Cardiometabolic disease (CMD) represents the major cause of mortality, accounting for one-third of all global deaths, 75% of which occur in middle- and low-income countries. CMD encompasses a broad spectrum of conditions characterized by limited prediction, based mainly on classical risk factors due to a lack of accurate molecular CMD predictors. Thus, there is an urgent need for improved diagnostics solutions to support early intervention and improve outcomes.
Early detection and adequate intervention are crucial to prevent CMD-associated complications, encouraging the quest for appropriate biomarkers with diverse applications ranging from risk assessment and screening to diagnosis and prognosis. Different circulating biomarkers for quantifying the CMD risk have been reported in the literature, such as C-reactive protein (CRP), leptin, and adiponectin. In addition, accumulating evidence in the literature points to the emergence of novel CMD biomarkers, such as cytokines, various metabolites, apelin, microRNAs, inflammasome molecules, and cardiac fibrosis markers. However, large meta-analyses have not sufficiently investigated and confirmed their biological roles in CMD diagnosis, prognosis, and/or potential reduction.
Further scientific efforts are required to identify appropriate molecular biomarkers for CMD. This may be achieved by using innovative approaches based on integrating multi-omics analyses, such as comparing and combining data obtained by genomic, epigenetic, transcriptomic, proteomic and metabolomic profiling. The use of data obtained by such high-throughput technologies may create a molecular framework of CMD, ultimately leading to the identification of appropriate and effective CVD biomarkers.
Therefore, we would like to encourage discussion regarding identifying, characterizing, and evaluating novel molecular biomarkers with potential applications in risk assessment, screening, diagnosis, and prognosis of CMD. We believe that the recent findings of a large number of studies using novel high-throughput methodologies for identifying genetic and epigenetic factors involved in CMD-associated complications such as insulin resistance, type 2 diabetes, and cardiovascular disease require a comprehensive and systematic approach to critically evaluate their roles in CMD and asses their potential applications as molecular biomarkers of CMD.
The current Research Topic aims to cover novel, promising research and clinical trends in the identification, characterization, assessment, and application of molecular biomarkers of CMD. Areas to be covered in this Research Topic may include, but are not limited to:
• Identification and experimental validation of novel molecular biomarkers of CMD
• Investigation of novel genetic, epigenetic, and biochemical biomarkers of CMD
• Experimental and clinical evaluation of miRNAs as potential epigenetic biomarkers of CMD
• Use of combinations of biomarkers and panels of biomarkers for CMD risk prediction, diagnosis, and prognosis
• Machine learning as a tool for the identification of potential novel molecular biomarkers of CMD
We are interested in publishing reviews and research articles.
Cardiometabolic disease (CMD) represents the major cause of mortality, accounting for one-third of all global deaths, 75% of which occur in middle- and low-income countries. CMD encompasses a broad spectrum of conditions characterized by limited prediction, based mainly on classical risk factors due to a lack of accurate molecular CMD predictors. Thus, there is an urgent need for improved diagnostics solutions to support early intervention and improve outcomes.
Early detection and adequate intervention are crucial to prevent CMD-associated complications, encouraging the quest for appropriate biomarkers with diverse applications ranging from risk assessment and screening to diagnosis and prognosis. Different circulating biomarkers for quantifying the CMD risk have been reported in the literature, such as C-reactive protein (CRP), leptin, and adiponectin. In addition, accumulating evidence in the literature points to the emergence of novel CMD biomarkers, such as cytokines, various metabolites, apelin, microRNAs, inflammasome molecules, and cardiac fibrosis markers. However, large meta-analyses have not sufficiently investigated and confirmed their biological roles in CMD diagnosis, prognosis, and/or potential reduction.
Further scientific efforts are required to identify appropriate molecular biomarkers for CMD. This may be achieved by using innovative approaches based on integrating multi-omics analyses, such as comparing and combining data obtained by genomic, epigenetic, transcriptomic, proteomic and metabolomic profiling. The use of data obtained by such high-throughput technologies may create a molecular framework of CMD, ultimately leading to the identification of appropriate and effective CVD biomarkers.
Therefore, we would like to encourage discussion regarding identifying, characterizing, and evaluating novel molecular biomarkers with potential applications in risk assessment, screening, diagnosis, and prognosis of CMD. We believe that the recent findings of a large number of studies using novel high-throughput methodologies for identifying genetic and epigenetic factors involved in CMD-associated complications such as insulin resistance, type 2 diabetes, and cardiovascular disease require a comprehensive and systematic approach to critically evaluate their roles in CMD and asses their potential applications as molecular biomarkers of CMD.
The current Research Topic aims to cover novel, promising research and clinical trends in the identification, characterization, assessment, and application of molecular biomarkers of CMD. Areas to be covered in this Research Topic may include, but are not limited to:
• Identification and experimental validation of novel molecular biomarkers of CMD
• Investigation of novel genetic, epigenetic, and biochemical biomarkers of CMD
• Experimental and clinical evaluation of miRNAs as potential epigenetic biomarkers of CMD
• Use of combinations of biomarkers and panels of biomarkers for CMD risk prediction, diagnosis, and prognosis
• Machine learning as a tool for the identification of potential novel molecular biomarkers of CMD
We are interested in publishing reviews and research articles.