Intensive Blood Pressure Treatment and Subclinical Brain Infarcts: A Secondary Analysis of SPRINT (Systolic Pressure Intervention Trial).

OBJECTIVE: Subclinical brain infarcts (SBI) increase the risk for stroke and dementia, but whether they should be considered equivalent to symptomatic stroke when determining blood pressure targets remains unclear. We tested whether intensive systolic blood pressure (SBP) treatment reduced the risk of new SBI or stroke and determined the association between SBI and cognitive impairment. METHODS: In this secondary analysis of SPRINT (Systolic Pressure Intervention Trial), participants ≥50 years old, with SBP 130-180mmHg and elevated cardiovascular risk but without known clinical stroke, dementia, or diabetes, were randomized to intensive (<120mmHg) or standard (<140mmHg) SBP treatment. Brain magnetic resonance images collected at baseline and follow-up were read for SBI. The occurrence of mild cognitive impairment (MCI) or probable dementia (PD) was evaluated. RESULTS: For 667 participants at baseline, SBI were identified in 75 (11%). At median 3.9 years follow-up, 12 of 457 had new SBI on magnetic resonance imaging (5 intensive, 7 standard), whereas 8 had clinical stroke (4 per group). Baseline SBI (subhazard ratio [sHR] = 3.90; 95% CI 1.49 to 10.24; p = 0.006), but not treatment group, was associated with new SBI or stroke. For participants with baseline SBI, intensive treatment reduced their risk for recurrent SBI or stroke (sHR = 0.050; 95% CI 0.0031 to 0.79; p = 0.033). Baseline SBI also increased risk for MCI or PD during follow-up (sHR = 2.38; 95% CI 1.23 to 4.61; p = 0.010). INTERPRETATION: New cerebral ischemic events were infrequent, but intensive treatment mitigated the increased risk for participants with baseline SBI, indicating primary prevention SBP goals are still appropriate when SBI are present. ANN NEUROL 2024;95:866-875.

MRI Investigation of the Association of Left Atrial and Left Atrial Appendage Hemodynamics with Silent Brain Infarction.

BACKGROUND: Left atrial (LA) myopathy is thought to be associated with silent brain infarctions (SBI) through changes in blood flow hemodynamics leading to thrombogenesis. 4D-flow MRI enables in-vivo hemodynamic quantification in the left atrium (LA) and LA appendage (LAA). PURPOSE: To determine whether LA and LAA hemodynamic and volumetric parameters are associated with SBI. STUDY TYPE: Prospective observational study. POPULATION: A single-site cohort of 125 Participants of the multiethnic study of atherosclerosis (MESA), mean age: 72.3 ± 7.2 years, 56 men. FIELD STRENGTH/SEQUENCE: 1.5T. Cardiac MRI: Cine balanced steady state free precession (bSSFP) and 4D-flow sequences. Brain MRI: T1- and T2-weighted SE and FLAIR. ASSESSMENT: Presence of SBI was determined from brain MRI by neuroradiologists according to routine diagnostic criteria in all participants without a history of stroke based on the MESA database. Minimum and maximum LA volumes and ejection fraction were calculated from bSSFP data. Blood stasis (% of voxels <10 cm/sec) and peak velocity (cm/sec) in the LA and LAA were assessed by a radiologist using an established 4D-flow workflow. STATISTICAL TESTS: Student's t test, Mann-Whitney U test, one-way ANOVA, chi-square test. Multivariable stepwise logistic regression with automatic forward and backward selection. Significance level P < 0.05. RESULTS: 26 (20.8%) had at least one SBI. After Bonferroni correction, participants with SBI were significantly older and had significantly lower peak velocities in the LAA. In multivariable analyses, age (per 10-years) (odds ratio (OR) = 1.99 (95% confidence interval (CI): 1.30-3.04)) and LAA peak velocity (per cm/sec) (OR = 0.87 (95% CI: 0.81-0.93)) were significantly associated with SBI. CONCLUSION: Older age and lower LAA peak velocity were associated with SBI in multivariable analyses whereas volumetric-based measures from cardiac MRI or cardiovascular risk factors were not. Cardiac 4D-flow MRI showed potential to serve as a novel imaging marker for SBI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Heart rate fragmentation and brain MRI markers of small vessel disease in MESA.

INTRODUCTION: Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. METHODS: In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. RESULTS: In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03). DISCUSSION: HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.

The association of upper airway anatomy with cognitive test performance: the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Numerous upper airway anatomy characteristics are risk factors for sleep apnea, which affects 26% of older Americans, and more severe sleep apnea is associated with cognitive impairment. This study explores the pathophysiology and links between upper airway anatomy, sleep, and cognition. METHODS: Participants in the Multi-Ethnic Study of Atherosclerosis underwent an upper airway MRI, polysomnography to assess sleep measures including the apnea-hypopnea index (AHI) and completed the Cognitive Abilities Screening Instrument (CASI). Two model selection techniques selected from among 67 upper airway measures those that are most strongly associated with CASI score. The associations of selected upper airway measures with AHI, AHI with CASI score, and selected upper airway anatomy measures with CASI score, both alone and after adjustment for AHI, were assessed using linear regression. RESULTS: Soft palate volume, maxillary divergence, and upper facial height were significantly positively associated with higher CASI score, indicating better cognition. The coefficients were small, with a 1 standard deviation (SD) increase in these variables being associated with a 0.83, 0.75, and 0.70 point higher CASI score, respectively. Additional adjustment for AHI very slightly attenuated these associations. Larger soft palate volume was significantly associated with higher AHI (15% higher AHI (95% CI 2%,28%) per SD). Higher AHI was marginally associated with higher CASI score (0.43 (95% CI 0.01,0.85) per AHI doubling). CONCLUSIONS: Three upper airway measures were weakly but significantly associated with higher global cognitive test performance. Sleep apnea did not appear to be the mechanism through which these upper airway and cognition associations were acting. Further research on the selected upper airway measures is recommended.

Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA.

INTRODUCTION: Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS: Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION: Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.

Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for Radiology Trainee Education.

Technological tools can redesign traditional approaches to radiology education, for example, with simulation cases and via computer-generated feedback. In this study, we investigated the use of an AI-powered, Bayesian inference-based clinical decision support (CDS) software to provide automated "real-time" feedback to trainees during interpretation of clinical and simulation brain MRI examinations. Radiology trainees participated in sessions in which they interpreted 3 brain MRIs: two cases from a routine clinical worklist (one without and one with CDS) and a teaching file-based simulation case with CDS. The CDS software required trainees to input imaging features and differential diagnoses, after which inferred diagnoses were displayed, and the case was reviewed with an attending neuroradiologist. An observer timed each case, including time spent on education, and trainees completed a survey rating their confidence in their findings and the educational value of the case. Ten trainees reviewed 75 brain MRI examinations during 25 reading sessions. Trainees had slightly lower confidence in their findings and diagnosis and rated the educational value slightly higher for simulation cases with CDS compared to clinical cases without CDS (p < 0.05). There were no significant differences in ratings of clinical cases with or without CDS. No differences in overall timing were found among the reading scenarios. Simulation cases with "CDS-provided feedback" may improve the educational value of interpreting imaging studies at a workstation without adding additional time. Further investigation will help drive innovation in trainee education, which may be particularly relevant in this era of increasing remote work and asynchronous attending review.

Kidney Disease, Hypertension Treatment, and Cerebral Perfusion and Structure.

RATIONALE & OBJECTIVE: The safety of intensive blood pressure (BP) targets is controversial for persons with chronic kidney disease (CKD). We studied the effects of hypertension treatment on cerebral perfusion and structure in individuals with and without CKD. STUDY DESIGN: Neuroimaging substudy of a randomized trial. SETTING & PARTICIPANTS: A subset of participants in the Systolic Blood Pressure Intervention Trial (SPRINT) who underwent brain magnetic resonance imaging studies. Presence of baseline CKD was assessed by estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (UACR). INTERVENTION: Participants were randomly assigned to intensive (systolic BP <120 mm Hg) versus standard (systolic BP <140 mm Hg) BP lowering. OUTCOMES: The magnetic resonance imaging outcome measures were the 4-year change in global cerebral blood flow (CBF), white matter lesion (WML) volume, and total brain volume (TBV). RESULTS: A total of 716 randomized participants with a mean age of 68 years were enrolled; follow-up imaging occurred after a median 3.9 years. Among participants with eGFR <60 mL/min/1.73 m2 (n = 234), the effects of intensive versus standard BP treatment on change in global CBF, WMLs, and TBV were 3.38 (95% CI, 0.32 to 6.44) mL/100 g/min, -0.06 (95% CI, -0.16 to 0.04) cm3 (inverse hyperbolic sine-transformed), and -3.8 (95% CI, -8.3 to 0.7) cm3, respectively. Among participants with UACR >30 mg/g (n = 151), the effects of intensive versus standard BP treatment on change in global CBF, WMLs, and TBV were 1.91 (95% CI, -3.01 to 6.82) mL/100 g/min, 0.003 (95% CI, -0.13 to 0.13) cm3 (inverse hyperbolic sine-transformed), and -7.0 (95% CI, -13.3 to -0.3) cm3, respectively. The overall treatment effects on CBF and TBV were not modified by baseline eGFR or UACR; however, the effect on WMLs was attenuated in participants with albuminuria (P = 0.04 for interaction). LIMITATIONS: Measurement variability due to multisite design. CONCLUSIONS: Among adults with hypertension who have primarily early kidney disease, intensive versus standard BP treatment did not appear to have a detrimental effect on brain perfusion or structure. The findings support the safety of intensive BP treatment targets on brain health in persons with early kidney disease. FUNDING: SPRINT was funded by the National Institutes of Health (including the National Heart, Lung, and Blood Institute; the National Institute of Diabetes and Digestive and Kidney Diseases; the National Institute on Aging; and the National Institute of Neurological Disorders and Stroke), and this substudy was funded by the National Institutes of Diabetes and Digestive and Kidney Diseases. TRIAL REGISTRATION: SPRINT was registered at ClinicalTrials.gov with study number NCT01206062.

