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Does SARS-CoV-2 infection have long-term effects on the kidney? SARS-CoV2 infection and the associated clinical disease, coronavirus disease-19 (COVID-19), have resulted in more than 20 million infections worldwide with an overall mortality rate of 3 9% Initially thought to be a disease of the respiratory system, the clinical syndrome of COVID-19 is actually multifaceted, also affecting the heart, vasculature, and neurological system Acute kidney injury (AKI) is a common complication, affecting up to 40% of patients hospitalized with COVID-19, with 20% of these patients needing acute hemodialysis Along with the shortage of ventilators that has been amply discussed, hospitals suffered from an acute shortage of dialysis machines during case surges Although the epidemiology of in-hospital AKI has been shown before in reports from China, Europe, and the United States of America, Fisher et al delved deeper into the effects of COVID-19 on the kidney They retrospectively evaluated AKI incidence, risk factors, and outcomes for hospitalized adult patients with and without COVID-19 in a large New York City health system They showed increased AKI incidence, greater need for acute dialysis, and higher mortality in those with COVID-19 They also identified several factors that were significantly associated with AKI, regardless of COVID-19 status Critically, they also assessed how many individuals recovered kidney function during hospitalization after an AKI episode Only 42% of patients with COVID-19 and AKI recovered baseline kidney function compared to 70% without COVID-19 Considering the substantial evidence linking in-hospital AKI, and more importantly, non-recovery of kidney function after an acute episode, with long-term mortality and worse kidney function, this may herald the onset of a coming post-COVID kidney disease epidemic This may be worsened by the fact that regular physician visits have decreased during this pandemic, and as a result, many patients with chronic disease may not be monitored regularly Although the research and medical community's focus has been on the acute care of patients with COVID-19, the long-term effects of infection are slowly coming into focus Integrated research and clinical care approaches are urgently needed to better understand and address what is likely to be a growing at-risk population

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

While increased obesity prevalence among persons of African ancestry (AAs) compared to persons of European ancestry (EAs) is linked to social, environmental and behavioral factors, there are no gene variants that are common and significantly associated with obesity in AA populations. We sought to explore the association between ancestry specific renal risk variants in the apolipoprotein L1 (APOL1) gene with obesity related traits in AAs.We conducted a genotype-phenotype association study from 3 electronic medical record linked cohorts (BioMe Biobank, BioVU, nuGENE); randomized controlled trials (genetic testing to understand and address renal disease disparities) and prospective cohort study (Jackson Heart Study). We analyzed association of APOL1 renal risk variants with cross-sectional measures of obesity (average body mass index (BMI), and proportion of overweight and obesity) and with measures of body composition (in Jackson Heart Study).We had data on 11,930 self-reported AA adults. Across cohorts, mean age was from 42 to 49 years and percentage female from 58% to 75.3%. Individuals who have 2 APOL1 risk alleles (14% of AAs) have 30% higher obesity odds compared to others (recessive model adjusted odds ratio 1.30; 95% confidence interval 1.16-1.41; P = 2.75 × 10-6). An additive model better fit the association, in which each allele (47% of AAs) increases obesity odds by 1.13-fold (adjusted odds ratio 1.13; 95% confidence interval 1.07-1.19; P = 3.07 × 10-6) and increases BMI by 0.36 kg/m2 (∼1 kg, for 1.7 m height; P = 2 × 10-4). APOL1 alleles are not associated with refined body composition traits overall but are significantly associated with fat free mass index in women [0.30 kg/m2 increment per allele; P = .03].Thus, renal risk variants in the APOL1 gene, found in nearly half of AAs, are associated with BMI and obesity in an additive manner. These variants could, either on their own or interacting with environmental factors, explain a proportion of ethnic disparities in obesity.

