Skip to main content
Log in

Differential ischemic stroke risk linked to novel subtypes of type 2 diabetes: insights from a Mendelian randomization analysis

  • Original Article
  • Published:
Endocrine Aims and scope Submit manuscript

Abstract

Purpose

This study employs a two-sample Mendelian randomization (MR) approach to investigate the variation in ischemic stroke risk across novel subtypes of adult-onset type 2 diabetes.

Methods

Leveraging pooled genome-wide association study (GWAS) data from the Swedish ANDIS cohort, we explored the association of four newly identified type 2 diabetes subtypes—severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD)—with ischemic stroke risk. The outcome data for ischemic stroke and its three subtypes (large artery, cardioembolic, and small vessel stroke) were sourced from the MEGASTROKE Consortium. Our analysis applied multiple MR methods, focusing on the inverse-variance weighted (IVW) technique, complemented by thorough sensitivity analyses to examine heterogeneity and potential horizontal pleiotropy.

Results

Our findings reveal a significant causal relationship between the SIDD subtype and small vessel stroke (OR = 1.06, 95% CI: 1.01–1.11, p = 0.025), while no causal associations were observed for SIRD with any stroke subtype. MOD was causally linked to small vessel stroke (OR = 1.07, 95% CI: 1.02–1.12, p = 0.004) and large artery stroke (OR = 1.07, 95% CI: 1.01–1.13, p = 0.015). Similarly, MARD showed a causal relationship with small vessel stroke (OR = 1.09, 95% CI: 1.03–1.16, p = 0.006) and overall ischemic stroke risk (OR = 1.04, 95% CI: 1.01–1.08, p = 0.010).

Conclusions

Our study highlights distinct causal links between specific type 2 diabetes subtypes and ischemic stroke risks, emphasizing the importance of subtype-specific prevention and treatment strategies.

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

No datasets were generated or analysed during the current study.

Abbreviations

CES :

Cardiogenic stroke

GWAS :

Genome-wide association studies

IS :

Ischemic stroke

IVW :

Inverse variance weighted

LD :

Linkage disequilibrium

LAS :

Large artery stroke

MR :

Mendelian randomization

OR :

Odds ratios

SVS :

Small vessel stroke

SNPs :

Single nucleotide polymorphisms

SIDD :

Severe insulin-deficiencyent diabetes

SIRD :

Severe insulin-resistant diabetes

MOD :

Mild obesity-related diabetes

MARD :

Mild age-related diabetes

References

  1. D. Kuriakose, Z. Xiao, Pathophysiology and treatment of stroke: present status and future perspectives. Int. J. Mol. Sci. 21, 7609 (2020). https://doi.org/10.3390/ijms21207609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. E.S. Donkor, Stroke in the 21st century: a snapshot of the burden, epidemiology, and quality of life. Stroke Res. Treat. 2018, 3238165 (2018). https://doi.org/10.1155/2018/3238165

    Article  PubMed  PubMed Central  Google Scholar 

  3. H.S. Markus, Stroke genetics. Hum. Mol. Genet. 20, R124–131 (2011). https://doi.org/10.1093/hmg/ddr345

    Article  CAS  PubMed  Google Scholar 

  4. M. Traylor, M. Farrall, E.G. Holliday, C. Sudlow, J.C. Hopewell, Y.-C. Cheng, M. Fornage, M.A. Ikram, R. Malik, S. Bevan, U. Thorsteinsdottir, M.A. Nalls, W. Longstreth, K.L. Wiggins, S. Yadav, E.A. Parati, A.L. DeStefano, B.B. Worrall, S.J. Kittner, M.S. Khan, A.P. Reiner, A. Helgadottir, S. Achterberg, I. Fernandez-Cadenas, S. Abboud, R. Schmidt, M. Walters, W.-M. Chen, E.B. Ringelstein, M. O’Donnell, W.K. Ho, J. Pera, R. Lemmens, B. Norrving, P. Higgins, M. Benn, M. Sale, G. Kuhlenbäumer, A.S.F. Doney, A.M. Vicente, H. Delavaran, A. Algra, G. Davies, S.A. Oliveira, C.N.A. Palmer, I. Deary, H. Schmidt, M. Pandolfo, J. Montaner, C. Carty, P.I.W. de Bakker, K. Kostulas, J.M. Ferro, N.R. van Zuydam, E. Valdimarsson, B.G. Nordestgaard, A. Lindgren, V. Thijs, A. Slowik, D. Saleheen, G. Paré, K. Berger, G. Thorleifsson, A. Hofman, T.H. Mosley, B.D. Mitchell, K. Furie, R. Clarke, C. Levi, S. Seshadri, A. Gschwendtner, G.B. Boncoraglio, P. Sharma, J.C. Bis, S. Gretarsdottir, B.M. Psaty, P.M. Rothwell, J. Rosand, J.F. Meschia, K. Stefansson, M. Dichgans, H.S. Markus, Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a metaanalysis of genome-wide association studies. Lancet. Neurology. 11, 951–962 (2012). https://doi.org/10.1016/S1474-4422(12)70234-X

