Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.

Diabetes Mellitus, Type 2 / complications Diabetic Nephropathies / metabolism Doublecortin-Like Kinases Fibrosis Genome-Wide Association Study Humans Intracellular Signaling Peptides and Proteins / genetics Kidney / metabolism Polymorphism, Single Nucleotide / genetics Protein Serine-Threonine Kinases / genetics
Diabetes complications Diabetic kidney disease Genetics Genome-wide association study; Meta-analysis; Transcriptomics

Journal

Diabetologia
ISSN: 1432-0428
Titre abrégé: Diabetologia
Pays: Germany
ID NLM: 0006777

Informations de publication

Date de publication:
09 2022
Historique:
received: 13 12 2021
accepted: 30 03 2022
pubmed: 29 6 2022
medline: 5 8 2022
entrez: 28 6 2022
Statut: ppublish

Résumé

Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m Altogether, the results point to novel genes contributing to the pathogenesis of DKD. The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

Identifiants

DOI: 10.1007/s00125-022-05735-0 PMID: 35763030 PMC: PMC9345823
pubmed: 35763030
doi: 10.1007/s00125-022-05735-0
pii: 10.1007/s00125-022-05735-0
pmc: PMC9345823
doi:

Substances chimiques

Intracellular Signaling Peptides and Proteins 0
DCLK1 protein, human EC 2.7.1.11
Doublecortin-Like Kinases EC 2.7.1.11
Protein Serine-Threonine Kinases EC 2.7.11.1

Types de publication

Journal Article Meta-Analysis Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1495-1509

Subventions

Organisme : NIDDK NIH HHS
ID : K99 DK127196
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK132299
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_22005
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : P30 DK020572
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_15025
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : R01 DK105154
Pays : United States

Informations de copyright

© 2022. The Author(s).

Références

Harjutsalo V, Thomas MC, Forsblom C, Groop P-H, FinnDiane Study Group (2018) Risk of coronary artery disease and stroke according to sex and presence of diabetic nephropathy in type 1 diabetes. Diabetes Obes Metab 20(12):2759–2767. https://doi.org/10.1111/dom.13456
doi: 10.1111/dom.13456 pubmed: 29962015
Groop PH, Thomas MC, Moran JL et al (2009) The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58(7):1651–1658
doi: 10.2337/db08-1543
World Health Organization (2020) The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death . 9 Dec 2020
Sandholm N, Van Zuydam N, Ahlqvist E et al (2017) The Genetic Landscape of Renal Complications in Type 1 Diabetes. J Am Soc Nephrol 28(2):557–574. https://doi.org/10.1681/ASN.2016020231
doi: 10.1681/ASN.2016020231 pubmed: 27647854
Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J (2004) Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes 53(9):2449–2454
doi: 10.2337/diabetes.53.9.2449
Salem RM, Todd JN, Sandholm N et al (2019) Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen. J Am Soc Nephrol 30(10):2000–2016. https://doi.org/10.1681/ASN.2019030218
doi: 10.1681/ASN.2019030218 pubmed: 31537649 pmcid: 6779358
Sandholm N, Salem RM, McKnight AJ et al (2012) New susceptibility Loci associated with kidney disease in type 1 diabetes. PLoS Genet 8(9):e1002921. https://doi.org/10.1371/journal.pgen.1002921
doi: 10.1371/journal.pgen.1002921 pubmed: 23028342 pmcid: 3447939
Sandholm N, McKnight AJ, Salem RM et al (2013) Chromosome 2q31.1 associates with ESRD in women with type 1 diabetes. J Am Soc Nephrol 24(10):1537–1543. https://doi.org/10.1681/ASN.2012111122
doi: 10.1681/ASN.2012111122 pubmed: 24029427 pmcid: 3785274
Pezzolesi MG, Poznik GD, Skupien J et al (2011) An intergenic region on chromosome 13q33.3 is associated with the susceptibility to kidney disease in type 1 and 2 diabetes. Kidney Int 80(1):105–111. https://doi.org/10.1038/ki.2011.64
doi: 10.1038/ki.2011.64 pubmed: 21412220 pmcid: 3774030
Sandholm N, Forsblom C, Makinen VP et al (2014) Genome-wide association study of urinary albumin excretion rate in patients with type 1 diabetes. Diabetologia 57(6):1143–1153. https://doi.org/10.1007/s00125-014-3202-3
doi: 10.1007/s00125-014-3202-3 pubmed: 24595857
Iyengar SK, Sedor JR, Freedman BI et al (2015) Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND). PLoS Genet 11(8):e1005352. https://doi.org/10.1371/journal.pgen.1005352
doi: 10.1371/journal.pgen.1005352 pubmed: 26305897 pmcid: 4549309
van Zuydam NR, Ahlqvist E, Sandholm N et al (2018) A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes. Diabetes 67(7):1414–1427. https://doi.org/10.2337/db17-0914
doi: 10.2337/db17-0914 pubmed: 29703844 pmcid: 6014557
Weeks EM, Ulirsch JC, Cheng NY et al (2020) Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. medRxiv 2020.09.08.20190561 (Preprint). 10 Sep 2020. Available from: https://doi.org/10.1101/2020.09.08.20190561 (accessed 21 Jun 2022)
de Leeuw CA, Mooij JM, Heskes T, Posthuma D (2015) MAGMA: generalized gene-set analysis of GWAS data. PLoS ComputBiol 11(4):e1004219. https://doi.org/10.1371/journal.pcbi.1004219
doi: 10.1371/journal.pcbi.1004219
Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S (2016) Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics. PLoS Comput Biol 12(1):e1004714. https://doi.org/10.1371/journal.pcbi.1004714
doi: 10.1371/journal.pcbi.1004714 pubmed: 26808494 pmcid: 4726509
Barbeira AN, Dickinson SP, Bonazzola R et al (2018) Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9(1):1825. https://doi.org/10.1038/s41467-018-03621-1
doi: 10.1038/s41467-018-03621-1 pubmed: 29739930 pmcid: 5940825
Qiu C, Huang S, Park J et al (2018) Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. NatMed 24(11):1721–1731. https://doi.org/10.1038/s41591-018-0194-4
doi: 10.1038/s41591-018-0194-4
Ko Y-A, Yi H, Qiu C et al (2017) Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease. Am J Hum Genet 100(6):940–953. https://doi.org/10.1016/j.ajhg.2017.05.004
doi: 10.1016/j.ajhg.2017.05.004 pubmed: 28575649 pmcid: 5473735
GTEx Consortium (2020) The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369(6509):1318–1330. https://doi.org/10.1126/science.aaz1776
doi: 10.1126/science.aaz1776
Gillies CE, Putler R, Menon R et al (2018) An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. Am J Hum Genet 103(2):232–244. https://doi.org/10.1016/j.ajhg.2018.07.004
doi: 10.1016/j.ajhg.2018.07.004 pubmed: 30057032 pmcid: 6081280
Smyth LJ, Kilner J, Nair V et al (2021) Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease: an exploratory study. ClinEpigenetics 13(1):99-021-01081–x. https://doi.org/10.1186/s13148-021-01081-x
doi: 10.1186/s13148-021-01081-x
Guan Y, Liang X, Ma Z et al (2021) A single genetic locus controls both expression of DPEP1/CHMP1A and kidney disease development via ferroptosis. Nat Commun 12(1):5078. https://doi.org/10.1038/s41467-021-25377-x
doi: 10.1038/s41467-021-25377-x pubmed: 34426578 pmcid: 8382756
Nair V, Komorowsky CV, Weil EJ et al (2018) A molecular morphometric approach to diabetic kidney disease can link structure to function and outcome. Kidney Int 93(2):439–449. https://doi.org/10.1016/j.kint.2017.08.013
doi: 10.1016/j.kint.2017.08.013 pubmed: 29054530
Looker HC, Mauer M, Saulnier P-J et al (2019) Changes in Albuminuria But Not GFR are Associated with Early Changes in Kidney Structure in Type 2 Diabetes. J Am Soc Nephrol 30(6):1049–1059. https://doi.org/10.1681/ASN.2018111166
doi: 10.1681/ASN.2018111166 pubmed: 31152118 pmcid: 6551789
Bulik-Sullivan BK, Loh PR, Finucane HK et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. NatGenet 47(3):291–295. https://doi.org/10.1038/ng.3211
doi: 10.1038/ng.3211
Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7:e34408. https://doi.org/10.7554/eLife.34408
doi: 10.7554/eLife.34408 pubmed: 29846171 pmcid: 5976434
Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965
doi: 10.1002/gepi.21965 pubmed: 27061298 pmcid: 4849733
Marouli E, Graff M, Medina-Gomez C et al (2017) Rare and low-frequency coding variants alter human adult height. Nature 542(7640):186–190. https://doi.org/10.1038/nature21039
doi: 10.1038/nature21039 pubmed: 28146470 pmcid: 5302847
Sheng X, Qiu C, Liu H et al (2020) Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease. PNAS 117(46):29013–29024. https://doi.org/10.1073/pnas.2005905117
doi: 10.1073/pnas.2005905117 pubmed: 33144501 pmcid: 7682409
Teumer A, Li Y, Ghasemi S et al (2019) Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria. Nat Commun 10(1):4130. https://doi.org/10.1038/s41467-019-11576-0
doi: 10.1038/s41467-019-11576-0 pubmed: 31511532 pmcid: 6739370
Wuttke M, Li Y, Li M et al (2019) A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51(6):957–972. https://doi.org/10.1038/s41588-019-0407-x
doi: 10.1038/s41588-019-0407-x pubmed: 31152163 pmcid: 6698888
Stanzick KJ, Li Y, Schlosser P et al (2021) Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals. Nat Commun 12(1):4350. https://doi.org/10.1038/s41467-021-24491-0
doi: 10.1038/s41467-021-24491-0 pubmed: 34272381 pmcid: 8285412
Mifsud B, Tavares-Cadete F, Young AN et al (2015) Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. NatGenet 47(6):598–606. https://doi.org/10.1038/ng.3286
doi: 10.1038/ng.3286
Wilson PC, Wu H, Kirita Y et al (2019) The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci U S A 116(39):19619–19625. https://doi.org/10.1073/pnas.1908706116
doi: 10.1073/pnas.1908706116 pubmed: 31506348 pmcid: 6765272
Ju W, Greene CS, Eichinger F et al (2013) Defining cell-type specificity at the transcriptional level in human disease. Genome Res 23(11):1862–1873. https://doi.org/10.1101/gr.155697.113
doi: 10.1101/gr.155697.113 pubmed: 23950145 pmcid: 3814886
Woroniecka KI, Park AS, Mohtat D, Thomas DB, Pullman JM, Susztak K (2011) Transcriptome analysis of human diabetic kidney disease. Diabetes 60(9):2354–2369. https://doi.org/10.2337/db10-1181
doi: 10.2337/db10-1181 pubmed: 21752957 pmcid: 3161334
Brennan EP, Mohan M, McClelland A et al (2018) Lipoxins Regulate the Early Growth Response-1 Network and Reverse Diabetic Kidney Disease. J Am Soc Nephrol 29(5):1437–1448. https://doi.org/10.1681/ASN.2017101112
doi: 10.1681/ASN.2017101112 pubmed: 29490938 pmcid: 5967780
Tartey S, Matsushita K, Vandenbon A et al (2014) Akirin2 is critical for inducing inflammatory genes by bridging IκB-ζ and the SWI/SNF complex. EMBO J 33(20):2332–2348. https://doi.org/10.15252/embj.201488447
doi: 10.15252/embj.201488447 pubmed: 25107474 pmcid: 4253523
Goto A, Matsushita K, Gesellchen V et al (2008) Akirins are highly conserved nuclear proteins required for NF-kappaB-dependent gene expression in drosophila and mice. NatImmunol 9(1):97–104
Mezzano S, Aros C, Droguett A et al (2004) NF-κB activation and overexpression of regulated genes in human diabetic nephropathy. Nephrology Dialysis Transplantation 19(10):2505–2512
doi: 10.1093/ndt/gfh207
Li Y, Chen R, Zhou Q et al (2012) LSm14A is a processing body-associated sensor of viral nucleic acids that initiates cellular antiviral response in the early phase of viral infection. Proc Natl Acad Sci U S A 109(29):11770–11775. https://doi.org/10.1073/pnas.1203405109
doi: 10.1073/pnas.1203405109 pubmed: 22745163 pmcid: 3406844
Lieberthal W, Levine JS (2009) The role of the mammalian target of rapamycin (mTOR) in renal disease. Journal of the American Society of Nephrology 20(12):2493–2502
doi: 10.1681/ASN.2008111186
Mariappan MM, Feliers D, Mummidi S, Choudhury GG, Kasinath BS (2007) High glucose, high insulin, and their combination rapidly induce laminin-beta1 synthesis by regulation of mRNA translation in renal epithelial cells. Diabetes 56(2):476–485
doi: 10.2337/db05-1334
Saeki K, Zhu M, Kubosaki A, Xie J, Lan MS, Notkins AL (2002) Targeted Disruption of the Protein Tyrosine Phosphatase-Like Molecule IA-2 Results in Alterations in Glucose Tolerance Tests and Insulin Secretion. Diabetes 51(6):1842–1850. https://doi.org/10.2337/diabetes.51.6.1842
doi: 10.2337/diabetes.51.6.1842 pubmed: 12031972
Kim SM, Theilig F, Qin Y et al (2009) Dense-core vesicle proteins IA-2 and IA-2{beta} affect renin synthesis and secretion through the {beta}-adrenergic pathway. Am J Physiol Renal Physiol 296(2):F382–F389. https://doi.org/10.1152/ajprenal.90543.2008
doi: 10.1152/ajprenal.90543.2008 pubmed: 19019914
Sheng J, Li H, Dai Q et al (2018) NR4A1 Promotes Diabetic Nephropathy by Activating Mff-Mediated Mitochondrial Fission and Suppressing Parkin-Mediated Mitophagy. Cell Physiol Biochem 48(4):1675–1693. https://doi.org/10.1159/000492292
doi: 10.1159/000492292 pubmed: 30077998
Voight BF, Peloso GM, Orho-Melander M et al (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380(9841):572–580. https://doi.org/10.1016/S0140-6736(12)60312-2
doi: 10.1016/S0140-6736(12)60312-2 pubmed: 22607825 pmcid: 3419820
Todd JN, Dahlstrom EH, Salem RM et al (2015) Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease. Diabetes 64(12):4238–4246. https://doi.org/10.2337/db15-0254
doi: 10.2337/db15-0254 pubmed: 26307587 pmcid: 4657582
Ahola AJ, Sandholm N, Forsblom C et al (2017) The serum uric acid concentration is not causally linked to diabetic nephropathy in type 1 diabetes. Kidney Int 91(5):1178–1185
doi: 10.1016/j.kint.2016.11.025
Jordan DM, Choi HK, Verbanck M et al (2019) No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study. PLoS medicine 16(1):e1002725
doi: 10.1371/journal.pmed.1002725