Inflammatory bowel disease (IBD) is a group of chronic diseases, affecting different parts of the gastrointestinal tract, that mainly comprises Crohn’s Disease (CD) and Ulcerative Colitis (UC). Most IBD genomic research to date has involved genome-wide association studies (GWAS) of common genetic variants, mostly in Europeans, resulting in the identification of over 200 risk loci. The incidence of IBD in Ashkenazi Jews (AJ) is particularly high compared to other population groups and rare protein-coding variants are significantly enriched in AJ. These variants are expected to have a larger phenotypic effect and are hypothesized to complement the missing heritability that cannot be fully addressed by GWAS in IBD. Therefore, we genetically identified 4,974 AJs IBD cases and controls from whole exome sequencing (WES) data from the NIDDK IBD Genetics Consortium (IBDGC). We selected credible rare variants with high predicted impact, aggregated them into genes, and performed gene burden and pathway enrichment analyses to identify 7 novel plausible IBD-causing genes:NCF1, CES1, ICAM1, INPP5D, ABCB1, IL33andTLR4. We further perform bulk and single-cell RNA sequencing, demonstrating the likely relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of AJ adult IBD displays a significant overlap with very early onset IBD (VEOIBD) genetics. At the variant level, we performed Phenome-wide association studies (PheWAS) in the UK Biobank to replicate risk sites in IBD and reveal shared risk sites with other diseases. Finally, we showed that a polygenic risk score (PRS) has high power to differentiate AJ IBD cases from controls when using rare and high impact variants.

BackgroundThere are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group.MethodsWe identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these groups, as well as cumulative incidence of death by HIV status.ResultsPatients did not differ significantly by HIV status by age, sex, or race/ethnicity due to the matching algorithm. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94).ConclusionsWe found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group.

Multimodal measurements and machine learning provide evidence for personalized dietary advice.

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 – 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

ImportanceDiabetic kidney disease (DKD) is the leading cause of kidney failure in the United States and predicting progression is necessary for improving outcomes.ObjectiveTo develop and validate a machine-learned, prognostic risk score (KidneyIntelX™) combining data from electronic health records (EHR) and circulating biomarkers to predict DKD progression.DesignObservational cohort studySettingTwo EHR linked biobanks: Mount Sinai BioMe Biobank and the Penn Medicine Biobank.ParticipantsPatients with prevalent DKD (G3a-G3b with all grades of albuminuria (A1-A3) and G1 & G2 with A2-A3 level albuminuria) and banked plasma.Main outcomes and measuresPlasma biomarkers soluble tumor necrosis factor 1/2 (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) were measured at baseline. Patients were divided into derivation [60%] and validation sets [40%]. The composite primary end point, progressive decline in kidney function, including the following: rapid kidney function decline (RKFD) (estimated glomerular filtration rate (eGFR) decline of ≥5 ml/min/1.73m2/year), ≥40% sustained decline, or kidney failure within 5 years. A machine learning model (random forest) was trained and performance assessed using standard metrics.ResultsIn 1146 patients with DKD the median age was 63, 51% were female, median baseline eGFR was 54 ml/min/1.73 m2, urine albumin to creatinine ratio (uACR) was 61 mg/g, and follow-up was 4.3 years. 241 patients (21%) experienced progressive decline in kidney function. On 10-fold cross validation in the derivation set (n=686), the risk model had an area under the curve (AUC) of 0.77 (95% CI 0.74-0.79). In validation (n=460), the AUC was 0.77 (95% CI 0.76-0.79). By comparison, the AUC for an optimized clinical model was 0.62 (95% CI 0.61-0.63) in derivation and 0.61 (95% CI 0.60-0.63) in validation. Using cutoffs from derivation, KidneyIntelX stratified 46%, 37% and 16.5% of validation cohort into low-, intermediate- and high-risk groups, with a positive predictive value (PPV) of 62% (vs. PPV of 37% for the clinical model and 40% for KDIGO; p < 0.001) in the high-risk group and a negative predictive value (NPV) of 91% in the low-risk group. The net reclassification index for events into high-risk group was 41% (pConclusions and RelevanceA machine learned model combining plasma biomarkers and EHR data improved prediction of progressive decline in kidney function within 5 years over KDIGO and standard clinical models in patients with early DKD.

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery. Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes(1,2). Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease(3,4), suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns(5-8), the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)(9), 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work(10), confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.