    Article  PubMed  Google Scholar 

  5. S.C. Larsson, A. Wallin, N. Håkansson, O. Stackelberg, M. Bäck, A. Wolk, Type 1 and type 2 diabetes mellitus and incidence of seven cardiovascular diseases. Int. J. Cardiol. 262, 66–70 (2018). https://doi.org/10.1016/j.ijcard.2018.03.099

    Article  PubMed  Google Scholar 

  6. S. Chatterjee, K. Khunti, M.J. Davies, Type 2 diabetes. Lancet 389, 2239–2251 (2017). https://doi.org/10.1016/S0140-6736(17)30058-2

    Article  CAS  PubMed  Google Scholar 

  7. A. Mahajan, J. Wessel, S.M. Willems, W. Zhao, N.R. Robertson, A.Y. Chu, W. Gan, H. Kitajima, D. Taliun, N.W. Rayner, X. Guo, Y. Lu, M. Li, R.A. Jensen, Y. Hu, S. Huo, K.K. Lohman, W. Zhang, J.P. Cook, B.P. Prins, J. Flannick, N. Grarup, V.V. Trubetskoy, J. Kravic, Y.J. Kim, D.V. Rybin, Yaghootkar, H. Müller-Nurasyid, M. Meidtner, K. Li-Gao, R. Varga, T.V. Marten, J. Li, J. Smith, A.V. An, P. Ligthart, S. Gustafsson, S. Malerba, G. Demirkan, A. Tajes, J.F. Steinthorsdottir, V. Wuttke, M. Lecoeur, C. Preuss, M. Bielak, L.F. Graff, M. Highland, H.M. Justice, A.E. Liu, D.J. Marouli, E. Peloso, G.M. Warren; H.R., ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Afaq, S., Afzal, S., Ahlqvist, E., Almgren, P., Amin, N., Bang, L.B., Bertoni, A.G., Bombieri, C., Bork-Jensen, J., Brandslund, I., Brody, J.A., Burtt, N.P., Canouil, M., Chen, Y.-D.I., Cho, Y.S., Christensen, C., Eastwood, S.V., Eckardt, K.-U., Fischer, K., Gambaro, G., Giedraitis, V., Grove, M.L., de Haan, H.G., Hackinger, S., Hai, Y., Han, S., Tybjærg-Hansen, A., Hivert, M.-F., Isomaa, B., Jäger, S., Jørgensen, M.E., Jørgensen, T., Käräjämäki, A., Kim, B.-J., Kim, S.S., Koistinen, H.A., Kovacs, P., Kriebel, J., Kronenberg, F., Läll, K., Lange, L.A., Lee, J.-J., Lehne, B., Li, H., Lin, K.-H., Linneberg, A., Liu, C.-T., Liu, J., Loh, M., Mägi, R., Mamakou, V., McKean-Cowdin, R., Nadkarni, G., Neville, M., Nielsen, S.F., Ntalla, I., Peyser, P.A., Rathmann, W., Rice, K., Rich, S.S., Rode, L., Rolandsson, O., Schönherr, S., Selvin, E., Small, K.S., Stančáková, A., Surendran, P., Taylor, K.D., Teslovich, T.M., Thorand, B., Thorleifsson, G., Tin, A., Tönjes, A., Varbo, A., Witte, D.R., Wood, A.R., Yajnik, P., Yao, J., Yengo, L., Young, R., Amouyel, P., Boeing, H., Boerwinkle, E., Bottinger, E.P., Chowdhury, R., Collins, F.S., Dedoussis, G., Dehghan, A., Deloukas, P., Ferrario, M.M., Ferrières, J., Florez, J.C., Frossard, P., Gudnason, V., Harris, T.B., Heckbert, S.R., Howson, J.M.M., Ingelsson, M., Kathiresan, S., Kee, F., Kuusisto, J., Langenberg, C., Launer, L.J., Lindgren, C.M., Männistö, S., Meitinger, T., Melander, O., Mohlke, K.L., Moitry, M., Morris, A.D., Murray, A.D., de Mutsert, R., Orho-Melander, M., Owen, K.R., Perola, M., Peters, A., Province, M.A., Rasheed, A., Ridker, P.M., Rivadineira, F., Rosendaal, F.R., Rosengren, A.H., Salomaa, V., Sheu, W.H.-H., Sladek, R., Smith, B.H., Strauch, K., Uitterlinden, A.G., Varma, R., Willer, C.J., Blüher, M., Butterworth, A.S., Chambers, J.C., Chasman, D.I., Danesh, J., van Duijn, C., Dupuis, J., Franco, O.H., Franks, P.W., Froguel, P., Grallert, H., Groop, L., Han, B.-G., Hansen, T., Hattersley, A.T., Hayward, C., Ingelsson, E., Kardia, S.L.R., Karpe, F., Kooner, J.S., Köttgen, A., Kuulasmaa, K., Laakso, M., Lin, X., Lind, L., Liu, Y., Loos, R.J.F., Marchini, J., Metspalu, A., Mook-Kanamori, D., Nordestgaard, B.G., Palmer, C.N.A., Pankow, J.S., Pedersen, O., Psaty, B.M., Rauramaa, R., Sattar, N., Schulze, M.B., Soranzo, N., Spector, T.D., Stefansson, K., Stumvoll, M., Thorsteinsdottir, U., Tuomi, T., Tuomilehto, J., Wareham, N.J., Wilson, J.G., Zeggini, E., Scott, R.A., Barroso, I., Frayling, T.M., Goodarzi, M.O., Meigs, J.B., Boehnke, M., Saleheen, D., Morris, A.P., Rotter, J.I., McCarthy, M.I., Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018). https://doi.org/10.1038/s41588-018-0084-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. P.W. Franks, M.I. McCarthy, Exposing the exposures responsible for type 2 diabetes and obesity. Science 354, 69–73 (2016). https://doi.org/10.1126/science.aaf5094

    Article  CAS  PubMed  Google Scholar 

  9. E. Ahlqvist, R.B. Prasad, L. Groop, 100 YEARS OF INSULIN: Towards improved precision and a new classification of diabetes mellitus. J. Endocrinol. 252, R59–R70 (2022). https://doi.org/10.1530/JOE-20-0596

    Article  CAS  Google Scholar 

  10. American Diabetes Association: 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 44, S15–S33 (2021). https://doi.org/10.2337/dc21-S002.

  11. T. Tuomi, N. Santoro, S. Caprio, M. Cai, J. Weng, L. Groop, The many faces of diabetes: a disease with increasing heterogeneity. Lancet. 383, 1084–1094 (2014). https://doi.org/10.1016/S0140-6736(13)62219-9

    Article  PubMed  Google Scholar 

  12. E. Ahlqvist, P. Storm, A. Käräjämäki, M. Martinell, M. Dorkhan, A. Carlsson, P. Vikman, R.B. Prasad, D.M. Aly, P. Almgren, Y. Wessman, N. Shaat, P. Spégel, H. Mulder, E. Lindholm, O. Melander, O. Hansson, U. Malmqvist, Å. Lernmark, K. Lahti, T. Forsén, T. Tuomi, A.H. Rosengren, L. Groop, Novel subgroups of adultonset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018). https://doi.org/10.1016/S2213-8587(18)30051-2

    Article  PubMed  Google Scholar 

  13. H. Tanabe, H. Saito, A. Kudo, N. Machii, H. Hirai, G. Maimaituxun, K. Tanaka, H. Masuzaki, T. Watanabe, K. Asahi, J. Kazama, M. Shimabukuro,, Factors associated with risk of diabetic complications in novel cluster-based diabetes subgroups: a japanese retrospective cohort study. J. Clinical Medicine 9, 2083 (2020). https://doi.org/10.3390/jcm9072083

