Epidemiology in wonderland: Big Data and precision medicine

Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort

BACKGROUND

Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS

All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION

The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION

Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Addressing persistent evidence gaps in cardiovascular sex differences research - the potential of clinical care data

Women have historically been underrepresented in cardiovascular clinical trials, resulting in a lack of sex-specific data. This is especially problematic in two situations, namely those where diseases manifest differently in women and men and those where biological differences between the sexes might affect the efficacy and/or safety of medication. There is therefore a pressing need for datasets with proper representation of women to address questions related to these situations. Clinical care data could fit this bill nicely because of their unique broad scope across both patient groups and clinical measures. This perspective piece presents the potential of clinical care data in sex differences research and discusses current challenges clinical care data-based research faces. It also suggests strategies to reduce the effect of these limitations, and explores whether clinical care data alone will be sufficient to close evidence gaps or whether a more comprehensive approach is needed.

Plastic diagnostics: The remaking of disease and evidence in personalized medicine

Politically authorized reports on personalized and precision medicine stress an urgent need for finer-grained disease categories and faster taxonomic revision, through integration of genomic and phenotypic data. Developing a data-driven taxonomy is, however, not as simple as it sounds. It is often assumed that an integrated data infrastructure is relatively easy to implement in countries that already have highly centralized and digitalized health care systems. Our analysis of initiatives associated with the Danish National Genome Center, recently launched to bring Denmark to the forefront of personalized medicine, tells a different story. Through a "meta-taxonomy" of taxonomic revisions, we discuss what a genomics-based disease taxonomy entails, epistemically as well as organizationally. Whereas policy reports promote a vision of seamless data integration and standardization, we highlight how the envisioned strategy imposes significant changes on the organization of health care systems. Our analysis shows how persistent tensions in medicine between variation and standardization, and between change and continuity, remain obstacles for the production as well as the evaluation of genomics-based taxonomies of difference. We identify inherent conflicts between the ideal of dynamic revision and existing regulatory functions of disease categories in, for example, the organization and management of health care systems. Moreover, we raise concerns about shifts in the regulatory regime of evidence standards, where clinical care increasingly becomes a vehicle for biomedical research.

Vagueness in Medicine: On Disciplinary Indistinctness, Fuzzy Phenomena, Vague Concepts, Uncertain Knowledge, and Fact-Value-Interaction

This article investigates five kinds of vagueness in medicine: disciplinary, ontological, conceptual, epistemic, and vagueness with respect to descriptive-prescriptive connections. First, medicine is a discipline with unclear borders, as it builds on a wide range of other disciplines and subjects. Second, medicine deals with many indistinct phenomena resulting in borderline cases. Third, medicine uses a variety of vague concepts, making it unclear which situations, conditions, and processes that fall under them. Fourth, medicine is based on and produces uncertain knowledge and evidence. Fifth, vagueness emerges in medicine as a result of a wide range of fact-value-interactions. The various kinds of vagueness in medicine can explain many of the basic challenges of modern medicine, such as overdiagnosis, underdiagnosis, and medicalization. Even more, it illustrates how complex and challenging the field of medicine is, but also how important contributions from the philosophy can be for the practice of medicine. By clarifying and, where possible, reducing or limiting vagueness, philosophy can help improving care. Reducing the various types of vagueness can improve clinical decision-making, informing individuals, and health policy making.

Counterpoint: Epidemiology's Dual Social Commitment-Science and Health

Matching epidemiology's aspirations to actual delivery of goods valuable for population health depends both on the scientific and operational capabilities of epidemiology and on the degree to which the goods meet its contract with society. Epidemiology's capabilities have advanced remarkably in recent decades, although research gaps have appeared during the current coronavirus disease 2019 (COVID-19) pandemic. Epidemiology's social contract reflecting a dual commitment to science and health could arguably be entirely met by producing research results under conditions variously described as objective, impartial, neutral, or independent and handing such results to decision makers and the public at large. However, a closer examination shows that those four terms address sharply distinct issues, with distinct practical implications, and that the epidemiologist responsibility is de facto involved beyond providing research results. Hence the epidemiologist's engagement should encompass arguing from a science-for-health viewpoint and proactively driving the results into decision processes on public health issues.

Trailblazing precision medicine in Europe: A joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany

Over the last decades, rapid technological and scientific advances have led to a merge of molecular sciences and clinical medicine, resulting in a better understanding of disease mechanisms and the development of novel therapies that exploit specific molecular lesions or profiles driving disease. Precision oncology is here used as an example, illustrating the potential of precision/personalized medicine that also holds great promise in other medical fields. Real-world implementation can only be achieved by dedicated healthcare connected centers which amass and build up interdisciplinary expertise reflecting the complexity of precision medicine. Networks of such centers are ideally suited for a nation-wide outreach offering access to precision medicine to patients independent of their place of residence. Two of these multicentric initiatives, Genomic Medicine Sweden (GMS) and the Centers for Personalized Medicine (ZPM) initiative in Germany have teamed up to present and share their views on core concepts, potentials, challenges, and future developments in precision medicine. Together with other initiatives worldwide, GMS and ZPM aim at providing a robust and sustainable framework, covering all components from technology development to clinical trials, ethical and legal aspects as well as involvement of all relevant stakeholders, including patients and policymakers in the field.

Child maltreatment by non-accidental burns: interest of an algorithm of detection based on hospital discharge database

OBJECTIVES

To build a detection algorithm of non-accidental pediatric burns (NAB) using hospital resumes from the French Hospital Discharge Database (HDD) and to describe cases with no judicial or administrative report.

