Skip to main content
SpringerLink
Log in

Metabolomic and genetic architecture of gestational diabetes subtypes

  • Article
  • Published:
Diabetologia Aims and scope Submit manuscript

Abstract

Aims/hypothesis

Physiological gestational diabetes mellitus (GDM) subtypes that may confer different risks for adverse pregnancy outcomes have been defined. The aim of this study was to characterise the metabolome and genetic architecture of GDM subtypes to address the hypothesis that they differ between GDM subtypes.

Methods

This was a cross-sectional study of participants in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study who underwent an OGTT at approximately 28 weeks’ gestation. GDM was defined retrospectively using International Association of Diabetes and Pregnancy Study Groups/WHO criteria, and classified as insulin-deficient GDM (insulin secretion <25th percentile with preserved insulin sensitivity) or insulin-resistant GDM (insulin sensitivity <25th percentile with preserved insulin secretion). Metabolomic analyses were performed on fasting and 1 h serum samples in 3463 individuals (576 with GDM). Genome-wide genotype data were obtained for 8067 individuals (1323 with GDM).

Results

Regression analyses demonstrated striking differences between the metabolomes for insulin-deficient or insulin-resistant GDM compared to those with normal glucose tolerance. After adjustment for covariates, 33 fasting metabolites, including 22 medium- and long-chain acylcarnitines, were uniquely associated with insulin-deficient GDM; 23 metabolites, including the branched-chain amino acids and their metabolites, were uniquely associated with insulin-resistant GDM; two metabolites (glycerol and 2-hydroxybutyrate) were associated with the same direction of association with both subtypes. Subtype differences were also observed 1 h after a glucose load. In genome-wide association studies, variants within MTNR1B (rs10830963, p=3.43×10−18, OR 1.55) and GCKR (rs1260326, p=5.17×10−13, OR 1.43) were associated with GDM. Variants in GCKR (rs1260326, p=1.36×10−13, OR 1.60) and MTNR1B (rs10830963, p=1.22×10−9, OR 1.49) demonstrated genome-wide significant association with insulin-resistant GDM; there were no significant associations with insulin-deficient GDM. The lead SNP in GCKR, rs1260326, was associated with the levels of eight of the 25 fasting metabolites that were associated with insulin-resistant GDM and ten of 41 1 h metabolites that were associated with insulin-resistant GDM.

Conclusions/interpretation

This study demonstrates that physiological GDM subtypes differ in their metabolome and genetic architecture. These findings require replication in additional cohorts, but suggest that these differences may contribute to subtype-related adverse pregnancy outcomes.

Graphical Abstract

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

AC:

Acylcarnitine

DC:

Dicarboxylate

FDR:

False discovery rate

GDM:

Gestational diabetes mellitus

GWAS:

Genome-wide association studies

HAPO:

Hyperglycemia and Adverse Pregnancy Outcome

PC:

Principal component

References

  1. Gregory EC, Ely DM (2022) Trends and characteristics in gestational diabetes: United States, 2016–2020. Natl Vital Stat Rep 71:1–15

    PubMed  Google Scholar 

  2. Sweeting A, Wong J, Murphy HR, Ross GP (2022) A clinical update on gestational diabetes mellitus. Endocr Rev 43:763–793. https://doi.org/10.1210/endrev/bnac003

    Article  PubMed  PubMed Central  Google Scholar 

  3. Benhalima K, Van Crombrugge P, Moyson C et al (2019) Characteristics and pregnancy outcomes across gestational diabetes mellitus subtypes based on insulin resistance. Diabetologia 62:2118–2128. https://doi.org/10.1007/s00125-019-4961-7

    Article  CAS  PubMed  Google Scholar 

  4. Madsen LR, Gibbons KS, Ma RCW et al (2021) Do variations in insulin sensitivity and insulin secretion in pregnancy predict differences in obstetric and neonatal outcomes? Diabetologia 64:304–312. https://doi.org/10.1007/s00125-020-05323-0

    Article  CAS  PubMed  Google Scholar 

  5. Powe CE, Allard C, Battista MC et al (2016) Heterogeneous contribution of insulin sensitivity and secretion defects to gestational diabetes mellitus. Diabetes Care 39:1052–1055. https://doi.org/10.2337/dc15-2672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang N, Song L, Sun B et al (2021) Contribution of gestational diabetes mellitus heterogeneity and prepregnancy body mass index to large-for-gestational-age infants – a retrospective case–control study. J Diabetes 13:307–317. https://doi.org/10.1111/1753-0407.13113

    Article  CAS  PubMed  Google Scholar 

  7. Immanuel J, Simmons D, Harreiter J et al (2021) Metabolic phenotypes of early gestational diabetes mellitus and their association with adverse pregnancy outcomes. Diabet Med 38:e14413. https://doi.org/10.1111/dme.14413

    Article  CAS  PubMed  Google Scholar 

  8. Selen DJ, Edelson PK, James K et al (2022) Physiological subtypes of gestational glucose intolerance and risk of adverse pregnancy outcomes. Am J Obstet Gynecol 226:241.e1-241.e14. https://doi.org/10.1016/j.ajog.2021.08.016

    Article  CAS  PubMed  Google Scholar 

  9. Powe CE, Hivert MF, Udler MS (2020) Defining heterogeneity among women with gestational diabetes mellitus. Diabetes 69:2064–2074. https://doi.org/10.2337/dbi20-0004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Liu Y, Kuang A, Bain JR et al (2021) Maternal metabolites associated with gestational diabetes mellitus and a postpartum disorder of glucose metabolism. J Clin Endocrinol Metab 106:3283–3294. https://doi.org/10.1210/clinem/dgab513

    Article  PubMed  PubMed Central  Google Scholar 

  11. Pervjakova N, Moen GH, Borges MC et al (2022) Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Hum Mol Genet 31:3377–3391. https://doi.org/10.1093/hmg/ddac050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Powe CE, Kwak SH (2020) Genetic studies of gestational diabetes and glucose metabolism in pregnancy. Curr Diab Rep 20:69. https://doi.org/10.1007/s11892-020-01355-3

    Article  PubMed  PubMed Central  Google Scholar 

  13. Metzger BE, Lowe LP, Dyer AR et al (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358:1991–2002. https://doi.org/10.1056/NEJMoa0707943

    Article  PubMed  Google Scholar 

  14. International Association of Diabetes and Pregnancy Groups (2010) International Association of Diabetes and Pregnancy Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 33:676–682. https://doi.org/10.2337/dc09-1848

    Article  CAS  Google Scholar 

  15. Radaelli T, Farrell KA, Huston-Presley L et al (2010) Estimates of insulin sensitivity using glucose and C-peptide from the hyperglycemia and adverse pregnancy outcome glucose tolerance test. Diabetes Care 33:490–494. https://doi.org/10.2337/dc09-1463

    Article  CAS  PubMed  Google Scholar 

  16. Stumvoll M, Mitrakou A, Pimenta W et al (2000) Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care 23:295–301. https://doi.org/10.2337/diacare.23.3.295

    Article  CAS  PubMed  Google Scholar 

  17. Stumvoll M, Van Haeften T, Fritsche A, Gerich J (2001) Oral glucose tolerance test indexes for insulin sensitivity and secretion based on various availabilities of sampling times. Diabetes Care 24:796–797. https://doi.org/10.2337/diacare.24.4.796

    Article  CAS  PubMed  Google Scholar 

  18. Scholtens DM, Bain JR, Reisetter AC et al (2016) Metabolic networks and metabolites underlie associations between maternal glucose during pregnancy and newborn size at birth. Diabetes 65:2039–2050. https://doi.org/10.2337/db15-1748

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chen L, Zhernakova DV, Kurilshikov A et al (2022) Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat Med 28:2333–2343. https://doi.org/10.1038/s41591-022-02014-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Benjamini Y, Yekutieli D (2001) The control of false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188. https://doi.org/10.1214/aos/1013699998

    Article  MathSciNet  Google Scholar 

  21. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9:432–441. https://doi.org/10.1093/biostatistics/kxm045

    Article  PubMed  Google Scholar 

  22. Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L (2012) The huge package for high-dimensional undirected graph estimation in R. J Mach Learn Res 13:1059–1062

    MathSciNet  PubMed  PubMed Central  Google Scholar 

  23. Csardi G, Nepusz T (2006) The igraph software package for complex network research. Interjournal Complex Systems 1695, https://igraph.org

  24. Kuang A, Hayes MG, Hivert MF, Balasubramanian R, Lowe WL Jr, Scholtens DM (2022) Network approaches to integrate analyses of genetics and metabolomics data with applications to fetal programming studies. Metabolites 12(6):512. https://doi.org/10.3390/metabo12060512

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hayes MG, Urbanek M, Hivert MF et al (2013) Identification of HKDC1 and BACE2 as genes influencing glycemic traits during pregnancy through genome-wide association studies. Diabetes 62(9):3282–3291. https://doi.org/10.2337/db12-1692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Laurie CC, Doheny KF, Mirel DB et al (2010) Quality control and quality assurance in genotypic data for genome-wide association studies. Genet Epidemiol 34:591–602. https://doi.org/10.1002/gepi.20516

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28:3326–3328. https://doi.org/10.1093/bioinformatics/bts606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Das S, Forer L, Schonherr S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287. https://doi.org/10.1038/ng.3656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Taliun D, Harris DN, Kessler MD et al (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590:290–299. https://doi.org/10.1038/s41586-021-03205-y

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang QY, You LH, Xiang LL, Zhu YT, Zeng Y (2021) Current progress in metabolomics of gestational diabetes mellitus. World J Diabetes 12:1164–1186. https://doi.org/10.4239/wjd.v12.i8.1164

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sikorski C, Azab S, de Souza RJ et al (2022) Serum metabolomic signatures of gestational diabetes in South Asian and white European women. BMJ Open Diabetes Res Care 10:e002733

    Article  PubMed  PubMed Central  Google Scholar 

  32. Karamitri A, Jockers R (2019) Melatonin in type 2 diabetes mellitus and obesity. Nat Rev Endocrinol 15:105–125. https://doi.org/10.1038/s41574-018-0130-1

    Article  CAS  PubMed  Google Scholar 

  33. Kivela A (1991) Serum melatonin during human pregnancy. Acta Endocrinol (Copenh) 124:233–237

    CAS  PubMed  Google Scholar 

  34. Ejaz H, Figaro JK, Woolner AMF, Thottakam BMV, Galley HF (2020) Maternal serum melatonin increases during pregnancy and falls immediately after delivery implicating the placenta as a major source of melatonin. Front Endocrinol (Lausanne) 11:623038. https://doi.org/10.3389/fendo.2020.623038

    Article  PubMed  Google Scholar 

  35. Lyssenko V, Nagorny CL, Erdos MR et al (2009) Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet 41:82–88. https://doi.org/10.1038/ng.288

    Article  CAS  PubMed  Google Scholar 

  36. Sparso T, Bonnefond A, Andersson E et al (2009) G-allele of intronic rs10830963 in MTNR1B confers increased risk of impaired fasting glycemia and type 2 diabetes through an impaired glucose-stimulated insulin release: studies involving 19,605 Europeans. Diabetes 58:1450–1456. https://doi.org/10.2337/db08-1660

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Vangipurapu J, Stancakova A, Pihlajamaki J et al (2011) Association of indices of liver and adipocyte insulin resistance with 19 confirmed susceptibility loci for type 2 diabetes in 6,733 non-diabetic Finnish men. Diabetologia 54:563–571. https://doi.org/10.1007/s00125-010-1977-4

    Article  CAS  PubMed  Google Scholar 

  38. Kwak SH, Kim SH, Cho YM et al (2012) A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes 61:531–541. https://doi.org/10.2337/db11-1034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu Y, Kuang A, Talbot O et al (2020) Metabolomic and genetic associations with insulin resistance in pregnancy. Diabetologia 63:1783–1795. https://doi.org/10.1007/s00125-020-05198-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Brouwers M, Jacobs C, Bast A, Stehouwer CDA, Schaper NC (2015) Modulation of glucokinase regulatory protein: a double-edged sword? Trends Mol Med 21:583–594. https://doi.org/10.1016/j.molmed.2015.08.004

    Article  CAS  PubMed  Google Scholar 

  41. Freinkel N (1980) Banting lecture 1980. Of pregnancy and progeny. Diabetes 29:1023–1035. https://doi.org/10.2337/diab.29.12.1023

    Article  CAS  PubMed  Google Scholar 

  42. Prentki M, Matschinsky FM, Madiraju SR (2013) Metabolic signaling in fuel-induced insulin secretion. Cell Metab 18:162–185. https://doi.org/10.1016/j.cmet.2013.05.018

    Article  CAS  PubMed  Google Scholar 

  43. Mitro SD, Wu J, Rahman ML et al (2021) Longitudinal plasma metabolomics profile in pregnancy – a study in an ethnically diverse U.S. pregnancy cohort. Nutrients 13(9):3080. https://doi.org/10.3390/nu13093080

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Schooneman MG, Vaz FM, Houten SM, Soeters MR (2013) Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62:1–8. https://doi.org/10.2337/db12-0466

    Article  CAS  PubMed  Google Scholar 

  45. Aichler M, Borgmann D, Krumsiek J et al (2017) N-acyl taurines and acylcarnitines cause an imbalance in insulin synthesis and secretion provoking β cell dysfunction in type 2 diabetes. Cell Metab 25:1334–1347. https://doi.org/10.1016/j.cmet.2017.04.012

    Article  CAS  PubMed  Google Scholar 

  46. Batchuluun B, Al Rijjal D, Prentice KJ et al (2018) Elevated medium-chain acylcarnitines are associated with gestational diabetes mellitus and early progression to type 2 diabetes and induce pancreatic β-cell dysfunction. Diabetes 67:885–897. https://doi.org/10.2337/db17-1150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Tanner HL, Dekker Nitert M, Callaway LK, Barrett HL (2021) Ketones in pregnancy: why is it considered necessary to avoid them and what is the evidence behind their perceived risk? Diabetes Care 44:280–289. https://doi.org/10.2337/dc20-2008

    Article  CAS  PubMed  Google Scholar 

  48. Sampson M, Lathen DR, Dallon BW et al (2017) β-hydroxybutyrate improves β-cell mitochondrial function and survival. J Insul Resist 2:2412–2785

    Google Scholar 

  49. Zhou YP, Grill V (1995) Long term exposure to fatty acids and ketones inhibits B-cell functions in human pancreatic islets of Langerhans. J Clin Endocrinol Metab 80:1584–1590. https://doi.org/10.1210/jcem.80.5.7745004

    Article  CAS  PubMed  Google Scholar 

  50. Biden TJ, Taylor KW (1983) Effects of ketone bodies on insulin release and islet-cell metabolism in the rat. Biochem J 212:371–377. https://doi.org/10.1042/bj2120371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rittig N, Svart M, Thomsen HH et al (2020) Oral D/L-3-hydroxybutyrate stimulates cholecystokinin and insulin secretion and slows gastric emptying in healthy males. J Clin Endocrinol Metab 105(10):e3597–e3605. https://doi.org/10.1210/clinem/dgaa483

    Article  Google Scholar 

  52. Gu X, Al Dubayee M, Alshahrani A et al (2020) Distinctive metabolomics patterns associated with insulin resistance and type 2 diabetes mellitus. Front Mol Biosci 7:609806. https://doi.org/10.3389/fmolb.2020.609806

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Newgard CB (2017) Metabolomics and metabolic diseases: where do we stand? Cell Metab 25:43–56. https://doi.org/10.1016/j.cmet.2016.09.018

    Article  CAS  PubMed  Google Scholar 

  54. Wang Q, Holmes MV, Davey Smith G, Ala-Korpela M (2017) Genetic support for a causal role of insulin resistance on circulating branched-chain amino acids and inflammation. Diabetes Care 40:1779–1786. https://doi.org/10.2337/dc17-1642

    Article  CAS  PubMed  Google Scholar 

  55. Mahendran Y, Jonsson A, Have CT et al (2017) Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels. Diabetologia 60:873–878. https://doi.org/10.1007/s00125-017-4222-6

    Article  CAS  PubMed  Google Scholar 

  56. Wurtz P, Soininen P, Kangas AJ et al (2012) Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36(3):648–655

    Article  PubMed  Google Scholar 

  57. Sun Y, Gao HY, Fan ZY, He Y, Yan YX (2020) Metabolomics signatures in type 2 diabetes: a systematic review and integrative analysis. J Clin Endocrinol Metab 105(4):dgz240. https://doi.org/10.1210/clinem/dgz240

    Article  PubMed  Google Scholar 

  58. Herrera E, Desoye G (2016) Maternal and fetal lipid metabolism under normal and gestational diabetic conditions. Horm Mol Biol Clin Investig 26:109–127. https://doi.org/10.1515/hmbci-2015-0025

    Article  CAS  PubMed  Google Scholar 

  59. Ginsberg HN, Zhang YL, Hernandez-Ono A (2005) Regulation of plasma triglycerides in insulin resistance and diabetes. Arch Med Res 36:232–240. https://doi.org/10.1016/j.arcmed.2005.01.005

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medicine, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Kristen Lee, M. Geoffrey Hayes & William L. Lowe Jr

  2. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Alan Kuang & Denise M. Scholtens

  3. Duke Molecular Physiology Institute, Durham, NC, USA

    James R. Bain, Michael J. Muehlbauer, Olga R. Ilkayeva & Christopher B. Newgard

  4. Department of Medicine, Duke University School of Medicine, Duke University, Durham, NC, USA

    James R. Bain, Olga R. Ilkayeva & Christopher B. Newgard

  5. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Camille E. Powe & Marie-France Hivert

  6. Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA

    Camille E. Powe

  7. Broad Institute of MIT and Harvard, Boston, MA, USA

    Camille E. Powe

  8. Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA

    Marie-France Hivert

  9. Department of Medicine, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada

    Marie-France Hivert

  10. Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada

    Marie-France Hivert

Authors
  1. Kristen Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alan Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James R. Bain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Geoffrey Hayes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael J. Muehlbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Olga R. Ilkayeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christopher B. Newgard
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Camille E. Powe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marie-France Hivert
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Denise M. Scholtens
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. William L. Lowe Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William L. Lowe Jr.

Ethics declarations

Acknowledgements

This work was presented, in part, at ENDO 2023 in Chicago, USA.

Data availability

Data are available from the corresponding author upon request.

Funding

This study was funded by NIH grants DK095963, DK117491, HD34242, HD34243, HG-004415 and R03CA211318. The study funder was not involved in the design of the study, the collection, analysis and interpretation of data or writing the report, and did not impose any restrictions regarding publication of the report.

Authors’ relationships and activities

The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.

Contribution statement

KL contributed to interpretation of the data and manuscript writing. AK contributed to analysis, interpretation of the data and manuscript writing. DMS and AK led the data analysis. JRB, MJM, MGH, ORI and CBN contributed to acquisition and interpretation of the data. M-FH and CEP contributed to interpretation of the data and manuscript writing. DMS and WLL were involved in all aspects of the study, including study design and data collection, analysis and interpretation. All authors made critical intellectual contributions to drafting and/or revising the manuscript, and all approved the final version. WLL is the guarantor of this work, and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1534 KB)

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

Lee, K., Kuang, A., Bain, J.R. et al. Metabolomic and genetic architecture of gestational diabetes subtypes. Diabetologia 67, 895–907 (2024). https://doi.org/10.1007/s00125-024-06110-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00125-024-06110-x

Keywords

Search

Navigation