Precision Medicine and Global Health: The Good, the Bad, and the Ugly

Precision emergency medicine

BACKGROUND

Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health.

METHODS

In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward.

RESULTS

Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty.

CONCLUSIONS

Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.

Precision and personalized medicine: What their current definition says and silences about the model of health they promote. Implication for the development of personalized health

The US National Human Genome Research Institute defines precision medicine as follows: "Precision medicine (generally considered analogous to personalized medicine or individualized medicine) is an innovative approach that uses information about an individual's genomic, environmental, and lifestyle information to guide decisions related to their medical management. The goal of precision medicine is to provide a more precise approach for the prevention, diagnosis, and treatment of disease." In this perspective article, we question this definition of precision medicine and the risks linked to its current practice and development. We highlight that in practice, precision medicine is based on the use of large volumes of biological data for individual purposes mostly in line with the biomedical model of health, which carries the risk of the biological reductionism of the person. A more comprehensive, precise, and even "personal" approach to health would require taking into account environmental, socio-economic, psychological, and biological determinants, an approach more in line with the biopsychosocial model of health. The role of environmental exposures, in a broad sense, is highlighted more and more, notably in the field of exposome research. Not considering the conceptual framework in which precision medicine is deployed leads to the concealment of the different responsibilities that can be mobilized within the health system. Anchoring precision medicine in a model that does not limit its definition to its biological and technical components makes it possible to envisage a personalized and more precise medicine, integrating a greater share of interventions centered on the skills and life contexts of individuals.

Algorithmic lifestyle optimization

OBJECTIVE

A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual.

MATERIALS AND METHODS

We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group.

RESULTS

Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individual's food intolerances and allergies to 19-56 foods.

DISCUSSION

ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individual's subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints.

CONCLUSION

ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED.

TREM2 R47H variant and risk for Alzheimer's disease: assessment in a Greek population and updated meta-analysis

INTRODUCTION

Rare coding variants in TREM2 and their association with the susceptibility towards Alzheimer's disease (AD) were recently studied in various ethnic groups with contradictory results. The T allele of the rs75932628 (p.R47H variant) has shown a positive risk association with AD in several studies; however, neither a study in Greece nor an updated meta-analysis have been conducted.

OBJECTIVE

To assess the association between TREM2 rs75932628 and late-onset (sporadic) AD in a Greek population, and perform a meta-analysis of current data.

MATERIALS AND METHODS

The rs75932628 was genotyped in a total of 327 patients with AD and 700 cognitively healthy controls. A systematic search and meta-analyses of studies presenting data regarding rs75932628 in AD cases and controls were also performed.

RESULTS

Three patients vs. none of the controls were found to carry the heterozygous risk allele of the rs75932628, yielding a significant association (p = 0.032), in the Greek sample. In the meta-analysis, the overall odds ratio (OR) under a fixed-effects model was 2.98 (Confidence Interval (CI):2.52-3.53) showing a significant association of the rs75932628-T allele with AD in the overall dataset, based on data from 27 studies (26200 AD cases and 142084controls). Caucasian population-only studies (n = 16) revealed a similar OR of 2.93 (CI:2.45-3.51), whereas Asian population-only studies (n = 5) had a non-significant OR of 0.84 (CI:0.19-3.74).

CONCLUSION

The rs75932628 was associated with AD in the Greek sample. Our meta-analysis, covering a total population of over 168,000 people, also showed a significant association of the allele with AD in Caucasian populations.

