Skip to main content
Log in

Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke

  • Original Investigation
  • Published:
Human Genetics Aims and scope Submit manuscript

Abstract

While carotid intima-media thickness (cIMT) as a noninvasive surrogate measure of atherosclerosis is widely considered a risk factor for stroke, the intrinsic link underlying cIMT and stroke has not been fully understood. We aimed to evaluate the clinical value of cIMT in stroke through the investigation of phenotypic and genetic relationships between cIMT and stroke. We evaluated phenotypic associations using observational data from UK Biobank (N = 21,526). We then investigated genetic relationships leveraging genomic data conducted in predominantly European ancestry for cIMT (N = 45,185) and any stroke (AS, Ncase/Ncontrol=40,585/406,111). Observational analyses suggested an increased hazard of stroke per one standard deviation increase in cIMT (cIMTmax-AS: hazard ratio (HR) = 1.39, 95%CI = 1.09–1.79; cIMTmean-AS: HR = 1.39, 95%CI = 1.09–1.78; cIMTmin-AS: HR = 1.32, 95%CI = 1.04–1.68). A positive global genetic correlation was observed (cIMTmax-AS: \({r}_{g}\)=0.23, P=9.44 × 10−5; cIMTmean-AS: \({r}_{g}\)=0.21, P=3.00 × 10−4; cIMTmin-AS: \({r}_{g}\)=0.16, P=6.30 × 10−3). This was further substantiated by five shared independent loci and 15 shared expression-trait associations. Mendelian randomization analyses suggested no causal effect of cIMT on stroke (cIMTmax-AS: odds ratio (OR)=1.12, 95%CI=0.97–1.28; cIMTmean-AS: OR=1.09, 95%CI=0.93–1.26; cIMTmin-AS: OR=1.03, 95%CI = 0.90–1.17). A putative association was observed for genetically predicted stroke on cIMT (AS-cIMTmax: beta=0.07, 95%CI = 0.01–0.13; AS-cIMTmean: beta=0.08, 95%CI = 0.01–0.15; AS-cIMTmin: beta = 0.08, 95%CI = 0.01–0.16) in the reverse direction MR, which attenuated to non-significant in sensitivity analysis. Our work does not find evidence supporting causal associations between cIMT and stroke. The pronounced cIMT-stroke association is intrinsic, and mostly attributed to shared genetic components. The clinical value of cIMT as a surrogate marker for stroke risk in the general population is likely limited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Data from UK Biobank is available for open access to scientific researcher (www.biobank.ac.uk). The UK Biobank analysis was conducted within the application 50538. The full GWAS summary statistics are available through the MEGASTROKE Consortium (http://megastroke.org/).

References

  • Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–525. https://doi.org/10.1093/ije/dyv080

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 40:304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  • Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Psychiatric Genomics C, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case, Control C, Duncan L, Perry JR, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241. https://doi.org/10.1038/ng.3406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG, Consortium E-I (2015) Using published data in mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30:543–552. https://doi.org/10.1007/s10654-015-0011-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Campbell BCV, Khatri P (2020) Stroke Lancet 396:129–142. https://doi.org/10.1016/S0140-6736(20)31179-X

    Article  PubMed  Google Scholar 

  • Chatterjee M, Ehrenberg A, Toska LM, Metz LM, Klier M, Krueger I, Reusswig F, Elvers M (2020) Molecular drivers of platelet activation: unraveling novel targets for anti-thrombotic and anti-thrombo-inflammatory therapy. Int J Mol Sci 21. https://doi.org/10.3390/ijms21217906

  • Cheng CY, Reich D, Haiman CA, Tandon A, Patterson N, Selvin E, Akylbekova EL, Brancati FL, Coresh J, Boerwinkle E, Altshuler D, Taylor HA, Henderson BE, Wilson JG, Kao WH (2012) African ancestry and its correlation to type 2 diabetes in African americans: a genetic admixture analysis in three U.S. population cohorts. PLoS ONE 7:e32840. https://doi.org/10.1371/journal.pone.0032840

