Scott, Christopher G.

Background Neprilysin is a metalloprotease involved in proteolysis of numerous peptides, including natriuretic peptides, and is of prognostic and therapeutic importance in heart failure with reduced ejection fraction. No studies have investigated circulating neprilysin in the community, its clinical correlates, or its relationship to cardiovascular disease in the general population. Methods and Results Plasma neprilysin was measured in 1536 participants from Olmsted County, Minnesota, using a commercially available sandwich ELISA assay. Clinical and echocardiographic correlates and subsequent outcomes were determined. Soluble neprilysin is non‐normally distributed in the community (median: 3.9 ng/mL; interquartile range: 1.0–43.0 ng/mL). There was no relationship between plasma neprilysin and age (Spearman correlation: −0.04, P =0.16); body mass index (Spearman correlation: −0.04, P =0.16); glomerular filtration rate (Spearman correlation: −0.007, P =0.8); or A‐, B‐, or C‐type natriuretic peptides (Spearman correlation: 0.03, P =0.22; −0.001, P =0.96; 0.01, P =0.67, respectively). Among tertiles of neprilysin, the lowest tertile group had the highest prevalence of smokers ( P <0.001), hypertension ( P =0.04), dyslipidemia ( P =0.03), and diastolic dysfunction ( P =0.02). Soluble neprilysin was not prospectively associated with death or heart failure over a median of 10.7 years. Conclusions In a large community‐based cohort, for the first time, we described the distribution of circulating neprilysin in the general community. We observed that neprilysin does not correlate with natriuretic peptide levels and is not independently associated with adverse outcomes. The novel associations observed between low soluble neprilysin levels and an adverse cardiometabolic and smoking profile requires further investigation.

Abstract Background: Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes. Methods: Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes. Results: Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [−2.2 cm3 (95% confidence interval, −2.7 to −1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm3) versus below (DV, −3.5 cm3 vs. −1.0 cm3; P &lt; 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition. Conclusions: High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition. Impact: We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Abstract Purpose: The evidence on associations of mammographic breast density with breast cancer risk by combination of tumor aggressiveness features is limited. We examined associations of breast density phenotypes with risk of aggressive breast tumor features by menopausal status, and current postmenopausal hormone therapy. Methods: This study included 2,635 invasive breast cancer cases and 4,059 controls from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses Health Study, Nurses Health Study II, and San Francisco Mammography Registry. Percent breast density, absolute dense and non-dense areas were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density measures with risk of breast tumor aggressiveness (defined as presence of 2 or more of the following tumor characteristics: size ≥2cm, grade 2 or 3, or positive nodes), stratified by menopausal status and current hormone therapy (i.e., premenopausal, postmenopausal/estrogen therapy, postmenopausal/combined therapy, and postmenopausal/no hormones). We also evaluated differences in the strength of associations across categories. In a secondary analysis, we examined these associations while excluding cases with mammogram date within 2 years of diagnosis. Results: Positive associations of percent density and dense area and inverse associations of non-dense area with breast cancer risk were stronger in aggressive vs. non-aggressive tumors (OR=2.62, 95%CI 2.08-3.31 vs. OR=1.94, 95%CI 1.62-2.33 for percent density≥51% vs. 11-25%, p-heterogeneity=0.001; OR=1.89, 95%CI 1.54-2.31 vs. OR=1.65, 95%CI 1.41-1.93 for dense area 4th vs. 2nd quartile, p-heterogeneity=0.015; OR=0.56, 95%CI 0.44-0.72 vs. OR=0.71, 95%CI 0.59-0.86 for non-dense area 4th vs 2nd quartile, p-heterogeneity=0.007, respectively). These patterns were similar across all menopausal and hormone therapy groups (P-interactions=0.62, 0.76, and 0.23, for percent density, dense area and non-dense area, respectively). Excluding cases diagnosed within 2 years of mammography resulted in similar findings. Conclusion: Mammographic density phenotypes were more strongly associated with aggressive cancer (having two or more of the following: size ≥2cm, grade 2 or 3, or positive nodes) vs. non-aggressive types of breast cancer across categories of menopause and hormone therapy types. Citation Format: Lusine Yaghjyan, Rulla Tamimi, Kimberly Bertrand, Christopher G. Scott, Matthew R. Jensen, Shane Pankratz, Kathleen Brandt, Daniel Visscher, Aaron Norman, Fergus Cough, John Shepherd, Bo Fan, Yunn-Yi Chen, Lin Ma, Andrew H. Beck, Steven R. Cummings, Karla Kerlikowske, Celine Vachon. Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B27.

Objective The presence of aortic paravalvular leak (PVL) is associated with lower survival, but a direct comparison of its impact after transcatheter aortic valve replacement (TAVR) versus surgical aortic valve replacement (SAVR) has not been performed. This study sought to determine the differential influence of PVL on survival following TAVR versus SAVR and in patients with varying levels of risk as defined by the Society of Thoracic Surgeons (STS) risk score. Methods Patients with and without postprocedural PVL were identified from 2290 patients undergoing TAVR or SAVR at Mayo Clinic between 2008 and 2014. The primary endpoint was overall survival. Results There were 588 patients with PVL (374 TAVR, 214 SAVR): age 78±11 years, 63% male and mean follow-up of 3±2 years. PVL was trivial/mild in 442 (75%) patients. In propensity-matched analyses (n=86 per group), the overall survival at 1 and 4 years was 93% and 56% vs 89% and 61% in patients with PVL after TAVR versus SAVR, respectively (p=0.43). The presence or degree of PVL severity had no influence on survival of patients with high STS score (≥8%), while the presence of greater than mild PVL predicted worse survival in those with STS score <8%. During the first year after PVL diagnosis, while either improvement or stable PVL grade was seen in the majority of patients, worsening of PVL grade was more common in the TAVR group (19%) versus the SAVR group (4%) (p<0.0001). Conclusions At mid-term follow-up, the presence of PVL was associated with equally unfavourable outcomes following SAVR or TAVR. In patients with high STS risk score, the presence of PVL was not independently associated with increased mortality.

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.