    Article  Google Scholar 

  14. X. Zou, X. Zhou, Z. Zhu, L. Ji, Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 7, 9–11 (2019). https://doi.org/10.1016/S2213-8587(18)30316-4

    Article  PubMed  Google Scholar 

  15. E. Sanderson, M.M. Glymour, M.V. Holmes, H. Kang, J. Morrison, M.R. Munafò, T. Palmer, C.M. Schooling, C. Wallace, Q. Zhao, G. Davey Smith, Mendelian randomization. Nat. Rev. Methods Primers. 2, 1–21 (2022). https://doi.org/10.1038/s43586-021-00092-5

    Article  CAS  Google Scholar 

  16. V.W. Skrivankova, R.C. Richmond, B.A.R. Woolf, N.M. Davies, S.A. Swanson, T.J. VanderWeele, N.J. Timpson, J.P.T. Higgins, N. Dimou, C. Langenberg, E.W. Loder, R.M. Golub, M. Egger, G. Davey Smith, J.B. Richards, Strengthening the reporting of observational studies in epidemiology using mendelian randomization (STROBE-MR): explanation and elaboration. BMJ 375, n2233 (2021). https://doi.org/10.1136/bmj.n2233

    Article  PubMed  PubMed Central  Google Scholar 

  17. R. Malik, G. Chauhan, M. Traylor, M. Sargurupremraj, Y. Okada, A. Mishra, L. Rutten-Jacobs, A.-K. Giese, S.W. van der Laan, S. Gretarsdottir, C.D. Anderson, M. Chong, H.H.H. Adams, T. Ago, P. Almgren, P. Amouyel, H. Ay, T.M. Bartz, O.R. Benavente, S. Bevan, G.B. Boncoraglio, R.D. Brown, A.S. Butterworth, C. Carrera, C.L. Carty, D.I. Chasman, W.-M. Chen, J.W. Cole, A. Correa, I. Cotlarciuc, C. Cruchaga, J. Danesh, P.I.W. de Bakker, A.L. DeStefano, M. den Hoed, Q. Duan, S.T. Engelter, G.J. Falcone, R.F. Gottesman, R.P. Grewal, V. Gudnason, S. Gustafsson, J. Haessler, T.B. Harris, A. Hassan, A.S. Havulinna, S.R. Heckbert, E.G. Holliday, G. Howard, F.-C. Hsu, H.I. Hyacinth, M.A. Ikram, E. ingelsson, M.R. Irvin, X. Jian, J. Jimenez-Conde, J.A. Johnson, J.W. Jukema, M. Kanai, K.L. Keene, B.M. Kissela, D.O. Kleindorfer, C. Kooperberg, M. Kubo, L.A. Lange, C.D. Langefeld, C. Langenberg, L.J. Launer, J.-M. Lee, R. Lemmens, D. Leys, C.M. Lewis, W.-Y. Lin, A.G. Lindgren, E. Lorentzen, P.K. Magnusson, J. Maguire, A. Manichaikul, P.F. McArdle, J.F. Meschia, B.D. Mitchell, T.H. Mosley, M.A. Nalls, T. Ninomiya, M.J. O’Donnell, B.M. Psaty, S.L. Pulit, K. Rannikmäe, A.P. Reiner, K.M. Rexrode, K. Rice, S.S. Rich, P.M. Ridker, N.S. Rost, P.M. Rothwell, J.I. Rotter, T. Rundek, R.L. Sacco, S. Sakaue, M.M. Sale, V. Salomaa, B.R. Sapkota, R. Schmidt, C.O. Schmidt, U. Schminke, P. Sharma, A. Slowik, C.L.M. Sudlow, C. Tanislav, T. Tatlisumak, K.D. Taylor, V.N.S. Thijs, G. Thorleifsson, U. Thorsteinsdottir, S. Tiedt, S. Trompet, C. Tzourio, C.M. van Duijn, M. Walters, N.J. Wareham, S. Wassertheil-Smoller, J.G. Wilson, K.L. Wiggins, Q. Yang, S. Yusuf, J.C. Bis, T. Pastinen, A. Ruusalepp, E.E. Schadt, S. Koplev, J.L.M. Björkegren, V. Codoni, M. Civelek, N.L. Smith, D.A. Tregouet, I.E. Christophersen, C. Roselli, S.A. Lubitz, P.T. Ellinor, E.S. Tai, J.S. Kooner, N. Kato, J. He, P. van