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Journal of Alzheimer’s Disease xx (20xx) x–xxDOI 10.3233/JAD-131736IOS Press

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Structural and Functional Brain Changesin Middle-Aged Type 2 Diabetic Patients:A Cross-Sectional Study

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Natalia Garcıa-Casaresa,b,∗, Marcelo L. Berthiera,b, Ricardo E. Jorgec, Pedro Gonzalez-Alegred,Antonio Gutierrez Cardob, Jose Rioja Villodresa,b, Laura Acione, Marıa Jose Ariza Corboa,b,Alejandro Nabrozidisb, Juan A. Garcıa-Arnesf and Pedro Gonzalez-Santosa,b,g

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aDepartment of Medicine, Faculty of Medicine, University of Malaga, Spain7

bCentro de Investigaciones Medico-Sanitarias (C.I.M.E.S), Malaga, Spain8

cDepartment of Psychiatry, Iowa City Veterans Administration Medical Center, The University of Iowa, West IowaCity, IA, USA

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dDepartment of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA11

eThe Iowa Consortium for Substance Abuse Research and Evaluation, The University of Iowa, Iowa City, IA, USA12

f Department of Endocrinology, Carlos-Haya Hospital, Malaga, Spain13

gDepartment of Internal Medicine, University Hospital Virgen de la Victoria, Malaga, Spain14

Accepted 27 November 2013

Abstract.BACKGROUND: Type 2 diabetes mellitus (T2DM) is an emerging risk factor for cognitive impairment. Whether this impair-ment is a direct effect of this metabolic disorder on brain function, a consequence of vascular disease, or both, remains unknown.Structural and functional neuroimaging studies in patients with T2DM could help to elucidate this question.OBJECTIVE: We designed a cross-sectional study comparing 25 T2DM patients with 25 age- and gender-matched healthycontrol participants. Clinical information, APOE genotype, lipid and glucose analysis, structural cerebral magnetic resonanceimaging including voxel-based morphometry, and F-18 fluorodeoxyglucose positron emission tomography were obtained in allsubjects.METHODS: Gray matter densities and metabolic differences between groups were analyzed using statistical parametric map-ping. In addition to comparing the neuroimaging profiles of both groups, we correlated neuroimaging findings with HbA1clevels, duration of T2DM, and insulin resistance measurement (HOMA-IR) in the diabetic patients group.RESULTS: Patients with T2DM presented reduced gray matter densities and reduced cerebral glucose metabolism in severalfronto-temporal brain regions after controlling for various vascular risk factors. Furthermore, within the T2DM group, longerdisease duration, and higher HbA1c levels and HOMA-IR were associated with lower gray matter density and reduced cerebralglucose metabolism in fronto-temporal regions.CONCLUSION: In agreement with previous reports, our findings indicate that T2DM leads to structural and metabolic abnor-malities in fronto-temporal areas. Furthermore, they suggest that these abnormalities are not entirely explained by the role ofT2DM as a cardiovascular risk factor.

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Keywords: cognition, magnetic resonance imaging, neuroimaging, positron emission tomography, type 2 diabetes mellitus33

∗Correspondence to: Natalia Garcıa Casares, Department ofMedicine, Faculty of Medicine, University of Malaga, BoulevardLouis Pasteur 32, 29010 Malaga, Spain. Tel.: +34952137354; Fax:+34952131615; E-mail: Nagcasares@uma.es.

ISSN 1387-2877/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved

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2 N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes

INTRODUCTION34

Type 2 diabetes mellitus (T2DM) has emerged35

as an important risk factor for cognitive impairment36

and dementia [1–5]. However, the pathophysiologi-37

cal mechanisms underlying cognitive dysfunction in38

T2DM are not well-known and multiple mechanisms39

have been postulated [4–6]. The metabolic distur-40

bances linked to T2DM affect multiple biochemical41

pathways that could potentially lead to neuronal dys-42

function and cognitive decline. For instance, chronic43

hyperglycemia and insulin resistance may accelerate44

neuronal aging through the effect of advanced glyca-45

tion end-products or chronic oxidative stress [7, 8].46

T2DM, and probably other peripheral/systemic47

insulin resistance states, serve as co-factors con-48

tributing to the pathogenesis or progression of49

neurodegeneration [9]. The weight of evidence50

suggests that diabetes increases the risk of both51

Alzheimer’s disease (AD) and vascular dementia, and52

that this risk occurs regardless of the age of onset of dia-53

betes [10]. However, this aspect is still controversial.54

Ahtiluoto et al. presented epidemiological evidence55

that diabetes increases the risk of vascular pathology56

in elderly patients with or without AD in a neuropatho-57

logic study [11]. However, since neurofibrillary tangles58

and dystrophic neurites are hallmarks of AD, other59

postmorten human studies suggest that T2DM alone60

is not sufficient to cause AD [12–13]. Gaining a bet-61

ter understanding of this process could help us devise62

new preventive and therapeutic interventions for this63

and other related cognitive disorders.64

Different approaches can be employed to dissect the65

pathogenic process underlying cognitive dysfunction66

in T2DM. Among these, the combination of vari-67

ous neuroimaging modalities represents a reasonable68

approach to investigate how brain dysfunction evolves69

in subjects with T2DM. Neuroimaging studies in the70

early stages of the disease might help us better char-71

acterize anatomical and functional abnormalities that72

predate the onset of cognitive impairment. In fact,73

structural magnetic resonance imaging (MRI)-based74

studies in patients with T2DM have shown global and75

regional cortical atrophy and the presence of hyperin-76

tense lesions in different locations [14–18]. Brundel77

et al. recently examined the cerebral blood flow and78

the brain volumes with MRI in patients with T2DM at79

baseline and after 4 years, and concluded that cerebral80

blood flow was associated with impaired cognition and81

decreased brain volume in cross-sectional analyses, but82

did not predict changes over time [19]. In addition, a83

study performed in 23 cognitively unimpaired adults84

with pre-diabetes or early T2DM and 6 healthy con- 85

trols showed a significant reduction in cerebral glucose 86

metabolism in fronto-temporo-parietal cortices in the 87

disease group [20]. 88

The studies mentioned above have provided very 89

helpful information on the impact of T2DM on 90

brain structure and function. They used single imag- 91

ing modalities, but a combination of structural and 92

functional neuroimaging studies in the same T2DM 93

subjects is lacking. To fill this gap, we designed a study 94

that included brain MRI and FDG-PET combined 95

with selected biochemical and genetic analyses in 25 96

middle-aged T2DM patients and 25 controls. Although 97

the sample size was relatively small, this novel experi- 98

mental approach should provide helpful information to 99

better characterize the footprint of T2DM on the brain. 100

MATERIALS AND METHODS 101

Subjects 102

We designed a cross-sectional study comparing 103

T2DM patients and control subjects at the Centro 104

de Investigaciones Medico-Sanitarias (CIMES) of the 105

University of Malaga (Spain). The study sample con- 106

sisted of 25 patients with T2DM and 25 control 107

subjects. Consecutive T2DM patients were identified 108

by an endocrinologist from a diabetes outpatient clinic 109

at the hospital affiliated to the University of Malaga. 110

Controls included non-consanguineous relatives of the 111

T2DM patients or subjects recruited through an adver- 112

tisement in a University newsletter. They were selected 113

according to guidelines for biomedical research on 114

brain function in normal volunteers [21]. 115

The inclusion criteria were the following: (A) a 116

diagnosis of T2DM according to the American Dia- 117

betes Association criteria for the diagnosis of diabetes 118

[22] confirmed by an endocrinologist, and a fasting 119

glucose concentration below 110 mg/dl and glycated 120

hemoglobin (Hb1Ac) <5.7% for control subjects; 121

(B) age between 45 and 65 years (to reduce the 122

influence of normal aging); (C) Mini-Mental State 123

Examination (MMSE) [23] score≥26 points; (D) func- 124

tional independence, assessed by scores (≤3.3) on the 125

Bayer Activities of Daily Living Scale [24]; right- 126

handedness (7 out of 10 activities performed with the 127

right hand) on the Edinburgh Handedness Inventory 128

[25]. Exclusion criteria included: (A) history of neu- 129

rological or psychiatric disease impairing cognitive 130

function (e.g., Parkinson’s disease or schizophrenia); 131

(B) previous clinical diagnosis of transient ischemic 132

attack/stroke or severe, uncontrolled cardiovascular 133

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N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes 3

