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Neuroimaging methods for assessing the brain in diabetes mellitus (literature review)

https://doi.org/10.20538/1682-0363-2020-2-189-194

Abstract

Diabetes mellitus (DM) is associated with changes in the structure of the brain and deterioration of cognitive functions from mild to moderate according to neuropsychological testing. With the growing DM epidemic and the increasing number of people living to old age, cognitive dysfunctions associated with DM can have serious consequences for the future of public and practical health. Chronic hyperglycemia, severe episodes of hypoglycemia, and microvascular complications are important risk factors common for type 1 and type 2 diabetes. DM is also associated with structural and functional changes in the brain, which can be diagnosed by various types of magnetic resonance imaging (MRI) of the brain. In this review, we investigate studies conducted over the past two decades to improve the understanding of how DM effects the brain function and structure. We also describe the changes characteristic of type 1 and type 2 diabetes during standard MRI, functional MRI and proton magnetic-resonance spectroscopy (proton MRS) as well as their features.

About the Authors

M. V. Matveeva
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



Yu. G. Samoilova
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



N. G. Zhukova
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



I. V. Tolmachov
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



K. S. Brazovskiy
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



O. P. Leiman
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



N. Yu. Fimushkina
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



M. A. Rotkank
Siberian State Medical University
Russian Federation
2, Moscow Trakt, Tomsk, 634050, Russian Federation



References

1. Xia W., Chen Y., Luo Y., Zhang D., Chen H., Ma J., Yin X. Decreased spontaneous brain activity and functional connectivity in type 1 diabetic patients without microvascular complications. Cell Physiol. Biochem. 2018; 51 (6): 2694–2703. DOI: 10.1159/000495960.

2. McCall A.L. The impact of diabetes on the CNS. Diabetes. 1992; 41 (5): 557–570. DOI: 10.2337/diab.41.5.557.

3. Strachan M.W., Price J.F., Frier B.M. Diabetes, cognitive impairment, and dementia. BMJ. 2008; 336 (7634): 6. DOI: 10.1136/bmj.39386.664016.BE.

4. Gohel M.G. Evaluation of glycemic control in patients with type 2 diabetes mellitus with and without microvascular complications International. Journal of Pharma and Bio Sciences. 2013; 4 (4): 794–802.

5. Kodl C.T., Seaquist E.R. Cognitive dysfunction and diabetes mellitus. Endocr. Rev. 2008; 29 (4): 494–511. DOI: 10.1210/er.2007-0034.

6. Bangen K.J., Werhane M.L., Weigand A.J., Edmonds E.C., Delano-Wood L., Thomas K.R., Nation D.A., Evangelista N.D., Clark A.L., Liu T.T., Bondi M.W. Reduced regional cerebral blood flow relates to poorer cognition in older adults with type 2 diabetes. Front. Aging Neurosci. 2018; 10: 270. DOI: 10.3389/fnagi.2018.00270.

7. Chen Y., Liu Z., Zhang J., Zhang J., Xu K., Zhang S., Wei D., Zhan Z. Altered brain activation patterns under different working memory loads in patients with type 2 diabetes. Diabetes Care. 2014; 37 (12): 3157–3163. DOI: 10.2337/dc14-1683.

8. Bertholdo D., Watcharakorn A., Castillo M. Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin. N. Am. 2013; 23 (3): 359–380. DOI: 10.1016/j.nic.2012.10.002.

9. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005; 54 (6): 1615–1625. DOI: 10.2337/diabetes.54.6.1615

10. Brands A.M., Biessels G.J., De Haan E.H., Kappelle L.J., Kessels R.P. The effects of type 1 diabetes on cognitive performance: a meta-analysis. Diabetes Care. 2005; 28 (3): 726–735. DOI: 10.2337/diacare.28.3.726.

11. Broadley M.M., White M.J., Andrew B. A systematic review and meta-analysis of executive function performance in type 1 diabetes mellitus. Psychosom. Med. 2017; 79 (6): 684–696. DOI: 10.1097/PSY.0000000000000460.

12. Gaudieri P.A., Chen R., Greer T.F., Holmes C.S. Cognitive function in children with type 1 diabetes: a meta-analysis. Diabetes Care. 2008; 31 (9): 1892–1897. DOI: 10.2337/dc07-2132.

13. Brands A.M., Kessels R.P., Hoogma R.P., Henselmans J.M., van der Beek Boter J.W., Kappelle L.J., de Haan E.H., Biessels G.J. Cognitive performance, psychological well-being, and brain magnetic resonance imaging in older patients with type 1 diabetes. Diabetes. 2006; 55 (6): 1800–1806. DOI: 10.2337/db05-1226.

14. Northam E.A., Anderson P.J., Werther G.A., Warne G.L., Adler R.G., Andrewes D. Neuropsychological complications of IDDM in children 2 years after disease onset. Diabetes Care. 1998; 21 (3): 379–384. DOI: 10.2337/diacare.21.3.379.

15. Lyoo I.K., Yoon S., Renshaw P.F., Hwang J., Bae S., Musen G., Kim J.E., Bolo N., Jeong H.S., Simonson D.C. Network-level structural abnormalities of cerebral cortex in type 1 diabetes mellitus. PLoS One. 2013; 8 (8): e71304. DOI: 10.1371/journal.pone.0071304.

16. Van Duinkerken E., Ijzerman R.G., Klein M., Moll A.C., Snoek F.J., Scheltens P., Pouwels P.J.W., Barkhof F., Diamant M., Tijms B.M. Disrupted subject-specific gray matter network properties and cognitive dysfunction in type 1 diabetes patients with and without proliferative retinopathy. Hum. Brain Mapp. 2016; 37 (3): 1194–1208. DOI: 10.1002/hbm.23096.

17. Antenor-Dorsey J.A.V., Meyer E., Rutlin J., Perantie D.C., White N.H., Arbelaez A.M., Shimony J.S., Hershey T. White matter microstructural integrity in youth with type 1 diabetes. Diabetes. 2013; 62 (2): 581–589. DOI: 10.2337/db12-0696.

18. Musen G., Lyoo I.K., Sparks C.R., Weinger K., Hwang J., Ryan C.M., Jimerson D.C., Hennen J., Renshaw P.F., Jacobson A.M. Effects of type 1 diabetes on gray matter density as measured by voxel-based morphometry. Diabetes. 2006; 55 (2): 326–333. DOI: 10.2337/diabetes.55.02.06.db05-0520.

19. Northam E.A., Rankins D., Lin A., Wellard R.M., Pell G.S., Finch S.J., Werther G.A., Cameron F.J. Central nervous system function in youth with type 1 diabetes 12 years after disease onset. Diabetes Сare. 2009; 32 (2): 445–450. DOI: 10.2337/dc08-1657.

20. Perantie D.C., Wu J., Koller J.M., Lim A., Warren S.L., Black K.J., Sadler M., White N.H., Hershey T. Regional brain volume differences associated with hyperglycemia and severe hypoglycemia in youth with type 1 diabetes. Diabetes Care. 2007; 30 (9): 2331–2337. DOI: 10.2337/dc07-0351.

21. Hershey T., Perantie D.C., Wu J., Weaver P.M., Black K.J., White N.H. Hippocampal volumes in youth with type 1 diabetes. Diabetes. 2010; 59 (1): 236–241.

22. DOI: 10.2337/db09-1117.

23. Marzelli M.J., Mazaika P.K., Barnea-Goraly N., Hershey T., Tsalikian E., Tamborlane W., Mauras N., White N.H., Buckingham B., Beck R.W., Ruedy K.J., Kollman C., Cheng P., Reiss A.L. Diabetes research in children network (DirecNet).

24. Neuroanatomical correlates of dysglycemia in young children with type 1 diabetes. Diabetes. 2014; 63 (1): 343–353. DOI: 10.2337/db13-0179.

25. Kodl C.T., Franc D.T., Rao J.P., Anderson F.S., Thomas W., Mueller B.A., Lim K.O., Seaquist E.R. Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes. 2008; 57 (1): 3083–3089. DOI: 10.2337/db08-0724.

26. Van Duinkerken E., Schoonheim M.M., Sanz-Arigita E.J., IJzerman R.G., Moll A.C., Snoek F.J., Ryan C.M., Klein M., Diamant M., Barkhof F. Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes. 2012; 61: 1814–1821. DOI: 10.2337/db11-1358.

27. Van Duinkerken E., Ryan C.M., Schoonheim M.M., Barkhof F., Klein M., Moll A.C., Diamant M., Ijzerman R.G., Snoek F.J. Subgenual cingulate cortex functional connectivity in relation to depressive symptoms and cognitive functioning in type 1 diabetes mellitus patients. Psychosom. Med. 2016; 78: 740–749.

28. Ryan J.P., Aizenstein H.J., Orchard T.J., Ryan C.M., Saxton J.A., Fine D.F., Nunley K.A., Rosano C. Age of childhood onset in type 1 diabetes and functional brain connectivity in midlife. Psychosom. Med. 2015; 77: 622.

29. Van Duinkerken E., Schoonheim M.M., IJzerman R.G., Moll A.C., Landeira-Fernandez J., Klein M., Diamant M., Snoek F.J., Barkhof F,. Wink A.-M. Altered eigenvector centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus. Hum. Brain Mapp. 2017; 38: 3623–3636.

30. Demuru M., van Duinkerken E., Fraschini M., Marrosu F., Snoek F.J., Barkhof F., Klein M., Diamant M., Hillebrand A. Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients. Neuroimage Clin. 2014; 5: 69–76.

31. Mangia S., Kumar A.F., Moheet A.A., Roberts R.J., Eberly L.E., Seaquist E.R., Tkáč I. Neurochemical profile of patients with type 1 diabetes measured by (1)H-MRS at 4 T. J. Cereb. Blood Flow Metab. 2013; 33: 754–759. DOI: 10.1038/jcbfm.2013.13.

32. Heikkila O., Lundbom N., Timonen M., Groop P.H., Heikkinen S., Makimattila S. Hyperglycaemia is associated with changes in the regional concentrations of glucose and myo-inositol within the brain. Diabetologia. 2009; 52: 534–540. DOI: 10.1007/s00125-008-1242-2.

33. Seaquist E.R., Tkac I., Damberg G., Thomas W., Gruetter R. Brain glucose concentrations in poorly controlled diabetes mellitus as measured by high-field magnetic resonance spectroscopy. Metabolism. 2005; 54: 1008–1013. DOI: 10.1016/j.metabol.2005.02.018.

34. Sarac K., Akinci A., Alkan A., Aslan M., Baysal T., Ozcan C. Brain metabolite changes on proton magnetic resonance spectroscopy in children with poorly controlled type 1 diabetes mellitus. Neuroradiology. 2005; 47: 562–565. DOI: 10.1007/s00234-005-1387-3.

35. Biessels G.J., Staekenborg S., Brunner E., Brayne C., Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 2006; 5 (1): 64–74.

36. Exalto L.G., Whitmer R.A., Kappele L.J., Biessels G.J. An update on type 2 diabetes, vascular dementia and Alzheimer’s disease. Exp. Gerontol. 2012; 47 (11): 858–864.

37. Yates K.F., Sweat V., Yau P.L., Turchiano M.M., Convit A. Impact of metabolic syndrome on cognition and brain: a selected review of the literature. Arterioscler. Thromb. Vasc. Biol. 2012; 32 (9): 2060–2067.

38. Geijselaers S.L.C., Sep S.J.S., Stehouwer C.D.A., Biessels G.J. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015; 3 (1): 75–89. DOI: 10.1016/S2213-8587(14)70148-2. Epub 2014 Aug 24.

39. Van Harten B., deLeeuw F.E., Weinstein H.C., Scheltens P., Biessels G.J. Brain imaging in patients with diabetes: a systematic review. Diabetes Care. 2006; 29: 2539–2548.

40. De Bresser J., Tiehuis A.M., van den Berg E., Reijmer Y.D., Jongen C., Kappelle L.J., Mali W.P., Viergever M.A., Biessels G.J. On behalf of the Utrecht Diabetic Encephalopathy Study Group. Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes. Diabetes Care. 2010; 33: 1309–1314.

41. Kooistra M., Geerlings M.I., Mali W.P., Vincken K.L., van der Graaf Y., Biessels G.J. SMART-MR Study Group. Diabetes mellitus and progression of vascular brain lesions and brain atrophy in patients with symptomatic atherosclerotic disease. The SMART-MR Study. J. Neurol. Sci. 2013; 332: 69–74. DOI: 10.1016/j.jns.2013.06.019.

42. Musen G., Jacobson A.M., Bolo N.R., Simonson D.C., Shenton M.E., McCartney R.L., Flores V.L., Hoogenboom W.S. Resting-state brain functional connectivity is altered in type 2 diabetes. Diabetes. 2012; 61: 2375–2379. DOI: 10.2337/db11-1669.

43. Sinha S., Ekka M., Sharma U., Pandey R.M., Jagannathan N.R. Assessment of changes in brain metabolites in Indian patients with type-2 diabetes mellitus using proton magnetic resonance spectroscopy. BMC Res. 2014; 7: 41. DOI: 10.1186/1756-0500-7-41.

44. Sahin I., Alkan A., Keskin L., Cikim A., Karakas H.M., Firat A.K., Sigirci A. Evaluation of in vivo cerebral metabolism on proton magnetic resonance spectroscopy in patients with impaired glucose tolerance and type 2 diabetes mellitus. J. Diabetes Complications. 2008; 22 (4): 254–260. DOI: 10.1016/j.jdiacomp.2007.03.007.

45. Groeneveld O., Reijmer Y., Heinen R., Kuijf H., Koekkoek P., Janssen J., Rutten G., Kappelle L., Biessels G. Cog-Id Study Group. Brain imaging correlates of mild cognitive impairment and early dementia in patients with type 2 diabetes mellitus. Nutr. Metab. Cardiovasc. Dis. 2018; 28 (12): 1253–1260. DOI: 10.1016/j.numecd.2018.07.008.

46. Macpherson H., Formica M., Harris E., Daly R.M. Brain functional alterations in type 2 diabetes – a systematic review of fMRI studies. Front. Neuroendocrinol. 2017; 47: 34–46. DOI: 10.1016/j.yfrne.2017.07.001.

47. Rae C.D. A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem. Res. 2014; 39 (1): 1–36. DOI: 10.1007/s11064-013-1199-5.

48. Gujar S.K., Maheshwari S., Björkman-Burtscher I., Sundgren P.C. Magnetic resonance spectroscopy. J. Neuroophthalmol. 2005; 25 (3): 217–226. DOI: 10.1097/01.wno.0000177307.21081.81.

49. Ross B., Bluml S. Magnetic resonance spectroscopy of the human brain. Anat. Rec. 2001; 265 (2): 54–84. DOI: 10.1002/ar.1058.


Review

For citations:


Matveeva M.V., Samoilova Yu.G., Zhukova N.G., Tolmachov I.V., Brazovskiy K.S., Leiman O.P., Fimushkina N.Yu., Rotkank M.A. Neuroimaging methods for assessing the brain in diabetes mellitus (literature review). Bulletin of Siberian Medicine. 2020;19(2):189-194. https://doi.org/10.20538/1682-0363-2020-2-189-194

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ISSN 1682-0363 (Print)
ISSN 1819-3684 (Online)