Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Emerging applications of metabolomics in drug discovery and precision medicine

Objectives
A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (mIoT). This paper reviews mIoT and big data in healthcare fields.

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Medical Internet of Things and Big Data in Healthcare

Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Integrative omics for health and disease.

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community.

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

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Wearables and the medical revolution.

Ten years ago, the proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was regarded as highly speculative. Today, the core elements of that vision are widely accepted and have been articulated in a series of recent reports by the US Institute of Medicine. Systems approaches to biology and medicine are now beginning to provide patients, consumers and physicians with personalized information about each individual's unique health experience of both health and disease at the molecular, cellular and organ levels. This information will make disease care radically more cost effective by personalizing care to each person's unique biology and by treating the causes rather than the symptoms of disease. It will also provide the basis for concrete action by consumers to improve their health as they observe the impact of lifestyle decisions. Working together in digitally powered familial and affinity networks, consumers will be able to reduce the incidence of the complex chronic diseases that currently account for 75% of disease-care costs in the USA.

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P4 medicine: how systems medicine will transform the healthcare sector and society

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

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A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Systems biology and the digital revolution are transforming healthcare into a proactive P4 medicine that is predictive, preventive, personalized and participatory. Systems biology – holistic, global and integrative in approach – has given rise to systems medicine, a systems approach to health and disease. Systems medicine promises to (1) provide deep insights into disease mechanisms, (2) make blood a diagnostic window for viewing health and disease for the individual, (3) stratify complex diseases into their distinct subtypes for an impedance match against proper drug treatments, (4) provide new approaches to drug target discovery, and (5) generate metrics for assessing wellness. P4 medicine, the clinical face of systems medicine, has two major objectives: to quantify wellness and to demystify disease. Patients and consumers will be major drivers in the realization of P4 medicine through their participation in social networks directed at improving their own healthcare. P4 medicine has striking implications for society – including the ability to turn around the ever-escalating costs of healthcare. The challenge in bringing P4 medicine to patients and consumers is twofold: first, inventing the strategies and technologies that will enable P4 medicine, and second, dealing with the impact of P4 medicine on society, including key ethical, social, legal, regulatory, and economic issues. Managing the societal problems will pose the most significant challenges. Strategic partnerships of a variety of types will be necessary to bring P4 medicine to patients.

http://scitechconnect.elsevier.com/wp-content/uploads/2014/02/Handbook-of-Systems-Biology.pdf

Systems Medicine and the Emergence of Proactive P4 Medicine: Predictive, Preventive, Personalized and Participatory

Pd18-04 prospective validation of an epigenetic sperm quality test for men seeking fertility care

Complex diseases have multifactorial etiologies making actionable diagnostic biomarkers difficult to identify. Diagnostic research must expand beyond single or a handful of genetic or epigenetic targets for complex disease and explore a broader system of biological pathways. With the objective to develop a diagnostic tool designed to analyze a comprehensive network of epigenetic profiles in complex diseases, we used publicly available DNA methylation data from over 2,400 samples representing 20 cell types and various diseases. This tool, rather than detecting differentially methylated regions at specific genes, measures the intra-individual methylation variability within gene promoters to identify global shifts away from healthy regulatory states. To assess this new approach, we explored three distinct questions: 1) Are profiles of epigenetic variability tissue-specific? 2) Do diseased tissues exhibit altered epigenetic variability compared to normal tissue? 3) Can epigenetic variability be detected in complex disease? Unsupervised clustering established that global epigenetic variability in promoter regions is tissue-specific and promoter regions that are the most epigenetically stable in a specific tissue are associated with genes known to be essential for its function. Furthermore, analysis of epigenetic variability in these most stable regions distinguishes between diseased and normal tissue in multiple complex diseases. Finally, we demonstrate the clinical utility of this new tool in the assessment of a multifactorial condition, male infertility. We show that epigenetic variability in purified sperm is correlated with live birth outcomes in couples undergoing intrauterine insemination (IUI), a common fertility procedure. Men with the least epigenetically variable promoters were almost twice as likely to father a child than men with the greatest number of epigenetically variable promoters. Interestingly, no such difference was identified in men undergoing in vitro fertilization (IVF), another common fertility procedure, suggesting this as a treatment to overcome higher levels of epigenetic variability when trying to conceive.

Kristin Brogaard

Papers

P4 medicine: how systems medicine will transform the healthcare sector and society

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Systems Medicine and the Emergence of Proactive P4 Medicine: Predictive, Preventive, Personalized and Participatory

Pd18-04 prospective validation of an epigenetic sperm quality test for men seeking fertility care

Tissue-specific DNA methylation variability and its potential clinical value