Increased endogenous acetate production (Ra) in rodents has been shown to activate the parasympathetic nervous system and thereby promote increased glucose-stimulated insulin secretion (GSIS), increased ghrelin secretion, hyperphagia and obesity.

To examine whether rates of acetate turnover are different in lean versus obese humans and whether increased acetate turnover promotes increased GSIS and increased ghrelin secretion in humans.

Basal acetate Ra was measured following an overnight fast in lean (BMI: 21.3 ± 1.1 Kg/m2) and obese (30.2 ± 0.9 Kg/m2, P = 0.00001) individuals. The subjects underwent two hyperglycemic (10 mmol/L) clamp studies to measure GSIS during a basal acetate infusion and during a high-dose acetate infusion increasing plasma acetate concentrations ∼5-fold.

Basal acetate Ra was 30% higher in the lean compared to the obese subjects (257 ± 27 vs. 173 ± 18 μmol/min; P = 0.025). Basal plasma insulin concentrations were 4-fold higher in the obese than the lean subjects (P = 0.008) and increased 5-fold during hyperglycemia in both groups, independent of changes in plasma acetate concentrations. Fasting plasma ghrelin concentrations were 35% lower in the obese compared to the lean subjects (P = 0.015). During the hyperglycemic clamp, plasma ghrelin decreased by 42% in the lean group (P < 0.022 vs. basal) and did not change in the obese group.

Rates of endogenous acetate turnover are ∼30% higher in the lean subjects compared to the obese subjects, and increasing plasma acetate turnover does not promote increased GSIS or ghrelin secretion in either group.

The need to diversify the biomedical research workforce is well documented. The importance of fostering the careers of fledgling underrepresented background (URB) biomedical researchers is evident in light of the national and local scarcity of URB scientists in biomedical research. The Career Education and Enhancement for Health Care Research Diversity (CEED) program at the University of Pittsburgh Institute for Clinical Research Education (ICRE) was designed to promote career success and help seal the “leaky pipeline” for URB researchers. In this study, we aimed to quantify CEED’s effect on several key outcomes by comparing CEED Scholars to a matched set of URB ICRE trainees not enrolled in CEED using data collected over 10 years.

We collected survey data on CEED Scholars from 2007 to 2017 and created a matched set of URB trainees not enrolled in CEED using propensity score matching in a 1:1 ratio. Poisson regression was used to compare the rate of publications between CEED and non-CEED URB trainees after adjusting for baseline number of publications.

CEED has 45 graduates. Seventy-six percent are women, 78% are non-White, and 33% are Hispanic/Latino. Twenty-four CEED Scholars were matched to non-CEED URB trainees. Compared to matched URB trainees, CEED graduates had more peer-reviewed publications (p=0.0261) and were more likely to be an assistant professor (p=0.0145).

Programs that support URB researchers can help expand and diversify the biomedical research workforce. CEED has been successful despite the challenges of a small demographic pool.

To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”

As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.

With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.

The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.

Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.