Correlative analysis of glycemic variability and brain stracture and cognitive function in type 2 diabetic patients

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• Original Article •

CHEN Mimi ZHOU Shanlei LIU Fujun WANG Jie LUO Wei DENG Datong YU Yongqiang

Cite this article as: Chen MM, Zhou SL, Liu FJ, et al. Correlative analysis of glycemic variability and brain stracture and cognitive function in type 2 diabetic patients[J]. Chin J Magn Reson Imaging, 2021, 12(11): 46-51. DOI:10.12015/issn.1674-8034.2021.11.010.

Abstract

[Abstract] Objective To investigate the correlation between glycemic variability and brain structure and cognitive function in patients with type 2 diabetes mellitus (T2DM).Materials and Methods: Seventy-one T2DM patients in our hospital were selected, and 70 healthy controls (HC) were recruited at the same time. All participants completed MRI scans and cognitive function tests. Among them, 36 of T2DM patients completed the collection of glycemic variability data through continuous glucose monitoring. Using 3.0 T MRI to obtain 3D T1 images, the two groups of gray matter volumes were statistically compared based on the cluster level, and the gray matter volume values of different brain regions were extracted, and the partial correlation analysis was used to analyze the gray matter volume values and glycemic variability indicators and cognitive test scores, in which gender, age and education level were used as covariates.Results Compared with the control group, the volume of gray matter in the right cerebellum 4/5 area, left caudate, left thalamus, left middle frontal gyrus and left medial superior frontal gyrus were decreased and multiple cognitive test scores were reduced in the T2DM group (P＜0.05). In T2DM group, correlation between brain gray matter volume and cognitive test scores: the gray matter volume of the left caudate was negatively correlated with TMT_A (Pr=-0.276, P=0.023). The gray matter volume of the left thalamus was positively correlated with AVLT (delay) (Pr=0.251, P=0.039). The gray matter volume of the left medial superior frontal gyrus was positively correlated with DST_forward (Pr=0.258, P=0.034). Correlation between brain gray matter volume and glycemic variability index: SDBG was negatively correlated with the left caudate and the left medial superior frontal gyrus (Pr=-0.449, P=0.009; Pr=-0.376, P=0.031). Correlation between glycemic variability index and cognitive test scores: MBG was negatively correlated with SDMT and VFT (Pr=-0.357, P=0.042; Pr=-0.374, P=0.032). SDBG was negatively correlated with DST_forward (Pr=-0.465, P=0.006). CV was negatively correlated with DST_forward (Pr=-0.383, P=0.028). MODD was negatively correlated (Pr=-0.562, P=0.002). TIR was positively correlated with DST_forward (Pr=0.406, P=0.032).Conclusions T2DM patients had gray matter volume atrophy in different brain regions and cognitive function decline. We also found that the greater of the glycemic variability, the more obvious of the gray matter atrophy and the worse the cognitive level of T2DM patients.

[Keywords] type 2 diabetes mellitus；glycemic variability；magnetic resonance imaging；cognitive function

Contributor Information

CHEN Mimi¹ ZHOU Shanlei² LIU Fujun¹ WANG Jie¹ LUO Wei³ DENG Datong² YU Yongqiang^1*

¹ Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China

² Department of Endocrinology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022

³ Department of Radiology, the Chaohu Hospital Affiliated to Anhui Medical University, Chaohu 238000, China

Yu YQ, E-mail: cjr.yuyongqiang@vip.163.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS This work was part of National Natural Science Foundation of China (No. 81771817).

Received 2021-07-22

Accepted 2021-09-18

DOI: 10.12015/issn.1674-8034.2021.11.010

Text

References

[1]

Yang JP, Yang XL, Zhao DN, et al. Association of time in range, as assessed by continuous glucose monitoring, with painful diabetic polyneuropathy[J]. J Diabetes Investig, 2021. 12(5): 828-836. DOI: 10.1111/jdi.13394.

