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

Feasibility of smartphone application and social media intervention on breast cancer survivors' health outcomes

Breast cancer survivors are at risk for poor health, with physical activity a possible treatment. Little research has examined how technology might promote breast cancer survivor physical activity or health. The aim of this study is to investigate the feasibility of employing a commercially available mobile health application- and social media-based health education intervention to improve breast cancer survivor physical activity or health.Ten breast cancer survivors (X̅ age = 45.80 ± 10.23 years; X̅ weight = 79.51 ± 20.85 kg) participated in this 10-week single-group pilot study from 2015 to 2016. Participants downloaded the MapMyFitness application, documented all physical activity with MapMyFitness, and were enrolled in a Social Cognitive Theory-based, Facebook-delivered health education intervention. Objectively measured physical activity, weight or body composition, cardiovascular fitness, psychosocial constructs, and quality of life indices were measured at baseline and 10 weeks. Intervention use and acceptability was evaluated during and following the intervention. Descriptive statistics were calculated for all study outcomes, with qualitative analyses performed regarding use and acceptability.At postintervention, average daily moderate-to-vigorous physical activity and steps increased by 2.6 min and 1,657, respectively, with notable decreases in weight (2.4 kg) and body fat percentage (2.3%). Physical activity-related social support and ability to engage in social roles or activity demonstrated the greatest improvements among all psychosocial and quality of life indices, respectively. Participants enjoyed the feedback and tracking features of MapMyFitness, with most finding the Facebook component helpful. All participants recommended the intervention for future use.Physical activity interventions combining commercially available mobile health applications and theoretically based social media-delivered health interventions may promote certain physiological, psychosocial, and quality of life outcomes among breast cancer survivors. Larger samples and randomized studies are warranted.

Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care

Background

Informing kidney transplant recipients of their prognosis and disease progression is of primary importance in a patient-centred vision of care. By participating in decisions from the outset, transplant recipients may be more adherent to complex medical regimens due to their enhanced understanding.

Methods

We proposed to include repeated measurements of serum creatinine (SCr), in addition to baseline characteristics, in order to obtain dynamic predictions of the graft failure risk that could be updated continuously during patient follow-up. Adult recipients from the French Données Informatisées et VAlidées en Transplantation (DIVAT) cohort transplanted for the first or second time from a heart-beating or living donor and alive with a functioning graft at 1 year post-transplantation were included.

Results

The model was composed of six baseline parameters, in addition to the SCr evolution. We validated the dynamic predictions by evaluating both discrimination and calibration accuracy. The area under the receiver operating characteristic curve varied from 0.72 to 0.76 for prediction times at 1 and 6 years post-transplantation, respectively, while calibration plots showed correct accuracy. We also provided an online application tool (https://shiny.idbc.fr/DynPG).

Conclusion

We have created a tool that, for the first time in kidney transplantation, predicts graft failure risk both at an individual patient level and dynamically. We believe that this tool would encourage willing patients into participative medicine.

Development of Strategic Plans for Advancing Nursing in Korea

PURPOSE

The aim of the study is to evaluate the current and prospective status of nursing in Korea and develop a strategic framework and plan to accommodate the increased demands on nurses in the changing health-care system.

METHODS

This study used a mixed-methods approach including a literature review, an online survey with health-care consumers, expert panel interviews, and an analysis of strengths, weaknesses, opportunities, and threats to develop the strategic plans and framework.

RESULTS

The vision of the strategic framework involved improving health and quality of life, and its mission was to elevate the status of Korea's nursing sector as a key health-care profession through high-quality and innovative nursing education, research, and practice. The five values in accordance with the mission and vision were innovation, creation, collaboration, excellence, and authenticity. Three strategic goals, namely, education, research, and practice, were identified, and 31 related strategic tasks were developed.

CONCLUSION

In response to the rising social demand for a paradigm shift in nursing care services, there is a need for advancements in nursing education, research, and practice in Korea. This study provide some recommendations to achieve these aims.

Clinical and research applications of a brain tumor tissue bank in the age of precision medicine

Marked progress has been made recently in the treatment of patients with central nervous system (CNS) tumors, especially gliomas. However, because of the relative rarity of these tumors compared with other malignancies, advances in the molecular/genetic analysis leading to future targeted treatments rely on systematic, organized tissue banking. Several large multi-institutional efforts have utilized major tissue banks that have yielded valuable information that may lead to a better understanding of the pathogenesis of CNS tumors. This manuscript portrays best practices for the establishment and maintenance of a well-organized CNS tumor bank. In addition, annotation for clinical and research needs is explained. The potential benefits to clinical care, as well as basic science and translational research are also described.

