PURPOSE:We describe a prospective cohort study (NCT05277116) conducted in phase IV of the electronic MEdical Records and GEnomics (eMERGE) Network to implement a multi-ancestry polygenic risk score for coronary heart disease (PRSCHD: PGS004696) and assess outcomes after return of results (RoR). METHODS:PRSCHD was considered alongside family history (FamHxCHD), monogenic risk from familial hypercholesterolemia (FH), and clinical risk factors, to return CHD risk as part of a Genome Informed Risk Assessment (GIRA) report. Participants with high PRSCHD (top 5th percentile) or FH received their results from study personnel, while participants with FamHxCHD were informed by mail/email. Results were placed in the electronic health record and communicated to the primary care provider. The primary outcome of initiation/intensification of lipid lowering therapy within 12 months after RoR is compared between participants with PRSCHD ≥95th percentile and those with PRSCHD 90th-94th percentile, using a regression discontinuity design. Secondary outcomes include ordering of screening tests, a new CHD diagnosis, and lifestyle changes. RESULTS:By April 2025, 20,421 adults were enrolled: mean age 50±15 years (range 18-75 years), 68% female, 50% belonging to health disparity groups, and 40% non-White by self-report. Prevalence of CHD, FamHxCHD, high PRSCHD and FH was 4.0%, 10.2%, 4.3% and 0.7%, respectively; 14.3% had at least one of the three CHD genetic risk factors and CHD risk estimates were highest in those who self-reported as Black. CONCLUSION:The prevalence of increased genetic risk for CHD was high and at least one of the three genetic risk factors for CHD was present in 14.2% of the cohort. Analyses are underway to assess outcomes after PRSCHD implementation in the context of FamHxCHD, FH, and clinical risk, across the age spectrum in a diverse cohort.

Transplantation of the larynx is a rare and controversial procedure. Unlike solid organ transplantation, in which the primary goal is preservation of life, laryngeal transplantation aims to improve a patient's quality-of-life by restoring function. Several successful laryngeal transplants have been reported in the scientific literature: restoring patient speech, improving swallowing and breathing, and providing substantial quality-of-life improvements. Nonetheless, laryngeal transplantation poses both scientific and ethical challenges. The novelty of the procedure, uncertainty about appropriate clinical indications, need for highly nuanced clinical evaluation and patient selection, and paucity of alternative treatment options add to the moral complexity of offering LT. In this paper, we argue that laryngeal transplantation is ethically supportable in a limited set of circumstances, specifically in situations where patients are struggling with severe compromises to their quality of life or experiencing functional impairments that threaten their sense of self.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 × 10-3) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Sharing biomedical research data can accelerate scientific discovery, leading funders and journals to increasingly mandate sharing. However, data openness must be balanced with protecting research participants from harm in an evolving legal and social landscape. Drawing on experiences from the Electronic Medical Records and Genomics (eMERGE-IV) Network—a US-based, multi-site consortium gathering genomic and medical data focused on underrepresented groups to refine disease risk prediction—we examine challenges in implementing data sharing that are “as open as possible, as closed as necessary.” Recent US legal developments, including the Dobbs decision and gender-affirming care bans, highlight the urgency of considering data-sharing risks and required the Network to rethink strategies to prevent individual- and group-level harms from genomic analyses. eMERGE-IV implemented several strategies to mitigate concerns, including cell suppression for race/ethnicity data and not extracting certain diagnostic codes from participants’ electronic health records. These decisions balanced immediate protection and long-term scientific benefits for relevant populations. Participant agreement to broad data sharing in informed consent is often required for research participation to make data as open as possible. No consent form, however, can define the terms of “as closed as necessary”—a construct that is subject to sociolegal changes across the life cycle of research studies. Providing protection requires robust data governance, including engagement with prospective and actual participants. The research enterprise must reconsider its consenting approach and develop transparent, inclusive governance structures responsive to evolving vulnerabilities while maintaining scientific progress. Public trust depends on the research enterprise successfully navigating these competing demands.

