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Background: Individuals with Alzheimer’s disease (AD) often present with coexisting vascular pathology that is expressed to different degrees and can lead to clinical heterogeneity. Objective: To examine the utility of unsupervised statistical clustering approaches in identifying neuropsychological (NP) test performance subtypes that closely correlate with carotid intima-media thickness (cIMT) in midlife. Methods: A hierarchical agglomerative and k-means clustering analysis based on NP scores (standardized for age, sex, and race) was conducted among 1,203 participants (age 48±5.3 years) from the Bogalusa Heart Study. Regression models assessed the association between cIMT ≥50th percentile and NP profiles, and global cognitive score (GCS) tertiles for sensitivity analysis. Results: Three NP profiles were identified: Mixed-low performance [16%, n = 192], scores ≥1 SD below the mean on immediate, delayed free recall, recognition verbal memory, and information processing; Average [59%, n = 704]; and Optimal [26%, n = 307] NP performance. Participants with greater cIMT were more likely to have a Mixed-low profile [OR = 3.10, 95% CI (2.13, 4.53), p

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics.Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively.The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

IntroductionAdvances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples.MethodsA participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time.ResultsParticipants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support.DiscussionThe importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.

Objective: Adiponectin (ApN), has anti-inflammatory and anti-atherogenic properties. Low levels of ApN are found even after normalization of glucose in patients with diabetes, and are implicated in atherosclerosis, and dementia onset but results vary across the lifespan. We assessed the relation of ApN levels in young adulthood, with midlife atherosclerosis, measured by carotid intima media thickness (cIMT), and with cognitive function among Black and White individuals from the Bogalusa Heart Study (BHS). Methods: A total of 604 participants from the BHS, a community cohort in rural Louisiana, were examined for ApN levels with lowest quartile of ApN levels (th percentile (>0.88mm), and a global cognitive score(GCS) standardized for age, race and sex. Adjusted models included education attendance. Results: Individuals with low-ApN were more likely men [ 56.2% (77), pth (P=0.001), and cIMT ≥ 50 th and GCS (P=0.002). ( Figure1) In mediation analysis, the ratio of Indirect effect/Total effect was 0.108, indicating that ~11% of the effect of low-ApN on GCS was mediated by cIMT. Education was not associated with low-ApN. Conclusion: Low-ApN levels in early adulthood was associated with poor cognition after ~10 years of follow-up, and the association was partially mediated by subclinical atherosclerosis. This suggests that ApN may play a role in neurodegeneration mechanisms and contribute to the development of dementia, underscoring the importance of addressing cardiometabolic influences earlier in the lifespan to prevent/delay cognitive decline.

Background Paclitaxel is touted as an essential medicine due to its extensive use as a chemotherapeutic for various cancers and an antiproliferative agent for restenosis. Due to recent concerns related to long-term mortality, paclitaxel (PTX)-based endovascular therapy is now surrounded by controversies. Objective Examine the inflammatory mediators driven by the systemic administration of PTX and explore the means to suppress these effects. Methods RNAseq analysis, cell and mouse models. Results RNAseq analysis of primary human endothelial cells (ECs) treated with PTX demonstrated transcriptional perturbations of a set of pro-inflammatory mediators, including monocyte chemoattractant protein-1 (MCP-1) and CD137, which were validated in EC lysates. These perturbations were abrogated with dexamethasone, a prototypic anti-inflammatory compound. The media of ECs pre-treated with PTX showed a significant increase in MCP-1 levels, which were reverted to baseline levels with DEX treatment. A group of mice harvested at different time points after PTX injection were analyzed for immediate and delayed effects of PTX. A 3-fold increase in MCP-1 was noted in blood and aortic ECs after 12 hours of PTX treatment. Similar changes in CD137 and downstream mediators such as tissue factor, VCAM-1 and E-selectin were noted in aortic ECs. Conclusions Our study shows that systemic PTX exposure upregulates atherothrombotic markers, and co-delivery of DEX can subdue the untoward toxic effects. Long-term studies are needed to probe the mechanisms driving systemic complications of PTX-based therapies and evaluate the clinical potential of DEX to mitigate risk.

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy and extent of information obtained from ML and metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analysing many chemical features with abundances that are correlated and ‘noisy’. Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics datasets without feature selection. The performance of the NN approach for predicting Parkinson’s disease (PD) from blood plasma metabolomics data was significantly higher than classical ML methods with a mean area under the curve of > 0.995. PD-specific markers that contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many other diseases using metabolomics and other untargeted ‘omics methods.

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis(OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-meter walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain.Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR=0.80 (0.64-1.00) and aOR=0.79(0.66-0.95), respectively].

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and has the ability to damage multiple organs. However, information on serum SARS-CoV-2 nucleic acid (RNAemia) in patients affected by coronavirus disease 2019 (COVID-19) is limited.MethodsPatients who were admitted to Zhongnan Hospital of Wuhan University with laboratory-confirmed COVID-19 were tested for SARS-COV-2 RNA in serum from 28 January 2020 to 9 February 2020. Demographic data, laboratory and radiological findings, comorbidities, and outcomes data were collected and analyzed.ResultsEighty-five patients were included in the analysis. The viral load of throat swabs was significantly higher than of serum samples. The highest detection of SARS-CoV-2 RNA in serum samples was between 11 and 15 days after symptom onset. Analysis to compare patients with and without RNAemia provided evidence that computed tomography and some laboratory biomarkers (total protein, blood urea nitrogen, lactate dehydrogenase, hypersensitive troponin I, and D-dimer) were abnormal and that the extent of these abnormalities was generally higher in patients with RNAemia than in patients without RNAemia. Organ damage (respiratory failure, cardiac damage, renal damage, and coagulopathy) was more common in patients with RNAemia than in patients without RNAemia. Patients with vs without RNAemia had shorter durations from serum testing SARS-CoV-2 RNA. The mortality rate was higher among patients with vs without RNAemia.ConclusionsIn this study, we provide evidence to support that SARS-CoV-2 may have an important role in multiple organ damage. Our evidence suggests that RNAemia has a significant association with higher risk of in-hospital mortality.

Qiu et al. present a novel deep learning strategy to generate high-resolution visualizations of Alzheimer’s disease risk in humans that are highly interpretable and can accurately predict Alzheimer’s disease status. They then test the model by comparing its performance to that of neurologists and neuropathological data., Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Endovascular deployment of drug-coated balloons (DCB) is an emerging strategy for the revascularization of arterial disease. Randomized clinical trials have demonstrated DCB effectiveness, but a recent meta-analysis reported increased mortality risk in humans with use of DCBs containing the common antiproliferative drug paclitaxel. While many factors could have contributed to adverse outcomes, current DCB designs have poor drug delivery efficiency, risk of systemic toxicity, and limited potential to retain therapeutic drug concentrations within the arterial wall following the procedure. Our study focuses on developing a strategy to enhance acute drug transfer from the balloon to the arterial wall over the short procedural window (∼30-120 s). We employed ultraviolet-ozone plasma (UVO) treatment to increase the hydrophilicity of a prototypical balloon material (Nylon-12) and subsequently applied a urea-paclitaxel coating previously shown to undergo favorable adhesive interactions with the arterial wall under simulated ex-vivo deployment. A series of assays were performed to characterize our experimental DCBs in terms of UVO-induced alterations in balloon surface hydrophobicity, formed coating microstructure, coating stability, and acute drug transfer to the arterial wall. Obtained results suggest that the UVO-based surface modification of angioplasty balloons is a promising design strategy and highlights the critical role of coating microstructure in determining the drug transfer efficiency in DCB therapy.

