Joshi Shashank, Shamanna Paramesh, Dharmalingam Mala, Vadavi Arun, Keshavamurthy Ashok, Shah Lisa, Mechanick Jeffrey I
Department of Diabetology and Endocrinology, Lilavati Hospital and Research center, Mumbai, India.
Department of Diabetes, Bangalore Diabetes Centre, Bangalore, Karnataka, India.
Endocr Pract. 2023 Dec;29(12):960-970. doi: 10.1016/j.eprac.2023.08.016. Epub 2023 Sep 29.
Postprandial hyperglycemia drives insulin resistance and inflammation, leading to metabolic dysfunction-associated fatty liver disease (MAFLD). Prediction of postprandial glycemic responses by digital twin (DT) technology can fashion a personalized nutrition, activity, and sleep to treat type 2 diabetes (T2D) and MAFLD. This study examines the effects of DT-enabled personalized nutrition, activity, and sleep on glycemic status, surrogate markers of MAFLD, and magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) in patients with T2D.
In an open-label randomized trial (2:1), 319 people with T2D were eligible to intervention (DT) or standard care (SC). DT patients followed personalized meal plans with foods suggested by artificial intelligence with least predicted postprandial glycemic response. The primary end point was to compare change in hemoglobin A1c (HbA1c) and medicine reduction between the DT and SC groups. Key secondary end points included remission to compare liver function test scores and visceral adiposity using MRI.
HbA1 was significantly better for DT than for SC (-2.9 [1.8] vs -0.3 [1.2]; P < .001) at 1 year with 72.7% remission of T2D. In patients with abnormal baseline values, significant improvements were seen in DT vs SC patients from baseline to 1 year in nonalcoholic fatty liver disease liver fat score (mean [SD]; -2.5 [2.0] vs -0.1 [1.5]; P < .001) and nonalcoholic fatty liver disease fibrosis score (-1.20 [0.9] vs -0.1 [1.0]; P < .001), respectively. Improvements are seen with DT compared with SC in other liver fat, fibrosis score, and %liver fat by MRI-PDFF.
At 1 year, DT-enabled personalized treatment significantly improved hyperglycemia and surrogate markers of MAFLD and MRI-PDFF.
餐后高血糖会引发胰岛素抵抗和炎症，进而导致代谢功能障碍相关脂肪性肝病（MAFLD）。通过数字孪生（DT）技术预测餐后血糖反应可为2型糖尿病（T2D）和MAFLD患者制定个性化的营养、活动和睡眠方案。本研究探讨了基于DT的个性化营养、活动和睡眠对T2D患者血糖状态、MAFLD替代标志物以及磁共振成像衍生的质子密度脂肪分数（MRI-PDFF）的影响。
在一项开放标签随机试验（2:1）中，319例T2D患者符合干预（DT）或标准护理（SC）条件。DT组患者遵循由人工智能推荐的个性化饮食计划，这些食物的餐后血糖反应预测值最低。主要终点是比较DT组和SC组之间糖化血红蛋白（HbA1c）的变化以及药物减量情况。关键次要终点包括缓解情况，即使用MRI比较肝功能测试评分和内脏脂肪量。
1年后，DT组的HbA1水平显著优于SC组（-2.9[1.8] vs -0.3[1.2]；P <.001），T2D缓解率为72.7%。在基线值异常的患者中，从基线到1年，DT组患者的非酒精性脂肪性肝病肝脏脂肪评分（均值[标准差]；-2.5[2.0] vs -0.1[1.5]；P <.001）和非酒精性脂肪性肝病纤维化评分（-1.20[0.9] vs -0.1[1.0]；P <.001）较SC组患者有显著改善。与SC组相比，DT组在其他肝脏脂肪、纤维化评分以及MRI-PDFF测量的肝脏脂肪百分比方面也有改善。
1年后，基于DT的个性化治疗显著改善了高血糖、MAFLD替代标志物以及MRI-PDFF。
