Dataset Information

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

ABSTRACT:

Background

Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk.

Methods

In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA_1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores.

Findings

Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA_1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m², -0·09 to 0·30).

Interpretation

PCSK9 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.

Funding

British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre.

SUBMITTER: Schmidt AF

PROVIDER: S-EPMC5266795 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

Schmidt Amand F AF Swerdlow Daniel I DI Holmes Michael V MV Patel Riyaz S RS Fairhurst-Hunter Zammy Z Lyall Donald M DM Hartwig Fernando Pires FP Horta Bernardo Lessa BL Hyppönen Elina E Power Christine C Moldovan Max M van Iperen Erik E Hovingh G Kees GK Demuth Ilja I Norman Kristina K Steinhagen-Thiessen Elisabeth E Demuth Juri J Bertram Lars L Liu Tian T Coassin Stefan S Willeit Johann J Kiechl Stefan S Willeit Karin K Mason Dan D Wright John J Morris Richard R Wanamethee Goya G Whincup Peter P Ben-Shlomo Yoav Y McLachlan Stela S Price Jackie F JF Kivimaki Mika M Welch Catherine C Sanchez-Galvez Adelaida A Marques-Vidal Pedro P Nicolaides Andrew A Panayiotou Andrie G AG Onland-Moret N Charlotte NC van der Schouw Yvonne T YT Matullo Giuseppe G Fiorito Giovanni G Guarrera Simonetta S Sacerdote Carlotta C Wareham Nicholas J NJ Langenberg Claudia C Scott Robert R Luan Jian'an J Bobak Martin M Malyutina Sofia S Pająk Andrzej A Kubinova Ruzena R Tamosiunas Abdonas A Pikhart Hynek H Husemoen Lise Lotte Nystrup LL Grarup Niels N Pedersen Oluf O Hansen Torben T Linneberg Allan A Simonsen Kenneth Starup KS Cooper Jackie J Humphries Steve E SE Brilliant Murray M Kitchner Terrie T Hakonarson Hakon H Carrell David S DS McCarty Catherine A CA Kirchner H Lester HL Larson Eric B EB Crosslin David R DR de Andrade Mariza M Roden Dan M DM Denny Joshua C JC Carty Cara C Hancock Stephen S Attia John J Holliday Elizabeth E O'Donnell Martin M Yusuf Salim S Chong Michael M Pare Guillaume G van der Harst Pim P Said M Abdullah MA Eppinga Ruben N RN Verweij Niek N Snieder Harold H Christen Tim T Mook-Kanamori Dennis O DO Gustafsson Stefan S Lind Lars L Ingelsson Erik E Pazoki Raha R Franco Oscar O Hofman Albert A Uitterlinden Andre A Dehghan Abbas A Teumer Alexander A Baumeister Sebastian S Dörr Marcus M Lerch Markus M MM Völker Uwe U Völzke Henry H Ward Joey J Pell Jill P JP Smith Daniel J DJ Meade Tom T Maitland-van der Zee Anke H AH Baranova Ekaterina V EV Young Robin R Ford Ian I Campbell Archie A Padmanabhan Sandosh S Bots Michiel L ML Grobbee Diederick E DE Froguel Philippe P Thuillier Dorothée D Balkau Beverley B Bonnefond Amélie A Cariou Bertrand B Smart Melissa M Bao Yanchun Y Kumari Meena M Mahajan Anubha A Ridker Paul M PM Chasman Daniel I DI Reiner Alex P AP Lange Leslie A LA Ritchie Marylyn D MD Asselbergs Folkert W FW Casas Juan-Pablo JP Keating Brendan J BJ Preiss David D Hingorani Aroon D AD Sattar Naveed N

The lancet. Diabetes & endocrinology 20161129 2

<h4>Background</h4>Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge ...[more]

PMID: 27908689

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Project description:BackgroundThis Mendelian randomization (MR) study aimed to explore the causal relationship between the genetic predisposition to type 2 diabetes mellitus (T2DM) and aortic dissection (AD), and to assess associations with genetically predicted glycemic traits. The study sought to verify the inverse relationship between T2DM and AD using a more robust and unbiased method, building on the observational studies previously established.Materials and methodsThe study employed a two-sample and multivariable MR approach to analyze genetic data from the DIAbetes Meta-ANalysis of Trans-Ethnic association studies (DIAMANTE) with 74,124 cases and 824,006 controls, and the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) involving up to 196,991 individuals. For AD data, FinnGen Release 10 was used, including 967 cases and 381,977 controls. The research focused on three foundational MR assumptions and controlled for confounders like hypertension. Genetic instruments were selected for their genome-wide significance, and multiple MR methods and sensitivity analyses were conducted.ResultsThe study revealed no significant effect of genetic predisposition to T2DM on the risk of AD. Even after adjusting for potential confounders, the results were consistent, indicating no causal relationship. Additionally, glycemic traits such as fasting glucose, fasting insulin, and HbA1c levels did not show a significant impact on AD susceptibility. The findings remained stable across various MR models and sensitivity analyses. In contrast, genetic liability to T2DM and glycemic traits showed a significant association with coronary artery disease (CAD), aligning with the established understanding.ConclusionContrary to previous observational studies, this study concludes that genetic predisposition to T2DM does not confer protection against AD. These findings underscore the imperative for further research, particularly in exploring the preventative potential of T2DM treatments against AD and to facilitate the development of novel therapeutic interventions.

