P4 medicine and osteoporosis: a systematic review

SOXC Transcription Factors as Diagnostic Biomarkers and Therapeutic Targets for Arthritis

Osteoarthritis (OA) and rheumatoid arthritis (RA) are two common disorders that disrupt the quality of life of millions of people. These two chronic diseases cause damage to the joint cartilage and surrounding tissues of more than 220 million people worldwide. Sex-determining region Y-related (SRY) high-mobility group (HMG) box C, SOXC, is a superfamily of transcription factors that have been recently shown to be involved in various physiological and pathological processes. These include embryonic development, cell differentiation, fate determination, and autoimmune diseases, as well as carcinogenesis and tumor progression. The SOXC superfamily includes SOX4, SOX11, and SOX12, all have a similar DNA-binding domain, i.e., HMG. Herein, we summarize the current knowledge about the role of SOXC transcription factors during arthritis progression and their potential utilization as diagnostic biomarkers and therapeutic targets. The involved mechanistic processes and signaling molecules are discussed. SOX12 appears to have no role in arthritis, however SOX11 is dysregulated and promotes arthritic progression according to some studies but supports joint maintenance and protects cartilage and bone cells according to others. On the other hand, SOX4 upregulation during OA and RA was documented in almost all studies including preclinical and clinical models. Molecular details have indicated that SOX4 can autoregulate its own expression besides regulating the expression of SOX11, a characteristic associated with the transcription factors that protects their abundance and activity. From analyzing the currently available data, SOX4 seems to be a potential diagnostic biomarker and therapeutic target of arthritis.

Association of amino acid metabolites with osteoporosis, a metabolomic approach: Bushehr elderly health program

INTRODUCTION AND OBJECTIVES

Amino acids are the most frequently reported metabolites associated with low bone mineral density (BMD) in metabolomics studies. We aimed to evaluate the association between amino acid metabolic profile and bone indices in the elderly population.

METHODS

400 individuals were randomly selected from 2384 elderly men and women over 60 years participating in the second stage of the Bushehr elderly health (BEH) program, a population-based prospective cohort study that is being conducted in Bushehr, a southern province of Iran. Frozen plasma samples were used to measure 29 amino acid and derivatives metabolites using the UPLC-MS/MS-based targeted metabolomics platform. We conducted Elastic net regression analysis to detect the metabolites associated with BMD of different sites and lumbar spine trabecular bone score, and also to examine the ability of the measured metabolites to differentiate osteoporosis.

RESULTS

We adjusted the analysis for possible confounders (age, BMI, diabetes, smoking, physical activity, vitamin D level, and sex). Valine, leucine, isoleucine, and alanine in women and tryptophan in men were the most important amino acids inversely associated with osteoporosis (OR range from 0.77 to 0.89). Sarcosine, followed by tyrosine, asparagine, alpha aminobutyric acid, and ADMA in women and glutamine in men and when both women and men were considered together were the most discriminating amino acids detected in individuals with osteoporosis (OR range from 1.15 to 1.31).

CONCLUSION

We found several amino acid metabolites associated with possible bone status in elderly individuals. Further studies are required to evaluate the utility of these metabolites as clinical biomarkers for osteoporosis prediction and their effect on bone health as dietary supplements.

Application of metabolomics in osteoporosis research

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.

Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.