Predictive Effect Of Infant BMI Peak And Adiposity Reboundon Metabolic Abnormalities In Preschool Children: A Prospective Cohort Study

Objective This study amis to describe the growth trajectory of BMI in children,to explore the relationship between the infant BMI peak,the adiposity rebound and metabolic abnormality in preschools,and to explore the prospective factors of metabolic abnormality in preschool children.Methods This study was a prospective cohort study design with 3,474 mother-child pairs from the Ma’anshan Birth Cohort Study（MABC）as the baseline population,and children born from October 2013 to April 2015 were followed up to 6 years old.Information on length/height,weight,head circumference,and chest circumference were collected at birth,42 days,3 months,6 months,9 months,1 year,1.5 years,2years,2.5 years,3 years,3.5 years,4 years,4.5 years,5 years,5.5 years,and 6 years,respectively.2 m L of venous blood was collected from 12 to 14 hours fasting perschools in the cohort children,and metabolic indices such as glucose,insulin,triacylglycerol（triglyceride,TG）,cholesterol（TC）,high-density lipoprotein cholesterol（HDL-C）,and low-density lipoprotein cholesterol（LDL-C）were measured using reagents provided by Roche Diagnostic Products（Shanghai）.The participants’right upper arm brachial blood pressure was measured,and the measurements were repeated three times with a 1 to 2 minutes interval each time,and the mean values were recorded.The criteria for determining the metabolic abnormalities in this study were defined with reference to the International Diabetes Federation’s diagnosis of metabolic syndrome over 12 years of age^[1].Based on the Maternal and Infant Health Record Form and the Health Record Form for Preschool Children developed by the group,a questionnaire was used to collect demographic information related to the children’s breastfeeding status,complementary food addition,diet,sleep duration,and outdoor activity time;the mother’s pre-pregnancy weight,pregnancy weight gain,presence of metabolic diseases,and education level.A total of 2,022 children were included in this study,and 164 gigantic children were excluded during the sensitivity analysis phase,for a total of 1,858 children.A fractional polynomial mixed-effects model of BMI for the study population was developed in Stata 13.0 statistical software to calculate the BMI growth function for individual children.Finally,the highest point（IP）and lowest point（AR）of the BMI function curve were determined using a segmentation method.To reduce bias,the difference between the maximum value of BMI and the subsequent BMI was specified to be≥0.1 kg/m~2.If the difference between the maximum value of BMI（Y₁）and the subsequent BMI value（Y）was<0.1 kg/m~2,Y₁ was taken as the maximum value,and the difference between Y₁ and the subsequent BMI value（Y₃）was compared again,and so on,until the maximum point was determined^[2];the minimum point was determined in the same way.Logistic regression analysis of the association of IP characteristics and AR characteristics with metabolic abnormalities in preschool children,and regression prediction models to predict the association.Results 2,022 children had an IP level of（18.39±1.60）kg/m~2,an IP age of（7.18±1.54）months,an AR time phase of（51.64±14.95）months,and an AR level of（15.48±1.22）kg/m~2;1,026 boys had an IP level of（18.63±1.56）kg/m~2,an IP age of（7.07±1.48）months,AR time phase of（51.29±15.46）months,and AR level of（15.61±1.21）kg/m~2;996 girls had an IP level of（18.15±1.59）kg/m~2,IP age of（7.30±1.59）months,AR time phase of（51.99±14.40）months,and AR level of（15.34±1.22）.There were gender differences in IP levels and age in infancy,with boys having higher IP levels than girls（18.63 kg/m~2 vs.18.15 kg/m~2,P<0.01）and earlier age than girls（7.07 months vs.7.30 months,P<0.01）;there was no statistical difference between the sexes in AR chronological phase（P>0.05）However,AR levels were higher in boys than in girls（15.61 kg/m2 vs.15.34 kg/m~2,P<0.01）.The detection rate of metabolic abnormality in preschool children was 16.9%.Adjusting for maternal comorbidity during pregnancy,pre-pregnancy BMI,education level,gender,feeding pattern up to 6 months of age,picky eating status,and daily activity time,logistic regression analysis showed that high IP level in infancy was a risk factor for metabolic abnormalities in preschool children compared with the normal group（RR=1.46,95%CI:1.07 to 1.97）,and low IP level and metabolic There was no statistical association between low IP levels and metabolic abnormalities（RR=0.88,95%CI:0.63 to 1.23）;no statistical association between earlier or later IP age and metabolic abnormalities in preschool children（RR=0.86,95%CI:0.62 to 1.20;RR=1.02,95%CI:0.74 to 1.39）;children in the high AR level group had a higher had a higher risk（RR=2.59,95%CI:1.92 to 3.52）,and there was no statistical association between low AR levels and metabolic abnormalities（RR=0.97,95%CI:0.68 to 1.39）;preschool children with earlier AR chronology had an increased risk of metabolic abnormalities（RR=1.82,95%CI:1.34 to 2.48）,and there was no statistical association between delayed AR and There was no statistical association between metabolic abnormalities（RR=0.94,95%CI:0.67 to 1.33）.In the sensitivity analysis phase,these results persisted after the exclusion of gigantic children.IP characteristics and AR characteristics can be used as predictors of metabolic abnormalities in preschool children,and the combination of the two has a better prediction effect（R=90.2%）.Conclusions The AR phase of children in Ma’anshan has an earlier trend,which may exist for a long time.There is also an increased risk of metabolic abnormalities in pre-school-age children.Compared with children with normal IP and AR characteristics,high BMI level at IP time point,earlier AR time point,and high BMI level at AR time point can increase the risk of metabolic abnormalities in preschool children.The combined effect of IP feature and AR feature has a better prediction effect on the metabolic abnormalities of preschool children,and can be used as a predictor of the metabolic abnormalities of preschool children.