Benign breast disease and breast cancer risk in African women: a case-control study.

PURPOSE: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. METHODS: BC cases and controls were enrolled in three sub-Saharan African countries, Nigeria, Cameroon, and Uganda, between 1998 and 2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. RESULTS: Of 6,274 participants, 55.6% (3,478) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most commonly reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] 1.47, 95% CI 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR 2.25, 95% CI 1.26-4.02). BBD did not significantly mediate the effects of any of the selected BC risk factors. CONCLUSIONS: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

Benign breast disease and breast cancer risk in African women: A case-control study.

Purpose: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. Methods: BC cases and matched controls were enrolled in three sub-Saharan African countries, Nigeria Cameroon, and Uganda, between 1998-2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. Results: Of 6418 participants, 55.7% (3572) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] = 1.47, 95% CI: 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR = 3.11, 95% CI: 1.78-5.44). BBD did not significantly mediate the effects of any of the selected BC risk factors. Conclusions: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Effect of an Educational Intervention for Primary Stroke Risk Reduction in Ghana and Nigeria: Pilot Randomized Controlled Trial.

BACKGROUND: Using tailored mobile health interventions to improve global vascular risk awareness and control is yet to be investigated for primary stroke prevention in Africa. METHODS: This 2-arm pilot randomized controlled trial involved 100 stroke-free adults with at least 2 vascular risk factors for stroke. Eligible participants were assigned randomly to a control arm offering 1-time counseling (n=50) or a 2-month educational intervention arm (n=50) comprising a stroke video and riskometer app aimed at improving stroke risk factor awareness and health-seeking behavioral modification to control total vascular risk. Reduction in total stroke risk score was the primary outcome while feasibility and process measures were secondary outcomes. RESULTS: All enrolled participants completed the 2-month follow-up (retention rate=100%). The mean (SD) age of participants was 59.5 (±12.5) years, 38% were males. The mean change in stroke risk score at 2 months was -11.9% (±14.2) in the intervention arm versus -1.2% (±9.1) in the control arm, P=0.0001. Stroke risk awareness improved by 16.1% (±24.7) in the intervention arm versus 8.9% (±24.7) in the control arm, P=0.08. The intervention arm had 11.1 mm Hg reduction in systolic blood pressure compared with 4.8 mm Hg reduction in the control arm. CONCLUSIONS: The intervention demonstrated a positive signal of effect over a 2-month period. A definitive clinical trial with a longer duration of follow-up is warranted on the premise of these promising findings from this pilot randomized clinical trial. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT05619406.

From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants - Atiku Abubakar, Peter Obi and Bola Tinubu - in the forthcoming 2023 Presidential election. Sentiment analysis was performed on the preprocessed dataset using three machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. This study spanned ten weeks starting from the candidates' declaration of intent to run for Presidency. Results: The sentiment models gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2%, 87.6% and 82.9% respectively for LSTM; 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively for BERT and 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively for LSVC. Result also showed that Peter Obi has the highest total impressions the highest positive sentiments, Tinubu has the highest network of active friends while Atiku has the highest number of followers. Conclusion: Sentiment analysis and other Natural Language Understanding tasks can aid in the understanding of the social media space in terms of public opinion mining. We conclude that opinion mining from Twitter can form a general basis for generating insights for election as well as modeling election outcomes.

Sleep quality, perivascular spaces and brain health markers in ageing - A longitudinal study in the Lothian Birth Cohort 1936.

