Remote digital health technologies for improving the care of people with respiratory disorders

Respiratory diseases are a leading cause of morbidity and mortality globally. However, existing systems of care, built around scheduled appointments, are not well designed to support the needs of people with chronic and acute respiratory conditions that can change rapidly and unexpectedly. Home-based and personal digital health technologies (DHTs) allow implementation of new models of care catering to the unique needs of individuals. The high number of respiratory triggers and unique responses to them require a personalised solution for each patient. The real-world, repetitive monitoring capabilities of DHTs enable identification of the normal operating characteristics for each individual and, therefore, recognition of the earliest deviations from that state. However, despite this potential, the number of clinical efficacy studies of DHTs is quite small. Evaluation of clinical effectiveness of DHTs in improving health quality in real-world settings is urgently needed.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students

Background

Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population.

Methods

We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group.

Results

We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group.

Conclusions

Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

Platelet RNA Biomarker of Ticagrelor-Responsive Genes Is Associated With Platelet Function and Cardiovascular Events

BACKGROUND

Identifying patients with the optimal risk:benefit for ticagrelor is challenging. The aim was to identify ticagrelor-responsive platelet transcripts as biomarkers of platelet function and cardiovascular risk.

METHODS

Healthy volunteers (n=58, discovery; n=49, validation) were exposed to 4 weeks of ticagrelor with platelet RNA data, platelet function, and self-reported bleeding measured pre-/post-ticagrelor. RNA sequencing was used to discover platelet genes affected by ticagrelor, and a subset of the most informative was summarized into a composite score and tested for validation. This score was further analyzed (1) in CD34+ megakaryocytes exposed to an P2Y12 inhibitor in vitro, (2) with baseline platelet function in healthy controls, (3) in peripheral artery disease patients (n=139) versus patient controls (n=30) without atherosclerosis, and (4) in patients with peripheral artery disease for correlation with atherosclerosis severity and risk of incident major adverse cardiovascular and limb events.

RESULTS

Ticagrelor exposure differentially expressed 3409 platelet transcripts. Of these, 111 were prioritized to calculate a Ticagrelor Exposure Signature score, which ticagrelor reproducibly increased in discovery and validation cohorts. Ticagrelor's effects on platelets transcripts positively correlated with effects of P2Y12 inhibition in primary megakaryocytes. In healthy controls, higher baseline scores correlated with lower baseline platelet function and with minor bleeding while receiving ticagrelor. In patients, lower scores independently associated with both the presence and extent of atherosclerosis and incident ischemic events.

CONCLUSIONS

Ticagrelor-responsive platelet transcripts are a biomarker for platelet function and cardiovascular risk and may have clinical utility for selecting patients with optimal risk:benefit for ticagrelor use.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Data-driven science and diversity in the All of Us Research Program

The National Institutes of Health's All of Us Research Program is an accessible platform that hosts genomic and phenotypic data to be collected from 1 million participants in the United States. Its mission is to accelerate medical research and clinical breakthroughs with a special emphasis on diversity.

Branched-Chain Amino Acids in Computed Tomography-Defined Adipose Depots and Coronary Artery Disease: A PROMISE Trial Biomarker Substudy

Background The interplay between branched-chain amino acid (BCAA) metabolism, an important pathway in adiposity and cardiometabolic disease, and visceral adipose depots such as hepatic steatosis (HS) and epicardial adipose tissue is unknown. We leveraged the PROMISE clinical trial with centrally adjudicated coronary computed tomography angiography imaging to determine relationships between adipose depots, BCAA dysregulation, and coronary artery disease (CAD). Methods and Results The PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial randomized 10 003 outpatients with stable chest pain to computed tomography angiography versus standard-of-care diagnostics. For this study, we included 1798 participants with available computed tomography angiography data and biospecimens. Linear and logistic regression were used to determine associations between a molar sum of BCAAs measured by nuclear magnetic resonance spectroscopy with body mass index, adipose traits, and obstructive CAD. Mendelian randomization was then used to determine if BCAAs are in the causal pathway for adipose depots or CAD. The study sample had a mean age of 60 years (SD, 8.0), body mass index of 30.6 (SD, 5.9), and epicardial adipose tissue volume of 57.3 (SD, 21.3) cm³/m²; 27% had HS, and 14% had obstructive CAD. BCAAs were associated with body mass index (multivariable beta 0.12 per SD increase in BCAA [95% CI, 0.08-0.17]; P=4×10^-8). BCAAs were also associated with HS (multivariable odds ratio [OR], 1.46 per SD increase in BCAAs [95% CI, 1.28-1.67]; P=2×10^-8), but BCAAs were associated only with epicardial adipose tissue volume (odds ratio, 1.18 [95% CI, 1.07-1.32]; P=0.002) and obstructive CAD (OR, 1.18 [95% CI, 1.04-1.34]; P=0.009) in univariable models. Two-sample Mendelian randomization did not support the role of BCAAs as within the causal pathways for HS or CAD. Conclusions BCAAs have been implicated in the pathogenesis of cardiometabolic diseases, and adipose depots have been associated with the risk of CAD. Leveraging a large clinical trial, we further establish the role of dysregulated BCAA catabolism in HS and CAD, although BCAAs did not appear to be in the causal pathway of either disease. This suggests that BCAAs may serve as an independent circulating biomarker of HS and CAD but that their association with these cardiometabolic diseases is mediated through other pathways.

Development of Competency-based Online Genomic Medicine Training (COGENT)

The fields of genetics and genomics have greatly expanded across medicine through the development of new technologies that have revealed genetic contributions to a wide array of traits and diseases. Thus, the development of widely available educational resources for all healthcare providers is essential to ensure the timely and appropriate utilization of genetics and genomics patient care. In 2020, the National Human Genome Research Institute released a call for new proposals to develop accessible, sustainable online education for health providers. This paper describes the efforts of the six teams awarded to reach the goal of providing genetic and genomic training modules that are broadly available for busy clinicians.

Lipoprotein Subclasses Associated With High-Risk Coronary Atherosclerotic Plaque: Insights From the PROMISE Clinical Trial

BACKGROUND More than half of major adverse cardiovascular events (MACE) occur in the absence of obstructive coronary artery disease and are often attributed to the rupture of high-risk coronary atherosclerotic plaque (HRP). Blood-based biomarkers that associate with imaging-defined HRP and predict MACE are lacking. METHODS AND RESULTS Nuclear magnetic resonance-based lipoprotein particle profiling was performed in the biomarker substudy of the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial (N=4019) in participants who had stable symptoms suspicious for coronary artery disease. Principal components analysis was used to reduce the number of correlated lipoproteins into uncorrelated lipoprotein factors. The association of lipoprotein factors and individual lipoproteins of significantly associated factors with core laboratory determined coronary computed tomographic angiography features of HRP was determined using logistic regression models. The association of HRP-associated lipoproteins with MACE was assessed in the PROMISE trial and validated in an independent coronary angiography biorepository (CATHGEN [Catheterization Genetics]) using Cox proportional hazards models. Lipoprotein factors composed of high-density lipoprotein (HDL) subclasses were associated with HRP. In these factors, large HDL (odds ratio [OR], 0.70 [95% CI, 0.56-0.85]; P<0.001) and medium HDL (OR, 0.84 [95% CI, 0.72-0.98]; P=0.028) and HDL size (OR, 0.82 [95% CI, 0.69-0.96]; P=0.018) were associated with HRP in multivariable models. Medium HDL was associated with MACE in PROMISE (hazard ratio [HR], 0.76 [95% CI, 0.63-0.92]; P=0.004), which was validated in the CATHGEN biorepository (HR, 0.91 [95% CI, 0.88-0.94]; P<0.001). CONCLUSIONS Large and medium HDL subclasses and HDL size inversely associate with HRP features, and medium HDL subclasses inversely associate with MACE in PROMISE trial participants. These findings may aid in the risk stratification of individuals with chest pain and provide insight into the pathobiology of HRP. REGISTRATION URL: https://clinicaltrials.gov; Unique identifier: NCT01174550.