der Harst, P. Elliott, J.C. Chambers, F. Takeuchi, A.D. Johnson, D.K. Sanghera, O. Melander, C. Jern, D. Strbian, I. Fernandez-Cadenas, W.T. Longstreth, A. Rolfs, J. Hata, D. Woo, J. Rosand, G. Pare, J.C. Hopewell, D. Saleheen, K. Stefansson, B.B. Worrall, S.J. Kittner, S. Seshadri, M. Fornage, H.S. Markus, J.M.M. Howson, Y. Kamatani, S. Debette, M. Dichgans, Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018). https://doi.org/10.1038/s41588-018-0058-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  19. N. Papadimitriou, N. Dimou, K.K. Tsilidis, B. Banbury, R.M. Martin, S.J. Lewis, N. Kazmi, T.M. Robinson, D. Albanes, K. Aleksandrova, S.I. Berndt, D. Timothy Bishop, H. Brenner, D.D. Buchanan, B. Bueno-de-Mesquita, P.T. Campbell, S. Castellví-Bel, A.T. Chan, J. Chang-Claude, M. Ellingjord-Dale, J.C. Figueiredo, S.J. Gallinger, G.G. Giles, E. Giovannucci, S.B. Gruber, A. Gsur, J. Hampe, H. Hampel, S. Harlid, T.A. Harrison, M. Hoffmeister, J.L. Hopper, L. Hsu, J. María Huerta, J.R. Huyghe, M.A. Jenkins, T.O. Keku, T. Kühn, C. La Vecchia, L. Le Marchand, C.I. Li, L. Li, A. Lindblom, N.M. Lindor, B. Lynch, S.D. Markowitz, G. Masala, A.M. May, R. Milne, E. Monninkhof, L. Moreno, V. Moreno, P.A. Newcomb, K. Offit, V. Perduca, P.D.P. Pharoah, E.A. Platz, J.D. Potter, G. Rennert, E. Riboli, M.-J. Sánchez, S.L. Schmit, R.E. Schoen, G. Severi, S. Sieri, M.L. Slattery, M. Song, C.M. Tangen, S.N. Thibodeau, R.C. Travis, A. Trichopoulou, C.M. Ulrich, F.J.B. van Duijnhoven, B. Van Guelpen, P. Vodicka, E. White, A. Wolk, M.O. Woods, A.H. Wu, U. Peters, M.J. Gunter, N. Murphy, Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun. 11, 597 (2020). https://doi.org/10.1038/s41467-020-14389-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. J. Bowden, W. Spiller, F. Del Greco M, N. Sheehan, J. Thompson, C. Minelli, G. Davey Smith, Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018). https://doi.org/10.1093/ije/dyy101

    Article  PubMed  PubMed Central  Google Scholar 

  21. M. Verbanck, C.-Y. Chen, B. Neale, R. Do, Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018). https://doi.org/10.1038/s41588-018-0099-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Q. Zhao, Y. Chen, J. Wang, D.S. Small, Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization. Int. J. Epidemiol. 48, 1478–1492 (2019). https://doi.org/10.1093/ije/dyz142

    Article  PubMed  Google Scholar 

  23. S. Burgess, A. Butterworth, S.G. Thompson, Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013). https://doi.org/10.1002/gepi.21758

    Article  PubMed  PubMed Central  Google Scholar 

  24. F.D. Greco M, C. Minelli, N.A. Sheehan, J.R. Thompson, Detecting pleiotropy in Mendelian randomization studies with summary data and a continuous outcome. Sta.t Med. 34, 2926–2940 (2015). https://doi.org/10.1002/sim.6522

