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Introduction:Estimating the effects of comorbidities on risk of all-cause dementia (ACD) could potentially better inform prevention strategies and identify novel risk factors compared to more common post-hoc analyses from predictive modeling. Methods:In a retrospective cohort study of patients with mild cognitive impairment (MCI) from US Veterans Affairs Medical Centers between 2009 and 2021, we used machine learning techniques from the treatment effect estimation literature to estimate individualized effects of 25 comorbidities (e.g., hypertension) on ACD risk within 10 years of MCI diagnosis. Age and healthcare utilization were adjusted for using exact matching. Results:After matching, of 19,797 MCI patients, 6,767 (34.18%) experienced ACD onset. Dyslipidemia (percentage point increase of ACD risk range across different treatment effect estimation techniques = 0.009–0.044), hypertension (range = 0.007–0.043), and diabetes (range = 0.007–0.191) consistently had non-zero average effects. Discussion:Our findings support known associations between dyslipidemia, hypertension, and diabetes that increase the risk of ACD in MCI patients, demonstrating the potential for these approaches to identify novel risk factors. 

INTRODUCTION Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all-cause dementia (ACD) conversion at 5 years. METHODS Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held-out data subset. RESULTS Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors. DISCUSSION EHR-based prediction model had good performance in identifying 5-year MCI to ACD conversion and has potential to assist triaging of at-risk patients. Highlights * Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all-cause dementia within 5 years. * Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18). * Age and vascular-related morbidities were predictors of dementia conversion. * Synthetic data was comparable to real data in modeling MCI to dementia conversion. Key Points * An electronic health record–based model using demographic and co-morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all-cause dementia (ACD) within 5 years. * Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5-year conversion from MCI to ACD. * High body mass index, alcohol abuse, and sleep apnea were protective factors for 5-year conversion from MCI to ACD. * Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health-care data with minimized patient privacy concern that could accelerate scientific discoveries. 

Cardiac tissue engineering is an emerging field providing tools to treat and study cardiovascular diseases (CVDs). In the past years, the integration of stem cell technologies with micro- and nanoengineering techniques has enabled the creation of novel engineered cardiac tissues (ECTs) with potential applications in disease modeling, drug screening, and regenerative medicine. However, a major unaddressed limitation of stem cell-derived ECTs is their immature state, resembling a neonatal phenotype and genotype. The modulation of the cellular microenvironment within the ECTs has been proposed as an efficient mechanism to promote cellular maturation and improve features such as cellular coupling and synchronization. The integration of biological and nanoscale cues in the ECTs could serve as a tool for the modification and control of the engineered tissue microenvironment. Here we present a proof-of-concept study for the integration of biofunctionalized gold nanoribbons (AuNRs) with hiPSC-derived isogenic cardiac organoids to enhance tissue function and maturation. We first present extensive characterization of the synthesized AuNRs, their PEGylation and cytotoxicity evaluation. We then evaluated the functional contractility and transcriptomic profile of cardiac organoids fabricated with hiPSC-derived cardiomyocytes (mono-culture) as well as with hiPSC-derived cardiomyocytes and cardiac fibroblasts (co-culture). We demonstrated that PEGylated AuNRs are biocompatible and do not induce cell death in hiPSC-derived cardiac cells and organoids. We also found an improved transcriptomic profile of the co-cultured organoids indicating maturation of the hiPSC-derived cardiomyocytes in the presence of cardiac fibroblasts. Overall, we present for the first time the integration of AuNRs into cardiac organoids, showing promising results for improved tissue function. 

An organotypic heart on-a-chip modeling long QT syndrome type 2 was created to study effect of R531W mutation in LQTS2 pathology. 

Introduction: Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer’s disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations. Methods: In a retrospective cohort study, EHRdata from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM). Results: The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM). Conclusion: Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.