Dapagliflozin reduces systemic inflammation in patients with type 2 diabetes without known heart failure.

OBJECTIVE: Sodium glucose cotransporter 2 (SGLT2) inhibitors significantly improve cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that dapagliflozin improves cardiac outcomes via beneficial effects on systemic and cardiac inflammation and cardiac fibrosis. RESEARCH AND DESIGN METHODS: This randomized placebo-controlled clinical trial enrolled 62 adult patients (mean age 62, 17% female) with type 2 diabetes (T2D) without known heart failure. Subjects were randomized to 12 months of daily 10 mg dapagliflozin or placebo. For all patients, blood/plasma samples and cardiac magnetic resonance imaging (CMRI) were obtained at time of randomization and at the end of 12 months. Systemic inflammation was assessed by plasma IL-1B, TNFα, IL-6 and ketone levels and PBMC mitochondrial respiration, an emerging marker of sterile inflammation. Global myocardial strain was assessed by feature tracking; cardiac fibrosis was assessed by T1 mapping to calculate extracellular volume fraction (ECV); and cardiac tissue inflammation was assessed by T2 mapping. RESULTS: Between the baseline and 12-month time point, plasma IL-1B was reduced (- 1.8 pg/mL, P = 0.003) while ketones were increased (0.26 mM, P = 0.0001) in patients randomized to dapagliflozin. PBMC maximal oxygen consumption rate (OCR) decreased over the 12-month period in the placebo group but did not change in patients receiving dapagliflozin (- 158.9 pmole/min/106 cells, P = 0.0497 vs. - 5.2 pmole/min/106 cells, P = 0.41), a finding consistent with an anti-inflammatory effect of SGLT2i. Global myocardial strain, ECV and T2 relaxation time did not change in both study groups. GOV REGISTRATION: NCT03782259.

Measles Outbreak Associated with a Migrant Shelter - Chicago, Illinois, February-May 2024.

Measles, a highly contagious respiratory virus with the potential to cause severe complications, hospitalization, and death, was declared eliminated from the United States in 2000; however, with ongoing global transmission, infections in the United States still occur. On March 7, 2024, the Chicago Department of Public Health (CDPH) confirmed a case of measles in a male aged 1 year residing in a temporary shelter for migrants in Chicago. Given the congregate nature of the setting, high transmissibility of measles, and low measles vaccination coverage among shelter residents, measles virus had the potential to spread rapidly among approximately 2,100 presumed exposed shelter residents. CDPH immediately instituted outbreak investigation and response activities in collaboration with state and local health departments, health care facilities, city agencies, and shelters. On March 8, CDPH implemented active case-finding and coordinated a mass vaccination campaign at the affected shelter (shelter A), including vaccinating 882 residents and verifying previous vaccination for 784 residents over 3 days. These activities resulted in 93% measles vaccination coverage (defined as receipt of ≥1 recorded measles vaccine dose) by March 11. By May 13, a total of 57 confirmed measles cases associated with residing in or having contact with persons from shelter A had been reported. Most cases (41; 72%) were among persons who did not have documentation of measles vaccination and were considered unvaccinated. In addition, 16 cases of measles occurred among persons who had received ≥1 measles vaccine dose ≥21 days before first known exposure. This outbreak underscores the need to ensure high vaccination coverage among communities residing in congregate settings.

Inpatient Screening, Brief Intervention, and Referral to Treatment for Alcohol Use Disorder in Patients Admitted with Alcohol-associated Liver Disease Is Not Universally Implemented in Practice, But Can Reduce Readmissions for Alcohol-associated Hepatitis.

Introduction: The management of alcohol-related liver disease requires a multidisciplinary approach to treat alcohol use disorder. We aimed to determine the proportion of actively drinking patients admitted for alcohol-associated hepatitis (AAH) or decompensated alcohol-related cirrhosis (DARLC) who were offered or underwent screening, brief intervention, and referral to treatment (SBIRT) for alcohol use disorder during admission and if inpatient SBIRT is associated with reduced readmissions for alcohol-related liver disease. Methods: We conducted a retrospective cohort study of actively drinking patients admitted to our institution from January 2017 to December 2021 with AAH or DARLC. Logistic regression was used to identify factors, such as conducting SBIRT, that were associated with 30-day and 90-day readmissions for recurrent AAH or DARLC. Results: There were 120 AAH admissions (mean age 47.7 ± 13.6 years), and 177 DARLC admissions (mean age 58.2 ± 9.5 years). SBIRT was conducted in only 51.7% of AAH admissions, and 23.7% of DARLC admissions. For AAH, conducting SBIRT was associated with significantly reduced 30-day (OR 0.098, P = 0.001, 95% CI 0.024-0.408) and 90-day (OR 0.166, P = 0.003, 95% CI 0.052-0.534) readmissions. For DARLC, there was no association between conducting SBIRT and 30-day or 90-day readmissions. Conclusion: SBIRT was conducted with actively drinking patients in only 51.7% of AAH admissions and 23.7% of DARLC admissions. Patients admitted for AAH who received inpatient SBIRT had decreased 30-day and 90-day readmission rates for AAH or DARLC.

