Neuroscience Letters 562 (2014) 1–6
Contents lists available at ScienceDirect
Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Gray matter volume abnormalities in type 2 diabetes mellitus with and without mild cognitive impairment夽 Yanwei Zhang a,d,1 , Xiao Zhang b,1 , Jiuquan Zhang a , Chen Liu a , Qiaoying Yuan c , Xuntao Yin a , Luqing Wei a , Jinguo Cui d , Ran Tao a,d , Ping Wei b,∗ , Jian Wang a,∗∗ a
Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China c Department of Gerontology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China d Department of Radiology, Bethune International Peace Hospital of People’s Liberty Army, Shijiazhuang 050082, Hebei Province, China b
h i g h l i g h t s • Widespread GM atrophy was found in T2DM patients both with and without MCI. • MTL atrophy was found with the occurrence of the MCI in T2DM patient. • MTG atrophy might be associated with an increased risk for MCI in T2DM.
a r t i c l e
i n f o
Article history: Received 25 September 2013 Received in revised form 12 December 2013 Accepted 4 January 2014 Keywords: Type 2 diabetes mellitus Mild cognitive impairment Voxel-based morphometry Gray matter
a b s t r a c t This study sought to evaluate the potential brain gray matter (GM) volume changes that occur with the transition from normal cognition to mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) using voxel-based morphometry (VBM). VBM analyses of brain GM based on magnetic resonance imaging (MRI) data were performed on 28 T2DM patients with MCI, 25 T2DM patients without MCI, 28 MCI patients and 29 healthy controls (HC). Compared with the HC, the T2DM patients both with and without MCI showed significantly decreased total GM volume. Furthermore, the VBM results indicated that the T2DM patients without MCI exhibited extensively decreased GM volume compared with the HC in certain brain regions, including the superior and middle temporal gyrus (MTG), the superior and medial frontal gyrus and the middle occipital gyrus. In addition to more extensive GM atrophy in the aforementioned brain regions, the medial temporal lobe also exhibited GM loss in the T2DM patients with MCI. Furthermore, relative to the patients without MCI, only the left MTG exhibited a lower GM volume in the T2DM patients with MCI, which was positively correlated with the total MoCA score (r = 0.699, P < 0.01). Finally, relative to MCI, the left MTG atrophy was also found in the T2DM patients with MCI. Our findings suggest that MTG atrophy was associated with an increased risk for MCI in T2DM patients. The brain structural changes in many brain regions may underlie the transition from normal cognition to MCI in T2DM patients. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
夽 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. ∗ Corresponding author at: Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China. Tel.: +86 23 6876 5213; fax: +86 23 6876 5713. ∗∗ Corresponding author at: Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China. Tel.: +86 23 6876 5419; fax: +86 23 6546 3026. E-mail addresses: [email protected] (P. Wei), [email protected] (J. Wang). 1 Co-first author. 0304-3940/$ – see front matter © 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neulet.2014.01.006
Diabetes can result in progressive damage to the central nervous system. Approximately 10.8–17.5% of diabetic patients develop cognitive dysfunction [5], including mild cognitive impairment (MCI) and dementia [4]. It has been reported that type 2 diabetes mellitus (T2DM) is associated with deficits in learning and memory [20], information processing speed, attention and executive function [25]. MCI has attracted increasing research interest as it represents the transitional state between normal aging and Alzheimer’s disease (AD) [22]. Previous study has shown that T2DM patients have a significantly increased risk of MCI [10]. It is now widely accepted that MCI is the most important at-risk state
2
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
for AD and may result in elevated mortality in diabetic patients [14,29]. Thus, research on the pathophysiological mechanisms of MCI related to T2DM is crucial for the prompt treatment and improved prognosis of T2DM patients. Magnetic resonance imaging (MRI) has become a novel and widely used technique to investigate the pathogenesis of various neuropsychiatric disorders. Previous structural MRI studies revealed atrophy of the gray matter (GM) in extensive brain regions on T2DM [8,31], most consistently in the middle temporal gyrus (MTG). However, results for other brain regions are inconsistent. For example, the medial temporal lobe (MTL) was found to be atrophied [12,16], or unchanged [8]. MTG [28] and MTL [9] may play the central role in cognitive decline associated with T2DM. The aforementioned studies mainly focused on non-demented T2DM patients, including both with and without MCI. Thus, this discordance could be due to the cognitive status in T2DM. It is necessary to explore the potential brain GM alterations of T2DM with or without MCI relative to the normal controls. Voxel-based morphometry (VBM) [2] is a nonbiased and fully automated whole-brain technique for indirect volumetry using voxel-by-voxel analysis. VBM has been widely used in characterizing subtle changes in brain GM structure in a variety of diseases associated with neurological and psychiatric dysfunction, such as AD and MCI [13,15]. The present study divided T2DM patients into non-MCI and MCI groups to explore potential GM volume changes using VBM analysis. In addition, the relationships between potential GM volume changes and cognitive impairment were also investigated.
2. Materials and methods 2.1. Participants Three age-, gender- and education-matched groups of participants were studied, including 28 T2DM patients with MCI, 25 T2DM patients without MCI and 29 healthy controls (HC). The patients with T2DM were diagnosed using the criteria recommended by the American Diabetes Association (ADA)-2010 [3]. The Beijing version of the Montreal Cognitive Assessment (MoCA) was applied to assess the cognitive situation of each participant as a brief screening tool for MCI [27]. The diagnosis of MCI was based on the criteria established in the 2006 European Alzheimer’s Disease Consortium [23], which include complaints of hypomnesis, mini mental state exam (MMSE) score > 24, MoCA score < 26, clinical dementia rating (CDR) ≥ 0.5 and normal activities of daily living (ADL) score. We composed a control group of 29 HC with no history or symptoms of diabetes, psychiatric or neurologic disease, and MoCA ≥ 26. All of the participants were right-handed and underwent the MMSE to exclude dementia. All patients underwent clinical and biochemical tests. The MRI scans were performed within the first 24 h after diagnosis. All of the patients were recruited from the general population in Departments of Endocrinology and Gerontology of Southwest Hospital. Participants with history of known stroke, dementia, alcoholism, head injury, Parkinson’s disease, epilepsy, major depression (excluded by the Hamilton depression rating scale) or other neurological or psychiatric illness (excluded by clinical assessment and case history), major medical illness (e.g., cancer, anemia, diabetic ketoacidosis and thyroid dysfunction), white matter hyperintensity lesions on MRI and severe visual or hearing loss were excluded from the study. This study protocol was approved by the Ethics Committee of Southwest Hospital, Chongqing, China. All participants were advised of the experimental objectives, procedures, and possible
risks before MRI scan. Written informed consent was obtained from each participant prior to the study. 2.2. MRI acquisition All imaging data were obtained using a Siemens 3-T TIM Trio MRI system (Erlangen, Germany) equipped with the standard eight-channel head coil. For each participant, conventional brain T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images were obtained to exclude organic disease and white matter (WM) hyperintensity lesions. All MR images were assessed by two experienced radiologists. Highresolution sagittal T1-weighted structural images were acquired using a three-dimensional (3D) magnetization-prepared rapidacquisition gradient echo (MPRAGE) sequence with the following parameters: TR = 1900 ms, TE = 2.52 ms, flip angle (FA) = 9◦ , slice thickness = 1 mm, matrix = 256 × 256, voxel size = 1 × 1 × 1 mm3 and 176 slices. 2.3. VBM data analysis The T1 weighted images were processed and examined using the VBM8 toolbox [1] (http://dbm.neuro.uni-jena.de/vbm8/) with default parameters running in the statistical parametric mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm). The processing steps were briefly presented as follows: (1) All T1-weighted brain structural images were spatially normalized into the identical Montreal Neurological Institute (MNI) coordinate system on a voxel-by-voxel basis [2]. (2) All standardized brain structural images were effectively segmented into GM, WM and cerebrospinal fluid (CSF) using the new Segment and Dartel modules included in SPM8. (3) The segmented, modulated GM images were smoothed with an 8-mm full-width at half-maximum (FWHM) isotropic Gaussian kernel. Global measures of GM, WM and CSF volumes were calculated from the modulated normalized segmented images. The total intracranial volume (ICV) was calculated from the sum of the global measures of the three tissue types. Voxel-by-voxel comparisons of GM volumes between groups were performed using two-sample t-tests based on the general linear model in SPM8 (age and gender as confounding covariates). We calculated the positive contrasts (HC > T2DM without MCI, HC > T2DM with MCI, T2DM without MCI > T2DM with MCI, MCI > T2DM with MCI) and the negative contrasts. The threshold level for the statistical significance of between-group differences among the three groups was set at P < 0.05 using the AlphaSim correction (with a combination of a threshold of P < 0.001 and a minimum cluster size of 151 voxels). This correction was conducted using the AlphaSim program in the REST software [7]. 2.4. Statistical analysis 2.4.1. Demographic and clinical characteristics analysis Statistical analysis was performed using the PASW version 18.0 statistical software package (PASW for Windows, Chicago, IL, USA). The differences in demographic and clinical characteristics between patients and controls were analyzed using one-way ANOVA and the two-tailed t-test, and Chi-square tests were used for gender differences. A P-value less than 0.05 was considered statistically significant. 2.4.2. Total GM volume comparisons Statistical differences in the total GM volume among the groups were estimated by an ANCOVA model with age, gender, education
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
3
Table 1 Demographic and clinical characteristics of the participants.
