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National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality., (Copyright © 2023 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Objective: To examine associations of cumulative exposure to proton pump inhibitors (PPIs) with total cardiovascular disease (CVD; composed of stroke, coronary heart disease, and heart failure [HF]) and HF alone in a cohort study of White and African American participants of the Atherosclerosis Risk in Communities (ARIC) study., Methods: Use of PPIs was assessed by pill bottle inspection at visit 1 (January 1, 1987 to 1989) and up to 10 additional times before baseline (visit 5; 2011 to 2013). We calculated cumulative exposure to PPIs as days of use from visit 1 to baseline. Participants (n=4346 free of total CVD at visit 5; mean age, 75 years) were observed for incident total CVD and HF events through December 31, 2016. We used Cox regression to measure associations of PPIs with total CVD and HF., Results: After adjustment for potential confounding variables, participants with a cumulative exposure to PPIs of more than 5.1 years had a 2.02-fold higher risk of total CVD (95% CI, 1.50 to 2.72) and a 2.21-fold higher risk of HF (95% CI, 1.51 to 3.23) than nonusers., Conclusion: Long-term PPI use was associated with twice the risk of total CVD and HF compared with nonusers. Our findings are in concordance with other research and suggest another reason to be cautious of PPI overuse., (Copyright © 2021 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Objective: To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR)., Patients and Methods: We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR., Results: The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance., Conclusion: This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice., (Copyright © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate the participants in the Mayo Clinic Biobank for their representativeness to the entire Employee and Community Health program (ECH) primary care population with regard to hospital utilization., Patients and Methods: Participants enrolled in the Mayo Clinic Biobank from April 1, 2009, to December 31, 2010, were linked to the ECH population. These individuals were categorized into risk tiers (0-4) on the basis of the number of health conditions present as of December 31, 2010. Outcomes were ascertained through December 31, 2011. Hazard ratios (HRs) and 95% CIs for risk of hospitalization, emergency department (ED) visits, and for risk of hospitalization and emergency department (ED) visits were estimated., Results: The 8927 Biobank participants were part of ECH (N=84,872). Compared with the entire ECH population, the Biobank-ECH participants were more likely to be female (64.3% vs 54.6%), older (median age, 58 years vs 47 years), and categorized to tier 0 (6.4% vs 24.0%). There were strong positive associations between tier (tier 4 vs combined tiers 0 and 1) and risk of hospitalization (HR, 5.8; 95% CI, 4.6-7.5) and ED visits (HR, 5.4; 95% CI, 4.2-6.8) among Biobank-ECH participants. Similar associations for risk of hospitalization (HR, 8.5; 95% CI, 7.8-9.3) and ED visits (HR, 6.9; 95% CI, 6.4-7.5) were observed for the entire ECH population., Conclusion: Although the Biobank-ECH participants were older and had more chronic conditions compared with the overall ECH population, the associations of risk tier with utilization outcomes were similar, supporting the use of the Biobank participants to assess biomarkers for health care outcomes in the primary care setting., (Copyright © 2013 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Objective: To investigate the association between 347 single-nucleotide polymorphisms within candidate genes of the tumor necrosis factor, interleukin 1 and interleukin 6 families with neutrophil count., Patients and Methods: Four hundred cases with heart failure after myocardial infarction (MI) were matched by age, sex, and date of incident MI to 694 controls (MI without post-MI heart failure). Both genotypes and neutrophil count at admission for incident MI were available in 314 cases and 515 controls., Results: We found significant associations between the TNFSF8 poly morphisms rs927374 (P=5.1 x 10(-5)) and rs2295800 (P=1.3 x 10(-4)) and neutrophil count; these single-nucleotide polymorphisms are in high linkage disequilibrium (r(2)=0.97). Associations persisted after controlling for clinical characteristics and were unchanged after adjusting for case-control status. For rs927374, the neutrophil count of GG homozygotes (7.6±5.1) was 16% lower than that of CC homozygotes (9.0±5.2)., Conclusion: The TNFSF8 polymorphisms rs927374 and rs2295800 were associated with neutrophil count. This finding suggests that post-MI inflammatory response is genetically modulated.

Objective: To create a cohort for cost-effective genetic research, the Mayo Genome Consortia (MayoGC) has been assembled with participants from research studies across Mayo Clinic with high-throughput genetic data and electronic medical record (EMR) data for phenotype extraction., Participants and Methods: Eligible participants include those who gave general research consent in the contributing studies to share high-throughput genotyping data with other investigators. Herein, we describe the design of the MayoGC, including the current participating cohorts, expansion efforts, data processing, and study management and organization. A genome-wide association study to identify genetic variants associated with total bilirubin levels was conducted to test the genetic research capability of the MayoGC., Results: Genome-wide significant results were observed on 2q37 (top single nucleotide polymorphism, rs4148325; P=5.0 × 10(-62)) and 12p12 (top single nucleotide polymorphism, rs4363657; P=5.1 × 10(-8)) corresponding to a gene cluster of uridine 5'-diphospho-glucuronosyltransferases (the UGT1A cluster) and solute carrier organic anion transporter family, member 1B1 (SLCO1B1), respectively., Conclusion: Genome-wide association studies have identified genetic variants associated with numerous phenotypes but have been historically limited by inadequate sample size due to costly genotyping and phenotyping. Large consortia with harmonized genotype data have been assembled to attain sufficient statistical power, but phenotyping remains a rate-limiting factor in gene discovery research efforts. The EMR consists of an abundance of phenotype data that can be extracted in a relatively quick and systematic manner. The MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases.