Microbiome in the Framework of Predictive, Preventive and Personalised Medicine

The human body is inhabited by trillions of diverse microorganisms collectively called "microbiome" or "microbiota". Microbiota consists of bacteria, viruses, fungi, protozoa, and archaea. Microbiome demonstrates multi-faceted effects on human physical and mental health. Per evidence there is a multi-functional interplay between the whole-body microbiome composition on the epithelial surfaces including skin, nasal and oral cavities, airway, gastro-intestinal and urogenital tracts on one hand and on the other hand, the individual health status. Microbiota composition as well as an option to modulate it - together create a highly attractive operation area for the translational bio/medical research with multi-professional expertise and healthcare-relevant output in the framework of predictive, preventive and personalised medicine (PPPM/3 PM). Advanced PPPM strategies implemented in the microbiome area are expected to significantly improve individual outcomes and overall cost-efficacy of healthcare. According to the accumulated research data, corresponding diagnostic and treatment approaches are applicable to primary care (health risk assessment in individuals with sub-optimal health conditions and prevention of a disease development), secondary care (personalised treatment of clinically manifested disorders preventing a disease progression) and tertiary care (making palliation to an optimal management of non-curable diseases). In the current book, we do highlight the implementation potential of the microbiome-relevant research in the framework of predictive diagnostics, targeted prevention and treatments tailored to the individualised patient profile.Copyright © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Introduction

Introducing Information Technology (IT) tools in today’s and future medicine is being increasingly considered all over the world, especially in developed countries. Indeed, these countries are suffering the most from population aging and chronic diseases. This induces original challenges not only for healthcare but also for wellbeing. In this sense, Predictive, Preventive and Personalised Medicine (3PM) has been identified since more than a decade as a research avenue having huge potential for developed societies. © Springer Nature Switzerland AG 2020.

Endothelin-1 axes in the framework of predictive, preventive and personalised (3P) medicine.

Endothelin-1 (ET-1) is involved in the regulation of a myriad of processes highly relevant for physical and mental well-being; female and male health; in the modulation of senses, pain, stress reactions and drug sensitivity as well as healing processes, amongst others. Shifted ET-1 homeostasis may influence and predict the development and progression of suboptimal health conditions, metabolic impairments with cascading complications, ageing and related pathologies, cardiovascular diseases, neurodegenerative pathologies, aggressive malignancies, modulating, therefore, individual outcomes of both non-communicable and infectious diseases such as COVID-19. This article provides an in-depth analysis of the involvement of ET-1 and related regulatory pathways in physiological and pathophysiological processes and estimates its capacity as a predictor of ageing and related pathologies,a sensor of lifestyle quality and progression of suboptimal health conditions to diseases for their targeted preventionand as a potent target for cost-effective treatments tailored to the person.

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine.

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach.

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the "participatory" medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

Cell-free nucleic acid patterns in disease prediction and monitoring-hype or hope?

Interest in the use of cell-free nucleic acids (CFNAs) as clinical non-invasive biomarker panels for prediction and prevention of multiple diseases has greatly increased over the last decade. Indeed, circulating CFNAs are attributable to many physiological and pathological processes such as imbalanced stress conditions, physical activities, extensive apoptosis of different origin, systemic hypoxic-ischemic events and tumour progression, amongst others. This article highlights the involvement of circulating CFNAs in local and systemic processes dealing with the question, whether specific patterns of CFNAs in blood, their detection, quantity and quality (such as their methylation status) might be instrumental to predict a disease development/progression and could be further utilised for accompanying diagnostics, targeted prevention, creation of individualised therapy algorithms, therapy monitoring and prognosis. Presented considerations conform with principles of 3P medicine and serve for improving individual outcomes and cost efficacy of medical services provided to the population.

Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations.

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of parameters measured, clear differences were found for "comet assay" and S100 expression patterns. In conclusion, multi-faceted cancer control within the palliative care of liver malignancies is crucial for improved disease outcomes including individualised patient profiling, predictive models and implementation of corresponding cost-effective risks mitigating measures detailed in the paper. The "proof-of-principle" model is presented.