Kullo, Iftikhar J.

AbstractBackgroundThe extent to which obesity and genetics determine postoperative complications is incompletely understood.MethodsWe performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome‐wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease‐9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity‐risk single‐nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components.ResultsIndividuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7–5.5, p < 3.1 × 10−20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2–2.3, p < 2.5 × 10−5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8–2.5], p = 1.4 × 10−6) and postoperative infection (OR 1.6 [95% CI 1.4–1.9], p = 3.1 × 10−6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures.ConclusionsClinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.

Abstract Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.

Objective— It is unclear to what extent genetic susceptibility variants are shared between peripheral artery disease (PAD) and coronary heart disease (CHD), both manifestations of atherosclerotic vascular disease. We investigated whether common and low-frequency/rare variants in loci associated with CHD are also associated with PAD. Approach and Results— Targeted sequencing of 41 genomic regions associated with CHD in genome-wide association studies was performed in 1749 PAD cases (65±11 years, 61% men) and 1855 controls (60±11 years, 56% men) of European ancestry. PAD cases had a resting/postexercise ankle-brachial index ≤0.9, or history of lower extremity revascularization; controls had no history of PAD. We tested the association of common (defined as minor allele frequency ≥5%) variants with PAD assuming an additive genetic model with adjustment for age and sex. To identify low-frequency/rare variants (minor allele frequency <5%) associated with PAD, we conducted gene-level analyses using sequence kernel association test and permutation test. After Bonferroni correction, we found common variants in SH2B3, ABO , and ZEB2 to be associated with PAD ( P <4.5×10 −5 ). At the gene level, the strongest associations were for LPL and SH2B3 . Conclusions— Targeted sequencing of 41 genomic regions associated with CHD revealed several common variants/genes to be associated with PAD, highlighting the basis of shared genetic susceptibility between CHD and PAD.

Background: The Asp358Ala variant (rs2228145; A>C) in the IL (interleukin)-6 receptor ( IL6R ) gene has been implicated in the development of abdominal aortic aneurysms (AAAs), but its effect on AAA growth over time is not known. We aimed to investigate the clinical association between the IL6R -Asp358Ala variant and AAA growth and to assess the effect of blocking the IL-6 signaling pathway in mouse models of aortic aneurysm rupture or dissection. Methods: Using data from 2863 participants with AAA from 9 prospective cohorts, age- and sex-adjusted mixed-effects linear regression models were used to estimate the association between the IL6R -Asp358Ala variant and annual change in AAA diameter (mm/y). In a series of complementary randomized trials in mice, the effect of blocking the IL-6 signaling pathways was assessed on plasma biomarkers, systolic blood pressure, aneurysm diameter, and time to aortic rupture and death. Results: After adjusting for age and sex, baseline aneurysm size was 0.55 mm (95% CI, 0.13–0.98 mm) smaller per copy of the minor allele [C] of the Asp358Ala variant. Change in AAA growth was −0.06 mm per year (−0.18 to 0.06) per copy of the minor allele; a result that was not statistically significant. Although all available worldwide data were used, the genetic analyses were not powered for an effect size as small as that observed. In 2 mouse models of AAA, selective blockage of the IL-6 trans-signaling pathway, but not combined blockage of both, the classical and trans-signaling pathways, was associated with improved survival ( P <0.05). Conclusions: Our proof-of-principle data are compatible with the concept that IL-6 trans-signaling is relevant to AAA growth, encouraging larger-scale evaluation of this hypothesis.

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a “virtual proteomic” approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08–1.58; P =0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66–0.94; P =0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Purpose We assessed the decision-making of individuals pursuing genomic sequencing without a requirement for pretest genetic counselling. We sought to describe the extent to which individuals who decline genetic counselling reported decisional conflict or struggled to make a decision to pursue genomic testing. Methods We administered a 100-item survey to 3037 individuals who consented to the Return of Actionable Variants Empirical study, a genomic medicine implementation study supported by the National Institutes of Health (USA) eMERGE consortium. The primary outcomes of interest were self-reported decisional conflict about the decision to participate in the study and time required to reach a decision. Results We received 2895 completed surveys (response rate=95.3%), and of these respondents 97.8% completed the decisional conflict scale in its entirety. A majority of individuals (63%) had minimal or no decisional conflict about the pursuit of genomic sequencing and were able to reach a decision quickly (78%). Multivariable logistic regression analyses identified several characteristics associated with decisional conflict, including lower education, lower health literacy, lower self-efficacy in coping, lack of prior experience with genetic testing, not discussing study participation with a family member or friend, and being male. Conclusion As genomic sequencing is used more widely, genetic counselling resources may not be sufficient to meet demand. Our results challenge the notion that all individuals need genetic counselling in order to make an informed decision about genomic sequencing.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P <0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real‐time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real‐time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real‐time risk calculator deployed at the point of care.

Familial Hypercholesterolemia (FH) is an inherited disorder associated with increased cardiovascular risk that requires patients to make multiple impactful decisions regarding the management of their condition. Patient decision aids (PDAs) can facilitate shared decision-making (SDM) and enable patients to make choices that are concordant with their goals and values. To inform the development of a PDA for patients with FH, we employed a qualitative inductive approach and focus group discussions with patients, physicians, and genetic counselors. We explored and analyzed the perceptions and understanding of these stakeholders related to a PDA for FH and identified important concepts to include in the PDA. Categories emerging from focus group discussions included: (a) perceptions of a PDA related to FH; (b) discussion about the content of a PDA related to FH; and (c) SDM. In general, stakeholders were in favor of developing tools which can inform and individualize discussion about genetic testing and treatment options for FH. Physicians valued a tool that facilitates knowledge transfer to FH patients. Patients desired a tool to help them understand the genetic aspects of and treatment options related to FH. Genetic counselors emphasized the inclusion of visual aids to support discussion with patients. Potential barriers to and facilitators of PDA implementation were identified. The input of various stakeholders will inform the development of a prototype tool that will be iteratively tested before implementation in the clinical setting.