A Study On T-cell Immunity Of Simple Obese Children And Its Related Factors

BackgroundObesity, a disorder of energy balance in which energy intake is greater than energy expenditure, is a wide variety of health problems including hypertension, dyslipidemia, cardiovascular disease, diabetes mellitus and certain types of cancer. It is a growing public health problem.A positive chronic imbalance between energy intake and expenditure leads to situations of obesity, which influence unspecific and specific immune response mediated by humoral and cell mediated mechanisms. Furthermore, several lines of evidence have supported a link between adipose tissue and immunocompetent cells. However, limited and often controversial information exist comparing immunity in obese and non-obese subjects as well as about the cellular and molecular mechanisms implicated. In general terms, clinical and epidemiologicaldata support the evidence that the incidence and severity of specific types of infection illnesses are higher in obese person as compared to lean individuals together with the occurrence of poor antibody responses to antigens in overweight subjects. But the mechanisms involved remain still unclear, and the underlying factors are not fully understood. Hypercholesterolaemia might play a key role in linking nutritional status with T-cell function. But previous studies on this field are all based on genetically obese animal models, and only single associated factor is analyzed on T-lymphocyte cell function. In fact, many factors such as the degree of obesity, the level of plasma lipoprotein, fasting plasma glucose, insulin sensitivity, excessive intake of certain nutrients may also induce some immune damage. What's the T-cell immune state on simple obese children? What's the extent of different obese degree on the immunity damages? Which factors may associate to cell immune damages? In this study, we will mainly focus on these questions.ObjectivesTo explore the impact of obesity on children's cell immunity by comparing the T-cell immune function and analyze the possible factors associated to T-cell immune.PatientsChildren with simple obesity, who consulted doctor at the clinic of children's health care, Women and Children's Hospital of Ningbo City during May 2004 and Dec 2004 were investigated. 45 children with simple obesity with age ranged from 7 to 15 years old were selected.20 age and sex matched healthy children were set as controls.The diagnosis of obesity was based on the standard of the Pediatrics textbook (the fifth edition, edited by Wang Mudi). Children with weight for height and sex is greater than the average by 30%~39% are diagnosed as media obesity, Body Mass Index (BMI) is 20-25 correspondingly; if it is by 40%~59% greater, then diagnosed as severe obesity, BMI is 25-30 correspondingly; when over by more than 60%, BMI is correspondingly more than 30, then diagnosed as extremely obesity. BMI of control is ranged from 15 to 18.BMI was calculated as weight /(height)2 (Kg/m2).MethodsFor all the children, T-lymphocyte sub-populations (CD3+, CD4+, CD8+, CD4+,CD8+, CD19+, NK), liver function, plasma lipoprotein, fasting plasma glucose, fasting plasma insulin, microelement wereexamined. T-lymphocyte sub-populations was measured by FACSCalibur (B&D company, U.S.A) flow cytometry. Fasting plasma glucose (FPG) was measured with the method of Creative Kinase。 The plasma lipoprotein was detected by enzyme method. Fasting plasma insulin (FINS) was measured with the method of radioimmunoassay. All of these inspections were conducted on fully automatic biochemistry instrument. Microelement was measured with the method of Atom absorbing.Statistical analysisStatistical analyses were performed using Statistical Packages for Social Science version 10.0. When comparing the means of HOMA-IR and HBCI by independent t test, the data were first transformed by natural logarithm (Ln) in order to make the data fit the Kurtosis distribution. One-way ANOVA was used to compare the degrees of BMI and age-matched healthy children. S-N-K test was used to compare two of them. Bivariate analysis and Multiple linear...