Race, sex, and mid-life changes in brain health: Cardia MRI substudy.

OBJECTIVE: To examine longitudinal race and sex differences in mid-life brain health and to evaluate whether cardiovascular health (CVH) or apolipoprotein E (APOE) ε4 explain differences. METHODS: The study included 478 Black and White participants (mean age: 50 years). Total (TBV), gray (GMV), white (WMV), and white matter hyperintensity (WMH) volumes and GM-cerebral blood flow (CBF) were acquired with 3T-magnetic resonance imaging at baseline and 5-year follow-up. Analyses were based on general linear models. RESULTS: There were race x sex interactions for GMV (P-interaction = .004) and CBF (P-interaction = .01) such that men showed more decline than women, and this was most evident in Blacks. Blacks compared to Whites had a significantly greater increase in WMH (P = .002). All sex-race differences in change were marginally attenuated by CVH and APOE ε4. CONCLUSION: Race-sex differences in brain health emerge by mid-life. Identifying new environmental factors beyond CVH is needed to develop early interventions to maintain brain health.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). METHODS: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. RESULTS: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aß) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Emerging Applications of Artificial Intelligence in Neuro-Oncology.

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial.

Importance: There are currently no proven treatments to reduce the risk of mild cognitive impairment and dementia. Objective: To evaluate the effect of intensive blood pressure control on risk of dementia. Design, Setting, and Participants: Randomized clinical trial conducted at 102 sites in the United States and Puerto Rico among adults aged 50 years or older with hypertension but without diabetes or history of stroke. Randomization began on November 8, 2010. The trial was stopped early for benefit on its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. The final date for follow-up of cognitive outcomes was July 22, 2018. Interventions: Participants were randomized to a systolic blood pressure goal of either less than 120 mm Hg (intensive treatment group; n = 4678) or less than 140 mm Hg (standard treatment group; n = 4683). Main Outcomes and Measures: The primary cognitive outcome was occurrence of adjudicated probable dementia. Secondary cognitive outcomes included adjudicated mild cognitive impairment and a composite outcome of mild cognitive impairment or probable dementia. Results: Among 9361 randomized participants (mean age, 67.9 years; 3332 women [35.6%]), 8563 (91.5%) completed at least 1 follow-up cognitive assessment. The median intervention period was 3.34 years. During a total median follow-up of 5.11 years, adjudicated probable dementia occurred in 149 participants in the intensive treatment group vs 176 in the standard treatment group (7.2 vs 8.6 cases per 1000 person-years; hazard ratio [HR], 0.83; 95% CI, 0.67-1.04). Intensive BP control significantly reduced the risk of mild cognitive impairment (14.6 vs 18.3 cases per 1000 person-years; HR, 0.81; 95% CI, 0.69-0.95) and the combined rate of mild cognitive impairment or probable dementia (20.2 vs 24.1 cases per 1000 person-years; HR, 0.85; 95% CI, 0.74-0.97). Conclusions and Relevance: Among ambulatory adults with hypertension, treating to a systolic blood pressure goal of less than 120 mm Hg compared with a goal of less than 140 mm Hg did not result in a significant reduction in the risk of probable dementia. Because of early study termination and fewer than expected cases of dementia, the study may have been underpowered for this end point. Trial Registration: ClinicalTrials.gov Identifier: NCT01206062.

Association of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions.

Importance: The effect of intensive blood pressure lowering on brain health remains uncertain. Objective: To evaluate the association of intensive blood pressure treatment with cerebral white matter lesion and brain volumes. Design, Setting, and Participants: A substudy of a multicenter randomized clinical trial of hypertensive adults 50 years or older without a history of diabetes or stroke at 27 sites in the United States. Randomization began on November 8, 2010. The overall trial was stopped early because of benefit for its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. Brain magnetic resonance imaging (MRI) was performed on a subset of participants at baseline (n = 670) and at 4 years of follow-up (n = 449); final follow-up date was July 1, 2016. Interventions: Participants were randomized to a systolic blood pressure (SBP) goal of either less than 120 mm Hg (intensive treatment, n = 355) or less than 140 mm Hg (standard treatment, n = 315). Main Outcomes and Measures: The primary outcome was change in total white matter lesion volume from baseline. Change in total brain volume was a secondary outcome. Results: Among 670 recruited patients who had baseline MRI (mean age, 67.3 [SD, 8.2] years; 40.4% women), 449 (67.0%) completed the follow-up MRI at a median of 3.97 years after randomization, after a median intervention period of 3.40 years. In the intensive treatment group, based on a robust linear mixed model, mean white matter lesion volume increased from 4.57 to 5.49 cm3 (difference, 0.92 cm3 [95% CI, 0.69 to 1.14]) vs an increase from 4.40 to 5.85 cm3 (difference, 1.45 cm3 [95% CI, 1.21 to 1.70]) in the standard treatment group (between-group difference in change, -0.54 cm3 [95% CI, -0.87 to -0.20]). Mean total brain volume decreased from 1134.5 to 1104.0 cm3 (difference, -30.6 cm3 [95% CI, -32.3 to -28.8]) in the intensive treatment group vs a decrease from 1134.0 to 1107.1 cm3 (difference, -26.9 cm3 [95% CI, 24.8 to 28.8]) in the standard treatment group (between-group difference in change, -3.7 cm3 [95% CI, -6.3 to -1.1]). Conclusions and Relevance: Among hypertensive adults, targeting an SBP of less than 120 mm Hg, compared with less than 140 mm Hg, was significantly associated with a smaller increase in cerebral white matter lesion volume and a greater decrease in total brain volume, although the differences were small. Trial Registration: ClinicalTrials.gov Identifier: NCT01206062.

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10-7). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; PEA=4.5×10-8) partially independent of known common signal ( PEA(conditional)=1.4×10-3). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; Pall=1.9×10-10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( Prs34136221=2.8×10-8). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.

ACCORDION MIND: results of the observational extension of the ACCORD MIND randomised trial.

AIMS/HYPOTHESIS: The Memory in Diabetes (MIND) substudy of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study, a double 2x2 factorial parallel-group randomised clinical trial, tested whether intensive compared with standard management of hyperglycaemia, BP or lipid levels reduced cognitive decline and brain atrophy in 2977 people with type 2 diabetes. We describe the results of the observational extension study, ACCORDION MIND (ClinicalTrials.gov registration no. NCT00182910), which aimed to measure the long-term effects of the three ACCORD interventions on cognitive and brain structure outcomes approximately 4 years after the trial ended. METHODS: Participants (mean diabetes duration 10 years; mean age 62 years at baseline) received a fourth cognitive assessment and a third brain MRI, targeted at 80 months post-randomisation. Primary outcomes were performance on the Digit Symbol Substitution Test (DSST) and total brain volume (TBV). The contrast of primary interest compared glycaemic intervention groups at the ACCORDION visit; secondary contrasts were the BP and lipid interventions. RESULTS: Of the surviving ACCORD participants eligible for ACCORDION MIND, 1328 (68%) were re-examined at the ACCORDION follow-up visit, approximately 47 months after the intensive glycaemia intervention was stopped. The significant differences in therapeutic targets for each of the three interventions (glycaemic, BP and lipid) were not sustained. We found no significant difference in 80 month mean change from baseline in DSST scores or in TBV between the glycaemic intervention groups, or the BP and lipid interventions. Sensitivity analyses of the sites with ≥70% participation at 80 months revealed consistent results. CONCLUSIONS/INTERPRETATION: The ACCORD interventions did not result in long-term beneficial or adverse effects on cognitive or brain MRI outcomes at approximately 80 months follow-up. Loss of separation in therapeutic targets between treatment arms and loss to follow-up may have contributed to the lack of detectable long-term effects. TRIAL REGISTRATION: ClinicalTrials.gov NCT00182910.

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.