Impaired Macromolecular Protein Pools in Fronto-Striato-Thalamic Circuits in Type 2 Diabetes Revealed by Magnetization Transfer Imaging

Subjects

The subject population consisted of 20 T2DM patients without mood disorders (aged 65.05 ± 11.95 years) and 26 nondiabetic HCs (aged 62.92 ± 12.71 years). Subjects were selected from a larger sample of a research program on diabetes and depression at the University of Illinois at Chicago. All participants were aged 30 years and older and recruited from the greater Chicago area through flyers, local advertisements, and relevant outpatient clinics. The study was approved by the University of Illinois at Chicago Institutional Review Board, and written informed consent was obtained from all participants.

The diagnosis of T2DM in patients was made by their primary care physicians and was confirmed using American Diabetes Association guidelines (26) (an elevated nonfasting hemoglobin A_1c [HbA_1c] level [>6.5% (48 mmol/mol)] or the use of antidiabetic medications [oral hypoglycemic and/or insulin] when enrolled for this study). T2DM patients reported using oral hypoglycemic medications and/or insulin for glycemic control (six patients with one oral hypoglycemic medication or insulin; seven with two or more hypoglycemic medications; two with both medications and insulin; five without any hypoglycemic medication or insulin). With respect to diabetic vascular complications (27), 5 patients had diabetic microvascular complications (e.g., diabetic nephropathy, neuropathy, and retinopathy), 1 had diabetic macrovascular complications (e.g., coronary artery disease, peripheral arterial disease, and myocardial infarction), 2 had both, and 12 were without any vascular complications to their diabetes. HCs were free of diabetes and had HbA_1c levels within normal limits. All participants received the Mini-Mental State Examination (MMSE) (28), the Structured Clinical Interview for DSM-IV (29), and the 17-item Hamilton Depression Rating Scale (HAM-D) (30), administered by a trained research assistant and a board-certified (A.K.) or board-eligible (O.A.) psychiatrist. Both control and patient subjects denied a history of depressed mood, obtained scores of 8 or lower on the HAM-D, were free of unstable medical conditions, and were of comparable age and sex.

Exclusion criteria included any current or past history of neurological and psychiatric disorders (e.g., dementia, stroke, seizure, transient ischemic attack, or depression), learning disability or attention deficit hyperactivity disorder, psychotropic medication, current or past history of substance abuse or dependence, history of head injury or loss of consciousness, a MMSE score less than 24, or any contraindication to MRI scan such as metal in the body, surgically implanted devices containing metal, claustrophobia, and pregnancy.

All participants were assessed for medical comorbidity using the Cumulative Illness Rating Scale (CIRS) (31) and for vascular comorbidities using the Framingham Stroke Risk Profile (FSRP) score (32). We also designed a modified FSRP (mFSRP) score in which the contribution of T2DM was removed to represent vascular complications only. All participants received a nonfasting blood draw to document HbA_1c levels, HDL cholesterol, and LDL cholesterol and to verify diabetes status. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and BMI were documented on each participant. All participants were also assessed for IQ using the Wechsler Test of Adult Reading (WTAR) (33). Demographic and T2DM-related clinical measures are summarized in Table 1. Obesity, hypertension, vascular diseases, and/or elevated bad cholesterol levels are often seen in patients with T2DM, and each of these comorbidities has been reported to exert their own effects on the human brain. It must be noted that in this study, these comorbidities (including the following variables: BMI, SBP, DBP, mFSRP, and LDL cholesterol) were of comparable levels between two groups (P > 0.20). So the possible effects of these comorbidities on the group difference in MTR have been minimized, and the findings from this study are primarily attributable to T2DM.

Neuropsychological Tests

A neuropsychological battery was conducted on each participant across three domains: learning and memory (California Verbal Learning Test [Second Edition] immediate total recall and long-delay free recall [34]; Wechsler Memory Scale [Third Edition] Logical Memory I and II and Visual Reproduction I and II [35]); attention and information processing (Stroop Color and Word tests [36]; Trail Making Test A; and Wechsler Adult Intelligence Scale [Third Edition] Digit Symbol-Coding [37]); and executive function (Delis-Kaplan Executive Function System Category Switching [38]; Trail Making Test B [39]; Stroop Interference Score [36]; Wechsler Adult Intelligence Scale [Third Edition] Digit Span Backward [37]; and Self-Ordered Pointing Task Total Errors [40]). Raw scores from the neuropsychological battery were standardized using the HC sample mean and SD. Relevant scores were reversed so that high scores consistently reflected better performance. Composite Z scores were calculated for each domain, and Cronbach α suggested that each variable measured a unidimensional latent construct (learning and memory, α = 0.88; attention and information processing, α = 0.85; executive function, α = 0.76).

MT Image Data Acquisition

MRI was performed on a Philips Achieva 3.0T scanner (Philips Medical Systems, Best, the Netherlands) with a body coil for transmission and a Philips 8-channel SENSE Head coil for reception. Subjects were equipped with soft ear plugs, positioned comfortably in the head coil using custom-made foam pads to minimize head motion, and instructed to remain still. MT images were acquired using a three-dimensional (3D) spoiled gradient echo sequence with multishot echo-planar imaging (EPI) readout and the following parameters: repetition time (TR)/echo time (TE) = 64/15 ms, flip angle = 9°, field of view (FOV) = 24 cm, 67 axial slices, slice thickness/gap = 2.2 mm/no gap, EPI factor = 7, and reconstructed voxel size = 0.83 × 0.83 × 2.2 mm³, with a nonselective five-lobed Sinc-Gauss off-resonance MT prepulse (B₁/Δf/duration = 10.5 μT/1.5 kHz/24.5 ms) optimized for maximum white matter/gray matter contrast (41). Image slices were parallel to the anterior commissure–posterior commissure line. Parallel imaging was used with a reduction factor of 2. Before the MT scan, high-resolution 3D T₁-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) images were acquired with the following sequence parameters: TR/TE = 8.4/3.9 ms, flip angle = 8°, FOV = 24 cm, 134 axial slices/no gap, and reconstructed voxel size = 0.83 × 0.83 × 1.1 mm³. In addition, T₂-weighted fluid-attenuated inversion recovery (FLAIR) images were also acquired using turbo spin echo sequence with the following sequence parameters: TR/inversion time (TI)/TE = 11,000/2,800/68 ms, FOV = 24 cm, 67 axial slices without gap, and reconstructed voxel size = 0.83 × 0.83 × 2.2 mm³, for delineation of hyperintense areas in the brain.

Image Processing

For each participant, T₁-weighted MPRAGE image, T₂-weighted FLAIR image, and MT images (with and without the off-resonance MT prepulse: M_s and M₀) were coregistered. MTR values were calculated on a voxel-by-voxel basis using coregistered M₀ and M_s with the formula MTR = (M₀ − M_s)/M₀. Regions of interest (ROIs) were placed on the coregistered high-resolution T₁-weighted image at the nodes of fronto-striato-thalamic circuits (21), including four subcortical regions (i.e., hCaud, putamen, globus pallidus, and thalamus) and four cortical regions (i.e., rostral anterior cingulate cortex [rACC], dorsal anterior cingulate cortex [dACC], dorsolateral prefrontal cortex [DLPFC], and lateral orbitofrontal cortex [lOFC]) in both hemispheres (see Fig. 1). Care was taken to ensure consistent placement of the subcortical ROIs for the MTR analysis. The slice displaying the most anterior margin of the genu of the corpus callosum (Montreal Neurological Institute (MNI) [x, y, z] coordinates, [1, 32, 6] mm) was chosen as the reference slice for placing the subcortical ROIs hCaud, putamen, and thalamus, because this landmark could be easily and consistently identified across subjects and these ROIs are visible at this slice level (4) (see Fig. 1A). In the case that putamen was not completely visualized on this slice, the next inferior slice was used as the reference slice (4). For the ROI of globus pallidus, the slice clearly displaying the anterior commissure (MNI coordinates [0, 2, −4] mm) was chosen as the reference slice (see Fig. 1B). During the placement of the subcortical ROIs, the coregistered FLAIR image was closely examined to ensure the ROIs were not placed in hyperintense areas. Moreover, we used constant volumes of ROIs in all of the defined subcortical regions, i.e., the volume was fixed to 73.3 mm³ for hCaud, putamen, and thalamus and was fixed to relatively smaller 55 mm³ for globus pallidus. These two volumes were selected so that the MTR calculation in each subcortical ROI could be devoid of any partial volume effects from adjoining brain regions and/or cerebrospinal fluid on all the involved subjects. For the four cortical ROIs (i.e., rACC, dACC, DLPFC, and lOFC), we used the FreeSurfer package (https://surfer.nmr.mgh.harvard.edu/) to parcellate these structures and evaluated MTR within cortical white matter to minimize partial volume effects from the cerebrospinal fluid. To better illustrate the anatomical location of each cortical region, both gray (green) and white (red) matter areas are displayed in Fig. 1C–E (the region of dACC is not shown for simplicity). Generation of ROI masks and calculation of MTR were performed using in-house developed programs.

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Figure 1

ROIs for MTR analysis.

Statistical Analysis

Clinical and demographic measures were analyzed using univariate ANOVA for continuous variables and χ² tests for categorical variables. Group differences in MTR in bilateral regions were assessed using a mixed-model analysis with diagnostic group as the between-group factor and hemisphere as a within-subject factor. Group differences in MTR in individual regions on each hemisphere were further analyzed using univariate ANCOVA controlling for age. Correlations between MTR and FSRP, mFSRP, HbA_1c level, or other related clinical variables were analyzed using partial Pearson product-moment correlations controlling for age. In the correlation analysis of MTR and HbA_1c, a natural log transformation was first performed on HbA_1c (i.e., log-transformed HbA_1c) to reduce the skewness of this variable when the sample was combined from both HC and T2DM groups. Correlations between MTR and neuropsychological task performance were also analyzed using partial Pearson product-moment correlations controlling for age and MMSE as initial covariates. Multiple comparison correction was performed using false discovery rate (FDR) approach (42–44) with the maximum acceptable FDR set at 0.15. All statistical analyses were carried out using SPSS version 18 (SPSS Inc., Chicago, IL).