A Comprehensive Review on the Metabolic Cooperation Role of Nuclear Factor E2-Related Factor 2 and Fibroblast Growth Factor 21 against Homeostasis Changes in Diabetes

Objective: Type 1 and type 2 diabetes are associated with metabolic disorders including hyperglycemia, hyperlipidemia, and inflammation, leading to the production of reactive oxygen species and nitrogen activators. In these cases, some of the body’s innate factors are activated to cope with these dangerous situations. The purpose of the review is to explain the collaboration between the nuclear factor E2-related factor 2 (NRF2) and fibroblast growth factor 21 (FGF21) in homeostasis and body metabolism with a focus on diabetes. Materials and methods: This review is based on searching the PubMed database, SCOPUS, Elsevier and citation lists of relevant publications. Subject heading and key words used include diabetes, oxidative stress, inflammation, NRF2, and FGF21. Only articles in English were included. Results: NRF2 and FGF21 are two attractive biomarkers for the diagnosis of specific metabolic disorders and therapeutic targets, which have been implicated as therapeutic targets for the management of diabetic complications. The combination of both factors leads to the regulation of antioxidant and anti-inflammatory responses and metabolic pathways. Conclusions: Given most studies of NRF2- and FGF21-based therapeutic interventions in animal models and the possibility of not achieving the same results in humans, further clinical studies are needed to determine the efficacy of NRF2 and FGF21 in treatment of patients with diabetes

Advancing Data-Driven Healthcare Models with Optimization-Based Calibration Methods

A number of health studies consider Markov models of individual-level disease progression. Typically these studies use simulation-based methods (SBMs) to calibrate the parameters of the models so that model outputs agree with target data related to the disease/condition under consideration. SBMs, however, require a large amount of computation time, ultimately limiting the complexity of models that these studies can actually calibrate in practice. The black-box nature of these methods also has left open a number of fundamental questions (e.g., understanding conditions under which the parameters of Markov models of disease progression are identifiable). This thesis develops new optimization-based calibration methods that require less computation time than SBMs, explores fundamental questions left open by SBMs, and constructs Markov models for Opioid Use Disorder (OUD) progression and Total Joint Replacement (TJR) recovery. In Chapter 2 of this thesis, we consider using disease prevalence target data to calibrate a class of discrete-time Markov chain (DTMC) models that have covariate-dependent transition probabilities. We formulate the calibration problem as a (deterministic) non-convex optimization problem and consider solving it with first order methods that just require relatively inexpensive matrix-vector multiplications (instead of simulations). We investigate the performance of our methods through computational experiments and apply them in a case study on Opioid Use Disorder. Chapter 3 of this thesis considers the problem of identifying the transition probabilities of time-homogeneous DTMC models of natural history of disease from target mortality data. We establish mathematical conditions under which the transition probabilities are identifiable, and we present a polynomial-time algorithm for computing the values of the transition probabilities when they are identifiable. Our approach is premised on an interesting connection to the theory of homogeneous symmetric polynomials. In Chapter 4, we propose a Markov Decision Process (MDP) model that concurrently uses multiple patient-reported and performance-based measurements as variables that define the state of the patients to dynamically assess the recovery progress of TJR patients. The model can be used as a tool to devise personalized post-discharge intervention plans for TJR patients