Implementation-effectiveness trial of systematic family health history based risk assessment and impact on clinical disease prevention and surveillance activities

BACKGROUND

Systematically assessing disease risk can improve population health by identifying those eligible for enhanced prevention/screening strategies. This study aims to determine the clinical impact of a systematic risk assessment in diverse primary care populations.

METHODS

Hybrid implementation-effectiveness trial of a family health history-based health risk assessment (HRA) tied to risk-based guideline recommendations enrolling from 2014-2017 with 12 months of post-intervention survey data and 24 months of electronic medical record (EMR) data capture.

SETTING

19 primary care clinics at four geographically and culturally diverse U.S. healthcare systems.

PARTICIPANTS

any English or Spanish-speaking adult with an upcoming appointment at an enrolling clinic.

METHODS

A personal and family health history based HRA with integrated guideline-based clinical decision support (CDS) was completed by each participant prior to their appointment. Risk reports were provided to patients and providers to discuss at their clinical encounter.

OUTCOMES

provider and patient discussion and provider uptake (i.e. ordering) and patient uptake (i.e. recommendation completion) of CDS recommendations.

MEASURES

patient and provider surveys and EMR data.

RESULTS

One thousand eight hundred twenty nine participants (mean age 56.2 [SD13.9], 69.6% female) completed the HRA and had EMR data available for analysis. 762 (41.6%) received a recommendation (29.7% for genetic counseling (GC); 15.2% for enhanced breast/colon cancer screening). Those with recommendations frequently discussed disease risk with their provider (8.7%-38.2% varied by recommendation, p-values ≤ 0.004). In the GC subgroup, provider discussions increased referrals to counseling (44.4% with vs. 5.9% without, P < 0.001). Recommendation uptake was highest for colon cancer screening (provider = 67.9%; patient = 86.8%) and lowest for breast cancer chemoprevention (0%).

CONCLUSIONS

Systematic health risk assessment revealed that almost half the population were at increased disease risk based on guidelines. Risk identification resulted in shared discussions between participants and providers but variable clinical action uptake depending upon the recommendation. Understanding the barriers and facilitators to uptake by both patients and providers will be essential for optimizing HRA tools and achieving their promise of improving population health.

TRIAL REGISTRATION

Clinicaltrials.gov number NCT01956773 , registered 10/8/2013.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Risk factors for cardiovascular disease among individuals with hepatic steatosis

Cardiovascular disease (CVD) is the leading cause of mortality in adults with hepatic steatosis (HS). However, risk factors for CVD in HS are unknown. We aimed to identify factors associated with coronary artery disease (CAD) and incident major adverse cardiovascular events (MACE) in individuals with HS. We performed a nested cohort study of adults with HS detected on coronary computed tomography in the PROspective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) trial. Obstructive CAD was defined as ≥50% coronary stenosis. MACE included hospitalization for unstable angina, nonfatal myocardial infarction, or all-cause death. Multivariate modeling, adjusted for age, sex, atherosclerotic CVD (ASCVD) risk score and body mass index, identified factors associated with obstructive CAD. Cox regression, adjusted for ASCVD risk score, determined the predictors of MACE. A total of 959 of 3,756 (mean age 59.4 years, 55.0% men) had HS. Obstructive CAD was present in 15.2% (145 of 959). Male sex (adjusted odds ratio [aOR] = 1.83, 95% confidence interval [CI] 1.18-1.2.84; p = 0.007), ASCVD risk score (aOR = 1.05, 95% CI 1.03-1.07; p < 0.001), and n-terminal pro-b-type natriuretic peptide (NT-proBNP; aOR = 1.90, 95% CI 1.38-2.62; p < 0.001) were independently associated with obstructive CAD. In the 25-months median follow-up, MACE occurred in 4.4% (42 of 959). Sedentary lifestyle (adjusted hazard ratio [aHR] = 2.53, 95% CI 1.27-5.03; p = 0.008) and NT-proBNP (aOR = 1.50, 95% CI 1.01-2.25; p = 0.046) independently predicted MACE. Furthermore, the risk of MACE increased by 3% for every 1% increase in ASCVD risk score (aHR = 1.03, 95% CI 1.01-1.05; p = 0.02). Conclusion: In individuals with HS, male sex, NT-pro-BNP, and ASCVD risk score are associated with obstructive CAD. Furthermore, ASCVD, NT-proBNP, and sedentary lifestyle are independent predictors of MACE. These factors, with further validation, may help risk-stratify adults with HS for incident CAD and MACE.