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Den Ruijter HM, Peters SA, Anderson TJ, Britton AR, Dekker JM, Eijkemans MJ, Engstrom G, Evans GW, de Graaf J, Grobbee DE, Hedblad B, Hofman A, Holewijn S, Ikeda A, Kavousi M, Kitagawa K, Kitamura A, Koffijberg H, Lonn EM, Lorenz MW, Mathiesen EB, Nijpels G, Okazaki S, O’Leary DH, Polak JF, Price JF, Robertson C, Rembold CM, Rosvall M, Rundek T, Salonen JT, Sitzer M, Stehouwer CD, Witteman JC, Moons KG, Bots ML (2012) Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA 308:796–803. https://doi.org/10.1001/jama.2012.9630

    Article  Google Scholar 

  • Folsom AR, Yatsuya H, Psaty BM, Shahar E, Longstreth WT Jr (2011) Carotid intima-media thickness, electrocardiographic left ventricular hypertrophy, and incidence of intracerebral hemorrhage. Stroke 42:3075–3079. https://doi.org/10.1161/STROKEAHA.111.623157

    Article  PubMed  PubMed Central  Google Scholar 

  • Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, Plagnol V (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10:e1004383. https://doi.org/10.1371/journal.pgen.1004383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimaki T, Raitoharju E, Kahonen M, Seppala I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252. https://doi.org/10.1038/ng.3506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang H, Fang M, Jostins L, Umicevic Mirkov M, Boucher G, Anderson CA, Andersen V, Cleynen I, Cortes A, Crins F, D’Amato M, Deffontaine V, Dmitrieva J, Docampo E, Elansary M, Farh KK, Franke A, Gori AS, Goyette P, Halfvarson J, Haritunians T, Knight J, Lawrance IC, Lees CW, Louis E, Mariman R, Meuwissen T, Mni M, Momozawa Y, Parkes M, Spain SL, Theatre E, Trynka G, Satsangi J, van Sommeren S, Vermeire S, Xavier RJ, Weersma C, Duerr RK, Mathew RH, Rioux CG, McGovern JD, Cho DPB, Georges JH, Daly M, Barrett MJ JC (2017) Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547:173–178. https://doi.org/10.1038/nature22969

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • International Consortium for Blood Pressure Genome-Wide, Association S, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ, Pihur V, Vollenweider P, O’Reilly PF, Amin N, Bragg-Gresham JL, Teumer A, Glazer NL, Launer L, Zhao JH, Aulchenko Y, Heath S, Sober S, Parsa A, Luan J, Arora P, Dehghan A, Zhang F, Lucas G, Hicks AA, Jackson AU, Peden JF, Tanaka T, Wild SH, Rudan I, Igl W, Milaneschi Y, Parker AN, Fava C, Chambers JC, Fox ER, Kumari M, Go MJ, van der Harst P, Kao WH, Sjogren M, Vinay DG, Alexander M, Tabara Y, Shaw-Hawkins S, Whincup PH, Liu Y, Shi G, Kuusisto J, Tayo B, Seielstad M, Sim X, Nguyen KD, Lehtimaki T, Matullo G, Wu Y, Gaunt TR, Onland-Moret NC, Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Vitart V, Braund PS, Kuznetsova T, Uiterwaal CS, Adeyemo A, Palmas W, Campbell H, Ludwig B, Tomaszewski M, Tzoulaki I, Palmer ND, consortium, Consortium CA, KidneyGen CK, Aspelund C, Garcia T, Chang M, O’Connell YP, Steinle JR, Grobbee NI, Arking DE, Kardia DE, Morrison SL, Hernandez AC, Najjar D, McArdle S, Hadley WL, Brown D, Connell MJ et al (2011) JM,. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478: 103-9. https://doi.org/10.1038/nature10405

