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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%.

Among female subjects of reproductive age, polycystic ovarian syndrome (PCOS) is a common endocrine disorder that may be linked to a number of health risks for cardiovascular diseases (CVDs). The study aims to determine some CVD risk variables and how they relate to body mass index (BMI) in female subjects with PCOS. Fifty healthy women without PCOS (controls) and 90 women with PCOS between the ages of 18 and 45 were enrolled in the study. Standard methods were used to evaluate the serum sex hormones, lipid profile, troponin-I, highly sensitive C-reactive protein (hs-CRP), fasting blood glucose, and atherogenic indices. Compared to controls, the mean age of women with PCOS was substantially lower ($p<0.001$). The mean values of BMI, waist circumference, and hip were not significantly different from one another. While the cardiometabolic variables were higher in women with PCOS than in healthy subjects, no significant difference between obese/overweight and nonobese women with PCOS in terms of mean BMI, fasting blood glucose, insulin, atherogenic index of plasma (AIP), troponin-I, hs-CRP, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol were observed. In PCOS-affected women, AIP ($r=0.712$, $p<0.001$), lipid accumulation product (LAP) ($r=0.764$, $p<0.001$), and hs-CRP ($r=0.666$, $p<0.001$) all showed positive correlations with BMI. Regardless of BMI status, there was an independent correlation between cardiovascular risk factors (CVRFs) and PCOS. This implies that regardless of a woman’s BMI, PCOS may increase her risk of CVD. Treatment combined with lifestyle modifications may be useful in lowering the risk of CVD in PCOS-afflicted Nigerian women. The finding suggests that PCOS itself, independent of weight, may increase the risk of heart disease. Nigerian women with PCOS are at increased risk of CVD, regardless of their BMI, and early detection, prevention, and treatment of CVRFs in women with PCOS is desirable.

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.