Quantitative assessment of myocardial perfusion based on dynamic single-photon emission computed tomography in patients with type 2 diabetes mellitus and non-obstructive coronary artery disease

Introduction. In patients with type 2 diabetes mellitus (T2DM), myocardial ischemia often develops in the presence of non-obstructive coronary artery disease (NOCAD), indicating a leading role of coronary microvascular dysfunction (CMD). Quantitative assessment of myocardial flow reserve (MFR) using dynamic SPECT with semiconductor detectors (CZT) represents a promising yet understudied approach in this cohort.

Aim: To determine the functional characteristics of the microcirculatory bed in patients with T2DM and NOCAD using dynamic CZT-SPECT myocardial perfusion imaging. Material and methods. The study included 75 patients with NOCAD: 36 with T2DM and 39 without diabetes. All patients underwent dynamic myocardial SPECT with ⁹⁹ᵐTc-MIBI and perfusion ECG‑gated SPECT. Semi-quantitative perfusion parameters, absolute values of myocardial blood flow (MBF) at rest and during stress, and MFR were assessed.

Results. Compared with the non-diabetic group, patients with T2DM demonstrated statistically significantly higher summed perfusion defect scores (p<0.01) and higher rest MBF (p=0.004) were found, whereas stress MBF did not differ significantly. The medians of MFR and absolute increase in blood flow were lower in the T2DM group (p<0.01 and p=0.03, respectively). Multiple regression analysis confirmed T2DM as an independent predictor of elevated resting MBF.

Conclusion. For the first time, using dynamic CZT-SPECT, it was demonstrated that in patients with T2DM and NOCAD the key hemodynamic abnormality is an increase in resting MBF, which leads to a reduction in MFR and may indicate early CMD. Quantitative assessment of MBF components by dynamic CZT-SPECT expands diagnostic capabilities and may serve as a basis for cardiovascular risk stratification in this patient group.

1. Singh H., Kumar R., Singh A., et al. Type 2 diabetes and vascular complications: current perspectives on pathophysiology and management strategies. Curr. Pharm. Des. 2026, Feb. 19. DOI: 10.2174/0113816128415070251009170250. Epub ahead of print.

2. Wolsk E., Jürgens M., Schou M., et al. Coronary microvascular dysfunction and left heart filling pressures in patients with type 2 diabetes. ESC Heart Fail. 2024;11(6):3551–3558. DOI: 10.1002/ehf2.14929

3. Markova T.N., Ovchinnikova M.A., Shkodkina V.V., Belyaeva O.A. Diabetic cardiomyopathy: definition, right to exist. Diabetes Mellitus. 2025;28(4):384–393. (In Russ.) DOI: 10.14341/DM13250 EDN: QINSHK

4. Falkovskaya A.Yu., Zyubanova I.V., Manukyan M.A., et al. Hypertension and diabetes mellitus: Clinical and pathogenetic features and state-of-the-art high-tech treatment capabilities (Review). Siberian Journal of Clinical and Experimental Medicine. 2021;36(3):14–22. (In Russ.) DOI: 10.14341/DM13250 EDN: QINSHK

5. Emerging Risk Factors Collaboration, Sarwar N., Gao P., Seshasai S.R., et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–2222. DOI: 10.1016/S0140-6736(10)60484-9

6. Taqueti V.R., Solomon S.D., Shah A.M., et al. Coronary microvascular dysfunction and future risk of heart failure with preserved ejection fraction. Eur. Heart J. 2018;39(10):840–849. DOI: 10.1093/eurheartj/ehx721

7. Kibel A., Selthofer-Relatic K., Drenjancevic I., et al. Coronary microvascular dysfunction in diabetes mellitus. J. Int. Med. Res. 2017;45(6):1901–1929. DOI: 10.1177/0300060516675504.

8. Di Carli M.F. Coronary microvascular dysfunction: identification, special populations, and management strategies. Heart Fail. Clin. 2025;21(2):201–214. DOI: 10.1016/j.hfc.2025.01.002

9. Ford T.J., Ong P., Sechtem U., et al., on behalf of the COVADIS Study Group. Assessment of vascular dysfunction in patients without obstructive coronary artery disease: why, how, and when. JACC Cardiovasc. Interv. 2020;13(16):1847–1864. DOI: 10.1016/j.jcin.2020.05.052

10. Horton W.B., Barrett E.J. Microvascular dysfunction in diabetes mellitus and cardiometabolic disease. Endocr. Rev. 2021;42(1):29–55. DOI: 10.1210/endrev/bnaa025