disease; (C) history of medical disorders that could134

influence cognitive functioning (e.g., obstructive sleep135

apnea); (D) history of alcohol or substance misuse136

during the year prior to enrolment; and (E) contraindi-137

cations to the performance of MRI or 18FDG-PET such138

as a pacemaker, pregnancy, or claustrophobia.139

The protocol was approved by the medical ethics140

committee of the University of Malaga and conducted141

in accordance with the Declaration of Helsinki. All the142

subjects gave their written informed consent following143

a complete description of the study.144

Medical history, vital signs, and cardiovascular145

risk factors146

An ad hoc standardized interview was used to deter-147

mine: T2DM duration (DD), oral anti-diabetic and148

insulin treatment, history of hypertension or use of149

blood-pressure lowering medication, dyslipidemia or150

use of lipid-lowering medication, and smoking status.151

Vital signs were measured daily on three consec-152

utive days by the same investigator. Hypertension153

was defined as an average systolic blood pressure154

≥140 mmHg or diastolic blood pressure ≥ 90 mmHg.155

The body mass index (BMI) was calculated as weight156

in kilograms divided by the square of the height157

in meters, and the waist circumference (WC) was158

measured in centimeters according to the American159

Heart Association/National Heart Lung and Blood160

Institute (AHA/NHLBI) [26]. Cardiovascular risk was161

calculated for each patient using the Framingham Car-162

diovascular Risk Profile score (FCRP) [27].163

Laboratory and genetic analyses164

All the laboratory and genetic determinations were165

analyzed in the Lipid Research Laboratory at the166

CIMES. Fasting triglycerides, cholesterol and glucose167

were determined by enzymatic-colorimetric methods168

in a Mindray BS-380 chemistry analyzer as described169

previously [28]. A well-validated index of insulin170

resistance (HOMA-IR) was calculated using fasting171

glucose and serum insulin levels (measured through a172

chemoluminescent immunoassay using the chemistry173

analyzer Immulite One, Siemens). The percentage of174

HbA1c was measured by Variant II HbA1c reagents175

(Bio-Rad). The apolipoprotein E (APOE) genotype176

(rs429358 and rs7412 polymorphisms) was deter-177

mined by real-time PCR using a previously described178

TaqMan assay [29]. The absence or presence of at179

least one �4 allele for the APOE gene (APOE �4) was180

determined.181

Neuroimaging 182

MRI image acquisition 183

All the patients and control subjects underwent 184

a MRI scan using a 3-Tesla whole-body research- 185

dedicated scanner (Intera, Philips Medical Systems, 186

Best, the Netherlands) and an eight-channel head coil. 187

For voxel-based morphometry (VBM), a T1-weighted 188

3D dataset was acquired using magnetization pre- 189

pared by a rapid acquisition gradient-echo (MPRAGE) 190

sequence with the following parameters: Repetition 191

time (TR) 9.9 ms; Echo time (TE 4.6 ms); Flip angle 192

8º; Matrix acquisition 256/256 r; field of view (FOV) 193

240 mm; slice thickness 1 mm (190 slices); voxel size 194

0.9 × 0.9 × 1 m. 195

Voxel based morphometry (VBM) 196

T1-weighted images were pre-processed using an 197

optimized VBM protocol [30] and the Statistical 198

Parametric Mapping 5 software (SPM5, Wellcome 199

Department of Imaging Neurosciences, University 200

College London; [http://www.fil.ion.ucl.ac.uk/spm]) 201

running on MATLAB 7.00 (Math-Works, Natick, MA, 202

USA). First, MPRAGE images were segmented into 203

gray matter, white matter, and cerebrospinal fluid 204

by using the standard unified segmentation model in 205

SPM5 [31]. To remove non brain tissue, the ‘clean-up’ 206

procedure was applied to the segmented gray matter, 207

white matter, and cerebrospinal fluid images to calcu- 208

late the total intracranial volume. All analyses were 209

performed in ICBM-152 space transforming individ- 210

ual images using the T1 template supplied with SPM5. 211

After correcting for intensity non-uniformity, esti- 212

mates of gray matter density were generated. Density 213

estimates were modulated with the Jacobian trans- 214

formation matrix to address local compression or 215

expansion due to spatial normalization. Finally, spa- 216

tially normalized images were modulated to ensure that 217

the overall amount of each tissue class was not altered 218

by the spatial normalization procedure and smoothed 219

with a 6-mm full-width at half-maximum (FWHM) 220

Gaussian kernel for subsequent statistical analysis. 221

PET data acquisition and processing 222

Preparation for the PET study included fasting for 223

at least 6 hours before the administration of 18F-FDG 224

and oral hydration with water. Patients and control 225

subjects refrained from drinks containing alcohol or 226

caffeine and from smoking for 12 hours prior to the 227

PET scan. Before injection of 18F-FDG, the blood glu- 228

cose level was measured in each participant according 229

to Society of Nuclear Medicine Procedure Guideline 230

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4 N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes

Table 1Demographic and clinical characteristics of the type 2 diabetic patients and control subjects

