Background Alterations in glucose metabolism,especially the postprandial glucose response(PPGR),are cru-cial contributors to metabolic dysfunction,which underlies the pathogenesis of metabolic syndrome.Personalized lo...Background Alterations in glucose metabolism,especially the postprandial glucose response(PPGR),are cru-cial contributors to metabolic dysfunction,which underlies the pathogenesis of metabolic syndrome.Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes.However,current methods such as invasive continuous glucose monitoring(CGM)or multi-omics data integration to assess PPGR have limita-tions,including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention.Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices,thereby,circumventing the limitations associated with the existing approaches.By identifying the most accurate model,we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.Methods This data-driven analysis used the experimental dataset from the SENSE(“Systemische Ernährungsmedizin”)study.Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days.Blood volume pulse,electrodermal activity,heart rate,skin temperature,and the corre-sponding CGM values were measured.Subsequently,four data-driven deep learning(DL)models-convolutional neural network,lightweight transformer,long short-term memory with attention,and Bi-directional LSTM(BiL-STM)were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.Results The proposed BiLSTM achieved the best interstitial glucose prediction performance,with an average root mean squared error of 13.42 mg/dL,an average mean absolute percentage error of 0.12,and only 3.01%values falling within area D in Clarke error grid analysis,incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.Conclusion The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches.Furthermore,it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.展开更多
基金supported by DAMP Foundation,Germany(Grant No.2020-14).
文摘Background Alterations in glucose metabolism,especially the postprandial glucose response(PPGR),are cru-cial contributors to metabolic dysfunction,which underlies the pathogenesis of metabolic syndrome.Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes.However,current methods such as invasive continuous glucose monitoring(CGM)or multi-omics data integration to assess PPGR have limita-tions,including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention.Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices,thereby,circumventing the limitations associated with the existing approaches.By identifying the most accurate model,we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.Methods This data-driven analysis used the experimental dataset from the SENSE(“Systemische Ernährungsmedizin”)study.Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days.Blood volume pulse,electrodermal activity,heart rate,skin temperature,and the corre-sponding CGM values were measured.Subsequently,four data-driven deep learning(DL)models-convolutional neural network,lightweight transformer,long short-term memory with attention,and Bi-directional LSTM(BiL-STM)were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.Results The proposed BiLSTM achieved the best interstitial glucose prediction performance,with an average root mean squared error of 13.42 mg/dL,an average mean absolute percentage error of 0.12,and only 3.01%values falling within area D in Clarke error grid analysis,incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.Conclusion The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches.Furthermore,it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.
