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Journal of Peking University (Health Sciences) ›› 2023, Vol. 55 ›› Issue (3): 415-420. doi: 10.19723/j.issn.1671-167X.2023.03.005

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Association between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years

Meng-jie CUI1,Qi MA1,Man-man CHEN1,Tao MA1,Xin-xin WANG2,Jie-yu LIU1,Yi ZHANG1,Li CHEN1,Jia-nuo JIANG1,Wen YUAN1,Tong-jun GUO1,Yan-hui DONG1,Jun MA1,Yi XING1,*()   

  1. 1. Institute of Child and Adolescent Health, Peking University School of Public Health, Beijing 100191, China
    2. Department of Epidemiology and Health Statistics, School of Public Health and Management, Ningxia Medical University, Yinchuan 750004, China
  • Received:2023-03-02 Online:2023-06-18 Published:2023-06-12
  • Contact: Yi XING E-mail:cyrss@126.com
  • Supported by:
    the National Natural Science Foundation of China(91846302);the Key R & D Projects of the Ministry of Science and Technology(2016YFA0501604)

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Abstract:

Objective: To analyze the association between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years, and to provide suggestions for the prevention and control of metabolic syndrome in Chinese children and adolescents. Methods: Data were collected from the research project "Development and Application of Technology and Related Standards for Prevention and Control of Major Diseases among Students" of public health industry in 2012. This project is a cross-sectional study design. A total of 65 347 students from 93 primary and secondary schools in 7 provinces including Guangdong were selected by stratified cluster random sampling method. Given the budget, 25% of the students were randomly selected to collect blood samples. In this study, 10 176 primary and middle school students aged 7 to 17 years with complete physical measurements and blood biochemical indicators were selected as research objects. Chi-square test was used to compare the distribution differences of growth patterns under different demographic characteristics. Birth weight, waist circumference and blood biochemical indexes were expressed in the form of mean ± standard deviation, and the differences among different groups were compared by variance analysis. Binary Logistic regression model was used to analyze the relationship between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years. Results: The prevalence of metabolic syndrome in children and adolescents was 6.56%, 7.18% in boys and 5.97% in girls. The risk of metabolic syndrome was higher in the catch-down growth group than in the normal growth group (OR=1.417, 95%CI: 1.19-1.69), and lower in the catch-up growth group(OR=0.66, 95%CI: 0.53-0.82). After adjusting for gender, age and so on, the risk of developing metabolic syndrome in the catch-down growth group was higher than that in the normal growth group (OR=1.25, 95%CI: 1.02-1.52), but there was no significant difference between the catch-up growth group and the normal growth group (OR=0.79, 95%CI: 0.62-1.01). Stratified analysis showed that the association between different growth patterns and metabolic syndrome was statistically significant in the 7-12 years group, urban population, and Han Chinese student population. Conclusion: There is a correlation between different growth patterns and metabolic syndrome in children and adolescents. The risk of developing metabolic syndrome in children and adolescents with catch-down growth is higher than that in the normal growth group, which suggests that attention should be paid to the growth and development of children and adolescents, timely correction of delayed growth and prevention of adverse health outcomes.

Key words: Growth pattern, Children, Adolescents, Metabolic syndrome

CLC Number: 

  • R725.8

Table 1

Distribution characteristics of growth patterns under different demographic characteristics"

