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Treatment of Obesity and Diabetes Using Oxytocin or Analogs in Patients and Mouse Models

Overview of attention for article published in PLOS ONE, May 2013
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Title
Treatment of Obesity and Diabetes Using Oxytocin or Analogs in Patients and Mouse Models
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0061477
Pubmed ID
Authors

Hai Zhang, Chenguang Wu, Qiaofen Chen, Xiaoluo Chen, Zhigang Xu, Jing Wu, Dongsheng Cai

Abstract

Obesity is important for the development of type-2 diabetes as a result of obesity-induced insulin resistance accompanied by impaired compensation of insulin secretion from pancreatic beta cells. Here, based on a randomized pilot clinical trial, we report that intranasal oxytocin administration over an 8-week period led to effective reduction of obesity and reversal of related prediabetic changes in patients. Using mouse models, we further systematically evaluated whether oxytocin and its analogs yield therapeutic effects against prediabetic or diabetic disorders regardless of obesity. Our results showed that oxytocin and two analogs including [Ser4, Ile8]-oxytocin or [Asu1,6]-oxytocin worked in mice to reverse insulin resistance and glucose intolerance prior to reduction of obesity. In parallel, using streptozotocin-induced diabetic mouse model, we found that treatment with oxytocin or its analogs reduced the magnitude of glucose intolerance through improving insulin secretion. The anti-diabetic effects of oxytocin and its analogs in these animal models can be produced similarly whether central or peripheral administration was used. In conclusion, oxytocin and its analogs have multi-level effects in improving weight control, insulin sensitivity and insulin secretion, and bear potentials for being developed as therapeutic peptides for obesity and diabetes.

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The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
Russia 1 <1%
Denmark 1 <1%
Germany 1 <1%
Unknown 200 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 17%
Student > Bachelor 27 13%
Student > Master 23 11%
Researcher 18 9%
Other 17 8%
Other 39 19%
Unknown 46 23%
Readers by discipline Count As %
Medicine and Dentistry 51 25%
Agricultural and Biological Sciences 17 8%
Biochemistry, Genetics and Molecular Biology 15 7%
Psychology 14 7%
Neuroscience 13 6%
Other 40 20%
Unknown 54 26%