Controlling energetic intake based on a novel logistic regression model for the metabolic syndrome in a Chinese population

The present study was designed to develop a novel method of energy calculation for controlling energetic intake in patients with the metabolic syndrome. Demographics and dietary data were recorded for 2582 obese subjects. Nutritional education was applied to all the patients. One year later, the data on age, sex, activity intensity coefficient, waistline, environmental temperature and BMI in subjects who lost >= 5% body weight were entered into a multivariate logistic regression analysis model. Energy requirement was calculated from the results of multivariate logistic regression. Four hundred and thirty-four metabolic syndrome patients were then randomly divided into the treated group (216) and the control group (218). The energetic intake in the experimental group was controlled based on the new energy requirement model. The traditional energy exchange method was used in the control group. The independent factors predicting metabolic syndrome prognosis, such as age, sex, activity intensity coefficient, waistline, environmental temperature and BMI, were identified by multivariate logistic regression analysis. The energy requirement model was then constructed by logistic regression analysis. After 6 months of energetic intake control based on the new model, the parameters of the experimental group were significantly different from those of the controls (all P<0.05): waistline, 89.65 (SD 5.54) v. 91.97 (SD 4.78) cm; BMI, 24.67 (SD 3.54) v. 25.87 (SD 2.65) kg/m(2); fasting blood glucose, 6.9 (SD 3.6) v. 8.7 (SD 4.6) mmol/l; 2 h PG, 8.7 (SD 5.7) v. 10.7 (SD 4.5) mmol/l; HbA(1)c, 7.7 (SD 1.6) v. 8.9 (SD 2.6) %; homoeostasis model insulin resistance index, 3.14 (SD 1.62) v. 4.32 (SD 2.25). The new energy requirement model can effectively improve the clinical outcomes of controlling energetic intake in metabolic syndrome patients.