  • Kang L, Jia H, Huang B, Lu S, Chen Z, Shen J, Zou Y, Wang C, Sun Y (2021) Identification of differently expressed mRNAs in atherosclerosis reveals CDK6 is regulated by circHIPK3/miR-637 Axis and promotes cell growth in human vascular smooth muscle cells. Front Genet 12:596169. https://doi.org/10.3389/fgene.2021.596169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T, Aulchenko Y, Lumley T, Kottgen A, Vasan RS, Rivadeneira F, Eiriksdottir G, Guo X, Arking DE, Mitchell GF, Mattace-Raso FU, Smith AV, Taylor K, Scharpf RB, Hwang SJ, Sijbrands EJ, Bis J, Harris TB, Ganesh SK, O’Donnell CJ, Hofman A, Rotter JI, Coresh J, Benjamin EJ, Uitterlinden AG, Heiss G, Fox CS, Witteman JC, Boerwinkle E, Wang TJ, Gudnason V, Larson MG, Chakravarti A, Psaty BM, van Duijn CM (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41:677–687. https://doi.org/10.1038/ng.384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B (2019) WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 47:W199–W205. https://doi.org/10.1093/nar/gkz401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu M, Guan Z, Shen Q, Flinter F, Dominguez L, Ahn JW, Collier DA, O’Brien T, Shen S (2016) Ulk4 regulates neural stem Cell Pool. Stem Cells 34:2318–2331. https://doi.org/10.1002/stem.2423

    Article  CAS  PubMed  Google Scholar 

  • Luo S, Zheng N, Lang B (2022) ULK4 in Neurodevelopmental and Neuropsychiatric disorders. Front Cell Dev Biol 10:873706. https://doi.org/10.3389/fcell.2022.873706

    Article  PubMed  PubMed Central  Google Scholar 

  • Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HHH, Ago T, Almgren P, Amouyel P, Ay H, Bartz TM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD Jr., Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PIW, DeStefano AL, den Hoed M, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jimenez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O’Donnell MJ, Psaty BM, Pulit SL, Rannikmae K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM et al (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 50:524–537. https://doi.org/10.1038/s41588-018-0058-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mishra A, Malik R, Hachiya T, Jurgenson T, Namba S, Posner DC, Kamanu FK, Koido M, Le Grand Q, Shi M, He Y, Georgakis MK, Caro I, Krebs K, Liaw YC, Vaura FC, Lin K, Winsvold BS, Srinivasasainagendra V, Parodi L, Bae HJ, Chauhan G, Chong MR, Tomppo L, Akinyemi R, Roshchupkin GV, Habib N, Jee YH, Thomassen JQ, Abedi V, Carcel-Marquez J, Nygaard M, Leonard HL, Yang C, Yonova-Doing E, Knol MJ, Lewis AJ, Judy RL, Ago T, Amouyel P, Armstrong ND, Bakker MK, Bartz TM, Bennett DA, Bis JC, Bordes C, Borte S, Cain A, Ridker PM, Cho K, Chen Z, Cruchaga C, Cole JW, de Jager PL, de Cid R, Endres M, Ferreira LE, Geerlings MI, Gasca NC, Gudnason V, Hata J, He J, Heath AK, Ho YL, Havulinna AS, Hopewell JC, Hyacinth HI, Inouye M, Jacob MA, Jeon CE, Jern C, Kamouchi M, Keene KL, Kitazono T, Kittner SJ, Konuma T, Kumar A, Lacaze P, Launer LJ, Lee KJ, Lepik K, Li J, Li L, Manichaikul A, Markus HS, Marston NA, Meitinger T, Mitchell BD, Montellano FA, Morisaki T, Mosley TH, Nalls MA, Nordestgaard BG, O’Donnell MJ, Okada Y, Onland-Moret NC, Ovbiagele B, Peters A, Psaty BM, Rich SS et al (2022) Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611:115–123. https://doi.org/10.1038/s41586-022-05165-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morrison J, Knoblauch N, Marcus JH, Stephens M, He X (2020) Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet 52:740–747. https://doi.org/10.1038/s41588-020-0631-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ohira T, Shahar E, Iso H, Chambless LE, Rosamond WD, Sharrett AR, Folsom AR (2011) Carotid artery wall thickness and risk of stroke subtypes: the atherosclerosis risk in communities study. Stroke 42:397–403. https://doi.org/10.1161/STROKEAHA.110.592261

    Article  PubMed  Google Scholar 

  • Pierce BL, Ahsan H, Vanderweele TJ (2011) Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40:740–752. https://doi.org/10.1093/ije/dyq151

    Article  PubMed  Google Scholar 

  • Senis YA, Sangrar W, Zirngibl RA, Craig AW, Lee DH, Greer PA (2003) Fps/Fes and Fer non-receptor protein-tyrosine kinases regulate collagen- and ADP-induced platelet aggregation. J Thromb Haemost 1:1062–1070. https://doi.org/10.1046/j.1538-7836.2003.t01-1-00124.x

    Article  CAS  PubMed  Google Scholar 

  • Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, Krauss RM, Stephens M (2015) A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 caucasians. PLoS ONE 10:e0120758. https://doi.org/10.1371/journal.pone.0120758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779. https://doi.org/10.1371/journal.pmed.1001779

    Article  PubMed  PubMed Central  Google Scholar 

  • Traylor M, Persyn E, Tomppo L, Klasson S, Abedi V, Bakker MK, Torres N, Li L, Bell S, Rutten-Jacobs L, Tozer DJ, Griessenauer CJ, Zhang Y, Pedersen A, Sharma P, Jimenez-Conde J, Rundek T, Grewal RP, Lindgren A, Meschia JF, Salomaa V, Havulinna A, Kourkoulis C, Crawford K, Marini S, Mitchell BD, Kittner SJ, Rosand J, Dichgans M, Jern C, Strbian D, Fernandez-Cadenas I, Zand R, Ruigrok Y, Rost N, Lemmens R, Rothwell PM, Anderson CD, Wardlaw J, Lewis CM, Markus HS, Helsinki Stroke SDPI-CASG, National Institute of Neurological D, Stroke Stroke Genetics, Investigators N (2021) UDLSS, International Stroke Genetics C Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies. Lancet Neurol 20: 351–361. https://doi.org/10.1016/S1474-4422(21)00031-4

  • van den Oord SC, Sijbrands EJ, ten Kate GL, van Klaveren D, van Domburg RT, van der Steen AF, Schinkel AF (2013) Carotid intima-media thickness for cardiovascular risk assessment: systematic review and meta-analysis. Atherosclerosis 228:1–11. https://doi.org/10.1016/j.atherosclerosis.2013.01.025

    Article  CAS  PubMed  Google Scholar 

  • Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Back M, Benetos A, Biffi A, Boavida JM, Capodanno D, Cosyns B, Crawford C, Davos CH, Desormais I, Di Angelantonio E, Franco OH, Halvorsen S, Hobbs FDR, Hollander M, Jankowska EA, Michal M, Sacco S, Sattar N, Tokgozoglu L, Tonstad S, Tsioufis KP, van Dis I, van Gelder IC, Wanner C, Williams B, Societies ESCNC, Group ESCSD (2021) 2021 ESC guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J 42:3227–3337. https://doi.org/10.1093/eurheartj/ehab484

    Article  PubMed  Google Scholar 

  • Willeit P, Tschiderer L, Allara E, Reuber K, Seekircher L, Gao L, Liao X, Lonn E, Gerstein HC, Yusuf S, Brouwers FP, Asselbergs FW, van Gilst W, Anderssen SA, Grobbee DE, Kastelein JJP, Visseren FLJ, Ntaios G, Hatzitolios AI, Savopoulos C, Nieuwkerk PT, Stroes E, Walters M, Higgins P, Dawson J, Gresele P, Guglielmini G, Migliacci R, Ezhov M, Safarova M, Balakhonova T, Sato E, Amaha M, Nakamura T, Kapellas K, Jamieson LM, Skilton M, Blumenthal JA, Hinderliter A, Sherwood A, Smith PJ, van Agtmael MA, Reiss P, van Vonderen MGA, Kiechl S, Klingenschmid G, Sitzer M, Stehouwer CDA, Uthoff H, Zou ZY, Cunha AR, Neves MF, Witham MD, Park HW, Lee MS, Bae JH, Bernal E, Wachtell K, Kjeldsen SE, Olsen MH, Preiss D, Sattar N, Beishuizen E, Huisman MV, Espeland MA, Schmidt C, Agewall S, Ok E, Asci G, de Groot E, Grooteman MPC, Blankestijn PJ, Bots ML, Sweeting MJ, Thompson SG, Lorenz MW, Prog IMT, the, Proof ASG (2020) Carotid Intima-Media Thickness Progression as Surrogate Marker for Cardiovascular Risk: Meta-Analysis of 119 Clinical Trials Involving 100 667 Patients. Circulation 142: 621–642. https://doi.org/10.1161/CIRCULATIONAHA.120.046361

  • Yeung MW, Wang S, van de Vegte YJ, Borisov O, van Setten J, Snieder H, Verweij N, Said MA, van der Harst P (2022) Twenty-five novel loci for carotid intima-media thickness: a genome-wide Association study in > 45 000 individuals and Meta-analysis of > 100 000 individuals. Arterioscler Thromb Vasc Biol 42:484–501. https://doi.org/10.1161/ATVBAHA.121.317007

    Article  CAS  PubMed  Google Scholar 

  • Zhang Y, Lu Q, Ye Y, Huang K, Liu W, Wu Y, Zhong X, Li B, Yu Z, Travers BG, Werling DM, Li JJ, Zhao H (2021) SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol 22:262. https://doi.org/10.1186/s13059-021-02478-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu X, Feng T, Tayo BO, Liang J, Young JH, Franceschini N, Smith JA, Yanek LR, Sun YV, Edwards TL, Chen W, Nalls M, Fox E, Sale M, Bottinger E, Rotimi C, Consortium CB, Liu Y, McKnight B, Liu K, Arnett DK, Chakravati A, Cooper RS, Redline S (2015) Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet 96:21–36. https://doi.org/10.1016/j.ajhg.2014.11.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, Robinson MR, McGrath JJ, Visscher PM, Wray NR, Yang J (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9:224. https://doi.org/10.1038/s41467-017-02317-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank Ming Wai Yeung, PhD, University of Groningen, University Medical Center Groningen, Department of Cardiology, for making the complete GWAS summary statistics publicly available. We are grateful to the MEGASTROKE Consortium for making the complete meta-GWAS summary statistics publicly available.

Funding

This study was supported by the National Key R&D Program of China (2022YFC3600600, 2022YFC3600604), the National Natural Science Foundation of China (U22A20359, 81874283, 81673255), the Recruitment Program for Young Professionals of China, the Promotion Plan for Basic Medical Sciences and the Development Plan for Cutting-Edge Disciplines, Sichuan University, and other Projects from West China School of Public Health and West China Fourth Hospital, Sichuan University. The sponsors of this study had no role in study design, data collection, analysis, interpretation, writing of the report, or the decision for submission.

Author information

Authors and Affiliations

Authors

Contributions

B.Z. and X.J. conceived and supervised the study. W.Z., J.Z., X.W., T.F., W.L., X.L., J.C., L.Z., C.X., H.C., C.Y., P.Y., Y.W., M.T., L.C., Y.L., Y.Z., and X.W. did the analyses. W.Z., B.Z., and X.J. drafted the manuscript with significant contributions from L.Z., C.Y., Y.Y., J.L., and Z.L. All authors contributed to the interpretations of the findings, critically revised the paper, and had final responsibility for the decision to submit for publication.

Corresponding authors

Correspondence to Xia Jiang or Ben Zhang.

Ethics declarations

Ethical approval

UK Biobank participants provided written informed consent, and ethical approval was granted by the National Health Service North West Multi-Centre Research Ethics Committee (11/NW/0382). All GWAS summary statistics are publicly available, and the corresponding studies have obtained proper IRB approval and participants’ consent.

Competing of interest

All authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Zhu, J., Wu, X. et al. Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke. Hum. Genet. (2024). https://doi.org/10.1007/s00439-024-02666-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00439-024-02666-1

Keywords

Navigation