COMPARATIVE EPIDEMIOLOGY AND MULTIPHYSICS MODELING OF ENDOMETRIOSIS AND POLYCYSTIC OVARY SYNDROME: LINKING REPRODUCTIVE PHENOTYPES TO MDH-EOF TRANSPORT

Abstract

Endometriosis and polycystic ovary syndrome (PCOS) are prevalent reproductive disorders associated with pelvic pain, menstrual dysfunction, infertility, and impaired quality of life. Their overlapping epidemiological and physiological characteristics remain insufficiently understood, particularly in resource-constrained populations. This study investigates the incidence and epidemiological correlates of endometriosis and PCOS while developing a coupled magnetohydrodynamic (MHD) and electroosmotic flow framework to characterize fluid transport mechanisms relevant to female reproductive physiology. An analytical epidemiological design is proposed for reproductive-age women, incorporating demographic, reproductive, clinical, lifestyle, and biochemical variables. Multivariable logistic regression will identify independent correlates and estimate adjusted odds ratios with 95% confidence intervals. Complementarily, a mathematical model of reproductive-tract fluid transport will integrate MHD forces, electroosmotic effects, pressure gradients, viscous resistance, and physiologically relevant boundary conditions. Dimensionless transformations and analytical and numerical techniques will be employed to examine velocity, temperature, concentration, wall shear stress, and transport characteristics under varying magnetic-field and electrokinetic parameters. The epidemiological analysis is expected to identify age, menstrual irregularities, infertility, obesity-related factors, family history, and metabolic characteristics as important correlates of disease occurrence and co-occurrence. The mathematical model is expected to demonstrate that magnetic and electroosmotic forces significantly influence reproductive-fluid velocity, transport efficiency, and wall shear characteristics. Integrating epidemiological evidence with multiphysics modelling may provide a novel framework for understanding reproductive disorders and supporting predictive, personalized, and technologically informed reproductive healthcare strategies in diverse clinical settings.