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Record W2560403714 · doi:10.1016/s2213-8587(16)30398-9

PCSK9 inhibition and diabetes: turning to Mendel for clues

2016· letter· en· W2560403714 on OpenAlexaff
Joo-Ho Lee, Robert A. Hegele

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2016
Typeletter
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsMedicineDiabetes mellitusMEDLINEBioinformaticsEndocrinology

Abstract

fetched live from OpenAlex

Inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) is a therapeutic advance;1Burke AC Dron JS Hegele RA Huff MW PCSK9: regulation and target for drug development for dyslipidemia.Annu Rev Pharmacol Toxicol. 2016; 8: 8Google Scholar patients who receive this type of therapy have dramatic reductions in plasma LDL cholesterol and regression of coronary atherosclerosis.2Nicholls SJ Puri R Anderson T et al.Effect of evolocumab on progression of coronary disease in statin-treated patients: the GLAGOV randomized clinical trial.JAMA. 2016; (published online Nov 15.)https://doi.org/10.1001/jama.2016.16951Crossref PubMed Scopus (664) Google Scholar But some endocrinologists privately express reservations about these drugs in patients with type 2 diabetes. Concerns range from potential worsening of glycaemia to unanticipated adverse outcomes, despite the fact that about 23% of participants in phase 2 and 3 studies of PCSK9 inhibitors had type 2 diabetes at baseline,3Hoe E Hegele RA Lipid management in diabetes with a focus on emerging therapies.Can J Diabetes. 2015; 39: S183-S190Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar and these participants had similar benefits and side-effects as participants without diabetes.4Sattar N Preiss D Robinson JG et al.Lipid-lowering efficacy of the PCSK9 inhibitor evolocumab (AMG 145) in patients with type 2 diabetes: a meta-analysis of individual patient data.Lancet Diabetes Endocrinol. 2016; 4: 403-410Summary Full Text Full Text PDF PubMed Scopus (112) Google Scholar Furthermore, the incidence of diabetes did not increase in a pooled analysis of ten phase 3 studies.5Colhoun HM Ginsberg HN Robinson JG et al.No effect of PCSK9 inhibitor alirocumab on the incidence of diabetes in a pooled analysis from 10 ODYSSEY phase 3 studies.Eur Heart J. 2016; 37: 2981-2989Crossref PubMed Scopus (111) Google Scholar But learning from the experience with statins, for which the associated risk of new-onset diabetes surfaced only after years of clinical use and was confirmed in large meta-analyses,6Sattar N PCSK9 inhibitors and diabetes risk: a question worth asking?.Eur Heart J. 2016; 37: 2990-2992Crossref PubMed Scopus (8) Google Scholar should we hedge our bets regarding diabetes risk with PCSK9 inhibitors? Concerns might evaporate when subgroups of patients with diabetes in outcomes trials of PCSK9 inhibitors are reported. But in the meantime, are there clues about possible diabetes-related concerns with PCSK9 inhibition? Mendelian randomisation is a form of instrument analysis that infers causality of an exposure by use of genetic variants as surrogates.7Swerdlow DI Kuchenbaecker KB Shah S et al.Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.Int J Epidemiol. 2016; 45: 1600-1616Crossref PubMed Scopus (122) Google Scholar For example, a recent mendelian randomisation analysis of more than 500 000 individuals showed that exposure to LDL cholesterol-lowering alleles of the genes HMGCR (which encodes HMG-CoA reductase, the target of statins), NPC1L1 (which encodes the target of ezetimibe), and PCSK9, each markedly reduced risk of coronary heart disease, while concurrently slightly increasing the risk of type 2 diabetes.8Lotta LA Sharp SJ Burgess S et al.Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: a meta-analysis.JAMA. 2016; 316: 1383-1391Crossref PubMed Scopus (249) Google Scholar These findings supported the known connection between statins and diabetes, and implicated additional mechanisms, including PCSK9 knock-down.6Sattar N PCSK9 inhibitors and diabetes risk: a question worth asking?.Eur Heart J. 2016; 37: 2990-2992Crossref PubMed Scopus (8) Google Scholar Increased diabetes risk with HMGCR loss-of-function variants is further linked with diabetes-associated traits, including increases in bodyweight, waist circumference, plasma insulin, and plasma glucose.9Swerdlow DI Preiss D Kuchenbaecker KB et al.HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials.Lancet. 2015; 385: 351-361Summary Full Text Full Text PDF PubMed Scopus (463) Google Scholar How similar is the metabolic footprint for the association between PCSK9 variants and diabetes? This question is addressed in The Lancet Diabetes & Endocrinology by Amand Schmidt and colleagues,10Schmidt AF Swerdlow DI Holmes MV et al.PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.Lancet Diabetes Endocrinol. 2016; (published online Nov 28.)http://dx.doi.org/10.1016/S2213-8587(16)30396-5Google Scholar who evaluated whether PCSK9 variants that lower LDL cholesterol are associated with markers of glycaemia. Using a mendelian randomisation framework, they selected four PCSK9 single nucleotide polymorphisms (SNPs) based on effect size and independence from each other and bundled these into a genetic score. Up to 550 000 genotyped patients were assessed for a range of phenotypes, including fasting glucose, HbA1c, bodyweight, and odds ratio of having type 2 diabetes. The investigators identified significant and directionally consistent associations between PCSK9 variants and four clinical phenotypes. Specifically, for each 1 mmol/L reduction of PCSK9-determined LDL cholesterol, there was an associated 1·03 kg (95% CI 0·24–1·82) bodyweight increase, a 0·006 (0·003–0·010) increase in waist-to-hip ratio, a 0·009 mmol/L (0·02–0·15) increase in fasting glucose, and a significant odds ratio of 1·29 (1·11–1·50) for type 2 diabetes. Thus, genetically determined loss of PCSK9 function increases risk of type 2 diabetes and related variables. Allele effects were additive and associations were identical for both incident and prevalent cases of diabetes. Therefore, analogous to HMGCR variants, PCSK9 variants that reduce LDL cholesterol and risk of coronary heart disease are associated with slightly increased risk of diabetes and related traits. Extrapolation suggests that pharmacological inhibition of PCSK9, as with pharmacological inhibition of HMG-CoA reductase by statins, might increase diabetes risk. A possible limitation of this analysis is that one PCSK9 SNP (rs11591147) had a larger effect on the associations than the other three SNPs tested; when rs11591147 was excluded, the regression was non-significant (p=0·437). In fact, none of the other three SNPs showed individual statistical significance for any phenotype and were sometimes even discordant—eg, for HbA1c and waist-to-hip ratio. So are the associations governed by the PCSK9 locus overall, or by some unique property of rs11591147 in particular? In the latter case, perhaps questioning a mechanistic relationship between diabetes and PCSK9 inhibition is premature. Similarly, despite the associations of diabetes with LDL cholesterol-lowering alleles from several genes (HMGCR, NPC1L1, and PCSK9), such studies cannot definitively establish whether the cause lies with LDL cholesterol lowering by any mechanism or by various mechanisms directed by the different gene products. Another possible limitation, as recognised by the study investigators, is that diabetes risk in carriers of the PCSK9 variant is cumulative over their lifetime. Patients would not necessarily have such increased risk from a short exposure to PSCK9 inhibitors starting in adulthood. Furthermore, monoclonal antibodies against PCSK9 interact with the circulating protein only, because they cannot access the intracellular space. Would risk from antibody-based PSCK9 inhibition differ from other biological agents, such as antisense RNA-based inhibitors (eg, inclisiran)? Such RNA-based inhibitors would affect both intracellular and extracellular PCSK9, which might better reflect the global loss-of-function at the DNA level. With the data available, we cannot yet say whether diabetes risk would result from all forms of PCSK9 inhibition. Schmidt and colleagues' findings extend those of Lotta and colleagues8Lotta LA Sharp SJ Burgess S et al.Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: a meta-analysis.JAMA. 2016; 316: 1383-1391Crossref PubMed Scopus (249) Google Scholar by associating PSCK9 variation with several diabetes-related traits, suggesting that pharmacological inhibition of PSCK9 might be associated with these phenotypes. A third report has now confirmed the association.11Ference BA Robinson JG Brook RD et al.Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes.N Engl J Med. 2016; 375: 2144-2153Crossref PubMed Scopus (453) Google Scholar However, extrapolation of this risk to therapeutic PSCK9 inhibition is confounded by variables such as duration of effect and distinctive cellular localisation of various knock-down modalities. Nonetheless, the replicated association signal from the mendelian randomisation studies indicates the need for vigilance regarding new-onset diabetes as the large, long-term PCSK9 outcomes studies progress to their conclusion over the next couple of years. RAH has received honoraria for membership on advisory boards or speakers' bureaus for Aegerion, Amgen, Boston Heart Diagnostics, Gemphire, Lilly, Merck, Pfizer, Sanofi, and Valeant. JL declares no competing interests. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation studyPCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins. Full-Text PDF Open Access

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)0.008
metaresearch head score (Gemma)0.027
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0010.006
Scholarly communication0.0040.011
Open science0.0020.003
Research integrity0.0070.019
Insufficient payload (model declined to judge)0.0210.008

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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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
GenreCommentary

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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Citations3
Published2016
Admission routes1
Has abstractyes

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