Microstructural correlates of 3D steady-state inhomogeneous magnetization transfer (ihMT) in the human brain white matter assessed by myelin water imaging and diffusion tensor imaging.

PURPOSE: To compare the recently introduced inhomogeneous magnetization transfer (ihMT) technique with more established MRI techniques including myelin water imaging (MWI) and diffusion tensor imaging (DTI), and to evaluate the microstructural attributes correlating with this new contrast method in the human brain white matter. METHODS: Eight adult healthy volunteers underwent T1 -weighted, ihMT, MWI, and DTI imaging on a 3T human scanner. The ihMT ratio (ihMTR), myelin water fraction (MWF), fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) values were calculated from different white matter tracts. The angle ( θ ) between the directions of the principal eigenvector, as measured by DTI, and the main magnetic field was calculated for all voxels from various fiber tracts. The ihMTR was correlated with MWF and DTI metrics. RESULTS: A strong correlation was found between ihMTR and MWF (ρ = 0.77, P < 0.0001). This was followed by moderate to weak correlations between ihMTR and DTI metrics: RD (ρ = -0.30, P < 0.0001), FA (ρ = 0.20, P < 0.0001), MD (ρ = -0.19, P < 0.0001), AD (ρ = 0.02, P < 0.0001). A strong correlation was found between ihMTR and θ (ρ = -0.541, P < 0.0001). CONCLUSION: The strong correlation with myelin water imaging and its low coefficient of variation suggest that ihMT has the potential to become a new structural imaging marker of myelin. The substantial orientational dependence of ihMT should be taken into account when evaluating and quantitatively interpreting ihMT results.

Associations of Early Kidney Disease With Brain Magnetic Resonance Imaging and Cognitive Function in African Americans With Type 2 Diabetes Mellitus.

BACKGROUND: Relationships between early kidney disease, neurocognitive function, and brain anatomy are poorly defined in African Americans with type 2 diabetes mellitus (T2DM). STUDY DESIGN: Cross-sectional associations were assessed between cerebral anatomy and cognitive performance with estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR) in African Americans with T2DM. SETTING & PARTICIPANTS: African Americans with cognitive testing and cerebral magnetic resonance imaging (MRI) in the African American-Diabetes Heart Study Memory in Diabetes (AA-DHS MIND; n=512; 480 with MRI) and Action to Control Cardiovascular Risk in Diabetes (ACCORD) MIND (n=484; 104 with MRI) studies. PREDICTORS: eGFR (CKD-EPI creatinine equation), spot UACR. MEASUREMENTS: MRI-based cerebral white matter volume (WMV), gray matter volume (GMV), and white matter lesion volume; cognitive performance (Mini-Mental State Examination, Digit Symbol Coding, Stroop Test, and Rey Auditory Verbal Learning Test). Multivariable models adjusted for age, sex, body mass index, scanner, intracranial volume, education, diabetes duration, hemoglobin A1c concentration, low-density lipoprotein cholesterol concentration, smoking, hypertension, and cardiovascular disease were used to test for associations between kidney phenotypes and the brain in each study; a meta-analysis was performed. RESULTS: Mean participant age was 60.1±7.9 (SD) years; diabetes duration, 12.1±7.7 years; hemoglobin A1c concentration, 8.3%±1.7%; eGFR, 88.7±21.6mL/min/1.73m2; and UACR, 119.2±336.4mg/g. In the fully adjusted meta-analysis, higher GMV associated with lower UACR (P<0.05), with a trend toward association with higher eGFR. Higher white matter lesion volume was associated with higher UACR (P<0.05) and lower eGFR (P<0.001). WMV was not associated with either kidney parameter. Higher UACR was associated with lower Digit Symbol Coding performance (P<0.001) and a trend toward association with higher Stroop interference; eGFR was not associated with cognitive tests. LIMITATIONS: Cross-sectional; single UACR measurement. CONCLUSIONS: In African Americans with T2DM, mildly high UACR and mildly low eGFR were associated with smaller GMV and increased white matter lesion volume. UACR was associated with poorer processing speed and working memory.

APOL1 renal-risk variants associate with reduced cerebral white matter lesion volume and increased gray matter volume.

To assess apolipoprotein L1 gene (APOL1) renal-risk-variant effects on the brain, magnetic resonance imaging (MRI)-based cerebral volumes and cognitive function were assessed in 517 African American-Diabetes Heart Study (AA-DHS) Memory IN Diabetes (MIND) and 2568 hypertensive African American Systolic Blood Pressure Intervention Trial (SPRINT) participants without diabetes. Within these cohorts, 483 and 197 had cerebral MRI, respectively. AA-DHS participants were characterized as follows: 60.9% female, mean age of 58.6 years, diabetes duration 13.1 years, estimated glomerular filtration rate of 88.2 ml/min/1.73 m(2), and a median spot urine albumin to creatinine ratio of 10.0 mg/g. In additive genetic models adjusting for age, sex, ancestry, scanner, intracranial volume, body mass index, hemoglobin A1c, statins, nephropathy, smoking, hypertension, and cardiovascular disease, APOL1 renal-risk-variants were positively associated with gray matter volume (ß = 3.4 × 10(-3)) and negatively associated with white matter lesion volume (ß = -0.303) (an indicator of cerebral small vessel disease) and cerebrospinal fluid volume (ß= -30707) (all significant), but not with white matter volume or cognitive function. Significant associations corresponding to adjusted effect sizes (ß/SE) were observed with gray matter volume (0.16) and white matter lesion volume (-0.208), but not with cerebrospinal fluid volume (-0.251). Meta-analysis results with SPRINT Memory and Cognition in Decreased Hypertension (MIND) participants who had cerebral MRI were confirmatory. Thus, APOL1 renal-risk-variants are associated with larger gray matter volume and lower white matter lesion volume suggesting lower intracranial small vessel disease.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.

Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition. The framework comprises two components: a 3D autoencoder model for perceptual compression and a conditional 3D Diffusion Probabilistic Model (DPM) for generating high-quality and diverse multi-contrast brain tumor MRI samples, guided by a conditional tumor mask. Unlike existing works that focused on generating either 2D multi-contrast or 3D single-contrast MRI samples, our models generate multi-contrast 3D MRI samples. We also integrated a conditional module within the UNet backbone of the DPM to capture the semantic class-dependent data distribution driven by the provided tumor mask to generate MRI brain tumor samples based on a specific brain tumor mask. We trained our models using two brain tumor datasets: The Cancer Genome Atlas (TCGA) public dataset and an internal dataset from the University of Texas Southwestern Medical Center (UTSW). The models were able to generate high-quality 3D multi-contrast brain tumor MRI samples with the tumor location aligned by the input condition mask. The quality of the generated images was evaluated using the Fréchet Inception Distance (FID) score. This work has the potential to mitigate the scarcity of brain tumor data and improve the performance of deep learning models involving brain tumor MRI data.

Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

[This corrects the article DOI: 10.1117/1.JMI.9.1.016001.].

Corrigendum to: A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

[This corrects the article DOI: 10.1093/noajnl/vdaa066.].

MRI-Based Deep Learning Method for Classification of IDH Mutation Status.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin-Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

Purpose: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. Approach: T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. Results: Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. Conclusions: Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

BACKGROUND: One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. METHODS: Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight subjects were non-co-deleted. A T2w image-only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the network performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS: 1p/19q-net demonstrated a mean cross-validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, SD = 0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ± 0.003 and 0.95 ± 0.01, respectively and a mean area under the curve of 0.95 ± 0.01. The whole tumor segmentation mean Dice score was 0.80 ± 0.007. CONCLUSION: We demonstrate high 1p/19q co-deletion classification accuracy using only T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow.

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network. METHODS: Multiparametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive and The Cancer Genome Atlas. Two separate networks were developed, including a T2w image-only network (T2-net) and a multicontrast (T2w, fluid attenuated inversion recovery, and T1 postcontrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D Dense-UNets. Three-fold cross-validation was performed to generalize the networks' performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS: T2-net demonstrated a mean cross-validation accuracy of 97.14% ± 0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ± 0.03, specificity of 0.98 ± 0.01, and an area under the curve (AUC) of 0.98 ± 0.01. TS-net achieved a mean cross-validation accuracy of 97.12% ± 0.09, with a sensitivity of 0.98 ± 0.02, specificity of 0.97 ± 0.001, and an AUC of 0.99 ± 0.01. The mean whole tumor segmentation Dice scores were 0.85 ± 0.009 for T2-net and 0.89 ± 0.006 for TS-net. CONCLUSION: We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone toward clinical translation.

Relationships between cerebral structure and cognitive function in African Americans with type 2 diabetes.

BACKGROUND: Relationships between cognitive function and brain structure remain poorly defined in African Americans with type 2 diabetes. METHODS: Cognitive testing and cerebral magnetic resonance imaging in African Americans from the Diabetes Heart Study Memory IN Diabetes (n = 480) and Action to Control Cardiovascular Risk in Diabetes MIND (n = 104) studies were examined for associations. Cerebral gray matter volume (GMV), white matter volume (WMV) and white matter lesion volume (WMLV) and cognitive performance (Mini-mental State Exam [MMSE and 3MSE], Digit Symbol Coding (DSC), Stroop test, and Rey Auditory Verbal Learning Test) were recorded. Multivariable models adjusted for age, sex, BMI, scanner, intracranial volume, education, diabetes duration, HbA1c, LDL-cholesterol, smoking, hypertension and cardiovascular disease assessed associations between cognitive tests and brain volumes by study and meta-analysis. RESULTS: Mean(SD) participant age was 60.1(7.9) years, diabetes duration 12.1(7.7) years, and HbA1c 8.3(1.7)%. In the fully-adjusted meta-analysis, lower GMV associated with poorer global performance on MMSE/3MSE (ß̂ = 7.1 × 10-3, SE 2.4 × 10-3, p = 3.6 × 10-3), higher WMLV associated with poorer performance on DSC (ß̂ = -3 × 10-2, SE 6.4 × 10-3, p = 5.2 × 10-5) and higher WMV associated with poorer MMSE/3MSE performance (ß̂ = -7.1 × 10-3, SE = 2.4 × 10-3, p = 3.6 × 10-3). CONCLUSIONS: In African Americans with diabetes, smaller GMV and increased WMLV associated with poorer performance on tests of global cognitive and executive function. These data suggest that WML burden and gray matter atrophy associate with cognitive performance independent of diabetes-related factors in this population.

Cerebral Structure and Cognitive Performance in African Americans and European Americans With Type 2 Diabetes.

Background: African Americans typically perform worse than European Americans on cognitive testing. Contributions of cardiovascular disease (CVD) risk factors and educational quality to cognitive performance and brain volumes were compared in European Americans and African Americans with type 2 diabetes. Methods: Association between magnetic resonance imaging-determined cerebral volumes of white matter (WMV), gray matter (GMV), white matter lesions (WMLV), hippocampal GMV, and modified mini-mental state exam (3MSE), digit symbol coding (DSC), Rey Auditory Verbal Learning Test (RAVLT), Stroop, and verbal fluency performance were assessed in Diabetes Heart Study Memory in Diabetes (MIND) participants. Marginal models incorporating generalized estimating equations were employed with serial adjustment for risk factors. Results: The sample included 520 African Americans and 684 European Americans; 56 per cent female with mean ± SD age 62.8 ± 10.3 years and diabetes duration 14.3 ± 7.8 years. Adjusting for age, sex, diabetes duration, BMI, HbA1c, total intracranial volume, scanner, statins, CVD, smoking, and hypertension, WMV (p = .001) was lower and WMLV higher in African Americans than European Americans (p = .001), with similar GMV (p = .30). Adjusting for age, sex, education, HbA1c, diabetes duration, hypertension, BMI, statins, CVD, smoking, and depression, poorer performance on 3MSE, RAVLT, and DSC were seen in African Americans (p = 6 × 10-23-7 × 10-62). Racial differences in cognitive performance were attenuated after additional adjustment for WMLV and nearly fully resolved after adjustment for wide-range achievement test (WRAT) performance (p = .0009-.65). Conclusions: African Americans with type 2 diabetes had higher WMLV and poorer cognitive performance than European Americans. Differences in cognitive performance were attenuated after considering WMLV and apparent poorer educational quality based on WRAT.

Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks.

The effect of Type 2 Diabetes (T2D) on brain health is poorly understood. This study aims to quantify the association between T2D and perfusion in the brain. T2D is a very common metabolic disorder that can cause long term damage to the renal and cardiovascular systems. Previous research has discovered the shape, volume and white matter microstructures in the brain to be significantly impacted by T2D. We propose a fully-connected deep neural network to classify the regional Cerebral Blood Flow into low or high levels, given 16 clinical measures as predictors. The clinical measures include diabetes, renal, cardiovascular and demographics measures. Our model enables us to discover any nonlinear association which might exist between the input features and target. Moreover, our end-to-end architecture automatically learns the most relevant features and combines them without the need for applying a feature selection method. We achieved promising classification performance. Furthermore, in comparison with six (6) classical machine learning algorithms and six (6) alternative deep neural networks similarly tuned for the task, our proposed model outperformed all of them.

Analysis of the relationships between type 2 diabetes status, glycemic control, and neuroimaging measures in the Diabetes Heart Study Mind.

AIMS: To examine the relationships between type 2 diabetes (T2D) status, glycemic control, and T2D duration with magnetic resonance imaging (MRI)-derived neuroimaging measures in European Americans from the Diabetes Heart Study (DHS) Mind cohort. METHODS: Relationships were examined using marginal models with generalized estimating equations in 784 participants from 514 DHS Mind families. Fasting plasma glucose, glycated hemoglobin, and diabetes duration were analyzed in 682 participants with T2D. Models were adjusted for potential confounders, including age, sex, history of cardiovascular disease, smoking, educational attainment, and use of statins or blood pressure medications. Association was tested with gray and white matter volume, white matter lesion volume, gray matter cerebral blood flow, and white and gray matter fractional anisotropy and mean diffusivity. RESULTS: Adjusting for multiple comparisons, T2D status was associated with reduced white matter volume (p = 2.48 × 10(-6)) and reduced gray and white matter fractional anisotropy (p ≤ 0.001) in fully adjusted models, with a trend toward increased white matter lesion volume (p = 0.008) and increased gray and white matter mean diffusivity (p ≤ 0.031). Among T2D-affected participants, neither fasting glucose, glycated hemoglobin, nor diabetes duration were associated with the neuroimaging measures assessed (p > 0.05). CONCLUSIONS: While T2D was significantly associated with MRI-derived neuroimaging measures, differences in glycemic control in T2D-affected individuals in the DHS Mind study do not appear to significantly contribute to variation in these measures. This supports the idea that the presence or absence of T2D, not fine gradations of glycemic control, may be more significantly associated with age-related changes in the brain.

Associations between anxiety and depression symptoms and cognitive testing and neuroimaging in type 2 diabetes.

AIMS: Anxiety, depression, accelerated cognitive decline, and increased risk of dementia are observed in individuals with type 2 diabetes. Anxiety and depression may contribute to lower performance on cognitive tests and differences in neuroimaging observed in individuals with type 2 diabetes. METHODS: These relationships were assessed in 655 European Americans with type 2 diabetes from 504 Diabetes Heart Study families. Participants completed cognitive testing, brain magnetic resonance imaging, the Brief Symptom Inventory Anxiety subscale, and the Center for Epidemiologic Studies Depression-10. RESULTS: In analyses adjusted for age, sex, educational attainment, and use of psychotropic medications, individuals with comorbid anxiety and depression symptoms had lower performance on all cognitive testing measures assessed (p≤0.005). Those with both anxiety and depression also had increased white matter lesion volume (p=0.015), decreased gray matter cerebral blood flow (p=4.43×10(-6)), decreased gray matter volume (p=0.002), increased white and gray matter mean diffusivity (p≤0.001), and decreased white matter fractional anisotropy (p=7.79×10(-4)). These associations were somewhat attenuated upon further adjustment for health status related covariates. CONCLUSIONS: Comorbid anxiety and depression symptoms were associated with cognitive performance and brain structure in a European American cohort with type 2 diabetes.

APOE Genotypes Associate With Cognitive Performance but Not Cerebral Structure: Diabetes Heart Study MIND.

OBJECTIVE: Dementia is a debilitating illness with a disproportionate burden in patients with type 2 diabetes (T2D). Among the contributors, genetic variation at the apolipoprotein E locus (APOE) is posited to convey a strong effect. This study compared and contrasted the association of APOE with cognitive performance and cerebral structure in the setting of T2D. RESEARCH DESIGN AND METHODS: European Americans from the Diabetes Heart Study (DHS) MIND (n = 754) and African Americans from the African American (AA)-DHS MIND (n = 517) were examined. The cognitive battery assessed executive function, memory, and global cognition, and brain MRI was performed. RESULTS: In European Americans and African Americans, the APOE E4 risk haplotype group was associated with poorer performance on the modified Mini-Mental Status Examination (P < 0.017), a measure of global cognition. In contrast to the literature, the APOE E2 haplotype group, which was overrepresented in these participants with T2D, was associated with poorer Rey Auditory Verbal Learning Test performance (P < 0.032). Nominal associations between APOE haplotype groups and MRI-determined cerebral structure were observed. CONCLUSIONS: Compared with APOE E3 carriers, E2 and E4 carriers performed worse in the cognitive domains of memory and global cognition. Identification of genetic contributors remains critical to understanding new pathways to prevent and treat dementia in the setting of T2D.

