Action of Nutritive and Non-Nutritive Sweeteners on Insulin Signalling and Cardiometabolic Risk Factors Using an Experimental Animal Model

Abstract

Background: The increase in the production and intake of nutritive sweeteners (NS) has been observed to parallel the rise in cardiometabolic risk factors. Due to health-related effects attributed to the intake of NS, there is an increase in the production and intake of low or no calorie sweeteners (LNCS). However, there is an increasing volume of evidence which implies that LNCS may contribute to the development of metabolic syndrome although they do not contribute to caloric intake. Experimental studies have explored the relationship between the intake of sweeteners (both caloric and non-caloric) on individual components of metabolic syndrome including insulin resistance without examining possible mechanisms involved. Aim: To determine the effect of nutritive and non-nutritive sweeteners on cardiometabolic risk factors and GLUT4 insulin signalling pathway using an experimental animal model. Methods: An experimental study was conducted at the Department of Animal Experimentation at the Noguchi Memorial Institute for Medical Research. Thirteen groups of female Sprague Dawley rats (6 per group) were administered different doses of sweeteners (3 dose groups per sweetener; low, medium and high): white sugar (0.035, 0.07, and 0.1 g/ml), brown sugar (0.036, 0.072, and 0.11 g/ml), honey (0.047, 0.094, and 0.14 g/ml) and stevia (0.007, 0.014, and 0.02 g/ml), control (distilled water) for 17 weeks. A specified amount of food was administered to the rats daily and their daily food consumption was estimated. During the 17 weeks, the rats were weighed weekly and their fasting plasma glucose was measured bi-weekly. At the end of the study, the rats were sacrificed, and their blood samples were obtained by cardiac puncture for further biochemical analysis. Organs (brain, pancreas, and muscles) were harvested for histological analysis. Lipid profiles consisting of total cholesterol (TC), low density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), and very low density lipoprotein (VLDL) were determined at the end of the study. Lipid ratios, Castelli Risk Index I (CRI-I), Castelli Risk Index II (CRI-II), and Atherogenic Index of Plasma (AIP) were estimated from the lipid profiles. Serum fasting insulin and serum GLUT4 protein concentrations were determined at the end of the study using a rat ELISA kit. Hexokinase 2 (HK2) enzyme concentration was assessed from muscle homogenate using a rat ELISA kit. The muscles and pancreas were further processed for histological analysis. GraphPad Prism software version 9.0 was used for the statistical analysis. Descriptive variables were expressed as means ± SEM. Statistical significance was set at p< 0.05. Two-way mixed ANOVA was used to compare the changes in body weight and fasting plasma glucose over time among the various experimental groups. One-way ANOVA was used to compare differences in the means of different variables (percent weight change, end line fasting plasma glucose, fasting serum, HOMA-IR, TC, TG, LDL, HDL, CRI-I and II and AIP) between the experimental groups. Tukey’s post hoc tests were used to detect where differences between the group were observed. Results: The average weight of all the rats increased over time in all the experimental groups. A two-way ANOVA showed a statistically significant interaction between duration and type of treatment on weight gain [F (28.22, 52.86) = 2.85, p<0.0001, ղ² = 0.897)]. Significant differences in percent weight gain were observed among the various treatment groups [(F (12, 65) = 3.953, p<0.001, ղ² = 0.42)]. Compared to the control, percent weight gain was highest in the experimental group administered with stevia high dose (72.7 ± 10.5) (p = 0.005). There was a statistically significant interaction between the duration of treatment and type of treatment on fasting blood glucose [F (5.116, 199.5) = 56.95, p < 0.001, ղ²=0.467]. Fasting Insulin concentration was significantly higher in the control than in all the experimental groups (p<0.0001). Similarly, the HOMA-IR was significantly higher in the control group than in all the treatment groups (p<0.0001). There were significant differences in LDL, HDL, TG, and VLDL between the different treatment groups (p=0.001, 0.038, 0.003, 0.010 respectively). The experimental group administered with high-dose white sugar had the highest CRI-I (1.79 ± 0.11) and CRI-II (0.49 ± 0.09). Both CRI-I and CRI-II had a dose-dependent increase. AIP was highest in the high-dose stevia experimental group (0.21 ± 0.07) compared to the control (p = 0.008). The AIP increased in a dose-dependent manner. GLUT4 concentration was highest in the control group (4.38 ± 0.28 ng/ml). There was a significant difference in GLUT4 protein concentration between the different treatment groups [F (12, 45) = 13.09, p<0.0001]. High doses of the sweeteners resulted in a decrease in HK2 within all the sweetener groups. A one way ANOVA revealed significant differences in HK2 concentration between the different experimental groups [F (12, 64) = 11.66, p=0.0001]. The control rat showed normal islets of Langerhans. The pancreas of high-dose white sugar, high-dose brown sugar, and medium-dose stevia showed some pathological changes. The skeletal muscle of the control rat showed adequately preserved myofibers with clear striation and peripheral myonuclei. Pathological changes were observed in the skeletal muscle of all doses of stevia. Conclusion: Increased consumption of white sugar and stevia can increase food consumption and weight gain. High intakes of white sugar and stevia may initiate and promote atherosclerosis. Also, the intake of sweeteners in high quantities can affect insulin signalling through a reduction in GLUT4 expression and HK2 which are key factors in the development of type 2 diabetes. Intake of white sugar, brown sugar, and stevia can induce structural changes in the pancreas and skeletal muscle. In comparison with the other sweeteners, honey had a lesser effect on the risk factors of cardiometabolic syndrome.