Sex (male/female) Age (years) Education (years) Duration of diabetes (years) Age at diagnosis (years) HbA1c (%) BMI (kg/m2 ) WHR MMSE MoCA WM volume
Control (n = 29)
Type 2 diabetes without MCI (n = 25)
Type 2 diabetes with MCI (n = 28)
MCI (n = 28)
t/F/2
p
12/17 55.48(9.09) 11.48(3.87) NA NA NA NA NA 28.37(1.61) 27.48(1.33) 534.97(68.79)
14/11 52.24(9.25) 12.98(3.28) 6.44(5.34) 45.8(9.34) 7.39(1.39) 24.29(3.54) 0.9373(0.0502) 27.84(1.80) 26.68(1.11) 536.23(67.20)
8/20 56.21(7.69) 11.29(3.93) 8.18(5.97) 48.04(7.43) 7.78(1.59) 24.35(3.92) 1.2839(1.7964) 27.53(1.71) 16.43(3.38) 508.76(61.64)
10/18 56.64(9.10) 11.21(3.76) NA NA NA NA NA 27.39(1.57) 21.25(3.38) 507.68(41.58)
2 = 4.428 F = 1.329 F = 1.285 t = −1.111 t = −0.969 t = −0.930 t = −0.063 t = −0.963 F = 1.954 2 = 88.436 F = 1.863
0.219 0.269 0.283 0.272 0.337 0.357 0.950 0.340 0.125 0.000* 0.140
Data are reported as mean (SD); HbA1c indicated glycosylated hemoglobin (%). Body mass index (BMI); waist hip ratio (WHR); mini mental state exam (MMSE); Montreal Cognitive Assessment (MoCA); WM (white matter). NA – not applicable. * Kruskal–Wallis test.
level, and ICV as covariates. Post hoc univariate tests with the Bonferroni correction were performed to follow the significant main effects yielded by the ANCOVA test (P < 0.05). 2.4.3. Correlation analysis Partial correlation analyses were performed to delineate the potential relationship between total MoCA score (include
visuospatial, executive, attention, language, delayed recall, and orientation domains) and impaired GM volume (as suggested in the VBM results) in the T2DM with MCI group (age, gender and educational level were controlled). To correct for differences in global atrophy between subjects, GM volume was standardized as follows: Standard Volume (%) = Raw Volume (ml)/Total GM volumes (ml) × 100.
Fig. 1. Comparison of total gray matter volume in the HC, T2DM without and with MCI groups (A). Correlation of the total MoCA score (B), delayed recal (C) and attention (D) with the left middle temporal gyrus volume in T2DM with MCI patients. The error bar represents the 95% confidence interval. HC, healthy controls.
4
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
Table 2 Comparisons of region of gray matter volume among healthy controls, patients with type 2 diabetes mellitus (T2DM) with and without MCI. Location
HC > T2DM without MCI Fusiform gyrus Middle temporal gyrus Superior temporal gyrus Middle occipital gyrus Middle occipital gyrus Precuneus Medial frontal gyrus Superior frontal gyrus Anterior and posterior cingulate gyrus HC > T2DM with MCI Superior temporal gyrus Superior temporal gyrus Middle temporal gyrus Middle temporal gyrus Fusiform gyrus Fusiform gyrus Middle occipital gyrus Middle occipital gyrus Inferior occipital gyrus Inferior occipital gyrus Precuneus Precuneus Inferior parietal lobule Superior frontal gyrus Anterior and posterior cingulate gyrus Uncus Amygdala Parahippocampa gyrus Hippocampus T2DM without MCI > T2DM with MCI Middle temporal gyrus MCI > T2DM with MCI Middle temporal gyrus Medial frontal gyrus Superior frontal gyrus MCI < T2DM with MCI Parahippocampa gyrus Middle occipital gyrus Cuneus Paracentral lobule
BA
Hemisphere
MNI coordinates
Voxels
X
Y
Z
Peak t value
19 20 48 19 19 19 10 11 23
L R L L R L L L L/R
−41 45 −45 −36 41 −27 −11 −12 −2
−67 −14 −12 −87 −87 −78 62 68 −35
−17 −21 −2 1 12 40 0 −11 41
391 1157 306 1602 1208 455 395 438 1780
4.31 4.51 3.70 4.08 3.87 4.77 3.90 3.68 4.13
21 21 20 20 19 19 19 19 18 19 19 19 40 11 23 38 36 20 27
L R R L L R L R R L L R R R L/R L L L R
−57 66 41 −39 −33 41 −35 32 23 −41 −14 18 41 18 −9 −29 −30 −35 17
−22 −21 11 6 −69 −55 −84 −82 −93 −86 −82 −49 −52 57 −30 9 −1 −16 −32
−5 −5 −39 −32 −11 −21 0 17 −17 −6 42 52 57 −12 33 −23 −29 −27 −3
1464 1564 1847 2542 1177 1158 930 684 730 543 1269 246 344 1107 4682 314 65 129 70
4.10 4.00 4.64 4.40 5.65 4.06 4.46 4.9 3.62 3.53 3.68 4.76 3.94 4.55 4.76 3.89 3.31 3.24 4.16
20
L
−45
2
−33
594
2.91
21 10 11
L L L
−55 −9 −14
−49 49 39
−1 12 −21
485 1221 118
3.37 4.37 3.18
30 19 18 4
R R R L
16 36 9 −6
−40 −76 −84 −30
−9 3 18 73
198 184 387 626
−2.79 −3.68 −3.37 −3.05
Note: L = left; R = right; MNI = Montreal Neurological Institute; BA = Brodmann area; HC = healthy controls.
3. Results 3.1. Demographic, clinical characteristics and biochemical test data No significant differences were identified for age, gender, educational level, duration of diabetes, age at diagnosis, HbA1c , BMI, WHR, MMSE and WM volume (Table 1, P > 0.05).
3.2. Total GM volume According to the ANCOVA test (Fig. 1A), there were no significant differences (P = 0.27) in total GM volume between the T2DM without MCI group (604.20 ± 4.46 ml) and the T2DM with MCI group (596.51 ± 4.24 ml). However, both of these groups demonstrated significantly reduced total GM volumes (both P < 0.05) compared with the HC group (620.24 ± 4.08 ml).
3.3. Regional brain GM volume changes among groups The significant inter-group volumetric differences in the brain GM anatomic regions were shown in Table 2.
3.3.1. HC versus T2DM without MCI Compared with the HC group, the T2DM without MCI group exhibited decreased GM volume in the superior temporal gyrus and MTG, the fusiform gyrus, the superior and medial frontal gyrus, the middle occipital gyrus, the precuneus, the angular gyrus and the bilateral cingulate regions (Fig. 2A). 3.3.2. HC versus T2DM with MCI In addition to more extensive GM atrophy in the aforementioned brain regions, the T2DM patients with MCI exhibited reduced GM in the limbic system (i.e., the hippocampus, parahippocampal gyrus, amygdala and uncus) compared with the HC group (Fig. 2B). 3.3.3. T2DM without MCI versus T2DM with MCI The T2DM patients with MCI exhibited lower GM volume in the left MTG than the T2DM patients without MCI (Fig. 2C). Partial correlation analysis revealed that GM volume decrease in the left MTG was positively correlated with the total MoCA score (r = 0.699, P < 0.01, Fig. 1B), mainly in delayed recall (r = 0.416, P = 0.038, Fig. 1C) and attention (r = 0.512, P = 0.009, Fig. 1D) domains. 3.3.4. T2DM with MCI versus MCI Compared with the MCI, the T2DM patients with MCI showed significant lower GM volume in the left MTG, the left superior and medial frontal gyrus, while higher GM volume in the right
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
5
Fig. 2. Brain regions with significant GM decreases in T2DM without MCI versus HC (A), T2DM with MCI versus HC (B) and T2DM without MCI versus T2DM with MCI (C), T2DM with MCI versus MCI (D). The colored bars (A–D) represent the T score corresponding to the colors in the figure. HC: healthy controls; L: left; R: right. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
parahippocampa gyrus, the left paracentral lobule, the right middle occipital gyrus and cuneus (Fig. 2D). 4. Discussion Our study revealed several important findings. First, the VBM results showed decreased GM volume in multiple regions in T2DM patients both with and without MCI compared with HC. However, the total GM volume decrease was more significant in T2DM patients with MCI than in T2DM patients without MCI compared with HC, suggesting more extensive GM impairment with the progression from normal cognition to MCI. In particular, MTL atrophy was found with the occurrence of the MCI in T2DM patient. Importantly, GM volume decrease in the left MTG was noted in T2DM patients with MCI than in T2DM patients without MCI, and this change was also significantly positively correlated with the total MoCA score. According to our VBM analyses, T2DM patients without MCI (with normal cognition) showed significant GM loss in multiple brain regions, including the temporal, frontal and occipital lobes. Previous studies have shown that T2DM patients experience diabetic metabolic disturbance, blood flow deregulation [17] and decreased ALFF (amplitude of low frequency fluctuation) values [28] in these regions. Notably, in the present study, the ANCOVA test revealed a significant difference in the total GM volume between the T2DM without MCI group and the HC group. Thus, combined with the above results of these studies, our results suggest that these regions might experience significant brain loss prior to the onset of clinically relevant MCI observed in T2DM patients. Therefore, if these patients do not receive timely therapeutic intervention, GM loss may be further aggravated. Compared with HC, the extent of GM volume loss was greater in the T2DM patients with MCI than in the patients without MCI in this study. These findings indicate that brain atrophy presents a tendency for extension during this progression, which is in support of
previous studies showing that neurodegeneration plays a key role in T2DM-related cognitive impairment [21]. Thus, during the progression from normal cognition to MCI, T2DM patients experience deterioration in brain volume, which indicates that T2DM could accelerate neurodegeneration. In addition, compared with HC, the MTL atrophy was also observed in T2DM patients with MCI rather than in T2DM patients without MCI. Similarly, previous MRI studies have reported significant atrophy in the MTLs of T2DM [9] and MCI patients [13]. In addition, MTL (including hippocampus and amygdala) are responsible for functions such as memory and behavior [9]. Taken together, these findings suggest that MTL atrophy likely reflects the occurrence of cognitive impairment in T2DM patients. In this study, the T2DM patients both with and without MCI presented temporal lobe volume decline compared with HC. Furthermore, relative to MCI, the left MTG atrophy was also found in T2DM patients with MCI. Consistent with these results, previous studies have demonstrated that GM thinning in the temporal lobes of T2DM patients [6,8]. One of the most intriguing findings of this study was the significant decrease of GM volume in the left MTG of T2DM patients with MCI compared with patients without MCI. Similarly, MCI patients showed reduced GM density in the MTG compared to controls [13]. These findings indicated that the temporal lobe could be the vulnerable site in T2DM-related cognitive decline, particularly in the MTG. Additionally, we found that the GM volume decrease in the left MTG was positively correlated with the total MoCA score in T2DM patients with MCI, particularly in delayed recall and attention domains. The MTG is closely associated with cued attention and working memory [11]. The MoCA is one of the most sensitive neuropsychological tests for distinguishing patients with MCI from healthy control subjects [18], and this test is considered to be more sensitive than the MMSE [27]. The pathological hallmarks of AD emerge in the brain regions associated with memory consolidation [30], and the MTG presents more significant early AD pathology compared to other brain regions [24].
6
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
Based on these findings, we argue that MTG atrophy may imply the existence of cognitive dysfunction and may reflect the impaired degree of cognition in T2DM patients. Some study limitations should be noted in this study. First, this preliminary study was limited to a small sample size with heterogeneous patient etiology, which may affect the statistical analysis and comprehensive interpretation of the results. Further studies using larger cohorts with homogeneous etiology are needed. Second, MOCA and MMSE are two simple screening instruments to identify cognitive impairment. More screening instruments are needed in the future study. Third, metabolic and vascular risk factors associated with T2DM, such as hyperinsulinemia, hypertension and dyslipidemia, may precede the actual onset of diabetes by many years, and these risk factors have been linked to the accelerated cognitive decline and a higher risk of dementia [19,26]. Therefore, although we made efforts to control for these factors, the data collection for variables such as the duration of T2DM, family history and subjective cognitive complaints were performed using selfreporting and the patients’ medical records, which may have led to recall bias. In conclusion, widespread GM atrophy was found in T2DM patients both with and without MCI. Furthermore, the brain atrophy in T2DM patients could become more widespread accomplished with the occurrence of the MCI. In particular, MTG atrophy was correlated with cognitive decline in T2DM patients and could therefore serve as a novel biomarker for early diagnosis and treatment. Together, the brain structural changes may underlie the transition from normal cognition to MCI in T2DM patients. Conflicts of interest The authors report no conflicts of interest. Acknowledgments The authors would like to thank Department of Endocrinology and Department of Gerontology of Southwest Hospital of Third Military Medical University for providing the T2DM patients and the clinical data. References [1] J. Ashburner, Computational anatomy with the SPM software, Magn. Reson. Imaging 27 (2009) 1163–1174. [2] J. Ashburner, K.J. Friston, Voxel-based morphometry—the methods, Neuroimage 11 (2000) 805–821. [3] A.D. Association, Standards of medical care in diabetes—2010, Diabetes Care 33 (Suppl. 1) (2010) S11–S61. [4] G.J. Biessels, I.J. Deary, C.M. Ryan, Cognition and diabetes: a lifespan perspective, Lancet Neurol. 7 (2008) 184–190. [5] D.G. Bruce, G.P. Casey, V. Grange, R.C. Clarnette, O.P. Almeida, J.K. Foster, F.J. Ives, T.M. Davis, Cognitive impairment, physical disability and depressive symptoms in older diabetic patients: the Fremantle Cognition in Diabetes Study, Diabetes Res. Clin. Pract. 61 (2003) 59–67. [6] M. Brundel, M. van den Heuvel, J. de Bresser, L.J. Kappelle, G.J. Biessels, Cerebral cortical thickness in patients with type 2 diabetes, J. Neurol. Sci. 299 (2010) 126–130. [7] Y. Chao-Gan, Z. Yu-Feng, DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI, Front. Syst. Neurosci. 4 (2010) 13. [8] Z. Chen, L. Li, J. Sun, L. Ma, Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL, Eur. J. Radiol. 81 (2012) 1870–1876.
