A diet high in fat leads to obesity in both humans and animals [22,23]. In both rats and mice, a positive relationship has been demonstrated between the levels of fat in the diet and body weight or fat gain; also, rats that consumed diets containing high quantities of fat gained weight faster than those on diets containing minimal fat [24–28].
The recorded increase of BMI in obese rats is in agreement with previous studies [29], in which they reported that BMI in obese rats is usually higher than 0.75 gm/cm2. The final weight and weight gain in obese rats were also coupled with high BMI, in agreement with the findings of Picklo et al [30], who showed the obesogenic effect of a saturated lipid diet in animal models. Unexpectedly, while both mangosteen pericarp extract and curcumin were effective in lowering the BMI of obese animals, both also induced weight gain in lean animals. This is similar to the findings of Husen et al [31], who reported an increase in body weight following mangosteen extract and curcumin after the decrease in body weight induced by STZ treatment.
The elevated levels of ROS after high caloric intake or inflammation can later result in increase of BMI or incidence of obesity. Adipose tissue can undergo pathological alterations induced by inflammation or oxidative stress, which in turn enhances the secretion of adipokines and affects the peripheral tissues that produce ROS, further promoting oxidative stress and the inflammatory response [32] (see also Cristancho & Lazar 2011). The concentration of the serum MDA can be used as an indicator of oxidative stress. MDA is one of the final products of the peroxidation of polyunsaturated fatty acids (PUFA). The concentration of MDA can be used as an indicator of cell or tissue damage due to the increase in lipid peroxidation. The unexpected non-significant increase of MDA and decrease in GSH levels, GST activity, and vitamin C levels in HFD-induced obese rats might be attributed to the addition of coconut oil to the high saturated fat diet given in our paradigm as an inducer of obesity. Multiple studies demonstrate the anti-oxidative effects of virgin coconut oil, supporting this hypothesis [33,34]. Table 2 also shows the independent and synergistic effects of mangosteen pericarp extract and curcumin on oxidative stress-related variables. While mangosteen pericarp extract did not demonstrate anti-oxidative effects in normal weight rats, it did induce a significant increase in GSH levels in obese rats. GST functions as antioxidant, and enzymes can catalyze the detoxification of xenobiotics via conjugation with GSH.
The diet-induced obesity animal model is one of the most common and reliable models used in obesity studies due to its similarity in modeling the most common route of obesity in humans, as well as related metabolic effects. As shown previously, this HFD models obesity via increased food intake, body weight gain, body fat accumulation, BMI increase, defects in antioxidant levels, and disruption in the lipid profile [22,28].
While LDL-C is responsible for the delivery of cholesterol to peripheral tissues, HDL-C mediates the inverse process of cholesterol transport from peripheral tissues [35]. The non-significant decrease of HDL-C in obese rats reported in the present study may be related to the anti-oxidative effect of coconut oil, a component of the HFD used here [33,34].
In general, ingestion of coconut oil can increase HDL-C [36]. It has been proposed that lauric acid, the main constituent of coconut oil, is the cornerstone of this pathway. Lauric acid accounts for 50% of the content of coconut oil. Although lauric acid is considered a medium-chain fatty acid (MCFA), 70% of lauric acid is transported as a long-chain fatty acid (LCFA), while the other 30% remains as a MCFA. Thus, there are two ways of transporting lauric acid in the body. When lauric acid reaches the liver, it serves as a substrate in the production of apoA1 and apoB, further contributing to the formation of both HDL-C and LDL-C [36].
Studies show that in contrast, to LDL-C, HDL-C may play an anti-atherogenic and anti-thrombotic role by protecting LDL-C particles against lipid peroxidation and reducing the deleterious effects of oxidized LDL-C [37]. Based on this report, the non-significant decrease of HDL-C in obese rats may be related to the presence of coconut oil in the HFD [38].
Moreover, our data is consistent with those of Wihastuti et al [39], who showed that 400 mg/kg body weight mangosteen pericarp extract affected LDL-C but no other lipid marker, and significantly reduced H2O2 levels and NF-κB expression. At concentrations of 800 mg/kg body weight, this extract was most effective in improving the lipid profile; this suggests that although in the present study mangosteen pericarp extract did induce a hypo-lipidemic effect in obese rats, the anti-atherogenic effects likely occurred via its anti-oxidative and anti-inflammatory effects [39].
This contradiction may also be attributed to the fact the amount of total xanthone in the mangosteen pericarp extract is strongly affected by the extraction capacity of the solvent to recover different phenolic constituents from various fruit origins, as well as the methods of transportation and storage [40–42]. Aisha, Abu-Salah & Ismail [43] have reported that toluene is the most efficient extraction solvent for mangosteen pericarp extract compared to 75% ethanol and methanol [44].
Obesity is a risk factor for the development of cardiovascular disease, diabetes mellitus, hyperlipidemia, and arteriosclerosis (Cannon, 2007). To treat and prevent obesity and obesity-related complications, an increasing number of people use hypoglycemia-inducing or weight-loss drugs. However, the long-term use of these drugs can damage the liver and kidney. Unsurprisingly, finding safe and effective weight-loss- and hypoglycemia-inducing agents is becoming increasingly urgent.
Table 4 reveals the level of serum glucose in the studied rats. While obesity itself did not induce elevation of blood glucose, mangosteen pericarp extract was effective in reducing glucose levels in both normal weight and obese animals. This is consistent with the previous work of Taher et al [45], which demonstrated that orally administered mangosteen pericarp extract at various doses demonstrated a hypoglycemic effect in streptozotocin (STZ)-induced diabetic rats and normoglycemic rats. Moreover, curcumin demonstrates hypoglycemic effects in both obese and normal weight rats; in obese rats, the synergistic effects of both of these extracts was much higher than each extract independently. This is also supported by Rivera-Mancía et al [46] and Sohaei et al [47], who showed the hypoglycemic effects of curcumin. As previously described, the use of curcumin in vitro and in animal models of diabetes revealed a variety of potential mechanisms of action to treat diabetes mellitus; however, clinical trials in humans have thus far been inconsistent with these findings.
Pearson’s correlation coefficient (PCC) is a statistical metric that measures the strength and direction of a linear relationship between two or more random variables [48]. Table 5 indicates the correlations between all variables measured in this study. It can be seen that obesity (BMI), antioxidant status (GSH levels), and dyslipidemia-related markers (CHOL, TAG, CHOL/HDL-C) were negatively or positively correlated in a manner that demonstrates the negative impact of oxidative stress and dyslipidemia on obesity.
Abruzzo et al [49] highlighted the advantage of using ROC curves as an outstanding statistical tool for the identification of biomarkers that are sufficiently sensitive and specific for the early diagnosis of obesity. Although its utility in prediction, risk valuation, and assessment of therapeutic interventions still requires further validation, ROC curves emphasize the most significant statistical differences between patients and controls and even animal models of diseases [50]. The AUC provides a useful measure to evaluate the predictive value of biomarkers. While an AUC value near 1 designates an excellent predictive marker, a curve that lies adjacent to the diagonal (AUC = 0.5) indicates no diagnostic usefulness. AUC values close to 1.00 are always accompanied by satisfactory values of specificity and sensitivity [51].
Table 6 demonstrates the ROC curves with AUC, specificity, and sensitivity in obese rats. Among the measured variables, BMI showed excellent predictive value as a marker of obesity, with AUC between 0.8 and 1 with satisfactory specificity and sensitivity. The other measured variables demonstrate relatively less predictive ability with any specificity and sensitivity.