Digital Twin Technology in Resolving Polycystic Ovary Syndrome and Improving Metabolic Health: A Comprehensive Case Study.

Q3 Medicine AACE Clinical Case Reports Pub Date : 2024-11-22 eCollection Date: 2025-01-01 DOI:10.1016/j.aace.2024.11.004

Paramesh Shamanna, Anuj Maheshwari, Ashok Keshavamurthy, Sanjay Bhat, Abhijit Kulkarni, Shivakumar R, Kumar K, Mukulesh Gupta, Mohamed Thajudeen, Ranjita Kulkarni, Shashikiran Patil, Shashank Joshi

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Abstract

Background: Clinical manifestations of polycystic ovary syndrome (PCOS) are heterogeneous, with hallmarks including anovulation, androgen excess, and insulin resistance.

Case report: A 38-year-old female with typical PCOS features presented with hypertension, obesity, and elevated fasting and postprandial insulin levels. She was enrolled in the Digital Twin (DT) platform, which uses artificial intelligence and Internet of Things to deliver personalized nutrition by predicting postprandial glucose responses and suggesting alternative foods with lower postprandial glucose response through a mobile app. After 360 days, significant improvements were observed. Weight decreased from 65.4 kg to 57.3 kg (-12.4%); body mass index lowered from 26.2 to 22.96 (-12.4%); Waist circumference reduced from 104 cm to 86.3 cm (-17.0%); clinic systolic blood pressure/diastolic blood pressure reduced from 144/93 to 102/80 mmHg (-29.17%/-13.98%); fasting insulin dropped from 27.6 to 15.5 μIU/mL (-43.8%); postprandial insulin decreased from 182.4 to 23.8 μIU/mL (-87.0%); Homeostatic Model Assessment of Insulin Resistance reduced from 6.47 to 3.48 (-46.2%); estimated glomerular filteration rate improved from 116 to 128 mL/min/1.73m2 (+10.3%); urine microalbumin creatinine ratio decreased from 596 to 73 mg/g (-87.8%). Ultrasound showed reduced ovarian volume and improved fatty liver infiltration, while computed tomography scan revealed significant reductions in epicardial (21.8%), pericardial (69.9%), and visceral fat (44.4%).

Discussion: This case shows the effective use of DT technology for managing PCOS, significantly improving weight, body mass index, insulin, blood pressure, and lipid profile. It supports the potential of artificial intelligence-driven, personalized interventions in chronic disease management.

Conclusion: This case highlights the potential of DT technology in managing PCOS, showing significant metabolic and reproductive improvements, suggesting promising future research directions.

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