The ability to predict rapid kidney function decline (RKFD) in patients with early stages of type 2 diabetic kidney disease (T2DKD) can improve long-term health outcomes through earlier intervention. Our objective was to develop a robust risk score for patients with early stages of T2DKD. We obtained EHR and plasma from patients with T2DKD (Kidney Disease Improving Global Outcomes [KDIGO] G1-G2, A2-A3 or G3a-G3b, A1-A3) from the Mount Sinai BioMe Biobank and the Penn Medicine Biobank and measured soluble tumor necrosis factor receptor (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) via a validated quantitative electrochemiluminescent assay. A machine learning model was trained using the EHR/biomarker data to predict RKFD, defined as eGFR decline of ≥5 ml/min/year or ≥40% sustained decline in eGFR or kidney failure within 5 years, and performance was compared to a clinical model and KDIGO risk-matrix. A total of 1146 subjects with a mean age of 63, baseline eGFR 54 ml/min, and median UACR 61 mg/g were randomly divided into 60% training (n=686) and 40% validation (n=460) sets. The median follow-up was 4.3 years and 241 (21%) experienced RKFD. The AUC of KidneyIntelX for RFKD was 0.85 (95% CI 0.84-0.86) in training and 0.77 (95% CI 0.76-0.79) in the validation set vs. AUC of 0.67 (95% CI 0.64-0.70) for a clinical model. Using pre-specified cutoffs from the training set, KidneyIntelX stratified 47%, 37% and 16% of the validation set into low-, intermediate- and high-risk strata, with a PPV of 62% (vs. 41% for KDIGO) in the high-risk group (p Disclosure L. Chan: None. G.N. Nadkarni: Advisory Panel; Self; Renalytix AI. Consultant; Self; AstraZeneca, Reata, Renalytix AI. Stock/Shareholder; Self; Renalytix AI. Other Relationship; Self; Renalytix AI. F. Fleming: Board Member; Self; Renalytix AI plc. F.E. Salem: None. B. Murphy: Board Member; Self; RenalytixAI. M.J. Donovan: Consultant; Self; Renalytix AI plc. S. Coca: Advisory Panel; Self; Renalytix AI plc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., CHF Solutions, Relypsa, Inc., Takeda Pharmaceutical Company Limited. Stock/Shareholder; Self; Renalytix AI plc. S.M. Damrauer: Consultant; Spouse/Partner; Rhythm Pharmaceuticals. Research Support; Self; CytoVas LLC, Renalytix AI plc. Funding Renalytix AI plc

The degree of myocardial injury, as reflected by troponin elevation, and associated outcomes among U.S. hospitalized patients with coronavirus disease-2019 (COVID-19) are unknown.The purpose of this study was to describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19.Patients with COVID-19 admitted to 1 of 5 Mount Sinai Health System hospitals in New York City between February 27, 2020, and April 12, 2020, with troponin-I (normal value 0.03 ng/ml) measured within 24 h of admission were included (n = 2,736). Demographics, medical histories, admission laboratory results, and outcomes were captured from the hospitals' electronic health records.The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD), including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. In all, 985 (36%) patients had elevated troponin concentrations. After adjusting for disease severity and relevant clinical factors, even small amounts of myocardial injury (e.g., troponin I0.03 to 0.09 ng/ml; n = 455; 16.6%) were significantly associated with death (adjusted hazard ratio: 1.75; 95% CI: 1.37 to 2.24; p 0.001) while greater amounts (e.g., troponin I0.09 ng/dl; n = 530; 19.4%) were significantly associated with higher risk (adjusted HR: 3.03; 95% CI: 2.42 to 3.80; p 0.001).Myocardial injury is prevalent among patients hospitalized with COVID-19; however, troponin concentrations were generally present at low levels. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation among patients hospitalized with COVID-19 is associated with higher risk of mortality.

ImportancePreliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.DesignObservational, retrospective study.SettingAdmitted to hospital between February 27 and April 15, 2020.ParticipantsPatients aged ≥18 years with laboratory confirmed COVID-19ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline).Main Outcomes and MeasuresFrequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.ResultsA total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.Conclusions and RelevanceAKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.Key PointsQuestionWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19?FindingsIn this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79.MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.

ImportanceThere is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality.ObjectiveTo assess association of clinical features on patient outcomes.Design, Setting, and ParticipantsIn this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases.ExposuresConfirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs.Main Outcomes and MeasuresEffects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated.ResultsHispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (ppConclusions and RelevanceWhile race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

The novel coronavirus disease-2019 (COVID-19) has affected nearly every country worldwide. Reports note increased thromboembolic events among hospitalized patients ([1][1],[2][2]), and there are anecdotal observations of improved outcomes with systemic anticoagulation (AC); however, the specific

Multifaceted nonpharmaceutical interventions supported by data restricted spread of COVID-19 in China.

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions.One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

BackgroundThe coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.MethodsDemographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.ConclusionsThis is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes

Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features-tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint-confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.

suPAR might serve as a biomarker for prediction of acute kidney injury with possible therapeutic implications.

