Gut-associated lymphoid tissue attrition associates with response to anti-α4β7 therapy in ulcerative colitis

Vedolizumab (VDZ) is a first-line treatment in ulcerative colitis (UC) that targets the α4β7- 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 (β7+) plasmablasts in circulation of VDZ-treated patients suggested that VDZ targets gut-associated lymphoid tissue (GALT). Anti-α4β7 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 β7+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 α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC.

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.

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.

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.

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.

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.

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.

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.

Gut-associated lymphoid tissue attrition associates with response to anti-α4β7 therapy in ulcerative colitis

Targeting the α4β7-MAdCAM-1 axis with vedolizumab (VDZ) is a front-line therapeutic paradigm in ulcerative colitis (UC). However, mechanism(s) of action (MOA) of VDZ remain relatively undefined. Here, we examined three distinct cohorts of patients with UC (n=83, n=60, and n=21), to determine the effect of VDZ on the mucosal and peripheral immune system. Transcriptomic studies with protein level validation were used to study drug MOA using conventional and transgenic murine models. We found a significant decrease in colonic and ileal naïve B and T cells and circulating gut-homing plasmablasts (β7+) in VDZ-treated patients, pointing to gut-associated lymphoid tissue (GALT) targeting by VDZ. Murine Peyer's patches (PP) demonstrated a significant loss cellularity associated with reduction in follicular B cells, including a unique population of epithelium-associated B cells, following anti-α4β7 antibody (mAb) administration. Photoconvertible (KikGR) mice unequivocally demonstrated impaired cellular entry into PPs in anti-α4β7 mAb treated mice. In VDZ-treated, but not anti-tumor necrosis factor-treated UC patients, lymphoid aggregate size was significantly reduced in treatment responders compared to non-responders, with an independent validation cohort further confirming these data. GALT targeting represents a novel MOA of α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC, and for the development of new therapeutic strategies.

Epitope-Specific IgE at 1 Year of Age Can Predict Peanut Allergy Status at 5 Years

BACKGROUND

Currently, there is no laboratory test that can accurately identify children at risk of developing peanut allergy. Utilizing a subset of children randomized to the peanut avoidance arm of the LEAP trial, we monitored the development of epitope-specific (ses-)IgE and ses-IgG4 from 4-11 months to 5 years of age.

OBJECTIVE

The aim of the study was to evaluate the prognostic ability of epitope-specific antibodies to predict the result of an oral food challenge (OFC) at 5 years.

METHODS

A Bead-Based Epitope Assay was used to quantitate IgE and IgG4 to 64 sequential (linear) epitopes from Ara h 1-3 proteins at 4-11 months, 1 and 2.5 years of age in 74 subjects (38 of them with a positive OFC at 5 years). Specific IgE (sIgE) to peanut and component proteins was measured using ImmunoCAP. Machine learning methods were used to identify the earliest time point to predict 5-year outcome, developing prognostic algorithms based only on 4-11 month samples, 1-year or 2.5-year, and a combination of them. Data from 74 children were iteratively split 3:1 into training and validation sets, and machine learning models were developed to predict the 5-year outcome. A test set (n = 90) from an independent cohort was used for final evaluation.

RESULTS

Elastic-Net algorithm combining ses-IgE and IgE to Ara h 1, 2, 3, and 9 proteins could predict the 5-year peanut allergy status of LEAP participants with an average validation accuracy of 64% at baseline. Samples taken at 1 year accurately predicted a 5-year OFC outcome with 83% accuracy. This performance remained consistent when evaluated on an independent CoFAR2 cohort with an accuracy of 78% for the 1-year model.

CONCLUSION

IgE antibody profiles at 1 year of age are predictive of peanut OFC at 5 years in children avoiding peanuts. If further confirmed, this model may enable early identification of infants who may benefit from early immunotherapeutic interventions.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers

Objective

To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials and Methods

Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app.

Results

We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age.

Discussion

We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection.

Conclusion

Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

HLA alleles and sustained peanut consumption promote IgG4 responses in subjects protected from peanut allergy

We investigated the interplay between genetics and oral peanut protein exposure in the determination of the immunological response to peanut using the targeted intervention in the LEAP clinical trial. We identified an association between peanut-specific IgG4 and HLA-DQA1*01:02 that was only observed in the presence of sustained oral peanut protein exposure. The association between IgG4 and HLA-DQA1*01:02 was driven by IgG4 specific for the Ara h 2 component. Once peanut consumption ceased, the association between IgG4-specific Ara h 2 and HLA-DQA1*01:02 was attenuated. The association was validated by observing expanded IgG4-specific epitopes in people who carried HLA-DQA1*01:02. Notably, we confirmed the previously reported associations with HLA-DQA1*01:02 and peanut allergy risk in the absence of oral peanut protein exposure. Interaction between HLA and presence or absence of exposure to peanut in an allergen- and epitope-specific manner implicates a mechanism of antigen recognition that is fundamental to driving immune responses related to allergy risk or protection.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.. [PMID: 36093350 PMCID: PMC9460972 DOI: 10.1101/2021.12.09.21267548]

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. 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). 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. 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.

Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression

An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications.

Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Background

The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact.

Objective

This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress.

Methods

HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally.

Results

A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial.

Conclusions

High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method-threshold gradient descent regularization (TGDR)-stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes-GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

BACKGROUND

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

OBJECTIVE

We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

METHODS

Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily.

RESULTS

Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01).

CONCLUSIONS

Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Evolution of epitope-specific IgE and IgG4 antibodies in children enrolled in the LEAP trial

BACKGROUND

In the LEAP (Learning Early About Peanut Allergy) trial, early consumption of peanut in high-risk infants was found to decrease the rate of peanut allergy at 5 years of age. Sequential epitope-specific (ses-)IgE is a promising biomarker of clinical peanut reactivity.

OBJECTIVE

We sought to compare the evolution of ses-IgE and ses-IgG₄ in children who developed (or not) peanut allergy and to evaluate the immunomodulatory effects of early peanut consumption on these antibodies.

METHODS

Sera from 341 children (LEAP cohort) were assayed at baseline, 1, 2.5, and 5 years of age, with allergy status determined by oral food challenge at 5 years. A bead-based epitope assay was used to quantitate ses-IgE and ses-IgG₄ to 64 sequential epitopes from Ara h 1 to Ara h 3 and was analyzed using linear mixed-effect models.

RESULTS

In children avoiding peanut who became peanut allergic, the bulk of peanut ses-IgE did not develop until after 2.5 years. Minimal increases of ses-IgE occurred after 1 year in consumers, but not to the same epitopes as those in children developing peanut allergy. No major changes in ses-IgE were seen in nonallergic or sensitized children. IgE in sensitized consumers was detected against peanut proteins. ses-IgG₄ increased over time in most children regardless of consumption or allergy status.

CONCLUSIONS

Early peanut consumption in infants at high risk of developing peanut allergy appears to divert the immunologic response to a presumably "protective" effect. In general, consumers tend to generate ses-IgG₄ earlier and in greater quantities than nonconsumers do, whereas only avoiders tend to generate significant quantities of ses-IgE.

A Molecular network approach reveals shared cellular and molecular signatures between chronic fatigue syndrome and other fatiguing illnesses

The molecular mechanisms of chronic fatigue syndrome (CFS, or Myalgic encephalomyelitis), a disease defined by extreme, long-term fatigue, remain largely uncharacterized, and presently no molecular diagnostic test and no specific treatments exist to diagnose and treat CFS patients. While CFS has historically had an estimated prevalence of 0.1-0.5% [1], concerns of a “long hauler” version of Coronavirus disease 2019 (COVID-19) that symptomatically overlaps CFS to a significant degree(Supplemental Table-1)and appears to occur in 10% of COVID-19 patients[2], has raised concerns of a larger spike in CFS [3]. Here, we established molecular signatures of CFS and a corresponding network-based disease context from RNA-sequencing data generated on whole blood and FACs sorted specific peripheral blood mononuclear cells (PBMCs) isolated from CFS cases and non-CFS controls. The immune cell type specific molecular signatures of CFS we identified, overlapped molecular signatures from other fatiguing illnesses, demonstrating a common molecular etiology. Further, after constructing a probabilistic causal model of the CFS gene expression data, we identified master regulator genes modulating network states associated with CFS, suggesting potential therapeutic targets for CFS.

Effect of Concomitant Therapy With Steroids and Tumor Necrosis Factor Antagonists for Induction of Remission in Patients With Crohn's Disease: A Systematic Review and Pooled Meta-analysis

BACKGROUND & AIMS

It is not clear whether concomitant therapy with corticosteroids and anti-tumor necrosis factor (TNF) agents is more effective at inducing remission in patients with Crohn's disease (CD) than anti-TNF monotherapy. We aimed to determine whether patients with active CD receiving corticosteroids during induction therapy with anti-TNF agents had higher rates of clinical improvement than patients not receiving corticosteroids during induction therapy.