Data ambiguity and clinical decision making: A qualitative case study of the use of predictive information technologies in a personalized cancer clinical trial

Personalized medicine aims to tailor the treatment to the specific characteristics of the individual patient. In the process, physicians engage with multiple sources of data and information to decide on a personalized treatment. This article draws on a qualitative case study of a clinical trial testing a method for matching treatments for advanced cancer patients. Specialists in the trial used data and information processed by a specifically developed drug-efficacy predictive algorithm and other information artifacts to make personalized clinical decisions. While using high-resolution data in the trial was expected to provide a more accurate basis for action, sociomaterial engagements of oncologists with data and its representation by artifacts paradoxically hindered personalized clinical decisions. I contend that the engagement between human discretion, ambiguous data, and malleable artifacts in this non-standardized trial produced moments of contradiction within entanglement. Sociomaterial approaches should acknowledge such conflicts in further analyses of medical practice transitions.

Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review

Disease management requires the use of mixed languages when discussing etiology, diagnosis, treatment, and follow-up. All phases require data management, and, in the optimal case, such data are interdisciplinary and uniform and clear to all those involved. Such semantic data interoperability is one of the technical building blocks that support emerging digital medicine, e-health, and P4-medicine (predictive, preventive, personalized, and participatory). In a world where infectious diseases are on a trend to become hard-to-treat threats due to antimicrobial resistance, semantic data interoperability is part of the toolbox to fight more efficiently against those threats. In this review, we will introduce semantic data interoperability, summarize its added value, and analyze the technical foundation supporting the standardized healthcare system interoperability that will allow moving forward to e-health. We will also review current usage of those foundational standards and advocate for their uptake by all infectious disease-related actors.

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

Personalised organs-on-chips: functional testing for precision medicine

Organs-on-chips are microfluidic systems with controlled, dynamic microenvironments in which cultured cells exhibit functions that emulate organ-level physiology. They can in principle be 'personalised' to reflect individual physiology, for example by including blood samples, primary human tissue, and cells derived from induced pluripotent stem cell-derived cells, as well as by tuning key physico-chemical parameters of the cell culture microenvironment based on personal health data. The personalised nature of such systems, combined with physiologically relevant read-outs, provides new opportunities for person-specific assessment of drug efficacy and safety, as well as personalised strategies for disease prevention and treatment; together, this is known as 'precision medicine'. There are multiple reports of how to personalise organs-on-chips, with examples including airway-on-a-chip systems containing primary patient alveolar epithelial cells, vessels-on-chips with shapes based on personal biomedical imaging data and lung-on-a-chip systems that can be exposed to various regimes of cigarette smoking. In addition, multi-organ chip systems even allow the systematic and dynamic integration of more complex combinations of personalised cell culture parameters. Current personalised organs-on-chips have not yet been used for precision medicine as such. The major challenges that affect the implementation of personalised organs-on-chips in precision medicine are related to obtaining access to personal samples and corresponding health data, as well as to obtaining data on patient outcomes that can confirm the predictive value of personalised organs-on-chips. We argue here that involving all biomedical stakeholders from clinicians and patients to pharmaceutical companies will be integral to transition personalised organs-on-chips to precision medicine.

Patient-centered simulations to assess the usefulness of the 70-gene signature for adjuvant chemotherapy administration in early-stage breast cancer

PURPOSE

From the MINDACT trial, Cardoso et al. did not demonstrate a significant efficacy for adjuvant chemotherapy (CT) for women with early-stage breast cancer presenting high clinical and low genomic risks. Our objective was to assess the usefulness of the 70-gene signature in this population by using an alternative endpoint: the number of Quality-Adjusted Life-Years (QALYs), i.e., a synthetic measure of quantity and quality of life.

METHODS

Based on the results of the MINDACT trial, we simulated a randomized clinical trial consisting of 1497 women with early-stage breast cancer presenting high clinical and low genomic risks. The individual preferences for the different health states and corresponding decrements were obtained from the literature.

RESULTS

The gain in terms of 5-year disease-free survival was 2.8% (95% CI from - 0.1 to 5.7%, from 90.4% for women without CT to 93.3% for women with CT). In contrast, due to the associated side effects, CT significantly reduced the number of QALYs by 62 days (95% CI from 55 to 70 days, from 4.13 years for women without CT to 3.96 years for women with CT).

CONCLUSION

Our results support the conclusions published by Cardoso et al. by providing additional evidence that the 70-gene signature can be used to avoid overtreatment by CT for women with high clinical risk but low genomic risk.

Patienthood and participation in the digital era

The 'digital era' of informatics and knowledge integration has changed the roles and experiences of patients, research participants and health consumers. No longer figured (merely) as passive recipients of healthcare services or as beneficiaries of top-down biomedical information, individuals are increasingly seen as active contributors in healthcare and research. They are positioned into multiple roles that are experienced simultaneously by those who access and co-produce digital content that can easily be transformed into data. This is contextualised by 'big data' technologies that have altered biomedicine, enabling collation and analysis of myriad data from digitised records to personal mobile data. Social media facilitate new formations of communities and knowledge enacted online, while novel kinds of commercial value emerge from digital networks that enable health data commodification. In this paper, we draw from exemplary digital era shifts towards participatory medicine to cast light on the rapprochements between patienthood, participation and consumption, and we explore how these rapprochements are mediated by, and materialise through, the use of participatory digital technologies and big data. We argue that there is a need to use new conceptual tools that account for the multiple roles and experiences of patient-participant-consumers that co-emerge through digital technologies. We must also ethically re-assess the rights and responsibilities of individuals in the digital era, and the implications of digital era changes for the future of biomedicine and healthcare.

Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models

Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. However, this is challenging since CPMs are usually derived using data sets where patients received treatment, often initiated postbaseline as "treatment drop-ins." This study proposes the use of marginal structural models (MSMs) to adjust for treatment drop-in. We illustrate the use of MSMs in the CPM framework through simulation studies that represent randomized controlled trials and real-world observational data and the example of statin initiation for cardiovascular disease prevention. The simulations include a binary treatment and a covariate, each recorded at two timepoints and having a prognostic effect on a binary outcome. The bias in predicted risk was examined in a model ignoring treatment, a model fitted on treatment-naïve patients (at baseline), a model including baseline treatment, and the MSM. In all simulation scenarios, all models except the MSM underestimated the risk of outcome given absence of treatment. These results were supported in the statin initiation example, which showed that ignoring statin initiation postbaseline resulted in models that significantly underestimated the risk of a cardiovascular disease event occurring within 10 years. Consequently, CPMs that do not acknowledge treatment drop-in can lead to underallocation of treatment. In conclusion, when developing CPMs to predict treatment-naïve risk, researchers should consider using MSMs to adjust for treatment drop-in, and also seek to exploit the ability of MSMs to allow estimation of individual treatment effects.

Precision gaming for health: Computer games as digital medicine

Health based games have significant potential as therapeutic interventions due to the inherent mechanisms associated with social and individual game play and their capacity for sensor integration, data capture analysis and patient feedback. Moreover, they are low cost and they can be deployed at the point of care across an evolving digital ecosystem. However, a robust evidence base to support their wider adoption as a clinical intervention for chronic diseases is lacking and significant methodological barriers exist for health games developers creating efficacious 'digital medicines'. Game design is complex and it must utilise validated game mechanics balanced with a creative and engaging game design. The aim of this review is therefore to outline the fundamental steps of game development for health professionals and to critically appraise the methodology for assessing health games as medical interventions. This requires (1) The adoption of clearly defined global language for health games development based on a targeted function as therapeutic agents. (2) The development of multidisciplinary teams with a broad portfolio of development and clinical skill sets. (3) The creation of health game engines specifically built to facilitate clinical game development. (4) Robust trial design and assessment of translational impact: If games are to be prescribed, their efficacy and toxicity must be based on a rigorous assessment of their use within a real world clinical environment. Trials for precision health games have specific challenges around blinding, learning curves, bias and confounding that are particularly problematic. We propose the adoption of the IDEAL-GAMES framework for game development that systematically assess and validates games through open registries. In conclusion we propose a new framework for assessing the robustness and clinical efficacy of games for health as clinical interventions in the clinical environment.

Development of an mHealth platform for HIV Care: Gathering User Perspectives Through Co-Design Workshops and Interviews

Background

Despite advances in testing and treatment, HIV incidence rates within European countries are at best stable or else increasing. mHealth technology has been advocated to increase quality and cost-effectiveness of health services while dealing with growing patient numbers. However, studies suggested that mHealth apps are rarely adopted and often considered to be of low quality by users. Only a few studies (conducted in the United States) have involved people living with HIV (PLWH) in the design of mHealth.

Objective

The goal of this study was to facilitate a co-design process among PLWH and clinicians across 5 clinical sites in the European Union to inform the development of an mHealth platform to be integrated into clinical care pathways. We aimed to (1) elicit experiences of living with HIV and of working in HIV care, (2) identify mHealth functionalities that are considered useful for HIV care, and (3) identify potential benefits as well as concerns about mHealth.

Methods

Between January and June 2016, 14 co-design workshops and 22 semistructured interviews were conducted, involving 97 PLWH and 63 clinicians. Data were analyzed thematically and iteratively, drawing on grounded theory techniques.

Results

Findings were established into 3 thematic clusters: (1) approaching the mHealth platform, (2) imagining the mHealth platform, and (3) anticipating the mHealth platform’s implications. Co-design participants approached the mHealth platform with pre-existing concerns arising from their experiences of receiving or providing care. PLWH particularly addressed issues of stigma and questioned how mHealth could enable them to manage their HIV. Clinicians problematized the compatibility of mHealth with existing information technology systems and questioned which patients should be targeted by mHealth. Imagining the potential of mHealth for HIV care, co-design participants suggested medical functionalities (accessing test results, managing medicines and appointments, and digital communication channels), social functionalities (peer support network, international travel, etc), and general features (security and privacy, credibility, language, etc). Co-design participants also anticipated potential implications of mHealth for self-management and the provision of care.

Conclusions

Our approach to co-design enabled us to facilitate early engagement in the mHealth platform, enabling patient and clinician feedback to become embedded in the development process at a preprototype phase. Although the technologies in question were not yet present, understanding how users approach, imagine, and anticipate technology formed an important source of knowledge and proved highly significant within the technology design and development process.

[Personalised COPD care: Where are we going?]

The concept of personalised medicine is recent but the underlying notions are not new: knowing how to adapt care to patients' characteristics is one of the components of the "art of medicine". The advances of science allow to refine considerably the applications of the concept in many fields of medicine including COPD: research has identified phenotypes, endotypes and treatable traits. Personalisation can be applied to all components of care. For instance, the decision to perform screening spirometry relies not only on risk factors (age, smoking, other exposures) but also on symptoms. Assessment of comorbidities often associated with COPD is based on risk factors and their combinations, variable between individuals. Rehabilitation and its components are in essence highly individualised, which a major condition for their success. Last but not least, personalisation of pharmacological therapy, which has long been rather poor, could not benefit from biomarkers of interest (predictive of response), such as blood eosinophil count. Practical strategies using these still need to be established, and new biomarkers may usefully enrich the collection!

Looking for trouble? Diagnostics expanding disease and producing patients

Novel tests give great opportunities for earlier and more precise diagnostics. At the same time, new tests expand disease, produce patients, and cause unnecessary harm in overdiagnosis and overtreatment. How can we evaluate diagnostics to obtain the benefits and avoid harm? One way is to pay close attention to the diagnostic process and its core concepts. Doing so reveals 3 errors that expand disease and increase overdiagnosis. The first error is to decouple diagnostics from harm, eg, by diagnosing insignificant conditions. The second error is to bypass proper validation of the relationship between test indicator and disease, eg, by introducing biomarkers for Alzheimer's disease before the tests are properly validated. The third error is to couple the name of disease to insignificant or indecisive indicators, eg, by lending the cancer name to preconditions, such as ductal carcinoma in situ. We need to avoid these errors to promote beneficial testing, bar harmful diagnostics, and evade unwarranted expansion of disease. Accordingly, we must stop identifying and testing for conditions that are only remotely associated with harm. We need more stringent verification of tests, and we must avoid naming indicators and indicative conditions after diseases. If not, we will end like ancient tragic heroes, succumbing because of our very best abilities.

Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool

BACKGROUND

The growth in the availability of personal genomic data to nonexperts poses multiple challenges to human-computer interaction research; data are highly sensitive, complex, and have health implications for individuals and families. However, there has been little research on how nonexpert users explore their genomic data.

OBJECTIVE

We focus on how to support nonexperts in exploring and comparing their own personal genomic report with those of other people. We designed and evaluated CrossGenomics, a novel tool for comparing personal genetic reports, which enables exploration of shared and unshared genetic variants. Focusing on communicating comparative impact, rarity, and certainty, we evaluated alternative novel interactive prototypes.

METHODS

We conducted 3 user studies. The first focuses on assessing the usability and understandability of a prototype that facilitates the comparison of reports from 2 family members. Following a design iteration, we studied how various prototypes support the comparison of genetic reports of a 4-person family. Finally, we evaluated the needs of early adopters-people who share their genetic reports publicly for comparing their genetic reports with that of others.

RESULTS

In the first study, sunburst- and Venn-based comparisons of two genomes led to significantly higher domain comprehension, compared with the linear comparison and with the commonly used tabular format. However, results show gaps between objective and subjective comprehension, as sunburst users reported significantly lower perceived understanding and higher levels of confusion than the users of the tabular report. In the second study, users who were allowed to switch between the different comparison views presented higher comprehension levels, as well as more complex reasoning than users who were limited to a single comparison view. In the third study, 35% (17/49) reported learning something new from comparing their own data with another person's data. Users indicated that filtering and toggling between comparison views were the most useful features.

CONCLUSIONS

Our findings (1) highlight features and visualizations that show strengths in facilitating user comprehension of genomic data, (2) demonstrate the value of affording users the flexibility to examine the same report using multiple views, and (3) emphasize users' needs in comparison of genomic data. We conclude with design implications for engaging nonexperts with complex multidimensional genomic data.

Precision Medicine for Neonatal Sepsis

Neonatal sepsis remains a significant cause of morbidity and mortality especially in the preterm infant population. The ability to promptly and accurately diagnose neonatal sepsis based on clinical evaluation and laboratory blood tests remains challenging. Advances in high-throughput molecular technologies have increased investigations into the utility of transcriptomic, proteomic and metabolomic approaches as diagnostic tools for neonatal sepsis. A systems-level understanding of neonatal sepsis, obtained by using omics-based technologies (at the transcriptome, proteome or metabolome level), may lead to new diagnostic tools for neonatal sepsis. In particular, recent omic-based studies have identified distinct transcriptional signatures and metabolic or proteomic biomarkers associated with sepsis. Despite the emerging need for a systems biology approach, future studies have to address the challenges of integrating multi-omic data with laboratory and clinical meta-data in order to translate outcomes into precision medicine for neonatal sepsis. Omics-based analytical approaches may advance diagnostic tools for neonatal sepsis. More research is needed to validate the recent systems biology findings in order to integrate multi-dimensional data (clinical, laboratory and multi-omic) for future translation into precision medicine for neonatal sepsis. This review will discuss the possible applications of omics-based analyses for identification of new biomarkers and diagnostic signatures for neonatal sepsis, focusing on the immune-compromised preterm infant and considerations for clinical translation.

Data Integration for Future Medicine (DIFUTURE)

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers.

OBJECTIVES

The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing.

GOVERNANCE AND POLICIES

Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change.

ARCHITECTURAL FRAMEWORK AND METHODOLOGY

The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing.

USE CASES

From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios.

DISCUSSION

Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

Autoimmune antibodies and asbestos exposure: Evidence from Wittenoom, Western Australia

BACKGROUND

Studies comparing different forms of asbestos are rare, and limited by the failure to compare results with unexposed populations. We compare autoimmune responses among former workers and residents of the crocidolite mining and milling town of Wittenoom, Western Australia, with an unexposed population.

METHODS

ANA testing using indirect immunofluorescence was performed on randomly selected serum samples from Wittenoom workers or residents and compared with those from participants of another unexposed cohort study.

RESULTS

ANA scores were higher in the Wittenoom participants compared with Busselton and the odds of being ANA positive was fivefold greater among Wittenoom participants than Busselton (OR 5.5, 95%CI 2.3-13.0).

CONCLUSIONS

This study is the first to report increased ANA positivity among persons exposed exclusively to crocidolite. This finding of a high frequency of positive ANA tests among crocidolite-exposed subjects may be an indicator for an increased risk of systemic autoimmune diseases and needs further scrutiny.

Potentials and Challenges of the Health Data Cooperative Model

INTRODUCTION

Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges.

RESULTS

An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the "core," the "app store," and the "big data." This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens' restraint, disclosure of clinical results, big data, and commercial interest. Limitations and Outlook: The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.

The Heimdall Framework for Supporting Characterisation of Learning Health Systems

BACKGROUND

Learning Health Systems (LHS) can focus population medicine and Evidence Based Practice; smart technology delivering the next generation of improved healthcare described as Precision Medicine, and yet researchers in the LHS domain presently lack the ability to recognise their relevant works as falling within this domain.

OBJECTIVE

To review LHS literature and develop a framework describing the domain that can be used as a tool to analyse the literature and support researchers to identify health informatics investigations as falling with the domain of LHS.

METHOD

A scoping review is used to identify literature on which analysis was performed. This resolved the ontology and framework. The ontology was applied to quantify the distribution of classifications of LHS solutions. The framework was used to analyse and characterise the various works within the body of LHS literature.

RESULTS

The ontology and framework developed was shown to be easily applicable to the literature, consistently describing and representing the goals, intentions and solutions of each LHS investigation in the literature. More proposed or potential solutions are described in the literature than implemented LHS. This suggests immaturity in the domain and points to the existence of barriers preventing LHS realisation.

CONCLUSION

The lack of an ontology and framework may have been one of the causes for the failure to describe research works as falling within the LHS domain. Using our ontology and framework, LHS research works could be easily classified, demonstrating the comprehensiveness of our approach in contrast to earlier efforts.

Can a collaborative healthcare network improve the care of people with epilepsy?

New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.

Studying microbial functionality within the gut ecosystem by systems biology

Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.

E-health beyond technology: analyzing the paradigm shift that lies beneath

Information and computer technology has come to play an increasingly important role in medicine, to the extent that e-health has been described as a disruptive innovation or revolution in healthcare. The attention is very much focused on the technology itself, and advances that have been made in genetics and biology. This leads to the question: What is changing in medicine today concerning e-health? To what degree could these changes be characterized as a 'revolution'? We will apply the work of Thomas Kuhn, Larry Laudan, Michel Foucault and other philosophers-which offers an alternative understanding of progress and revolution in medicine to the classic discovery-oriented approach-to our analysis. Nowadays, the long-standing curative or reactive paradigm in medicine is facing a crisis due to an aging population, a significant increase in chronic diseases and the development of more expensive diagnostic tools and therapies. This promotes the evolution towards a new paradigm with an emphasis on preventive medicine. E-health constitutes an essential part of this new paradigm that seeks to solve the challenges presented by an aging population, skyrocketing costs and so forth. Our approach changes the focus from the technology itself toward the underlying paradigm shift in medicine. We will discuss the relevance of this approach by applying it to the surge in digital self-tracking through health apps and wearables: the recognition of the underlying paradigm shift leads to a more comprehensive understanding of self-tracking than a solely discovery-oriented or technology-focused view can provide.

