Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria.

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants.

Ecosystem Barriers to Innovation Adoption in Clinical Practice.

Despite increasing ability to understand and correct molecular derangements in disease, genomics and novel phenotypic assays are unevenly deployed in clinical practice. This has hampered translational research and our ability to identify clinically actionable subtypes of disease. Historic examples illustrate how the perspectives of stakeholders across the healthcare ecosystem can influence adoption of innovations in healthcare.

Cholesterol Metabolism by Uncultured Human Gut Bacteria Influences Host Cholesterol Level.

The human microbiome encodes extensive metabolic capabilities, but our understanding of the mechanisms linking gut microbes to human metabolism remains limited. Here, we focus on the conversion of cholesterol to the poorly absorbed sterol coprostanol by the gut microbiota to develop a framework for the identification of functional enzymes and microbes. By integrating paired metagenomics and metabolomics data from existing cohorts with biochemical knowledge and experimentation, we predict and validate a group of microbial cholesterol dehydrogenases that contribute to coprostanol formation.

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels.

Use of Narrative Concepts in Electronic Health Records to Validate Associations Between Genetic Factors and Response to Treatment of Inflammatory Bowel Diseases.

Crohn's disease (CD) and ulcerative colitis (UC) are heterogeneous. With availability of therapeutic classes with distinct immunologic mechanisms of action, it has become imperative to identify markers that predict likelihood of response to each drug class. However, robust development of such tools has been challenging because of need for large prospective cohorts with systematic and careful assessment of treatment response using validated indices.

Factors associated with clinical inertia in type 2 diabetes mellitus patients treated with metformin monotherapy.

To assess demographic and clinical characteristics associated with clinical inertia in a real-world cohort of type 2 diabetes mellitus patients not at hemoglobin A1c goal (<7%) on metformin monotherapy. Adult (≥18 years) type 2 diabetes mellitus patients who received care at Massachusetts General Hospital/Brigham and Women's Hospital and received a new metformin prescription between 1992 and 2010 were included in the analysis. Clinical inertia was defined as two consecutive hemoglobin A1c measures ≥7% ≥3 months apart while remaining on metformin monotherapy (i.

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes).

MELD-Na score predicts incident major cardiovascular events, in patients with nonalcoholic fatty liver disease (NAFLD).

Background: Cardiovascular disease (CVD) is the leading cause of mortality among adults with nonalcoholic fatty liver disease (NAFLD), however accurate tools for identifying NAFLD patients at highest CVD risk are lacking.

Methods: Using a validated algorithm, we identified a retrospective cohort of 914 NAFLD patients without known CVD. Fibrosis severity was estimated using the FIB-4 index.

The MELD-Plus: A generalizable prediction risk score in cirrhosis.

Background And Aims: Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.

Methods: We developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications.

Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making.

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data.

Use of Chronic Oral Anticoagulation and Associated Outcomes Among Patients Undergoing Percutaneous Coronary Intervention.

Background: Contemporary rates of oral anticoagulant (OAC) therapy and associated outcomes among patients undergoing percutaneous coronary intervention (PCI) have been poorly described.

Methods And Results: Using data from an integrated health care system from 2009 to 2014, we identified patients on OACs within 30 days of PCI. Outcomes included in-hospital bleeding and mortality.

Natural Product Screening Reveals Naphthoquinone Complex I Bypass Factors.

Deficiency of mitochondrial complex I is encountered in both rare and common diseases, but we have limited therapeutic options to treat this lesion to the oxidative phosphorylation system (OXPHOS). Idebenone and menadione are redox-active molecules capable of rescuing OXPHOS activity by engaging complex I-independent pathways of entry, often referred to as "complex I bypass." In the present study, we created a cellular model of complex I deficiency by using CRISPR genome editing to knock out Ndufa9 in mouse myoblasts, and utilized this cell line to develop a high-throughput screening platform for novel complex I bypass factors.

The Use of Smartphones for Health Research.

