Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Personal career decisions during medical training are not complicated, they are complex

BACKGROUND

For medical training to be deemed successful, in addition to gaining the skills required to make appropriate clinical decisions, trainees must learn how to make good personal decisions. These decisions may affect satisfaction with career choice, work-life balance, and their ability to maintain/improve clinical performance over time-outcomes that can impact future wellness. Here, the authors introduce a decision-making framework with the goal of improving our understanding of personal decisions.

METHODS

Stemming from the business world, the Cynefin framework describes five decision-making domains: clear, complicated, complex, chaotic, and confusion, and a key inference of this framework is that decision-making can be improved by first identifying the decision-making domain. Personal decisions are largely complex-so applying linear decision-making strategies is unlikely to help in this domain.

RESULTS

The available data suggest that the outcomes of personal decisions are suboptimal, and the authors propose three mechanisms to explain these findings: (1) Complex decision is susceptible to attribute substitution where we subconsciously trade these decisions for easier decisions; (2) predictions are prone to cognitive biases, such as assuming our situation will remain constant (linear projection fallacy), believing that accomplishing a goal will deliver lasting happiness (arrival bias), or overestimating benefits and underestimating costs of future tasks (planning fallacy); and (3) complex decisions have an inherently higher failure rate than complicated decisions because they are the result of an ongoing, dynamic person-by-situation interaction and, as such, have more time to fail and more ways to do so.

DISCUSSION

Based upon their view that personal decisions are complex, the authors propose strategies to improve satisfaction with personal decisions, including increasing awareness of biases that may impact personal decisions. Recognising that the outcome of personal decisions can change over time, they also suggest additional interventions to manage these decisions, such as different forms of mentoring.

Diagnostic staging and stratification in psychiatry and oncology: clarifying their conceptual, epistemological and ethical implications

Staging and stratification are two diagnostic approaches that have introduced a more dynamic outlook on the development of diseases, thus participating in blurring the line between the normal and the pathological. First, diagnostic staging, aiming to capture how diseases evolve in time and/or space through identifiable and gradually more severe stages, may be said to lean on an underlying assumption of "temporal determinism". Stratification, on the other hand, allows for the identification of various prognostic or predictive subgroups based on specific markers, relying on a more "mechanistic" or "statistical" form of determinism. There are two medical fields in which these developments have played a significant role and have given rise to sometimes profound nosological transformations: oncology and psychiatry. Drawing on examples from these two fields, this paper aims to provide much needed conceptual clarifications on both staging and stratification in order to outline how several epistemological and ethical issues may, in turn, arise. We argue that diagnostic staging ought to be detached from the assumption of temporal determinism, though it should still play an essential role in adapting interventions to stage. In doing so, it would help counterbalance stratification's own epistemological and ethical shortcomings. In this sense, the reflections and propositions developed in psychiatry can offer invaluable insights regarding how adopting a more transdiagnostic and cross-cutting perspective on temporality and disease dynamics may help combine both staging and stratification in clinical practice.

Lung-function trajectories: relevance and implementation in clinical practice

Lung development starts in utero and continues during childhood through to adolescence, reaching its peak in early adulthood. This growth is followed by gradual decline due to physiological lung ageing. Lung-function development can be altered by several host and environmental factors during the life course. As a result, a range of lung-function trajectories exist in the population. Below average trajectories are associated with respiratory, cardiovascular, metabolic, and mental health comorbidities, as well as with premature death. This Review presents progressive research into lung-function trajectories and assists the implementation of this knowledge in clinical practice as an innovative approach to detect poor lung health early, monitor respiratory disease progression, and promote lung health. Specifically, we propose that, similar to paediatric height and weight charts used globally to monitor children's growth, lung-function charts could be used for both children and adults to monitor lung health status across the life course. To achieve this proposal, we introduce our free online Lung Function Tracker tool. Finally, we discuss challenges and opportunities for effective implementation of the trajectory concept at population level and outline an agenda for crucial research needed to support such implementation.

Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores

Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Implementation of precision medicine in healthcare-A European perspective

The technical development of high-throughput sequencing technologies and the parallel development of targeted therapies in the last decade have enabled a transition from traditional medicine to personalized treatment and care. In this way, by using comprehensive genomic testing, more effective treatments with fewer side effects are provided to each patient-that is, precision or personalized medicine (PM). In several European countries-such as in England, France, Denmark, and Spain-the governments have adopted national strategies and taken "top-down" decisions to invest in national infrastructure for PM. In other countries-such as Sweden, Germany, and Italy with regionally organized healthcare systems-the profession has instead taken "bottom-up" initiatives to build competence networks and infrastructure to enable equal access to PM. In this review, we summarize key learnings at the European level on the implementation process to establish sustainable governance and organization for PM at the regional, national, and EU/international levels. We also discuss critical ethical and legal aspects of implementing PM, and the importance of access to real-world data and performing clinical trials for evidence generation, as well as the need for improved reimbursement models, increased cross-disciplinary education and patient involvement. In summary, PM represents a paradigm shift, and modernization of healthcare and all relevant stakeholders-that is, healthcare, academia, policymakers, industry, and patients-must be involved in this system transformation to create a sustainable, non-siloed ecosystem for precision healthcare that benefits our patients and society at large.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

Polymorphisms of the GCLC Gene Are Novel Genetic Markers for Susceptibility to Psoriasis Associated with Alcohol Abuse and Cigarette Smoking

The aim of this pilot study was to investigate whether single nucleotide polymorphisms (SNP) in the gene encoding the catalytic subunit of glutamate cysteine ligase (GCLC) are associated with the risk and clinical features of psoriasis. A total of 944 unrelated individuals, including 474 patients with a diagnosis of psoriasis and 470 healthy controls, were recruited for the study. Six common SNPs in the GCLC gene were genotyped using the MassArray-4 system. Polymorphisms rs648595 (OR = 0.56, 95% CI 0.35-0.90; P_perm = 0.017) and rs2397147 (OR = 0.54, 95% CI 0.30-0.98; P_perm = 0.05) were associated with susceptibility to psoriasis in males. In the male group, diplotype rs2397147-C/C × rs17883901-G/G was associated with a decreased risk of psoriasis (FDR-adjusted p = 0.014), whereas diplotype rs6933870-G/G × rs17883901-G/G (FDR-adjusted p = 0.045) showed an association with an increased disease risk in females. The joint effects of SNPs with tobacco smoking (rs648595 and rs17883901) and alcohol abuse (rs648595 and rs542914) on psoriasis risk were observed (P_perm ≤ 0.05). We also found multiple sex-independent associations between GCLC gene polymorphisms and various clinical features such as earlier disease onset, the psoriatic triad, and specific localizations of skin lesions. The present study is the first to show that polymorphisms of the GCLC gene are significantly associated with the risk of psoriasis and related to its clinical features.