Personalised Medicine Approaches to Screening and Prevention

One of the promised benefits of 'personalised' medicine is that it will have a positive impact on disease screening and prevention, by enabling more person-specific estimates of risk, and hence more personalised strategies for screening and risk reduction. This article summarises some of the areas in which these approaches are already being implemented, but also outlines some of the potential difficulties and limitations - and challenges the assumption that 'personalised' approaches are necessarily superior for prevention and screening.

Application of Personalized, Predictive, Preventative, and Participatory (P4) Medicine to Obstructive Sleep Apnea. A Roadmap for Improving Care?

Dr. Leroy Hood promotes a paradigm to advance medical care that he calls P4 medicine. The four Ps are: personalized, predictive, preventative, and participatory. P4 medicine encourages a convergence of systems medicine, the digital revolution, and consumer-driven healthcare. Might P4 medicine be applicable to obstructive sleep apnea (OSA)? OSA should be personalized in that there are different structural and physiological pathways to disease. Obesity is a major risk factor. The link between obesity and OSA is likely to be fat deposits in the tongue compromising the upper airway. Clinical features at presentation also vary between patients. There are three distinct subgroups: (1) patients with a primary complaint of insomnia, (2) relatively asymptomatic patients with a high prevalence of cardiovascular comorbidities, and (3) excessively sleepy patients. Currently, there have been limited efforts to identify subgroups of patients on the basis of measures obtained by polysomnography. Yet, these diagnostic studies likely contain considerable predictive information. Likewise, there has currently been limited application of -omic approaches. Determining the relative role of obesity and OSA for particular consequences is challenging, because they both affect the same molecular pathways. There is evidence that the effects of OSA are modified by the level of obesity. These insights may lead to improvements in predicting outcomes to personalized therapies. The final P-participatory-is ideally suited to OSA, with technology to obtain extensive data remotely from continuous positive airway pressure machines. Providing adherence data directly to patients increases their use of continuous positive airway pressure. Thus, the concept of P4 medicine is very applicable to obstructive sleep apnea and can be the basis for future research efforts.

Representing a "revolution": how the popular press has portrayed personalized medicine

PURPOSE

This study investigated the portrayal of "personalized" and "precision" medicine (PM) in North American news over the past decade. Content analysis of print and online news was conducted to determine how PM has been defined and to identify the frames used to discuss PM, including associated topics, benefits, and concerns.

METHODS

A data set was built using the FACTIVA database, searching for popular North American publications with the terms "personalized (personalised) medicine" and/or "precision medicine" from 1 January 2005 to 15 March 2016. The final set of publications totaled 774.

RESULTS

PM is almost exclusively defined as related to genetics and is often part of a story related to cancer. The PM story is overwhelmingly one of highlighting (potential) benefits and optimism, especially in shorter publications, and ones where PM is not the main focus. This promotional PM discourse has remained fairly consistent over the past decade.

CONCLUSION

The numerous concerns associated with PM have received little attention over the past decade, especially in articles more likely to be encountered by a more general audience. This promotion of PM serves as an example of the science hyping that takes place in science reportage and may have implications for consumers, public expectations, and related health policy.

Integrative health care - Toward a common understanding: A mixed method study

OBJECTIVE

To generate a multidisciplinary stakeholder-informed definition of integrative health care (IHC).

METHODS

A mixed-method study design was used, employing the use of focus groups/semi-structured interviews (phase-1) and document analysis (phases 2 and 3). Phase-1 recruited a purposive sample of Australian health consumers/health providers. Phase-2 interrogated websites of international IHC organisations for definitions of IHC. Phase-3 systematically searched bibliographic databases for articles defining IHC. Data were analysed using thematic analysis.

RESULTS

Data were drawn from 54 health consumers/providers (phase-1), 23 IHC organisation webpages (phase-2) and 23 eligible articles (phase-3). Seven themes emerged from the data. Consensus was reached on a single, 65-word definition of IHC.

CONCLUSION

An unambiguous definition of IHC is critical to establishing a clearer identity for IHC, as well as providing greater clarity for consumers, health providers and policy makers. In recognising the need for a clearer description, we propose a scientifically-grounded, multi-disciplinary stakeholder-informed definition of IHC.

The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

Advances in high-throughput molecular biology and electronic health records (EHR), coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Big data require collection and analysis of data at an unprecedented scale and represents a paradigm shift in health care, offering (1) the capacity to generate new knowledge more quickly than traditional scientific approaches; (2) unbiased collection and analysis of data; and (3) a holistic understanding of biology and pathophysiology. Big data promises more personalized and precision medicine for patients with improved accuracy and earlier diagnosis, and therapy tailored to an individual’s unique combination of genes, environmental risk, and precise disease phenotype. This promise comes from data collected from numerous sources, ranging from molecules to cells, to tissues, to individuals and populations—and the integration of these data into networks that improve understanding of heath and disease. Big data-driven science should play a role in propelling comparative medicine and “one medicine” (i.e., the shared physiology, pathophysiology, and disease risk factors across species) forward. Merging of data from EHR across institutions will give access to patient data on a scale previously unimaginable, allowing for precise phenotype definition and objective evaluation of risk factors and response to therapy. High-throughput molecular data will give insight into previously unexplored molecular pathophysiology and disease etiology. Investigation and integration of big data from a variety of sources will result in stronger parallels drawn at the molecular level between human and animal disease, allow for predictive modeling of infectious disease and identification of key areas of intervention, and facilitate step-changes in our understanding of disease that can make a substantial impact on animal and human health. However, the use of big data comes with significant challenges. Here we explore the scope of “big data,” including its opportunities, its limitations, and what is needed capitalize on big data in one medicine.

Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine

Clinical practice guidelines have been developed for many common conditions based on data from randomized controlled trials. When medicine is informed solely by clinical practice guidelines, however, the patient is not treated as an individual, but rather as member of a group. Precision medicine, as defined herein, characterizes unique biological characteristics of the individual or of specimens obtained from an individual to tailor diagnostics and therapeutics to a specific patient. These unique biological characteristics are defined by the tools of precision medicine: genomics, proteomics, metabolomics, epigenomics, pharmacogenomics, and other “-omics.” Personalized medicine, as defined herein, uses additional information about the individual derived from knowing the patient as a person. These unique personal characteristics are defined by the tools of personalized medicine—personomics—which take into account an individual’s personality, preferences, values, goals, health beliefs, social support network, financial resources, and unique life circumstances that affect how and when a given health condition will manifest in that person and how that condition will respond to treatment. In this paradigm, precision medicine may be considered a necessary step in the evolution of medical care to personalized medicine, with personomics as the missing link.

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits

In Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

Can learning health systems help organisations deliver personalised care?

There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions.

Systems toxicology meta-analysis of in vitro assessment studies: biological impact of a candidate modified-risk tobacco product aerosol compared with cigarette smoke on human organotypic cultures of the aerodigestive tract

Systems biology combines comprehensive molecular analyses with quantitative modeling to understand the characteristics of a biological system as a whole. Leveraging a similar approach, systems toxicology aims to decipher complex biological responses following exposures. This work reports a systems toxicology meta-analysis in the context of in vitro assessment of a candidate modified-risk tobacco product (MRTP) using three human organotypic cultures of the aerodigestive tract (buccal, bronchial, and nasal epithelia). Complementing a series of functional measures, a causal network enrichment analysis of transcriptomic data was used to compare quantitatively the biological impact of aerosol from the Tobacco Heating System (THS) 2.2, a candidate MRTP, with 3R4F cigarette smoke (CS) at similar nicotine concentrations. Lower toxicity was observed in all cultures following exposure to THS2.2 aerosol compared with 3R4F CS. Because of their morphological differences, a smaller exposure impact was observed in the buccal (stratified epithelium) compared with the bronchial and nasal (pseudostratified epithelium). However, the causal network enrichment approach supported a similar mechanistic impact of CS across the three cultures, including the impact on xenobiotic, oxidative stress, and inflammatory responses. At comparable nicotine concentrations, THS2.2 aerosol elicited reduced and more transient effects on these processes. To demonstrate the benefits of additional data modalities, we employed a newly established targeted mass-spectrometry marker panel to further confirm the reduced cellular stress responses elicited by THS2.2 aerosol compared with 3R4F CS in the nasal culture. Overall, this work demonstrates the applicability and robustness of the systems toxicology approach for in vitro inhalation toxicity assessment.

Innovation, productivity, and pricing: Capturing value from precision medicine technology in Canada

For new technology and innovation such as precision medicine to become part of the solution for the fiscal sustainability of Canadian Medicare, decision-makers need to change how services are priced rather than trying to restrain emerging technologies like precision medicine for short-term cost savings. If provincial public payers shift their thinking to be public purchasers, value considerations would direct reform of the reimbursement system to have prices that adjust with technologically driven productivity gains. This strategic shift in thinking is necessary if Canadians are to benefit from the promised benefits of innovations like precision medicine.

Stability of Proteins in Dried Blood Spot Biobanks

An important motivation for the construction of biobanks is to discover biomarkers that identify diseases at early, potentially curable stages. This will require biobanks from large numbers of individuals, preferably sampled repeatedly, where the samples are collected and stored under conditions that preserve potential biomarkers. Dried blood samples are attractive for biobanking because of the ease and low cost of collection and storage. Here we have investigated their suitability for protein measurements. Ninety-two proteins with relevance for oncology were analyzed using multiplex proximity extension assays (PEA) in dried blood spots collected on paper and stored for up to 30 years at either +4 °C or -24 °C.Our main findings were that (1) the act of drying only slightly influenced detection of blood proteins (average correlation of 0.970), and in a reproducible manner (correlation of 0.999), (2) detection of some proteins was not significantly affected by storage over the full range of three decades (34 and 76% of the analyzed proteins at +4 °C and -24 °C, respectively), whereas levels of others decreased slowly during storage with half-lives in the range of 10 to 50 years, and (3) detectability of proteins was less affected in dried samples stored at -24 °C compared with at +4 °C, as the median protein abundance had decreased to 80 and 93% of starting levels after 10 years of storage at +4 °C or -24 °C, respectively. The results of our study are encouraging as they suggest an inexpensive means to collect large numbers of blood samples, even by the donors themselves, and to transport, and store biobanked samples as spots of whole blood dried on paper. Combined with emerging means to measure hundreds or thousands of protein, such biobanks could prove of great medical value by greatly enhancing discovery as well as routine analysis of blood biomarkers.

Novel approaches to develop community-built biological network models for potential drug discovery

INTRODUCTION

Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

Current and future perspectives on the management of polypharmacy

BACKGROUND

Because of ageing populations, the growth in the number of people with multi-morbidity and greater compliance with disease-specific guidelines, polypharmacy is becoming increasingly common. Although the correct drug treatment in patients with complex medical problems can improve clinical outcomes, quality of life and life expectancy, polypharmacy is also associated with an increased risk of adverse drug events, some severe enough to result in hospital admission and even death. Hence, having systems in place to ensure that medications are started only when there is a suitable indication, ensuring patients are fully aware of the benefits and complications that may arise from their treatment, and reviewing patients regularly to ensure their medication regime remains appropriate, are essential.

DISCUSSION

The development and rapid uptake of electronic patient records - particularly in primary care settings where the majority of prescribing takes place - makes monitoring of patients more straightforward than in the past; and allows identification of sub-groups of patients at particularly high risk of adverse drug events and complications. It also facilitates 'deprescribing' the process by which medications are reviewed and stopped if not clinically beneficial. In recent years, we have also seen the development of smartphone 'apps' to improve communication between patients and healthcare professionals, improve people's understanding of their conditions and their treatment, and maintain a record of changes made to patient's medication. In the longer term, developments such as the introduction of artificial intelligence and clinical decision support systems also have the potential to improve prescribing and minimise the risks from polypharmacy. Finally, there is considerable scope to improve the quality of prescribing and reduce risks from poly-pharmacy using non-medical groups such as pharmacists, specialist nurses and physician assistants. Polypharmacy has increased in recent decades and will continue to increase as populations age and the number of people with multiple long-term conditions increases. As with all areas of medicine, the evidence-base in this area continues to evolve. Further trials on the impact on patients with polypharmacy of new interventions such as technology-based solutions and the use of different professional groups are needed to improve the evidence-base in this area.

Precision wildlife medicine: applications of the human-centred precision medicine revolution to species conservation

The current species extinction crisis is being exacerbated by an increased rate of emergence of epizootic disease. Human-induced factors including habitat degradation, loss of biodiversity and wildlife population reductions resulting in reduced genetic variation are accelerating disease emergence. Novel, efficient and effective approaches are required to combat these epizootic events. Here, we present the case for the application of human precision medicine approaches to wildlife medicine in order to enhance species conservation efforts. We consider how the precision medicine revolution, coupled with the advances made in genomics, may provide a powerful and feasible approach to identifying and treating wildlife diseases in a targeted, effective and streamlined manner. A number of case studies of threatened species are presented which demonstrate the applicability of precision medicine to wildlife conservation, including sea turtles, amphibians and Tasmanian devils. These examples show how species conservation could be improved by using precision medicine techniques to determine novel treatments and management strategies for the specific medical conditions hampering efforts to restore population levels. Additionally, a precision medicine approach to wildlife health has in turn the potential to provide deeper insights into human health and the possibility of stemming and alleviating the impacts of zoonotic diseases. The integration of the currently emerging Precision Medicine Initiative with the concepts of EcoHealth (aiming for sustainable health of people, animals and ecosystems through transdisciplinary action research) and One Health (recognizing the intimate connection of humans, animal and ecosystem health and addressing a wide range of risks at the animal-human-ecosystem interface through a coordinated, collaborative, interdisciplinary approach) has great potential to deliver a deeper and broader interdisciplinary-based understanding of both wildlife and human diseases.

Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.

Mining the fecal proteome: from biomarkers to personalised medicine

INTRODUCTION

Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance. Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current 'Proteomics Toolbox', explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform. Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.

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.

Integrated biomarker profiling of smokers with periodontitis

BACKGROUND

In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups.

AIM

The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers).

MATERIAL & METHODS

The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups.

RESULTS

Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups.

CONCLUSIONS

Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.