[2]

Ceriello A, Colagiuri S. International diabetes federation guideline for management of postmeal glucose: a review of recommendations[J]. Diabet Med, 2008. 25(10): 1151-1156. DOI: 10.1111/j.1464-5491.2008.02565.x.

[3]

Mitsuishi S, Nishimura R, Harashima SI, et al. The effect of novel glucose monitoring system (flash glucose monitoring) on mental well-being and treatment satisfaction in Japanese people with diabetes[J]. Adv Ther, 2018, 35(1): 72-80. DOI: 10.1007/s12325-017-0649-x.

[4]

Cao WH, Tian S, Zhang HQ, et al. Association of low-density lipoprotein receptor-related protein 1 and its rs1799986 polymorphism with mild cognitive impairment in Chinese patients with type 2 Diabetes[J]. Front Neurosci, 2020, 14: 743. DOI: 10.3389/fnins.2020.00743.

[5]

Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation[J]. Diabet Med, 1998, 15(7): 539-553. DOI: 10.1002/(sici)1096-9136(199807)15:7＜539::Aid-dia668＞3.0.Co;2-s.

[6]

Nasreddine ZS, Phillips NA, Bédirian V, et al. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment[J]. J Am Geriatr Soc, 2005, 53(4): 695-699. DOI: 10.1111/j.1532-5415.2005.53221.x.

[7]

Gudala K, Bansal D, Schifano F, et al. Diabetes mellitus and risk of dementia: a Meta-analysis of prospective observational studies[J]. J Diabetes Investig, 2013. 4(6): 640-650. DOI: 10.1111/jdi.12087.

[8]

Li C, Zuo ZW, Liu DH, et al. Type 2 diabetes mellitus may exacerbate gray matter atrophy in patients with early-onset mild cognitive impairment[J]. Front Neurosci, 2020, 14: 856. DOI: 10.3389/fnins.2020.00856.

[9]

Redel JM, DiFrancesco M, Vannest J, et al. Brain gray matter volume differences in obese youth with type 2 diabetes: a pilot study[J]. J Pediatr Endocrinol Metab, 2018, 31(3): 261-268. DOI: 10.1515/jpem-2017-0349.

[10]

de la Monte SM. Insulin resistance and neurodegeneration: progress towards the development of new therapeutics for Alzheimer's disease[J]. Drugs, 2017, 77(1): 47-65. DOI: 10.1007/s40265-016-0674-0.

[11]

Bloch K, Vanichkin A, Gil-Ad I, et al. Insulin delivery to the brain using intracranial implantation of alginate-encapsulated pancreatic islets[J]. J Tissue Eng Regen Med, 2017, 11(11): 3263-3272. DOI: 10.1002/term.2235.

[12]

Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging[J]. Neuron, 2013, 80(3): 807-815. DOI: 10.1016/j.neuron.2013.10.044.

[13]

Bruchhage MMK, Correia S, Malloy P, et al. Machine learning classification identifies cerebellar contributions to early and moderate cognitive decline in Alzheimer's disease[J]. Front Aging Neurosci, 2020, 12: 524024. DOI: 10.3389/fnagi.2020.524024.

[14]

Tang FY, Zhu DL, Ma WY, et al. Differences changes in cerebellar functional connectivity between mild cognitive impairment and Alzheimer's disease: a seed-based approach[J]. Front Neurol, 2021, 12: 645171. DOI: 10.3389/fneur.2021.645171.

[15]

Ou SQ, Wei PH, Fan XT, et al. Delineating the decussating dentato-rubro-thalamic tract and its connections in humans using diffusion spectrum imaging techniques[J]. Cerebellum, 2021, DOI: 10.1007/s12311-021-01283-2.

[16]

Ohara M, Hiromura M, Nagaike H, et al. Relationship between glucose variability evaluated by continuous glucose monitoring and clinical factors, including glucagon-stimulated insulin secretion in patients with type 2 diabetes[J]. Diabetes Res Clin Pract, 2019, 158: 107904. DOI: 10.1016/j.diabres.2019.107904.

[17]

Endocrinology Branch of Chinese Medical Association. Expert consensus on management of glycemic variability of diabetes mellitus[J]. Drug Evaluation, 2017, 14(17): 5-8, 14. DOI: 1672-2809(2017)17-0005-05.

[18]

Vergès B, Pignol E, Rouland A, et al. Glycemic variability assessment with a 14-day continuous glucose monitoring system: when and how long to measure MAGE (Mean Amplitude of Glucose Excursion) for Optimal Reliability?[J]. J Diabetes Sci Technol, 2021. [ DOI: ]. DOI: 10.1177/1932296821992060.

[19]

Zinman B, Marso SP, Poulter NR, et al. Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2)[J]. Diabetologia, 2018, 61(1): 48-57. DOI: 10.1007/s00125-017-4423-z.

[20]

Vigersky RA, McMahon C. The relationship of hemoglobin A1C to time-in-range in patients with diabetes[J]. Diabetes Technol Ther, 2019, 21(2): 81-85. DOI: 10.1089/dia.2018.0310.

[21]

Xia W, Luo Y, Chen YC, et al. Glucose fluctuations are linked to disrupted brain functional architecture and cognitive impairment[J]. J Alzheimers Dis, 2020, 74(2): 603-613. DOI: 10.3233/jad-191217.

[22]

Wang KL, Han Y, Liu CC, et al. Correlative analysis of glycemic variability and mild cognitive impairment in elderly patients with type 2 diabetes[J]. Chin J Med, 2021, 56(7): 768-772. DOI: 10.3969/j.issn.1008-1070.2021.07.022.

[23]

Ding WC, Wang LH, Yang J, et al. Relevance of cognitive level to hypoglycemia and glucose variability in type 2 diabetes mellitus[J]. Chin J Clin Res, 2019, 32(9): 1163-1167. DOI: 10.13429/j.cnki.cjcr.2019.09.003.

[24]

Guo XL, Geng CJ, Chen LH. Value of 3.0 T magnetic resonance imaging plus susceptibility weighted imaging in evaluating basal ganglia volume in patients with Parkinson's disease at different stages[J]. J Pract Clin Med, 2021, 25(11): 18-21. DOI: 10.7619/jcmp.20211242.

[25]

Wei JW, Zhang ZG, Yao ZQ, et al. Modulation of sustained attention by theta-tACS over the lateral and medial frontal cortices[J]. Neural Plast, 2021, 2021: 5573471. DOI: 10.1155/2021/5573471.

[26]

Hawkins BT, Lundeen TF, Norwood KM, et al. Increased blood-brain barrier permeability and altered tight junctions in experimental diabetes in the rat: contribution of hyperglycaemia and matrix metalloproteinases[J]. Diabetologia, 2007, 50(1): 202-211. DOI: 10.1007/s00125-006-0485-z.

[27]

Quincozes-Santos A, Bobermin LD, de Assis AM, et al. Fluctuations in glucose levels induce glial toxicity with glutamatergic, oxidative and inflammatory implications[J]. Biochim Biophys Acta Mol Basis Dis, 2017, 1863(1): 1-14. DOI: 10.1016/j.bbadis.2016.09.013.

[28]

Xue B, Wang L, Zhang Z, et al. Puerarin may protect against Schwann cell damage induced by glucose fluctuation[J]. J Nat Med, 2017, 71(3): 472-481. DOI: 10.1007/s11418-016-1067-0.

[29]

Park MH, Hong JT. Roles of NF-κB in cancer and inflammatory diseases and their therapeutic approaches[J]. Cells, 2016. 5(2). DOI: 10.3390/cells5020015.

[30]

Hsieh CF, Liu CK, Lee CT, et al. Acute glucose fluctuation impacts microglial activity, leading to inflammatory activation or self-degradation[J]. Sci Rep, 2019, 9(1): 840. DOI: 10.1038/s41598-018-37215-0.

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