Associations of a polygenic risk score with coronary artery disease phenotypes in the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial

A polygenic risk score (PGS) is associated with obstructive coronary artery disease (CAD) independent of traditional risk factors. Coronary computed tomography angiography (CTA) can characterize coronary plaques, including features of highrisk CAD. However, it is unknown if a PGS is associated with obstructive CAD and high-risk CAD phenotypes in patients with symptoms suggestive of CAD.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources

OBJECTIVE

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

MATERIALS AND METHODS

Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.

RESULTS

Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.

DISCUSSION

Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Digital health-enabled genomics: Opportunities and challenges

Digital health solutions, with apps, virtual care, and electronic medical records, are gaining momentum across all medical disciplines, and their adoption has been accelerated, in part, by the COVID-19 pandemic. Personal wearables, sensors, and mobile technologies are increasingly being used to identify health risks and assist in diagnosis, treatment, and monitoring of health and disease. Genomics is a vanguard of digital healthcare as we witness a convergence of the fields of genomic and digital medicine. Spurred by the acute need to increase health literacy, empower patients' preference-sensitive decisions, or integrate vast amounts of complex genomic data into the clinical workflow, there has been an emergence of digital support tools in genomics-enabled care. We present three use cases that demonstrate the application of these converging technologies: digital genomics decision support tools, conversational chatbots to scale the genetic counseling process, and the digital delivery of comprehensive genetic services. These digital solutions are important to facilitate patient-centered care delivery, improve patient outcomes, and increase healthcare efficiencies in genomic medicine. Yet the development of these innovative digital genomic technologies also reveals strategic challenges that need to be addressed before genomic digital health can be broadly adopted. Alongside key evidentiary gaps in clinical and cost-effectiveness, there is a paucity of clinical guidelines, policy, and regulatory frameworks that incorporate digital health. We propose a research agenda, guided by learning healthcare systems, to realize the vision of digital health-enabled genomics to ensure its sustainable and equitable deployment in clinical care.

A hands-free stool sampling system for monitoring intestinal health and disease

Analysis of stool offers simple, non-invasive monitoring for many gastrointestinal (GI) diseases and access to the gut microbiome, however adherence to stool sampling protocols remains a major challenge because of the prevalent dislike of handling one's feces. We present a technology that enables individual stool specimen collection from toilet wastewater for fecal protein and molecular assay. Human stool specimens and a benchtop test platform integrated with a commercial toilet were used to demonstrate reliable specimen collection over a wide range of stool consistencies by solid/liquid separation followed by spray-erosion. The obtained fecal suspensions were used to perform occult blood tests for GI cancer screening and for microbiome 16S rRNA analysis. Using occult blood home test kits, we found overall 90% agreement with standard sampling, 96% sensitivity and 86% specificity. Microbiome analysis revealed no significant difference in within-sample species diversity compared to standard sampling and specimen cross-contamination was below the detection limit of the assay. Furthermore, we report on the use of an analogue turbidity sensor to assess in real time loose stools for tracking of diarrhea. Implementation of this technology in residential settings will improve the quality of GI healthcare by facilitating increased adherence to routine stool monitoring.

Global priorities for large-scale biomarker-based prospective cohorts

The focus of this paper is on strategic approaches for establishing population-based prospective cohorts that collect and store biological samples from very large numbers of participants to help identify the determinants of common health outcomes. In particular, it aims to address key issues related to investigation of genetic, as well as social, environmental, and ancestral, diversity; generation of detailed genetic and other types of assay data; collection of detailed lifestyle and environmental exposure information; follow-up and characterization of incident health outcomes; and overcoming obstacles to data sharing and access (including capacity building). It concludes that there is a need for strategic planning at an international level (rather than the current ad hoc approach) toward the development of a carefully selected set of deeply characterized large-scale prospective cohorts that are readily accessible by researchers around the world.

Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.

Health coaching and genetic risk testing in primary care: Randomized controlled trial

OBJECTIVE

Accessible interventions are needed to prevent coronary heart disease (CHD) and Type 2 diabetes (T2D). This prospective, randomized, controlled trial evaluated remote health coaching (HC), genetic risk testing (GRT), or both added to standardized risk assessment (SRA) in at-risk military primary care patients.

METHOD

Using a 2 × 2 factorial longitudinal design, 200 Air Force at-risk participants provided primary outcomes at baseline, 3-, 6- (HC endpoint), and 12-months. Secondary measures were taken less often. Per protocol analyses used linear models and logistic regression; intent-to-treat (ITT) analyses used mixed models.

RESULTS

Compared with those not receiving HC, the HC group was 3.6 times more likely to report moderate to intense physical activity at 6-months (p = .0009), and 2.9 times more likely to report such at 12-months (p = .0065). ITT longitudinal model did not reach significance (p = .0885). The HC group reported lower emotional representations of illness at 6-weeks and lower depression at 6 months. There were no other significant findings. HC and GRT interacted; higher T2D risk participants receiving HC were 4.7 times more likely to report higher stage of change for exercise at 6-months, and lost 2.2 kg more by 12-months. Lower T2D risk participants receiving HC perceived greater control over CHD risk at 6-weeks, and averaged lower 6-month depression.

CONCLUSIONS

Remote HC after SRA increased physical activity, which was sustained 6-months later. Incorporating GRT into SRA warrants further exploration regarding the potential to leverage HC for weight loss in elevated T2D risk participants, and for depression in lower T2D risk participants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A precision medicine approach to stress testing using metabolomics and microribonucleic acids

Both transcriptomics and metabolomics hold promise for identifying acute coronary syndrome (ACS) but they have not been used in combination, nor have dynamic changes in levels been assessed as a diagnostic tool. We assessed integrated analysis of peripheral blood miRNA and metabolite analytes to distinguish patients with myocardial ischemia on cardiac stress testing. We isolated and quantified miRNA and metabolites before and after stress testing from seven patients with myocardial ischemia and 1:1 matched controls. The combined miRNA and metabolomic data were analyzed jointly in a supervised, dimension-reducing discriminant analysis. We implemented a baseline model (T0) and a stress-delta model. This novel integrative analysis of the baseline levels of metabolites and miRNA expression showed modest performance for distinguishing cases from controls. The stress-delta model showed worse performance. This pilot study shows potential for an integrated precision medicine approach to cardiac stress testing.

Differential chromatin accessibility in peripheral blood mononuclear cells underlies COVID-19 disease severity prior to seroconversion.. [PMID: 35411343 PMCID: PMC8996625 DOI: 10.21203/rs.3.rs-1479864/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associate with mild or moderate symptoms are already robust and include severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity is marked by upregulation classical antiviral pathways including those regulating IRF1 and IRF7, whereas in moderate disease these classical antiviral signals diminish suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

Importance

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.

Objective

To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.

Design, Setting, and Participants

This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.

Interventions

The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.

Main Outcomes and Measures

Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement.

Results

Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.

Conclusions and Relevance

In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination

Background

Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another.

Methods

This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies.

Results

Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets.

Conclusions

In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01025-x.

OUP accepted manuscript

AIMS

Gene expression biosignatures may hold promise to individualize antiplatelet therapy in conjunction with current guidelines and risk scores. The Aspirin Response Signature (ARS) score is comprised of a weighted sum of correlated, pro-thrombotic gene transcripts measured in whole blood. In prior work where volunteers were exposed to aspirin 325 mg daily, higher ARS score was associated with lower platelet function; separately, in a clinical cohort of patients, higher ARS scores were associated with increased risk of adverse cardiovascular events. To better understand this apparent paradox, we measured ARS gene expression and score in volunteers to determine aspirin dose-response and ticagrelor relationships with ARS score and separately in patients to assess whether ARS is associated with incident bleeding.