[9] T. den Heijer, S.E. Vermeer, E.J. van Dijk, N.D. Prins, P.J. Koudstaal, A. Hofman, M.M.B. Breteler, Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI, Diabetologia 46 (2003) 1604–1610. [10] S.A. Ebady, M.A. Arami, M.H. Shafigh, Investigation on the relationship between diabetes mellitus type 2 and cognitive impairment, Diabetes Res. Clin. Pract. 82 (2008) 305–309. [11] M.D. Fox, M. Corbetta, A.Z. Snyder, J.L. Vincent, M.E. Raichle, Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 10046–10051. [12] S.M. Gold, I. Dziobek, V. Sweat, A. Tirsi, K. Rogers, H. Bruehl, W. Tsui, S. Richardson, E. Javier, A. Convit, Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes, Diabetologia 50 (2007) 711–719. [13] A. Hamalainen, S. Tervo, M. Grau-Olivares, E. Niskanen, C. Pennanen, J. Huuskonen, M. Kivipelto, T. Hanninen, M. Tapiola, M. Vanhanen, M. Hallikainen, E.-L. Helkala, A. Nissinen, R. Vanninen, H. Soininen, Voxel-based morphometry to detect brain atrophy in progressive mild cognitive impairment, Neuroimage 37 (2007) 1122–1131. [14] A.L. Hunderfund, R.O. Roberts, T.C. Slusser, C.L. Leibson, Y.E. Geda, R.J. Ivnik, E.G. Tangalos, R.C. Petersen, Mortality in amnestic mild cognitive impairment: a prospective community study, Neurology 67 (2006) 1764–1768. [15] G.B. Karas, P. Scheltens, S.A. Rombouts, P.J. Visser, R.A. van Schijndel, N.C. Fox, F. Barkhof, Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease, Neuroimage 23 (2004) 708–716. [16] E.S.C. Korf, L.R. White, P. Scheltens, L.J. Launer, Brain aging in very old men with type 2 diabetes: the Honolulu-Asia Aging Study, Diabetes Care 29 (2006) 2268–2274. [17] D. Last, D.C. Alsop, A.M. Abduljalil, R.P. Marquis, C. de Bazelaire, K. Hu, J. Cavallerano, V. Novak, Global and regional effects of type 2 diabetes on brain tissue volumes and cerebral vasoreactivity, Diabetes Care 30 (2007) 1193–1199. [18] J.Y. Lee, L. Dong Woo, S.J. Cho, D.L. Na, J. Hong Jin, S.K. Kim, L. You Ra, J.H. Youn, M. Kwon, J.H. Lee, C. Maeng Je, Brief screening for mild cognitive impairment in elderly outpatient clinic: validation of the Korean version of the Montreal Cognitive Assessment, J. Geriatr. Psychiatry Neurol. 21 (2008) 104–110. [19] J.A. Luchsinger, Diabetes, related conditions, and dementia, J. Neurol. Sci. 299 (2010) 35–38. [20] R.J. McCrimmon, C.M. Ryan, B.M. Frier, Diabetes and cognitive dysfunction, Lancet (2012). [21] C. Moran, T.G. Phan, J. Chen, L. Blizzard, R. Beare, A. Venn, G. Munch, A.G. Wood, J. Forbes, T.M. Greenaway, S. Pearson, V. Srikanth, Brain atrophy in type 2 diabetes: regional distribution and influence on cognition, Diabetes Care 36 (2013) 4036–4042. [22] R.C. Petersen, Mild cognitive impairment: current research and clinical implications, Semin. Neurol. 27 (2007) 22–31. [23] F. Portet, P.J. Ousset, P.J. Visser, G.B. Frisoni, F. Nobili, P. Scheltens, B. Vellas, J. Touchon, Mild cognitive impairment (MCI) in medical practice: a critical review of the concept and new diagnostic procedure. Report of the MCI Working Group of the European Consortium on Alzheimer’s disease, J. Neurol. Neurosurg. Psychiatry 77 (2006) 714–718. [24] M. Ray, W. Zhang, Analysis of Alzheimer’s disease severity across brain regions by topological analysis of gene co-expression networks, BMC Syst. Biol. 4 (2010) 136. [25] P.J. Spauwen, S. Kohler, F.R. Verhey, C.D. Stehouwer, M.P. van Boxtel, Effects of type 2 diabetes on 12-year cognitive change: results from the Maastricht Aging Study, Diabetes Care. 36 (2013) 1554–1561. [26] H. Umegaki, S. Iimuro, T. Shinozaki, A. Araki, T. Sakurai, K. Iijima, Y. Ohashi, H. Ito, Risk factors associated with cognitive decline in the elderly with type 2 diabetes: pooled logistic analysis of a 6-year observation in the Japanese Elderly Diabetes Intervention Trial, Geriatr. Gerontol. Int. 12 (Suppl. 1) (2012) 110–116. [27] K.A. Whitney, B. Mossbarger, S.M. Herman, S.L. Ibarra, Is the Montreal cognitive assessment superior to the mini-mental state examination in detecting subtle cognitive impairment among middle-aged outpatient U.S. Military veterans? Arch. Clin. Neuropsychol.: Off. J. Natl. Acad. Neuropsychol. 27 (2012) 742–748. [28] W. Xia, S. Wang, Z. Sun, F. Bai, Y. Zhou, Y. Yang, P. Wang, Y. Huang, Y. Yuan, Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study, Psychoneuroendocrinology. 38 (2013) 2493–2501. [29] W.L. Xu, E. von Strauss, C.X. Qiu, B. Winblad, L. Fratiglioni, Uncontrolled diabetes increases the risk of Alzheimer’s disease: a population-based cohort study, Diabetologia 52 (2009) 1031–1039. [30] H. Zetterberg, K. Blennow, E. Hanse, Amyloid beta and APP as biomarkers for Alzheimer’s disease, Exp. Gerontol. 45 (2010) 23–29. [31] H. Zhou, W.J. Lu, Z.J. Zhang, F. Bai, J.H. Chang, G.J. Teng, Study of cognitive function and brain volume in type 2 diabetic patients, Zhonghua Yi Xue Za Zhi 90 (2010) 327–331.
Contents lists available at ScienceDirect
Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Gray matter volume abnormalities in type 2 diabetes mellitus with and without mild cognitive impairment夽 Yanwei Zhang a,d,1 , Xiao Zhang b,1 , Jiuquan Zhang a , Chen Liu a , Qiaoying Yuan c , Xuntao Yin a , Luqing Wei a , Jinguo Cui d , Ran Tao a,d , Ping Wei b,∗ , Jian Wang a,∗∗ a
Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China c Department of Gerontology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China d Department of Radiology, Bethune International Peace Hospital of People’s Liberty Army, Shijiazhuang 050082, Hebei Province, China b
h i g h l i g h t s • Widespread GM atrophy was found in T2DM patients both with and without MCI. • MTL atrophy was found with the occurrence of the MCI in T2DM patient. • MTG atrophy might be associated with an increased risk for MCI in T2DM.
a r t i c l e
i n f o
Article history: Received 25 September 2013 Received in revised form 12 December 2013 Accepted 4 January 2014 Keywords: Type 2 diabetes mellitus Mild cognitive impairment Voxel-based morphometry Gray matter
a b s t r a c t This study sought to evaluate the potential brain gray matter (GM) volume changes that occur with the transition from normal cognition to mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) using voxel-based morphometry (VBM). VBM analyses of brain GM based on magnetic resonance imaging (MRI) data were performed on 28 T2DM patients with MCI, 25 T2DM patients without MCI, 28 MCI patients and 29 healthy controls (HC). Compared with the HC, the T2DM patients both with and without MCI showed significantly decreased total GM volume. Furthermore, the VBM results indicated that the T2DM patients without MCI exhibited extensively decreased GM volume compared with the HC in certain brain regions, including the superior and middle temporal gyrus (MTG), the superior and medial frontal gyrus and the middle occipital gyrus. In addition to more extensive GM atrophy in the aforementioned brain regions, the medial temporal lobe also exhibited GM loss in the T2DM patients with MCI. Furthermore, relative to the patients without MCI, only the left MTG exhibited a lower GM volume in the T2DM patients with MCI, which was positively correlated with the total MoCA score (r = 0.699, P < 0.01). Finally, relative to MCI, the left MTG atrophy was also found in the T2DM patients with MCI. Our findings suggest that MTG atrophy was associated with an increased risk for MCI in T2DM patients. The brain structural changes in many brain regions may underlie the transition from normal cognition to MCI in T2DM patients. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
夽 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. ∗ Corresponding author at: Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China. Tel.: +86 23 6876 5213; fax: +86 23 6876 5713. ∗∗ Corresponding author at: Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China. Tel.: +86 23 6876 5419; fax: +86 23 6546 3026. E-mail addresses: [email protected] (P. Wei), [email protected] (J. Wang). 1 Co-first author. 0304-3940/$ – see front matter © 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neulet.2014.01.006
Diabetes can result in progressive damage to the central nervous system. Approximately 10.8–17.5% of diabetic patients develop cognitive dysfunction [5], including mild cognitive impairment (MCI) and dementia [4]. It has been reported that type 2 diabetes mellitus (T2DM) is associated with deficits in learning and memory [20], information processing speed, attention and executive function [25]. MCI has attracted increasing research interest as it represents the transitional state between normal aging and Alzheimer’s disease (AD) [22]. Previous study has shown that T2DM patients have a significantly increased risk of MCI [10]. It is now widely accepted that MCI is the most important at-risk state
2
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
for AD and may result in elevated mortality in diabetic patients [14,29]. Thus, research on the pathophysiological mechanisms of MCI related to T2DM is crucial for the prompt treatment and improved prognosis of T2DM patients. Magnetic resonance imaging (MRI) has become a novel and widely used technique to investigate the pathogenesis of various neuropsychiatric disorders. Previous structural MRI studies revealed atrophy of the gray matter (GM) in extensive brain regions on T2DM [8,31], most consistently in the middle temporal gyrus (MTG). However, results for other brain regions are inconsistent. For example, the medial temporal lobe (MTL) was found to be atrophied [12,16], or unchanged [8]. MTG [28] and MTL [9] may play the central role in cognitive decline associated with T2DM. The aforementioned studies mainly focused on non-demented T2DM patients, including both with and without MCI. Thus, this discordance could be due to the cognitive status in T2DM. It is necessary to explore the potential brain GM alterations of T2DM with or without MCI relative to the normal controls. Voxel-based morphometry (VBM) [2] is a nonbiased and fully automated whole-brain technique for indirect volumetry using voxel-by-voxel analysis. VBM has been widely used in characterizing subtle changes in brain GM structure in a variety of diseases associated with neurological and psychiatric dysfunction, such as AD and MCI [13,15]. The present study divided T2DM patients into non-MCI and MCI groups to explore potential GM volume changes using VBM analysis. In addition, the relationships between potential GM volume changes and cognitive impairment were also investigated.
2. Materials and methods 2.1. Participants Three age-, gender- and education-matched groups of participants were studied, including 28 T2DM patients with MCI, 25 T2DM patients without MCI and 29 healthy controls (HC). The patients with T2DM were diagnosed using the criteria recommended by the American Diabetes Association (ADA)-2010 [3]. The Beijing version of the Montreal Cognitive Assessment (MoCA) was applied to assess the cognitive situation of each participant as a brief screening tool for MCI [27]. The diagnosis of MCI was based on the criteria established in the 2006 European Alzheimer’s Disease Consortium [23], which include complaints of hypomnesis, mini mental state exam (MMSE) score > 24, MoCA score < 26, clinical dementia rating (CDR) ≥ 0.5 and normal activities of daily living (ADL) score. We composed a control group of 29 HC with no history or symptoms of diabetes, psychiatric or neurologic disease, and MoCA ≥ 26. All of the participants were right-handed and underwent the MMSE to exclude dementia. All patients underwent clinical and biochemical tests. The MRI scans were performed within the first 24 h after diagnosis. All of the patients were recruited from the general population in Departments of Endocrinology and Gerontology of Southwest Hospital. Participants with history of known stroke, dementia, alcoholism, head injury, Parkinson’s disease, epilepsy, major depression (excluded by the Hamilton depression rating scale) or other neurological or psychiatric illness (excluded by clinical assessment and case history), major medical illness (e.g., cancer, anemia, diabetic ketoacidosis and thyroid dysfunction), white matter hyperintensity lesions on MRI and severe visual or hearing loss were excluded from the study. This study protocol was approved by the Ethics Committee of Southwest Hospital, Chongqing, China. All participants were advised of the experimental objectives, procedures, and possible
risks before MRI scan. Written informed consent was obtained from each participant prior to the study. 2.2. MRI acquisition All imaging data were obtained using a Siemens 3-T TIM Trio MRI system (Erlangen, Germany) equipped with the standard eight-channel head coil. For each participant, conventional brain T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images were obtained to exclude organic disease and white matter (WM) hyperintensity lesions. All MR images were assessed by two experienced radiologists. Highresolution sagittal T1-weighted structural images were acquired using a three-dimensional (3D) magnetization-prepared rapidacquisition gradient echo (MPRAGE) sequence with the following parameters: TR = 1900 ms, TE = 2.52 ms, flip angle (FA) = 9◦ , slice thickness = 1 mm, matrix = 256 × 256, voxel size = 1 × 1 × 1 mm3 and 176 slices. 2.3. VBM data analysis The T1 weighted images were processed and examined using the VBM8 toolbox [1] (http://dbm.neuro.uni-jena.de/vbm8/) with default parameters running in the statistical parametric mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm). The processing steps were briefly presented as follows: (1) All T1-weighted brain structural images were spatially normalized into the identical Montreal Neurological Institute (MNI) coordinate system on a voxel-by-voxel basis [2]. (2) All standardized brain structural images were effectively segmented into GM, WM and cerebrospinal fluid (CSF) using the new Segment and Dartel modules included in SPM8. (3) The segmented, modulated GM images were smoothed with an 8-mm full-width at half-maximum (FWHM) isotropic Gaussian kernel. Global measures of GM, WM and CSF volumes were calculated from the modulated normalized segmented images. The total intracranial volume (ICV) was calculated from the sum of the global measures of the three tissue types. Voxel-by-voxel comparisons of GM volumes between groups were performed using two-sample t-tests based on the general linear model in SPM8 (age and gender as confounding covariates). We calculated the positive contrasts (HC > T2DM without MCI, HC > T2DM with MCI, T2DM without MCI > T2DM with MCI, MCI > T2DM with MCI) and the negative contrasts. The threshold level for the statistical significance of between-group differences among the three groups was set at P < 0.05 using the AlphaSim correction (with a combination of a threshold of P < 0.001 and a minimum cluster size of 151 voxels). This correction was conducted using the AlphaSim program in the REST software [7]. 2.4. Statistical analysis 2.4.1. Demographic and clinical characteristics analysis Statistical analysis was performed using the PASW version 18.0 statistical software package (PASW for Windows, Chicago, IL, USA). The differences in demographic and clinical characteristics between patients and controls were analyzed using one-way ANOVA and the two-tailed t-test, and Chi-square tests were used for gender differences. A P-value less than 0.05 was considered statistically significant. 2.4.2. Total GM volume comparisons Statistical differences in the total GM volume among the groups were estimated by an ANCOVA model with age, gender, education
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
3
Table 1 Demographic and clinical characteristics of the participants.
