Comorbidity patterns in cardiovascular diseases: the role of life-stage and socioeconomic status.

Cardiovascular diseases stand as a prominent global cause of mortality, their intricate origins often entwined with comorbidities and multimorbid conditions. Acknowledging the pivotal roles of age, sex, and social determinants of health in shaping the onset and progression of these diseases, our study delves into the nuanced interplay between life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Leveraging data from a cross-sectional survey encompassing Mexican adults, we unearth a robust association between these variables and the prevalence of comorbidities linked to cardiovascular conditions. To foster a comprehensive understanding of multimorbidity patterns across diverse life-stages, we scrutinize an extensive dataset comprising 47,377 cases diagnosed with cardiovascular ailments at Mexico's national reference hospital. Extracting sociodemographic details, primary diagnoses prompting hospitalization, and additional conditions identified through ICD-10 codes, we unveil subtle yet significant associations and discuss pertinent specific cases. Our results underscore a noteworthy trend: younger patients of lower socioeconomic status exhibit a heightened likelihood of cardiovascular comorbidities compared to their older counterparts with a higher socioeconomic status. By empowering clinicians to discern non-evident comorbidities, our study aims to refine therapeutic designs. These findings offer profound insights into the intricate interplay among life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Armed with data-supported approaches that account for these factors, clinical practices stand to be enhanced, and public health policies informed, ultimately advancing the prevention and management of cardiovascular disease in Mexico.

Network Biology and Medicine to Rescue: Applications for Retinal Disease Mechanisms and Therapy.

Inherited retinal degenerations (IRDs) are clinically and genetically heterogenous blinding diseases that manifest through dysfunction of target cells, photoreceptors, and retinal pigment epithelium (RPE) in the retina. Despite knowledge of numerous underlying genetic defects, current therapeutic approaches, including gene centric applications, have had limited success, thereby asserting the need of new directions for basic and translational research. Human diseases have commonalities that can be represented in a network form, called diseasome, which captures relationships among disease genes, proteins, metabolites, and patient meta-data. Clinical and genetic information of IRDs suggest shared relationships among pathobiological factors, making these a model case for network medicine. Characterization of the diseasome would considerably improve our understanding of retinal pathologies and permit better design of targeted therapies for disrupted regions within the integrated disease network. Network medicine in synergy with the ongoing artificial intelligence revolution can boost therapeutic developments, especially gene agnostic treatment opportunities.

Exploring the molecular underpinning of psoriasis and its associated comorbidities through network approach: cross talks of genes and pathways.

Patients with psoriasis often complain of several linked disorders including autoimmune and cardiometabolic diseases. Understanding of molecular link between psoriasis and associated comorbidities would be of great interest at the point of patient care management. Integrative unbiased network approach, indicates significant unidirectional gene overlap between psoriasis and its associated comorbid condition including obesity (31 upregulated and 26 downregulated), ischemic stroke (14 upregulated and 2 downregulated), dyslipidaemia (5 upregulated, 5 downregulated), atherosclerosis (8 upregulated and 1 downregulated) and type II diabetes (5 upregulated, 5 downregulated). The analysis revealed substantial gene sharing among the different psoriasis-associated comorbidities. Molecular comorbidity index determining the strength of the interrelation between psoriasis and its comorbidities indicates prevalence of dyslipidaemia followed by type II diabetes among psoriasis patients. The Jaccard coefficient indices revealed psoriasis shared maximum number of biological pathways with dyslipidaemia followed by type 2 diabetes, ischemic stroke, obesity and atherosclerosis. Moreover, pathway annotation highlighted nearly 45 shared pathways amongst psoriasis and its comorbidities and a substantial number of shared pathways was found among multi-morbidities. Overall, the present study established conceivable link between psoriasis and comorbid diseases. The shared genes and overlapped pathways may be explored as a common productive target for psoriasis and its comorbid conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03533-y.

miR2Trait: an integrated resource for investigating miRNA-disease associations.

MicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function causes cascading effects leading to pathophenotypes. A better understanding of the role of miRNAs in diseases is essential for human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogs, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. miR2Trait provides resources to compute the disease enrichment of a user-given set of miRNAs and analyze the miRNA profile of a specified diseasome. Reproducible examples demonstrating use-cases for each of these resource components are illustrated. Furthermore we used these tools to construct pairwise miRNA-miRNA and disease-disease enrichment networks, and identified 23 central miRNAs that could underlie major regulatory functions in the human genome. miR2Trait is available as an open-source command-line interface in Python3 (URL: https://github.com/miR2Trait) with a companion wiki documenting the scripts and data resources developed, under MIT license for commercial and non-commercial use. A minimal web-based implementation has been made available at https://sas.sastra.edu/pymir18. Supplementary information is available at: https://doi.org/10.6084/m9.figshare.8288825.v3.

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.

Human Immune System Diseasome Networks and Female Oviductal Microenvironment: New Horizons to be Discovered.

Human hypofertility and infertility are two worldwide conditions experiencing nowadays an alarming increase due to a complex ensemble of events. The immune system has been suggested as one of the responsible for some of the etiopathogenic mechanisms involved in these conditions. To shed some light into the strong correlation between the reproductive and immune system, as can be inferred by the several and valuable manuscripts published to date, here we built a network using a useful bioinformatic tool (DisGeNET), in which the key genes involved in the sperm-oviduct interaction were linked. This constitutes an important event related with Human fertility since this interaction, and specially the spermatozoa, represents a not-self entity immunotolerated by the female. As a result, we discovered that some proteins involved in the sperm-oviduct interaction are implicated in several immune system diseases while, at the same time, some immune system diseases could interfere by using different pathways with the reproduction process. The data presented here could be of great importance to understand the involvement of the immune system in fertility reduction in Humans, setting the basis for potential immune therapeutic tools in the near future.

Two way network construction and analysis of mRNA, miRNA and lncRNA reveals critical regulators and regulatory modules in cardiovascular diseases.

BACKGROUND: Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population. OBJECTIVE: The objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression. METHOD: We applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used 'diseasome to interactome' and 'interactome to diseasome' approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition. RESULTS: The top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions. CONCLUSION: The identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.

Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare.

BACKGROUND: Migraine is a complex neurological disorder with high co-existing morbidity burden. The aim of our study was to examine the overall morbidity and phenotypic diseasome for migraine among people of working age using real world data collected as a part of routine clinical practice. METHODS: Electronic medical records (EMR) of patients with migraine (n = 17,623) and age- and gender matched controls (n = 17,623) were included in this retrospective analysis. EMRs were assessed for the prevalence of ICD-10 codes, those with at least two significant phi correlations, and a prevalence >2.5% in migraine patients were included to phenotypic disease networks (PDN) for further analysis. An automatic subnetwork detection algorithm was applied in order to cluster the diagnoses within the PDNs. The diagnosis-wise connectivity based on the PDNs was compared between migraine patients and controls to assess differences in morbidity patterns. RESULTS: The mean number of diagnoses per patient was increased 1.7-fold in migraine compared to controls. Altogether 1337 different ICD-10 codes were detected in EMRs of migraine patients. Monodiagnosis was present in 1% and 13%, and the median number of diagnoses was 12 and 6 in migraine patients and controls. The number of significant phi-correlations was 2.3-fold increased, and cluster analysis showed more clusters in those with migraine vs. controls (9 vs. 6). For migraine, the PDN was larger and denser and exhibited one large cluster containing fatigue, respiratory, sympathetic nervous system, gastrointestinal, infection, mental and mood disorder diagnoses. Migraine patients were more likely affected by multiple conditions compared to controls, even if no notable differences in morbidity patterns were identified through connectivity measures. Frequencies of ICD-10 codes on a three character and block level were increased across the whole diagnostic spectrum in migraine. CONCLUSIONS: Migraine was associated with an increased multimorbidity, evidenced by multiple different approaches in the study. A systematic increase in the morbidity across the whole spectrum of ICD-10 coded diagnoses, and when interpreting PDNs, were detected in migraine patients. However, no specific diagnoses explained the morbidity. The results reflect clinical praxis, but also undoubtedly, the pathophysiological phenotypes related to migraine, and emphasize the importance of better understanding migraine-related morbidity.

Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.

Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic. We describe here a method to identify and rank protein-topic relationships by calculating the semantic similarity between a protein and a query term in the biomerical literature while adjusting for the impact and immediacy of associated research articles. We term the calculated metric the weighted copublication distance (WCD) and show that it compares well to related approaches in predicting benchmark protein lists in multiple biological processes. We used WCD to extract prioritized "popular proteins" across multiple cell types, subanatomical regions, and standardized vocabularies containing over 20 000 human disease terms. The collection of protein-disease associations across the resulting human "diseasome" supports data analytical workflows to perform reverse protein-to-disease queries and functional annotation of experimental protein lists. We envision that the described improvement to the popular proteins strategy will be useful for annotating protein lists and guiding method development efforts as well as generating new hypotheses on understudied disease proteins using bibliometric information.

Molecular Mechanisms of Syndromic Cryptorchidism: Data Synthesis of 50 Studies and Visualization of Gene-Disease Network.

Background: Cryptorchidism is one of the most frequent congenital birth defects in male children and is present in 2-4% of full-term male births. It has several possible health effects including reduced fertility, increased risk for testicular neoplasia, testicular torsion, and psychological consequences. Cryptorchidism is often diagnosed as comorbid; copresent with other diseases. It is also present in clinical picture of several syndromes. However, this field has not been systematically studied. The aim of the present study was to catalog published cases of syndromes which include cryptorchidism in the clinical picture and associated genomic information. Methods: The literature was extracted from Public/Publisher MEDLINE and Web of Science databases, using the keywords including: syndrome, cryptorchidism, undescended testes, loci, and gene. The obtained data was organized in a table according to the previously proposed standardized data format. The results of the study were visually represented using Gephi and karyotype view. Results: Fifty publications had sufficient data for analysis. Literature analysis resulted in 60 genomic loci, associated with 44 syndromes that have cryptorchidism in clinical picture. Genomic loci included 38 protein-coding genes and 22 structural variations containing microdeletions and microduplications. Loci, associated with syndromic cryptorchidism are located on 16 chromosomes. Visualization of retrieved data is presented in a gene-disease network. Conclusions: The study is ongoing and further studies will be needed to develop a complete catalog with the data from upcoming publications. Additional studies will also be needed for revealing of molecular mechanisms associated with syndromic cryptorchidism and revealing complete diseasome network.

Genetic Profiling and Comorbidities of Zika Infection.

Background: The difficulty in distinguishing infection by Zika virus (ZIKV) from other flaviviruses is a global health concern, particularly given the high risk of neurologic complications (including Guillain-Barré syndrome [GBS]) with ZIKV infection. Methods: We developed quantitative frameworks to compare and explore infectome, diseasome, and comorbidity of ZIKV infections. We analyzed gene expression microarray and RNA-Seq data from ZIKV, West Nile fever (WNF), chikungunya, dengue, yellow fever, Japanese encephalitis virus, GBS, and control datasets. Using neighborhood-based benchmarking and multilayer network topology, we constructed relationship networks based on the Online Mendelian Inheritance in Man database and our identified significant genes. Results: ZIKV infections showed dysregulation in expression of 929 genes. Forty-seven genes were highly expressed in both ZIKV and dengue infections. However, ZIKV shared <15 significant transcripts with other flavivirus infections. Notably, dysregulation of MAFB and SELENBP1 was common to ZIKV, dengue, and GBS infection; ATF5, TNFAIP3, and BAMB1 were common to ZIKV, dengue, and WNF; and NAMPT and PMAlP1 were common to ZIKV, GBS, and WNF. Phylogenetic, ontologic, and pathway analyses showed that ZIKV infection most resembles dengue fever. Conclusions: We have developed methodologies to investigate disease mechanisms and predictions for infectome, diseasome, and comorbidities quantitatively, and identified particular similarities between ZIKV and dengue infections.

Cancerome: A hidden informative subnetwork of the diseasome.

Neoplastic disorders are a leading cause of mortality and morbidity worldwide. Studying the relationships between different cancers using high throughput-generated data may elucidate undisclosed aspects of cancer etiology, diagnosis, and treatment. Several studies have described relationships between different diseases based on genes, proteins, pathways, gene ontology, comorbidity, symptoms, and other features. In this study, we first constructed an integrated human disease network based on nine different biological aspects, including molecular, functional, and clinical features. Next, we extracted the cancerome as a cancer-related subnetwork. Further investigation of cancerome could reveal hidden mechanisms of cancer and could be useful in developing new diagnostic tests and effective new drugs.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

CanisOme--The protein signatures of Canis lupus familiaris diseases.

Although the applications of Proteomics in Human Biomedicine have been explored for some time now, in animal and veterinary research, the potential of this resource has just started to be explored, especially when companion animal health is considered. In the last years, knowledge on the Canis lupus familiaris proteome has been accumulating in the literature and a resource compiling all this information and critically reviewing it was lacking. This article presents such a resource for the first time. CanisOme is a database of all proteins identified in Canis lupus familiaris tissues, either in health or in disease, annotated with information on the proteins present on the sample and on the donors. This database reunites information on 549 proteins, associated with 63 dog diseases and 33 dog breeds. Examples of how this information may be used to produce new hypothesis on disease mechanisms is presented both through the functional analysis of the proteins quantified in canine cutaneous mast cell tumors and through the study of the interactome of C. lupus familiaris and Leishmania infantum. Therefore, the usefulness of CanisOme for researchers looking for protein biomarkers in dogs and interested in a comprehensive analysis of disease mechanisms is demonstrated. BIOLOGICAL SIGNIFICANCE: This paper presents CanisOme, a database of proteomic studies with relevant protein annotation, allowing the enlightenment of disease mechanisms and the discovery of novel disease biomarkers for C. lupus familiaris. This knowledge is important not only for the improvement of animal health but also for the use of dogs as models for human health studies.

Diagnosis - the limiting focus of taxonomy.

RATIONALE, AIMS AND OBJECTIVES: The focus on the diagnosis is a pivotal aspect of medical practice since antiquity. Diagnostic taxonomy helped to categorize ailments to improve medical care, and in its social sense resulted in validation of the sick role for some, but marginalization or stigmatization for others. In the medical industrial complex, diagnostic taxonomy structured health care financing, management and practitioner remuneration. However, with increasing demands from multiple agencies, there are increasing unintended and unwarranted consequences of our current taxonomies and diagnostic processes resulting from the conglomeration of underpinning concepts, theories, information and motivations. RESULTS: We argue that the increasing focus on the diagnosis resulted in excessive compartmentalization - 'partialism' - of medical practice, diminishing medical care and being naively simplistic in light of the emerging understanding of the interconnected nature of the diseasome. The human is a complex organic system of interconnecting dynamics and feedback loops responding to internal and external forces including genetic, epigenetic and environmental attractors, rather than the sum of multiple discrete organs which can develop isolated diseases or multiple morbidities. Solutions to these unintended consequences of many contemporary health system processes involve revisiting the nature of diagnostic taxonomies and the processes of their construction. A dynamic taxonomic framework would shift to more relevant attractors at personal, clinical and health system levels recognizing the non-linear nature of health and disease. Human health at an individual, group and population level is the ability to adapt to internal and external stressors with resilience throughout the life course, yet diagnostic taxonomies are increasingly constructed around fixed anchors. CONCLUSIONS: Understanding diagnosis as dissecting, pigeonholing or bean counting (learning by dividing) is no longer useful, the challenge for the future is to understand the big picture (learning by connecting). Diagnostic categorization needs to embrace a meta-learning approach open to human variability.

Insights from Chromosome-Centric Mapping of Disease-Associated Genes: Chromosome 12 Perspective.

In line with the aims of the Chromosome-based Human Proteome Project and the Biology/Disease-based Human Proteome Project, we have been studying differentially expressed transcripts and proteins in gliomas­the most prevalent primary brain tumors. Here, we present a perspective on important insights from this analysis in terms of their co-expression, co-regulation/de-regulation, and co-localization on chromosome 12 (Chr. 12). We observe the following: (1) Over-expression of genes mapping onto amplicon regions of chromosomes may be considered as a biological validation of mass spectrometry data. (2) Their co-localization further suggests common determinants of co-expression and co-regulation of these clusters. (3) Co-localization of "missing" protein genes of Chr. 12 in close proximity to functionally related genes may help in predicting their functions. (4) Further, integrating differentially expressed gene-protein sets and their ontologies with medical terms associated with clinical phenotypes in a chromosome-centric manner reveals a network of genes, diseases, and pathways­a diseasome network. Thus, chromosomal mapping of disease data sets can help uncover important regulatory and functional links that may offer new insights for biomarker development.

A survey on computational approaches to identifying disease biomarkers based on molecular networks.

The disease biomarkers can help make accurate diagnosis and therefore give appropriate interventions. In the past years, the accumulation of various kinds of 'omics' data, e.g. genomics and transcriptomics, makes it possible to identify disease biomarkers in a more efficient way. In particular, the molecular networks that describe the functional relationships among molecules enable the identification of disease biomarkers from a systematic perspective. In this paper, we surveyed the recent progress on the computational approaches that have been developed to identify disease biomarkers based on molecular networks. In addition, we introduced the popular resources about human interactomes and regulatomes as well as human diseasomes, whose availability makes it possible to predict the disease biomarkers with the utility of networks.

Syntropy, genetic testing and personalized medicine.

The concept of syntropic diseases was proposed at the beginning of the last century to emphasize the phenomenon of nonrandom co-occurrence of human disorders. Common genes underlying specific syntropic diseases were called syntropic genes. The application of this concept to contemporary genomic studies will facilitate the understanding of the molecular basis of complex diseases, provide future direction for discovering new targets for therapy and prognosis, and may even lead to the reassessment of disease classification for the practice of more precise personalized medicine. With the acceptance of the syntropic genes theory, new genetic tests, focused on markers pointing to a set of pathogenetically linked diseases rather than to a single nosology, can be developed.