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Insulin Signaling in Type 2 Diabetes EXPERIMENTAL AND MODELING ANALYSES REVEAL MECHANISMS OF INSULIN RESISTANCE IN HUMAN ADIPOCYTES*

Overview of attention for article published in Journal of Biological Chemistry, February 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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9 X users

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Title
Insulin Signaling in Type 2 Diabetes EXPERIMENTAL AND MODELING ANALYSES REVEAL MECHANISMS OF INSULIN RESISTANCE IN HUMAN ADIPOCYTES*
Published in
Journal of Biological Chemistry, February 2013
DOI 10.1074/jbc.m112.432062
Pubmed ID
Authors

Cecilia Brännmark, Elin Nyman, Siri Fagerholm, Linnéa Bergenholm, Eva-Maria Ekstrand, Gunnar Cedersund, Peter Strålfors

Abstract

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

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The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 2 1%
Italy 1 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Russia 1 <1%
United States 1 <1%
Philippines 1 <1%
Unknown 183 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 17%
Student > Bachelor 31 16%
Researcher 23 12%
Student > Master 23 12%
Student > Postgraduate 9 5%
Other 33 17%
Unknown 40 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 23%
Biochemistry, Genetics and Molecular Biology 37 19%
Medicine and Dentistry 22 12%
Engineering 13 7%
Neuroscience 7 4%
Other 24 13%
Unknown 45 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2022.
All research outputs
#4,801,932
of 25,373,627 outputs
Outputs from Journal of Biological Chemistry
#12,295
of 85,241 outputs
Outputs of similar age
#48,697
of 296,785 outputs
Outputs of similar age from Journal of Biological Chemistry
#83
of 651 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 85,241 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 296,785 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 651 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.