Project description:BackgroundProstate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomisation (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c-lowering drug targets on risk of PrCa.Methods and findingsSingle-nucleotide polymorphisms (SNPs) associated with LDL-c (P < 5 × 10-8) from the Global Lipids Genetics Consortium genome-wide association study (GWAS) (N = 1,320,016) and located in and around the HMGCR, NPC1L1, and PCSK9 genes were used to proxy the therapeutic inhibition of these targets. Summary-level data regarding the risk of total, advanced, and early-onset PrCa were obtained from the PRACTICAL consortium. Validation analyses were performed using genetic instruments from an LDL-c GWAS conducted on male UK Biobank participants of European ancestry (N = 201,678), as well as instruments selected based on liver-derived gene expression and circulation plasma levels of targets. We also investigated whether putative mediators may play a role in findings for traits previously implicated in PrCa risk (i.e., lipoprotein a (Lp(a)), body mass index (BMI), and testosterone). Applying two-sample MR using the inverse-variance weighted approach provided strong evidence supporting an effect of genetically proxied inhibition of PCSK9 (equivalent to a standard deviation (SD) reduction in LDL-c) on lower risk of total PrCa (odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.76 to 0.96, P = 9.15 × 10-3) and early-onset PrCa (OR = 0.70, 95% CI = 0.52 to 0.95, P = 0.023). Genetically proxied HMGCR inhibition provided a similar central effect estimate on PrCa risk, although with a wider 95% CI (OR = 0.83, 95% CI = 0.62 to 1.13, P = 0.244), whereas genetically proxied NPC1L1 inhibition had an effect on higher PrCa risk with a 95% CI that likewise included the null (OR = 1.34, 95% CI = 0.87 to 2.04, P = 0.180). Analyses using male-stratified instruments provided consistent results. Secondary MR analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.90 per SD reduction in PCSK9 expression, 95% CI = 0.86 to 0.95, P = 5.50 × 10-5) and circulating plasma levels of PCSK9 (OR = 0.93 per SD reduction in PCSK9 protein levels, 95% CI = 0.87 to 0.997, P = 0.04) on PrCa risk. Colocalization analyses identified strong evidence (posterior probability (PPA) = 81.3%) of a shared genetic variant (rs553741) between liver-derived PCSK9 expression and PrCa risk, whereas weak evidence was found for HMGCR (PPA = 0.33%) and NPC1L1 expression (PPA = 0.38%). Moreover, genetically proxied PCSK9 inhibition was strongly associated with Lp(a) levels (Beta = -0.08, 95% CI = -0.12 to -0.05, P = 1.00 × 10-5), but not BMI or testosterone, indicating a possible role for Lp(a) in the biological mechanism underlying the association between PCSK9 and PrCa. Notably, we emphasise that our estimates are based on a lifelong exposure that makes direct comparisons with trial results challenging.ConclusionsOur study supports a strong association between genetically proxied inhibition of PCSK9 and a lower risk of total and early-onset PrCa, potentially through an alternative mechanism other than the on-target effect on LDL-c. Further evidence from clinical studies is needed to confirm this finding as well as the putative mediatory role of Lp(a).

Project description:Aims/hypothesisEpidemiological data on the associations of circulating fatty acid levels with type 2 diabetes are inconsistent. We conducted a two-sample Mendelian randomisation study to explore the causal associations of plasma levels of ten fatty acids with type 2 diabetes and glycaemic traits.MethodsThirteen SNPs associated with circulating levels of ten individual fatty acids at the genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables for the exposures. For the outcomes, summary-level data were obtained from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium for type 2 diabetes (898,130 individuals) and from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for the glycaemic traits (up to 46,186 non-diabetic individuals). The inverse-variance weighted method was used for analyses.ResultsGenetic predisposition to higher plasma levels of eight of the ten fatty acids were statistically significantly associated with lower or higher odds of type 2 diabetes. The OR per one SD increment of each fatty acid was 0.93 (95% CI 0.90, 0.96; p = 2.21 × 10-5) for α-linolenic acid, 0.96 (95% CI 0.94, 0.98; p = 1.85 × 10-4) for linoleic acid, 0.86 (95% CI 0.81, 0.91; p = 6.68 × 10-7) for palmitoleic acid, 0.87 (95% CI 0.81, 0.93; p = 2.21 × 10-5) for oleic acid, 1.08 (95% CI 1.03, 1.12; p = 0.002) for eicosapentaenoic acid, 1.04 (95% CI 1.02, 1.07; p = 0.001) for docosapentaenoic acid, 1.03 (95% CI 1.02, 1.05; p = 2.51 × 10-5) for arachidonic acid and 1.09 (95% CI 1.03, 1.15; p = 0.003) for stearic acid. The same eight fatty acids were also associated with fasting glucose levels and HOMA-B. The associations, except that for palmitoleic acid, were driven by variants in FADS1/2.Conclusions/interpretationGenetic predisposition to higher circulating levels of eight out of ten fatty acids was associated with type 2 diabetes, fasting glucose and islet beta cell function. However, the associations, except that for palmitoleic acid, were driven by variants in FADS1/2, which encode enzymes with a key role in fatty acid metabolism.