MATERIALS AND METHODS

Children aged 0-16 years old hospitalized at the University Hospital of Tours from 2012 to 2017 with a coded burn were included. "Probable" or "possible" HDD cases of NAB were defined based on the International Classification of Diseases 10th version codes during the inclusion stay or the previous year. A chart review was performed on all the HDD cases and HDD non cases matched on sex and age with a 1:2 ratio. Performance parameters were estimated for three clinical definitions of child maltreatment: excluding neglect, including neglect in a restrictive definition, and in a broad definition. For clinical cases, report to the judicial or administrative authorities was searched.

RESULTS

Among the 253 included children, 83 "probable" cases and 153 non-cases were analyzed. Sensitivity varied from 48 (_95%CI [36-60], excluding neglect) to 90% [55-100] and specificity from 70 [63;77] to 68% [61;74]. The proportion of clinical cases with no report without justification varied from 0 (excluding neglect) to > 85% (with the broadest definition); all corresponded to possible isolated neglect.

CONCLUSION

The performances of the algorithm varied tremendously according to the clinical definition of child maltreatment. Neglect is obviously complex and tough to clinically detect. Training for healthcare professionals and qualitative studies on obstacles to report should be added to this work.

Genetic Approaches for Sports Performance: How Far Away Are We?

Humans vary in their ‘natural ability’ related to sports performance. One facet of natural ability reflects so-called intrinsic ability or the ability to do well with minimal training. A second facet of natural ability is how rapidly an individual adapts to training; this is termed trainability. A third facet is the upper limit achievable after years of prolonged intense training; this represents both intrinsic ability and also trainability. There are other features of natural ability to consider, for example body size, because some events, sports, or positions favor participants of different sizes. In this context, the physiological determinants of elite endurance performance, especially running and cycling, are well known and can be used as a template to discuss these general issues. The key determinants of endurance performance include maximal oxygen uptake \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\dot{V}{\text{O}}_{2\hbox{max} } )$$\end{document}(V˙O2max), the lactate threshold, and running economy (efficiency in the case of cycling or other sports). In this article, I use these physiological determinants to explore what is known about the genetics of endurance performance. My main conclusion is that at this time there are very few, if any, obvious relationships between these key physiological determinants of performance and DNA sequence variation. Several potential reasons for this lack of relationship will be discussed.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Precise hepatectomy in the intelligent digital era

In the past 20 years, the concept of surgery has undergone profound changes. Surgical practice has shifted from emphasizing the complete elimination of lesions to achieving optimal rehabilitation in patients. Collaborative optimization of surgery consists of three core elements, removal of lesions, organ protection and injury close monitoring, and controlled surgical intervention. As a result, the traditional surgical paradigm has quietly transformed into a modern precision surgical paradigm. In this review, we summarized the latest breakthroughs and applications of precision medicine in liver surgery. In addition, we also outlined the progresses that have been made in precision liver surgery, the opportunities and challenges that may encountered in the future.

Fifteen years of epidemiology in BMC Medicine

BMC Medicine was launched in November 2003 as an open access, open peer-reviewed general medical journal that has a broad remit to publish "outstanding and influential research in all areas of clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities". Here, I discuss the last 15 years of epidemiological research published by BMC Medicine, with a specific focus on how this reflects changes occurring in the field of epidemiology over this period; the impact of 'Big Data'; the reinvigoration of debates about causality; and, as we increasingly work across and with many diverse disciplines, the use of the name 'population health science'. Reviewing all publications from the first volume to the end of 2018, I show that most BMC Medicine papers are epidemiological in nature, and the majority of them are applied epidemiology, with few methodological papers. Good research must address important translational questions that should not be driven by the increasing availability of data, but should take appropriate advantage of it. Over the next 15 years it would be good to see more publications that integrate results from several different methods, each with different sources of bias, in a triangulation framework.

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

Time to care: why the humanities and the social sciences belong in the science of health

Health is more than the absence of disease. It is also more than a biological phenomenon. It is inherently social, psychological, cultural and historical. While this has been recognised by major health actors for decades, open questions remain as to how to build systems that reflect the complexity of health, disease and sickness, and in a context that is increasingly technologised. We argue that an urgent change of approach is necessary. Methods and concepts from the humanities and social science must be embedded in the concepts and methods of the health sciences if we are to promote sustainable interventions capable of engaging with the recognised complexity of health, disease and sickness. Our vision is one of radical interdisciplinarity, integrating aspects of biological, psychological, social and humanities approaches across areas of urgent health need. Radical interdisciplinarity, we argue, entails the practical, methodological and conceptual integration of these approaches to health.

Epidemiologists of the Future: Data Collectors or Scientists?

Epidemiology is the study of epidemics. It is a biological science that includes expertise in many disciplines in social and behavioral sciences. Epidemiology is also a key component of preventive medicine and public health. Unfortunately, over recent years, academic epidemiology has lost its relationship with preventive medicine, as well as much of its focus on epidemics. The new "-omics" technologies to measure risk factors and phenotypes, and advances in genomics (e.g., host susceptibility) consistent with good epidemiology methods will likely enhance epidemiology research. There is a need based on these new technologies to modify training, especially for the first-level doctorate epidemiologist.

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.

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.

Digital diabetes: Perspectives for diabetes prevention, management and research

Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies - which the authors suggest be grouped under the term 'digitosome' - constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes - its prevention, management, technology and research - and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes.