Evidence map›PCSK9 inhibitors × lipids›PMID 27701660›Full record

SynthesisJAMA2016

Association Between Low-Density Lipoprotein Cholesterol-Lowering Genetic Variants and Risk of Type 2 Diabetes: A Meta-analysis.

Luca A Lotta, Stephen J Sharp, Stephen Burgess, John R B Perry, Isobel D Stewart, Sara M Willems, Jian'an Luan, Eva Ardanaz, Larraitz Arriola, Beverley Balkau and 31 more

Registry-linked trialOpen access · bronzeAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in JAMA, 2016. The graph read 1 number from its abstract, feeding 1 cell of the map, but none could be read as for or against, so it casts no vote. It also reports an association that does not count as treatment evidence, such as difference -1.19 (-1.38 to -1.02) for type 2 diabetes. It is linked to trial NCT04485871 (White Adipose Tissue LDL Receptors and Omega-3 as Modulators of the Risk for Type 2 Diabetes in Subjects With Normal Plasma LDL Cholesterol), which is not on this map. Cited by 189 papers, 13 of them syntheses that pooled it.

1number the graph read from it
0cells of the map it votes in
189citing papers in PubMed, 13 pooled it
50.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

Read, but not usablea number the graph found but could not read as for or against

Type 2 diabetesPCSK9 genetic variants vs non-carriers, in PCSK9an association or prognostic statement, not a treatment comparison · dyslipidemia, ascvdfeeds one cell of the map
reduction -1.19-1.38 to -1.02P = .03
For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95% CI, 1.02-1.38; P = .03).
FeedsPCSK9 inhibitors×lipids

clause the extractor read what became the number

2 · Its place on the map

Where it lands on the map

Rows are treatments, columns are outcomes. The coloured squares are the cells this paper feeds, coloured by the vote it casts there. Click one to jump to what this paper adds to it.

supports the treatmentfavours the comparatorno clear differenceread, but no usable result
3 · What it changes

What it adds to each cell

For every cell the paper feeds: the belief in the claim with and without this paper, and this paper's estimate drawn against every other readable study in the cell. The ringed dot is this paper.

PCSK9 inhibitors×lipids

No readable resultOpen on the map →What to test next →

34 readable studies in this cell: 29 favour the treatment, 2 find no difference, 3 favour the comparator.

Belief with this paper
0.93established · 26 families support, 2 contradict · against placebo
Without itNot a counted family in this claim, so removing it changes nothing.
← favours the treatmentfavours the comparator →
0 · no effect
NCT017638662,067 enrolled · 2013
Δ -71.4-77.5 to -65.3
NCT034008001,617 enrolled · 2017
Δ -53.5-56.7 to -50.4
NCT02662569986 enrolled · 2016
Δ -70.3-75.4 to -65.2
NCT04807400892 enrolled · 2021
Least Squares Mean -31.8-37.9 to -25.8
NCT01380730631 enrolled · 2011
Δ -66.1-71.5 to -60.7
NCT01763827615 enrolled · 2013
Δ -57.1-61.1 to -53.1
NCT01984424511 enrolled · 2013
Δ -37.8-42.3 to -33.3
NCT02833844467 enrolled · 2017
Δ -56.9-61.5 to -52.3
NCT04929249450 enrolled · 2021
Δ -53.0-60.0 to -46.0
NCT02739984424 enrolled · 2016
Δ -64.1-68.2 to -60.1
NCT02642159413 enrolled · 2016
Δ -32.5-38.1 to -27.0
NCT01375777411 enrolled · 2011
Δ -47.2-54.5 to -39.9

This paper's own estimate is on a different scale from the rest of the cell, so it is not drawn here.

4 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04485871 narecruitingnot on this mapstarted 2019, after this paper: background citation

White Adipose Tissue LDL Receptors and Omega-3 as Modulators of the Risk for Type 2 Diabetes in Subjects With Normal Plasma LDL Cholesterol

TypeinterventionalSponsorInstitut de Recherches Cliniques de MontrealRan2019 to 2027Enrolled48ConditionsType 2 Diabetes, Inflammation, Insulin Sensitivity/Resistance, Fatty Acids, Omega-3ArmsOmega-3 fatty acids
5 · Its place in the literature

Who cites it

189 citing papers in PubMed, 13 syntheses or guidelines pooled it, 414 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Pooled it
  5. Pooled it
  6. American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2022
    Guideline
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Pooled it
  12. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease.The Cochrane database of systematic reviews · 2017 · on this map
    Pooled it
  13. Pooled it
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. Trial
  19. Trial
  20. Trial

129 more citing papers are in PubMed but not listed here.

6 · The record

Corrections and comments

7 · Who and what money

Authors and funding

41 authors at 20 institutions in 8 countries.

Luca A LottaMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Stephen J SharpMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Stephen BurgessDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
John R B PerryMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Isobel D StewartMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Sara M WillemsMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Jian'an LuanMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Eva ArdanazNavarre Public Health Institute (ISPN), Pamplona, Spain.
Larraitz ArriolaCIBER Epidemiología y Salud Pública (CIBERESP), Spain.
Beverley BalkauInserm, CESP, U1018, Villejuif, France.
Heiner BoeingGerman Institute of Human Nutrition Potsdam-Rehbruecke, Germany.
Panos DeloukasThe Wellcome Trust Sanger Institute, Cambridge, United Kingdom.
Nita G ForouhiMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Paul W FranksLund University, Malmö, Sweden.
Sara GrioniEpidemiology and Prevention Unit, Milan, Italy.
Rudolf KaaksGerman Cancer Research Centre (DKFZ), Heidelberg, Germany.
Timothy J KeyUniversity of Oxford, Oxford, United Kingdom.
Carmen NavarroCIBER Epidemiología y Salud Pública (CIBERESP), Spain.
Peter M NilssonLund University, Malmö, Sweden.
Kim OvervadDepartment of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark.
Domenico PalliCancer Research and Prevention Institute (ISPO), Florence, Italy.
Salvatore PanicoDipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy.
Jose-Ramón QuirósPublic Health Directorate, Asturias, Spain.
Elio RiboliSchool of Public Health, Imperial College London, United Kingdom.
Olov RolandssonUmeå University, Umeå, Sweden.
Carlotta SacerdoteUnit of Cancer Epidemiology, Citta' della Salute e della Scienza Hospital-University of Turin and Center for Cancer Prevention (CPO), Torino, Italy.
Elena C SalamancaCIBER Epidemiología y Salud Pública (CIBERESP), Spain.
Nadia SlimaniInternational Agency for Research on Cancer, Lyon, France.
Annemieke Mw SpijkermanNational Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
Anne TjonnelandDanish Cancer Society Research Center, Copenhagen, Denmark.
Rosario TuminoASP Ragusa, Italy.
Daphne L van der ANational Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
Yvonne T van der SchouwUniversity Medical Center Utrecht, Utrecht, the Netherlands.
Mark I McCarthyOxford Centre for Diabetes, Endocrinology and Metabolism, and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom.
Inês BarrosoThe Wellcome Trust Sanger Institute, Cambridge, United Kingdom.
Stephen O'RahillyMetabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom.
David B SavageMetabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom.
Naveed SattarInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, United Kingdom.
Claudia LangenbergMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Robert A Scott *MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Nicholas J Wareham *MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
University of Cambridge · GBMRC Epidemiology Unit · GBNational Institute for Public Health and the Environment · NLUniversity of Oxford · GBWellcome Sanger Institute · GBAalborg University Hospital · DKAgenzia Regionale Sanitaria della Puglia · ITAndalusian School of Public Health · ESBasque Government · ESCentre de recherche en Epidémiologie et Santé des Populations · FRCentre International de Recherche sur le Cancer · FRCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública · ESDanish Cancer Society · DKFederico II University Hospital · ITGerman Cancer Research Center · DEGerman Institute of Human Nutrition · DEGobierno del Principado de Asturias · ESImperial College London · GBItalian institute for Genomic Medicine · ITLund University · SE

Funding

Cancer Research UK 16491Medical Research Council G0800270Medical Research Council MC_QA137853Medical Research Council MC_UU_00002/7Medical Research Council MC_UU_12012/1Medical Research Council MC_UU_12012/5Wellcome TrustWorld Health Organization 001
8 · The paper itself

Abstract

The marked sentences are the ones the graph read a number from.

Importance: Low-density lipoprotein cholesterol (LDL-C)-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, similar to the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are associated with the risk of type 2 diabetes. Objective: To investigate whether LDL-C-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (ie, HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. Design, Setting, and Participants: The associations with type 2 diabetes and coronary artery disease of LDL-C-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50 775 individuals with type 2 diabetes and 270 269 controls and 60 801 individuals with coronary artery disease and 123 504 controls. Data collection took place in Europe and the United States between 1991 and 2016. Exposures: Low-density lipoprotein cholesterol-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, and LDLR. Main Outcomes and Measures: Odds ratios (ORs) for type 2 diabetes and coronary artery disease. Results: Low-density lipoprotein cholesterol-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (OR for a genetically predicted 1-mmol/L [38.7-mg/dL] reduction in LDL-C of 0.61 [95% CI, 0.42-0.88]; P = .008) and directly associated with type 2 diabetes (OR for a genetically predicted 1-mmol/L reduction in LDL-C of 2.42 [95% CI, 1.70-3.43]; P < .001). For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95% CI, 1.02-1.38; P = .03). For a given reduction in LDL-C, genetic variants were associated with a similar reduction in coronary artery disease risk (I2 = 0% for heterogeneity in genetic associations; P = .93). However, associations with type 2 diabetes were heterogeneous (I2 = 77.2%; P = .002), indicating gene-specific associations with metabolic risk of LDL-C-lowering alleles. Conclusions and Relevance: In this meta-analysis, exposure to LDL-C-lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL-C-lowering therapy.

Indexed as

Genetic VariationAdultAgedATP Binding Cassette Transporter, Subfamily G, Member 5Cholesterol, LDLCohort StudiesCoronary Artery DiseaseDiabetes Mellitus, Type 2Drug Therapy, CombinationEzetimibeGenetic Association StudiesHumansHydroxymethylglutaryl-CoA Reductase InhibitorsHydroxymethylglutaryl CoA ReductasesLipoproteinsMembrane ProteinsABCG5 protein, humanATP Binding Cassette Transporter, Subfamily G, Member 5Cholesterol, LDLEzetimibeHMGCR protein, humanHydroxymethylglutaryl-CoA Reductase InhibitorsHydroxymethylglutaryl CoA ReductasesLDLR protein, humanLipoproteinsMembrane ProteinsMembrane Transport ProteinsNPC1L1 protein, humanPCSK9 protein, humanProprotein Convertase 9Receptors, LDLSimvastatin

Identifiers

PMID27701660
PMCPMC5386134
OpenAlexW2529053508

What Socratic holds

Texttitle and abstract
LicenceTDM
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.