Rashmi Kulkarni equal contributor,
equal contributor Contributed equally to this work with: Rashmi Kulkarni, Jhankar Acharya
Affiliation: Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, India
Jhankar Acharya equal contributor,
equal contributor Contributed equally to this work with: Rashmi Kulkarni, Jhankar Acharya
Affiliation: Department of Zoology, University of Pune, Pune, Maharashtra, India
Saroj Ghaskadbi,
Affiliation: Department of Zoology, University of Pune, Pune, Maharashtra, India
Pranay Goel mail
* E-mail: [email protected]
Affiliation: Mathematics and Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, India
Published: June 27, 2014
DOI: 10.1371/journal.pone.0100897
Abstract
Cellular and animal studies suggest that oxidative stress could be the central defect underlying both beta-cell dysfunction and insulin resistance in type 2 diabetes mellitus. A reduction of glycemic stress in diabetic patients on therapy alleviates systemic oxidative stress and improves insulin resistance and beta-cell secretion. Monitoring oxidative stress systematically with glucose can potentially identify an individual's recovery trajectory. To determine a quantitative model of serial changes in oxidative stress, as measured via the antioxidant glutathione, we followed patients newly diagnosed with diabetes over 8 weeks of starting anti-diabetic treatment. We developed a mathematical model which shows recovery is marked with a quantal response. For each individual the model predicts three theoretical quantities: an estimate of maximal glutathione at low stress, a glucose threshold for half-maximal glutathione, and a rate at which recovery progresses. Individual patients are seen to vary considerably in their response to glucose control. Thus, model estimates can potentially be used to determine whether an individual patient's response is better or worse than average in terms of each of these indices; they can therefore be useful in reassessing treatment strategy. We hypothesize that this method can aid the personalization of effective targets of glucose control in anti-diabetic therapy.
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