Cerebral structural changes in diabetic kidney disease: African American-Diabetes Heart Study MIND.

OBJECTIVE: Albuminuria and reduced kidney function are associated with cognitive impairment. Relationships between nephropathy and cerebral structural changes remain poorly defined, particularly in African Americans (AAs), a population at higher risk for both cognitive impairment and diabetes than European Americans. We examined the relationship between urine albumin:creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), and cerebral MRI volumes in 263 AAs with type 2 diabetes. RESEARCH DESIGN AND METHODS: Cross-sectional associations between renal parameters and white matter (WM), gray matter (GM), hippocampal, and WM lesion (WML) volumes were assessed using generalized linear models adjusted for age, education, sex, BMI, hemoglobin A1c (HbA1c) level, and hypertension. RESULTS: Participants had a mean (SD) age of 60.2 years (9.7 years), and 62.7% were female. Mean diabetes duration was 14.3 years (8.9 years), HbA1c level was 8.2% (2.2%; 66 mmol/mol), eGFR was 86.0 mL/min/1.73 m(2) (23.2 mL/min/1.73 m(2)), and UACR was 155.8 mg/g (542.1 mg/g; median 8.1 mg/g). Those with chronic kidney disease (CKD) (eGFR <60 mL/min/1.73 m(2) or UACR >30 mg/g) had smaller GM and higher WML volumes. Higher UACR was significantly associated with higher WML volume and greater atrophy (larger cerebrospinal fluid volumes), and smaller GM and hippocampal WM volumes. A higher eGFR was associated with larger hippocampal WM volumes. Consistent with higher WML volumes, participants with CKD had significantly poorer processing speed and working memory. These findings were independent of glycemic control. CONCLUSIONS: We found albuminuria to be a better marker of cerebral structural changes than eGFR in AAs with type 2 diabetes. Relationships between albuminuria and brain pathology may contribute to poorer cognitive performance in patients with mild CKD.

Subclinical Atherosclerosis Is Inversely Associated With Gray Matter Volume in African Americans With Type 2 Diabetes.

OBJECTIVE: Relative to European Americans, African Americans manifest lower levels of computed tomography-based calcified atherosclerotic plaque (CP), a measure of subclinical cardiovascular disease (CVD). Potential relationships between CP and cerebral structure are poorly defined in the African American population. We assessed associations among glycemic control, inflammation, and CP with cerebral structure on MRI and with cognitive performance in 268 high-risk African Americans with type 2 diabetes. RESEARCH DESIGN AND METHODS: Associations among hemoglobin A1c (HbA1c), C-reactive protein (CRP), and CP in coronary arteries, carotid arteries, and the aorta with MRI volumetric analysis (white matter volume, gray matter volume [GMV], cerebrospinal fluid volume, and white matter lesion volume) were assessed using generalized linear models adjusted for age, sex, African ancestry proportion, smoking, BMI, use of statins, HbA1c, hypertension, and prior CVD. RESULTS: Participants were 63.4% female with mean (SD) age of 59.8 years (9.2), diabetes duration of 14.5 years (7.6), HbA1c of 7.95% (1.9), estimated glomerular filtration rate of 86.6 mL/min/1.73 m(2) (24.6), and coronary artery CP mass score of 215 mg (502). In fully adjusted models, GMV was inversely associated with coronary artery CP (parameter estimate [ß] -0.47 [SE 0.15], P = 0.002; carotid artery CP (ß -1.92 [SE 0.62], P = 0.002; and aorta CP [ß -0.10 [SE 0.03] P = 0.002), whereas HbA1c and CRP did not associate with cerebral volumes. Coronary artery CP also associated with poorer global cognitive function on the Montreal Cognitive Assessment. CONCLUSIONS: Subclinical atherosclerosis was associated with smaller GMV and poorer cognitive performance in African Americans with diabetes. Cardioprotective strategies could preserve GMV and cognitive function in high-risk African Americans with diabetes.