Significant disparities exist in kidney disease, with blacks facing a higher burden of CKD and kidney failure. In addition to socioeconomic/health system factors, these disparities are linked to two sequence variants (G1 and G2) in the apoL1 ( APOL1 ) gene. The high-risk APOL1 genotype (two copies

SummaryBlood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including 563,946 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering the full allele frequency spectrum of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood cell traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell GWAS to interrogate clinically meaningful variants across the full allelic spectrum of human variation.

SUMMARYMost loci identified by GWAS have been found in populations of European ancestry (EA). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EA individuals, we identified 5,552 trait-variant associations at P−9, including 71 novel loci not found in EA populations. We also identified novel ancestry-specific variants not found in EA, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional, and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EA-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations, and compared genetic architecture and the impact of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Publisher's version (útgefin grein), The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease., We provide all investigator and study-specific acknowledgements in Supplementary Note 1, and funding sources in Supplementary Note 2.

Summary Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation., Graphical Abstract, Highlights • Largest genome-wide association study of blood cell traits to date • Empiric assessments of omnigenic and infinitesimal models of polygenic variation • Functional insights into how genetic variants impact human hematopoiesis • Assessment of the effect of polygenic trait scores upon blood diseases, Analysis of blood cell traits in the UK Biobank and other cohorts illuminates the full genetic architecture of hematopoietic phenotypes, with evidence supporting the omnigenic model for complex traits and linking polygenic burden with monogenic blood diseases.

Electronic Health Record (EHR) data is a rich source for powerful biomedical discovery but it consists of a wide variety of data types that are traditionally difficult to model. Furthermore, many machine learning frameworks that utilize these data for predictive tasks do not fully leverage the inter-connectivity structure and therefore may not be fully optimized. In this work, we propose a relational, deep heterogeneous network learning method that operates on EHR data and addresses these limitations. In this model, we used three different node types: patient, lab, and diagnosis. We show that relational graph learning naturally encodes structured relationships in the EHR and outperforms traditional multilayer perceptron models in the prediction of thousands of diseases. We evaluated our model on EHR data derived from MIMIC-III, a public critical care data set, and show that our model has improved prediction of numerous disease diagnoses.

JCPT_supplementary_materials_revision for Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype by Adam J. Russak, Farhan Chaudhry, Jessica K. De Freitas, Garrett Baron, Fayzan F. Chaudhry, Solomon Bienstock, Ishan Paranjpe, Akhil Vaid, Mohsin Ali, Shan Zhao, Sulaiman Somani, Felix Richter, Tejeshwar Bawa, Phillip D. Levy, Riccardo Miotto, Girish N. Nadkarni, Kipp W. Johnson and Benjamin S. Glicksberg in Journal of Cardiovascular Pharmacology and Therapeutics

Patients with CKD are at higher risk of developing peripheral artery disease (PAD) and have worse outcomes than persons without CKD ([1][1],[2][2]). Current PAD treatment strategies include exercise, medications, and revascularization techniques. However, it is likely that persons with advanced CKD

Background and objectives Patients are informed of the risk of kidney biopsy–related complications using data from nonhospitalized patients, which may underestimate the risk for hospitalized patients. We evaluated the rate and risk factors of kidney biopsy–related complications in hospitalized patients with acute kidney disease (AKD) to better estimate the risk in this population. Design, setting, participants, & measurements We used data from the Yale biopsy cohort to evaluate rates of kidney biopsy–related complications including adjudicated procedure-related bleeding requiring blood transfusions or angiographic interventions, medium- or large-sized hematomas, reimaging after biopsy including abdominal ultrasonography or computed tomography, and death in hospitalized patients with AKD (including AKI). We evaluated univariable and multivariable association of risk factors with transfusions. We compared rates of complications between hospitalized and nonhospitalized patients. Results Between 2015 and 2017, 159 hospitalized patients underwent a kidney biopsy for AKD evaluation, of which 80 (51%) had stage 1 AKI, 42 (27%) had stage 2 (or higher) AKI, and 27 (17%) had AKD (without AKI). Of these, 12 (8%; 95% confidence interval [95% CI], 5% to 15%) required a transfusion, three (2%; 95% CI, 1% to 5%) required an intervention, 11 (7%; 95% CI, 4% to 12%) had hematoma, and 31 (20%; 95% CI, 14% to 26%) required reimaging after biopsy. Of the four (3%; 95% CI, 1% to 6%) deaths during hospitalization, none were related to the biopsy. Female sex, lower platelet count, and higher BUN were associated with postbiopsy transfusions on univariable and multivariable analyses. Trainee as proceduralist and larger needle gauge were associated with transfusions in univariable, but not multivariable, analysis. Nonhospitalized patients had lower rates of transfusion than hospitalized patients, although the latter also had lower prebiopsy hemoglobin and greater surveillance after biopsy. Conclusions Hospitalized patients experience higher risk of postbiopsy complications than previously reported and several factors, such as lower platelet count, female sex, and higher BUN, are associated with this risk.

Dialysis-requiring acute kidney injury (D-AKI) is a serious complication in hospitalized heart failure (HF) patients. However, data on national trends are lacking after 2002.We used the Nationwide Inpatient Sample (2002-2013) to identify HF hospitalizations with and without D-AKI. We analyzed trends in incidence, in-hospital mortality, length of stay (LoS), and cost. We calculated adjusted odds ratios (aORs) for predictors of D-AKI and for outcomes including in-hospital mortality and adverse discharge (discharge to skilled nursing facilities, nursing homes, etc).We identified 11,205,743 HF hospitalizations. Across 2002-2013, the incidence of D-AKI doubled from 0.51% to 1.09%. We found male sex, younger age, African-American and Hispanic race, and various comorbidities and procedures, such as sepsis and mechanical ventilation, to be independent predictors of D-AKI in HF hospitalizations. D-AKI was associated with higher odds of in-hospital mortality (aOR 2.49, 95% confidence interval [CI] 2.36-2.63; P .01) and adverse discharge (aOR 2.04, 95% CI 1.95-2.13; P .01). In-hospital mortality and attributable risk of mortality due to D-AKI decreased across 2002-2013. LoS and cost also decreased across this period.The incidence of D-AKI in HF hospitalizations doubled across 2002-2013. Despite declining in-hospital mortality, LoS, and cost, D-AKI was associated with worse outcomes.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

Endoscopic retrograde cholangiopancreatography (ERCP) can be challenging in patients with decompensated cirrhosis (DC) due to increased risk of adverse events related to liver dysfunction. Limited data exist regarding its national utilization in patients with DC. We aim to determine the trends in utilization and outcomes of ERCP among patients with DC in US hospitalizations. We identified hospitalizations undergoing ERCP (diagnostic and therapeutic) between 2000 and 2013 from the National Inpatient Sample (NIS) database and used validated ICD9-CM codes to identify DC hospitalizations. We utilized Cochrane–Armitage test to identify changes in trends and multivariable survey regression modeling for adjusted odds ratios (aOR) for adverse outcomes and mortality predictors. There were 43782 cases of ERCPs performed in DC patients during the study period. Absolute number of ERCPs performed in this population from 2000 to 2013 showed an upward trend; however, the proportion of DC patients undergoing ERCP remained stable. We noted significant decrease in utilization of diagnostic ERCP and an increase of therapeutic ERCPs (P

TO THE EDITOR: Vitamin B12 (VB12), or cobalamin, is a water-soluble vitamin. It serves as an enzyme cofactor in the 1-carbon metabolism pathway, which plays a key role in a wide range of biological processes, including red blood cell (RBC) formation, DNA synthesis, and myelination of the central

G1/G2 variants in the Apolipoprotein L1 (APOL1) gene are associated with end-stage renal disease (ESRD) in people with African ancestry. Plasma biomarkers may have utility for risk stratification in APOL1 high-risk individuals of African ancestry. To evaluate this, we measured tumor necrosis factor receptor 1/2 (TNFR1/2) and kidney injury molecule-1 (KIM1) in baseline plasma specimens from individuals of African ancestry with high-risk APOL1 genotype. Biomarker association with a composite renal outcome of ESRD or 40% sustained decline in estimated glomerular filtration rate (eGFR) was then determined and then assessed as improvement in area under curve. Among the 498 participants, the median age was 56 years, 67.7% were female, and the baseline eGFR was 83.3 ml/min/1.73 m2 with 80 reaching outcome over 5.9 years. TNFR1, TNFR2, and KIM1 at enrollment were independently associated with renal outcome continuously (adjusted hazard ratio 2.0 [95% confidence interval 1.3-3.1]; 1.5 [1.2-1.9]; and 1.6 [1.3-1.9] per doubling in levels, respectively) or by tertiles. The area under the curve significantly improved from 0.75 with the clinical model to 0.79 with the biomarker-enhanced model. The event rate was 40% with all 3 biomarkers elevated (adjusted odds ratio of 5.3 (2.3-12.0) vs. 17% (adjusted odds ratio 1.8 [0.9-3.6] with 1 or 2 elevated and 7% with no biomarkers elevated. Thus, plasma concentrations of TNFR1, TNFR2, and KIM1 are independently associated with renal outcome and improve discrimination or reclassification of African ancestry individuals with a high-risk APOL1 genotype and preserve renal function. Elevation of all markers had higher risk of outcome and can assist with better clinical prediction and improved clinical trial efficiency by enriching event rates.

