Lipid traits and type 2 diabetes risk in African ancestry individuals: A Mendelian Randomization study
Opeyemi Soremekun,
Ville Karhunen,
Yiyan He,
Skanda Rajasundaram,
Bowen Liu,
Apostolos Gkatzionis,
Chisom Soremekun,
Brenda Udosen,
Hanan Musa,
Sarah Silva,
Christopher Kintu,
Richard Mayanja,
Mariam Nakabuye,
Tafadzwa Machipisa,
Amy Mason,
Marijana Vujkovic,
Verena Zuber,
Mahmoud Soliman,
Joseph Mugisha,
Oyekanmi Nash,
Pontiano Kaleebu,
Moffat Nyirenda,
Tinashe Chikowore,
Dorothea Nitsch,
Stephen Burgess,
Dipender Gill,
Segun Fatumo
Affiliations
Opeyemi Soremekun
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Ville Karhunen
Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
Yiyan He
Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
Skanda Rajasundaram
Kellogg College, University of Oxford, Oxford, UK; Faculty of Medicine, Imperial College London, London, UK
Bowen Liu
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
Apostolos Gkatzionis
MRC Integrative Epidemiology Unit, University of Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, UK
Chisom Soremekun
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Brenda Udosen
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Hanan Musa
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Sarah Silva
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
Christopher Kintu
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Richard Mayanja
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Mariam Nakabuye
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
Tafadzwa Machipisa
Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa; Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA) & Cape Heart Institute (CHI), University of Cape Town, South Africa; Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON L8L 2X2, Canada
Amy Mason
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
Marijana Vujkovic
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Verena Zuber
Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK
Mahmoud Soliman
Discipline of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
Joseph Mugisha
MRC/UVRI and LSHTM, Entebbe, Uganda
Oyekanmi Nash
H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
Pontiano Kaleebu
MRC/UVRI and LSHTM, Entebbe, Uganda
Moffat Nyirenda
MRC/UVRI and LSHTM, Entebbe, Uganda
Tinashe Chikowore
Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
Dorothea Nitsch
Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
Stephen Burgess
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Dipender Gill
Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK; Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
Segun Fatumo
The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK; Corresponding author at: The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda.
Summary: Background: Dyslipidaemia is highly prevalent in individuals with type 2 diabetes mellitus (T2DM). Numerous studies have sought to disentangle the causal relationship between dyslipidaemia and T2DM liability. However, conventional observational studies are vulnerable to confounding. Mendelian Randomization (MR) studies (which address this bias) on lipids and T2DM liability have focused on European ancestry individuals, with none to date having been performed in individuals of African ancestry. We therefore sought to use MR to investigate the causal effect of various lipid traits on T2DM liability in African ancestry individuals. Methods: Using univariable and multivariable two-sample MR, we leveraged summary-level data for lipid traits and T2DM liability from the African Partnership for Chronic Disease Research (APCDR) (N = 13,612, 36.9% men) and from African ancestry individuals in the Million Veteran Program (Ncases = 23,305 and Ncontrols = 30,140, 87.2% men), respectively. Genetic instruments were thus selected from the APCDR after which they were clumped to obtain independent instruments. We used a random-effects inverse variance weighted method in our primary analysis, complementing this with additional sensitivity analyses robust to the presence of pleiotropy. Findings: Increased genetically proxied low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels were associated with increased T2DM liability in African ancestry individuals (odds ratio (OR) [95% confidence interval, P-value] per standard deviation (SD) increase in LDL-C = 1.052 [1.000 to 1.106, P = 0.046] and per SD increase in TC = 1.089 [1.014 to 1.170, P = 0.019]). Conversely, increased genetically proxied high-density lipoprotein cholesterol (HDL-C) was associated with reduced T2DM liability (OR per SD increase in HDL-C = 0.915 [0.843 to 0.993, P = 0.033]). The OR on T2DM per SD increase in genetically proxied triglyceride (TG) levels was 0.884 [0.773 to 1.011, P = 0.072] . With respect to lipid-lowering drug targets, we found that genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with increased T2DM liability (OR per SD decrease in genetically proxied LDL-C = 1.68 [1.03-2.72, P = 0.04]) but we did not find evidence of a relationship between genetically proxied proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and T2DM liability. Interpretation: Consistent with MR findings in Europeans, HDL-C exerts a protective effect on T2DM liability and HMGCR inhibition increases T2DM liability in African ancestry individuals. However, in contrast to European ancestry individuals, LDL-C may increase T2DM liability in African ancestry individuals. This raises the possibility of ethnic differences in the metabolic effects of dyslipidaemia in T2DM. Funding: See the Acknowledgements section for more information.