Apolipoprotein E4 and meningeal lymphatics in Alzheimer disease: a conceptual framework

The potential existence and roles of the meningeal lymphatic system in normal and pathological brain function have been a long-standing enigma. Recent evidence suggests that meningeal lymphatic vessels are present in both the mouse and human brain; in mice, they seem to play a role in clearing toxic amyloid-beta peptides, which have been connected with Alzheimer disease (AD). Here, we review the evidence linking the meningeal lymphatic system with human AD. Novel findings suggest that the recently described meningeal lymphatic vessels could be linked to, and possibly drain, the efferent paravascular glial lymphatic (glymphatic) system carrying cerebrospinal fluid, after solute and immune cell exchange with brain interstitial fluid. In so doing, the glymphatic system could contribute to the export of toxic solutes and immune cells from the brain (an exported fluid we wish to describe as glymph, similarly to lymph) to the meningeal lymphatic system; the latter, by being connected with downstream anatomic regions, carries the glymph to the conventional cervical lymphatic vessels and nodes. Thus, abnormal function in the meningeal lymphatic system could, in theory, lead to the accumulation, in the brain, of amyloid-beta, cellular debris, and inflammatory mediators, as well as immune cells, resulting in damage of the brain parenchyma and, in turn, cognitive and other neurologic dysfunctions. In addition, we provide novel insights into APOE4-the leading genetic risk factor for AD-and its relation to the meningeal lymphatic system. In this regard, we have reanalyzed previously published RNA-Seq data to show that induced pluripotent stem cells (iPSCs) carrying the APOE4 allele (either as APOE4 knock-in or stemming from APOE4 patients) express lower levels of (a) genes associated with lymphatic markers, and (b) genes for which well-characterized missense mutations have been linked to peripheral lymphedema. Taking into account this evidence, we propose a new conceptual framework, according to which APOE4 could play a novel role in the premature shrinkage of meningeal lymphatic vessels (meningeal lymphosclerosis), leading to abnormal meningeal lymphatic functions (meningeal lymphedema), and, in turn, reduction in the clearance of amyloid-beta and other macromolecules and inflammatory mediators, as well as immune cells, from the brain, exacerbation of AD manifestations, and progression of the disease. Altogether, these findings and their potential interpretations may herald novel diagnostic tools and therapeutic approaches in patients with AD.

Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

From warrior genes to translational solutions: novel insights into monoamine oxidases (MAOs) and aggression

The pervasive and frequently devastating nature of aggressive behavior calls for a collective effort to understand its psychosocial and neurobiological underpinnings. Regarding the latter, diverse brain areas, neural networks, neurotransmitters, hormones, and candidate genes have been associated with antisocial and aggressive behavior in humans and animals. This review focuses on the role of monoamine oxidases (MAOs) and the genes coding for them, in the modulation of aggression. During the past 20 years, a substantial number of studies using both pharmacological and genetic approaches have linked the MAO system with aggressive and impulsive behaviors in healthy and clinical populations, including the recent discovery of MAALIN, a long noncoding RNA (lncRNA) regulating the MAO-A gene in the human brain. Here, we first provide an overview of the MAOs and their physiological functions, we then summarize recent key findings linking MAO-related enzymatic and gene activity and aggressive behavior, and, finally, we offer novel insights into the mechanisms underlying this association. Using the existing experimental evidence as a foundation, we discuss the translational implications of these findings in clinical practice and highlight what we believe are outstanding conceptual and methodological questions in the field. Ultimately, we propose that unraveling the specific role of MAO in aggression requires an integrated approach, where this question is pursued by combining psychological, radiological, and genetic/genomic assessments. The translational benefits of such an approach include the discovery of novel biomarkers of aggression and targeting the MAO system to modulate pathological aggression in clinical populations.

Artificial intelligence for good health: a scoping review of the ethics literature

Background

Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective?

Methods

Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed.

Results

Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs).

Conclusions

The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews

BACKGROUND

The etiologies of chronic neurological diseases, which heavily contribute to global disease burden, remain far from elucidated. Despite available umbrella reviews on single contributing factors or diseases, no study has systematically captured non-purely genetic risk and/or protective factors for chronic neurological diseases.

METHODS

We performed a systematic analysis of umbrella reviews (meta-umbrella) published until September 20th, 2018, using broad search terms in MEDLINE, SCOPUS, Web of Science, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature, ProQuest Dissertations & Theses, JBI Database of Systematic Reviews and Implementation Reports, DARE, and PROSPERO. The PRISMA guidelines were followed for this study. Reference lists of the identified umbrella reviews were also screened, and the methodological details were assessed using the AMSTAR tool. For each non-purely genetic factor association, random effects summary effect size, 95% confidence and prediction intervals, and significance and heterogeneity levels facilitated the assessment of the credibility of the epidemiological evidence identified.

RESULTS

We identified 2797 potentially relevant reviews, and 14 umbrella reviews (203 unique meta-analyses) were eligible. The median number of primary studies per meta-analysis was 7 (interquartile range (IQR) 7) and that of participants was 8873 (IQR 36,394). The search yielded 115 distinctly named non-genetic risk and protective factors with a significant association, with various strengths of evidence. Mediterranean diet was associated with lower risk of dementia, Alzheimer disease (AD), cognitive impairment, stroke, and neurodegenerative diseases in general. In Parkinson disease (PD) and AD/dementia, coffee consumption, and physical activity were protective factors. Low serum uric acid levels were associated with increased risk of PD. Smoking was associated with elevated risk of multiple sclerosis and dementia but lower risk of PD, while hypertension was associated with lower risk of PD but higher risk of dementia. Chronic occupational exposure to lead was associated with higher risk of amyotrophic lateral sclerosis. Late-life depression was associated with higher risk of AD and any form of dementia.

CONCLUSIONS

We identified several non-genetic risk and protective factors for various neurological diseases relevant to preventive clinical neurology, health policy, and lifestyle counseling. Our findings could offer new perspectives in secondary research (meta-research).

Machine learning for precision medicine

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine's multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine's "big data", in the context of genetics, genomics, and beyond.

CYP1A2 rs762551 polymorphism and risk for amyotrophic lateral sclerosis

BACKGROUND

Genetic variability is considered to confer susceptibility to amyotrophic lateral sclerosis (ALS). Oxidative stress is a significant contributor to ALS-related neurodegeneration, and it is regulated by cytochromes P450 (CYPs), such as CYP1A2; these are responsible for the oxidative metabolism of both exogenous and endogenous substrates in the brain, subsequently impacting ALS. The function of CYP1A2 is largely affected by genetic variability; however, the impact of CYP1A2 polymorphisms in ALS remains underinvestigated.

OBJECTIVE

This study aims to examine the possible association of ALS with the CYP1A2 rs762551 polymorphism, which codes for the high inducibility form of the enzyme.

METHODS

One hundred and fifty-five patients with sporadic ALS and 155 healthy controls were genotyped for the CYP1A2 rs762551. Statistical testing for the association of CYP1A2 rs762551 with risk for ALS was performed using SNPstats.

RESULTS

The CYP1A2 rs762551 C allele was associated with a decreased risk of ALS development. In the subgroup analysis according to the ALS site of onset, an association between CYP1A2 rs762551 and limb and bulbar onset of ALS was shown. Cox proportional-hazard regression analyses revealed a significant effect of the CYP1A2 rs762551 on the age of onset of ALS.

CONCLUSIONS

Based on our results, a primarily potential link between the CYP1A2 rs762551 polymorphism and ALS risk could exist.

COVID-19 Immunopathology and the Central Nervous System: Implication for Multiple Sclerosis and Other Autoimmune Diseases with Associated Demyelination

In the frame of the coronavirus disease 2019 (COVID-19) pandemic, recent reports on SARS-CoV-2 potential neuroinvasion placed neurologists on increased alertness in order to assess early neurological manifestations and their potentially prognostic value for the COVID-19 disease. Moreover, the management of chronic neurological diseases, such as Multiple Sclerosis (MS), underwent guided modifications, such as an Extended Interval Dose (EID) of Disease-Modifying Treatment (DMT) administration, in order to minimize patients' exposure to the health system, thus reducing the risk of SARS-CoV-2 infection. In this review, we summarize existing evidence of key immune pathways that the SARS-CoV-2 modifies during COVID-19 and the relevant implication for MS and other autoimmune diseases with associated demyelination (such as Systemic lupus erythematosus and Antiphospholipid syndrome), including the context of potential neuroinvasion by SARS-Cov-2 and the alterations that DMT induces to the immune system. Moreover we hereby aim to provide an overview of the possible consequences that COVID-19 may carry for the Central Nervous System (CNS) in People with MS (PwMS) and other demyelinating diseases, which are likely to pose challenges for treating Neurologists with respect to the long-term disease management of these diseases.

PIWI family proteins as prognostic markers in cancer: a systematic review and meta-analysis

BACKGROUND

P-element-induced-wimpy-testis-(PIWI)-like proteins are implicated in germ cells' regulation and detected in numerous cancer types. In this meta-analysis, we aimed to associate, for the first time, the prognosis in cancer patients with intratumoral expression of PIWI family proteins.

METHODS

PubMed, Embase, and Web of Knowledge databases were searched, and studies investigating the association between intratumoral mRNA or protein expression of different PIWI family proteins and survival, metastasis, or recurrence of various cancer types were reviewed. Study qualities were assessed using the REMARK criteria. Studies' heterogeneity was evaluated using I2 index and Cochran Q test. Publication bias was assessed by funnel plots and Egger's regression. Pooled hazard ratios (HR) with 95% confidence intervals (95% CIs) were calculated for different PIWI family proteins separately. Specifically, log of calculated HR was pooled using random-effects model.

RESULTS

Twenty-six studies (4299 participants) were included. The pooled HR of mortality in high versus low expression of PIWIL1, PIWIL2, and PIWIL4 was 1.87 (95% CI: 1.31-2.66, p < 0.05), 1.09 (95% CI: 0.58-2.07, p = 0.79), and 0.44 (95% CI: 0.25-0.76, p < 0.05), respectively. The pooled HR of recurrence in high versus low expression of PIWIL1 and PIWIL2 was 1.72 (95% CI: 1.20-2.49, p < 0.05) and 1.98 (95% CI: 0.65-5.98, p = 0.23), respectively.

CONCLUSIONS

Highly variable results were observed for different cancer types. Higher PIWIL1 and lower piwil4 and PIWIL4 expression levels could potentially indicate worse prognosis in cancer. These proteins' expressions could be used for personalized prognosis and treatment in the future.

Microbiome in Multiple Sclerosis; Where Are We, What We Know and Do Not Know

An increase of multiple sclerosis (MS) incidence has been reported during the last decade, and this may be connected to environmental factors. This review article aims to encapsulate the current advances targeting the study of the gut-brain axis, which mediates the communication between the central nervous system and the gut microbiome. Clinical data arising from many research studies, which have assessed the effects of administered disease-modifying treatments in MS patients to the gut microbiome, are also recapitulated.

Helicobacter pylori infection and gastric cancer biology: tempering a double-edged sword

Helicobacter pylori (H. pylori) infection affects an estimated 4.4 billion people globally. Moreover, H. pylori presents the most significant risk factor for gastric cancer and low-grade mucosa-associated lymphoid tissue (MALT) lymphoma, and it is the first example of bacterial infection linked to carcinogenesis. Here, we contend that H. pylori research, which focuses on a cancer-causing pathogen resident in a relatively accessible organ, the stomach, could constitute an exemplar for microbial-related carcinogenesis in less tractable organs, such as the pancreas and lung. In this context, molecular biological approaches that could reap rewards are reviewed, including: (1) gastric cancer dynamics, particularly the role of stem cells and the heterogeneity of neoplastic cells, which are currently being investigated at the single-cell sequencing level; (2) mechanobiology, and the role of three-dimensional organoids and matrix metalloproteases; and (3) the connection between H. pylori and host pathophysiology and the gut microbiome. In the context of H. pylori's contribution to gastric cancer, several important conundrums remain to be fully elucidated. From among them, this article discusses (1) why H. pylori infection, which causes both gastric and duodenal inflammation, is only linked to gastric cancer; (2) whether a "precision oncomicrobiology" approach could enable a fine-tuning of the expression of only cancer-implicated H. pylori genes while maintaining beneficial H. pylori-mediated factors in extra-gastric tissues; and (3) the feasibility of using antibiotics targeting the microbial DNA damage system, which shares commonalities with mechanisms for human cell replication, as chemopreventives. Additional therapeutic perspectives are also discussed.

Needle Biopsy Adequacy in the Era of Precision Medicine and Value-Based Health Care

CONTEXT.—

Needle biopsy of diseased tissue is an essential diagnostic tool that is becoming even more important as precision medicine develops. However, the capability of this modality to efficiently provide samples adequate for diagnostic and prognostic analysis remains quite limited relative to current diagnostic needs. For physicians and patients, inadequate biopsy frequently leads to diagnostic delay, procedure duplication, or insufficient information about tumor biology leading to delay in treatment; for health systems, this results in substantial incremental costs and inefficient use of scarce specialized diagnostic resources.

OBJECTIVE.—

To review current needle biopsy technology, devices, and practice with a perspective to identify current limitations and opportunities for improvement in the context of advancing precision medicine.

DATA SOURCES.—

PubMed searches of fine-needle aspiration and core needle biopsy devices and similar technologies were made generally, by tissue site, and by adequacy as well as by health economics of these technologies.

CONCLUSIONS.—

Needle biopsy adequacy can be improved by recognizing the importance of this diagnostic tool by promoting common criteria for needle biopsy adequacy; by optimizing needle biopsy procedural technique, technologies, clinical practice, professional education, and quality assurance; and by bundling biopsy procedure costs with downstream diagnostic modalities to provide better accountability and incentives to improve the diagnostic process.

Strategic Opportunities for Leveraging Low-cost, High-impact Technological Innovations to Promote Cardiovascular Health in India

Accelerated epidemiological transition in India over the last 40 years has resulted in a dramatic increase in the burden of cardiovascular diseases and the related risk factors of diabetes and hypertension. This increase in disease burden has been accompanied by pervasive health disparities associated with low disease detection rates, inadequate awareness, poor use of evidence-based interventions, and low adherence rates among patients in rural regions in India and those with low socioeconomic status. Several research groups in India have developed innovative technologies and care-delivery models for screening, diagnosis, clinical management, remote-monitoring, self-management, and rehabilitation for a range of chronic conditions. These innovations can leverage advances in sensor technology, genomic tools, artificial intelligence, big-data analytics, and so on, for improving access to and delivering quality and affordable personalized medicine in primary care. In addition, several health technology start-ups are entering this booming market that is set to grow rapidly. Innovations outside biomedical space (eg, protection of traditional wisdom in diet, lifestyle, yoga) are equally important and are part of a comprehensive solution. Such low-cost, culturally tailored, robust innovations to promote health and reduce disparities require partnership among multi-sectors including academia, industry, civil society, and health systems operating in a conducive policy environment that fosters adequate public and private investments. In this article, we present the unique opportunity for India to use culturally tailored, low-cost, high-impact technological innovations and strategies to ameliorate the perennial challenges of social, policy, and environmental challenges including poverty, low educational attainment, culture, and other socioeconomic factors to promote cardiovascular health and advance health equity.

Biomarkers for bipolar disorder: current status and challenges ahead

INTRODUCTION

Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.

Big Data and the Study of Social Inequalities in Health: Expectations and Issues

Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: (i) the perimeter and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health. To face these issues, it seems essential to integrate different approaches into a close dialog, integrating methodological, societal, and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.