Multimodal single-cell analyses reveal mechanisms of perianal fistula in diverse patients with Crohn's disease.

BACKGROUND: Crohn's disease complicated by perianal fistulae is more prevalent and severe in patients of African ancestry. METHODS: We profiled single cells from diverse patients with Crohn's disease with perianal fistula from colorectal mucosa and fistulous tracts. Immunofluorescence was performed to validate predicted cell-cell interactions. Unstimulated monocytes were chronically cultured in diverse cohorts. A subset was analyzed by single-nucleus RNA + ATAC sequencing. FINDINGS: Fistulous tract cells from complete proctectomies demonstrated enrichment of myeloid cells compared to paired rectal tissues. Ligand-receptor analysis highlights myeloid-stromal cross-talk and cellular senescence, with cellular co-localization validated by immunofluorescence. Chitinase-3 like-protein-1 (CHI3L1) is a top upregulated gene in stromal cells from fistulae expressing both destructive and fibrotic gene signatures. Monocyte cultures from patients of African ancestry and controls demonstrated differences in CHI3L1 and oncostatin M (OSM) expression upon differentiation compared to individuals of European ancestry. Activating protein-1 footprints are present in ATAC-seq peaks in stress response genes, including CHI3L1 and OSM; genome-wide chromatin accessibility including JUN footprints was observed, consistent with reported mechanisms of inflammatory memory. Regulon analyses confirm known cell-specific transcription factor regulation and implicate novel ones in fibroblast subsets. All pseudo-bulked clusters demonstrate enrichment of genetic loci, establishing multicellular contributions. In the most significant African American Crohn's genetic locus, upstream of prostaglandin E receptor 4, lymphoid-predominant ATAC-seq peaks were observed, with predicted RORC footprints. CONCLUSIONS: Population differences in myeloid-stromal cross-talk implicate fibrotic and destructive fibroblasts, senescence, epigenetic memory, and cell-specific enhancers in perianal fistula pathogenesis. The transcriptomic and epigenetic data provided here may guide optimization of promising mesenchymal stem cell therapies for perianal fistula. FUNDING: This work was supported by grants U01DK062422, U24DK062429, and R01DK123758.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review.

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

Gut-associated lymphoid tissue attrition associates with response to anti-α4ß7 therapy in ulcerative colitis.

Vedolizumab (VDZ) is a first-line treatment in ulcerative colitis (UC) that targets the α4ß7- mucosal vascular addressin cell adhesion molecule 1 (MAdCAM-1) axis. To determine the mechanisms of action of VDZ, we examined five distinct cohorts of patients with UC. A decrease in naïve B and T cells in the intestines and gut-homing (ß7+) plasmablasts in circulation of VDZ-treated patients suggested that VDZ targets gut-associated lymphoid tissue (GALT). Anti-α4ß7 blockade in wild-type and photoconvertible (KikGR) mice confirmed a loss of GALT size and cellularity because of impaired cellular entry. In VDZ-treated patients with UC, treatment responders demonstrated reduced intestinal lymphoid aggregate size and follicle organization and a reduction of ß7+IgG+ plasmablasts in circulation, as well as IgG+ plasma cells and FcγR-dependent signaling in the intestine. GALT targeting represents a previously unappreciated mechanism of action of α4ß7-targeted therapies, with major implications for this therapeutic paradigm in UC.

Evidence That Anemia Accelerates AS Progression Via Shear-Induced TGF-ß1 Activation: Heyde's Syndrome Comes Full Circle.

The severity of aortic stenosis (AS) is associated with acquired von Willebrand syndrome (AVWS) and gastrointestinal bleeding, leading to anemia (Heyde's syndrome). We investigated how anemia is linked with AS and AVWS using the LA100 mouse model and patients with AS. Induction of anemia in LA100 mice increased transforming growth factor (TGF)-ß1 activation, AVWS, and AS progression. Patients age >75 years with severe AS had higher plasma TGF-ß1 levels and more severe anemia than AS patients age <75 years, and there was a correlation between TGF-ß1 and anemia. These data are compatible with the hypothesis that the blood loss anemia of Heyde's syndrome contributes to AS progression via WSS-induced activation of platelet TGF-ß1 and additional gastrointestinal bleeding via WSS-induced AVWS.

Gene coexpression networks reveal a broad role for lncRNAs in inflammatory bowel disease.

The role of long noncoding RNAs (lncRNAs) in disease is incompletely understood, but their regulation of inflammation is increasingly appreciated. We addressed the extent of lncRNA involvement in inflammatory bowel disease (IBD) using biopsy-derived RNA-sequencing data from a large cohort of deeply phenotyped patients with IBD. Weighted gene correlation network analysis revealed gene modules of lncRNAs coexpressed with protein-coding genes enriched for biological pathways, correlated with epithelial and immune cell signatures, or correlated with distal colon expression. Correlation of modules with clinical features uncovered a module correlated with disease severity, with an enriched interferon response signature containing the hub lncRNA IRF1-AS1. Connecting genes to IBD-associated single nucleotide polymorphisms (SNPs) revealed an enrichment of SNP-adjacent lncRNAs in biologically relevant modules. Ulcerative colitis-specific SNPs were enriched in distal colon-related modules, suggesting that disease-specific mechanisms may result from altered lncRNA expression. The function of the IBD-associated SNP-adjacent lncRNA IRF1-AS1 was explored in human myeloid cells, and our results suggested IRF1-AS1 promoted optimal production of TNF-α, IL-6, and IL-23. A CRISPR/Cas9-mediated activation screen in THP-1 cells revealed several lncRNAs that modulated LPS-induced TNF-α responses. Overall, this study uncovered the expression patterns of lncRNAs in IBD that identify functional, disease-relevant lncRNAs.

Heterogeneous Effects of CPAP in Non-Sleepy OSA on CVD Outcomes: Post-hoc Machine Learning Analysis of the ISAACC Trial (ECSACT Study).

RATIONALE: Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects. OBJECTIVES: Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. METHODS: Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. RESULTS: A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (>19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). CONCLUSIONS: We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP. Among the training dataset, those with longer OSA event duration and hypercholesterolemia had nearly 2.5-times more CVD events with CPAP compared to UC, while those with shorter OSA event duration had roughly half the rate of CVD events if randomized to CPAP.

Deep resolution of clinical, cellular, and transcriptomic inflammatory markers during 52 weeks of IL-17A inhibition by secukinumab.

BACKGROUND: Secukinumab, an anti-IL-17A monoclonal antibody, induces histological and molecular resolution of psoriatic plaques by 12 weeks. However, the long-term effects of secukinumab on molecular resolution of psoriatic inflammation remain unknown. OBJECTIVE: To investigate the molecular resolution of psoriasis following 52-weeks of secukinumab treatment. METHODS: NCT01537432 was a two-part Phase 2, randomised, double-blinded, placebo-controlled, 52-week study of patients with moderate to severe psoriasis receiving secukinumab 300 mg. Psoriatic lesional and non-lesional skin biopsies were obtained at baseline, Week 12, and Week 52, and the composition of the residual disease genomic profile (RDGP, i.e., "molecular scar") of biopsies from secukinumab-responders was analysed. RESULTS: After 52 weeks of treatment, 14/24 enrolled patients were considered clinical responders (≥75% improvement in Psoriasis Area and Severity Index [PASI]; PASI75), 4/24 were considered non-responders (

Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients.

OBJECTIVE: Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR. METHODS: A secondary analysis of a randomized multicenter SAVR trial of embolic protection devices (NCT02389894) was conducted, excluding participants with clinical stroke or incomplete neuroimaging (N = 298; 39% female, 7% non-White, 74 ± 7 years). Delirium during in-hospital recovery was serially screened using the Confusion Assessment Method. Parcellation and tractography atlas-based neuroimaging methods were used to determine infarct locations and cortical connectivity effects. Mixed-effect, zero-inflated gaussian modeling analyses, accounting for brain region-specific infarct characteristics, were conducted to examine for differences within and between groups by delirium status and perioperative neuroprotection device strategy. RESULTS: 23.5% participants experienced postoperative delirium. Delirium was associated with significantly increased lesion volumes in the right cerebellum and temporal lobe white matter, while diffusion weighted imaging infarct-related structural disconnection (DWI-ISD) was observed in frontal and temporal lobe regions (p-FDR < 0.05). Fewer brain regions demonstrated DWI-ISD loss in the suction-based neuroprotection device group, relative to filtration-based device or standard aortic cannula. INTERPRETATION: Structural disconnection from acute infarcts was greater in patients who experienced postoperative delirium, suggesting that the impact from covert perioperative infarcts may not be as clinically "silent" as commonly assumed.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS: 130 000 critical care admissions across both health systems. INTERVENTION: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences.