Project description:Aims/hypothesisThe aim of this study was to use Mendelian randomisation (MR) to identify the causal risk factors for type 2 diabetes.MethodsWe first conducted a review of meta-analyses and review articles to pinpoint possible risk factors for type 2 diabetes. Around 170 possible risk factors were identified of which 97 risk factors with available genetic instrumental variables were included in MR analyses. To reveal more risk factors that were not included in our MR analyses, we conducted a review of published MR studies of type 2 diabetes. For our MR analyses, we used summary-level data from the DIAbetes Genetics Replication And Meta-analysis consortium (74,124 type 2 diabetes cases and 824,006 controls of European ancestry). Potential causal associations were replicated using the FinnGen consortium (11,006 type 2 diabetes cases and 82,655 controls of European ancestry). The inverse-variance weighted method was used as the main analysis. Multivariable MR analysis was used to assess whether the observed associations with type 2 diabetes were mediated by BMI. We used the Benjamini-Hochberg method that controls false discovery rate for multiple testing.ResultsWe found evidence of causal associations between 34 exposures (19 risk factors and 15 protective factors) and type 2 diabetes. Insomnia was identified as a novel risk factor (OR 1.17 [95% CI 1.11, 1.23]). The other 18 risk factors were depression, systolic BP, smoking initiation, lifetime smoking, coffee (caffeine) consumption, plasma isoleucine, valine and leucine, liver alanine aminotransferase, childhood and adulthood BMI, body fat percentage, visceral fat mass, resting heart rate, and four plasma fatty acids. The 15 exposures associated with a decreased risk of type 2 diabetes were plasma alanine, HDL- and total cholesterol, age at menarche, testosterone levels, sex hormone binding globulin levels (adjusted for BMI), birthweight, adulthood height, lean body mass (for women), four plasma fatty acids, circulating 25-hydroxyvitamin D and education years. Eight associations remained after adjustment for adulthood BMI. We additionally identified 21 suggestive risk factors (p < 0.05), such as alcohol consumption, breakfast skipping, daytime napping, short sleep, urinary sodium, and certain amino acids and inflammatory factors.Conclusions/interpretationThe present study verified several previously reported risk factors and identified novel potential risk factors for type 2 diabetes. Prevention strategies for type 2 diabetes should be considered from multiple perspectives on obesity, mental health, sleep quality, education level, birthweight and smoking.

Project description:Objective To test the hypothesis that low density lipoprotein (LDL) cholesterol due to genetic variation in the genes responsible for LDL cholesterol metabolism and biosynthesis(PCSK9 and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), respectively) is associated with a high risk of Alzheimer's disease, vascular dementia, any dementia, and Parkinson's disease in the general population.Design Mendelian randomisation study.Setting Copenhagen General Population Study and Copenhagen City Heart Study.Participants 111 194 individuals from the Danish general population.Main outcome measures Risk of Alzheimer's disease, vascular dementia, all dementia, and Parkinson's disease.Results In observational analyses, the multifactorially adjusted hazard ratio for Parkinson's disease in participants with an LDL cholesterol level <1.8 mmol/L versus ≥4.0 mmol/L was 1.70 (95% confidence interval 1.03 to 2.79), whereas the corresponding hazard ratios for Alzheimer's disease, vascular dementia, or any dementia did not differ from 1.0. PCSK9 and HMGCR variants combined were associated with a 9.3% lower LDL cholesterol level. In genetic, causal analyses adjusted for age, sex, and year of birth, the risk ratios for a lifelong 1 mmol/L lower LDL cholesterol level were 0.57 (0.27 to 1.17) for Alzheimer's disease, 0.81 (0.34 to 1.89) for vascular dementia, 0.66 (0.34 to 1.26) for any dementia, and 1.02 (0.26 to 4.00) for Parkinson's disease. Summary level data from the International Genomics of Alzheimer's Project using Egger Mendelian randomisation analysis gave a risk ratio for Alzheimer's disease of 0.24 (0.02 to 2.79) for 26 PCSK9 and HMGCR variants, and of 0.64 (0.52 to 0.79) for 380 variants of LDL cholesterol level lowering.Conclusion Low LDL cholesterol levels due to PCSK9 and HMGCR variants had no causal effect on high risk of Alzheimer's disease, vascular dementia, any dementia, or Parkinson's disease; however, low LDL cholesterol levels may have a causal effect in reducing the risk of Alzheimer's disease.

Dataset Information

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

Background

Methods

Findings

Interpretation

Funding

Publications

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

Similar Datasets

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