    Article  Google Scholar 

  25. G. Hemani, K. Tilling, G. Davey Smith, Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017). https://doi.org/10.1371/journal.pgen.1007081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. F. Giacco, M. Brownlee, Oxidative stress and diabetic complications. Circ. Res. 107, 1058–1070 (2010). https://doi.org/10.1161/CIRCRESAHA.110.223545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. M.J. Chapman, Metabolic syndrome and type 2 diabetes: lipid and physiological consequences. Diabetes Vas. Dis. Res. 4, S5–S8 (2007). https://doi.org/10.3132/dvdr.2007.050

    Article  Google Scholar 

  28. A. Guilherme, J.V. Virbasius, V. Puri, M.P. Czech, Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat. Rev. Mol. Cell. Biol. 9, 367–377 (2008). https://doi.org/10.1038/nrm2391

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. A.E. Achari, S.K. Jain, Adiponectin, a therapeutic target for obesity, diabetes, and endothelial dysfunction. Int. J. Mol. Sci. 18, 1321 (2017). https://doi.org/10.3390/ijms18061321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. I. Tabas, G. García-Cardeña, G.K. Owens, Recent insights into the cellular biology of atherosclerosis. J. Cell. Biol. 209, 13–22 (2015). https://doi.org/10.1083/jcb.201412052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. K.N. Keane, V.F. Cruzat, R. Carlessi, P.I.H. de Bittencourt, P. Newsholme, Molecular events linking oxidative stress and inflammation to insulin resistance and β-cell dysfunction. Oxid. Med. Cell. Longev. 2015, 181643 (2015). https://doi.org/10.1155/2015/181643

    Article  PubMed  PubMed Central  Google Scholar 

  32. F. Bonomini, L.F. Rodella, R. Rezzani, Metabolic syndrome, aging and involvement of oxidative stress. Aging. Dis. 6, 109–120 (2015). https://doi.org/10.14336/AD.2014.0305

    Article  PubMed  PubMed Central  Google Scholar 

  33. N. Labinskyy, P. Mukhopadhyay, J. Toth, G. Szalai, M. Veres, G. Losonczy, J.T. Pinto, P. Pacher, P. Ballabh, A. Podlutsky, S.N. Austad, A. Csiszar, Z. Ungvari, Longevity is associated with increased vascular resistance to high glucose-induced oxidative stress and inflammatory gene expression in Peromyscus leucopus. Am. J. Physiol. Heart Circ. Physiol. 296, H946–956 (2009). https://doi.org/10.1152/ajpheart.00693.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Z. Ungvari, G. Kaley, R. de Cabo, W.E. Sonntag, A. Csiszar, Mechanisms of vascular aging: new perspectives. J Gerontol A Biol Sci Med Sci 65, 1028–1041 (2010). https://doi.org/10.1093/gerona/glq113

    Article  PubMed  Google Scholar 

  35. K.-S. Hong, S. Yegiaian, M. Lee, J. Lee, J.L. Saver, Declining stroke and vascular event recurrence rates in secondary prevention trials over the past 50 years and consequences for current trial design. Circulation 123, 2111–2119 (2011). https://doi.org/10.1161/CIRCULATIONAHA.109.934786

    Article  PubMed  PubMed Central  Google Scholar 

  36. A.K. Boehme, C. Esenwa, M.S.V. Elkind, Stroke risk factors, genetics, and prevention. Circ. Res. 120, 472–495 (2017). https://doi.org/10.1161/CIRCRESAHA.116.308398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Xing, L., Peng, F., Liang, Q., Dai, X., Ren, J., Wu, H., Yang, S., Zhu, Y., Jia, L., Zhao, S.: Clinical characteristics and risk of diabetic complications in data-driven clusters among type 2 diabetes. Front. Endocrinol. 12 (2021). https://doi.org/10.3389/fendo.2021.617628.

  38. Fedotkina, O., Sulaieva, O., Ozgumus, T., Cherviakova, L., Khalimon, N., Svietleisha, T., Buldenko, T., Ahlqvist, E., Asplund, O., Groop, L., Nilsson, P.M., Lyssenko, V.: Novel reclassification of adult diabetes is useful to distinguish stages of β-cell function linked to the risk of vascular complications: the dolce study from northern ukraine. Front. Genet. 12 (2021). https://doi.org/10.3389/fgene.2021.637945.

