Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

Genetically influenced externalizing and internalizing risk pathways as novel prevention targets

Objective: Risky substance use among college students is widespread, and associated with numerous adverse consequences. Current interventions focus primarily on students' current substance use; we hypothesize that shifting focus from current use to underlying risk factors is a complementary approach that may improve effectiveness of prevention/intervention programming. This approach aligns with the personalized medicine movement, which aims to harness knowledge about underlying etiological factors to provide individuals with specific information about their unique risk profiles and personalized recommendations, to motivate and enable individuals to better self-regulate their health. Method: Our group is building and evaluating an online Personalized Feedback Program (PFP) for college students that provides feedback about the individual's underlying genetically influenced externalizing and internalizing risk factors for substance use, along with personalized recommendations/resources. The project capitalizes on work from a university-wide research project (Spit for Science; S4S), in which > 12,000 students (˜70% of 5 years of incoming freshmen) are being followed longitudinally to assess substance use and related factors across the college years. In this article, we describe our foundational work to develop the PFP. Results: From the S4S data, we have identified risk factors across four domains (Sensation Seeking, Impulsivity, Extraversion, and Neuroticism) that are correlated with college students' substance use. We developed an online self-guided PFP, in collaboration with professionals from student affairs, and using feedback from students, with the ultimate goal of conducting a randomized clinical trial. Conclusion: The provision of personalized risk information represents a novel approach to complement and extend existing college substance use programming. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care

There is growing interest in the role of predictive analytics in sport, where such extensive data collection provides an exciting opportunity for the development and utilisation of prediction models for medical and performance purposes. Clinical prediction models have traditionally been developed using regression-based approaches, although newer machine learning methods are becoming increasingly popular. Machine learning models are considered 'black box'. In parallel with the increase in machine learning, there is also an emergence of proprietary prediction models that have been developed by researchers with the aim of becoming commercially available. Consequently, because of the profitable nature of proprietary systems, developers are often reluctant to transparently report (or make freely available) the development and validation of their prediction algorithms; the term 'black box' also applies to these systems. The lack of transparency and unavailability of algorithms to allow implementation by others of 'black box' approaches is concerning as it prevents independent evaluation of model performance, interpretability, utility, and generalisability prior to implementation within a sports medicine and performance environment. Therefore, in this Current Opinion article, we: (1) critically examine the use of black box prediction methodology and discuss its limited applicability in sport, and (2) argue that black box methods may pose a threat to delivery and development of effective athlete care and, instead, highlight why transparency and collaboration in prediction research and product development are essential to improve the integration of prediction models into sports medicine and performance.

The Emotional Dimension of Value: A Proposal for Its Quantitative Measurement

The first goal of this paper is to develop a theoretical and practical framework which can help to measure the emotional value generated by organizations in quantitative terms. Its second goal is to use data obtained from the UCAN (Union of Food and Agriculture Cooperatives of Navarre) in Spain as a case study to illustrate the quantification of the emotional value generated, with a view to factoring that value into a social accounting system. Ever greater recognition of the social role of organizations in recent years has led to a need for a consistent definition of the concept of socio-emotional value, and for instruments that can be used to measure that value in terms of generic social accounting. Taking the current lack of standardization in such models and instruments, especially those that deal with emotional value as its starting point, the paper proposes a new instrument for measuring that value quantitatively in such a way as to overcome some of the limitations of earlier proposals. The underlying perspective is that the monetary values identified in market and non-market transactions do not accurately account for all the value generated for different stakeholders, and that adjustments are required through a correction factor applied to the value variables identified. The quantification of the socio-emotional value generated by an organization is seen as a more comprehensive indicator of its performance, given that it provides more information and takes into account the value generated for stakeholders as a whole in all dimensions.

Why do people seek out polygenic risk scores for complex disorders, and how do they understand and react to results?

We sought to explore individuals' motivations for using their direct-to-consumer genetic testing data to generate polygenic risk scores (PRSs) using a not-for-profit third-party tool, and to assess understanding of, and reaction to their results. Using a cross-sectional design, users of Impute.me who had already accessed PRS results were invited to complete an online questionnaire asking about demographics, motivations for seeking PRSs, understanding and interpretation of PRSs, and two validated scales regarding reactions to results-the Impact of Event Scale Revised (IES-R) and the Feelings About genomiC Testing Results (FACToR). Independent samples T-tests and ANOVA were used to explore associations between the variables. 227 individuals participated in the study. The most frequently reported motivation was general curiosity (98.2%). Only 25.6% of participants correctly answered all questions assessing understanding/interpretation of PRSs. Over half of participants (60.8%) experienced a negative reaction (upset, anxious, and/or sad on FACToR scale) after receiving their PRSs and 5.3% scored over the threshold for potential post-traumatic stress disorder on the IES-R. Lower understanding about PRS was associated with experiencing a negative psychological reaction (P values <0.001). Higher quality pre-test information, particularly to improve understanding, and manage expectations for PRS may be useful in limiting negative psychological reactions.

Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores

To date, interpretation of genomic information has focused on single variants conferring disease risk, but most disorders of major public concern have a polygenic architecture. Polygenic risk scores (PRSs) give a single measure of disease liability by summarizing disease risk across hundreds of thousands of genetic variants. They can be calculated in any genome-wide genotype data-source, using a prediction model based on genome-wide summary statistics from external studies. As genome-wide association studies increase in power, the predictive ability for disease risk will also increase. Although PRSs are unlikely ever to be fully diagnostic, they may give valuable medical information for risk stratification, prognosis, or treatment response prediction. Public engagement is therefore becoming important on the potential use and acceptability of PRSs. However, the current public perception of genetics is that it provides "yes/no" answers about the presence/absence of a condition, or the potential for developing a condition, which in not the case for common, complex disorders with polygenic architecture. Meanwhile, unregulated third-party applications are being developed to satisfy consumer demand for information on the impact of lower-risk variants on common diseases that are highly polygenic. Often, applications report results from single-nucleotide polymorphisms (SNPs) and disregard effect size, which is highly inappropriate for common, complex disorders where everybody carries risk variants. Tools are therefore needed to communicate our understanding of genetic vulnerability as a continuous trait, where a genetic liability confers risk for disease. Impute.me is one such tool, whose focus is on education and information on common, complex disorders with polygenetic architecture. Its research-focused open-source website allows users to upload consumer genetics data to obtain PRSs, with results reported on a population-level normal distribution. Diseases can only be browsed by International Classification of Diseases, 10th Revision (ICD-10) chapter-location or alphabetically, thus prompting the user to consider genetic risk scores in a medical context of relevance to the individual. Here, we present an overview of the implementation of the impute.me site, along with analysis of typical usage patterns, which may advance public perception of genomic risk and precision medicine.

SEPSIS. Educational and Best Practice Frontiers. Beyond the Boundaries of Fatality, Enhancing Clinical Skills and Precision Medicine

Dissemination and exploitation of knowledge regarding affordable clinical skills and innovative precision medicine, two current topics in active development in medicine, may contribute to improve also sepsis management. Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection. Sepsis is strongly related to all body organs or to systemic diseases and to the quality of the best-practice in use, which is particularly critical in surgical or intervention techniques. Trauma, surgical and mini-invasive procedures, vascular or endoscopic interventions, otolaryngology, obstetrics-gynecological and urological procedures, malnutrition, dental, skin, chronic liver, kidney and respiratory disease are frequently involved. Accordingly, apart from the clinical risk analysis and management of the process of care, the actual factors that may be easily neglected are the techniques used, the personal skills of the health professionals and the quality of the equipment. The quest for biomarkers consistent with the unmet needs of medical doctors and of their patient and the efforts for overcoming bacterial antibiotic resistances are currently the main foci of medical research. In addition, in this regard, research and innovation would benefit from greater knowledge, skills and use of bioinformatics and omics. The caveats related to in-silico approaches must be flagged: algorithms may equally warrant scientific innovations or hide the lack of them; a patient is more than a set of covariates. Epidemiology and prevention includes all the actions suitable for achieving an adequate hygiene and immunization of populations and for safer hospital policies and procedures during Patients’ stays. In any subset, the most unresolved critical point in sepsis is a timely diagnosis. This is impaired by low degrees of suspicion for the possibility of emerging sepsis, by the shortage of use of the simplest microbiological testing but, equally or more, by the insufficient diffusion of non-invasive imaging skills suitable to detect and monitor the emerging sites and sources of infection. In primary care, in emergency facilities, in hospital wards and in intensive care units, inclusion of appropriate knowledge, skills, expertise and imaging equipment must be extended as much as possible. The low cost of UltraSound machines and of increasing bioinformatics literacy by e-learning, makes such investments affordable even in limited-resources contexts. Frontier educational and best practice intervention enhancing affordable clinical skills and innovative precision medicine may lead beyond the boundaries of fatal outcomes in sepsis.

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