Journal article Open Access

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Camerlingo Nunzio; Vettoretti Martina; Del Favero Simone; Facchinetti Andrea; Sparacino Giovanni


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    <subfield code="a">eng</subfield>
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    <subfield code="a">in-silico clinical trials</subfield>
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    <subfield code="a">parametric modelling</subfield>
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    <subfield code="a">machine learning</subfield>
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    <subfield code="a">support vector machine</subfield>
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    <subfield code="a">This study is part of the Hypo-RESOLVE project. The project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 777460. The JU receives support from the European Union's Horizon 2020 research and innovation program and EFPIA and T1D Exchange, JDRF, International Diabetes Federation (IDF), The Leona M. and Harry B. Helmsley Charitable Trust.</subfield>
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    <subfield code="u">University of Padova</subfield>
    <subfield code="a">Vettoretti Martina</subfield>
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    <subfield code="u">University of Padova</subfield>
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    <subfield code="a">Del Favero Simone</subfield>
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    <subfield code="u">University of Padova</subfield>
    <subfield code="a">Facchinetti Andrea</subfield>
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    <subfield code="u">University of Padova</subfield>
    <subfield code="0">(orcid)0000-0002-3248-1393</subfield>
    <subfield code="a">Sparacino Giovanni</subfield>
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    <subfield code="u">https://zenodo.org/record/4139754/files/Camerlingo_2020_JDST_MealModels.pdf</subfield>
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    <subfield code="c">2020-09-17</subfield>
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    <subfield code="p">Journal of Diabetes Science and Technology</subfield>
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    <subfield code="u">University of Padova</subfield>
    <subfield code="0">(orcid)0000-0003-3222-2479</subfield>
    <subfield code="a">Camerlingo Nunzio</subfield>
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    <subfield code="a">Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator</subfield>
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    <subfield code="a">Hypoglycaemia - REdefining SOLutions for better liVEs</subfield>
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    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
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    <subfield code="a">&lt;p&gt;Background:&lt;/p&gt;

&lt;p&gt;In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Methods:&lt;/p&gt;

&lt;p&gt;Data of 32 T1D subjects under free-living conditions for 4874&amp;thinsp;days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHO&lt;sub&gt;B&lt;/sub&gt;), lunch (CHO&lt;sub&gt;L&lt;/sub&gt;), dinner (CHO&lt;sub&gt;D&lt;/sub&gt;), and snack (CHO&lt;sub&gt;S&lt;/sub&gt;); breakfast timing (T&lt;sub&gt;B&lt;/sub&gt;); and time between breakfast-lunch (T&lt;sub&gt;BL&lt;/sub&gt;) and between lunch-dinner (T&lt;sub&gt;LD&lt;/sub&gt;). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Results:&lt;/p&gt;

&lt;p&gt;Resulting PDF models were: gamma (CHO&lt;sub&gt;B&lt;/sub&gt;, CHO&lt;sub&gt;S&lt;/sub&gt;), lognormal (CHO&lt;sub&gt;L&lt;/sub&gt;, T&lt;sub&gt;B&lt;/sub&gt;), loglogistic (CHO&lt;sub&gt;D&lt;/sub&gt;), and generalized-extreme-values (T&lt;sub&gt;BL&lt;/sub&gt;, T&lt;sub&gt;LD&lt;/sub&gt;). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (&amp;alpha;&amp;thinsp;=&amp;amp;thinsp;0.05).&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Conclusions:&lt;/p&gt;

&lt;p&gt;The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.&lt;/p&gt;</subfield>
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    <subfield code="a">10.1177/1932296820952123</subfield>
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