Background and ObjectivesCarotid intima-media thickness (c-IMT) is a measurement of atherosclerosis, a progressive disease that develops as early as childhood and has been linked with cognitive impairment and dementia in the elderly. However, the relationship between c-IMT and midlife cognitive function and the race and social disparities in this relationship remain unclear. We examined the association between c-IMT and cognitive function in midlife among Black and White participants from a semirural community-based cohort in Bogalusa, Louisiana.MethodsIn this cross-sectional analysis of participants from the Bogalusa Heart Study, linear regression models were used to determine the association between c-IMT dichotomized above the 50th percentile (>0.87 mm), an a demographically standardized global cognitive score (GCS), and individual cognitive domain–based z scores. Stratified analyses were performed to evaluate the impact of race and the individual's education status.ResultsA total of 1,217 participants (age 48 ± 5.28 years) were included; 66% (804) self-identified as White, and 34% (413) self-identified as Black. Of those, 58% (708) were women, and 42% (509) were men. Having a c-IMT ≥50th percentile was inversely associated with GCS (B ± SE −0.39 ± 0.18, p = 0.03), independently of cardiovascular risk factors (CVRFs) and achieved education. The effect remained significant in Black and White participants after adjustment for CVRFs (Black participants: B ± SE −1.25 ± 0.45, p = 0.005; White participants: B ± SE −0.92 ± 0.35, p = 0.008) but not for education. The interaction between c-IMT ≥50th percentile and education was significant (p = 0.03), and stratified analysis showed an association with GCS among those with lower achieved education (B ± SE −0.81 ± 0.33, p = 0.013) independently of major CVRFs.DiscussionSubclinical atherosclerosis, measured as c-IMT, was associated with worse midlife cognitive function, independently of major CVRFs. The association was buffered by education and may be stronger among Black than White participants, likely due to corresponding structural and social determinants. These findings underscore the importance of establishing preventive measures in midlife and suggest subclinical atherosclerosis as a potential target to prevent cognitive decline.

Background: Higher levels of non-atherogenic cholesterol (HDL) may be protective against vascular risk and inflammation, mechanisms accompanying cognitive impairment in late life. However, the role of atherogenic lipoproteins remains controversial. Evidence suggests that CV risk burden in middle age may be more closely associated with late-life brain health. Therefore, we aimed to explore the relationship between atherogenic cholesterol (non-HDL) in a community-based cohort of midlife adults. Methods: A total of 1,266 adult participants (58.9% Woman, 34.5% African American) from the Bogalusa Heart Study, with cognitive function assessment at age 48 ± 5 years and at least 3 observations of lipoproteins since childhood. A standardized global cognitive score (GCS) was obtained from a battery of tests covering major neuropsychological domains. Non-HDL trajectories were created using latent class trajectory modeling. Linear regression models were used to determine the association between non-HDL trajectories and GCS in midlife. Results: The mean follow-up for non-HDL and other traditional cardiovascular (CV) risk factors was 41.8 ± 3.4 years. Four distinct trajectory classes were identified: class 1 (n= 451 [ 36%]), class 2(n=168 [13.2%]), class 3 (n=530 [41.8%]), and class 4 (n=117 [9 %] (Figure 1). With class 1 as reference, and after adjustment for sex, race, education, cholesterol medication, hypertension, and diabetes comorbidities. Class 2 and class 4 trajectories were associated with lower GCS (Beta= -0.65 [SE 0.28]; p =0.022, and Beta = -0.06 [0.31]; p=0.057, respectively). (Table 1) Conclusions: These findings suggest that highly-increasing atherogenic cholesterol trajectories over time, particularly from early adulthood to middle age, may adversely affect cognitive performance in midlife, independent of other dementia risk factors. This underscores the importance of addressing CV risk factors earlier in the life course, as their cumulative burden could have a detrimental impact on brain health.

