A population approach to precision medicine

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

Malmö Breast ImaginG database: objectives and development

Purpose

We describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles.

Approach

The M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries.

Results

To date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM.

Conclusions

We describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.

Advanced molecular approaches in male infertility diagnosis

In the recent years a special attention has been given to a major health concern namely to male infertility, defined as the inability to conceive after 12 months of regular unprotected sexual intercourse, taken into account the statistics that highlight that sperm counts have dropped by 50-60% in recent decades. According to the WHO, infertility affects approximately 9% of couples globally, and the male factor is believed to be present in roughly 50% of cases, with exclusive responsibility in 30%. The aim of this manuscript is to present an evidence-based approach for diagnosing male infertility that includes finding new solutions for diagnosis and critical outcomes, retrieving up-to-date studies and existing guidelines. The diverse factors that induce male infertility generated in a vast amount of data that needed to be analysed by a clinician before a decision could be made for each individual. Modern medicine faces numerous obstacles as a result of the massive amount of data generated by the molecular biology discipline. To address complex clinical problems, vast data must be collected, analysed, and used, which can be very challenging. The use of artificial intelligence (AI) methods to create a decision support system can help predict the diagnosis and guide treatment for infertile men, based on analysis of different data as environmental and lifestyle, clinical (sperm count, morphology, hormone testing, karyotype, etc.) and "omics" bigdata. Ultimately, the development of AI algorithms will assist clinicians in formulating diagnosis, making treatment decisions, and predicting outcomes for assisted reproduction techniques.

Personalized multicomponent exercise programs using smartphone technology among older people: protocol for a randomized controlled trial

BACKGROUND

Optimal application of the recently updated World Health Organization (WHO) guidelines for exercise in advanced age necessitates an accurate adjustment for the age-related increasing variability in biological age and fitness levels, alongside detailed recommendations across a range of motor fitness components, including balance, strength, and flexibility. We previously developed and validated a novel tool, designed to both remotely assess these fitness components, and subsequently deliver a personalized exercise program via smartphone. We describe the design of a prospective randomized control trial, comparing the effectiveness of the remotely delivered personalized multicomponent exercise program to either WHO exercise guidelines or no intervention.

METHODS

Participants (n = 300) are community dwelling, healthy, functionally independent, cognitively intact volunteers aged ≥65 at low risk for serious fall injuries, assigned using permuted block randomization (age/gender) to intervention, active-control, or control group. The intervention is an 8-week program including individually tailored exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular); active-controls receive exercising counselling according to WHO guidelines; controls receive no guidance. Primary outcome is participant fitness level, operationalized as 42 digital markers generated from 10 motor fitness measures (balance, strength, flexibility); measured at baseline, mid-trial (4-weeks), trial-end (8-weeks), and follow-up (12-weeks). Target sample size is 300 participants to provide 99% power for moderate and high effect sizes (Cohen's f = 0.25, 0.40 respectively).

DISCUSSION

The study will help understand the value of individualized motor fitness assessment used to generate personalized multicomponent exercise programs, delivered remotely among older adults.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04181983.

Personalized, Predictive, Participatory, Precision, and Preventive (P5) Medicine in Rotator Cuff Tears

Rotator cuff (RC) disease is a common musculoskeletal disorder of the shoulder entailing pain, with reduced functionality and quality of life. The main objective of this study was to present a perspective of the current scientific evidence about personalized, predictive, participatory, precision, and preventive approaches in the management of RC diseases. The personalized, predictive, participatory, precision and preventive (P5) medicine model is an interdisciplinary and multidisciplinary approach that will provide researchers and clinicians with a comprehensive patrimony of knowledge in the management of RC diseases. The ability to define genetic predispositions in conjunction with the evaluation of lifestyle and environmental factors may boost the tailoring of diagnosis and therapy in patients suffering from RC diseases.

Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues

Clinical use of polygenic risk scores (PRS) will look very different to the more familiar monogenic testing. Here we argue that despite these differences, most of the ethical, legal, and social issues (ELSI) raised in the monogenic setting, such as the relevance of results to family members, the approach to secondary and incidental findings, and the role of expert mediators, continue to be relevant in the polygenic context, albeit in modified form. In addition, PRS will reanimate other old debates. Their use has been proposed both in the practice of clinical medicine and of public health, two contexts with differing norms. In each of these domains, it is unclear what endpoints clinical use of PRS should aim to maximize and under what constraints. Reducing health disparities is a key value for public health, but clinical use of PRS could exacerbate race-based health disparities owing to differences in predictive power across ancestry groups. Finally, PRS will force a reckoning with pre-existing questions concerning biomarkers, namely the relevance of self-reported race, ethnicity and ancestry, and the relationship of risk factors to disease diagnoses. In this Opinion, we argue that despite the parallels to the monogenic setting, new work is urgently needed to gather data, consider normative implications, and develop best practices around this emerging branch of genomics.

