Use of big data from health insurance for assessment of cardiovascular outcomes.

Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation).

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.

Objective: Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

Methods: In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information.

Background: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots.

Identification of the Transcription Factor ATF3 as a Direct and Indirect Regulator of the LDLR.

Coronary artery disease (CAD) is a complex, multifactorial disease caused, in particular, by inflammation and cholesterol metabolism. At the molecular level, the role of tissue-specific signaling pathways leading to CAD is still largely unexplored. This study relied on two main resources: (1) genes with impact on atherosclerosis/CAD, and (2) liver-specific transcriptome analyses from human and mouse studies.

Vascular Tissue Specific miRNA Profiles Reveal Novel Correlations with Risk Factors in Coronary Artery Disease.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Non-coding RNAs have already been linked to CVD development and progression. While microRNAs (miRs) have been well studied in blood samples, there is little data on tissue-specific miRs in cardiovascular relevant tissues and their relation to cardiovascular risk factors.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events).

Cistromic Reprogramming of the Diurnal Glucocorticoid Hormone Response by High-Fat Diet.

The glucocorticoid receptor (GR) is a potent metabolic regulator and a major drug target. While GR is known to play integral roles in circadian biology, its rhythmic genomic actions have never been characterized. Here we mapped GR's chromatin occupancy in mouse livers throughout the day and night cycle.

Inferring Interaction Networks From Multi-Omics Data.

A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge.