Due to the increased prevalence of obesity in children and adolescents, various studies have been conducted to discover which associations and risk factors increase the likelihood of obesity in children. Although it is still difficult to fully grasp all the risk factors related to obesity, it is of great significance to control and prevent obesity by combining diet, exercise, physiological factors and psychological factors . The short-term and long-term effects of obesity on children's health are a major issue due to adverse psychological and health consequences . Potential negative psychological outcomes are depressive symptoms, poor body image, low self-esteem, risk of eating disorders, and behavioral and learning problems; negative health consequences include insulin resistance, type 2 diabetes, asthma, hypertension, and nonalcoholic steatohepatitis [17, 18]. Obese children are more likely to become obese adults, and therefore increase their risk of multiple diseases before they even reach puberty .
The human lipidomic profile reflects lipid metabolism, including the early phase of pathophysiological changes associated with diseases. Wang et al. observed a significant reduction in the levels of five lysophosphatidylcholines (LPC) species (LPC18:2, LPC18:1, LPC20:2, LPC20:1, and LPC20:0) in the obese group compared with the normal-weight group . In addition, lower total LPC, LPC18:0, LPC18:2, and LPC20:4 levels were measured in obese and obese subjects with type 2 diabetes than in nonobese adults. A difference in the LPC profile was not observed between obese individuals and obese subjects with type 2 diabetes . Moreover, Wallace et al. reported associations between the levels of several LPC species, BMI, and inflammatory markers . The authors identified higher levels of LPC14:0 and LPC18:0 and a lower concentration of LPC18:1 in obese subjects compared with lean subjects .
Obesity can be estimated in several ways: Body mass index (BMI), the ratio of weight to squared height , is used as the most common indicator of obesity. It is convenient and simple, but it can cause changes in cardiovascular and metabolic performance between individuals, but there are alternative methods of body fat distribution. Higher WHR indicates more intraperitoneal cavity and is associated with a higher risk of type 2 diabetes, cardiovascular disease and mortality ]. At the same time, waist circumference can also be used. Similar to WHR , it is considered a more direct and reliable method. Generally, body fat percentage (BFP) is a method in the body to measure the ratio of adipose tissue to lean meat and water , and most are determined using bioelectrical impedance. BFP is not related to BMI, it is associated with an increase in all-cause mortality, and it is generally suggested to estimate obesity better than BMI . Therefore, this study aimed at Chinese teenagers, a group with relatively stable diet and lifestyle, carried out a lipidomics study to observe the development process of obesity and to screen out some biochemical indicators for predicting obesity.
In the present study, the levels of TG, 18-Hydroxycortisol, Isohumulinone A, and 11-Dihydro-12-norneoquassin were up-regulated in obesity group, while PC, PE, LysoPC, LysoPE, and PI were significantly down-regulated in obesity group than in control and overweight individuals. 1.82_480.3095 m/z was annotated as PC (15:0/0:0), PE (18:0/0:0), LysoPC (15:0), and LysoPE (0:0/18:0). 6.10_861.5490 m/z was annotated as PI (14:0/22:2(13Z, 16Z))- PI (22:2(13Z,16Z)/14:0) (Additional file 3). According to the Fig. 1, eight metabolites generated only in 1.11_396.2412 m/z was annotated as 18-Hydroxycortisol, Isohumulinone A, and 11-Dihydro-12-norneoquassin; 4.86_902.5761 m/z was annoted as PI (18:0/20:5 (5Z,8Z,11Z,14Z,17Z)); 10.13_949.7263 m/z was annoted as TG (20:4 (5Z,8Z,11Z,14Z) /20:3(5Z,8Z,11Z) /18:3 (9Z,12Z,15Z)) (Additional file 2). These data suggested that the development of obesity may does not have to be through overweight, and it may develop directly to obesity due to some changes in lipid metabolism.
There are also some limitations in our study. First of all, it was a cross-sectional study which only addressed the alterations of lipidomic profiling in normal, overweight and obesity students. Furthermore, subjects were selected into groups just according to BMI rather than selected randomly, therefore, this can produce selection bias. In addition, there is a small sample study. So based on the above limitations, more large-scale population studies are still needed in the future investigation.