Leveraging implementation science to improve implementation outcomes in precision medicine

BACKGROUND AND PURPOSE

Introduction of omics technologies in clinical practice means increased use of validated biomarkers, through precision medicine (PM). Although implementation science (IS) affords an array of theoretical approaches that can potentially explain PM intervention uptake, their relevance and applicability in PM implementation has not been empirically tested. This article identifies and examines existing implementation frameworks for their applicability in PM, demonstrating how different IS theories can be used to generate testable implementation hypotheses in PM.

METHODS

A three-step methodology was employed to search and select implementation models: a scoping search in Google Scholar produced 15 commonly used models in healthcare; a systematic search in PUBMED and Web of Science using the names of each model as keywords in search strings produced 290 publications for screening and abstraction; finally, a citation frequency search in the 3 databases produced most cited models that were included in the narrative synthesis.

RESULTS

Main concepts and constructs associated with each of the 15 models were identified. Four most cited frameworks in healthcare were: REAIM, CFIR, PRISM and PARiHS. Corresponding constructs were mapped and examined for potential congruence to PM. A generalized PM implementation conceptual framework was developed showing how omics biomarker uptake relates to their evidence base, patient and provider engagement and Big data capabilities of involved organizations.

CONCLUSION

We demonstrated how implementation complexities in PM can be addressed by explicit use of implementation theories. The work here may provide a reference for further research of empirically testing and refining the identified implementation constructs.

Precision community: a mixed methods study to identify determinants of adoption and implementation of targeted cancer therapy in community oncology

Background

Precision medicine has enormous potential to improve cancer outcomes. Over one third of the 1.5 million Americans diagnosed with cancer each year have genetic mutations that could be targeted with an FDA-approved drug to treat their disease more effectively. However, the current uptake of targeted cancer therapy in clinical practice is suboptimal. Tumor testing is not widely used, and treatments based on molecular and genomic profiling are often not prescribed when indicated. Challenges with the uptake of precision medicine may disproportionately impact cancer patients in rural communities and other underserved populations. The objective of this study is to identify the determinants of adoption and implementation of precision cancer therapy to design an implementation strategy for community oncology practices, including those in rural areas.

Methods

This study is an explanatory sequential mixed methods study to identify factors associated with the use of targeted cancer therapy. Levels of targeted therapy use will be ascertained by secondary analysis of medical records to identify concordance with 18 national guideline recommendations for use of precision medicine in the treatment of breast, colorectal, lung, and melanoma skin cancer. Concurrently, facilitators and barriers associated with the use of precision cancer therapy will be elicited from interviews with up to a total of 40 oncologists, administrators, pathology, and pharmacy staff across the participating sites. Qualitative analysis will be a template analysis based on the Theoretical Domains Framework. Quantitative data aggregated at the practice level will be used to rank oncology practices’ adherence to targeted cancer therapy guidelines. Determinants will be compared among high and low users to isolate factors likely to facilitate targeted therapy use. The study will be conducted in eight community oncology practices, with an estimated 4121 targeted therapy treatment decision-making opportunities over a 3-year period.

Discussion

Despite unprecedented investment in precision medicine, translation into practice is suboptimal. Our study will identify factors associated with the uptake of precision medicine in community settings. These findings will inform future interventions to increase equitable uptake of evidence-based targeted cancer treatment.

Precision Medicine Approaches to Health Disparities Research

Ethn Dis. 2020;30(Suppl 1):129-134; doi:10.18865/ed.30.S1.129

Developing a "Tailor-Made" Precision Lifestyle Medicine Intervention for Weight Control among Middle-aged Latino Men

Objective

To identify what is important to middle-aged Latino men and their personal goals and values as foundations for a future precision lifestyle medicine intervention that is rooted in Self-Determination Theory.

Design

We used a phenomenological, thematic approach to analyze data from 20 semi-structured, individual interviews with Latino men aged 35-60 years.

Setting

Community-based settings between November 2017 and May 2018 in South Florida.

Participants

Latino or Hispanic men who were aged 35-60 years. The mean age of the men was 49.8 years.

Results

Two key themes emerged: a) the characteristics that these men say define what it means to be a man; and b) the characteristics that these men say define what is important to them. "What defines a man" includes three primary subthemes: a) the attributes, characteristics and behaviors that participants understood to be ideals that a man should embody and the roles he should fulfill; b) lessons learned growing up about what it means to be a man; and c) how Latino men relate to the ideal of machismo. "What defines me" includes the subthemes: a) comparing themselves with the ideal of machismo; b) caring for family and others; and c) supporting and modeling positive behavior for their children.

Conclusions

We found key candidate mechanisms that may be novel yet critical foundations on which to build a precision lifestyle medicine intervention for Latino men. We identified actionable psychosocial factors that map onto motivational constructs that can shape behaviors that are essential for weight control and be a useful foundation for improving the health of middle-aged Latino men.

The building blocks of precision oral health in early childhood: the ZOE 2.0 study

Improving children's oral health is a long-standing area of priority and sustained efforts by many stakeholders. Despite these efforts, dental caries, particularly early childhood caries (ECC), persists as a clinical and dental public health problem with multilevel consequences. Despite recent successes in the non-restorative management of dental caries, remarkably little has been done in the domain of ECC prevention. There is promise and expectation that meaningful improvements in early childhood oral health and ECC prevention can be made via the advent of precision medicine in the oral health domain. We posit that precision dentistry, including genomic influences, may be best examined in the context of well-characterized communities (versus convenience clinical samples) and the impact of contextual factors including geography and social disadvantage may be explainable via mechanistic (i.e., biological) research. This notion is aligned with the population approach in precision medicine, which calls for the latter to be predictive, preventive, personalized, and participatory. The article highlights research directions that must be developed for precision dentistry and precision dental public health to be realized. In this context, we describe the rationale, activities, and early insights gained from the ZOE 2.0 study - a large-scale, community-based, genetic epidemiologic study of early childhood oral health. We anticipate that this long-term research program will illuminate foundational domains for the advancement of precision dentistry and precision dental public health. Ultimately, this new knowledge can help catalyze the development of effective preventive and therapeutic modalities via actions at the policy, community, family, and person level.

Typologies of Suicide: A Critical Literature Review

This review highlights proposed suicide typologies and identifies areas of future research. The current study is an illustrative, rather than exhaustive, qualitative review of theoretical and empirically derived typologies of suicide decedents. Theoretical and empirical typologies of suicide delineate between groups of suicide decedents based on individual, motivational, psychiatric, interpersonal, socio-demographic, and other variables. Certain core themes emerge across theoretical typologies including escape, aggression, intrapsychic pain, and relational concerns. Empirical typologies have identified unique patterns of life stressors, mental health history, health care utilization, and suicide method among suicide decedents. Future research should build on existing typological models of suicide to delineate when, and for whom, particular typologies of suicide may inform targeted prevention efforts. Researchers and clinicians should consider the characteristics and needs of particular high-risk groups when translating typological research into meaningful suicide prevention and intervention.

Searching Deep and Wide: Advances in the Molecular Understanding of Dental Caries and Periodontal Disease

During the past decades, remarkable progress has been made in the understanding of the molecular basis of the 2 most common oral diseases, dental caries and periodontal disease. Improvements in our knowledge of the diseases' underlying biology have illuminated previously unrecognized aspects of their pathogenesis. Importantly, the key role of the oral (supragingival and subgingival) microbiome is now well recognized, and both diseases are now best understood as dysbiotic. From a host susceptibility standpoint, some progress has been made in dissecting the "hyperinflammatory" trait and other pathways of susceptibility underlying periodontitis, and novel susceptibility loci have been reported for dental caries. Nevertheless, there is a long road to the translation of these findings and the realization of precision oral health. There is promise and hope that the rapidly increasing capacity of generating multiomics data layers and the aggregation of study samples and cohorts comprising thousands of participants will accelerate the discovery and translation processes. A first key element in this process has been the identification and interrogation of biologically informed disease traits-these "deep" or "precise" traits have the potential of revealing biologically homogeneous disease signatures and genetic susceptibility loci that might present with overlapping or heterogeneous clinical signs. A second key element has been the formation of international consortia with the goals of combining and harmonizing oral health data of thousands of individuals from diverse settings-these "wide" collaborative approaches leverage the power of large sample sizes and are aimed toward the discovery or validation of genetic influences that would otherwise be impossible to detect. Importantly, advancements via these directions require an unprecedented engagement of systems biology and team science models. The article highlights novel insights into the molecular basis of dental caries and chronic periodontitis that have been gained from recent and ongoing studies involving "deep" and "wide" analytical approaches.

When Genetics Meets Religion: What Scientists and Religious Leaders Can Learn from Each Other

INTRODUCTION

To date scientists and religious leaders have not yet engaged in sustained face-to-face conversation concerning precision public health-related genetic technologies.

OBJECTIVES

To elucidate areas of commonality and divergence in scientists' and religious leaders' views of precision genetic technologies, and extract lessons conveyed by religious leaders to scientists, and scientists to religious leaders through participatory dialogue.

METHODS

Six 1.5-h dialogue sessions were held between 6 religious leaders, 8 University of Michigan scientists, and 3 additional public health/genetic counseling graduate students between October 2016 and September 2017, followed by an open conference at the Ann Arbor Public Library (n = 46). Statements were organized into thematically arranged duets comparing views of scientists and religious leaders. Duets were further ordered into interpretive levels. Comparative techniques were used to assure category agreement and face validity.

RESULTS

The analysis yielded 20 duets and 3 interpretive levels (expositional; implications and consequences; and integrative, bridging concepts). Scientists emphasized the value of epigenetic testing for health promotion, and cost saving for some forms of early genetic testing for adult-onset conditions. Religious leaders stressed care for an individual's willingness to change over technical fixes for behavioral conditions and, together with public participants, the importance of allocating money for societal needs. Both expressed caution on the use of nuclear transfer for mitochondrial DNA replacement and secondary uses of genetic data. Lay conference participants pointed towards a middle ground on the release of genetically edited mosquitoes for disease eradication.

DISCUSSION

Scientists stressed the value of professional guidance; religious leaders listened to family needs. Dialogues met four literature-based criteria for stakeholder involvement in deliberative processes.

CONCLUSION

While scientists and religious leaders differ in their points of emphasis and faith orientations (professional competency versus drawing on compassion), they can successfully collaborate in reaching mutual understanding and specific areas of agreement on precision genetic technologies relating to public health.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Public health digitalization in Europe

BACKGROUND

As digitalization is progressively permeating all aspects of society, how can be it fruitfully employed to sustain the public health goals of quality, accessibility, efficiency and equity in health care and prevention? In this paper, we reflect on the potential of applying digital tools to public health and discuss some key challenges.

METHODS

We first define 'digitalization' in its broader sense, as well as applied to public health. We then build a conceptual framework where key public health domains are associated to available digital technologies in a matrix that help to identify digital features that bolster public health action. We also provide illustrative data and evidence on the application of digital solutions on selected public health areas. In the second part, we identify the strategic pillars for a successful European strategy for public health digitalization and we outline how the approach being pursued by the European Public Health Association (EUPHA) applies to digital health.

RESULTS

From a public health perspective, digitalization is being touted as providing several potential benefits and advantages, including support for the transition from cure to prevention, helping to put people and patients at the center of care delivery, supporting patient empowerment and making healthcare system more efficient, safer and cheaper. These benefits are enabled through the following features of digital technologies: (i) Personalization and precision; (ii) Automation; (iii) Prediction; (iv) Data analytics and (v) Interaction.

CONCLUSION

A successful European strategy for public health digitalization should integrate the following pillars: political commitment, normative frameworks, technical infrastructure, targeted economic investments, education, research, monitoring and evaluation. EUPHA acknowledges digitalization is an asset for public health and is working both to promote the culture of "public health digitalization", as well as to enable its planning, implementation and evaluation at the research, practice and policy level.

An Adaptive, Contextual, Technology-Aided Support (ACTS) System for Chronic Illness Self-Management

Policy Points Fundamental changes are needed in how complex chronic illness conditions are conceptualized and managed. Health management plans for chronic illness need to be integrated, adaptive, contextual, technology aided, patient driven, and designed to address the multilevel social environment of patients' lives. Such primary care-based health management plans are feasible today but will be even more effective and sustainable if supported by systems thinking, technological advances, and policies that create and reinforce home, work, and health care collaborations.

CONTEXT

The current health care system is failing patients with chronic illness, especially those with complex comorbid conditions and social determinants of health challenges. The current system combined with unsustainable health care costs, lack of support for primary care in the United States, and aging demographics create a frightening probable future.

