Abstract-Pulse pressure, the difference between systolic and diastolic blood pressure, is an independent risk factor for cardiovascular disease. Increased pulse pressure reflects reduced compliance of arteries and is a marker of atherosclerosis. To locate genes that affect pulse pressure, a genome-wide linkage scan for quantitative trait loci influencing pulse pressure was performed using variance components methods as implemented in sequential oligogenic linkage analysis routines. The analysis sample included 10 798 participants in 3320 families who were recruited as part of the Family Blood Pressure Program and were phenotyped with an oscillometric blood pressure measurement device using a consistent protocol across centers. Pulse pressure was adjusted for the effects of sex, age, age 2 , age-by-sex interaction, age 2 -by-sex interaction, body mass index, and field center to remove sources of variation other than the genetic effects related to pulse pressure. Significant linkage was observed on chromosome 18 (logarithm of odds [LOD]ϭ3.2) in a combined racial sample, chromosome 20 (LODϭ4.4), and 17 (LODϭ3.6) in Hispanics, chromosome 21 (LODϭ4.3) in whites, chromosome 19 (LODϭ3.1) in a combined sample of blacks and whites, and chromosome 7 (logarithm of odds [LOD]ϭ3.1) in blacks from the GenNet Network. Our genome scan shows significant evidence for linkage for pulse pressure in multiple areas of the genome, supporting previous published linkage studies. The identification of these loci for pulse pressure and the apparent congruence with other blood pressure phenotypes provide increased support that these regions contain genes influencing blood pressure phenotypes.

Background:Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more.Methods:In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered.Results:Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months.Conclusions:These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

BACKGROUND:The lipoprotein scavenger receptor BI (SCARB1) rs10846744 noncoding variant is significantly associated with atherosclerotic disease independently of traditional cardiovascular risk factors. We identified a potentially novel connection between rs10846744, the immune checkpoint inhibitor lymphocyte activation gene 3 (LAG3), and atherosclerosis. METHODS:In vitro approaches included flow cytometry, lipid raft isolation, phosphosignaling, cytokine measurements, and overexpressing and silencing LAG3 protein. Fasting plasma LAG3 protein was measured in hyperalphalipoproteinemic (HALP) and Multi-Ethnic Study of Atherosclerosis (MESA) participants. RESULTS:In comparison with rs10846744 reference (GG homozygous) cells, LAG3 protein levels by flow cytometry (P < 0.001), in lipid rafts stimulated and unstimulated (P = 0.03), and phosphosignaling downstream of B cell receptor engagement of CD79A (P = 0.04), CD19 (P = 0.04), and LYN (P = 0.001) were lower in rs10846744 risk (CC homozygous) cells. Overexpressing LAG3 protein in risk cells and silencing LAG3 in reference cells confirmed its importance in phosphosignaling. Secretion of TNF-α was higher (P = 0.04) and IL-10 was lower (P = 0.04) in risk cells. Plasma LAG3 levels were lower in HALP carriers of the CC allele (P < 0.0001) and by race (P = 0.004). In MESA, race (P = 0.0005), age (P = 0.003), lipid medications (P = 0.03), smoking history (P < 0.0001), and rs10846744 genotype (P = 0.002) were independent predictors of plasma LAG3. In multivariable regression models, plasma LAG3 was significantly associated with HDL-cholesterol (HDL-C) (P = 0.007), plasma IL-10 (P < 0.0001), and provided additional predictive value above the Framingham risk score (P = 0.04). In MESA, when stratified by high HDL-C, plasma LAG3 was associated with coronary heart disease (CHD) (odds ratio 1.45, P = 0.004).CONCLUSION: Plasma LAG3 is a potentially novel independent predictor of HDL-C levels and CHD risk.

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Background Cognitive function is essential to effective self‐management of heart failure (HF). Alzheimer's disease and Alzheimer's disease‐related dementias (AD/ADRD) can coexist with HF, but its exact prevalence and impact on health care utilization and death are not well defined. Methods Residents from 7 southeast Minnesota counties with a first‐ever diagnosis code for HF between January 1, 2013 and December 31, 2018 were identified. Clinically diagnosed AD/ADRD was ascertained using the Centers for Medicare and Medicaid (CMS) Chronic Conditions Data Warehouse algorithm. Patients were followed through March 31, 2020. Cox and Andersen‐Gill models were used to examine associations between AD/ADRD (before and after HF) and death and hospitalizations, respectively. Results Among 6336 patients with HF (mean age [SD] 75 years [14], 48% female), 644 (10%) carried a diagnosis of AD/ADRD at index HF diagnosis. The 3‐year cumulative incidence of AD/ADRD after HF diagnosis was 17%. During follow‐up (mean [SD] 3.2 [1.9] years), 2618 deaths and 15,475 hospitalizations occurred. After adjustment, patients with AD/ADRD before HF had nearly a 2.7 times increased risk of death, but no increased risk of hospitalization compared to those without AD/ADRD. When AD/ADRD was diagnosed after the index HF date, patients experienced a 3.7 times increased risk of death and a 73% increased risk of hospitalization compared to those who remain free of AD/ADRD. Conclusions In a large, community cohort of patients with incident HF, the burden of AD/ADRD is quite high as more than one‐fourth of patients with HF received a diagnosis of AD/ADRD either before or after HF diagnosis. AD/ADRD markedly increases the risk of adverse outcomes in HF underscoring the need for future studies focused on holistic approaches to improve outcomes.

Genomic, proteomic, epigenomic, and other “omic” data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called “the omic funnel.” This model parallels the classic “Data, Information, Knowledge, Wisdom pyramid” and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

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Aim: Pharmacogenomics (PGx) tests are performed on whole-blood or saliva specimens. In patients with a transplanted liver, PGx results may be discordant with hepatic drug metabolizing enzyme activity. We evaluate the incidence and impact of PGx testing in liver transplant recipients, detail potential errors and describe clinical decision support (CDS) solution implemented. Materials & methods: A retrospective cohort study of liver transplant recipients at Mayo Clinic who underwent PGx testing between 1 January 1996 and 7 October 2019 were characterized. Impact of a CDS solution was evaluated. Results: There were 129 PGx tests in 117 patients. PGx testing incidence increased before (per year incidence rate ratio = 1.45, 95% CI: 1.20–1.74, p < 0.001) and after transplant (incidence rate ratio = 1.48, 95% CI: 1.27–1.72, p < 0.001). Three erroneous PGx tests were avoided 6 months following CDS implementation. Conclusion: Incidence of PGx testing in liver transplant recipients is increasing, leading to erroneous therapeutic decisions. CDS interventions and education are needed to prevent errors.