiaKidney diseasePeritoneal dialysisInternal medicineHydroxymethylglutaryl-CoA Reductase InhibitorsDialysisDiseaseIntensive care medicineStatin

Abstract

fetched live from OpenAlex

Dyslipidemia is a potent cardiovascular (CV) risk factor in the general population. Elevated low-density lipoprotein cholesterol (LDL-C) and/or low high-density lipoprotein (HDL-C) are well-established CV risk factors, but more precise determinants of risk include increased apoprotein B (ApoB), lipoprotein(a) [Lp(a)], intermediate and very low-density lipoprotein (IDL-C, VLDL-C; "remnant particles"), and small dense LDL particles. Lipoprotein metabolism is altered in association with declining glomerular filtration rate such that patients with non dialysis-dependent chronic kidney disease (CKD) have lower levels of HDL-C, higher triglyceride, ApoB, remnant IDL-C, remnant VLDL-C, and Lp(a), and a greater proportion of oxidized LDL-C. Similar abnormalities are prevalent in hemodialysis (HD) patients, who often manifest proatherogenic changes in LDL-C in the absence of increased levels. Patients treated with peritoneal dialysis (PD) have a similar but more severe dyslipidemia compared to HD patients due to stimulation of hepatic lipoprotein synthesis by glucose absorption from dialysate, increased insulin levels, and selective protein loss in the dialysate analogous to the nephrotic syndrome. In the dialysis-dependent CKD population, total cholesterol is directly associated with increased mortality after controlling for the presence of malnutrition-inflammation. Treatment with statins reduces CV mortality in the general population by approximately one third, irrespective of baseline LDL-C or prior CV events. Statins have similar, if not greater, efficacy in altering the lipid profile in patients with dialysis-dependent CKD (HD and PD) compared to those with normal renal function, and are well tolerated in CKD patients at moderate doses (<or=20 mg/day atorvastatin or simvastatin). Statins reduce C-reactive protein as well as lipid moieties such as ApoB, remnants IDL and VLDL-C, and oxidized and small dense LDL-C fraction. Large observational studies demonstrate that statin treatment is independently associated with a 30%-50% mortality reduction in patients with dialysis-dependent CKD (similar between HD- and PD-treated patients). One recent randomized controlled trial evaluated the ability of statin treatment to reduce mortality in type II diabetics treated with HD ("4D"); the primary end point of death from cardiac cause, myocardial infarction, and stroke was not significantly reduced. However, results of this trial may not apply to other end-stage renal disease populations. Two ongoing randomized controlled trials (SHARP and AURORA) are underway evaluating the effect of statins on CV events and death in patients with CKD (including patients treated with HD and PD). Recruitment to future trials should be given a high priority by nephrologists and, until more data are available, consideration should be given to following published guidelines for the treatment of dyslipidemia in CKD. Additional consideration could be given to treating all dialysis patients felt to be at risk of CV disease (irrespective of cholesterol level), given the safety and potential efficacy of statins. This is especially relevant in patients treated with PD, given their more atherogenic lipid profile and the lack of randomized controlled trials in this population.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.307
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations71
Published2006
Admission routes1
Has abstractyes

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