METHODS

Recent developments, including integrated behavioral health, community resources to address social determinants, population health infrastructure, patient-centered digital-health self-management support, and complexity science have the potential to help address these alarming trends. This article describes, first, the opportunity to integrate these trends and, second, a proposal for an integrated, patient-directed, adaptive, contextual, and technology-aided support (ACTS) system, based on a patient's life context and home/primary care/work-setting "support triangle."

FINDINGS

None of these encouraging trends is a panacea, and although most have been described previously, they have not been integrated. Here we discuss an example of integration using these components and how our proposed model (termed My Own Health Report) can be applied, along with its strengths, limitations, implications, and opportunities for practice, policy, and research.

CONCLUSIONS

This ACTS system builds on and extends the current chronic illness management approaches. It is feasible today and can produce even more dramatic improvements in the future.

Leveraging system sciences methods in clinical trial evaluation: An example concerning African American women diagnosed with breast cancer via the Patient Navigation in Medically Underserved Areas study

BACKGROUND

Systems science methodologies offer a promising assessment approach for clinical trials by: 1) providing an in-silico laboratory to conduct investigations where purely empirical research may be infeasible or unethical; and, 2) offering a more precise measurement of intervention benefits across individual, network, and population levels. We propose to assess the potential of systems sciences methodologies by quantifying the spillover effects of randomized controlled trial via empirical social network analysis and agent-based models (ABM).

DESIGN/METHODS

We will evaluate the effects of the Patient Navigation in Medically Underserved Areas (PNMUA) study on adult African American participants diagnosed with breast cancer and their networks through social network analysis and agent-based modeling. First, we will survey 100 original trial participants (50 navigated, 50 non-navigated) and 150 of members of their social networks (75 from navigated, 75 non-navigated) to assess if navigation results in: 1) greater dissemination of breast health information and breast healthcare utilization throughout the trial participants' networks; and, 2) lower incremental costs, when incorporating navigation effects on trial participants and network members. Second, we will compare cost-effectiveness models, using a provider perspective, incorporating effects on trial participants versus trial participants and network members. Third, we will develop an ABM platform, parameterized using published data sources and PNMUA data, to examine if navigation increases the proportion of early stage breast cancer diagnoses.

DISCUSSION

Our study results will provide promising venues for leveraging systems science methodologies in clinical trial evaluation.

Precision health: Advancing symptom and self-management science

BACKGROUND

Precision health considers individual lifestyle, genetics, behaviors, and environment context and facilitates interventions aimed at helping individuals achieve well-being and optimal health.

PURPOSE

To present the Nursing Science Precision Health (NSPH) Model and describe the integration of precision health concepts within the domains of symptom and self-management science as reflected in the National Institute of Nursing Research P30 Centers of Excellence and P20 Exploratory Centers.

METHODS

Center members developed the NSPH Model and the manuscript based on presentations and discussions at the annual NINR Center Directors Meeting and in follow-up telephone meetings.

DISCUSSION

The NSPH Model comprises four precision components (measurement; characterization of phenotype including lifestyle and environment; characterization of genotype and other biomarkers; and intervention target discovery, design, and delivery) that are underpinned by an information and data science infrastructure.

CONCLUSION

Nurse scientist leadership is necessary to realize the vision of precision health as reflected in the NSPH Model.

Precision health research and implementation reviewed through the conNECT framework

BACKGROUND

Precision health is a population-based approach that incorporates big-data strategies to understand the complex interactions between biological, environmental, lifestyle, and psychosocial factors that influence health.

PURPOSE

A promising tool to facilitate precision health research and its dissemination is the ConNECT Framework.

METHODS

Here, we discuss the relationship of the five broad and synergistic principles within the ConNECT Framework as they may apply to nursing science research: (1) Integrating Context, (2) Fostering a Norm of Inclusion, (3) Ensuring Equitable Diffusion of Innovations, (4) Harnessing Communication Technology, and (5) Prioritizing Specialized Training.

DISCUSSION/CONCLUSION

The principles within this framework can be used by nurse scientists and educators to guide and disseminate precision health research.

Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial

IMPORTANCE

The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome.

OBJECTIVE

To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months.

DESIGN, SETTING, AND PARTICIPANTS

This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018.

INTERVENTIONS

The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care.

MAIN OUTCOMES AND MEASURES

Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics.

RESULTS

Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]).

CONCLUSIONS AND RELEVANCE

The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention.

TRIAL REGISTRATION

ClinicalTrial.gov identifier: NCT01286350.

Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

Caring for trafficked and unidentified patients in the EHR shadows: Shining a light by sharing the data

OBJECTIVE

Healthcare providers have key roles in the prevention of, detection of, and interventions for human trafficking. Yet caring for trafficked persons is particularly challenging: patients whose identities are unknown, unreliable, or false could receive subpar care from providers delivering care in a vacuum of relevant information. The application of precision medicine principles and integration of biometric data (including genetic information) could facilitate patient identification, enable longitudinal medical records, and improve continuity and quality of care for this vulnerable patient population. Scant empirical data exist regarding healthcare system preparedness and care for the needs of this vulnerable population nor data on perspectives on the use and risks of biometrics or genetic information for trafficked patients.

METHODS

To address this gap, we conducted mixed-methods research involving semi-structured interviews with key informants, which informed a subsequent broad survey of physicians and registered nurses.

RESULTS

Our findings support the perception that trafficked persons obtain care yet remain unnoticed or undocumented in the electronic health record. Our survey findings further reveal that healthcare providers remain largely unaware of human trafficking issues and are inadequately prepared to provide patient-centered care for trafficked and unidentified patients.

CONCLUSION

Meaningful efforts to design and implement precision medicine initiatives in an inclusive way that optimizes impacts are unlikely to succeed without concurrent efforts to increase general awareness of and preparedness to care for trafficked persons. Additional research is needed to examine properly the potential utility for biometrics to improve the delivery of care for trafficked patients.

Difficulties and challenges in the development of precision medicine

The rapid development of precision medicine is introducing a new era of significance in medicine. However, attaining precision medicine is an ambitious goal that is bound to encounter some challenges. Here, we have put forward some difficulties or questions that should be addressed by the progress in this field. The proposed issues include the long road to precision medicine for all types of diseases as the unknown domains of the human genome hinder the development of precision medicine. The challenges in the acquisition and analysis of large amounts of omics data, including difficulties in the establishment of a library of biological samples and large-scale data analysis, as well as the challenges of informed consent and medical ethics in precision medicine, must be overcome to attain the goals of precision medicine. To date, precision medicine programs have accomplished many preliminary achievements and will help to drive a dramatic revolution in clinical practices for the medical community. Through these advances, the diagnosis and treatment of many diseases will achieve many breakthroughs. This project is just beginning and requires a great deal of time and money. Precision medicine also requires extensive collaboration. Ultimately, these difficulties can be overcome. We should realize that precision medicine is good for patients, but there is still a long way to go.

Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis

Motivation

Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse.

Results

We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.

Experiences and perspectives on the return of secondary findings among genetic epidemiologists

PURPOSE

While there has been a recent increase in scholarship around developing policies for the return of results from genetic sequencing, it is not clear whether these approaches are appropriate for genetic epidemiology studies. Because genetic epidemiological research increasingly utilizes genome sequencing methods, particularly in large data sets where researchers did not directly ascertain the subjects, it is important to understand researchers' perspectives on the return of results.

METHODS

We conducted an online survey of members of the International Genetic Epidemiology Society to document the diversity of experiences and impressions regarding return of results. The survey contained both closed and open-ended questions.

RESULTS

Among our respondents who enroll their own research participants, only 21% return secondary findings. Most respondents do not search their sequence data for clinically actionable findings not associated with their disease of interest. Many feel that genetic epidemiologists have a unique perspective on the return of results and that research studies should not follow the same procedures as clinical sequencing studies.

CONCLUSION

Precision medicine initiatives that rely on both clinical and "big data" genomic research should account for variation in researcher perspectives and study design limitations when developing policies and standard practices regarding the return of results.

Precision Medicine: Familiarity, Perceived Health Drivers, and Genetic Testing Considerations Across Health Literacy Levels in a Diverse Sample

A clear awareness of a patient's knowledge, values, and perspectives is an important component of effective genetic counseling. Advances in precision medicine, however, have outpaced our understanding of patient perceptions of this new approach. Patient views may differ across the three domains of precision medicine (genetics, behavioral, and environmental determinants of health), ethnic/racial groups, and health literacy levels. This study describes and compares group differences in familiarity, perceptions, and preferences for precision medicine in a diverse sample. Between 2016 and 2017, 252 participants completed a 10-15-min survey in three primary care clinics in Florida and Tennessee. The final sample was 42.5% African American/Black, 25.8% Hispanic/Latino, 25.0% White, and 6.7% other ethnicity/race. Less than a quarter of participants reported being familiar with the term "precision medicine," but were more familiar with basic genetic terms. Participants with higher health literacy reported greater familiarity with terms (p ≤ .003). African Americans/Black participants were more likely to identify ethnicity/race and discrimination as influencing their health (p ≤ .004). When deciding to get a genetic test, individuals across ethnic/racial groups shared similar considerations. Those with higher health literacy, however, gave significantly greater importance to provider trust (p ≤ .008). Given the recent emergence of precision medicine, at present there may be limited differences in patient perceptions across ethnic/racial groups. Culturally sensitive efforts, tailored to health literacy level, may aid equitable precision medicine uptake.

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.

How Healthy Are Survey Respondents Compared with the General Population?: Using Survey-linked Death Records to Compare Mortality Outcomes

BACKGROUND

National surveys are used to capture US health trends and set clinical guidelines, yet the sampling frame often includes those in noninstitutional households, potentially missing those most vulnerable for poor health. Declining response rates in national surveys also represent a challenge, and existing inputs to survey weights have limitations. We compared mortality rates between those who respond to surveys and the general population over time.

METHODS

Survey respondents from 20 waves of the National Health Interview Survey from 1990 to 2009 who have been linked to death records through 31 December 2011 were included. For each cohort in the survey, we estimated their mortality rates along with that cohort's mortality rate in the census population using vital statistics records, and differences were examined using Poisson models.

RESULTS

In all years, survey respondents had lower mortality rates compared with the general population when data were both weighted and unweighted. Among men, survey respondents in the weighted sample had 0.86 (95% confidence interval = 0.853, 0.868) times the mortality rate of the general population (among women, RR = 0.887; 95% confidence interval, 0.879, 0.895). Differences in mortality are evident along all points of the life course. Differences have remained relatively stable over time.

CONCLUSION

Survey respondents have lower death rates than the general US population, suggesting that they are a systematically healthier source population. Incorporating nonhousehold samples and revised weighting strategies to account for sample frame exclusion and nonresponse may allow for more rigorous estimation of the US population's health.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Precision annotation of digital samples in NCBI's gene expression omnibus

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open ‘big data’ under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

Precisely Where Are We Going? Charting the New Terrain of Precision Prevention

In addition to genetic data, precision medicine research gathers information about three factors that modulate gene expression: lifestyles, environments, and communities. The relevant research tools-epidemiology, environmental assessment, and socioeconomic analysis-are those of public health sciences rather than molecular biology. Because these methods are designed to support inferences and interventions addressing population health, the aspirations of this research are expanding from individualized treatment toward precision prevention in public health. The purpose of this review is to explore the emerging goals and challenges of such a shift to help ensure that the genomics community and public policy makers understand the ethical issues at stake in embracing and pursuing precision prevention. Two emerging goals bear special attention in this regard: (a) public health risk reduction strategies, such as screening, and (b) the application of genomic variation studies to understand and reduce health disparities among population groups.

Effect of genomics-related literacy on non-communicable diseases

Recent progress in genomic research has raised expectations for the development of personalized preventive medicine, although genomics-related literacy of patients will be essential. Thus, enhancing genomics-related literacy is crucial, particularly for individuals with low genomics-related literacy because they might otherwise miss the opportunity to receive personalized preventive care. This should be especially emphasized when a lack of genomics-related literacy is associated with elevated disease risk, because patients could therefore be deprived of the added benefits of preventive interventions; however, whether such an association exists is unclear. Association between genomics-related literacy, calculated as the genomics literacy score (GLS), and the prevalence of non-communicable diseases was assessed using propensity score matching on 4646 participants (males: 1891; 40.7%). Notably, the low-GLS group (score below median) presented a higher risk of hypertension (relative risk (RR) 1.09, 95% confidence interval (CI) 1.03-1.16) and obesity (RR 1.11, 95% CI 1.01-1.22) than the high-GLS group. Our results suggest that a low level of genomics-related literacy could represent a risk factor for hypertension and obesity. Evaluating genomics-related literacy could be used to identify a more appropriate population for health and educational interventions.

Toward Precision Healthcare: Context and Mathematical Challenges

Precision medicine refers to the idea of delivering the right treatment to the right patient at the right time, usually with a focus on a data-centered approach to this task. In this perspective piece, we use the term "precision healthcare" to describe the development of precision approaches that bridge from the individual to the population, taking advantage of individual-level data, but also taking the social context into account. These problems give rise to a broad spectrum of technical, scientific, policy, ethical and social challenges, and new mathematical techniques will be required to meet them. To ensure that the science underpinning "precision" is robust, interpretable and well-suited to meet the policy, ethical and social questions that such approaches raise, the mathematical methods for data analysis should be transparent, robust, and able to adapt to errors and uncertainties. In particular, precision methodologies should capture the complexity of data, yet produce tractable descriptions at the relevant resolution while preserving intelligibility and traceability, so that they can be used by practitioners to aid decision-making. Through several case studies in this domain of precision healthcare, we argue that this vision requires the development of new mathematical frameworks, both in modeling and in data analysis and interpretation.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

The optimal design and patient selection for interventional trials in radiogenomics seem trivial at first sight. However, radiogenomics do not give binary information like in e.g. targetable mutation biomarkers. Here, the risk to develop severe side effects is continuous, with increasing incidences of side effects with higher doses and/or volumes. In addition, a multi-SNP assay will produce a predicted probability of developing side effects and will require one or more cut-off thresholds for classifying risk into discrete categories. A classical biomarker trial design is therefore not optimal, whereas a risk factor stratification approach is more appropriate. Patient selection is crucial and this should be based on the dose-response relations for a specific endpoint. Alternatives to standard treatment should be available and this should take into account the preferences of patients. This will be discussed in detail.

Population-Based Precision Cancer Screening: A Symposium on Evidence, Epidemiology, and Next Steps

Precision medicine, an emerging approach for disease treatment that takes into account individual variability in genes, environment, and lifestyle, is under consideration for preventive interventions, including cancer screening. On September 29, 2015, the National Cancer Institute sponsored a symposium entitled "Precision Cancer Screening in the General Population: Evidence, Epidemiology, and Next Steps". The goal was two-fold: to share current information on the evidence, practices, and challenges surrounding precision screening for breast, cervical, colorectal, lung, and prostate cancers, and to allow for in-depth discussion among experts in relevant fields regarding how epidemiology and other population sciences can be used to generate evidence to inform precision screening strategies. Attendees concluded that the strength of evidence for efficacy and effectiveness of precision strategies varies by cancer site, that no one research strategy or methodology would be able or appropriate to address the many knowledge gaps in precision screening, and that issues surrounding implementation must be researched as well. Additional discussion needs to occur to identify the high priority research areas in precision cancer screening for pertinent organs and to gather the necessary evidence to determine whether further implementation of precision cancer screening strategies in the general population would be feasible and beneficial. Cancer Epidemiol Biomarkers Prev; 25(11); 1449-55. ©2016 AACR.

From "Personalized" to "Precision" Medicine: The Ethical and Social Implications of Rhetorical Reform in Genomic Medicine

Since the late 1980s, the human genetics and genomics research community has been promising to usher in a "new paradigm for health care"-one that uses molecular profiling to identify human genetic variants implicated in multifactorial health risks. After the completion of the Human Genome Project in 2003, a wide range of stakeholders became committed to this "paradigm shift," creating a confluence of investment, advocacy, and enthusiasm that bears all the marks of a "scientific/intellectual social movement" within biomedicine. Proponents of this movement usually offer four ways in which their approach to medical diagnosis and health care improves upon current practices, arguing that it is more "personalized," "predictive," "preventive," and "participatory" than the medical status quo. Initially, it was personalization that seemed to best sum up the movement's appeal. By 2012, however, powerful opinion leaders were abandoning "personalized medicine" in favor of a new label: "precision medicine." The new label received a decisive seal of approval when, in January 2015, President Obama unveiled plans for a national "precision medicine initiative" to promote the development and use of genomic tools in health care.

Towards Evidence-based Precision Medicine: Extracting Population Information from Biomedical Text using Binary Classifiers and Syntactic Patterns

Precision Medicine is an emerging approach for prevention and treatment of disease that considers individual variability in genes, environment, and lifestyle for each person. The dissemination of individualized evidence by automatically identifying population information in literature is a key for evidence-based precision medicine at the point-of-care. We propose a hybrid approach using natural language processing techniques to automatically extract the population information from biomedical literature. Our approach first implements a binary classifier to classify sentences with or without population information. A rule-based system based on syntactic-tree regular expressions is then applied to sentences containing population information to extract the population named entities. The proposed two-stage approach achieved an F-score of 0.81 using a MaxEnt classifier and the rule- based system, and an F-score of 0.87 using a Nai've-Bayes classifier and the rule-based system, and performed relatively well compared to many existing systems. The system and evaluation dataset is being released as open source.

Precision medicine in cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.

267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation

Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ∼10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms.