Profiling genetic variants in cardiovascular disease genes among a Heterogeneous cohort of Mendelian conditions patients and electronic health records

Introduction: This study addresses the rising cardiovascular disease (CVD) rates in the United Arab Emirates (UAE) by investigating the occurrence and impact of genetic variants in CVD-related genes. Methods: We collected all genes linked to heritable CVD from public and diagnostic databases and mapped them to their corresponding biological processes and molecular pathways. We then evaluated the types and burden of genetic variants within these genes in 343 individuals from the Emirati Mendelian Study Cohort and 3,007 national electronic health records. Results: We identified a total of 735 genes associated with heritable CVD, covering a range of cardiovascular conditions. Enrichment analysis revealed key biological processes and pathways, including Apelin, FoxO, and Ras signaling, that are implicated across all forms of heritable CVD. Analysis of a UAE cohort of 3,350 individuals showed a predominance of rare and unique CVD variants specific to the population. The study found a significant burden of pathogenic variants in families with CVD within the Emirati Mendelian cohort and re-assessed the pathogenicity of 693 variants from national health records, leading to the discovery of new CVD-causing variants. Discussion: This study underscores the importance of continuously updating our understanding of genes and pathways related to CVD. It also highlights the significant underrepresentation of the UAE population in public databases and clinical literature on CVD genetics, offering valuable insights that can inform future research and intervention strategies

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived insilico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of diseasespecific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy=0.73), Meta-RNN (Accuracy=0.72), ClinPred (Accuracy=0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy=0.70). While on HGMD dataset they were ClinPred (Accuracy=0.72), MetaRNN (Accuracy=0.71), CADD (Accuracy=0.69), Fathmm-MKL (Accuracy=0.68), and Fathmm-XF (Accuracy=0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions

Insurance for climate change and environmental risk: a bibliometric review

Purpose This study aims to examine the research output on climate change, environmental risks and insurance from 1986 to 2020, thereby revealing the development of the literature through collaborative networks. The relationship between insurance, climate change and environmental threats has gained research attention. This study describes the interaction between insurance, climate change and environmental risk. Design/methodology/approach This study is a bibliometric analysis of the literature and assessed the current state of science. A total of 97 academic papers from top-level journals listed in the Scopus database are shortlisted. Findings The understanding of climate change, environmental risks and insurance is shaped and enhanced through the collaborative network maps of researchers. Their reach expands across different networks, core themes and streams, as these topics develop. Research limitations/implications Data for this study were generated from English-written journal articles listed in the Scopus database only; subsequently, this study was representative of high-quality papers published in the areas of climate change, environmental risks and insurance. Practical implications The results of this study can be useful to academic researchers to aid their understanding of climate change, environmental risks and insurance research development, to identify the current context and to develop a future research agenda. Social implications The findings of this study can improve the understanding of industry practitioners about climate change and global warming challenges, and how insurance can be used as a tool to address such challenges. Originality/value This study is a novel attempt. To the best of the authors’ knowledge, this is one of the first studies to better understand climate change, environmental risks and insurance as a research topic by examining its evolution in an academic context through visualization, coupling and bibliometric analysis. This bibliometric study is unique in reviewing climate change literature and providing a future research agenda. Using bibliometric data, this study addressed the technical aspects and the value it adds to actual practice. Bibliometric indicators quantitatively and qualitatively evaluate emerging disciplinary progress in this topic. </jats:sec