The liver is an early systemic target of Ebola virus (EBOV), but characterization beyond routine histopathology and viral antigen distribution is limited. We hypothesized Ebola virus disease (EVD) systemic proinflammatory responses would be reflected in temporally altered liver myeloid phenotypes. We utilized multiplex fluorescent immunohistochemistry (mfIHC), multispectral whole slide imaging, and image analysis to quantify molecular phenotypes of myeloid cells in the liver of rhesus macaques ( Macaca mulatta; n = 21) infected with EBOV Kikwit. Liver samples included uninfected controls ( n = 3), 3 days postinoculation (DPI; n = 3), 4 DPI ( n = 3), 5 DPI ( n = 3), 6 DPI ( n = 3), and terminal disease (6–8 DPI; n = 6). Alterations in hepatic macrophages occurred at ≥ 5 DPI characterized by a 1.4-fold increase in CD68+ immunoreactivity and a transition from primarily CD14−CD16+ to CD14+CD16− macrophages, with a 2.1-fold decrease in CD163 expression in terminal animals compared with uninfected controls. An increase in the neutrophil chemoattractant and alarmin S100A9 occurred within hepatic myeloid cells at 5 DPI, followed by rapid neutrophil influx at ≥ 6 DPI. An acute rise in the antiviral myxovirus resistance protein 1 (MxA) occurred at ≥ 4 DPI, with a predilection for enhanced expression in uninfected cells. Distinctive expression of major histocompatibility complex (MHC) class II was observed in hepatocytes during terminal disease. Results illustrate that EBOV causes macrophage phenotype alterations as well as neutrophil influx and prominent activation of interferon host responses in the liver. Results offer insight into potential therapeutic strategies to prevent and/or modulate the host proinflammatory response to normalize hepatic myeloid functionality.

ObjectiveTo 1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over two years in individuals without or with early knee osteoarthritis and 2) identify influential predictors in the model and quantify their effect on cartilage worsening.DesignAn ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure statistic, and their marginal effect on the outcome was quantified by g-computation.ResultsOf 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5th-97.5thpercentile) AUC across the 100 held-out test sets was 0.73 (0.65-0.79). Presence of baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying, and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening.ConclusionsAn ensemble machine learning approach incorporating gait, physical activity, and clinical/demographic features showed good performance for predicting cartilage worsening over two years. While identifying potential intervention targets from the model is challenging, these results suggest that lateral ground reaction force impulse, time spent lying, and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.Summary boxWhat are the findings?Machine learning models predicted cartilage worsening in persons without or with early knee osteoarthritis from gait, physical activity, and clinical and demographic characteristics with a median AUC of 0.73 across 100 held-out test sets.High lateral ground reaction force impulse, more time spent lying, and low vertical ground reaction force unloading rate were associated with increased risk of cartilage worsening over two years.How might it impact on clinical practice in the future?Gait and physical activity are some of the only modifiable risk factors for knee osteoarthritis; this study identified three potential intervention targets to slow early knee osteoarthritis progression.

Quantifying heterogeneity in Alzheimer’s disease (AD) risk is critical for individualized care and management. Recent attempts to assess AD heterogeneity have used structural (magnetic resonance imaging (MRI)-based) or functional (Aβ or tau) imaging, which focused on generating quartets of atrophy patterns and protein spreading, respectively. Here we present a computational framework that facilitated the identification of subtypes based on their risk of progression to AD. We used cerebrospinal fluid (CSF) measures of Aβ from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n=544, discovery cohort) as well as the National Alzheimer’s Coordinating Center (NACC) (n=508, validation cohort), and risk-stratified individuals with mild cognitive impairment (MCI) into quartiles (high-risk (H), intermediate-high risk (IH), intermediate-low risk (IL), and low-risk (L)). Patients were divided into subgroups utilizing patterns of brain atrophy found in each of these risk-stratified quartiles. We found H subjects to have a greater risk of AD progression compared to the other subtypes at 2- and 4-years in both the discovery and validation cohorts (ADNI: H subtype versus all others, p < 0.05 at 2 and 4 years; NACC: H vs. IL and LR at 2 years, p < 0.05, and a trend toward higher risk vs. IH, and p < 0.05 vs. IH, and L risk groups at 48 months with a trend toward lower survival vs. IL). Using MRI-based neural models that fused various deep neural networks with survival analysis, we then predicted MCI to AD conversion. We used these models to identify subtype-specific regions that demonstrate the largest levels of atrophy-related importance, which had minimal overlap (Average pairwise Jaccard Similarity in regions between the top 5 subtypes, 0.25±0.05 (± std)). Neuropathologic changes characteristic of AD were present across all subtypes in comparable proportions (Chi-square test, p>0.05 for differences in ADNC, n=31). Our risk-based approach to subtyping individuals provides an objective means to intervene and tailor care management strategies at early stages of cognitive decline.