In Patients With Obesity, the Number of Adipose Tissue Mast Cells Is Significantly Lower in Subjects With Type 2 Diabetes

Abstract

Type 2 diabetes (T2D) is a rising global health problem mainly caused by obesity and a sedentary lifestyle. In healthy individuals, white adipose tissue (WAT) has a relevant homeostatic role in glucose metabolism, energy storage, and endocrine signaling. Mast cells contribute to these functions promoting WAT angiogenesis and adipogenesis. In patients with T2D, inflammation dramatically impacts WAT functioning, which results in the recruitment of several leukocytes, including monocytes, that enhance this inflammation. Accordingly, the macrophages population rises as the WAT inflammation increases during the T2D status worsening. Since mast cell progenitors cannot arrive at WAT, the amount of WAT mast cells depends on how the new microenvironment affects progenitor and differentiated mast cells. Here, we employed a flow cytometry-based approach to analyze the number of mast cells from omental white adipose tissue (o-WAT) and subcutaneous white adipose tissue (s-WAT) in a cohort of 100 patients with obesity. Additionally, we measured the number of mast cell progenitors in a subcohort of 15 patients. The cohort was divided in three groups: non-T2D, pre-T2D, and T2D. Importantly, patients with T2D have a mild condition (HbA1c <7%). The number of mast cells and mast cell progenitors was lower in patients with T2D in both o-WAT and s-WAT in comparison to subjects from the pre-T2D and non-T2D groups. In the case of mast cells in o-WAT, there were statistically significant differences between non-T2D and T2D groups (p = 0.0031), together with pre-T2D and T2D groups (p=0.0097). However, in s-WAT, the differences are only between non-T2D and T2D groups (p=0.047). These differences have been obtained with patients with a mild T2D condition. Therefore, little changes in T2D status have a huge impact on the number of mast cells in WAT, especially in o-WAT. Due to the importance of mast cells in WAT physiology, their decrease can reduce the capacity of WAT, especially o-WAT, to store lipids and cause hypoxic cell deaths that will trigger inflammation.

Keywords: T2D; adipogenesis; adipose tissue; angiogenesis; flow cytometry; inflammation; mast cell; obesity.

Figure 1

Flow cytometry. (A–E) Flow cytometry gating employed to identify mast cells and mast cell progenitors in white adipose tissue. The red dots in the first scatter plot are the autofluorescent beads employed in the quantification. (F) Mast cell progenitor gate in colonic mucosa. MC (mast cells), MCp (Mast cell progenitors).

Figure 2

Differences in mast cells per gram of tissue in omental white adipose tissue depending on the type 2 diabetes status. The data comes from the big cohort (n = 100). MC (mast cells), T2D (type 2 diabetes), “ns” (p-value > 0.05), “ ** ” (0,01 > p-value).

Figure 3

Differences in mast cells per gram of tissue in subcutaneous white adipose tissue depending on the type 2 diabetes status. The data comes from the big cohort (n = 100). MC (mast cells), T2D (type 2 diabetes), “ns” (p-value > 0.05), “ * ” (0.05 > p-value > 0.01).

Figure 4

Differences in mast cells per gram of tissue between subcutaneous and omental white adipose tissue in patients without type 2 diabetes. The data comes from the big cohort (n = 100). MC (mast cells), s-WAT (subcutaneous white adipose tissue), o-WAT (omental white adipose tissue), T2D (type 2 diabetes), “ * ” (0.05 > p-value > 0.01).

Figure 5

Differences in mast cells per gram of tissue between subcutaneous and omental white adipose tissue in patients with pre-type 2 diabetes. The data comes from the big cohort (n = 100). MC (mast cells), s-WAT (subcutaneous white adipose tissue), o-WAT (omental white adipose tissue), T2D (type 2 diabetes), “ * ” (0.05 > p-value > 0.01).

Figure 6

Differences in mast cells per gram of tissue between subcutaneous and omental white adipose tissue in patients with type 2 diabetes. The data comes from the big cohort (n = 100). MC (mast cells), s-WAT (subcutaneous white adipose tissue), o-WAT (omental white adipose tissue), T2D (type 2 diabetes), “ns” (p-value > 0.05).

Figure 7

Principal component analysis (PCA). This technique allows us to observe the patterns of our data reducing its dimension. The data comes from the big cohort (n = 100). NO (patients without type 2 diabetes), PRE (patients with pre-type 2 diabetes), YES (patients with type 2 diabetes), BMI (Body Mass Index), MC_V (mast cells from omental white adipose tissue), MC_S (mast cells from subcutaneous white adipose tissue), WH index (waist hip index).

Figure 8

Linear discriminant analysis (LDA). This technique finds the linear combinations of variables that better separate the observations. The data comes from the big cohort (n = 100). NO (patients without type 2 diabetes), PRE (patients with pre-type 2 diabetes), YES (patients with type 2 diabetes), LD1 (linear discriminant 1), LD2 (linear discriminant 2).

Figure 9

Random Forests Analysis. This technique measures the importance of the variables to discriminate the three levels of the categorical variable “Type 2 diabetes”. The data comes from the big cohort (n = 100). MC_V (mast cells in omental white adipose tissue), MC_S (mast cells in subcutaneous white adipose tissue), WH index (waist-hip index).