Quantitative bias analysis of the association of type 2 diabetes mellitus with 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153)

An association between serum concentrations of persistent organic pollutants (POPs), such as 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153), and risk of type 2 diabetes mellitus (T2DM) has been reported. Conditional on body mass index (BMI) and waist circumference (WC), a higher serum PCB-153 concentration may be a marker of T2DM risk because it reflects other aspects of obesity that are related to T2DM risk and to PCB-153 clearance. To estimate the amount of residual confounding by other aspects of obesity, we performed a quantitative bias analysis on the results of a specific study. A physiologically-based pharmacokinetic (PBPK) model was developed to predict serum levels of PCB-153 for a simulated population. T2DM status was assigned to simulated subjects based on age, sex, BMI, WC, and visceral adipose tissue mass. The distributions of age, BMI, WC, and T2DM prevalence of the simulated population were tailored to closely match the target population. Analysis of the simulated data showed that a small part of the observed association appeared to be due to residual confounding. For example, the predicted odds ratio of T2DM that would have been obtained had the results been adjusted for visceral adipose tissue mass, for the ≥90th percentile of PCB-153 serum concentration, was 6.60 (95% CI 2.46-17.74), compared with an observed odds ratio of 7.13 (95% CI 2.65-19.13). Our results predict that the association between PCB-153 and risk of type 2 diabetes mellitus would not be substantially changed by additional adjustment for visceral adipose tissue mass in epidemiologic analyses. Confirmation of these predictions with longitudinal data would be reassuring.

Path Sampling Methods for Enzymatic Quantum Particle Transfer Reactions

The mechanisms of enzymatic reactions are studied via a host of computational techniques. While previous methods have been used successfully, many fail to incorporate the full dynamical properties of enzymatic systems. This can lead to misleading results in cases where enzyme motion plays a significant role in the reaction coordinate, which is especially relevant in particle transfer reactions where nuclear tunneling may occur. In this chapter, we outline previous methods, as well as discuss newly developed dynamical methods to interrogate mechanisms of enzymatic particle transfer reactions. These new methods allow for the calculation of free energy barriers and kinetic isotope effects (KIEs) with the incorporation of quantum effects through centroid molecular dynamics (CMD) and the full complement of enzyme dynamics through transition path sampling (TPS). Recent work, summarized in this chapter, applied the method for calculation of free energy barriers to reaction in lactate dehydrogenase (LDH) and yeast alcohol dehydrogenase (YADH). We found that tunneling plays an insignificant role in YADH but plays a more significant role in LDH, though not dominant over classical transfer. Additionally, we summarize the application of a TPS algorithm for the calculation of reaction rates in tandem with CMD to calculate the primary H/D KIE of YADH from first principles. We found that the computationally obtained KIE is within the margin of error of experimentally determined KIEs and corresponds to the KIE of particle transfer in the enzyme. These methods provide new ways to investigate enzyme mechanism with the inclusion of protein and quantum dynamics.