A Mendelian Randomization Study

Objective We employed Mendelian randomization to explore the effects of genetic predisposition to type 2 diabetes (T2D), hyperglycemia, insulin resistance, and pancreatic β-cell dysfunction on risk of stroke subtypes and related cerebrovascular phenotypes. Methods We selected instruments for genetic predisposition to T2D (74,124 cases, 824,006 controls), HbA1c levels (n = 421,923), fasting glucose levels (n = 133,010), insulin resistance (n = 108,557), and β-cell dysfunction (n = 16,378) based on published genome-wide association studies. Applying 2-sample Mendelian randomization, we examined associations with ischemic stroke (60,341 cases, 454,450 controls), intracerebral hemorrhage (1,545 cases, 1,481 controls), and ischemic stroke subtypes (large artery, cardioembolic, small vessel stroke), as well as with related phenotypes (carotid atherosclerosis, imaging markers of cerebral white matter integrity, and brain atrophy). Results Genetic predisposition to T2D and higher HbA1c levels were associated with higher risk of any ischemic stroke, large artery stroke, and small vessel stroke. Similar associations were also noted for carotid atherosclerotic plaque, fractional anisotropy, a white matter disease marker, and markers of brain atrophy. We further found associations of genetic predisposition to insulin resistance with large artery and small vessel stroke, whereas predisposition to β-cell dysfunction was associated with small vessel stroke, intracerebral hemorrhage, lower gray matter volume, and total brain volume. Conclusions This study supports causal effects of T2D and hyperglycemia on large artery and small vessel stroke. We show associations of genetically predicted insulin resistance and β-cell dysfunction with large artery and small vessel stroke that might have implications for antidiabetic treatments targeting these mechanisms. Classification of Evidence This study provides Class II evidence that genetic predisposition to T2D and higher HbA1c levels are associated with a higher risk of large artery and small vessel ischemic stroke. RELATED ARTICLE

[1]  M. Dichgans,et al.  Genetically Predicted Blood Pressure Across the Lifespan , 2020, Hypertension.

[2]  B. Lewis,et al.  2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD , 2020 .

[3]  N. Timpson,et al.  STROBE-MR: Guidelines for strengthening the reporting of Mendelian randomization studies , 2019 .

[4]  Stephen Burgess,et al.  PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations , 2019, Bioinform..

[5]  C. Sudlow,et al.  Genetically Determined Levels of Circulating Cytokines and Risk of Stroke: Role of Monocyte Chemoattractant Protein-1 , 2019, Circulation.

[6]  Mark E Bastin,et al.  Associations between vascular risk factors and brain MRI indices in UK Biobank , 2019, bioRxiv.

[7]  Andrew D. Johnson,et al.  GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes , 2018, Nature Communications.

[8]  C. Sudlow,et al.  Genome‐wide meta‐analysis identifies 3 novel loci associated with stroke , 2018, Annals of neurology.

[9]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[10]  M. Sabatine,et al.  Opportunities and Challenges in Mendelian Randomization Studies to Guide Trial Design. , 2018, JAMA cardiology.

[11]  G. Smith,et al.  Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization—A Job for the Humble Heterogeneity Statistic? , 2018, American journal of epidemiology.

[12]  Gad Getz,et al.  Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis , 2018, PLoS medicine.

[13]  H. Markus,et al.  Causal Impact of Type 2 Diabetes Mellitus on Cerebral Small Vessel Disease , 2018, Stroke.

[14]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[15]  Andrew D. Johnson,et al.  Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes , 2018, Nature Genetics.

[16]  G. Davey Smith,et al.  Problems in interpreting and using GWAS of conditional phenotypes illustrated by “alcohol GWAS” , 2018, Molecular Psychiatry.

[17]  A. Scheen,et al.  Impact of glucose-lowering therapies on risk of stroke in type 2 diabetes. , 2017, Diabetes & metabolism.

[18]  Christian Gieger,et al.  Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis , 2017, PLoS Medicine.

[19]  Fernando Pires Hartwig,et al.  Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption , 2017, bioRxiv.

[20]  G. Smith,et al.  Mendelian randomization in cardiometabolic disease: challenges in evaluating causality , 2017, Nature Reviews Cardiology.

[21]  J. Shaw,et al.  IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. , 2011, Diabetes research and clinical practice.

[22]  Iris M Heid,et al.  A multitrait GWAS sheds light on insulin resistance , 2016, Nature Genetics.

[23]  Laura Lovato,et al.  Effects of High Density Lipoprotein Raising Therapies on Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus, with or without Renal Impairment: The Action to Control Cardiovascular Risk in Diabetes Study , 2016, American Journal of Nephrology.

[24]  Ashutosh Kumar Singh,et al.  Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, The Lancet.

[25]  A. Tahrani,et al.  Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus , 2016, Nature Reviews Endocrinology.

[26]  D. Feng,et al.  Disproportionately Elevated Proinsulin Levels as an Early Indicator of β-Cell Dysfunction in Nondiabetic Offspring of Chinese Diabetic Patients , 2016, International journal of endocrinology.

[27]  L. Zhong,et al.  Effects of intensive glucose lowering in treatment of type 2 diabetes mellitus on cardiovascular outcomes: A meta-analysis of data from 58,160 patients in 13 randomized controlled trials. , 2016, International journal of cardiology.

[28]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[29]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[30]  D. Nickerson,et al.  A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians , 2014, bioRxiv.

[31]  Lisa J. Martin,et al.  Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. , 2014, American journal of human genetics.

[32]  M. Fornage,et al.  Guidelines for the Primary Prevention of Stroke: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2011, Stroke.

[33]  Mary G. George,et al.  Factors influencing the decline in stroke mortality: A statement for healthcare professionals from the American Heart Association/American Stroke Association , 2013 .

[34]  A. Butterworth,et al.  Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data , 2013, Genetic epidemiology.

[35]  T. Stijnen,et al.  Insulin Resistance and Risk of Incident Cardiovascular Events in Adults without Diabetes: Meta-Analysis , 2012, PloS one.

[36]  Tanya M. Teslovich,et al.  Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways , 2012, Nature Genetics.

[37]  E. Oetjen,et al.  Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes , 2011, Diabetes.

[38]  P. Sham,et al.  Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases , 2011, Genetic epidemiology.

[39]  G. Moneta Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies , 2011 .

[40]  Michael E. Miller,et al.  ACTION TO CONTROL CARDIOVASCULAR RISK IN DIABETES STUDY GROUP. EFFECTS OF INTENSIVE GLUCOSE LOWERING IN TYPE 2 DIABETES , 2010 .

[41]  N. Sattar,et al.  Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials , 2009, The Lancet.

[42]  S. Gabriel,et al.  The Structure of Haplotype Blocks in the Human Genome , 2002, Science.

[43]  C. Berne,et al.  Proinsulin Is an Independent Predictor of Coronary Heart Disease: Report From a 27-Year Follow-Up Study , 2002, Circulation.

[44]  David Lee Gordon,et al.  Classification of Subtype of Acute Ischemic Stroke: Definitions for Use in a Multicenter Clinical Trial , 1993, Stroke.