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Editorial: Statin Potency, LDL Receptors and New Onset Diabetes

2014· editorial· en· W2290366635 on OpenAlexaboutno aff
Vasilios G. Athyros, Niki Katsiki, Asterios Karagiannis, Dimitri P. Mikhailidis

Bibliographic record

VenueCurrent Vascular Pharmacology · 2014
Typeeditorial
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStatinEndocrinologyDiabetes mellitusInternal medicinePathogenesisNodInsulin

Abstract

fetched live from OpenAlex

New onset diabetes (NOD) has been regarded as one of the factors to be considered before deciding to prescribe a statin [1- 3]. The recent Canadian Network for Observational Drug Effect Studies Investigators [2] study and an older meta-analysis [3] suggest that the administration of higher potency statins is associated with an increased risk of NOD compared with lower potency statins [1-3]. The potential increase in caloric and fat intake during statin treatment may play a role in the pathogenesis of statin-related NOD [4]. However, the exact mechanisms involved in the pathogenesis of NOD associated with statin treatment remain to be defined. Recent evidence suggests that the prevalence of NOD is significantly lower in familial hypercholesterolemia (FH) patients (n=14,296) compared with their unaffected relatives (n=24,684) [5]. For receptor-negative and receptor-defective low density lipoprotein receptor (LDLr) mutations, the odds ratio was 0.35 (0.27-0.45) and 0.51 (0.42-0.62) (p for trend <0.001), respectively, compared with unaffected relatives [5]. FH patients may be more motivated to follow lifestyle measures (diet, exercise and not smoking) which may contribute to the reported decrease in the risk of NOD in patients with FH treated with statins. Statins may cause NOD, especially in predisposed individuals, through several mechanisms [1, 6]. They may affect insulin secretion, reduce translocation of glucose transporter 4 resulting in hyperglycaemia and hyperinsulinaemia, decrease intracellular signalling through the reduction of several downstream products, and finally they may decrease leptin and adiponectin levels [6]. However, the correlation between statin intensity and risk of NOD has not been fully defined. We could not find definitive information in the literature stating if activation of sterol regulatory element-binding proteins (SREBPs) occurs in patients with FH as part of the attempt to increase hepatic LDLr numbers. In contrast, statins increase LDLr numbers through activation of SREBPs 1a, 1c and 2 [7], which are also causally related to insulin resistance [8]. Thus, the more potent the statin, the greater the possible increase in SREBPs and LDLr as well as the reduction in plasma LDL cholesterol (LDL-C); however, the “cost” may be in terms of insulin resistance and a higher incidence of NOD. Thus, it is imperative to carefully balance the risk-benefit ratio [1, 9]. However, is this the case for antibodies against proprotein convertase subtilisin kexin-9 (PCSK9), which prolong the life of the LDLr? These drugs have no apparent adverse effect on SREBPs [10]. In fact, a study using a novel antibody (1B20) against PCSK9 has shown that its combination with simvastatin in dyslipidaemic rhesus monkeys reduced LDL-C more than either agent alone, while at the same time decreasing liver mRNA levels of SREBPregulated genes [10]. This mRNA effect remains to be confirmed in humans. The phase III studies using antibodies against PCSK9 (e.g. evolocumab or alirocumab) have not reported any trend for increased NOD [11, 12]; however the issue of NOD should be carefully monitored in these on-going trials. It took some 20 years to realise that statin-induced NOD occurs. If there is a similar problem with PCSK9 inhibitors (potentially the next blockbuster in hypolipidaemic treatment [13]) we should know as soon as possible. Moreover, we need to define the risk of NOD in patients treated with a statin and PCSK9 inhibitor combination. It may be that by lowering the statin dose required to reach LDLC targets, there will also be a decrease in NOD. This potential benefit may help justify, at least in part, the cost of using PCSK9 inhibitors. A recent study reported an off-target effect of cholesterol ester transfer protein (CETP) inhibitors, a new drug category aimed at increasing high density lipoprotein cholesterol (HDL-C) levels and reduce atherosclerosis; these drugs are still under investigation in phase III trials [14]. CETP inhibitors reduce the levels of mature SREBP 2, leading to attenuated transcription of hepatic LDLr and PCSK9 [14]. Thus, it is possible that these drugs will not have the diabetogenic effects of statins. We also need to consider the evidence that currently available lipid lowering drugs, like ezetimibe and colesevelam, may also have beneficial effects on insulin sensitivity. However, the evidence for ezetimibe comes from relatively small studies [15- 19] with design limitations and the results were not always consistent [20]. This may reflect which patient category was studied. It is also of interest that ezetimibe down-regulated liver SREBP-1c and improved hepatic insulin resistance in mice fed a high-fat diet although there were also effects on other mediators [21]. Colesevelam is actually licensed (in some countries) to lower glucose levels although the mechanisms involved are not clear [22]. The above evidence suggests that ezetimibe and colesevelam as well as two new drug categories (i.e. PCSK9 and CETP inhibitors) for the treatment of dyslipidaemia might decrease/avoid the risk of NOD. This will hopefully lead to combinations with statins of lesser intensity or at lower doses which may result in a reduced risk of statin-related NOD. The key issue is whether such combinations will be as effective as statin monotherapy in terms of lowering the risk of vascular events.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0060.001
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0140.012

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 head0.317
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations7
Published2014
Admission routes1
Has abstractyes

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