Clinical and demographic characteristics Diabetics Controls p value

Age (years) 60.0 ± 4.6 57.8 ± 5.4 0.115Gender M/F 17/8 14/11 0.382Education (years)1 18.3 ± 3.6 18.9 ± 4.0 0.562MMSE score1 28.8 ± 1.3 29.7 ± 0.6 0.002*Weight1 81.91 ± 3.4 72.9 ± 8.2 0.004*Height 169.16 ± 0.8 167.6 ± 6.3 0.430Body mass index (Kg/m2) 28.6 ± 4.1 26.0 ± 3.2 0.014*Waist circumference (cm) 102.1 ± 10.8 94.2 ± 8.7 0.006*Systolic blood pressure (mmHg) 131.8 ± 10.8 121.2 ± 8.8 0.0002†Diastolic blood pressure (mmHg) 79.2 ± 6.4 74.6 ± 5.6 0.009*Antihypertensive treatment 11 (44%) 0 (0%) 0.0002†Lipid lowering treatment 11 (44%) 0 (0%) 0.0002†Total cholesterol (mg/dl) 196.5 ± 31.2 230.9 ± 36.3 0.001*HDL cholesterol (mg/dl) 54.09 ± 14.2 59.7 ± 14.4 0.257LDL cholesterol (mg/dl) 132.40 ± 29.5 158.48 ± 30.9 0.004*Triglycerides (mg/dl) 1 189.4 ± 107.2 130.8 ± 78.0 0.004*Smoking 8 (32%) 0 (0%) 0.0024†Cardiovascular Risk (%)1 17.6 ± 8.9 7.5 ± 3.6 0.0002†Apolipoprotein E�4 genotype 9 (36%) 5 (20%) 0.367Current HbA1c (%) 6.67 ± 0.76 5.32 ± 0.07 0.0001†HOMA-IR1 3.6 ± 2.93 1.4 ± 1.2 0.004*

Data are presented as mean ± S.D, n (%). *p<0.05; †p < 0.001; MMSE indicates Mini-Mental State Examination; (M/F) Male/Female; 1WilcoxonRank Sum test p-value reported.

for FDG PET Brain Imaging [32]. All the participants231

were PET scanned in a euglycemic state, and in no case232

was insulin injection required before, during or after233

the completion of the 18FDG-PET. None of the par-234

ticipants experienced hyperglycemia or other adverse235

effects during the PET scan. The subjects received an236

approximate dose of 370 MBq [18F] FDG in resting237

conditions with eyes closed and in an environment with238

dimmed ambient light. Forty minutes after injection of239

the radiotracer, PET acquisition was performed in a GE240

Discovery ST PET scanner for 20 minutes in 3D mode241

with a field of view of 15.7 cm and a pixel size 2.3 mm,242

after CT for attenuation correction purposes. SPM5243

was used for realignment, transformation into standard244

stereotactic space, smoothing (6 mm FWHM), and sta-245

tistical analyses. We used the PMOD software version246

3.2 (PMOD Technologies, Ltd., Zurich, Switzerland)247

for partial volume correction [33].248

Statistical analysis249

For inter-group comparisons of population charac-250

teristics, 2-sample t tests were used for continuous251

variables, the Wilcoxon Rank Sum test for such vari-252

ables when the assumptions for the t test were violated,253

and the chi squared test for dichotomous variables.254

The �-level was set at p < 0.05 for these two-tailed sta-255

tistical comparisons using SPSS (version 17.0; SPSS256

Inc., Chicago, IL, USA). For image analysis (MRI and257

PET), comparison of smoothed brain images between 258

groups was done using volumetric analysis in SPM5. 259

VBM and cerebral glucose metabolism were com- 260

pared between T2DM subjects and controls using the 261

unpaired student t-test. In addition, for VBM, total 262

intracranial volume was also added as a covariate. To 263

adjust for the confounding variables, multiple regres- 264

sion models were used in SPM5. 265

In the T2DM group, correlation analyses were 266

performed between gray matter densities and 267

hypometabolic regions and the Hb1Ac, DD, and 268

HOMA-IR. T maps from the statistical comparisons 269

were transformed into Z values, and the statistical sig- 270

nificance was estimated using random Gaussian field 271

methods. The statistical model uses an alpha with 272

p < 0.001 (voxel level, uncorrected) to define regions 273

of significant difference and an extent threshold of 10 274

voxels. We converted the Montreal Neurological Insti- 275

tute coordinates of voxel of maximal statistical signif- 276

icance into the Talairach and Tournoux system [34]. 277

RESULTS 278

Background characteristics 279

The characteristics of the study population are sum- 280

marized in Table 1. The T2DM group had a higher 281

weight and higher FCRP scores than the controls. 282

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N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes 5

Fig. 1. Red areas refer to significant Brodmann areas (BA) of reduced gray matter density in VBM (A) and reduced cerebral glucose metabolism(B) between type 2 diabetic patients compared with healthy control subjects. Results show fronto-temporal regions: Left frontal BA32 and lefttemporal BA38 (sagittal planes) (A); Left prefrontal BA10 (sagittal plane) and left inferior temporal BA20 (axial plane) (B).

Otherwise, the T2DM subjects and the controls did not283

differ in age, gender, educational level, or functional284

status as measured with the Bayer Activities of Daily285

Living Scale [24].286

In the T2DM group, the mean DD of T2DM was287

135 ± 94.8 months and the mean HbA1c percentage288

for the previous 3 years was 6.67 ± 0.766. Twenty-two289

diabetic patients were treated with metformin and three290

with insulin. None of the participants reported mod-291

erate/severe hypoglycemic episodes according to the292

Diabetes Control Complications Trial strict criteria in293

which the event leads to coma or unconsciousness [35].294

These results indicate that we had recruited compara-295

ble T2DM and control cases on non-diagnostic criteria296

and that the former group was relatively homogeneous.297

Laboratory findings298

As expected, the T2DM subjects had higher fasting299

glucose and fasting insulin levels than the controls. The300

HOMA-IR was significantly higher in the T2DM group301

than the controls (the three subjects receiving insulin302

were excluded from the analysis of this variable). Inter-303

estingly, the T2DM subjects had significantly lower304

serum cholesterol levels than the controls. There was305

no significant difference between the T2DM and the 306

control group concerning the APOE �4 genotypes. 307

Neuroimaging studies 308

The features of the neuroimaging results described 309

below show that the brain areas affected were very 310

limited portions of the cerebral cortex. 311

Voxel-based morphometry (VBM) 312

Gray matter densities were assessed for T2DM 313

subjects and controls of similar age, gender, and edu- 314

cation controlling for total intracranial volume and 315

cardiovascular risk factors (weight, blood pressure, 316

antihypertensive treatment, lipid lowering treatment, 317

total cholesterol, LDL cholesterol, triglycerides, and 318

smoking). The findings are summarized in Table 2 319

and depicted in Figure 1A. Overall, the T2DM group 320

had lower gray matter densities in the premotor cortex 321

[Brodmann’s area (BA) 6], the anterior cingulate cortex 322

(BA32), the rostral pole of the superior temporal gyrus 323

(BA38), and part of the BA36 of the left hemisphere 324

when compared to the controls.

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Table 2VBM: Differences in gray matter volume between patients with T2DM and control subjects and 18FDG-PET: Differences in glucose metabolism

between patients with T2DM and normal control subjects

VBM Regions Coordinates (mm) Size T Z(diabetic versus controls) (voxel)

x y z

L. anterior cingulate (BA32) –16 43 7 50 5.05 4.44L. rostral pole superior temporal gyrus (BA38) –24 4 –42 56 3.79 3.49L. (BA36) –26 2 –34 56 3.90 3.58L. premotor cortex (BA6) –34 5 57 10 3.76 3.4718FDG-PET Regions Coordinates (mm) Size T Z(diabetic versus controls) (voxel)

x y z

L. prefrontal cortex (BA10) –8 43 13 94 5.82 4.94L. inferior temporal gyrus (BA20) –57 –11 –21 11 4.69 4.18L. premotor cortex (BA6) –30 –5 52 17 4.12 3.74

Coordinates X, Y, Z refer to the anatomical location of peak voxels defined by the standard Talairach space [34]. All results significant on voxellevel uncorrected p < 0.001. R indicates right; L left; and BA Brodmann’s area.

18FDG-PET325

The T2DM patients showed significantly reduced326

cerebral glucose metabolism in the left prefrontal327

(BA10) and premotor (BA6) areas, and bilateral middle328

(BA21) and inferior (BA20) temporal gyri when com-329

pared with the control subjects of similar age, gender,330

and education, controlling for weight, blood pressure,331

antihypertensive treatment, lipid lowering treatment,332

total cholesterol, LDL cholesterol, triglycerides,333

and smoking. The differences in cerebral glucose334

metabolism between the T2DM and control groups are335

summarized in Table 2 and depicted in Fig. 1B.336

Effect of glycemic control and duration of illness337

on neuroimaging variables338

After assessing the impact of T2DM on brain struc-339

ture and function compared to non-diabetic controls,340

we aimed to determine whether glycemic control,341

DD, and insulin resistance correlated with the neu-342

roimaging findings within the T2DM group. We first343

asked whether HbA1c, as an index of glycemic con-344

trol, correlated with the VBM analysis. This analysis345

showed a negative correlation between HbA1c levels346

and gray matter density in the right medial prefrontal347

cortex (BA11) and left angular gyrus (BA39) (Fig-348

ure 2A-C and Table 3). HbA1c did not correlate349

with findings in 18FDG-PET. Disease duration cor-350

related negatively with gray matter density in the351

left orbital prefrontal gyrus (BA47), right premotor352

(BA8), and right prefrontal (BA46) areas, left ros-353

tral pole of the superior temporal gyrus (BA38), left354

inferior temporal gyrus (BA20), and right superior355

temporal gyrus (BA40) (Table 3). Furthermore, DD356

correlated negatively with reduced cerebral glucose 357

metabolism in the right and left medial prefrontal cor- 358

tex (BA11), right orbital prefrontal cortex (BA 45/47), 359

right angular gyrus (BA39), and left anterior cingulate 360

(BA32) (Fig. 2D-F and Table 4). Finally, we correlated 361

the results of brain imaging with insulin resistance 362

measured with the HOMA-IR. HOMA-IR correlated 363

negatively with gray matter density in the left mid- 364

dle temporal gyrus (BA21), left caudal fusiform gyrus 365

(BA37), left superior parietal precuneus (BA7), left 366

superior temporal gyrus (BA40), and right angular 367

gyrus (BA39). HOMA-IR correlated negatively with 368

cerebral glucose metabolism in the left middle tem- 369

poral gyrus (BA21) and left insula (BA13) (Tables 3 370

and 4). 371

DISCUSSION 372

We describe a cross-sectional study in 25 subjects 373

with T2DM and 25 controls that combines structural 374

and functional brain imaging studies with clinical, bio- 375

chemical, and genetic analyses. Although limited by 376

a relatively small sample size, previous publications 377

reported either structural or functional brain imaging 378

studies. Therefore, this is the first study to combine 379

both imaging modalities in a comparable group of 380

T2DM patients and controls, providing very valuable 381

information that will help us better understand the 382

effects of T2DM on the brain. 383

The main findings of our study are that relatively 384

young, well-controlled, and functionally intact patients 385

with T2DM present structural and metabolic changes 386

in the frontal and temporal regions, and that these 387

changes correlate with parameters of disease severity 388

and duration. These findings hold true even after con- 389

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N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes 7

Fig. 2. Negatively correlated gray matter density in VBM with lifetime average HbA1c for the type 2 diabetic patients group in the orbitofrontalcortex. Results shown on coronal (A), sagittal (B), and axial (C) planes. Red areas refer to regions of reduced gray matter density. Negativelycorrelated cerebral glucose metabolism in 18FDG-PET with diabetes duration in the orbitofrontal cortex. Results shown on coronal (D), sagittal(E), and axial (F) planes. Red areas refer to regions of reduced cerebral glucose metabolism.

Table 3VBM correlations between gray matter density and HbA1c levels, diabetes duration, and insulin resistance index (HOMA-IR) in the diabetic

group

VBM Regions Coordinates (mm) Size T Z R(Diabetic group) (voxel)

x y z

HbA1c correlationsR. prefrontal cortex (BA11) 6 22 –20 53 3.96 4.64 –0.72L. angular gyrus (BA39) –36 –69 26 10 3.96 3.96 –0.51Diabetes duration correlationsR. prefrontal cortex (BA46) 50 23 27 27 6.13 4.50 –0.72R. superior temporal gyrus (BA40) 51 –50 49 13 5.15 4.02 –0.61L. superior temporal gyrus (BA38) –28 16 –36 12 5.04 3.97 –0.73L. inferior temporal gyrus(BA20) –57 –3 –30 11 4.40 3.61 –0.70L. orbital prefrontal gyrus (BA47) –18 32 –23 11 4.19 3.48 –0.64R. premotor cortex (BA8) 46 16 43 13 4.07 3.41 –0.64HOMA-IR correlationsL. middle temporal gyrus (BA21) –63 –50 1 24 5.08 3.99 –0.67L. parietal lobule precuneus (BA7) –10 –46 48 25 4.85 3.86 –0.65L. superior temporal gyrus (BA40) –57 –41 26 11 5.17 4.03 –0.60L. caudal fusiform gyrus(BA37) –42 –64 3 27 5.27 4.09 –0.72R. angular gyrus(BA39) 53 –65 24 25 5.32 4.11 –0.78

Coordinates X, Y, Z refer to the anatomical location of peak voxels defined by the standard Talairach space [34]. All results significant on voxellevel p < 0.001 (uncorrected). R, right; L, left; BA; Brodmann’s area.

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8 N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes

Table 418FDG-PET correlations between cerebral glucose metabolism, diabetes duration, and insulin resistance index (HOMA-IR) in the diabetic group

18FDG-PET Regions Coordinates (mm) Size T Z R(Diabetic group) (voxel)

x y z

Diabetes duration correlationsL. medial prefrontal cortex (BA11) –10 24 –21 189 4.72 3.79 –0.77R. medial prefrontal cortex (BA11) 8 53 –19 55 5.06 3.98 –0.73R. prefrontal cortex (BA10) 22 57 10 15 4.52 3.68 –0.68R. orbital prefrontal gyrus (BA47) 48 35 –8 10 4.55 3.69 –0.63R. orbital prefrontal gyrus (BA45) 55 11 18 20 5.17 4.04 –0.72R. angular gyrus (BA39) 42 –68 38 25 5.61 4.26 –0.75L. anterior cingulate (BA32) –4 40 16 10 4.55 3.69 –0.64HOMA-IR correlationsL. middle temporal gyrus (BA21) –61 –50 4 17 5.41 4.16 –0.75L. insula (BA13) –40 –21 14 72 5.54 4.22 –0.78

Coordinates X, Y, Z refer to the anatomical location of peak voxels defined by the standard Talairach space [34]. All results significant on voxellevel p < 0.001 (uncorrected). R, right; L, left; BA; Brodmann’s area.

Fig. 3. Negatively correlated cerebral glucose metabolism in 18FDG-PET with IR-HOMA for type 2 diabetic patients group in the left insula.Results shown on coronal (A), sagittal (B) and axial (C) planes. Red areas refer to regions of reduced cerebral glucose metabolism. Negativelycorrelated gray matter density in VBM with HOMA-IR in the left precuneus and left middle temporal gyurs, results shown on coronal (D); leftmiddle temporal gyrus shown on sagittal (E) left middle temporal gyrus and caudal fusiform gyrus shown in axial (F) planes. Red areas refer toregions of reduced gray matter density.

trolling for cardiovascular risk factors, suggesting that390

diabetes presents an independent effect on brain struc-391

ture and function. We acknowledge that these results392

should be taken with a note of caution as, due to the393

small sample size inherent to a single-site study of 394

these characteristics, multiple corrections for all the 395

study variables could not be performed. Therefore, we 396

cannot exclude the possibility that some of our findings 397

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N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes 9

are not in fact a consequence of T2DM. Nevertheless,398

they are of great interest and support the emerg-399

ing hypothesis suggesting that T2DM independently400

affects brain function, implicating it in neurodegener-401

ative processes. Furthermore, our study provides the402

basis for larger, multi-site studies to replicate and403

extend these interesting results.404

We aimed to address the influence of T2DM on brain405

structure and function before age-related degenerative406

diseases occur. Most publications on this topic have407

focused on elderly diabetic individuals [36–38], and408

neuroimaging studies in middle-aged T2DM patients409

are scarce [39]. We reasoned that selecting a group410

of relatively young diabetic subjects lacking cognitive411

complaints or functional impairment would increase412

the clinical relevance of early structural or metabolic413

changes that might herald a progressively deteriorating414

course warranting treatment intervention. We recruited415

a control group very similar demographically to the416

T2DM subjects.417

As mentioned above, we cannot completely exclude418

the possibility that the differences found between the419

patients and the controls in our sample are due to420

variables that were not controlled for in multiple com-421

parisons, given the small sample size. However, the422

similarities of our findings with previous, similarly423

small and uncontrolled studies, suggest they are due424

to the presence of T2DM. Previous cross-sectional425

studies in patients with T2DM have reported a reduc-426

tion in prefrontal, anterior cingulate, and orbitofrontal427

regions and the temporal cortex and hippocampal brain428

volumes [14–19, 37–41]. In agreement with these429

reports, the areas showing reduced gray matter den-430

sity in our T2DM subjects involved the premotor area431

(BA6), anterior cingulate cortex (BA32), and rostral432

pole of the temporal superior gyrus (BA38) and BA36.433

Our results showed left hemisphere predominance. A434

recent voxel-based morphometry study in 16 T2DM435

subjects showed significant gray matter density reduc-436

tion in fronto-temporal regions, mainly on the right437

side, when compared with controls [41]. Furthermore,438

18FDG-PET analysis showed hypometabolism in left439

frontal (premotor and prefrontal) areas (BA6/10) and440

left and right inferior and middle temporal regions441

(BA20/BA21) of T2DM patients. Baker et al. [20]442

analyzed cerebral glucose metabolism in 23 cogni-443

tively unimpaired adults with pre-diabetes or early444

T2DM and six healthy controls. They also found a sig-445

nificant reduction in cerebral glucose metabolism in446

fronto-temporo-parietal cortices in the disease group.447

However, these authors did not include structural brain448

imaging.449

Once we established that the differences between 450

T2DM patients and controls in our sample were con- 451

sistent with previous studies, we asked whether disease 452

duration and severity correlated with our findings on 453

brain structure and function, as suggested by previ- 454

ous studies [20, 42–44]. Cukierman-Yaffe et al. [44] 455

showed a correlation between glycemic control and 456

cognitive function in patients with T2DM. Launder 457

et al. demonstrated that diabetic patients from the 458

ACCORD MIND study who received an intensive 459

glucose-lowering treatment reducing HbA1c to less 460

than 6% have greater brain volume than those who 461

received a standard treatment reducing HbA1c to not 462

less than 7–7.9%, though the cognitive outcomes did 463

not differ between groups. However they hypothesized 464

that structural brain changes in non-elderly T2DM 465

cases occur before cognitive changes [45]. In a sample 466

of patients with type 1 diabetes, Musen et al. [42] found 467

that indicators of disease severity (higher levels of 468

HbA1c, longer DD, severe hypoglycemic events, and 469

severity of retinopathy) correlated with cortical and/or 470

subcortical gray matter atrophy. Although our study 471

population included well-controlled T2DM patients 472

and the mean Hb1Ac percentage in the diabetic group 473

was 6.67 ± 0.766%, we had similar findings. However 474

we did not find significant correlations for cerebral glu- 475

cose metabolism and HbA1c, probably because of the 476

small range of HbA1c percentages within the T2DM 477

cases due to their medication and the small sample size. 478

We found that longer duration of diabetes and greater 479

insulin resistance were linked to reduced gray matter 480

densities and metabolism in prefrontal and temporal 481

areas. 482

Tomita et al. [46] used functional imaging to 483

evaluate the brain accumulation and distribution of 484

amyloid-� (A�). They used PET with an A� tracer 485

(BF-227) to study 14 controls and 15 patients with AD, 486

four of them diabetic and 11 non-diabetic. Although 487

their sample size makes it difficult to draw consis- 488

tent conclusions, they found a similar burden of A� 489

accumulation in all the AD patients, whether diabetic 490

or not, suggesting that diabetes does not affect brain 491

levels of A� deposition. This is consistent with the 492

failure of most previous studies to find an effect of 493

diabetes alone [12–13] on A� plaques and neurofib- 494

rillary tangles in the cerebral cortex and with the 495

findings that brain insulin resistance in AD occurs 496

in the absence of diabetes [47]. On the other hand, 497

Kuczynski et al. [48] linked the Framingham Cardio- 498

vascular Risk Profile score, which estimates the risk of 499

various cardiovascular disease outcomes, with reduced 500

cerebral glucose metabolism, predominantly in the left 501

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10 N. Garcıa-Casares et al. / Brain Changes in Type 2 Diabetes

prefrontal cortex. This suggested the possibility that502

the influence of T2DM on brain function they found503

is a consequence of a population with higher cardio-504

vascular risk factors. In our study, the mean Hb1Ac505

percentage of the diabetic group is at the threshold506

for presenting cardiovascular disease. Furthermore,507

the structural and metabolic changes we found per-508

sisted after controlling for the effect of risk factors509

for cardiovascular diseases. Therefore, together with510

the findings in patients with pre-diabetes or early dis-511

ease discussed earlier, our study suggests that T2DM512

contributes to brain dysfunction through a mechanism513

independent of cardiovascular disease (e.g., amyloid514

deposition) [5–8]. While the findings from this and pre-515

vious studies need to be replicated in larger samples,516

future prospective studies should also address whether517

these patients progress to having primary degenera-518

tive pathology (such as AD) or to vascular cognitive519

disorders.520

Our study is limited by a small sample size, and that521

all the T2DM participants were receiving antidiabetic522

treatment. In addition, the cross-sectional design does523

not allow establishing causal relationships or assess-524

ing the progression of structural and functional brain525

changes. However, this is the first study to combine526

structural and functional imaging modalities in a rela-527

tively young and functional diabetic population and528

with a larger sample than previous reports. Collec-529

tively, our work supports previous reports indicating530

that T2DM is an independent risk factor for age-related531

cognitive disease, suggesting a direct effect on brain532

structure and function. The structural and functional533

T2DM brain abnormalities may occur long before534

the clinical cognitive impairment. Thus, our results535

suggest the potential utility of neuroimaging for mon-536

itoring from the early stages of diabetes/peripheral537

insulin resistance to identify individuals at risk for538

cognitive impairment.539

Systematic reviews of the literature report a cogni-540

tive profile of mild to moderate decrements in cognitive541

functioning in patients with type 2 diabetes [49, 50].542

These decrements are most consistently found in cog-543

nitive domains such as information-processing speed544

and executive functioning, which are mostly dependent545

on the frontal lobe, and also verbal memory, strongly546

left-lateralized in the medial temporal lobe. Given547

that our results show a fronto-temporal predominance,548

further studies in T2DM patients including neuropsy-549

chological and imaging correlations could help to550

understand this process. Our preliminary results, which551

need to be confirmed in larger, longitudinal studies,552

could lead to the design of novel therapeutic inter-553

ventions for the treatment or prevention of individuals 554

potentially at risk for cognitive impairment. 555

ACKNOWLEDGMENTS 556

The authors wish to acknowledge Francisco 557

Alfaro, Centro de Investigaciones Medico-Sanitarias 558

(CIMES), University of Malaga, for the MRI technical 559

assistance. 560

Authors’ disclosures available online (http://www.j- 561

alz.com/disclosures/view.php?id=2038). 562

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