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia. We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature-feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%-70% accuracy with Naïve Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature-feature correlations and their results can vary between cohort studies. By understanding these biases, we can reduce discrepancies in feature ranking and identify a minimal set of features needed for accurate classification of dementia.

Transcatheter Edge-to-Edge Mitral Valve Repair in a Patient With Anderson-Fabry Disease.

Severe degenerative mitral regurgitation (DMR) is one cardiac manifestation of the multiorgan metabolic enzyme disorder Anderson-Fabry Disease (AFD). Although DMR is normally managed surgically, many patients with AFD are unsuitable for this. We present the first case of mitral transcatheter edge-to-edge repair in a patient with AFD.

Omada: robust clustering of transcriptomes through multiple testing.

BACKGROUND: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions. FINDINGS: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. CONCLUSIONS: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

Mendelian Randomization Study With Clinical Follow-Up Links Metabolites to Risk and Severity of Pulmonary Arterial Hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes. METHODS AND RESULTS: Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue remodeling in PAH. Our multivariable MR analysis suggests that the effect of l-proline betaine is actually mediated indirectly via homostachydrine. CONCLUSIONS: Our data present a method for study of the metabolome in the context of PAH, and suggests several candidates for further evaluation and translational research.

Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure.

Objective: Sodium glucose cotransporter 2 (SGLT2) inhibitors significantly improve cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that dapagliflozin improves cardiac outcomes via beneficial effects on systemic and cardiac inflammation and cardiac fibrosis. Research and Design Methods: This randomized placebo-controlled clinical trial enrolled 62 adult patients (mean age 62, 17% female) with type 2 diabetes (T2D) without known heart failure. Subjects were randomized to 12 months of daily 10 mg dapagliflozin or placebo. For all patients, blood/plasma samples and cardiac magnetic resonance imaging (CMRI) were obtained at time of randomization and at the end of 12 months. Systemic inflammation was assessed by plasma IL-1B, TNFα, IL-6 and ketone levels and PBMC mitochondrial respiration, an emerging marker of sterile inflammation. Cardiac fibrosis was assessed by T1 mapping to calculate extracellular volume fraction (ECV); cardiac tissue inflammation was assessed by T2 mapping. Results: Between the baseline and 12-month time point, plasma IL-1B was reduced (-1.8 pg/mL, P=0.003) while ketones were increased (0.26 mM, P=0.0001) in patients randomized to dapagliflozin. PBMC maximal oxygen consumption rate (OCR) decreased over the 12-month period in the placebo group but did not change in patients receiving dapagliflozin (-158.9 pmole/min/106cells, P=0.0497 vs -45.2 pmole/min/106cells, P=0.41), a finding consistent with an anti-inflammatory effect of SGLT2i. ECV and T2 relaxation time did not change in both study groups. Conclusion: This study demonstrates that 12 months of dapagliflozin reduces IL-1B mediated systemic inflammation but affect cardiac fibrosis in T2D. Clinical Trialgov Registration: NCT03782259.

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.

Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation.

Introduction: Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Methods: Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Results: Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. Discussion: The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

CD4+ T cells are homeostatic regulators during Mtb reinfection.

Immunological priming - either in the context of prior infection or vaccination - elicits protective responses against subsequent Mycobacterium tuberculosis (Mtb) infection. However, the changes that occur in the lung cellular milieu post-primary Mtb infection and their contributions to protection upon reinfection remain poorly understood. Here, using clinical and microbiological endpoints in a non-human primate reinfection model, we demonstrate that prior Mtb infection elicits a long-lasting protective response against subsequent Mtb exposure and that the depletion of CD4+ T cells prior to Mtb rechallenge significantly abrogates this protection. Leveraging microbiologic, PET-CT, flow cytometric, and single-cell RNA-seq data from primary infection, reinfection, and reinfection-CD4+ T cell depleted granulomas, we identify differential cellular and microbial features of control. The data collectively demonstrate that the presence of CD4+ T cells in the setting of reinfection results in a reduced inflammatory lung milieu characterized by reprogrammed CD8+ T cell activity, reduced neutrophilia, and blunted type-1 immune signaling among myeloid cells, mitigating Mtb disease severity. These results open avenues for developing vaccines and therapeutics that not only target CD4+ and CD8+ T cells, but also modulate innate immune cells to limit Mtb disease.