Background Acute kidney injury (AKI), as defined by peak increase in serum creatinine, is independently associated with increased risk of mortality and length of stay. Studies have suggested that the duration of AKI may be an important additional or independent prognostic marker of increased mortality in patients with AKI across clinical settings. We performed a systematic review and meta-analysis of published studies to assess the impact of duration of AKI on outcomes. Methods Various bibliographic databases (MEDLINE, Embase, Cochrane Library, CINAHL and Web of Science) were searched through database inception to December 2015. Human, longitudinal studies with patients aged 18 or above describing outcomes of duration of AKI were included. Duration of AKI categorized as “Short” if AKI duration was ≤2 days or labeled as “transient AKI”; “Medium” for AKI durations 3–6 days and “Long” for AKI duration of ≥7 days or “non-recovered”. Various outcomes looked at were Long term mortality, cardiovascular events, chronic kidney disease (CKD). Results Eighteen studies were deemed eligible for the systematic review. The outcome of long-term mortality with duration of AKI was reported in 8 studies. The pooled Risk Ratio (RR) for long-term mortality generally was higher for longer duration of AKI: short duration of AKI (n = 8 studies, RR 1.42, 95% CI 1.21–1.66), medium duration (n = 4 studies, RR 1.92, 95% CI 1.34–2.75), and long duration (n = 8 studies, RR 2.28, 95% CI 1.77–2.94) duration of AKI. Further, Duration of AKI was independently associated with higher risk of cardiovascular outcomes and incident CKD Stage 3 when stratified within each stage of AKI. Conclusion Duration of AKI was independently associated with long term mortality, cardiovascular(CV) events, and development of incident CKD Stage 3. Electronic supplementary material The online version of this article (10.1186/s12882-018-0876-7) contains supplementary material, which is available to authorized users.

Background An early posttransplant biomarker/surrogate marker for kidney allograft loss has the potential to guide targeted interventions. Previously published findings, including results from the Clinical Trials in Organ Transplantation (CTOT)-01 study, showed that elevated urinary chemokine CXCL9 levels and elevated frequencies of donor-reactive interferon gamma (IFNγ)-producing T cells by enzyme-linked immunosorbent spot (ELISPOT) assay associated with acute cellular rejection within the first year and with lower 1-year posttransplant estimated glomerular filtration rate (eGFR). How well these biomarkers correlate with late outcomes, including graft loss, is unclear. Methods In CTOT-17, we obtained 5-year outcomes in the CTOT-01 cohort and correlated them with (a) biomarker results and (b) changes in eGFR (Chronic Kidney Disease Epidemiology Collaboration formula) over the initial 2 years posttransplant using univariable analysis and multivariable logistic regression. Results Graft loss occurred in 14 (7.6%) of 184 subjects 2 to 5 years posttransplant. Neither IFNγ ELISPOTs nor urinary CXCL9 were informative. In contrast, a 40% or greater decline in eGFR from 6 months to 2 years posttransplant independently correlated with 13-fold odds of 5-year graft loss (adjusted odds ratio, 13.1; 95% confidence interval, 3.0-56.6), a result that was validated in the independent Genomics of Chronic Allograft Rejection cohort (n = 165; adjusted odds ratio, 11.2). Conclusions We conclude that although pretransplant and early posttransplant ELISPOT and chemokine measurements associate with outcomes within 2 years posttransplant, changes in eGFR between 3 or 6 months and 24 months are better surrogates for 5-year outcomes, including graft loss.

Introduction Chronic kidney disease (CKD), a progressive loss of renal function, can lead to serious complications if underdiagnosed. Many studies suggest that the oral microbiota plays important role in the health of the host; however, little is known about the association between the oral microbiota and CKD pathogenesis. Methods In this study, we surveyed the oral microbiota in saliva, the left and right molars, and the anterior mandibular lingual area from 77 participants (18 with and 59 without CKD), and tested their association with CKD to identify microbial features that may be predictive of CKD status. Results The overall oral microbiota composition significantly differed by oral locations and was associated with CKD status in saliva and anterior mandibular lingual samples. In CKD patients, we observed a significant enrichment of Neisseria and depletion of Veillonella in both sample types and a lower prevalence of Streptococcus in saliva after adjustment for other comorbidities. Furthermore, we detected a negative association of Neisseria and Streptococcus genera with the kidney function as measured by estimated glomerular filtration rate. Neisseria abundance also correlated with plasma interleukin-18 levels. Conclusion We demonstrate the association of the oral microbiome with CKD and inflammatory kidney biomarkers, highlighting a potential role of the commensal bacteria in CKD pathogenesis. A better understanding of the interplay between the oral microbiota and CKD may help in the development of new strategies to identify at-risk individuals or to serve as a novel target for therapeutic intervention.

Diabetes is a leading cause of ESKD worldwide, resulting in substantial patient morbidity and healthcare costs. Fenofibrate and other fibrate therapies may improve lipid profiles and prevent cardiovascular events in people with CKD, and thus may be useful as adjunctive therapies to reduce the risk

Deep Learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus-disease 2019 (COVID-19) pandemic where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL) which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR data set to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full data set and a restricted data set. CL models consistently outperform CEL models with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC., Deep learning models applied on EHR data often utilize cross-entropy loss (CEL) as the primary optimization function. But CEL may not be suitable for real-world scenarios with imbalanced data. We develop a learning framework that incorporates both CEL and contrastive loss (CL) to tackle this issue. Our framework achieves better predictive performance and feature interpretability, particularly for imbalanced data.

While increased obesity prevalence among persons of African ancestry (AAs) compared to persons of European ancestry (EAs) is linked to social, environmental and behavioral factors, there are no gene variants that are common and significantly associated with obesity in AA populations. We sought to explore the association between ancestry specific renal risk variants in the apolipoprotein L1 (APOL1) gene with obesity related traits in AAs.We conducted a genotype-phenotype association study from 3 electronic medical record linked cohorts (BioMe Biobank, BioVU, nuGENE); randomized controlled trials (genetic testing to understand and address renal disease disparities) and prospective cohort study (Jackson Heart Study). We analyzed association of APOL1 renal risk variants with cross-sectional measures of obesity (average body mass index (BMI), and proportion of overweight and obesity) and with measures of body composition (in Jackson Heart Study).We had data on 11,930 self-reported AA adults. Across cohorts, mean age was from 42 to 49 years and percentage female from 58% to 75.3%. Individuals who have 2 APOL1 risk alleles (14% of AAs) have 30% higher obesity odds compared to others (recessive model adjusted odds ratio 1.30; 95% confidence interval 1.16-1.41; P = 2.75 × 10-6). An additive model better fit the association, in which each allele (47% of AAs) increases obesity odds by 1.13-fold (adjusted odds ratio 1.13; 95% confidence interval 1.07-1.19; P = 3.07 × 10-6) and increases BMI by 0.36 kg/m2 (∼1 kg, for 1.7 m height; P = 2 × 10-4). APOL1 alleles are not associated with refined body composition traits overall but are significantly associated with fat free mass index in women [0.30 kg/m2 increment per allele; P = .03].Thus, renal risk variants in the APOL1 gene, found in nearly half of AAs, are associated with BMI and obesity in an additive manner. These variants could, either on their own or interacting with environmental factors, explain a proportion of ethnic disparities in obesity.

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 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.

Background & Aims Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of COVID-19, we investigated intestinal infection with SARS-CoV-2, its effect on pathogenesis, and clinical significance. Methods Human intestinal biopsy tissues were obtained from patients with COVID-19 (n = 19) and uninfected control individuals (n = 10) for microscopic examination, cytometry by time of flight analyses, and RNA sequencing. Additionally, disease severity and mortality were examined in patients with and without GI symptoms in 2 large, independent cohorts of hospitalized patients in the United States (N = 634) and Europe (N = 287) using multivariate logistic regressions. Results COVID-19 case patients and control individuals in the biopsy cohort were comparable for age, sex, rates of hospitalization, and relevant comorbid conditions. SARS-CoV-2 was detected in small intestinal epithelial cells by immunofluorescence staining or electron microscopy in 15 of 17 patients studied. High-dimensional analyses of GI tissues showed low levels of inflammation, including down-regulation of key inflammatory genes including IFNG, CXCL8, CXCL2, and IL1B and reduced frequencies of proinflammatory dendritic cells compared with control individuals. Consistent with these findings, we found a significant reduction in disease severity and mortality in patients presenting with GI symptoms that was independent of sex, age, and comorbid illnesses and despite similar nasopharyngeal SARS-CoV-2 viral loads. Furthermore, there was reduced levels of key inflammatory proteins in circulation in patients with GI symptoms. Conclusions These data highlight the absence of a proinflammatory response in the GI tract despite detection of SARS-CoV-2. In parallel, reduced mortality in patients with COVID-19 presenting with GI symptoms was observed. A potential role of the GI tract in attenuating SARS-CoV-2–associated inflammation needs to be further examined.

Objective To examine rates of readmission after parathyroidectomy in patients with chronic kidney disease and determine primary etiologies, timing, and risk factors for these unplanned readmissions. Study Design Retrospective cohort study. Setting Nationwide Readmissions Database. Subjects and Methods The Nationwide Readmissions Database was queried for parathyroidectomy procedures performed in patients with chronic kidney disease between January 2013 and November 2013. Patient-, admission-, and hospital-level characteristics were compared for patients with and without at least 1 unplanned 30-day readmission. Outcomes of interest included rates, etiology, and timing of readmission. Multivariate logistic regression was used to identify predictors of 30-day readmission. Results There were 2756 parathyroidectomies performed in patients with chronic kidney disease with an unplanned readmission rate of 17.2%. Hypocalcemia/hungry bone syndrome accounted for 40% of readmissions. Readmissions occurred uniformly throughout the 30 days after discharge, but readmissions for hypocalcemia/hungry bone syndrome peaked in the first 10 days and decreased over time. Weight loss/malnutrition at time of parathyroidectomy and length of stay of 5 to 6 days conferred increased risk of readmission with adjusted odds ratios (aOR) of 3.31 (95% confidence interval [CI], 1.55-7.05; P = .002) and 1.87 (95% CI, 1.10-3.19; P = .02), respectively. Relative to primary hyperparathyroidism, parathyroidectomies performed for secondary hyperparathyroidism (aOR, 2.53; 95% CI, 1.07-5.95; P = .03) were associated with higher risk of readmission. Conclusion Postparathyroidectomy readmission rates for patients with chronic kidney disease are nearly 5 times that of the general population. Careful consideration of postoperative care and electrolyte management is crucial to minimize preventable readmissions in this vulnerable population.

OBJECTIVE Sodium-glucose cotransporter-2 (SGLT2) inhibitors are new medications that improve cardiovascular and renal outcomes in patients with type 2 diabetes (T2D). However, the Food and Drug Administration has issued alerts regarding increased acute kidney injury (AKI) risk with canagliflozin and dapagliflozin. We aimed to assess the real-world risk of AKI in new SGLT2 inhibitor users in two large health care utilization cohorts of patients with T2D. RESEARCH DESIGN AND METHODS We used longitudinal data from the Mount Sinai chronic kidney disease registry and the Geisinger Health System cohort. We selected SGLT inhibitor users and nonusers (patients with T2D without SGLT2 inhibitor prescription). We determined AKI by the KDIGO (Kidney Disease: Improving Global Outcomes) definition (AKIKDIGO). We performed 1:1 nearest-neighbor propensity matching and calculated unadjusted hazard ratios (HRs) and adjusted HRs (aHRs; accounting for covariates poorly balanced) for AKI in primary and sensitivity analyses. RESULTS We identified 377 SGLT2 inhibitor users and 377 nonusers in the Mount Sinai cohort, of whom 3.8 and 9.7%, respectively, had an AKIKDIGO event over a median follow-up time of 14 months. The unadjusted hazards of AKIKDIGO were 60% lower in users (HR 0.4 [95% CI 0.2–0.7]; P = 0.01), which was unchanged (aHR 0.4 [95% CI 0.2–0.7]; P = 0.004) postadjustment. Similarly, we identified 1,207 SGLT2 inhibitor users and 1,207 nonusers in the Geisinger cohort, of whom 2.2 and 4.6% had an AKIKDIGO event. AKIKDIGO unadjusted hazards were lower in users (HR 0.5 [95% CI 0.3–0.8]; P < 0.01) with modest attenuation postadjustment for covariates (aHR 0.6 [95% CI 0.4–1.1]; P = 0.09). These estimates did not qualitatively change across several sensitivity analyses. CONCLUSIONS Our findings do not suggest an increased risk of AKI associated with SGLT2 inhibitor use in patients with T2D in two large health systems.

Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score (similar to 6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 x 10(-49)), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications.