Sharp, Richard R.

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.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), 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 PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD 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 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

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.

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/m 2 ), and 49% met for obesity (BMI≥30 kg/m 2 ). 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.