A mathematical model of adiponectin resistance. (7th June 2020)

• Record Type: Journal Article
• Authors: Sinha, Arvind Kumar; Namdev, Nishant; Kumar, Awanish
• Is Part Of: Journal of theoretical biology. Volume 494(2020)
• Publication Date: 2020-06-07
• Subjects: Adiponectin -- Obesity -- Fat mass -- Rough set -- Modeling; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2020.110246
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: Adiponectin is often associated with obesity. We propose a mathematical model of the adiponectin − adiponectin receptors framework. We take different parameters and their default values for simulation process. We apply the rough set as secretion of adiponectin is uncertain with time. Abstract: Adiponectin is often associated with obesity. The obese body displays a significant decrease in adiponectin expression and plasma levels. Higher adiponectin also results in lower expression of pro-inflammatory cytokine TNF-α from adipose tissue. Low adiponectin levels show to exist significantly in the case of insulin resistance. Adiponectin levels are found to be significantly lower in people with type 2 diabetes. In this paper, we proposed a mathematical model of the adiponectin − adiponectin receptors framework, based on the assumption that the secretion of adiponectin is inversely proportional to fat mass. Here, we show that an increase in obesity or adiposity results in a decrease in the adiponectin plasma level, which contributes to the development of adiponectin resistance. In this model, we have used different parameters and their default values, to perform a simulation based on the model. Further, experimentally, the plasma adiponectin concentration is ( ≈ 0.015) significantly lowered in the diabetic group compared to the non-diabetic group. In this model, we have obtained the plasma adiponectin concentration level ( ≈ 0.014) in the simulation process. So this model is only differing from 1 × 10 −3 significant digits. We have achieved the degree of accuracy of adiponectin resistance is 93.33%. Therefore, these advances offer novel insights into the mathematical approach.