Introduction: Diabetes mellitus and polycystic ovary syndrome (PCOS) are among the most prevalent chronic conditions worldwide, together affecting millions of women of reproductive age. Their coexistence substantially amplifies the risks of infertility, cardiometabolic complications, and long-term disability. Despite shared and interconnected pathophysiology, current management strategies often address metabolic and reproductive domains separately, resulting in suboptimal outcomes. Digital twin medicine—an emerging paradigm that creates a dynamic virtual replica of an individual for continuous simulation—offers a disruptive approach to unify these domains and guide personalized therapy. In this context, the present study aims to develop an integrated digital twin
framework capable of modeling both metabolic risk and reproductive health, thereby enabling simulation-based optimization of long-term therapeutic strategies in patients with diabetes and PCOS.
Methods: Retrospective datasets, including electronic medical records, laboratory results, and hormonal panels, were used to construct individualized digital twins. Each twin integrates glucose–insulin dynamics, body composition, lipid metabolism, and hypothalamic–pituitary–ovarian signaling pathways. Machine learning algorithms and mechanistic systems models calibrated the twins against historical patient trajectories. In silico simulations were then conducted to evaluate multiple interventions, ranging from lifestyle modifications and anti-diabetic therapies to ovulation-inducing agents and combination regimens. Outcomes were assessed across glycemic control (HbA1c, insulin resistance), weight management, menstrual cycle regulation, and fertility markers.
Results: Preliminary simulations revealed that digital twins can accurately reproduce retrospective disease trajectories and predict future states. Integrated interventions targeting both metabolic and reproductive parameters consistently outperformed single-domain therapies, achieving simulated reductions in HbA1c, improvements in BMI, normalization of androgen levels, and restoration of ovulatory function. Furthermore, scenario testing demonstrated the potential of digital twins to individualize therapy selection, minimize trial-and-error prescribing, and anticipate long-term complications.
Conclusion: This work positions digital twin medicine as a pioneering framework for chronic disease management at the intersection of metabolism and reproduction. By virtually uniting diabetes and PCOS management, the model offers clinicians a decision-support tool with direct translational impact—improving patient outcomes, reducing healthcare costs, and setting the foundation for personalized reproductive-metabolic care.
Keywords: Digital twin, personalized medicine, diabetes, PCOS, reproductive health