BACKGROUND: Sleep is thought to play a major role in brain health and general wellbeing. However, few longitudinal studies have explored the relationship between sleep habits and imaging markers of brain health, particularly markers of brain waste clearance such as perivascular spaces (PVS), of neurodegeneration such as brain atrophy, and of vascular disease, such as white matter hyperintensities (WMH). We explore these associations using data collected over 6 years from a birth cohort of older community-dwelling adults in their 70s. METHOD: We analysed brain MRI data from ages 73, 76 and 79 years, and self-reported sleep duration, sleep quality and vascular risk factors from community-dwelling participants in the Lothian Birth Cohort 1936 (LBC1936) study. We calculated sleep efficiency (at age 76), quantified PVS burden (at age 73), and WMH and brain volumes (age 73 to 79), calculated the white matter damage metric, and used structural equation modelling (SEM) to explore associations and potential causative pathways between indicators related to brain waste cleaning (i.e., sleep and PVS burden), brain and WMH volume changes during the 8th decade of life. RESULTS: Lower sleep efficiency was associated with a reduction in normal-appearing white matter (NAWM) volume (ß = 0.204, P = 0.009) from ages 73 to 79, but not concurrent volume (i.e. age 76). Increased daytime sleep correlated with less night-time sleep (r = -0.20, P < 0.001), and with increasing white matter damage metric (ß = -0.122, P = 0.018) and faster WMH growth (ß = 0.116, P = 0.026). Shorter night-time sleep duration was associated with steeper 6-year reduction of NAWM volumes (ß = 0.160, P = 0.011). High burden of PVS at age 73 (volume, count, and visual scores), was associated with faster deterioration in white matter: reduction of NAWM volume (ß = -0.16, P = 0.012) and increasing white matter damage metric (ß = 0.37, P < 0.001) between ages 73 and 79. On SEM, centrum semiovale PVS burden mediated 5% of the associations between sleep parameters and brain changes. CONCLUSION: Sleep impairments, and higher PVS burden, a marker of impaired waste clearance, were associated with faster loss of healthy white matter and increasing WMH in the 8th decade of life. A small percentage of the effect of sleep in white matter health was mediated by the burden of PVS consistent with the proposed role for sleep in brain waste clearance.

A framework for advancing sustainable magnetic resonance imaging access in Africa.

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings.

Breast cancer risk prediction in African women using Random Forest Classifier.

INTRODUCTION: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. AIM: In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. METHODS: A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. RESULTS: We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features. CONCLUSION: Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.

Association between white matter hyperintensities and stroke in a West African patient population: Evidence from the Stroke Investigative Research and Educational Network study.

BACKGROUND: This study is part of the Stroke Investigative Research and Educational Network (SIREN), the largest study of stroke patients in Africa to date, with computed tomography (CT) or magnetic resonance (MR) imaging data for each patient to confirm stroke. Prior imaging studies performed using high-field MR (≥1.5T) have shown that white matter hyperintensities (WMH), signs of microangiopathy in the subcortical brain, are correlated with many stroke risk factors as well as poor stroke outcomes. The aim of this study was the evaluation of MR images (0.3T-1.5T) from the SIREN study to determine associations between WMH volumes in West African patients and both stroke outcomes and stroke risk factors identified in the SIREN study. MATERIALS AND METHODS: Brain MR images of 130 Western African stroke patients (age â€‹= â€‹57.87 â€‹± â€‹14.22) were processed through Lesion Segmentation Toolbox of the Statistical Parametric Mapping software to extract all areas of hyperintensity in the brain. WMH was separated from stroke lesion hyperintensity and WMH volume was computed and summed. A stepwise linear regression and multivariate analysis was performed between patients' WMH volume and sociodemographic and clinical indices. RESULTS: Multivariate analysis showed that high WMH volume was statistically significantly positively correlated with age (ߠ​= â€‹0.44, p â€‹= â€‹0.001), waist/hip ratio (ߠ​= â€‹0.22, p â€‹= â€‹0.03), and platelet count (ߠ​= â€‹0.19, p â€‹= â€‹0.04) after controlling for head size in a Western African stroke population. CONCLUSION: Associations between WMH and age and waist/hip ratio previously identified in Western countries were demonstrated for the first time in a resource-limited, homogeneous black African community using low-field MR scanners.

Association between scripture memorization and brain atrophy using magnetic resonance imaging.

We investigated the association between scripture memorization and brain tissue using magnetic resonance imaging techniques. Participants comprised 63 healthy adults between the ages of 35 and 80 years old with no neurological or psychological disorders. Of these, 19 had completely memorized the Quran, 28 had partially memorized parts of Quran while 16, the control group, had not committed the Quran into their memory. White matter, grey matter and cerebrospinal fluid volumes were calculated. The brain tissue volumes of those who memorized the entire Quran and those who memorized only a small portion were compared with the control group using one­way ANOVA implemented in SPSS. There was no significant effect of age between the three groups (p>0.50). The group who completely memorized the Quran had larger grey matter and white matter volumes than the control group. Our results showed that those who memorized scripture had more brain tissues preserved compared with those who had not memorized scripture. These findings suggest that engaging our brains by memorizing scripture may increase brain health.

Polygenic Architecture of Human Neuroanatomical Diversity.

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured

Time Course of Normalization of Functional ß-Cell Capacity in the Diabetes Remission Clinical Trial After Weight Loss in Type 2 Diabetes.

OBJECTIVE: To assess functional ß-cell capacity in type 2 diabetes during 2 years of remission induced by dietary weight loss. RESEARCH DESIGN AND METHODS: A Stepped Insulin Secretion Test with Arginine was used to quantify functional ß-cell capacity by hyperglycemia and arginine stimulation. Thirty-nine of 57 participants initially achieved remission (HbA1c <6.5% [<48 mmol/mol] and fasting plasma glucose <7 mmol/L on no antidiabetic drug therapy) with a 16.4 ± 7.7 kg weight loss and were followed up with supportive advice on avoidance of weight regain. At 2 years, 20 participants remained in remission in the study. A nondiabetic control (NDC) group, matched for age, sex, and weight after weight loss with the intervention group, was studied once. RESULTS: During remission, median (interquartile range) maximal rate of insulin secretion increased from 581 (480-811) pmol/min/m2 at baseline to 736 (542-998) pmol/min/m2 at 5 months, 942 (565-1,240) pmol/min/m2 at 12 months (P = 0.028 from baseline), and 936 (635-1,435) pmol/min/m2 at 24 months (P = 0.023 from baseline; n = 20 of 39 of those initially in remission). This was comparable to the NDC group (1,016 [857-1,507] pmol/min/m2) by 12 (P = 0.064) and 24 (P = 0.244) months. Median first-phase insulin response increased from baseline to 5 months (42 [4-67] to 107 [59-163] pmol/min/m2; P < 0.0001) and then remained stable at 12 and 24 months (110 [59-201] and 125 [65-166] pmol/min/m2, respectively; P < 0.0001 vs. baseline) but lower than that of the NDC group (250 [226-429] pmol/min/m2; P < 0.0001). CONCLUSIONS: A gradual increase in assessed functional ß-cell capacity occurred after weight loss, becoming similar to that of NDC group participants by 12 months. This result was unchanged at 2 years with continuing remission of type 2 diabetes.

Hepatic Lipoprotein Export and Remission of Human Type 2 Diabetes after Weight Loss.

The role of hepatic lipoprotein metabolism in diet-induced remission of type 2 diabetes is currently unclear. Here, we determined the contributions of hepatic VLDL1-triglyceride production rate and VLDL1-palmitic acid content to changes in intra-pancreatic fat and return of first phase insulin response in a subgroup of the Diabetes Remission Clinical Trial. Liver fat, VLDL1-triglyceride production, and intra-pancreatic fat decreased after weight loss and remained normalized after 24 months of remission. First-phase insulin response remained increased only in those maintaining diabetes remission. Compared with those in remission at 24 months, individuals who relapsed after initial remission had a greater rise in the content of VLDL1-triglyceride and VLDL1-palmitic acid, re-accumulated intra-pancreatic fat, and lost first-phase response by 24 months. Thus, we observed temporal relationships between VLDL1-triglyceride production, hepatic palmitic acid flux, intra-pancreatic fat, and ß-cell function. Weight-related disordered fat metabolism appears to drive development and reversal of type 2 diabetes.