METHODS AND RESULTS

Blood samples were collected from volunteers (N = 188) who were exposed to 4 weeks of daily aspirin 81 mg, daily aspirin 325 mg, and/or twice-daily ticagrelor 90 mg. ARS scores were calculated from whole blood RNA qPCR, and platelet function and protein expression were assessed in platelet-rich plasma. In mixed linear regression models, aspirin 81 mg exposure was not associated with changes in ARS gene expression or score. Aspirin 325 mg exposure resulted in a 6.0% increase in ARS gene expression (P = 7.5 × 10-9 vs. baseline, P = 2.1 × 10-4 vs. aspirin 81 mg) and an increase in expression of platelet proteins corresponding to ARS genes. Ticagrelor exposure resulted in a 30.7% increase in ARS gene expression (P < 1 × 10-10 vs. baseline and each aspirin dose) and ARS score (P = 7.0 × 10-7 vs. baseline, P = 3.6 × 10-6 and 5.59 × 10-4 vs. aspirin 81 and 325 mg, respectively). Increases in ARS gene expression or score were associated with the magnitude of platelet inhibition across agents. To assess the association between ARS scores and incident bleeding, ARS scores were calculated in patients undergoing cardiac catheterization (N = 1421), of whom 25.4% experienced bleeding events over a median 6.2 years of follow-up. In a Cox model adjusting for demographics and baseline antithrombotic medication use, patients with ARS scores above the median had a higher risk of incident bleeding [hazard ratio 1.26 (95% CI 1.01-1.56), P = 0.038].

CONCLUSIONS

The ARS is an Antiplatelet Response Signature that increases in response to greater platelet inhibition due to antiplatelet therapy and may represent a homeostatic mechanism to prevent bleeding. ARS scores could inform future strategies to prevent bleeding while maintaining antiplatelet therapy's benefit of ischaemic cardiovascular event protection.

A comparison of host response strategies to distinguish bacterial and viral infection

OBJECTIVES

Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI).

METHODS

In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests.

RESULTS

The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively.

CONCLUSIONS

A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

Comparing the Diagnostic Accuracy of Clinician Judgment to a Novel Host Response Diagnostic for Acute Respiratory Illness

Background

Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated.

Methods

Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors.

Results

Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P<.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%).

Conclusions

Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis.

Aspirin effects on platelet gene expression are associated with a paradoxical, increase in platelet function

Aspirin has known effects beyond inhibiting platelet cyclooxygenase-1 (COX-1) that have been incompletely characterized. Transcriptomics can comprehensively characterize the on- and off-target effects of medications. We used a systems pharmacogenomics approach of aspirin exposure in volunteers coupled with serial platelet function and purified platelet mRNA sequencing to test the hypothesis that aspirin's effects on the platelet transcriptome are associated with platelet function. We prospectively recruited 74 adult volunteers for a randomized crossover study of 81- vs. 325 mg/day, each for 4 weeks. Using mRNA sequencing of purified platelets collected before and after each 4-week exposure, we identified 208 aspirin-responsive genes with no evidence for dosage effects. In independent cohorts of healthy volunteers and patients with diabetes, we validated aspirin's effects on five genes: EIF2S3, CHRNB1, EPAS1, SLC9A3R2 and HLA-DRA. Functional characterization of the effects of aspirin on mRNA as well as platelet ribosomal RNA demonstrated that aspirin may act as an inhibitor of protein synthesis. Database searches for small molecules that mimicked the effects of aspirin on platelet gene expression in vitro identified aspirin but no other molecules that share aspirin's known mechanisms of action. The effects of aspirin on platelet mRNA were correlated with higher levels of platelet function both at baseline and after aspirin exposure-an effect that counteracts aspirin's known antiplatelet effect. In summary, this work collectively demonstrates a dose-independent effect of aspirin on the platelet transcriptome that counteracts the well-known antiplatelet effects of aspirin.

The Host Response to Viral Infections Reveals Common and Virus-Specific Signatures in the Peripheral Blood

Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections.