Because of their growing popularity and functionality, smartphones are increasingly valuable potential tools for health and medical research. Using ResearchKit, Apple's open-source platform to build applications ("apps") for smartphone research, collaborators have developed apps for researching asthma, breast cancer, cardiovascular disease, type 2 diabetes, and Parkinson disease. These research apps enhance widespread participation by removing geographical barriers to participation, provide novel ways to motivate healthy behaviors, facilitate high-frequency assessments, and enable more objective data collection.

Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.

The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information.

Comparative Effectiveness of Infliximab and Adalimumab in Crohn's Disease and Ulcerative Colitis.

Background: The availability of monoclonal antibodies to tumor necrosis factor α has revolutionized management of Crohn's disease (CD) and ulcerative colitis. However, limited data exist regarding comparative effectiveness of these agents to inform clinical practice.

Methods: This study consisted of patients with CD or ulcerative colitis initiation either infliximab (IFX) or adalimumab (ADA) between 1998 and 2010.

Using an Electronic Medical Records Database to Identify Non-Traditional Cardiovascular Risk Factors in Nonalcoholic Fatty Liver Disease.

Objectives: Among adults with nonalcoholic fatty liver disease (NAFLD), 25% of deaths are attributable to cardiovascular disease (CVD). CVD risk reduction in NAFLD requires not only modification of traditional CVD risk factors but identification of risk factors unique to NAFLD.

Methods: In a NAFLD cohort, we sought to identify non-traditional risk factors associated with CVD.

Statin Use Is Associated With Reduced Risk of Colorectal Cancer in Patients With Inflammatory Bowel Diseases.

Background & Aims: Inflammatory bowel diseases (IBDs) such as Crohn's disease and ulcerative colitis are associated with an increased risk of colorectal cancer (CRC). Chemopreventive strategies have produced weak or inconsistent results. Statins have been associated inversely with sporadic CRC.

Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record.

Background And Aims: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study sought to design a classification algorithm for NAFLD within the electronic medical record (EMR) for the development of large-scale longitudinal cohorts.

Creation of a new longitudinal corpus of clinical narratives.

The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (CAD) in their first record, and continue to have it in subsequent records; patients who do not have a diagnosis of CAD in the first record, but develop it by the last record; patients who do not have a diagnosis of CAD in any record. This paper details the process used to select records for this corpus and provides an overview of novel research uses for this corpus.

Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort.

Background: Electronic health records, increasingly a part of healthcare, provide a wealth of untapped narrative free text data that have the potential to accurately inform clinical outcomes.

Methods: From a validated cohort of patients with Crohn's disease or ulcerative colitis, we identified patients with ≥1 coded or narrative mention of monoclonal antibodies to tumor necrosis factor α. Chart review by ascertained true use of therapy, time of initiation, and cessation of treatment, and also clinical response stratified as nonresponse, partial, or complete response at 1 year.

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.

Background: Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations.

Common Genetic Variants Influence Circulating Vitamin D Levels in Inflammatory Bowel Diseases.

Background: The accuracy and utility of electronic health record (EHR)-derived phenotypes in replicating genotype-phenotype relationships have been infrequently examined. Low circulating vitamin D levels are associated with severe outcomes in inflammatory bowel disease (IBD); however, the genetic basis for vitamin D insufficiency in this population has not been examined previously.

Methods: We compared the accuracy of physician-assigned phenotypes in a large prospective IBD registry to that identified by an EHR algorithm incorporating codified and structured data.

The kinase DYRK1A reciprocally regulates the differentiation of Th17 and regulatory T cells.

The balance between Th17 and T regulatory (Treg) cells critically modulates immune homeostasis, with an inadequate Treg response contributing to inflammatory disease. Using an unbiased chemical biology approach, we identified a novel role for the dual specificity tyrosine-phosphorylation-regulated kinase DYRK1A in regulating this balance. Inhibition of DYRK1A enhances Treg differentiation and impairs Th17 differentiation without affecting known pathways of Treg/Th17 differentiation.