Postprandial glucose variability in type 1 diabetes: The individual matters beyond the meal

Bozzetto, Lutgarda; Pacella, Daniela; Cavagnuolo, Luisa; Capuano, M.; Corrado, A.; Scidà, Giuseppe; Costabile, Giuseppina; Rivellese, Angela Albarosa; Annuzzi, Giovanni

doi:10.1016/j.diabres.2022.110089

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…In other words, postprandial glucose responses seem strongly related on individual characteristics of the subject. This is consistent with the findings in [53], [58], [59], reporting that, in general, the human postprandial glucose response is almost constant in the same subject, while it changes across different ones.…”

Section: Discussionsupporting

confidence: 93%

“…Consistently with the findings in [45], [53], [57], it results that some nutritional factors (such as fibers and lipids) can have a greater impact in middle term prediction respect to other ones.…”

Section: Discussionsupporting

confidence: 88%

“…• from PH = 30 min, a positive statistical significance were obtained when the nutritional factors were individually considered as input of FFNN, demonstrating that meal composition had a significant effect on post-prandial blood glucose. As expected, #3 Carbohydrates remained the factor that abruptly impacts BGL predictions [45], but also #4 Proteins and #5 Fibers played an important role from 30 min after the meal [53].…”

Section: B Ai4pg Resultssupporting

confidence: 74%

“…This result reflects the need to model inter-individual variability. Indeed, a significant part of postprandial glucose variability is generally related to individual characteristics [53].…”

Section: B Ai4pg Resultsmentioning

confidence: 99%

“…The aim of this study is to verify the impact of the nutritional factors in the final predictions. Indeed, the importance of the nutritional factors on the BGL reported in the literature [53] can lead toward the hypothesis that nutritional factors can affect the BGL predictions. The preliminary results on DirecNet showed that the proposed ML-model predicts blood glucose levels comparable with the state-of-the-art methods reported in literature.…”

Section: Discussionmentioning

confidence: 99%

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Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning

et al. 2023

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Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP), combining autonomous insulin delivery and blood glucose monitoring, is a promising solution. However, state-of-the-art APs require several information for bolus delivery, such as the estimated carbohydrate intake over the meals. This is mainly related to the limited knowledge of the determinants of PGR. Although meal carbohydrates are mostly considered as the major factor influencing PGR, other food components play a relevant role in PGRs, and thus, should be taken into account. Based on these considerations, a study to determine the effect of nutritional factors (i.e., carbohydrates, proteins, lipids, fibers, and energy intake) in the short and middle term on Blood Glucose Levels (BGLs) prediction was conducted by Machine Learning (ML) methods. A ML model able to predict the BGLs after 15, 30, 45, and 60 minutes from the meal leveraging on insulin doses, blood glucose, and nutritional factors in T1D patients on AP systems was implemented. More specifically, to investigate the impact of the nutritional factors on the model predictions, a Feed-Forward Neural Network, was fed with several dispositions of BGLs, insulin, and nutritional factors. Both public and self-produced data were used to validate the proposal. The results suggest that patient-specific information about nutritional factors can be significant for middle term postprandial BGLs predictions.

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Section: Discussionsupporting

confidence: 93%