Auteurs

Niina Sandholm (N)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
ORCID: 0000-0003-4322-6942

Joanne B Cole (JB)

Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
ORCID: 0000-0001-9520-2788

Viji Nair (V)

Michigan Medicine, Ann Arbor, MI, USA.

Xin Sheng (X)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
ORCID: 0000-0002-3620-3577

Hongbo Liu (H)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
ORCID: 0000-0002-0733-8616

Emma Ahlqvist (E)

Department of Clinical Sciences, Lund University Diabetes Centre, Lund University and Skåne University Hospital, Malmö, Sweden.
ORCID: 0000-0002-6513-2384

Natalie van Zuydam (N)

Pat Macpherson Centre for Pharmacogenetics & Pharmacogenomics, Cardiovascular & Diabetes Medicine, School of Medicine, University of Dundee, Dundee, UK.
Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
ORCID: 0000-0002-9809-1398

Emma H Dahlström (EH)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
ORCID: 0000-0003-1271-8696

Damian Fermin (D)

Michigan Medicine, Ann Arbor, MI, USA.

Laura J Smyth (LJ)

Molecular Epidemiology Research Group, Centre for Public Health, Queen's University Belfast, Belfast, UK.
ORCID: 0000-0001-6419-4826

Rany M Salem (RM)

Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
ORCID: 0000-0001-8816-6862

Carol Forsblom (C)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
ORCID: 0000-0002-3354-7454

Erkka Valo (E)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
ORCID: 0000-0003-0672-554X

Valma Harjutsalo (V)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Finnish Institute for Health and Welfare, Helsinki, Finland.
ORCID: 0000-0003-3568-9779

Eoin P Brennan (EP)

Diabetes Complications Research Centre, Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland.
ORCID: 0000-0003-4908-5474

Gareth J McKay (GJ)

Molecular Epidemiology Research Group, Centre for Public Health, Queen's University Belfast, Belfast, UK.
ORCID: 0000-0001-8197-6280

Darrell Andrews (D)

Diabetes Complications Research Centre, Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland.
ORCID: 0000-0002-3452-6858

Ross Doyle (R)

Diabetes Complications Research Centre, Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland.
ORCID: 0000-0002-0169-4572

Helen C Looker (HC)

Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.

Robert G Nelson (RG)

Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.
ORCID: 0000-0001-7307-3786

Colin Palmer (C)

Pat Macpherson Centre for Pharmacogenetics & Pharmacogenomics, Cardiovascular & Diabetes Medicine, School of Medicine, University of Dundee, Dundee, UK.
ORCID: 0000-0002-6415-6560

Amy Jayne McKnight (AJ)

Molecular Epidemiology Research Group, Centre for Public Health, Queen's University Belfast, Belfast, UK.
ORCID: 0000-0002-7482-709X

Catherine Godson (C)

Diabetes Complications Research Centre, Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland.
ORCID: 0000-0003-0655-1041

Alexander P Maxwell (AP)

Molecular Epidemiology Research Group, Centre for Public Health, Queen's University Belfast, Belfast, UK.
Regional Nephrology Unit, Belfast City Hospital, Belfast, Northern Ireland, UK.
ORCID: 0000-0002-6110-7253

Leif Groop (L)

Department of Clinical Sciences, Lund University Diabetes Centre, Lund University and Skåne University Hospital, Malmö, Sweden.
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
ORCID: 0000-0002-0187-3263

Mark I McCarthy (MI)

Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Matthias Kretzler (M)

Michigan Medicine, Ann Arbor, MI, USA.
ORCID: 0000-0003-4064-0582

Katalin Susztak (K)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
ORCID: 0000-0002-1005-3726

Joel N Hirschhorn (JN)

Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.
Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.
Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.

Jose C Florez (JC)

Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
ORCID: 0000-0002-1730-9325

Per-Henrik Groop (PH)

Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland. per-henrik.groop@helsinki.fi.
Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. per-henrik.groop@helsinki.fi.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. per-henrik.groop@helsinki.fi.
Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia. per-henrik.groop@helsinki.fi.
ORCID: 0000-0003-4055-6954

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Maladies endocriniennes › Diabète › Diabète de type 2 › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Maladies endocriniennes › Diabète › Complications du diabète › Néphropathies diabétiques › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines et peptides de signalisation intracellulaire › Protein-Serine-Threonine Kinases › Kinases de type doublecortine › Genome-wide meta-analysis and omics...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Fibrose › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Épidémiologie moléculaire › Étude d'association pangénomique › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines et peptides de signalisation intracellulaire › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Appareil urogénital › Voies urinaires › Rein › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Genome-wide meta-analysis and omics...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines et peptides de signalisation intracellulaire › Protein-Serine-Threonine Kinases › Genome-wide meta-analysis and omics...