Veterans Affairs Medical Center, Salt Lake City, UT, USA.
Diabetol Metab Syndr. 2013 Jul 1;5(1):33. doi: 10.1186/1758-5996-5-33.
Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.
Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals' attributes with model error.
Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).
The preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.
本研究旨在开发并验证 2 型糖尿病患者运动时急性血糖反应的预测模型。
综合了三项既往运动研究的数据（56 例患者，488 次运动）作为开发数据集。采用混合效应最小绝对收缩和选择算子（LASSO）筛选 12 个潜在预测因子中的自变量。采用 Lindemann Merenda 和 Gold（LMG）算法测试各预测因子的相对重要性。采用似然比检验测试模型结构。采用留一法交叉验证评估开发数据集的模型准确性。前瞻性采集的 47 例患者（436 次运动）的数据作为验证数据集。将预测值与实测值之间的差值在实测值中所占的百分比定义为模型的准确性。通过计算患者运动后 3 次测量中模型误差不超过 1 次的例数，评估模型的整体应用价值。采用图形方法评估个体间模型的准确性。在事后分析中，采用混合效应逻辑回归分析患者特征与模型误差之间的关系。
运动前的时间（分钟）、进食后时间（分钟）的非线性变换、餐后状态、糖化血红蛋白（HbA1c）、磺脲类药物的使用情况、年龄和运动次数被确定为新的预测因子。运动前的时间（分钟）、其非线性变换和 HbA1c 共同解释了血糖反应变异的 19.6%。磺脲类药物的使用情况、年龄和运动次数各自解释的变异小于 1.0%。在开发数据集，与仅包含随机截距的模型相比，增加了对运动前血糖的随机斜率后，模型拟合度更好（似然比 34.5，p<0.001）。验证集的交叉验证准确性为 83.3%。在验证集，总体准确性为 80.2%。模型在餐前运动中的准确性（83.6%）高于餐后运动（74.5%）。31/47 例患者在第三次运动后，模型误差不超过 1 次。个体间模型误差差异较大，与运动前血糖的个体内变异性呈弱相关（优势比 1.49，95%置信区间 1.23-1.75）。
本研究初步开发和验证了 2 型糖尿病患者运动时急性血糖反应的预测模型，并讨论了进一步改进该模型的方法。
