Abstract
Digital twin (DT) technology-real-time, data-driven virtual models of individuals-offers transformative potential in managing cardiometabolic-based chronic disease (CMBCD). The novel CMBCD model configures a progression from interacting adiposity, dysglycemia, hypertension, dyslipidemia, and residual drivers to certain cardiovascular diseases (CVDs) such as atherosclerosis, heart failure, and atrial fibrillation. Insulin resistance and type 2 diabetes (T2D) are central to this cascade, requiring management tactics where DT serves as a precision-medicine tool. A systematic search across PubMed, Embase, Web of Science, Scopus, and Cochrane identified 12 eligible studies (retrospective cohort, randomized clinical trials, and frameworks) to explore the role of DT in T2D and CMBCD. Due to data duplication and lack of homogeneity, meta-analysis was not feasible, prompting this narrative synthesis. DT-guided benefits were identified across the CMBCD spectrum: (1) adiposity - body mass index reduction by 1.8 kg/m² with decreased visceral adiposity; (2) dysglycemia - hemoglobin A1c reduction as much as 1.8% with 89% achieving glycemic targets, as well as fewer medications used, time-in-range (TIR) improved 69.7% to 86.9%, and time-above-range decreased; (3) hypertension - normal blood pressure cases increased from 46.4% to 63.1% and outperforming standard care; (4) dyslipidemia - triglycerides decreased 18.8% and high density lipoprotein (HDL) increased 6.8%; (5) residual - metabolic dysfunction-associated steatotic liver disease improvements; and (6) complications - improved chronic kidney disease, retinopathy, and cataract prediction, comprehensive CVD risk reduction, and medication de-escalation. In sum, DT appears promising for comprehensive and personalized T2D care and cardiometabolic risk reduction. Broader validation in diverse populations, refined implementation strategies, and eventual systematic reviews and meta-analyses are needed.