Sleep and brain morphological changes in the eighth decade of life.

OBJECTIVE: Sleep is important for brain health. We analysed associations between usual sleep habits and magnetic resonance imaging (MRI) markers of neurodegeneration (brain atrophy), vascular damage (white matter hyperintensities, WMH) and waste clearance (perivascular spaces, PVS) in older community-dwelling adults. METHOD: We collected self-reported usual sleep duration, quality and medical histories from the Lothian Birth Cohort 1936 (LBC1936) age 76 years and performed brain MRI. We calculated sleep efficiency, measured WMH and brain volumes, quantified PVS, and assessed associations between sleep measures and brain markers in multivariate models adjusted for demographic and medical history variables. RESULTS: In 457 subjects (53% males, mean age 76 ± 0.65 years), we found: brain and white matter loss with increased weekend daytime sleep (ß = -0.114, P = 0.03; ß = -0.122, P = 0.007 respectively), white matter loss with less efficient sleep (ß = 0.132, P = 0.011) and PVS increased with interrupted sleep (OR 1.84 95% CI, P = 0.025). CONCLUSION: Cross-sectional associations of sleep parameters with brain atrophy and more PVS suggest adverse relationships between usual sleep habits and brain health in older people that should be evaluated longitudinally.

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images.

BACKGROUND: The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. MATERIALS AND METHODS: We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results. RESULTS: Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001). CONCLUSIONS: Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes.

Remission of Human Type 2 Diabetes Requires Decrease in Liver and Pancreas Fat Content but Is Dependent upon Capacity for ß Cell Recovery.

The Diabetes Remission Clinical Trial reported return and persistence of non-diabetic blood glucose control in 46% of people with type 2 diabetes of up to 6 years duration. Detailed metabolic studies were performed on a subgroup (intervention, n = 64; control, n = 26). In the intervention group, liver fat content decreased (16.0% ± 1.3% to 3.1% ± 0.5%, p < 0.0001) immediately after weight loss. Similarly, plasma triglyceride and pancreas fat content decreased whether or not glucose control normalized. Recovery of first-phase insulin response (0.04[-0.05-0.32] to 0.11[0.0005-0.51] nmol/min/m2, p < 0.0001) defined those who returned to non-diabetic glucose control and this was durable at 12 months (0.11[0.005-0.81] nmol/min/m2, p = 0.0001). Responders were similar to non-responders at baseline but had shorter diabetes duration (2.7 ± 0.3 versus 3.8 ± 0.4 years; p = 0.02). This study demonstrates that ß cell ability to recover long-term function persists after diagnosis, changing the previous paradigm of irreversible loss of ß cell function in type 2 diabetes.

Longitudinal serum S100ß and brain aging in the Lothian Birth Cohort 1936.

Elevated serum and cerebrospinal fluid concentrations of S100ß, a protein predominantly found in glia, are associated with intracranial injury and neurodegeneration, although concentrations are also influenced by several other factors. The longitudinal association between serum S100ß concentrations and brain health in nonpathological aging is unknown. In a large group (baseline N = 593; longitudinal N = 414) of community-dwelling older adults at ages 73 and 76 years, we examined cross-sectional and parallel longitudinal changes between serum S100ß and brain MRI parameters: white matter hyperintensities, perivascular space visibility, white matter fractional anisotropy and mean diffusivity (MD), global atrophy, and gray matter volume. Using bivariate change score structural equation models, correcting for age, sex, diabetes, and hypertension, higher S100ß was cross-sectionally associated with poorer general fractional anisotropy (r = -0.150, p = 0.001), which was strongest in the anterior thalamic (r = -0.155, p < 0.001) and cingulum bundles (r = -0.111, p = 0.005), and survived false discovery rate correction. Longitudinally, there were no significant associations between changes in brain imaging parameters and S100ß after false discovery rate correction. These data provide some weak evidence that S100ß may be an informative biomarker of brain white matter aging.