Wen Song, Li Yanyan, Xu Chenglin, Jin Jianlan, Xu Zhimin, Yuan Yue, Chen Lijiao, Ren Yishu, Gong Min, Wang Congcong, Dong Meiyuan, Zhou Yingfan, Yuan Xinlu, Li Fufeng, Zhou Ligang
Department of Endocrinology, Shanghai Pudong Hospital, Fudan University, Pudong Medical Center, Shanghai, 201399, People's Republic of China.
Fudan Zhangjiang Institute, Fudan University, Shanghai, 201203, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Oct 29;17:4049-4068. doi: 10.2147/DMSO.S491897. eCollection 2024.
We aim to examine and reestablish the correlational and linear regression relationships, as well as the predictive value, between the significant facial and tongue features and the metabolic parameters in type 2 diabetes mellitus (T2DM).
From March to May 2024, we studied 269 patients with T2DM in the endocrinology department of Shanghai Pudong Hospital. The patients' facial and tongue characteristics were sampling by a tongue imaging device equipped with artificial intelligence (AI) (XiMaLife, Sinology, China) of automated and advanced machine learning algorithms. Then, the imaging features were examined in relation to the blood examination.
Multiple facial and tongue features, as well as dimensional facial and tongue color parameters, were significantly correlated with glycated hemoglobin A1c (HbA1c) (r < 0.3, p < 0.05), glycated albumin (GA) (-0.20 < 0.30, p < 0.05), C-peptide (-0.20.20, p < 0.05), plasma insulin (r < 0.30, p < 0.05), fasting plasma glucose (FPG) (r < 0.3, p < 0.05), significant hepatic and renal function indicators (-0.30 < r < 0.20, p<0.05), cardiac injury markers (-0.30 < r < 0.30, p < 0.05), tumor markers (-0.5 < r < 0.5, p < 0.05), thyroid function (-0.15 < r < 0.55, p < 0.05), and blood cell count, including white blood cells (r < 0.2, p < 0.05), and hemoglobin (Hb) (-0.30 < r < 0.3, 0.0001. The correlational results demonstrated that the tongue's characteristics and signs may be linked with the dynamic of the metabolic status of T2DM. In order to examine the causal relationships, we performed linear regression analyses, which revealed that various facial and tongue imaging parameters partially determined the metabolic indicators. The predictive value of imaging features was evaluated by receiver operating characteristic curve (ROC) to assess metabolic status in T2DM.
This study demonstrated that metabolic status, renal and hepatic, cardiac, and thyroid function, the proportion of blood cells, and Hb in T2DM were intimately associated with facial and tongue features. The precise analysis of facial and tongue features through AI and advanced machine learning could be used to predict T2DM's conditions and progression.
我们旨在研究并重新建立2型糖尿病（T2DM）患者面部和舌部显著特征与代谢参数之间的相关性、线性回归关系以及预测价值。
2024年3月至5月，我们对上海浦东医院内分泌科的269例T2DM患者进行了研究。使用配备人工智能（AI）（中国信诺西玛生命公司的XiMaLife）的舌成像设备，通过先进的自动化机器学习算法对患者的面部和舌部特征进行采样。然后，将成像特征与血液检查结果进行关联分析。
多个面部和舌部特征以及面部和舌部的维度颜色参数与糖化血红蛋白A1c（HbA1c）（r < 0.3，p < 0.05）、糖化白蛋白（GA）（-0.20 < r < 0.30，p < 0.05）、C肽（-0.20 < r < 0.20，p < 0.05）、血浆胰岛素（r < 0.30，p < 0.05）、空腹血糖（FPG）（r < 0.3，p < 0.05）、重要的肝肾功能指标（-0.30 < r < 0.20，p < 0.05）、心脏损伤标志物（-0.30 < r < 0.30，p < 0.05）、肿瘤标志物（-0.5 < r < 0.5，p < 0.05）、甲状腺功能（-0.15 < r < 0.55，p < 0.05）以及血细胞计数，包括白细胞（r < 0.2，p < 0.05）和血红蛋白（Hb）（-0.30 < r < 0.3，p < 0.0001）显著相关。相关性结果表明，舌部特征和体征可能与T2DM代谢状态的动态变化有关。为了检验因果关系，我们进行了线性回归分析，结果显示各种面部和舌部成像参数部分决定了代谢指标。通过受试者工作特征曲线（ROC）评估成像特征对T2DM代谢状态的预测价值。
本研究表明，T2DM患者的代谢状态、肝肾功能、心脏和甲状腺功能、血细胞比例以及Hb与面部和舌部特征密切相关。通过人工智能和先进的机器学习对面部和舌部特征进行精确分析，可用于预测T2DM的病情和进展。
