Digital Twin Technology Advances Precision Care in in Cardiometabolic Risk Management: AHJ, December 2025

Understanding the Role of Digital Twins in Cardiometabolic Health

Cardiometabolic disease spans a progressive continuum, beginning with abnormal adiposity and advancing through dysglycemia, dyslipidemia, hypertension, and ultimately major cardiovascular events. Traditional tools such as BMI or single-point lab tests often fall short in predicting disease progression or identifying early risk. Digital twin technology, by contrast, integrates multiple streams of real-time data—clinical records, physiological signals, imaging results, and lifestyle metrics—to create a dynamic model of a patient’s unique cardiometabolic profile.

Study Overview

To evaluate the impact of digital twin technology across cardiometabolic conditions, researchers conducted a systematic search of PubMed, Embase, Web of Science, Scopus, and the Cochrane Library. Twelve studies met the inclusion criteria. However, several of these studies used overlapping patient cohorts, and the heterogeneity in study design prevented a meta-analysis. The authors therefore conducted a narrative synthesis, organizing the evidence across different stages of cardiometabolic disease, from abnormal adiposity to cardiovascular outcomes.

Key Findings

• Across the 12 included studies, digital twin technology consistently demonstrated meaningful clinical improvements.
• In abnormal adiposity, DT-guided interventions resulted in measurable reductions in visceral fat and a notable decrease in BMI.
• For dysglycemia and type 2 diabetes, digital twins were associated with substantial HbA1c reductions, higher achievement of glycemic targets, reduced medication requirements, and significant improvements in continuous glucose monitoring measures. These findings highlight the ability of DTs to personalize glycemic management with greater precision than traditional care.
• In hypertension-based chronic disease, digital twin interventions improved blood pressure control, increasing the proportion of individuals achieving normal readings and allowing some patients to safely discontinue antihypertensive therapy.
• Lipid profiles also improved, with reductions in triglycerides and increases in HDL cholesterol.
• Beyond traditional risk factors, digital twin models showed promise in predicting complications such as chronic kidney disease, retinopathy, cataracts, and hepatic fibrosis, with high diagnostic accuracy.

Early cardiovascular outcome studies suggested improved overall risk profiles and potential for medication de-escalation, although long-term data remain limited. However, the narrative review also underscores critical limitations. The current body of evidence is small, with several studies sharing overlapping populations and many pilot trials offering short-term insights rather than long-term outcomes. Standardizing digital twin platforms, assessing cost-effectiveness, and validating results across diverse populations will be essential before widespread clinical adoption is feasible.

Potential Clinical Inference: A New Frontier in Personalized Cardiometabolic Care

For practicing clinicians, this emerging evidence positions digital twin technology as a powerful precision-medicine tool capable of enhancing risk prediction, treatment personalization, and early intervention. Digital twins can simulate a patient’s disease course, anticipate treatment response, and identify high-risk trajectories that might otherwise go undetected. This could be especially valuable in managing type 2 diabetes, hypertension, and obesity, where treatment inertia and fluctuating control frequently complicate care.

Reference: Seyedi SA, González-Rivas JP, Mellacheruvu P, Mellacheruvu A, Aledavood SP, Mechanick JI. Cardiometabolic Risk Reduction with Digital Twinning: A Narrative Review. American Heart Journal. 2025 Dec 1;290:10-1.

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