METHODS

We systematically searched the MEDLINE, Embase, and CENTRAL databases, through January 20, 2016, for randomized trials of anti-TNF agents approved for treatment of CD and identified 14 trials (5 of adalimumab, 5 of certolizumab, and 4 of infliximab). We conducted a pooled meta-analysis of individual patient and aggregated data from these trials. We compared data from participants who continued oral corticosteroids during induction with anti-TNF therapy to those treated with anti-TNF agents alone. The endpoints were clinical remission (CD activity index [CDAI] scores <150) and clinical response (a decrease in CDAI of 100 points) at the end of induction (weeks 4-14 of treatment).

RESULTS

We included 4354 patients who received induction therapy with anti-TNF agents, including 1653 [38.0%] who were receiving corticosteroids. The combination of corticosteroids and an anti-TNF agent induced clinical remission in 32.0% of patients, whereas anti-TNF monotherapy induced clinical remission in 35.5% of patients (odds ratio [OR], 0.93; 95% CI, 0.74-1.17). The combination of corticosteroids and an anti-TNF agent induced a clinical response in 42.7% of patients, whereas anti-TNF monotherapy induced a clinical response in 46.8% (OR 0.84; 95% CI, 0.73-0.96). These findings did not change with adjustment for baseline CDAI scores and concurrent use of immunomodulators.

CONCLUSIONS

Based on a meta-analysis of data from randomized trials of anti-TNF therapies in patients with active CD, patients receiving corticosteroids during induction therapy with anti-TNF agents did not have higher rates of clinical improvement compared with patients not receiving corticosteroids during induction therapy. Given these findings and the risks of corticosteroid use, clinicians should consider early weaning of corticosteroids during induction therapy with anti-TNF agents for patients with corticosteroid-refractory CD.

The erythema Q-score, an imaging biomarker for redness in skin inflammation

Physician rating of cutaneous erythema is central to clinical dermatological assessment as well as quantification of outcome measures in clinical trials in a number of dermatologic conditions. However, issues with inter‐rater reliability and variability in the setting of higher Fitzpatrick skin types make visual erythema assessment unreliable. We developed and validated a computer‐assisted image‐processing algorithm (EQscore) to reliably quantify erythema (across a range of skin types) in the dermatology clinical setting.

Our image processing algorithm evaluated erythema based upon green light suppression differentials between affected and unaffected skin. A group of four dermatologists used a 4‐point Likert scale as a human evaluation of similar erythematous patch tests. The algorithm and dermatologist scores were compared across 164 positive patch test reactions. The intra‐class correlation coefficient of groups and the correlation coefficient between groups were calculated. The EQscore was validated on and independent image set of psoriasis, minimal erythema dose testing and steroid‐induced blanching images. The reliability of the erythema quantification method produced an intra‐class correlation coefficient of 0.84 for the algorithm and 0.67 for dermatologists. The correlation coefficient between groups was 0.85. The EQscore demonstrated high agreement with clinical scoring and superior reliability compared with clinical scoring, avoiding the pitfalls of erythema underrating in the setting of pigmentation. The EQscore is easily accessible (http://lab.rockefeller.edu/krueger/EQscore), user‐friendly, and may allow dermatologists to more readily and accurately rate the severity of dermatological conditions and the response to therapeutic treatments.

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues

SIGNIFICANCE

Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics.

AIM

We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration.

APPROACH

Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves.

RESULTS

Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs.

CONCLUSIONS

From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

Molecular and Cellular Responses to the TYK2/JAK1 Inhibitor PF-06700841 Reveal Reduction of Skin Inflammation in Plaque Psoriasis

The IL-23/T helper type 17 cell axis is a target for psoriasis. The TYK2/Janus kinase 1 inhibitor PF-06700841 will directly suppress TYK2-dependent IL-12 and IL-23 signaling and Janus kinase 1-dependent signaling in cells expressing these signaling molecules, including T cells and keratinocytes. This clinical study sought to define the inflammatory gene and cellular pathways through which PF-06700841 improves the clinical manifestations of psoriasis. Patients (n = 30) with moderate-to-severe psoriasis were randomized to once-daily 30 mg (n = 14) or 100 mg (n = 7) PF-06700841 or placebo (n = 9) for 28 days. Biopsies were taken from nonlesional and lesional skin at baseline and weeks 2 and 4. Changes in the psoriasis transcriptome and genes induced by IL-17 in keratinocytes were evaluated with microarray profiling and reverse transcriptase-PCR. Reductions in IL-17A, IL-17F, and IL-12B mRNA were observed as early as 2 weeks and approximately 70% normalization of lesional gene expression after 4 weeks. Immunohistochemistry showed significant decreases in markers of keratinocyte activation, epidermal thickness, KRT16 and Ki-67 expression, and immune cell infiltrates CD3⁺/CD8⁺ (T cells) and CD11c (dendritic cells) after 2 weeks of treatment, corresponding with improvement in histologic score. PF-06700841 improves clinical symptoms of chronic plaque psoriasis by inhibition of proinflammatory cytokines that require TYK2 and Janus kinase 1 for signal transduction.

Impact of Zostavax Vaccination on T-Cell Accumulation and Cutaneous Gene Expression in the Skin of Older Humans After Varicella Zoster Virus Antigen-Specific Challenge

Background

The live attenuated vaccine Zostavax was developed to prevent varicella zoster virus (VZV) reactivation that causes herpes zoster (shingles) in older humans. However, the impact of vaccination on the cutaneous response to VZV is not known.

Methods

We investigated the response to intradermal VZV antigen challenge before and after Zostavax vaccination in participants >70 years of age by immunohistological and transcriptomic analyses of skin biopsy specimens collected from the challenge site.

Results

Vaccination increased the proportion of VZV-specific CD4⁺ T cells in the blood and promoted the accumulation of both CD4⁺ and CD8⁺ T cells in the skin after VZV antigen challenge. However, Zostavax did not alter the proportion of resident memory T cells (CD4⁺ and CD8⁺) or CD4⁺Foxp3⁺ regulatory T cells in unchallenged skin. After vaccination, there was increased cutaneous T-cell proliferation at the challenge site and also increased recruitment of T cells from the blood, as indicated by an elevated T-cell migratory gene signature. CD8⁺ T-cell–associated functional genes were also highly induced in the skin after vaccination.

Conclusion

Zostavax vaccination does not alter the abundance of cutaneous resident memory T cells but instead increases the recruitment of VZV-specific T cells from the blood and enhances T-cell activation, particularly cells of the CD8⁺ subset, in the skin after VZV antigen challenge.

Prostate Cancer in World Trade Center Responders Demonstrates Evidence of an Inflammatory Cascade

An excess incidence of prostate cancer has been identified among World Trade Center (WTC) responders. In this study, we hypothesized that WTC dust, which contained carcinogens and tumor-promoting agents, could facilitate prostate cancer development by inducing DNA damage, promoting cell proliferation, and causing chronic inflammation. We compared expression of immunologic and inflammatory genes using a NanoString assay on archived prostate tumors from WTC Health Program (WTCHP) patients and non-WTC patients with prostate cancer. Furthermore, to assess immediate and delayed responses of prostate tissue to acute WTC dust exposure via intratracheal inhalation, we performed RNA-seq on the prostate of normal rats that were exposed to moderate to high doses of WTC dust. WTC prostate cancer cases showed significant upregulation of genes involved in DNA damage and G₂-M arrest. Cell-type enrichment analysis showed that Th17 cells, a subset of proinflammatory Th cells, were specifically upregulated in WTC patients. In rats exposed to WTC dust, we observed upregulation of gene transcripts of cell types involved in both adaptive immune response (dendritic cells and B cells) and inflammatory response (Th17 cells) in the prostate. Unexpectedly, genes in the cholesterol biosynthesis pathway were also significantly upregulated 30 days after acute dust exposure. Our results suggest that respiratory exposure to WTC dust can induce inflammatory and immune responses in prostate tissue. IMPLICATIONS: WTC-related prostate cancer displayed a distinct gene expression pattern that could be the result of exposure to specific carcinogens. Our data warrant further epidemiologic and cellular mechanistic studies to better understand the consequences of WTC dust exposure.Visual Overview: http://mcr.aacrjournals.org/content/molcanres/17/8/1605/F1.large.jpg.

Microbial Engraftment and Efficacy of Fecal Microbiota Transplant for Clostridium Difficile in Patients With and Without Inflammatory Bowel Disease

BACKGROUND

Recurrent and severe Clostridium difficile infections (CDI) are treated with fecal microbiota transplant (FMT). Uncertainty exists regarding FMT effectiveness for CDI with underlying inflammatory bowel disease (IBD) and regarding its effects on disease activity and effectiveness in transferring the donor microbiota to patients with and without IBD.

METHODS

Subjects with and without IBD who underwent FMT for recurrent or severe CDI between 2013 and 2016 at The Mount Sinai Hospital were followed for up to 6 months. The primary outcome was CDI recurrence 6 months after FMT. Secondary outcomes were (1) CDI recurrence 2 months after FMT; (2) frequency of IBD flare after FMT; (3) microbiota engraftment after FMT; (and 4) predictors of CDI recurrence.

RESULTS

One hundred thirty-four patients, 46 with IBD, were treated with FMT. Follow-up was available in 83 and 118 patients at 6 and 2 months, respectively. There was no difference in recurrence in patients with and without IBD at 6 months (38.7% vs 36.5%; P > 0.99) and 2 months (22.5% vs 17.9%; P = 0.63). Proton pump inhibitor use, severe CDI, and comorbid conditions were predictors of recurrence. Pre-FMT microbiota was not predictive of CDI recurrence. Subjects with active disease requiring medication escalation had reduced engraftment, with no difference in engraftment based on CDI recurrence or IBD endoscopic severity at FMT.

CONCLUSIONS

Inflammatory bowel disease did not affect CDI recurrence rates 6 months after FMT. Pre-FMT microbiota was not predictive of recurrence, and microbial engraftment was impacted in those requiring IBD treatment escalation, though not by CDI recurrence or IBD disease severity.

An Integrated Model of Atopic Dermatitis Biomarkers Highlights the Systemic Nature of the Disease

Current atopic dermatitis (AD) models link epidermal abnormalities in lesional skin to cytokine activation. However, there is evolving evidence of systemic immune activation and detectable abnormalities in nonlesional skin. Because some of the best single correlations with severity (Scoring of AD, or SCORAD) are detected not only in lesional but also nonlesional skin and blood, more complex biomarker models of AD are needed. We thus performed extensive biomarker measures in these compartments using univariate and multivariate approaches to correlate disease biomarkers with SCORAD and with a combined hyperplasia score [thickness and keratin 16 (K16) mRNA] at baseline and after cyclosporine A treatment in 25 moderate to severe AD patients. Increases in serum cytokines and chemokines (IL-13, IL-22, CCL17) were found in AD versus healthy individuals and were reduced with treatment. SCORAD correlated with immune (IL-13, IL-22) and epidermal (thickness, K16) measures in lesional and, even more strongly, in nonlesional AD. Serum cytokines also had higher correlations with nonlesional markers at baseline and with treatment. Multivariate U statistics improved baseline and treatment-response SCORAD correlations. Nonlesional models showed the strongest correlations, with further improvement upon integration of serum markers. Even better correlations were obtained between biomarkers and the hyperplasia score. Larger cohorts are needed to confirm these preliminary data.

Meta-Analysis from Gene Expression Profiles of Lupus Affected Tissues Reveals Novel Immune Cell Contributions

Immunologic mechanisms causing tissue damage in autoimmune diseases such as SLE are not fully understood. To gain additional insight, gene expression profiles obtained from lupus affected skin, synovium and kidney were obtained and compared to meta-analyzed data obtained from active lupus B, T and myeloid cells. More than 300 arrays from lupus patients and appropriate controls were analyzed to determine differentially expressed (DE) genes (8279 discoid lupus skin, 5465 synovium, 6381 glomerulus, 5587 tubulointerstitum). Notably, the majority of lupus affected tissue DE genes were detected in more than one tissue and 439 were differentially expressed in all tissues. A variety of approaches assessed the molecular pathways and cellular phenotypes accounting for these common lupus affected tissue DE genes. Curated STRING-based interaction analysis identified a number of pathways including co-stimulation of T cells, activation of B and myeloid cells, antigen presentation, TLR signaling and p38 activation; from a total of 193 IPA-documented pathways, 59 were common to all tissues including p38 signaling, TLR signaling, maturation of dendritic cells, B cell activation and ICOS-ICOSL in T cells. Novel bioinformatics approaches documented that more than 50% of the DE genes in the tissues were associated with immune cell function and 11–18% were unique to the immune system. Further analysis using LINCS and additional novel software highlighted specific molecular pathways of immune cell activation including the JAK/STAT and the IL12 pathways. These results demonstrate the value of comprehensive application of orthogonal curated bioinformatics tools in identifying the role of immune cells in lupus pathogenesis and tissue damage.