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Cardiovascular diseases are the leading cause of death worldwide. The development of models to support clinical decision is of great importance in the management of these diseases. This work aims to improve the performance exhibited by risk assessment scores that are applied in the clinical practice. This methodology has three main phases: (i) representation of scores as a decision tree; (ii) optimization of the decision tree thresholds using data from recent clinical datasets; (iii) transformation of the optimized decision tree into a new score.

This approach was validated in a cardiovascular disease secondary prevention context, supported by a dataset provided by the Portuguese Society of Cardiology ($N=13902$). The respective performance was assessed using statistical metrics and was compared with GRACE score, the reference in Portuguese clinical practice. The new model originated a better balance between the sensitivity and specificity when compared with the GRACE, originating an accuracy improvement of approximately 22%.

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected “lead SNPs” via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.

Osteoarthritis (OA) significantly compromises the life quality of affected individuals and imposes a substantial economic burden on our society. Unfortunately the pathogenesis of the disease is till poorly understood and no effective medications have been developed. OA is a complex disease that involves both genetic and environmental influences. To elucidate the complex interlinked structure of metabolic processes associated with OA, we developed a differential correlation network approach to detecting the interconnection of metabolite pairs whose relationships are significantly altered due to the diseased process. Through topological analysis of such a differential network, we identified key metabolites that played an important role in governing the connectivity and information flow of the network. Identification of these key metabolites suggests the association of their underlying cellular processes with OA and may help elucidate the pathogenesis of the disease and the development of novel targeted therapies.