Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial

Ying Yu, Qian Sun, Lin-Feng Yan, Yu-Chuan Hu, Hai-Yan Nan, Yang Yang, Zhi-Cheng Liu, Wen Wang, Guang-Bin Cui, Ying Yu, Qian Sun, Lin-Feng Yan, Yu-Chuan Hu, Hai-Yan Nan, Yang Yang, Zhi-Cheng Liu, Wen Wang, Guang-Bin Cui

Abstract

Background: Type 2 diabetes mellitus (T2DM) is a risk factor for dementia. Mild cognitive impairment (MCI), an intermediary state between normal cognition and dementia, often occurs during the prodromal diabetic stage, making early diagnosis and intervention of MCI very important. Latest neuroimaging techniques revealed some underlying microstructure alterations for diabetic MCI, from certain aspects. But there still lacks an integrated multimodal MRI system to detect early neuroimaging changes in diabetic MCI patients. Thus, we intended to conduct a diagnostic trial using multimodal MRI techniques to detect early diabetic MCI that is determined by the Montreal Cognitive Assessment (MoCA).

Methods: In this study, healthy controls, prodromal diabetes and diabetes subjects (53 subjects/group) aged 40-60 years will be recruited from the physical examination center of Tangdu Hospital. The neuroimaging and psychometric measurements will be repeated at a 0.5 year-interval for 2.5 years' follow-up. The primary outcome measures are 1) Microstructural and functional alterations revealed with multimodal MRI scans including structure magnetic resonance imaging (sMRI), resting state functional magnetic resonance imaging (rs-fMRI), diffusion kurtosis imaging (DKI), and three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL); 2) Cognition evaluation with MoCA. The second outcome measures are obesity, metabolic characteristics, lifestyle and quality of life.

Discussion: The study will provide evidence for the potential use of multimodal MRI techniques with psychometric evaluation in diagnosing MCI at prodromal diabetic stage so as to help decision making in early intervention and improve the prognosis of T2DM.

Trial registration: This study has been registered to ClinicalTrials.gov ( NCT02420470 ) on April 2, 2015 and published on July 29, 2015.

Keywords: Microstructural alterations; Microvascular alterations; Mild cognitive impairment; Neuroimaging techniques; Prodromal diabetic stage; Type 2 diabetes mellitus.