Objectives To describe reasons why patients declined to participate in the Tapestry whole exome sequencing study and examine demographic trends in responses. Patients and Methods The Tapestry study enrolled patients 18 years of age and over, beginning July 1, 2020, through May 31, 2024. This study includes 12,705 recruited subjects who declined to participate before February 14, 2022, but provided reasons for declination (RPs). RPs were selected from 5 discrete reasons for declination and had the opportunity to add a free-text comment. Comments were classified into 7 theme-based categories by the reviewers for analysis. Demographics of the subjects, including age, race, ethnicity, having a primary care provider, and rural–urban commuting area, were compared based on consent status and their reasons for declination. Results RPs had a mean age of 61 years, were 90.1% White, and were 57.4% female. The most common reasons for declination were concerns about storing genetic data in electronic health records, the complexity of the process, and discomfort with genetic research. Significant differences in reasons for declination were found by sex, age, race, ethnicity, primary care provider status, and rural–urban commuting area. Conclusion Our study highlights demographic and structural factors influencing genomic study non-participation. Distinct barriers were identified among active decliners, including privacy concerns, logistical issues, and mistrust. These findings emphasize the need for targeted education and provider engagement to support informed decision-making, reduce post-genomic sequencing regret, and promote equity in participation in large-scale personalized medicine initiatives.

IntroductionIn the United States (US), Tribes are sovereign nations and have the right to oversee research conducted with Tribal citizens. However, it is unclear who should approve research protocols when data from American Indian and Alaska Native (AIAN) people are collected off Tribal lands. As genetic research continues to advance and transform the delivery of healthcare, equitable inclusion of AIAN people is necessary, but oversight of research needs clarity.MethodsWe held a 3-day workshop with US thought leaders on genetic and other health research with AIAN people in urban areas to explore views and values on this issue and to discuss potential policy and practice solutions.ResultsThirty-six individuals attended. Solidarity surfaced as a foundational motivation for Tribal Nations to review research conducted with AIAN people, whether on Tribal lands or not. Understanding data from Indigenous perspectives was identified as a way to ensure appropriate AIAN community protections are in place. Three discrete areas to improve policy were suggested–Tribal, Academic Institution, and National–to protect AIAN people participating in research both on and off Tribal lands.DiscussionResearchers, whether Indigenous or not, must recognize Tribal sovereignty and operate in solidarity with the applicable and most appropriate ethical principles and regulations.

The Electronic Medical Records and Genomics (eMERGE) Network developed and implemented a genome-informed risk assessment (GIRA) to communicate genomic (polygenic risk scores [PRSs], integrated risk scores [IRSs], and monogenic results), clinical, and family history-based risk for 11 chronic diseases and provide recommended healthcare recommendations. GIRA reports have now been returned to 23,840 participants and their providers in a large prospective cohort study. We present here the study design and analysis framework for assessing the attributable impact of GIRA return. Pre-specified outcomes include (1) provider/participant adoption of recommended healthcare actions, (2) new diagnosis of disease, (3) treatment initiation/intensification, and (4) clinical outcomes (surrogate markers or clinical events). We assess outcomes in high risk vs. not-high-risk participants, adjusting for covariates. We evaluate the effect of PRS/IRS at pre-established high-risk thresholds using regression discontinuity (RD), a quasi-experimental method that mimics randomization near a cutoff, enabling estimation of causal effects and controlling for unobserved confounders. Monogenic and family history-based risk stratification are analyzed using logistic regression. With 23,840 participants and 12 months of follow-up, the study is powered to detect differences of 2%-11% with 80% power (α = 0.05 in the adoption outcome). Longer follow-up will be required to enable assessment of new disease diagnosis, treatment changes, and clinical outcomes. Through innovative RD analyses and defined outcomes and comparison groups, this study will provide new insights into the real-world clinical impact of genomic risk assessment, address critical evidence gaps, advance understanding of genomic medicine outcomes, and inform future research.

BACKGROUND:To maximize the potential benefits of genomic medicine for all, it is crucial to research and support the delivery of genomic medicine in under-researched healthcare settings. METHODS:This project investigated experiences of healthcare providers caring for low-resourced patients in a Federally Qualified Health Center in Phoenix, Arizona (FQHC) using a mixed-methods, cross-sectional case study. Interviews were conducted with 14 healthcare providers working in a FQHC, involved with delivering genomic testing (specifically, polygenic risk scores [PRS] and monogenic testing) to patients enrolled in the electronic Medical Records and Genomics (eMERGE) network, specifically the eMERGE IV study. RESULTS:Providers expressed a general confidence in genomic testing in practice. Quantitatively, most providers expressed confidence in communicating genomic test results to patients. However, qualitative findings emphasize time constraints, insufficient training, lack of human resources, and concerns for follow-up care and financial barriers, which are pronounced in FQHC settings. CONCLUSIONS:The potential of genomic testing to improve patient outcomes is strong, but system-level support for providers is necessary to strive towards sustainable and equitable implementation of genomic medicine, particularly in FQHC and other under-resourced primary care settings.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Genomic innovations, including pharmacogenomics, are becoming increasingly relevant to routine medical care and hold promise for advancing prevention, early diagnosis, and treatment in primary care. However, integrating genetic services in this setting requires navigating a fragmented health care system, guided by national recommendations but marked by variability in reimbursement, clinician training, and infrastructure across regions. This article presents insights from a multidisciplinary Mayo Clinic task force examining the clinical, ethical, and implementation aspects of genomic services for adult primary care patients. We highlight the role of primary care clinicians in identifying individuals at risk for inherited conditions, such as hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia. The article outlines current implementation models, emphasizes the value of family history, and addresses persistent challenges including limited clinician confidence, time constraints, and inconsistent access to services due to insurance and reimbursement variability. Educational and systems-level approaches, including core competencies, clinical decision support, and ethical frameworks, are essential to support primary care clinicians and to ensure responsible, scalable implementation. We also explore how genomics can reinforce the primary care mission by enabling patient-centered care, improving outcomes, and proactively addressing gaps in access. Ethical considerations, such as patient autonomy, informed consent, and privacy-sensitive communication.

BackgroundAs artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice. ObjectiveThis study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine. MethodsWe interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods. ResultsPhysicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability. ConclusionsOur results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.

Purpose The Sangre Por Salud (SPS) Biobank was established to facilitate biomedical research opportunities for the Latino community by creating an easily accessible prospective cohort for scientists interested in studying health conditions and health disparities in this population.Participants Individuals self-identifying as Latino, aged 18–85 years, were prospectively recruited from the primary care Internal Medicine clinic at Mountain Park Health Center in Phoenix, Arizona. After obtaining informed consent, detailed medical history questionnaires were captured, and blood samples were obtained for common laboratory tests. Participants authorised the research team to access their electronic health records for research purposes. In addition, participants had serum, plasma and DNA samples isolated and stored at the Mayo Clinic Arizona Biorepository Laboratory for long-term storage and future access. As part of the study, participants consented and agreed to be contacted for potential participation in future research studies.Findings to date 3756 participants provided informed consent, of whom 3733 completed all study questionnaires, an oral glucose tolerance test and had blood collected and stored. The SPS cohort is predominantly composed of females (72%), with a median age at time of consent of 42 years. All participants self-identified as Hispanic/Latino, 45% were married, 53% were employed for wages and 60% had less than a high school degree. Around 25% of participants met diagnostic criteria for overweight (BMI 25–29 kg/m2), and 49% met for obesity (BMI≥30 kg/m2). At time of recruitment, hypertension, hyperlipidaemia and depression affected 22%, 20% and 13% of the cohort, respectively.Future plans We plan to regularly update the participants’ electronic health records and self-reported health data to longitudinal research. Additionally, we plan to obtain a more comprehensive genomic analysis on the entire cohort, ensuring greater research interest and investigation into the underlying genetic factors that contribute to disease susceptibility in this cohort.

Background: As more healthcare institutions consider providing preemptive pharmacogenomic (PGx) testing to greater numbers of their patients, it will be important to consider the potential concerns patients may have about the generation of preemptive PGx information. To date, few studies have examined the nature and incidence of patient concerns about preemptive PGx testing. Methods: We conducted a longitudinal survey study of 5000 patients receiving preemptive PGx testing in the Mayo Clinic RIGHT study. We assessed patient concerns regarding issues of data confidentiality, cost implications, comprehension of results, and potential disruption of pre-existing medication regimens. Participants were surveyed before and after they received PGx results from the RIGHT study. Results: We achieved 92.8% and 74.4% response rates on the pre- and post-results surveys, respectively. Participants had low levels of concern about PGx testing overall. However, 25.5% of participants were “quite/extremely concerned” about insurance implications, and 30.1% were “quite/extremely” concerned about increased out-of-pocket costs for prescription medications that might result from PGx testing. These same concerns were significantly reduced on the post-results survey. Patients who initially expressed concerns regarding their ability to understand PGx results were more likely to report having difficulty understanding results on the post-results survey. Conclusions: Our findings suggest that as healthcare institutions look to increase preemptive PGx screening, attention should be given to potential concerns patients may have around such testing. Educational interventions aimed at supporting patient understanding of PGx results and addressing potential concerns will be important elements of a successful PGx program.

Claims abound that advances in artificial intelligence (AI) will permeate virtually every aspect of medicine and transform clinical practice. Simultaneously, concerns about the safety and equity of health care AI have prompted ethical and regulatory scrutiny from multiple oversight bodies. Positioned at the intersection of these perspectives, academic medical centers (AMCs) are charged with navigating the safe and responsible implementation of health care AI. Decisions about the use of AI at AMCs are complicated by uncertainties regarding the risks posed by these technologies and a lack of consensus on best practices for managing these risks. In this article, we highlight several potential harms that may arise in the adoption of health care AI, with a focus on risks to patients, clinicians, and medical practice. In addition, we describe several strategies that AMCs might adopt now to address concerns about the safety and ethical uses of health care AI. Our analysis aims to support AMCs as they seek to balance AI innovation with proactive oversight.

Background:Measures of genetic predisposition can improve prediction of risk of cardiometabolic diseases but more data is needed in groups under-represented in genomics research. In this study, we investigated the impact of genetic risk factors for coronary heart disease (CHD) - polygenic risk, monogenic risk [in the form of familial hypercholesterolemia (FH)], and family history (FamHx) - on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults - eMERGE IV (eIV) and All of Us (AoU). Methods:CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a polygenic risk score for CHD (PRSCHD, PGS004698), ascertained FH as presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in a first-degree family member. We employed Pooled Cohort Equations (PCE) to estimate the 10-year risk of CHD for adults ≥40 y and modeled the association of conventional risk factors with CHD in adults <40 y. We analyzed the impact of PRSCHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results:We analyzed data for 19,348 participants from eIV (age 50.6±15.0, 68% female, 40.5% non-White) and 239,645 participants from AoU (age 55.4±17.0, 60.6% female, 48% non-White). The effects of PRSCHD and FamHx on CHD were independent and additive in the two cohorts and incorporating both into PCE for eIV participants significantly improved discrimination (C-statistic increased from 0.719 to 0.753; P-diff=9.1×10-3) and reclassified risk in 18.7% and 20.2% of participants at the 7.5% and 10% 10-y CHD risk thresholds, respectively. Between the 7.5% and 10% 10-y CHD risk thresholds, incorporating PRSCHD and FamHx into the PCE improved the net benefit of the risk prediction models across all SIRE groups. Conclusion:PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the US. Incorporating PRSCHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants at actionable 10-y CHD risk thresholds, and improved net benefit of the PCE, motivating the addition of these factors to clinical risk algorithms.

As applications of artificial intelligence (AI) integrate rapidly into healthcare, there is a pressing need for educational strategies to prepare various health professionals to identify, interrogate, and address AI-related ethical challenges. However, few pedagogical resources exist to support end users as they consider the ethical dimensions of healthcare AI. Involving highly technical elements and emerging regulatory structures, healthcare AI presents unique challenges to educators. The rapid pace of AI innovation and lack of transparency behind AI algorithms can limit opportunities to examine nuanced ethical themes related to algorithmic biases and validation. These limitations have far­ reaching implications when applied in practice, raising broader ethical concerns related to end-use and public trust, distribution of accountability for clinical decisions, and oversight of healthcare AI. While evidence supports the use of case-based learning in ethics education, the complexity of AI technologies demands careful consideration for how to integrate case-based learning into ethics education. In this paper, the authors describe pedagogical strategies used in an AI ethics course for healthcare professionals and biomedical scientists. Drawing on their experiences in teaching the course over three years, the authors describe the use of AI cases to promote consideration of ethical implications of AI among professionals whose careers may be impacted by the integration of AI-enabled technologies into healthcare. The authors close with reflections and lessons learned on the promises and challenges of graduate education as a tool in the responsible integration of AI into healthcare.