Sex (male/female) Age (years) Education (years) Duration of diabetes (years) Age at diagnosis (years) HbA1c (%) BMI (kg/m2 ) WHR MMSE MoCA WM volume
Control (n = 29)
Type 2 diabetes without MCI (n = 25)
Type 2 diabetes with MCI (n = 28)
MCI (n = 28)
t/F/2
p
12/17 55.48(9.09) 11.48(3.87) NA NA NA NA NA 28.37(1.61) 27.48(1.33) 534.97(68.79)
14/11 52.24(9.25) 12.98(3.28) 6.44(5.34) 45.8(9.34) 7.39(1.39) 24.29(3.54) 0.9373(0.0502) 27.84(1.80) 26.68(1.11) 536.23(67.20)
8/20 56.21(7.69) 11.29(3.93) 8.18(5.97) 48.04(7.43) 7.78(1.59) 24.35(3.92) 1.2839(1.7964) 27.53(1.71) 16.43(3.38) 508.76(61.64)
10/18 56.64(9.10) 11.21(3.76) NA NA NA NA NA 27.39(1.57) 21.25(3.38) 507.68(41.58)
2 = 4.428 F = 1.329 F = 1.285 t = −1.111 t = −0.969 t = −0.930 t = −0.063 t = −0.963 F = 1.954 2 = 88.436 F = 1.863
0.219 0.269 0.283 0.272 0.337 0.357 0.950 0.340 0.125 0.000* 0.140
Data are reported as mean (SD); HbA1c indicated glycosylated hemoglobin (%). Body mass index (BMI); waist hip ratio (WHR); mini mental state exam (MMSE); Montreal Cognitive Assessment (MoCA); WM (white matter). NA – not applicable. * Kruskal–Wallis test.
level, and ICV as covariates. Post hoc univariate tests with the Bonferroni correction were performed to follow the significant main effects yielded by the ANCOVA test (P < 0.05). 2.4.3. Correlation analysis Partial correlation analyses were performed to delineate the potential relationship between total MoCA score (include
visuospatial, executive, attention, language, delayed recall, and orientation domains) and impaired GM volume (as suggested in the VBM results) in the T2DM with MCI group (age, gender and educational level were controlled). To correct for differences in global atrophy between subjects, GM volume was standardized as follows: Standard Volume (%) = Raw Volume (ml)/Total GM volumes (ml) × 100.
Fig. 1. Comparison of total gray matter volume in the HC, T2DM without and with MCI groups (A). Correlation of the total MoCA score (B), delayed recal (C) and attention (D) with the left middle temporal gyrus volume in T2DM with MCI patients. The error bar represents the 95% confidence interval. HC, healthy controls.
4
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
Table 2 Comparisons of region of gray matter volume among healthy controls, patients with type 2 diabetes mellitus (T2DM) with and without MCI. Location
HC > T2DM without MCI Fusiform gyrus Middle temporal gyrus Superior temporal gyrus Middle occipital gyrus Middle occipital gyrus Precuneus Medial frontal gyrus Superior frontal gyrus Anterior and posterior cingulate gyrus HC > T2DM with MCI Superior temporal gyrus Superior temporal gyrus Middle temporal gyrus Middle temporal gyrus Fusiform gyrus Fusiform gyrus Middle occipital gyrus Middle occipital gyrus Inferior occipital gyrus Inferior occipital gyrus Precuneus Precuneus Inferior parietal lobule Superior frontal gyrus Anterior and posterior cingulate gyrus Uncus Amygdala Parahippocampa gyrus Hippocampus T2DM without MCI > T2DM with MCI Middle temporal gyrus MCI > T2DM with MCI Middle temporal gyrus Medial frontal gyrus Superior frontal gyrus MCI < T2DM with MCI Parahippocampa gyrus Middle occipital gyrus Cuneus Paracentral lobule
BA
Hemisphere
MNI coordinates
Voxels
X
Y
Z
Peak t value
19 20 48 19 19 19 10 11 23
L R L L R L L L L/R
−41 45 −45 −36 41 −27 −11 −12 −2
−67 −14 −12 −87 −87 −78 62 68 −35
−17 −21 −2 1 12 40 0 −11 41
391 1157 306 1602 1208 455 395 438 1780
4.31 4.51 3.70 4.08 3.87 4.77 3.90 3.68 4.13
21 21 20 20 19 19 19 19 18 19 19 19 40 11 23 38 36 20 27
L R R L L R L R R L L R R R L/R L L L R
−57 66 41 −39 −33 41 −35 32 23 −41 −14 18 41 18 −9 −29 −30 −35 17
−22 −21 11 6 −69 −55 −84 −82 −93 −86 −82 −49 −52 57 −30 9 −1 −16 −32
−5 −5 −39 −32 −11 −21 0 17 −17 −6 42 52 57 −12 33 −23 −29 −27 −3
1464 1564 1847 2542 1177 1158 930 684 730 543 1269 246 344 1107 4682 314 65 129 70
4.10 4.00 4.64 4.40 5.65 4.06 4.46 4.9 3.62 3.53 3.68 4.76 3.94 4.55 4.76 3.89 3.31 3.24 4.16
20
L
−45
2
−33
594
2.91
21 10 11
L L L
−55 −9 −14
−49 49 39
−1 12 −21
485 1221 118
3.37 4.37 3.18
30 19 18 4
R R R L
16 36 9 −6
−40 −76 −84 −30
−9 3 18 73
198 184 387 626
−2.79 −3.68 −3.37 −3.05
Note: L = left; R = right; MNI = Montreal Neurological Institute; BA = Brodmann area; HC = healthy controls.
3. Results 3.1. Demographic, clinical characteristics and biochemical test data No significant differences were identified for age, gender, educational level, duration of diabetes, age at diagnosis, HbA1c , BMI, WHR, MMSE and WM volume (Table 1, P > 0.05).
3.2. Total GM volume According to the ANCOVA test (Fig. 1A), there were no significant differences (P = 0.27) in total GM volume between the T2DM without MCI group (604.20 ± 4.46 ml) and the T2DM with MCI group (596.51 ± 4.24 ml). However, both of these groups demonstrated significantly reduced total GM volumes (both P < 0.05) compared with the HC group (620.24 ± 4.08 ml).
3.3. Regional brain GM volume changes among groups The significant inter-group volumetric differences in the brain GM anatomic regions were shown in Table 2.
3.3.1. HC versus T2DM without MCI Compared with the HC group, the T2DM without MCI group exhibited decreased GM volume in the superior temporal gyrus and MTG, the fusiform gyrus, the superior and medial frontal gyrus, the middle occipital gyrus, the precuneus, the angular gyrus and the bilateral cingulate regions (Fig. 2A). 3.3.2. HC versus T2DM with MCI In addition to more extensive GM atrophy in the aforementioned brain regions, the T2DM patients with MCI exhibited reduced GM in the limbic system (i.e., the hippocampus, parahippocampal gyrus, amygdala and uncus) compared with the HC group (Fig. 2B). 3.3.3. T2DM without MCI versus T2DM with MCI The T2DM patients with MCI exhibited lower GM volume in the left MTG than the T2DM patients without MCI (Fig. 2C). Partial correlation analysis revealed that GM volume decrease in the left MTG was positively correlated with the total MoCA score (r = 0.699, P < 0.01, Fig. 1B), mainly in delayed recall (r = 0.416, P = 0.038, Fig. 1C) and attention (r = 0.512, P = 0.009, Fig. 1D) domains. 3.3.4. T2DM with MCI versus MCI Compared with the MCI, the T2DM patients with MCI showed significant lower GM volume in the left MTG, the left superior and medial frontal gyrus, while higher GM volume in the right
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
5
Fig. 2. Brain regions with significant GM decreases in T2DM without MCI versus HC (A), T2DM with MCI versus HC (B) and T2DM without MCI versus T2DM with MCI (C), T2DM with MCI versus MCI (D). The colored bars (A–D) represent the T score corresponding to the colors in the figure. HC: healthy controls; L: left; R: right. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
parahippocampa gyrus, the left paracentral lobule, the right middle occipital gyrus and cuneus (Fig. 2D). 4. Discussion Our study revealed several important findings. First, the VBM results showed decreased GM volume in multiple regions in T2DM patients both with and without MCI compared with HC. However, the total GM volume decrease was more significant in T2DM patients with MCI than in T2DM patients without MCI compared with HC, suggesting more extensive GM impairment with the progression from normal cognition to MCI. In particular, MTL atrophy was found with the occurrence of the MCI in T2DM patient. Importantly, GM volume decrease in the left MTG was noted in T2DM patients with MCI than in T2DM patients without MCI, and this change was also significantly positively correlated with the total MoCA score. According to our VBM analyses, T2DM patients without MCI (with normal cognition) showed significant GM loss in multiple brain regions, including the temporal, frontal and occipital lobes. Previous studies have shown that T2DM patients experience diabetic metabolic disturbance, blood flow deregulation [17] and decreased ALFF (amplitude of low frequency fluctuation) values [28] in these regions. Notably, in the present study, the ANCOVA test revealed a significant difference in the total GM volume between the T2DM without MCI group and the HC group. Thus, combined with the above results of these studies, our results suggest that these regions might experience significant brain loss prior to the onset of clinically relevant MCI observed in T2DM patients. Therefore, if these patients do not receive timely therapeutic intervention, GM loss may be further aggravated. Compared with HC, the extent of GM volume loss was greater in the T2DM patients with MCI than in the patients without MCI in this study. These findings indicate that brain atrophy presents a tendency for extension during this progression, which is in support of
previous studies showing that neurodegeneration plays a key role in T2DM-related cognitive impairment [21]. Thus, during the progression from normal cognition to MCI, T2DM patients experience deterioration in brain volume, which indicates that T2DM could accelerate neurodegeneration. In addition, compared with HC, the MTL atrophy was also observed in T2DM patients with MCI rather than in T2DM patients without MCI. Similarly, previous MRI studies have reported significant atrophy in the MTLs of T2DM [9] and MCI patients [13]. In addition, MTL (including hippocampus and amygdala) are responsible for functions such as memory and behavior [9]. Taken together, these findings suggest that MTL atrophy likely reflects the occurrence of cognitive impairment in T2DM patients. In this study, the T2DM patients both with and without MCI presented temporal lobe volume decline compared with HC. Furthermore, relative to MCI, the left MTG atrophy was also found in T2DM patients with MCI. Consistent with these results, previous studies have demonstrated that GM thinning in the temporal lobes of T2DM patients [6,8]. One of the most intriguing findings of this study was the significant decrease of GM volume in the left MTG of T2DM patients with MCI compared with patients without MCI. Similarly, MCI patients showed reduced GM density in the MTG compared to controls [13]. These findings indicated that the temporal lobe could be the vulnerable site in T2DM-related cognitive decline, particularly in the MTG. Additionally, we found that the GM volume decrease in the left MTG was positively correlated with the total MoCA score in T2DM patients with MCI, particularly in delayed recall and attention domains. The MTG is closely associated with cued attention and working memory [11]. The MoCA is one of the most sensitive neuropsychological tests for distinguishing patients with MCI from healthy control subjects [18], and this test is considered to be more sensitive than the MMSE [27]. The pathological hallmarks of AD emerge in the brain regions associated with memory consolidation [30], and the MTG presents more significant early AD pathology compared to other brain regions [24].
6
Y. Zhang et al. / Neuroscience Letters 562 (2014) 1–6
Based on these findings, we argue that MTG atrophy may imply the existence of cognitive dysfunction and may reflect the impaired degree of cognition in T2DM patients. Some study limitations should be noted in this study. First, this preliminary study was limited to a small sample size with heterogeneous patient etiology, which may affect the statistical analysis and comprehensive interpretation of the results. Further studies using larger cohorts with homogeneous etiology are needed. Second, MOCA and MMSE are two simple screening instruments to identify cognitive impairment. More screening instruments are needed in the future study. Third, metabolic and vascular risk factors associated with T2DM, such as hyperinsulinemia, hypertension and dyslipidemia, may precede the actual onset of diabetes by many years, and these risk factors have been linked to the accelerated cognitive decline and a higher risk of dementia [19,26]. Therefore, although we made efforts to control for these factors, the data collection for variables such as the duration of T2DM, family history and subjective cognitive complaints were performed using selfreporting and the patients’ medical records, which may have led to recall bias. In conclusion, widespread GM atrophy was found in T2DM patients both with and without MCI. Furthermore, the brain atrophy in T2DM patients could become more widespread accomplished with the occurrence of the MCI. In particular, MTG atrophy was correlated with cognitive decline in T2DM patients and could therefore serve as a novel biomarker for early diagnosis and treatment. Together, the brain structural changes may underlie the transition from normal cognition to MCI in T2DM patients. Conflicts of interest The authors report no conflicts of interest. Acknowledgments The authors would like to thank Department of Endocrinology and Department of Gerontology of Southwest Hospital of Third Military Medical University for providing the T2DM patients and the clinical data. References [1] J. Ashburner, Computational anatomy with the SPM software, Magn. Reson. Imaging 27 (2009) 1163–1174. [2] J. Ashburner, K.J. Friston, Voxel-based morphometry—the methods, Neuroimage 11 (2000) 805–821. [3] A.D. Association, Standards of medical care in diabetes—2010, Diabetes Care 33 (Suppl. 1) (2010) S11–S61. [4] G.J. Biessels, I.J. Deary, C.M. Ryan, Cognition and diabetes: a lifespan perspective, Lancet Neurol. 7 (2008) 184–190. [5] D.G. Bruce, G.P. Casey, V. Grange, R.C. Clarnette, O.P. Almeida, J.K. Foster, F.J. Ives, T.M. Davis, Cognitive impairment, physical disability and depressive symptoms in older diabetic patients: the Fremantle Cognition in Diabetes Study, Diabetes Res. Clin. Pract. 61 (2003) 59–67. [6] M. Brundel, M. van den Heuvel, J. de Bresser, L.J. Kappelle, G.J. Biessels, Cerebral cortical thickness in patients with type 2 diabetes, J. Neurol. Sci. 299 (2010) 126–130. [7] Y. Chao-Gan, Z. Yu-Feng, DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI, Front. Syst. Neurosci. 4 (2010) 13. [8] Z. Chen, L. Li, J. Sun, L. Ma, Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL, Eur. J. Radiol. 81 (2012) 1870–1876.
[9] T. den Heijer, S.E. Vermeer, E.J. van Dijk, N.D. Prins, P.J. Koudstaal, A. Hofman, M.M.B. Breteler, Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI, Diabetologia 46 (2003) 1604–1610. [10] S.A. Ebady, M.A. Arami, M.H. Shafigh, Investigation on the relationship between diabetes mellitus type 2 and cognitive impairment, Diabetes Res. Clin. Pract. 82 (2008) 305–309. [11] M.D. Fox, M. Corbetta, A.Z. Snyder, J.L. Vincent, M.E. Raichle, Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 10046–10051. [12] S.M. Gold, I. Dziobek, V. Sweat, A. Tirsi, K. Rogers, H. Bruehl, W. Tsui, S. Richardson, E. Javier, A. Convit, Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes, Diabetologia 50 (2007) 711–719. [13] A. Hamalainen, S. Tervo, M. Grau-Olivares, E. Niskanen, C. Pennanen, J. Huuskonen, M. Kivipelto, T. Hanninen, M. Tapiola, M. Vanhanen, M. Hallikainen, E.-L. Helkala, A. Nissinen, R. Vanninen, H. Soininen, Voxel-based morphometry to detect brain atrophy in progressive mild cognitive impairment, Neuroimage 37 (2007) 1122–1131. [14] A.L. Hunderfund, R.O. Roberts, T.C. Slusser, C.L. Leibson, Y.E. Geda, R.J. Ivnik, E.G. Tangalos, R.C. Petersen, Mortality in amnestic mild cognitive impairment: a prospective community study, Neurology 67 (2006) 1764–1768. [15] G.B. Karas, P. Scheltens, S.A. Rombouts, P.J. Visser, R.A. van Schijndel, N.C. Fox, F. Barkhof, Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease, Neuroimage 23 (2004) 708–716. [16] E.S.C. Korf, L.R. White, P. Scheltens, L.J. Launer, Brain aging in very old men with type 2 diabetes: the Honolulu-Asia Aging Study, Diabetes Care 29 (2006) 2268–2274. [17] D. Last, D.C. Alsop, A.M. Abduljalil, R.P. Marquis, C. de Bazelaire, K. Hu, J. Cavallerano, V. Novak, Global and regional effects of type 2 diabetes on brain tissue volumes and cerebral vasoreactivity, Diabetes Care 30 (2007) 1193–1199. [18] J.Y. Lee, L. Dong Woo, S.J. Cho, D.L. Na, J. Hong Jin, S.K. Kim, L. You Ra, J.H. Youn, M. Kwon, J.H. Lee, C. Maeng Je, Brief screening for mild cognitive impairment in elderly outpatient clinic: validation of the Korean version of the Montreal Cognitive Assessment, J. Geriatr. Psychiatry Neurol. 21 (2008) 104–110. [19] J.A. Luchsinger, Diabetes, related conditions, and dementia, J. Neurol. Sci. 299 (2010) 35–38. [20] R.J. McCrimmon, C.M. Ryan, B.M. Frier, Diabetes and cognitive dysfunction, Lancet (2012). [21] C. Moran, T.G. Phan, J. Chen, L. Blizzard, R. Beare, A. Venn, G. Munch, A.G. Wood, J. Forbes, T.M. Greenaway, S. Pearson, V. Srikanth, Brain atrophy in type 2 diabetes: regional distribution and influence on cognition, Diabetes Care 36 (2013) 4036–4042. [22] R.C. Petersen, Mild cognitive impairment: current research and clinical implications, Semin. Neurol. 27 (2007) 22–31. [23] F. Portet, P.J. Ousset, P.J. Visser, G.B. Frisoni, F. Nobili, P. Scheltens, B. Vellas, J. Touchon, Mild cognitive impairment (MCI) in medical practice: a critical review of the concept and new diagnostic procedure. Report of the MCI Working Group of the European Consortium on Alzheimer’s disease, J. Neurol. Neurosurg. Psychiatry 77 (2006) 714–718. [24] M. Ray, W. Zhang, Analysis of Alzheimer’s disease severity across brain regions by topological analysis of gene co-expression networks, BMC Syst. Biol. 4 (2010) 136. [25] P.J. Spauwen, S. Kohler, F.R. Verhey, C.D. Stehouwer, M.P. van Boxtel, Effects of type 2 diabetes on 12-year cognitive change: results from the Maastricht Aging Study, Diabetes Care. 36 (2013) 1554–1561. [26] H. Umegaki, S. Iimuro, T. Shinozaki, A. Araki, T. Sakurai, K. Iijima, Y. Ohashi, H. Ito, Risk factors associated with cognitive decline in the elderly with type 2 diabetes: pooled logistic analysis of a 6-year observation in the Japanese Elderly Diabetes Intervention Trial, Geriatr. Gerontol. Int. 12 (Suppl. 1) (2012) 110–116. [27] K.A. Whitney, B. Mossbarger, S.M. Herman, S.L. Ibarra, Is the Montreal cognitive assessment superior to the mini-mental state examination in detecting subtle cognitive impairment among middle-aged outpatient U.S. Military veterans? Arch. Clin. Neuropsychol.: Off. J. Natl. Acad. Neuropsychol. 27 (2012) 742–748. [28] W. Xia, S. Wang, Z. Sun, F. Bai, Y. Zhou, Y. Yang, P. Wang, Y. Huang, Y. Yuan, Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study, Psychoneuroendocrinology. 38 (2013) 2493–2501. [29] W.L. Xu, E. von Strauss, C.X. Qiu, B. Winblad, L. Fratiglioni, Uncontrolled diabetes increases the risk of Alzheimer’s disease: a population-based cohort study, Diabetologia 52 (2009) 1031–1039. [30] H. Zetterberg, K. Blennow, E. Hanse, Amyloid beta and APP as biomarkers for Alzheimer’s disease, Exp. Gerontol. 45 (2010) 23–29. [31] H. Zhou, W.J. Lu, Z.J. Zhang, F. Bai, J.H. Chang, G.J. Teng, Study of cognitive function and brain volume in type 2 diabetic patients, Zhonghua Yi Xue Za Zhi 90 (2010) 327–331.
