1,720,987 research outputs found
Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine
No full textIntroduction: Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high-risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state.Objective: We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy.Conclusion: The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network-oriented biomarkers and drugs in the management of DM
Cardiovascular risk factors and molecular routes underlying endothelial dysfunction: Novel opportunities for primary prevention
No full textOne of the major challenges of cardiovascular primary prevention approach is the absence of early biomarkers of endothelial dysfunction which may be useful for identifying at-risk subjects. Endothelial dysfunction is a systemic disorder in which traditional cardiovascular risk factors, such as aging, gender, hypertension, smoking, hyperglycemia, and dyslipidemia, as well as emerging risk determinants, such as fetal factors, gut microbiome alteration, clonal hematopoiesis, air pollution, and sleep disorders act synergistically to tip the endothelial balance in favor of vasoconstrictive, pro-inflammatory, and pro-thrombotic phenotypes. Endothelial dysfunction can start already in fetal life and may be regained once detrimental stimuli are removed. The hallmark of endothelial dysfunction is a marked reduction of nitric oxide (NO) bioavailability owing to epigenetic-sensitive dysregulation of the endothelial nitric oxide synthase (eNOS) gene and upregulation of reactive oxygen species (ROS) in endothelial cells (ECs). Advance in liquid-based assays and molecular biology tools are providing novel potential EC-specific biomarkers for prediction and diagnosis of endothelial dysfunction. Significant associations between clinically useful indexes of endothelial dysfunction, mainly brachial artery flow-mediated dilation (FMD), and increased number of endothelial microparticles (EMPs), increased levels of endoglin and endocan, as well as reduced levels of irisin were observed in subjects with one or more traditional risk factors. However, none entered in clinical practice yet. Smoking cessation, weight loss, physical exercise, and diet control are the milestones of cardiovascular primary prevention, and they may restore endothelial function via epigenetic-sensitive pathways able to reduce inflammation and oxidative stress and increase NO production . We briefly summarize well-known and novel molecular routes driving early endothelial dysfunction mainly in human ECs and related potential biomarkers which may add predictive or diagnostic value to the traditional non-invasive techniques. Also, we focus on clinical trials investigating lifestyle modifications and their impact on molecular routes involved in restoring endothelial function
Transgenerational Epigenetic Inheritance of Cardiovascular Diseases: A Network Medicine Perspective
No full textIntroduction: The ability to identify early epigenetic signatures underlying the inheritance of cardiovascular risk, including trans- and intergenerational effects, may help to stratify people before cardiac symptoms occur. Methods: Prospective and retrospective cohorts and case–control studies focusing on DNA methylation and maternal/paternal effects were searched in Pubmed from 1997 to 2023 by using the following keywords: DNA methylation, genomic imprinting, and network analysis in combination with transgenerational/intergenerational effects. Results: Maternal and paternal exposures to traditional cardiovascular risk factors during critical temporal windows, including the preconceptional period or early pregnancy, may perturb the plasticity of the epigenome (mainly DNA methylation) of the developing fetus especially at imprinted loci, such as the insulin-like growth factor type 2 (IGF2) gene. Thus, the epigenome is akin to a “molecular archive” able to memorize parental environmental insults and predispose an individual to cardiovascular diseases onset in later life. Direct evidence for human transgenerational epigenetic inheritance (at least three generations) of cardiovascular risk is lacking but it is supported by epidemiological studies. Several blood-based association studies showed potential intergenerational epigenetic effects (single-generation studies) which may mediate the transmittance of cardiovascular risk from parents to offspring. Discussion: In this narrative review, we discuss some relevant examples of trans- and intergenerational epigenetic associations with cardiovascular risk. In our perspective, we propose three network-oriented approaches which may help to clarify the unsolved issues regarding transgenerational epigenetic inheritance of cardiovascular risk and provide potential early biomarkers for primary prevention
Unexplored horizons on sex bias and progression of heart failure with preserved ejection fraction
No full textClinical Role of Epigenetics and Network Analysis in Eye Diseases: A Translational Science Review
No full textNetwork medicine is a molecular-bioinformatic approach analyzing gene-gene interactions that can perturb the human interactome. This review focuses on epigenetic changes involved in several ocular diseases, such as DNA methylation, histone and nonhistone post-translational modifications, and noncoding RNA regulators. Although changes in aberrant DNA methylation play a major role in the pathogenesis of most ocular diseases, histone modifications are seldom investigated. Hypermethylation in TGM-2 and hypomethylation in MMP-2/CD24 promoter genes may play a crucial role in pterygium development; hypermethylation in regulatory regions of GSTP1 and OGG1 genes appear to be diagnostic biomarkers of cataract; hypomethylation of TGF-β1 promoter may trigger glaucoma onset; hypermethylation of the LOXL1 gene might be associated with pseudoexfoliation syndrome. A large panel of upregulated micro-RNAs (miRNAs), including hsa-hsa-miR-494, hsa-let-7e, hsa-miR-513-1, hsa-miR-513-2, hsa-miR-518c, hsa-miR-129-1, hsa-miR-129-2, hsa-miR-198, hsa-miR-492, hsa-miR-498, hsa-miR-320, hsa-miR-503, and hsa-miR-373,∗ may have a putative role in the development of retinoblastoma. Hypermethylation of H3K4 and hypomethylation of H3K27 at the TGFBIp locus are putative pathogenic mechanisms involved in corneal dystrophies. Determining how, where, and when specific epigenetic changes trigger ocular diseases may provide useful clinical biomarkers for their prevention, diagnosis, and management, as well as innovative drug targets. PF-04523655, a 19-nucleotide methylated double-stranded siRNA targeting the RTP80 gene, showed a dose-related improvement in best-corrected visual acuity (BCVA) in patients affected by diabetic macular edema. The observed results support a clinical network-based research program aimed to clarify the role of epigenetic regulators in the development of ocular diseases and personalized therapy
"Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine
No full textAlthough decades of the reductionist approach achieved great milestones in optimizing the immunosuppres-sion therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cel-lular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision med-icine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The applica-tion of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation
Pursuing functional biomarkers in complex disease: Focus on pulmonary arterial hypertension
No full textA major gap in diagnosis, classification, risk stratification, and prediction of therapeutic response exists in pulmonary arterial hypertension (PAH), driven in part by a lack of functional biomarkers that are also disease-specific. In this regard, leveraging big data-omics analyses using innovative approaches that integrate network medicine and machine learning correlated with clinically useful indices or risk stratification scores is an approach well-positioned to advance PAH precision medicine. For example, machine learning applied to a panel of 48 cytokines, chemokines, and growth factors could prognosticate PAH patients with immune-dominant subphenotypes at elevated or low-risk for mortality. Here, we discuss strengths and weaknesses of the most current studies evaluating omics-derived biomarkers in PAH. Progress in this field is offset by studies with small sample size, pervasive limitations in bioinformatics, and lack of standardized methods for data processing and interpretation. Future success in this field, in turn, is likely to hinge on mechanistic validation of data outputs in order to couple functional biomarker data with target-specific therapeutics in clinical practice
Unsolved Issues on Beneficial Effects of Combination Therapy With Sotatercept in Pulmonary Arterial Hypertension
No full textSynergistic Effects of a Novel Combination of Natural Compounds Prevent H2O2-Induced Oxidative Stress in Red Blood Cells
No full textNovel strategies to prevent the “storage lesions” of red blood cells (RBCs) are needed to prevent the risk of adverse effects after blood transfusion. One option could be the supplementation of stored blood bags with natural compounds that may increase the basal load of antioxidant protection and the shelf life of RBCs. In this pilot study, we investigated for the first time potential synergistic effects of a triple combination of well-known anti-oxidant compounds curcumin (curc), vitamin E (vit E), and vitamin C (vit C). Briefly, we established an ex vivo model of H2O2-induced oxidative stress and measured the hemolysis ratio (HR) (%) and thiobarbituric acid reactive substances (TBARS) levels in RBCs with or without pre-exposure for 30 min with increasing concentrations of curc, vit E, and vit C and then exposed to 10 mM H2O2. for 60 min. Exposure of RBCs to a triple combination of curc, vit E, and vit C at the highest concentration (100 µM) completely prevented H2O2-induced hemolysis. Surprisingly, we found that pre-treatment of RBCs with curc 100 µM alone completely prevented hemolysis as compared to vit E and vit C alone or in combination at the same concentration. On the other hand, pre-treatment with the triple combination of curc, vit E, and vit C 100 µM was required to totally prevent lipid peroxidation, as compared to curc 100 µM alone, supporting their synergistic effects in preventing RBCs membrane peroxidation. Further experiments are ongoing to investigate the anti-aging effects of the triple combination of curc, vit E, and vit C on cold-stored bags
Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases
No full textAlthough bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD
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