Shamanna Paramesh, Maheshwari Anuj, Keshavamurthy Ashok, Bhat Sanjay, Kulkarni Abhijit, R Shivakumar, K Kumar, Gupta Mukulesh, Thajudeen Mohamed, Kulkarni Ranjita, Patil Shashikiran, Joshi Shashank
Bangalore Diabetes Centre, Bangalore, Karnataka, India.
Shri Hari Kamal Diabetes and Heart Clinic, Lucknow, Uttar Pradesh, India.
AACE Clin Case Rep. 2024 Nov 22;11(1):70-74. doi: 10.1016/j.aace.2024.11.004. eCollection 2025 Jan-Feb.
Clinical manifestations of polycystic ovary syndrome (PCOS) are heterogeneous, with hallmarks including anovulation, androgen excess, and insulin resistance.
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%).
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.
This case highlights the potential of DT technology in managing PCOS, showing significant metabolic and reproductive improvements, suggesting promising future research directions.
多囊卵巢综合征（PCOS）的临床表现具有异质性，其特征包括无排卵、雄激素过多和胰岛素抵抗。
一名具有典型PCOS特征的38岁女性，伴有高血压、肥胖，空腹和餐后胰岛素水平升高。她被纳入数字孪生（DT）平台，该平台利用人工智能和物联网，通过预测餐后血糖反应并通过移动应用程序推荐餐后血糖反应较低的替代食物，来提供个性化营养。360天后，观察到显著改善。体重从65.4千克降至57.3千克（-12.4%）；体重指数从26.2降至22.96（-12.4%）；腰围从104厘米降至86.3厘米（-17.0%）；诊所收缩压/舒张压从144/93降至102/80毫米汞柱（-29.17%/-13.98%）；空腹胰岛素从27.6降至15.5微国际单位/毫升（-43.8%）；餐后胰岛素从182.4降至23.8微国际单位/毫升（-87.0%）；胰岛素抵抗稳态模型评估从6.47降至3.48（-46.2%）；估计肾小球滤过率从116提高到128毫升/分钟/1.73平方米（+10.3%）；尿微量白蛋白肌酐比值从596降至73毫克/克（-87.8%）。超声显示卵巢体积减小，脂肪肝浸润改善，而计算机断层扫描显示心外膜脂肪（21.8%）、心包脂肪（69.9%）和内脏脂肪（44.4%）显著减少。
该病例显示了DT技术在管理PCOS方面的有效应用，显著改善了体重、体重指数、胰岛素、血压和血脂状况。它支持了人工智能驱动的个性化干预在慢性病管理中的潜力。
该病例突出了DT技术在管理PCOS方面的潜力，显示出显著的代谢和生殖改善，为未来的研究方向提供了希望。