Figures

Fig. 1
Fig. 1
Flowchart of the current prospective diagnostic trial

References

    1. Strachan MW, Reynolds RM, Marioni RE, Price JF. Cognitive function, dementia and type 2 diabetes mellitus in the elderly. Nat Rev Endocrinol. 2011;7(2):108–114. doi: 10.1038/nrendo.2010.228.
    1. Luchsinger JA, Reitz C, Patel B, Tang MX, Manly JJ, Mayeux R. Relation of diabetes to mild cognitive impairment. Arch Neurol. 2007;64(4):570–575. doi: 10.1001/archneur.64.4.570.
    1. Ritchie K, Carriere I, Ritchie CW, Berr C, Artero S, Ancelin ML. Designing prevention programmes to reduce incidence of dementia: prospective cohort study of modifiable risk factors. BMJ. 2010;341:c3885. doi: 10.1136/bmj.c3885.
    1. Mehrabian S, Raycheva M, Gateva A, Todorova G, Angelova P, Traykova M, Stankova T, Kamenov Z, Traykov L. Cognitive dysfunction profile and arterial stiffness in type 2 diabetes. J Neurol Sci. 2012;322(1-2):152–156. doi: 10.1016/j.jns.2012.07.046.
    1. Profenno LA, Porsteinsson AP, Faraone SV. Meta-analysis of Alzheimer’s disease risk with obesity, diabetes, and related disorders. Biol Psychiatry. 2010;67(6):505–512. doi: 10.1016/j.biopsych.2009.02.013.
    1. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256(3):183–194. doi: 10.1111/j.1365-2796.2004.01388.x.
    1. Ajilore O, Narr K, Rosenthal J, Pham D, Hamilton L, Watari K, Elderkin-Thompson V, Darwin C, Toga A, Kumar A. Regional cortical gray matter thickness differences associated with type 2 diabetes and major depression. Psychiatry Res. 2010;184(2):63–70. doi: 10.1016/j.pscychresns.2010.07.003.
    1. Brundel M, van den Heuvel M, de Bresser J, Kappelle LJ, Biessels GJ. Utrecht Diabetic Encephalopathy Study G: Cerebral cortical thickness in patients with type 2 diabetes. J Neurol Sci. 2010;299(1-2):126–130. doi: 10.1016/j.jns.2010.08.048.
    1. Kumar A, Haroon E, Darwin C, Pham D, Ajilore O, Rodriguez G, Mintz J. Gray matter prefrontal changes in type 2 diabetes detected using MRI. J Magn Reson Imaging. 2008;27(1):14–19. doi: 10.1002/jmri.21224.
    1. Hsu JL, Chen YL, Leu JG, Jaw FS, Lee CH, Tsai YF, Hsu CY, Bai CH, Leemans A. Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage. 2012;59(2):1098–1105. doi: 10.1016/j.neuroimage.2011.09.041.
    1. Hoogenboom WS, Marder TJ, Flores VL, Huisman S, Eaton HP, Schneiderman JS, Bolo NR, Simonson DC, Jacobson AM, Kubicki M, et al. Cerebral white matter integrity and resting-state functional connectivity in middle-aged patients with type 2 diabetes. Diabetes. 2014;63(2):728–738. doi: 10.2337/db13-1219.
    1. Zhang A, Ajilore O, Zhan L, Gadelkarim J, Korthauer L, Yang S, Leow A, Kumar A. White matter tract integrity of anterior limb of internal capsule in major depression and type 2 diabetes. Neuropsychopharmacology. 2013;38(8):1451–1459. doi: 10.1038/npp.2013.41.
    1. Cui Y, Jiao Y, Chen YC, Wang K, Gao B, Wen S, Ju S, Teng GJ. Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes. 2014;63(2):749–760. doi: 10.2337/db13-0519.
    1. Musen G, Jacobson AM, Bolo NR, Simonson DC, Shenton ME, McCartney RL, Flores VL, Hoogenboom WS. Resting-state brain functional connectivity is altered in type 2 diabetes. Diabetes. 2012;61(9):2375–2379. doi: 10.2337/db11-1669.
    1. Yang S, Ajilore O, Wu M, Lamar M, Kumar A. Impaired macromolecular protein pools in fronto-striato-thalamic circuits in type 2 diabetes revealed by magnetization transfer imaging. Diabetes. 2015;64(1):183–192. doi: 10.2337/db14-0316.
    1. Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, Li Y, Pirraglia E, De Santi S, Reisberg B, Wisniewski T, et al. FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009;36(5):811–822. doi: 10.1007/s00259-008-1039-z.
    1. Hauser T, Schonknecht P, Thomann PA, Gerigk L, Schroder J, Henze R, Radbruch A, Essig M. Regional cerebral perfusion alterations in patients with mild cognitive impairment and Alzheimer disease using dynamic susceptibility contrast MRI. Acad Radiol. 2013;20(6):705–711. doi: 10.1016/j.acra.2013.01.020.
    1. Zhao L, Fielden SW, Feng X, Wintermark M, Mugler JP, 3rd, Meyer CH. Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction. Neuroimage. 2015;121:205–216. doi: 10.1016/j.neuroimage.2015.07.018.
    1. Takahashi H, Ishii K, Hosokawa C, Hyodo T, Kashiwagi N, Matsuki M, Ashikaga R, Murakami T. Clinical application of 3D arterial spin-labeled brain perfusion imaging for Alzheimer disease: comparison with brain perfusion SPECT. AJNR Am J Neuroradiol. 2014;35(5):906–911. doi: 10.3174/ajnr.A3780.
    1. Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 2010;23(7):698–710. doi: 10.1002/nbm.1518.
    1. Alsop DC, Detre JA, Golay X, Gunther M, Hendrikse J, Hernandez-Garcia L, Lu H, MacIntosh BJ, Parkes LM, Smits M, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med. 2015;73(1):102–116. doi: 10.1002/mrm.25197.
    1. Wang T, Li Y, Guo X, Huang D, Ma L, Wang DJ, Lou X. Reduced perfusion in normal-appearing white matter in mild to moderate hypertension as revealed by 3D pseudocontinuous arterial spin labeling. J Magn Reson Imaging. 2016;43(3):635–43.
    1. Van den Berghe G, Schoonheydt K, Becx P, Bruyninckx F, Wouters PJ. Insulin therapy protects the central and peripheral nervous system of intensive care patients. Neurology. 2005;64(8):1348–1353. doi: 10.1212/01.WNL.0000158442.08857.FC.
    1. Yu K, Zhang S, Wang Q, Wang X, Qin Y, Wang J, Li C, Wu Y, Wang W, Lin H. Development of a computerized tool for the chinese version of the montreal cognitive assessment for screening mild cognitive impairment. Int Psychogeriatr. 2014;3:1–7.
    1. Kennan RP, Takahashi K, Pan C, Shamoon H, Pan JW. Human cerebral blood flow and metabolism in acute insulin-induced hypoglycemia. J Cereb Blood Flow Metab. 2005;25(4):527–534. doi: 10.1038/sj.jcbfm.9600045.
    1. Torres Aleman I. Role of insulin-like growth factors in neuronal plasticity and neuroprotection. Adv Exp Med Biol. 2005;567:243–258. doi: 10.1007/0-387-26274-1_10.
    1. Tsai TH, Sun CK, Su CH, Sung PH, Chua S, Zhen YY, Leu S, Chang HW, Yang JL, Yip HK. Sitagliptin attenuated brain damage and cognitive impairment in mice with chronic cerebral hypo-perfusion through suppressing oxidative stress and inflammatory reaction. J Hypertens. 2015;33(5):1001–1013. doi: 10.1097/HJH.0000000000000529.
    1. Jones DT, Machulda MM, Vemuri P, McDade EM, Zeng G, Senjem ML, Gunter JL, Przybelski SA, Avula RT, Knopman DS, et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology. 2011;77(16):1524–1531. doi: 10.1212/WNL.0b013e318233b33d.
    1. Rusinek H, Ha J, Yau PL, Storey P, Tirsi A, Tsui WH, Frosch O, Azova S, Convit A. Cerebral perfusion in insulin resistance and type 2 diabetes. J Cereb Blood Flow Metab. 2015;35(1):95–102. doi: 10.1038/jcbfm.2014.173.
    1. Schopf V, Windischberger C, Kasess CH, Lanzenberger R, Moser E. Group ICA of resting-state data: a comparison. Magma. 2010;23(5-6):317–325. doi: 10.1007/s10334-010-0212-0.
    1. De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S. Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging. 2011;29(10):1410–1416. doi: 10.1016/j.mri.2011.04.006.
    1. Lu J, Li D, Li F, Zhou A, Wang F, Zuo X, Jia XF, Song H, Jia J. Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J Geriatr Psychiatry Neurol. 2011;24(4):184–190. doi: 10.1177/0891988711422528.

Source: PubMed

3
Prenumerera