Bielinski, Suzette J.

Introduction: Chronic kidney disease affects nearly half of heart failure (HF) patients, underscoring the importance of regular kidney function monitoring. However, longitudinal trajectories of kidney function in HF are not well characterized. Identifying clinical factors linked to long-term changes is critical for improving monitoring and informing early clinical decisions. Hypothesis: We hypothesized that longitudinal kidney function and its associations with clinical variables in HF patients differ from that of demographically similar comparators from the same community. Methods: We analyzed patients with validated HF from a community-based cohort and comparators individually matched to age and sex selected from participants in the Mayo Clinic Biobank cohort. Individuals with end-stage kidney disease, dialysis or kidney transplantation prior to enrollment were excluded. Serum creatinine-based estimated glomerular filtration rate (eGFR) from outpatient and inpatient visits was assessed from enrollment for up to 10 years of follow-up. The median of eGFR for 90-day intervals was used to estimate the follow-up eGFR (Figure 1) . We modeled percentage change from baseline and fit linear mixed-effect models with time, HF status, and their interaction to test whether the eGFR trajectories differed between the two groups. In addition, we examined the association between eGFR trajectories and clinical variables. Results: We studied 1182 HF patients and 825 comparators with at least three eGFR measurements. Median follow-up was 5 years (IQR: 3-10) for HF patients and 8 years (6-9) for comparators. In total, 16245 serum creatinine measurements from HF patients and 9858 from comparators were analyzed. Median age at enrollment was 76 years (67-84) for HF patients and 78 years (70-83) for comparators. A greater decline in percentage change in eGFR was observed in HF patients (-4.23%, 95% CI: -4.75 to -3.70% per year) than in comparators (-1.90%, 95% CI: -2.51 to -1.29% per year; P<0.001, Figure 2 ). Among HF patients, the decline in percentage change in eGFR was greater in the presence of diabetes, hyperlipidemia, or a higher comorbidity burden. Conclusions: In this community study, HF patients exhibited a greater percentage decline in eGFR compared to community comparators. Among HF patients, eGFR decline was more strongly associated with comorbidities—particularly metabolic risk factors—highlighting the relevance of the cardiovascular-kidney-metabolic syndrome in HF.

Abstract Background Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. Objective To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. Fitness for Purpose Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. Fitness for Reuse Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. Conclusions The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.

BACKGROUND: Heart failure (HF) is a heterogeneous syndrome with high mortality. The need for a new taxonomy of HF is recognized; up to now, such phenomapping efforts have primarily used clinical data. Proteomics offers potential for more precise phenotypic identification and mechanistic insights. However, few phenomapping studies have used this approach, and all have focused on targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. METHODS: We measured over 7000 plasma proteins in a population-based cohort of 1351 patients with HF, used k-means clustering to identify distinct phenogroups, and compared their clinical characteristics and all-cause mortality. RESULTS: Three proteomics-defined phenogroups were identified, with substantial differences in survival (phenogroup 1 5-year survival probability, 65% [95% CI, 61%–68%]; phenogroup 2, 45% [40%–51%]; phenogroup 3, 26% [22%–30%]), independent of clinical characteristics. Phenogroups also exhibited differences in several measures suggesting poorer health, including NT-proBNP (N-terminal pro-B-type natriuretic peptide), kidney function, and Meta-Analysis Global Group in Chronic Heart Failure scores, but did not differ by ejection fraction or New York Heart Association class. CONCLUSIONS: Our study demonstrates that molecular phenomapping can stratify patients with HF into distinct subgroups that go beyond predefined clinical classifications.

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.

Abstract Background: Pharmacogenomic studies on antidepressant treatment outcomes could be conducted using previously collected data from electronic health record (EHR)-linked biobanks. However, absence of EHR based outcome measures is an unmet need in designing such studies We aimed to define EHR-derived antidepressant outcome measures and explore their utility in showing associations between treatment outcomes and Cytochrome P450 (CYP) metabolizer phenotypes in a proof-of-concept study. Methods: Using data from the EHR-linked cohort, Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, we collected prescription data and patient health questionnaire 9 (PHQ-9) scores to compute 3 proxy measures for antidepressant response, efficacy, and acceptability: change in PHQ-9 scores, longest treatment interval with a single antidepressant, and antidepressant non-refill. Subsequently, we tested the association of both prescription-based outcomes with DNA-predicted CYP metabolizer phenotypes in European-ancestry participants. Results: We identified 3920 RIGHT 10K participants with at least 1 antidepressant prescription and European-ancestry. Participants had a mean age of 61 years and 72% were women. Implementation of the PHQ-9 outcome was not feasible because of missingness. Of both prescription-based outcomes, antidepressant non-refill reproduced several known antidepressant-CYP interactions. However, the pilot was limited by small subgroups of participants with non-normal metabolizer phenotypes. Conclusions: Derived from structured data, antidepressant non-refill is a promising outcome measure for EHR-linked biobanks that partially reproduced antidepressant-CYP interactions. However, testing on larger datasets is necessary to understand whether it would be a useful for pharmacogenomic research.

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.

Abstract Objectives The aims of the study were to identify conditions diagnosed in at least 10% of midlife women living in the US upper midwest and to assess prevalence by age, race, ethnicity, and sociodemographic status. Methods The Rochester Epidemiology Project was used to conduct a cross-sectional prevalence study of 86,946 women between 40 and 59 years residing in a 27-county region of the United States on January 1, 2020. Diagnostic billing codes were extracted and grouped into broader condition categories using the Clinical Classification System Refined. The prevalence of 424 conditions was calculated by age, race, ethnicity, and area deprivation index quartiles. Logistic regression was used to examine associations between participant characteristics and conditions that affected 10% or more of the study population. Results Twenty-eight conditions affected ≥10% of women, and eight conditions increased by ≥45% between the ages of 40 and 59 (disorders of lipid metabolism, hypertension, sleep/wake disorders, thyroid disorders, esophageal disorders, osteoarthritis, tendon and synovial disorders, and menopausal disorders; all test for trend P < 0.01). Black women had a significantly higher prevalence of hypertension and esophageal disorders at all ages (adjusted P values <0.05). Women living in more deprived areas had a significantly higher prevalence of hyperlipidemia, hypertension, sleep/wake disorders, and esophageal disorders (adjusted P values <0.05). Women living in less deprived areas had a significantly higher prevalence of thyroid disorders at age 40 to 44 and menopausal disorders at ages 50 to 59 (adjusted P values <0.05). Conclusions These data suggest that additional attention should focus on Black women and women with a lower socioeconomic status to ensure that common midlife conditions are diagnosed and treated.

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.