Benjamin Gochanour

BACKGROUND: Although diabetes is a known driver of heart failure with preserved ejection fraction, whether antecedent hyperinsulinemia drives preclinical diastolic dysfunction (DD) progression before overt hyperglycemia remains unknown. Therefore, we investigated whether elevated endogenous insulin and insulin resistance independently predict longitudinal DD progression and adverse cardiovascular outcomes. METHODS: In a community-based cohort study, we evaluated 1191 participants from the Olmsted County Heart Function Study (median age, 58.7 [interquartile range, 52.4–66.6 years]) who underwent echocardiography at baseline and after 4 years. Fasting insulin and homeostatic model assessment of insulin resistance were assessed. The primary end point was DD progression. Secondary outcomes (mortality, heart failure) were assessed in 2030 participants followed for a median of 10.97 (95% CI, 10.90–11.05) years for heart failure and a median of 18.1 (95% CI, 18.0–18.2) years for mortality. RESULTS: DD progression occurred in 7.5% of participants. Baseline fasting insulin and homeostatic model assessment of insulin resistance strongly predicted progression ( P P P <0.001). In secondary analyses, elevated insulin independently predicted all-cause (hazard ratio, 1.11 per 1-SD [95% CI, 1.02–1.21]) and cardiovascular mortality (hazard ratio, 1.16 [95% CI, 1.02–1.33]). Homeostatic model assessment of insulin resistance was similarly predictive of all-cause (hazard ratio, 1.11 [95% CI, 1.03–1.20]) and cardiovascular mortality (hazard ratio, 1.16 [95% CI, 1.02–1.32]). CONCLUSIONS: Elevated endogenous insulin and insulin resistance are predictors of progressive DD and long-term mortality, even after accounting for body mass index. Metabolic dysregulation may drive myocardial stiffening before diabetes onset, identifying a potential early therapeutic window for heart failure with preserved ejection fraction prevention.

Background: Clinical guidelines recommend high-intensity statin therapy for patients with acute coronary syndrome (ACS). However, high-intensity statins have been underused in this population. Objective: The objective of this study was to evaluate the feasibility of a pharmacist-initiated, team-based intervention for the delivery of individualized, guideline-directed, lipid-lowering therapy for patients with ACS. Methods: Patients admitted with ACS to cardiology hospital services at Mayo Clinic from August 1, 2021, to June 19, 2022, were assigned to a pharmacist-initiated, team-based intervention group or control group using a stepped wedge cluster study design. For the intervention group, pharmacists reviewed electronic health records and provided recommendations for lipid lowering therapy in hospital and at follow-up. In the control group, patients received usual care. Neither care team, nor study team were blinded to study assignments. The primary outcome was the proportion of patients with ACS discharged on high-intensity statins in the intervention group compared to controls. Secondary outcomes were (1) proportion of patients in the intervention group with a specific templated pharmacist intervention note in their electronic health records, (2) frequency of low-density lipoprotein (LDL) measurements in hospital, (3) proportion of patients with information related to lipid follow-up in their discharge summary, and (4) proportion of patients that received LDL monitoring at the outpatient follow-up 4 to 12 weeks post discharge. Results: There were 410 patients included in this study (median age 68, IQR 60-78 years) of whom 285 (69.5%) were male. Of the 402 patients alive at discharge, 355 (88.3%) were discharged taking a high-intensity statin, with no significant difference (P=.89) observed between groups. Lipid levels were measured in the hospital for 176/210 (83.8%) patients in the intervention group and 155/200 (77.5%) patients in the control group (P=.14). Fifty-four of 205 (26.3%) intervention patients alive at discharge had lipid-related recommendations in their discharge summary compared to 27/197 (13.7%) controls (P=.002). Forty-seven of 81 (58%) patients with lipid management recommendations provided in the discharge summary had LDL measured in the follow-up period compared with only 119/321 (37.1%) patients without these recommendations (P=.001). Of the 402 patients who survived to discharge, 166 (41.3%) had LDL measured at follow-up; the median LDL level was 63.5 (IQR 49-79) mg/dL, and distributions were similar by group (P=.95). Only 101/166 (60.8%) patients had follow-up LDL values below the target of 70 mg/dL. Conclusions: During hospitalization, there was no group difference in the primary outcome of high-intensity statin therapy. Feasibility of an effective pharmacist-initiated intervention for improvement of lipid management was demonstrated by entry of recommendations in the discharge summary and related adjustment in outpatient statin therapy. The main opportunity for future improvement in lipid management of patients with ACS is in longitudinal patient follow-up.

Introduction: Non-invasive assays are needed to better discriminate patients with prostate cancer (PCa) to avoid over-treatment of indolent disease. We analyzed 14 methylated DNA markers (MDMs) from urine samples of patients with biopsy-proven PCa relative to healthy controls and further studied discrimination of clinically significant PCa (csPCa) from healthy controls and Gleason 6 cancers. Methods: To evaluate the panel, urine from 24 healthy male volunteers with no clinical suspicion for PCa and 24 men with biopsy-confirmed disease across all Gleason scores was collected. Blinded to clinical status, DNA from the supernatant was analyzed for methylation signal within specific DNA sequences across 14 genes (HES5, ZNF655, ITPRIPL1, MAX.chr3.6187, SLCO3A1, CHST11, SERPINB9, WNT3A, KCNB2, GAS6, AKR1B1, MAX.chr3.8028, GRASP, ST6GALNAC2) by target enrichment long-probe quantitative-amplified signal assays. Results: Utilizing an overall specificity cut-off of 100% for discriminating normal controls from PCa cases across the MDM panel resulted in 71% sensitivity (95% CI: 49–87%) for PCa detection (4/7 Gleason 6, 8/12 Gleason 7, 5/5 Gleason 8+) and 76% (50–92%) for csPCa (Gleason ≥ 7). At 100% specificity for controls and Gleason 6 patients combined, MDM panel sensitivity was 59% (33–81%) for csPCa (5/12 Gleason 7, 5/5 Gleason 8+). Conclusions: MDMs assayed in urine offer high sensitivity and specificity for detection of clinically significant prostate cancer. Prospective evaluation is necessary to estimate discrimination of patients as first-line screening and as an adjunct to prostate-specific antigen (PSA) testing.

Agent‐based modelling (ABM) shows promise for animal movement studies. However, a robust, open‐source and spatially explicit ABM coding platform is currently lacking. We present abmR, an R package for conducting continental‐scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations. Potential future additions to this open‐source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with other R movement modelling packages.

Agent-based modeling (ABM) shows promise for animal movement studies. However, a robust, open-source, and spatially explicit ABM coding platform is currently lacking. We present abmR, an R package for conducting continental-scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein-Uhlenbeck (OU) process. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations. Potential future additions to this open-source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with other R movement modeling packages.