Development of a prediction model of postpartum hospital use using an equity-focused approach.

BACKGROUND: Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification. OBJECTIVE: This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach. STUDY DESIGN: We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use. RESULTS: The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications. CONCLUSION: The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.

Investigation of plaque psoriasis relapse after secukinumab withdrawal in patients from two Phase 3 studies.

BACKGROUND: Secukinumab is effective against a range of psoriatic manifestations. Investigating psoriasis (PsO) relapse following secukinumab discontinuation could provide insights into long-term PsO remission. OBJECTIVE: To examine PsO relapse rates upon treatment discontinuation following one year of secukinumab treatment. METHODS: This study (NCT01544595) is an extension of the Phase 3 ERASURE/FIXTURE studies in patients with moderate-to-severe plaque PsO. After one year of secukinumab 300 mg or 150 mg treatment, Week 52 PASI75 responders were randomly assigned to receive placebo. Upon relapse, patients receiving placebo were switched to their previous secukinumab dose. The study primary outcome was non-relapse rate after secukinumab withdrawal. RESULTS: Following the last dose of secukinumab 300 mg, 21% and 10% of patients who switched to placebo did not relapse at one and two years after discontinuation, respectively. Patients who received secukinumab 150 mg for one year showed a lower proportion of non-relapse following treatment discontinuation (14% and 6%) at one and two years, respectively). Non-relapsing patients maintained low mean PASI (2.8) at one year drug-free versus baseline (20.9); 1.7 at two years drug-free versus baseline (19.2). Disease duration (P=0.017) and severity (P=0.022) were significantly associated with time-to-relapse in patients initially treated with secukinumab 300 mg; patients with shorter disease duration and lower baseline PASI remained relapse-free for longer. CONCLUSIONS: Following discontinuation of secukinumab, a proportion of patients stayed relapse-free. Further, patients with shorter disease duration remained relapse-free for longer, suggesting that earlier treatment with secukinumab may result in long-term clinical control of moderate-to-severe PsO.

Myeloid cell influx into the colonic epithelium is associated with disease severity and non-response to anti-Tumor Necrosis Factor Therapy in patients with Ulcerative Colitis.

Ulcerative colitis (UC) is an idiopathic chronic inflammatory disease of the colon with sharply rising global prevalence. Dysfunctional epithelial compartment (EC) dynamics are implicated in UC pathogenesis although EC-specific studies are sparse. Applying orthogonal high-dimensional EC profiling to a Primary Cohort (PC; n=222), we detail major epithelial and immune cell perturbations in active UC. Prominently, reduced frequencies of mature BEST4+OTOP2+ absorptive and BEST2+WFDC2+ secretory epithelial enterocytes were associated with the replacement of homeostatic, resident TRDC+KLRD1+HOPX+ γδ+ T cells with RORA+CCL20+S100A4+ TH17 cells and the influx of inflammatory myeloid cells. The EC transcriptome (exemplified by S100A8, HIF1A, TREM1, CXCR1) correlated with clinical, endoscopic, and histological severity of UC in an independent validation cohort (n=649). Furthermore, therapeutic relevance of the observed cellular and transcriptomic changes was investigated in 3 additional published UC cohorts (n=23, 48 and 204 respectively) to reveal that non-response to anti-Tumor Necrosis Factor (anti-TNF) therapy was associated with EC related myeloid cell perturbations. Altogether, these data provide high resolution mapping of the EC to facilitate therapeutic decision-making and personalization of therapy in patients with UC.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods: Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results: We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion: In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions: These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

Density of Biopsy Sampling Required to Ensure Accurate Histological Assessment of Inflammation in Active Ulcerative Colitis.

BACKGROUND: Histological response to treatment is an important outcome in patients with ulcerative colitis (UC). The accuracy of biopsy-based measurements of inflammation may be limited by error imposed by natural microscopic heterogeneity on the scale of individual biopsies. We determined the magnitude of this error, its histological correlates, and the density of biopsy sampling within mucosal regions of interest required to meet specified benchmarks for accuracy. METHODS: A total of 994 sequential 1-mm digital microscopic images (virtual biopsies) from consecutive colectomies from patients with clinically severe UC were scored by 2 pathologists. Agreement statistics for Geboes subscores and Nancy (NHI) and Robarts Histological Indices (RHI) between random samples from 1 to 10 biopsies and a reference mean score across a 2-cm region of mucosa were calculated using bootstrapping with 2500 iterations. RESULTS: The agreement statistics improved across all indices as the biopsy density increased, with the largest proportional gains occurring with addition of the second and third biopsies. One biopsy achieved moderate to good agreement with 95% confidence for NHI and RHI corresponding to scale-specific errors of 0.40 (0.25-0.66) and 3.02 (2.08-5.36), respectively; and 3 biopsies achieved good agreement with 95% confidence corresponding to scale-specific errors of 0.22 (0.14-0.39) and 1.87 (1.19-3.25), respectively. Of the individual histological features, erosions and ulcers had the greatest impact on the agreement statistics. CONCLUSIONS: In the setting of active colitis, up to 3 biopsy samples per region of interest may be required to overcome microscopic heterogeneity and ensure accurate histological grading.

Double-Blind, Placebo-Controlled Study of E-B-FAHF-2 in Combination With Omalizumab-Facilitated Multiallergen Oral Immunotherapy.

BACKGROUND: Oral immunotherapy (OIT) is limited by adverse events, and most patients require continued treatment to maintain their increased threshold. Adjunctive treatments have been explored to increase the safety and efficacy of OIT. OBJECTIVE: This study aimed to determine the safety and efficacy of enhanced, butanol purified Food Allergy Herbal Formula-2 (E-B-FAHF-2) for inducing remission in subjects undergoing omalizumab-facilitated multiallergen OIT (multi-OIT). METHODS: In this double-blind, placebo-controlled clinical trial, subjects were randomized 1:1 to receive either E-B-FAHF-2 or placebo, starting 2 months before OIT and continuing throughout OIT. All subjects received a 4-month course of omalizumab, starting 2 months before OIT through the 2-month OIT build-up phase. After 24 months of multi-OIT (maintenance dose of 1000 mg of each allergen), desensitization and remission were assessed. The primary objective was to determine if subjects in the E-B-FAHF-2 group (EOIT) were more likely than the placebo group (OIT) to develop remission to all 3 allergens treated with multi-OIT, as defined by the absence of dose-limiting symptoms to a cumulative dose of 4444 mg of protein after discontinuing treatment for 3 months. RESULTS: Thirty-three subjects were randomized. A total of 63.6% were desensitized to 4444 mg of protein for each allergen at 26 months, and 24.2% met the primary outcome of remission at 29 months, with no difference between the treatment groups. There was good adherence (>85%) with study medications, with no difference between the treatment groups. There was no difference in reported overall adverse events between the treatment groups. CONCLUSION: Omalizumab-facilitated multifood OIT was safe and effective, and remission was achieved in about a quarter of subjects. However, outcomes were not improved by the addition of E-B-FAHF-2.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Baroreflex sensitivity is associated with markers of hippocampal gliosis and dysmyelination in patients with psychosis.

PURPOSE: Hippocampal dysfunction plays a key role in the pathology of psychosis. Given hippocampal sensitivity to changes in cerebral perfusion, decreased baroreflex function could contribute to psychosis pathogenesis. This study had two aims: (1) To compare baroreflex sensitivity in participants with psychosis to two control groups: participants with a nonpsychotic affective disorder and participants with no history of psychiatric disease; (2) to examine the relationship between hippocampal neurometabolites and baroreflex sensitivities in these three groups. We hypothesized that baroreflex sensitivity would be reduced and correlated with hippocampal neurometabolite levels in participants with psychosis, but not in the control groups. METHODS: We assessed baroreflex sensitivity during the Valsalva maneuver separated into vagal and adrenergic components. Metabolite concentrations for cellular processes were quantitated in the entire multivoxel hippocampus using H1-MR spectroscopic (MRS) imaging and were compared with baroreflex sensitivities in the three groups. RESULTS: Vagal baroreflex sensitivity (BRS-V) was reduced in a significantly larger proportion of participants with psychosis compared with patients with nonpsychotic affective disorders, whereas participants with psychosis had increased adrenergic baroreflex sensitivity (BRS-A) compared with participants with no history of psychiatric disease. Only in psychotic cases were baroreflex sensitivities associated with hippocampal metabolite concentrations. Specifically, BRS-V was inversely correlated with myo-inositol, a marker of gliosis, and BRS-A was positively correlated with energy dependent dysmyelination (choline, creatine) and excitatory activity (GLX). CONCLUSIONS: Abnormal baroreflex sensitivity is common in participants with psychosis and is associated with MRS markers of hippocampal pathology. Future longitudinal studies are needed to examine causality.