Personalized glucose-insulin model based on signal analysis
Glucose plasma measurements for diabetes patients are generally presented as a glucose concentration-time profile with 15–60 min time scale intervals. This limited resolution obscures detailed dynamic events of glucose appearance and metabolism. Measurement intervals of 15 min or more could contribu...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2017
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| Online Access: | https://hdl.handle.net/10356/86627 http://hdl.handle.net/10220/44149 |
| _version_ | 1826125570328494080 |
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| author | Goede, Simon L. de Galan, Bastiaan E. Leow, Melvin Khee Shing |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Goede, Simon L. de Galan, Bastiaan E. Leow, Melvin Khee Shing |
| author_sort | Goede, Simon L. |
| collection | NTU |
| description | Glucose plasma measurements for diabetes patients are generally presented as a glucose concentration-time profile with 15–60 min time scale intervals. This limited resolution obscures detailed dynamic events of glucose appearance and metabolism. Measurement intervals of 15 min or more could contribute to imperfections in present diabetes treatment. High resolution data from mixed meal tolerance tests (MMTT) for 24 type 1 and type 2 diabetes patients were used in our present modeling. We introduce a model based on the physiological properties of transport, storage and utilization. This logistic approach follows the principles of electrical network analysis and signal processing theory. The method mimics the physiological equivalent of the glucose homeostasis comprising the meal ingestion, absorption via the gastrointestinal tract (GIT) to the endocrine nexus between the liver, pancreatic alpha and beta cells. This model demystifies the metabolic ‘black box’ by enabling in silico simulations and fitting of individual responses to clinical data. Five-minute intervals MMTT data measured from diabetic subjects result in two independent model parameters that characterize the complete glucose system response at a personalized level. From the individual data measurements, we obtain a model which can be analyzed with a standard electrical network simulator for diagnostics and treatment optimization. The insulin dosing time scale can be accurately adjusted to match the individual requirements of characterized diabetic patients without the physical burden of treatment |
| first_indexed | 2024-10-01T06:38:30Z |
| format | Journal Article |
| id | ntu-10356/86627 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:38:30Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/866272020-11-01T05:24:59Z Personalized glucose-insulin model based on signal analysis Goede, Simon L. de Galan, Bastiaan E. Leow, Melvin Khee Shing Lee Kong Chian School of Medicine (LKCMedicine) Appearance Profile Model Identification Glucose plasma measurements for diabetes patients are generally presented as a glucose concentration-time profile with 15–60 min time scale intervals. This limited resolution obscures detailed dynamic events of glucose appearance and metabolism. Measurement intervals of 15 min or more could contribute to imperfections in present diabetes treatment. High resolution data from mixed meal tolerance tests (MMTT) for 24 type 1 and type 2 diabetes patients were used in our present modeling. We introduce a model based on the physiological properties of transport, storage and utilization. This logistic approach follows the principles of electrical network analysis and signal processing theory. The method mimics the physiological equivalent of the glucose homeostasis comprising the meal ingestion, absorption via the gastrointestinal tract (GIT) to the endocrine nexus between the liver, pancreatic alpha and beta cells. This model demystifies the metabolic ‘black box’ by enabling in silico simulations and fitting of individual responses to clinical data. Five-minute intervals MMTT data measured from diabetic subjects result in two independent model parameters that characterize the complete glucose system response at a personalized level. From the individual data measurements, we obtain a model which can be analyzed with a standard electrical network simulator for diagnostics and treatment optimization. The insulin dosing time scale can be accurately adjusted to match the individual requirements of characterized diabetic patients without the physical burden of treatment Accepted version 2017-12-15T03:19:04Z 2019-12-06T16:26:05Z 2017-12-15T03:19:04Z 2019-12-06T16:26:05Z 2017 Journal Article Goede, S. L., de Galan, B. E., & Leow, M. K. S. (2017). Personalized glucose-insulin model based on signal analysis. Journal of Theoretical Biology, 419, 333-342. 0022-5193 https://hdl.handle.net/10356/86627 http://hdl.handle.net/10220/44149 10.1016/j.jtbi.2016.12.018 en Journal of Theoretical Biology © 2017 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Theoretical Biology, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.jtbi.2016.12.018]. 26 p. application/pdf |
| spellingShingle | Appearance Profile Model Identification Goede, Simon L. de Galan, Bastiaan E. Leow, Melvin Khee Shing Personalized glucose-insulin model based on signal analysis |
| title | Personalized glucose-insulin model based on signal analysis |
| title_full | Personalized glucose-insulin model based on signal analysis |
| title_fullStr | Personalized glucose-insulin model based on signal analysis |
| title_full_unstemmed | Personalized glucose-insulin model based on signal analysis |
| title_short | Personalized glucose-insulin model based on signal analysis |
| title_sort | personalized glucose insulin model based on signal analysis |
| topic | Appearance Profile Model Identification |
| url | https://hdl.handle.net/10356/86627 http://hdl.handle.net/10220/44149 |
| work_keys_str_mv | AT goedesimonl personalizedglucoseinsulinmodelbasedonsignalanalysis AT degalanbastiaane personalizedglucoseinsulinmodelbasedonsignalanalysis AT leowmelvinkheeshing personalizedglucoseinsulinmodelbasedonsignalanalysis |