Abstract
Heart disease is the leading cause of morbidity and mortality in individuals with diabetes, due largely to risks associated with ischaemic injuries such as myocardial infarction (MI). We use human population genetic data to demonstrate that classical cardiovascular disease risk biomarkers, including common measures of hyperglycaemia, do not fully account for the increased risk of post-MI mortality in patients with diabetes. This study therefore systematically evaluates glycaemic stress underpinning cardiovascular risk in diabetes. Here, we show using in vivo studies in adult male mice and in vitro models that glycaemic variability, rather than sustained hyperglycaemia alone, is a key risk factor for cardiomyocyte dysfunction and increased susceptibility to myocardial injury in diabetes. We further demonstrate that patient plasma assays can elucidate the predictive potential of glycaemic variability as a primary contributor to cardiomyocyte dysfunction and subclinical cardiac injury in diabetes. These findings provide preclinical models for mechanistic and drug discovery studies and inform strategies for managing cardiovascular outcomes in patients with diabetes.
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Data availability
The raw and processed bulk RNA sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus database (GSE279917, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE279917]). The metabolomics data generated in this study have been deposited in the MetaboLights database (MTBLS11411, [https://www.ebi.ac.uk/metabolights/MTBLS11411]). Demographic characteristics of human participants are provided in Table 1 and Supplementary Data 1. The authors affirm that all human research participants provided written informed consent for publication of the de-identified demographic and clinical summary data presented. To minimise the risk of indirect identification, individual-level data have not been included, and only summary data are presented. Full individual-level datasets containing potentially identifiable information are not publicly available, in accordance with participant consent and privacy considerations. Researchers who meet the criteria for access for academic research purposes may request these data from the corresponding author, Nathan Palpant (email: n.palpant@uq.edu.au); requests will be reviewed within 2–4 weeks and fulfilled under standard ethics oversight. Individual-level UK Biobank data are available through application to the UK Biobank resource. Custom analysis scripts, phenotype definitions, and metadata required to reanalyse the data are available from the corresponding author upon request. A table of reagents and resources related to this project is provided in Supplementary Data 2. Source data are provided with this paper.
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Acknowledgements
This work was supported by funding from the NHMRC (MRFCDDM000033 to N.P., K.S., and G.K.; 2007625 to N.P.; 2007919 and 1159959 to K.S.; 2035090 to G.K.; 2034488 to A.W.), the Ian Potter Foundation (31111380 to N.P.), and the National Heart Foundation of Australia (106721 to N.P.). We acknowledge the School of Biomedical Sciences Histology Facility at The University of Queensland for providing the excellent research environment and core facilities that enable this research. We particularly thank Ms. Tania Henderson, Dr. Jason Huang, and Dr. Darryl Whitehead for assistance with tissue sectioning, Masson’s trichrome staining, and slide scanning. We also acknowledge Dr. Sophie Shen for providing her graphic design suggestions. This work uses data provided by patients and collected by the NHS as part of their care and support.
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Y.C. conceptualised and designed the study, carried out experiments, analysed data and wrote the manuscript. M.R. contributed to the study design, establishment of in vitro glycaemic stress modelling, in vitro IRI modelling and data interpretation. J.O. contributed to in vivo modelling of GV, mouse MI/R surgery and echocardiography assessment. D.M. and S.S. contributed to the UK Biobank cohort analysis and epidemiological data interpretation. W.S. performed RNA-sequencing data processing and DE analysis on in vitro cG versus vG models. C.F. contributed to cardiac differentiation from human pluripotent stem cells, literature reviews on acute mortality and LVEF function in DM and non-DM populations. C.V., A.W. and M.T. contributed to the cardiac tissue damage chip model development and analysis. C. Tan contributed to Masson’s trichrome histology, including tissue processing and data interpretation. Z.W., D.D., T.S. and R.P. contributed to the metabolomics study. H.C. and C. Tan contributed to hiPSC differentiation into cardiac lineage and quality control. H.C., U.T. and E. Dragicevic assisted in setting up the CardioExcyte 96 contractility platform. J.S. and K.S. provided pre-diabetic HFD-induced mice and technical support in the glycaemic variability mice model. N.S. and G.K. provided Hi1a and technical support on the drug efficacy study. H.B., E.S. Dorey and K.S. provided human clinical plasma samples from patients with diabetes and assisted with patient demographic data interpretation. N.P. contributed to the experimental design, data interpretation, manuscript writing and overall project supervision.
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N.P. and G.K. are co-founders and equity holders in Infensa Bioscience, a company developing therapeutics for ischaemic heart disease, which is related to the subject matter of this study. N.S. and H.C. are employees of Infensa Bioscience. The remaining authors declare no competing interests.
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Cao, Y., Redd, M.A., Outhwaite, J.E. et al. Glycaemic variability underlies myocyte dysfunction and myocardial injury risk in diabetes. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71809-x
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DOI: https://doi.org/10.1038/s41467-026-71809-x
