Predicting Optimal Basal Insulin Infusion Patterns in Kids
We here demonstrate that in the largest cohort of children and adolescents with type 1 diabetes on CSII thus far reported in the literature, four major distinct BR patterns had been programmed by independent clinical diabetes teams during the course of diabetes. Logistic regression analysis revealed that unsupervised assignment of a child by hierarchical clustering to one of the four different BR patterns was obviously based on the same prediction factors in both independent datasets, i.e., age, to a much lesser extent duration of diabetes, and rarely sex. In essence, the youngest children showed the highest insulin infusion rates in the late evening before midnight (BR F), older school children had higher insulin at midnight up to the early morning (BR D), and pubertal children showed the typical dawn-dusk pattern (BR BC). Therefore, and owing to the fact that in total >7,000 children had been investigated in both of our studies together, we conclude that these patterns likely represent a realistic approximation to the real age-dependent circadian distribution of insulin needs. One hundred and sixty patients of the study did not cluster into one of the four patterns. This may be due to particular therapeutic needs in individual patients or due to individual clinical situations. While we believe that the statistical bias is acceptable for the whole picture, these data point to the fact that in addition to characteristic BR patterns children with diabetes on CSII may have very variable metabolic needs to be handled by their BR.
Initiation of CSII and continuous clinical follow-up of a child with type 1 diabetes on insulin pump would profit significantly from knowledge and consideration of the individual circadian BR distribution. Therefore, we developed a mathematical prediction model that calculates the maximum probability for a given child to be treated with a certain BR pattern. In order to verify the biological significance, we performed these calculations independently in both of the two datasets. With use of test patients of a given age, duration of diabetes, and sex, the resulting probability curves were almost identical in the two datasets concerning BR F and BR BC reflecting the youngest and the oldest age-groups (Fig. 2A and C). Probabilities for assignment to patterns D and AG varied more between datasets 1 and 2 but generally showed the same age dependence (Fig. 2B and D). We conclude that this difference is most likely due to the differences in the age distribution of the two cohorts with younger children in dataset 2. The high similarity of the patterns and the good reproducibility of the probabilities for clustering to BR patterns comparing the two datasets support an overriding biological significance of our findings independent of the given cohort 1 or 2. The differences of the circadian distribution of insulin needs are likely to be due to the continuously changing neuroendocrine hormonal background from early childhood to adolescence, e.g., changing sleep patterns influencing growth hormone secretion in the small child, changing physical activity, growth, body proportions, growth spurt, puberty, and sex steroids. In this sense, increasing sex steroid secretion during puberty of a child with type 1 diabetes on CSII enhancing growth hormone secretion during the night would increase early morning insulin resistance resulting in higher insulin needs and, hence, a higher probability of being treated with a BC BR pattern. We conclude that based on our mathematical model, it is possible to predict a "best fit" BR pattern for individual children with type 1 diabetes treated with CSII.
Continuous glucose monitoring by glucose sensors is at the advent of a revolution in CSII treatment in children with type 1 diabetes. Furthermore, different diabetes research groups all over the world work on closing the loop between continuous glucose sensing and insulin delivery via insulin pumps. A perfect system would actually act completely automatically like the healthy β-cells of the normal pancreas. One of several difficult tasks to solve is programming suitable computer algorithms matching subcutaneous insulin delivery via the pump with the continuous physiological changes of insulin sensitivity and insulin needs during the course of day and night. We suggest that our large-scale data provide valuable information for modulating mathematical prediction models in closed-loop algorithms by providing relevant information on age-dependent changes and circadian variation of insulin sensitivity in children. In this context, our prediction equations could be used to approximate decision corridors for insulin delivery in individual children set on closed-loop CSII.
Conclusions
We here demonstrate that in the largest cohort of children and adolescents with type 1 diabetes on CSII thus far reported in the literature, four major distinct BR patterns had been programmed by independent clinical diabetes teams during the course of diabetes. Logistic regression analysis revealed that unsupervised assignment of a child by hierarchical clustering to one of the four different BR patterns was obviously based on the same prediction factors in both independent datasets, i.e., age, to a much lesser extent duration of diabetes, and rarely sex. In essence, the youngest children showed the highest insulin infusion rates in the late evening before midnight (BR F), older school children had higher insulin at midnight up to the early morning (BR D), and pubertal children showed the typical dawn-dusk pattern (BR BC). Therefore, and owing to the fact that in total >7,000 children had been investigated in both of our studies together, we conclude that these patterns likely represent a realistic approximation to the real age-dependent circadian distribution of insulin needs. One hundred and sixty patients of the study did not cluster into one of the four patterns. This may be due to particular therapeutic needs in individual patients or due to individual clinical situations. While we believe that the statistical bias is acceptable for the whole picture, these data point to the fact that in addition to characteristic BR patterns children with diabetes on CSII may have very variable metabolic needs to be handled by their BR.
Initiation of CSII and continuous clinical follow-up of a child with type 1 diabetes on insulin pump would profit significantly from knowledge and consideration of the individual circadian BR distribution. Therefore, we developed a mathematical prediction model that calculates the maximum probability for a given child to be treated with a certain BR pattern. In order to verify the biological significance, we performed these calculations independently in both of the two datasets. With use of test patients of a given age, duration of diabetes, and sex, the resulting probability curves were almost identical in the two datasets concerning BR F and BR BC reflecting the youngest and the oldest age-groups (Fig. 2A and C). Probabilities for assignment to patterns D and AG varied more between datasets 1 and 2 but generally showed the same age dependence (Fig. 2B and D). We conclude that this difference is most likely due to the differences in the age distribution of the two cohorts with younger children in dataset 2. The high similarity of the patterns and the good reproducibility of the probabilities for clustering to BR patterns comparing the two datasets support an overriding biological significance of our findings independent of the given cohort 1 or 2. The differences of the circadian distribution of insulin needs are likely to be due to the continuously changing neuroendocrine hormonal background from early childhood to adolescence, e.g., changing sleep patterns influencing growth hormone secretion in the small child, changing physical activity, growth, body proportions, growth spurt, puberty, and sex steroids. In this sense, increasing sex steroid secretion during puberty of a child with type 1 diabetes on CSII enhancing growth hormone secretion during the night would increase early morning insulin resistance resulting in higher insulin needs and, hence, a higher probability of being treated with a BC BR pattern. We conclude that based on our mathematical model, it is possible to predict a "best fit" BR pattern for individual children with type 1 diabetes treated with CSII.
Continuous glucose monitoring by glucose sensors is at the advent of a revolution in CSII treatment in children with type 1 diabetes. Furthermore, different diabetes research groups all over the world work on closing the loop between continuous glucose sensing and insulin delivery via insulin pumps. A perfect system would actually act completely automatically like the healthy β-cells of the normal pancreas. One of several difficult tasks to solve is programming suitable computer algorithms matching subcutaneous insulin delivery via the pump with the continuous physiological changes of insulin sensitivity and insulin needs during the course of day and night. We suggest that our large-scale data provide valuable information for modulating mathematical prediction models in closed-loop algorithms by providing relevant information on age-dependent changes and circadian variation of insulin sensitivity in children. In this context, our prediction equations could be used to approximate decision corridors for insulin delivery in individual children set on closed-loop CSII.
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