Experience and Perceptions of a Family Health History Risk Assessment Tool among Multi-Ethnic Asian Breast Cancer Patients

A family health history-based risk assessment is particularly valuable for guiding cancer screening and treatment strategies, yet an optimal implementation depends upon end-users' values and needs. This is not only true prior to disease development, but also for those already affected. The aim of this study is to explore perceptions of the value of knowing one's family health history (FHH)-based risk, experience using a patient-facing FHH tool and the potential of the tool for wider implementation. Twenty multi-ethnic Asian patients undergoing breast cancer treatment in Singapore completed an FHH-based risk assessment. Semi-structured one-on-one interviews were conducted and data were thematically analyzed. All participants were female and slightly more than half were Chinese. The acceptance and usage of an FHH risk assessment tool for cancers and its broader implementation was affected by a perceived importance of personal control over early detection, patient concerns of anxiety for themselves and their families due to risk results, concerns for genetic discrimination, adequacy of follow-up care plans and Asian cultural beliefs toward disease and dying. This study uniquely sheds light on the factors affecting Asian breast cancer patients' perceptions about undergoing an FHH-based risk assessment, which should inform steps for a broader implementation in Asian healthcare systems.

Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test

OBJECTIVES

Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test.

DESIGN

Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results.

SETTING

Four U.S. emergency departments.

PATIENTS

Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis.

INTERVENTIONS

Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes.

MEASUREMENTS AND MAIN RESULTS

Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance.

CONCLUSIONS

The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

IMPORTANCE

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

OBJECTIVE

To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

DESIGN, SETTING, AND PARTICIPANTS

The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

EXPOSURES

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

MAIN OUTCOMES AND MEASURES

The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project's strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study's implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches

BACKGROUND

Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined.

METHODS

In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers.

RESULTS

Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection).

CONCLUSIONS

Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.

Association of Hepatic Steatosis With Major Adverse Cardiovascular Events, Independent of Coronary Artery Disease

BACKGROUND & AIMS

Hepatic steatosis has been associated with increased risk of major adverse cardiovascular events (MACE) but it is not clear whether steatosis is independently associated with risk of MACE. We investigated whether steatosis is associated with risk of MACE independently of the presence and extent of baseline coronary artery disease, assessed by comprehensive contrast-enhanced computed tomography angiography (CTA).

METHODS

We conducted a nested cohort study of 3756 subjects (mean age, 60.6 years; 48.4% men) who underwent coronary CTA at 193 sites in North America, from July 2010 through September 2013, as part of the PROMISE study, which included noninvasive cardiovascular analyses of symptomatic outpatients without coronary artery disease. Independent core laboratory readers measured hepatic and splenic attenuation, using non-contrast computed tomography images to identify steatosis, and evaluated coronary plaques and stenosis in coronary CTA images. We collected data on participants' cardiovascular risk factors, presence of metabolic syndrome, and body mass index. The primary endpoint was an adjudicated composite of MACE (death, myocardial infarction, or unstable angina) during a median follow-up time of 25 months.

RESULTS

Among the 959 subjects who had steatosis (25.5% of the cohort), 42 had MACE (4.4%), whereas among the 2797 subjects without steatosis, 73 had MACE (2.6%) (hazard ratio [HR] for MACE in subjects with steatosis, 1.69; 95% CI, 1.16-2.48; P = .006 for MACE in subjects with vs without steatosis). This association remained after adjustment for atherosclerotic cardiovascular disease risk scores, significant stenosis, and metabolic syndrome (adjusted HR, 1.72; 95% CI, 1.16-2.54; P = .007) or obesity (adjusted HR, 1.75; 95% CI, 1.19-2.59; P = .005). Steatosis remained independently associated with MACE after adjustment for all CTA measures of plaques and stenosis.

CONCLUSIONS

Hepatic steatosis is associated with MACE independently of other cardiovascular risk factors or extent of coronary artery disease. Strategies to reduce steatosis might reduce risk of MACE. ClinicalTrials.gov no: NCT01174550.

Heparin-based blood purification attenuates organ injury in baboons with Streptococcus pneumoniae pneumonia

Bacterial pneumonia is a major cause of morbidity and mortality worldwide despite the use of antibiotics, and novel therapies are urgently needed. Building on previous work, we aimed to 1) develop a baboon model of severe pneumococcal pneumonia and sepsis with organ dysfunction and 2) test the safety and efficacy of a novel extracorporeal blood filter to remove proinflammatory molecules and improve organ function. After a dose-finding pilot study, 12 animals were inoculated with Streptococcus pneumoniae [5 × 10⁹ colony-forming units (CFU)], given ceftriaxone at 24 h after inoculation, and randomized to extracorporeal blood purification using a filter coated with surface-immobilized heparin sulfate (n = 6) or sham treatment (n = 6) for 4 h at 30 h after inoculation. For safety analysis, four uninfected animals also underwent purification. At 48 h, necropsy was performed. Inoculated animals developed severe pneumonia and septic shock. Compared with sham-treated animals, septic animals treated with purification displayed significantly less kidney injury, metabolic acidosis, hypoglycemia, and shock (P < 0.05). Purification blocked the rise in peripheral blood S. pneumoniae DNA, attenuated bronchoalveolar lavage (BAL) CCL4, CCL2, and IL-18 levels, and reduced renal oxidative injury and classical NLRP3 inflammasome activation. Purification was safe in both uninfected and infected animals and produced no adverse effects. We demonstrate that heparin-based blood purification significantly attenuates levels of circulating S. pneumoniae DNA and BAL cytokines and is renal protective in baboons with severe pneumococcal pneumonia and septic shock. Purification was associated with less severe acute kidney injury, metabolic derangements, and shock. These results support future clinical studies in critically ill septic patients.

An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility

BACKGROUND

While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.

RESULTS

Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.

CONCLUSIONS

Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .

Association of Metabolic Phenotypes With Coronary Artery Disease and Cardiovascular Events in Patients With Stable Chest Pain

OBJECTIVE

Obesity and metabolic syndrome are associated with major adverse cardiovascular events (MACE). However, whether distinct metabolic phenotypes differ in risk for coronary artery disease (CAD) and MACE is unknown. We sought to determine the association of distinct metabolic phenotypes with CAD and MACE.

RESEARCH DESIGN AND METHODS

We included patients from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) who underwent coronary computed tomography (CT) angiography. Obesity was defined as a BMI ≥30 kg/m² and metabolically healthy as less than or equal to one metabolic syndrome component except diabetes, distinguishing four metabolic phenotypes: metabolically healthy/unhealthy and nonobese/obese (MHN, MHO, MUN, and MUO). Differences in severe calcification (coronary artery calcification [CAC] ≥400), severe CAD (≥70% stenosis), high-risk plaque (HRP), and MACE were assessed using adjusted logistic and Cox regression models.

RESULTS

Of 4,381 patients (48.4% male, 60.5 ± 8.1 years of age), 49.4% were metabolically healthy (30.7% MHN and 18.7% MHO) and 50.6% unhealthy (22.3% MUN and 28.4% MUO). MHO had similar coronary CT findings as compared with MHN (severe CAC/CAD and HRP; P > 0.36 for all). Among metabolically unhealthy patients, those with obesity had similar CT findings as compared with nonobese (P > 0.10 for all). However, both MUN and MUO had unfavorable CAD characteristics as compared with MHN (P ≤ 0.017 for all). A total of 130 events occurred during follow-up (median 26 months). Compared with MHN, MUN (hazard ratio [HR] 1.61 [95% CI 1.02-2.53]) but not MHO (HR 1.06 [0.62-1.82]) or MUO (HR 1.06 [0.66-1.72]) had higher risk for MACE.

CONCLUSIONS

In patients with stable chest pain, four metabolic phenotypes exhibit distinctly different CAD characteristics and risk for MACE. Individuals who are metabolically unhealthy despite not being obese were at highest risk in our cohort.

Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network

PURPOSE

A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions.

METHODS

The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation.

RESULTS

Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression.

CONCLUSION

IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.