11. Schumann C., Bourque J.M. Coronary microvascular dysfunction: filling the research gaps with careful patient selection. J. Nucl. Cardiol. 2019;26(6):1853–1856. DOI: 10.1007/s12350-018-1449-7

12. Ben-Haim S., Murthy V.L., Breault C., et al. Quantification of myocardial perfusion reserve using dynamic SPECT imaging in humans: a feasibility study. J. Nucl. Med. 2013;54(6):873–879. DOI: 10.2967/jnumed.112.109652

13. Agostini D., Roule V., Nganoa C., et al. First validation of myocardial flow reserve assessed by dynamic 99mTc-sestamibi CZT-SPECT camera: head to head comparison with 15O-water PET and fractional flow reserve in patients with suspected coronary artery disease. The WATERDAY study. Eur. J. Nucl. Med. Mol. Imaging. 2018;45(7):1079–1090. DOI: 10.1007/s00259-018-3958-7

14. Carvelli M.V., Masoli O.H., Gobbo M., et al. Prognostic impact of myocardial flow reserve assessed by cadmium-zinc-telluride single-photon emission computed tomography. J. Nucl. Cardiol. 2026:61:102774. DOI: 10.1016/j.nuclcard.2026.102774

15. Yahiro D.S., Leite L.F., Azevedo G.L., et al. Comparison of PET-CT and CZTSPECT on myocardial blood flow and flow reserve measurement: a systematic review and meta-analysis. J. Nucl. Cardiol. 2025;52:102279. DOI: 10.1016/j.nuclcard.2025.102279

16. Mochula A.V., Nalesnik E.O., Vorobyeva D.A., et al. Microvascular dysfunction assessed by dynamic SPECT has a prognostic value in patients with a first acute myocardial infarction in a 3-year follow-up study. Eur. J. Nucl. Med. Mol. Imaging. 2026;53(4):2674–2685. DOI: 10.1007/s00259-025-07615-2

17. Maltseva A.N., Mochula A.V., Grakova E.V., et al. Comparative analysis of standard indices of myocardial perfusion scintigraphy and quantitative indicators of myocardial dynamic single-photon emission computed tomography in patients with non-obstructive coronary artery disease. Russian Journal of Cardiology. 2024;29(12):6166. (In Russ.) DOI: 10.15829/1560-4071-2024-6166.

18. Lee J.E., Kim B.G., Won J.C. Molecular pathways in diabetic cardiomyopathy and the role of anti-hyperglycemic drugs beyond their glucose lowering effect. J. Lipid Atheroscler. 2025;14(1):54–76. DOI: 10.12997/jla.2025.14.1.54

19. Sørensen M.H., Bojer A.S., Pontoppidan J.R.N., et al. Reduced myocardial perfusion reserve in type 2 diabetes is caused by increased perfusion at rest and decreased maximal perfusion during stress. Diabetes Care. 2020;43(6):1285–1292. DOI: 10.2337/dc19-2172

20. Mochula A., Maltseva A., Kopeva K., et al. The influence of kinetic models and attenuation correction on cadmium-zinc-telluride single-photon emission computed tomography (CZT SPECT)-derived myocardial blood flow and reserve: correlation with invasive angiography data. J. Clin. Med. 2024;13:1271. DOI: 10.3390/jcm13051271

21. Pop-Busui R., Boulton A.J., Feldman E.L., et al. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–154. DOI: 10.2337/dc16-2042

22. Seravalle G., Grassi G. Obesity and hypertension. Pharmacol. Res. 2017;122:1–7. DOI: 10.1016/j.phrs.2017.05.013

23. Aljizeeri A., Ahmed A.I., Suliman I., et al. Incremental prognostic value of positron emission tomography-derived myocardial flow reserve in patients with and without diabetes mellitus. Eur. Heart J. Cardiovasc. Imaging. 2023;24(5):563–571. DOI: 10.1093/ehjci/jead023

24. Assante R., Mainolfi C.G., Zampella E., et al. Relation between myocardial blood flow and cardiac events in diabetic patients with suspected coronary artery disease and normal myocardial perfusion imaging. J. Nucl. Cardiol. 2021;28(4):1222–1233. DOI: 10.1007/s12350-021-02533-w

25. Vohra S., Sakul S., Agarwal V., Patel K.K. Quantification of myocardial blood flow using PET: current status, clinical applications, and future directions. Prog. Cardiovasc. Dis. 2025;93:10–20. DOI: 10.1016/j.pcad.2025.09.011