Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.
Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.
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使用 1 周 CGM 家庭测试、夜间血糖增量和标准化液体混合早餐，并通过机器学习增强分类来预测 1 期和 2 期糖尿病的免疫风险。
DOI：
10.1089/dia.2023.0064
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发表时间：
2023
影响因子：
5.4
通讯作者：
Farhy,LeonS
中科院分区：
文献类型：
--
作者：
Montaser,Eslam;Breton,MarcD;Brown,SueA;DeBoer,MarkD;Kovatchev,Boris;Farhy,LeonS
背景：预测1型糖尿病(T1D)的风险是一项重大挑战。方法：60例T1D患者的健康亲属，平均 ± 标准差年龄为23.7 ± 10.7岁，HbA1c为5.3% ± 0.3%，体重指数为23.8 ± 5.6 kg/m2(n= 21)，1(n= 18)，和≥2(n= 21)自身抗体登记在美国国立卫生研究院试验网的辅助研究中。参与者戴了一周的CGM，吃了三份标准化的液体混合餐(SLMM)，而不是三顿早餐。血糖结果根据每周、隔夜(12：00-06：00)和SLMM CGM后的轨迹计算，跨组比较，并用于四个有监督的机器学习自身抗体状态分类器。结果：在所有计算的血糖指标中，只有三个指标在自身抗体组之间存在差异：每周百分比时间和GT；180 /dL(T180)每周(P= 0.04)，隔夜CGM增量AUC(P= 0.005)，以及SLMM CGM后75 分钟的T180(P= 0.004)。一旦将过夜和SLMM后的特征结合到机器学习分类器中，线性支持向量机模型在使用AUC-ROC≥0.81对自身抗体阳性和自身抗体阴性的参与者进行分类时取得了最好的性能。结论：将机器学习与潜在的自我管理的1周CGM家庭测试相结合的新技术可以帮助提高T1D风险检测，而不需要去医院或使用医学实验室。NCT02663661。
Background:Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification.Methods:Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2with zero (n= 21), one (n= 18), and ≥2 (n= 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00–06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.Results:Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P= 0.04), overnight CGM incremental AUC (P= 0.005), and T180 for 75 min post-SLMM CGM traces (P= 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81.Conclusion:A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory.Trial registration:ClinicalTrials.gov registration no. NCT02663661.