  39. L. Bennet, C. Nilsson, D. Mansour-Aly, A. Christensson, L. Groop, E. Ahlqvist, Adult-onset diabetes in Middle Eastern immigrants to Sweden: Novel subgroups and diabetic complications—The All New Diabetes in Scania cohort diabetic complications and ethnicity. Diabetes/Metabolism Res. Rev. 37, e3419 (2021). https://doi.org/10.1002/dmrr.3419

    Article  CAS  Google Scholar 

  40. J.M. Dennis, B.M. Shields, W.E. Henley, A.G. Jones, A.T. Hattersley, Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442–451 (2019). https://doi.org/10.1016/S2213-8587(19)30087-7

    Article  PubMed  PubMed Central  Google Scholar 

  41. O.P. Zaharia, K. Strassburger, A. Strom, G.J. Bönhof, Y. Karusheva, S. Antoniou, K. Bódis, D.F. Markgraf, V. Burkart, K. Müssig, J.-H. Hwang, O. Asplund, L. Groop, E. Ahlqvist, J. Seissler, P. Nawroth, S. Kopf, S.M. Schmid, M. Stumvoll, A.F.H. Pfeiffer, S. Kabisch, S. Tselmin, H.U. Häring, D. Ziegler, O. Kuss, J. Szendroedi, M. Roden, B.-F. Belgardt, A. Buyken, J. Eckel, G. Geerling, H. Al-Hasani, C. Herder, J.-H. Hwang, A. Icks, J. Kotzka, O. Kuss, E. Lammert, D. Markgraf, K. Müssig, W. Rathmann, M. Roden, J. Szendroedi, D. Ziegler, Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 7, 684–694 (2019). https://doi.org/10.1016/S2213-8587(19)30187-1

    Article  PubMed  Google Scholar 

  42. W.K. Chung, K. Erion, J.C. Florez, A.T. Hattersley, M.-F. Hivert, C.G. Lee, M.I. McCarthy, J.J. Nolan, J.M. Norris, E.R. Pearson, L. Philipson, A.T. McElvaine, W.T. Cefalu, S.S. Rich, P.W. Franks, Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD. Diabetologia 63, 1671–1693 (2020). https://doi.org/10.1007/s00125-020-05181-w

  43. M. Noh, H. Kwon, C.H. Jung, S.U. Kwon, M.S. Kim, W.J. Lee, J.Y. Park, Y. Han, H. Kim, T.-W. Kwon, Y.-P. Cho, Impact of diabetes duration and degree of carotid artery stenosis on major adverse cardiovascular events: a single-center, retrospective, observational cohort study. Cardiovasc. Diabetol. 16, 74 (2017). https://doi.org/10.1186/s12933-017-0556-0

    Article  PubMed  PubMed Central  Google Scholar 

  44. M. Kosiborod, M.B. Gomes, A. Nicolucci, S. Pocock, W. Rathmann, M.V. Shestakova, H. Watada, I. Shimomura, H. Chen, J. Cid-Ruzafa, P. Fenici, N. Hammar, F. Surmont, F. Tang, K. Khunti, DISCOVER investigators: Vascular complications in patients with type 2 diabetes: prevalence and associated factors in 38 countries (the DISCOVER study program). Cardiovasc. Diabetol. 17, 150 (2018). https://doi.org/10.1186/s12933-018-0787-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. S. Verma, S.C. Bain, T. Monk Fries, C.D. Mazer, M.A. Nauck, R.E. Pratley, S. Rasmussen, H.A. Saevereid, B. Zinman, J.B. Buse, Duration of diabetes and cardiorenal efficacy of liraglutide and semaglutide: A post hoc analysis of the LEADER and SUSTAIN 6 clinical trials. Diabetes Obes. Metab. 21, 1745–1751 (2019). https://doi.org/10.1111/dom.13698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all the genetics consortiums for making the GWAS summary data publicly available.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhichao Ruan and Jinxi Zhao. The first draft of the manuscript was written by Zhichao Ruan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jinxi Zhao.

Ethics declarations

Conflict of interest

The 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.

Supplemenary information

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

Ruan, Z., Zhao, J. Differential ischemic stroke risk linked to novel subtypes of type 2 diabetes: insights from a Mendelian randomization analysis. Endocrine (2024). https://doi.org/10.1007/s12020-024-03842-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12020-024-03842-z

Keywords

Navigation