Demographic index Catch-down growth(n=3 101) Normal growth(n=4 233) Catch-up growth(n=2 842) Total P value
Gender，n(%) 0.970
  Male 1 531 (30.43) 2 099 (41.72) 1 401 (27.85) 5 031
  Female 1 570 (30.52) 2 134 (41.48) 1 441 (28.01) 5 145
Age/years，n(%) < 0.001
  7-12 1 788 (28.33) 2 668 (42.27) 1 856 (29.40) 6 312
  13-17 1 313 (33.98) 1 565 (40.50) 986 (25.52) 3 864
District，n(%) 0.001
  Urban 1 688 (31.78) 2 214 (41.68) 1 410 (26.54) 5 312
  Rural 1 413 (29.05) 2 019 (41.50) 1 432 (29.44) 4 864
Nutrition status，n(%) < 0.001
  Normal or below 2 368 (28.19) 3 514 (41.83) 2 518 (29.98) 8 400
  Overweight or above 733 (41.27) 719 (40.48) 324 (18.24) 1 776
Nation，n(%) 0.007
  Han 2 756 (30.06) 3 833 (41.81) 2 579 (28.13) 9 168
  Others 202 (36.33) 217 (39.03) 137 (24.64) 556
Education level of father，n(%) < 0.001
  Junior high school or below 1 228 (29.38) 1 643 (39.31) 1 309 (31.32) 4 180
  Senior high school or equivalent 760 (28.61) 1 145 (43.11) 751 (28.28) 2 656
  College or above 945 (31.88) 1 313 (44.30) 706 (23.82) 2 964
Education level of mother，n(%) < 0.001
  Junior high school or below 1 311 (29.03) 1 759 (38.95) 1 446 (32.02) 4 516
  Senior high school or equivalent 722 (29.81) 1 037 (42.82) 663 (27.37) 2 422
  College or above 908 (31.76) 1 296 (45.33) 655 (22.91) 2 859
Birth weight/kg，${\bar x}$±s 3.27±0.52 3.28±0.47 3.30±0.48 10 176 0.050
BMI/(kg/m2)，${\bar x}$±s 19.66±3.99 18.77±3.83 18.11±3.60 10 176 < 0.001
WC/cm，${\bar x}$±s 69.31±10.99 65.79±10.48 62.82±10.04 10 176 < 0.001
SBP/mmHg，${\bar x}$±s 107.03±12.21 104.49±11.74 102.59±11.73 10 176 < 0.001
DBP/mmHg，${\bar x}$±s 67.77±8.88 65.99±8.68 65.13±8.78 10 176 < 0.001
TG/(mmol/L)，${\bar x}$±s 0.95±0.49 0.92±0.46 0.92±0.46 10 176 0.030
HDL-C/(mmol/L)，${\bar x}$±s 1.34±0.32 1.37±0.34 1.38±0.32 10 176 < 0.001
FBG/(mmol/L)，${\bar x}$±s 4.74±0.67 4.76±0.62 4.70±0.61 10 176 0.001

Table 2

Logistic regression analysis of relationship between different growth patterns and MS"

Growth patterns n(%)# Mode 1* Model 2*
OR(95%CI) P value OR(95%CI) P value
Normal growth 4 233 (6.40) 1 - 1 -
Catch-down growth 3 101 (8.84) 1.42 (1.19-1.69) < 0.001 1.25 (1.02-1.52) 0.03
Catch-up growth 2 842 (4.33) 0.66 (0.53-0.82) < 0.001 0.79 (0.62-1.01) 0.06

Table 3

Stratification analysis of the relationship between different growth patterns and MS"

Stratification factor Catch-down growth Catch-up growth
OR(95%CI) P value OR(95%CI) P value
Gender
  Male 1.28 (0.98-1.69) 0.07 0.71 (0.50-1.02) 0.07
  Female 1.23 (0.92-1.64) 0.16 0.85 (0.61-1.19) 0.34
Age/years
  7-12 1.41 (1.07-1.85) 0.02 0.66 (0.46-0.95) 0.03
  13-17 1.13 (0.84-1.51) 0.42 0.90 (0.64-1.26) 0.53
District
  Urban 1.38 (1.04-1.84) 0.03 0.66 (0.44-0.98) 0.04
  Rural 1.13 (0.85-1.49) 0.40 0.87 (0.64-1.20) 0.39
Nation
  Han 1.24 (1.10-1.52) 0.04 0.80 (0.62-1.03) 0.08
  Others 1.15 (0.47-2.79) 0.76 0.56 (0.17-1.81) 0.33
Birth weight
  Normal 1.22 (0.98-1.50) 0.07 0.81 (0.63-1.05) 0.11
  Low birth weight 1.92 (0.46-8.04) 0.37 3.64 (0.70-19.04) 0.13
  High birth weight 1.30 (0.70-2.42) 0.40 0.28 (0.09-0.85) 0.03
Nutrition status
  Normal or below 1.31 (0.96-1.79) 0.09 0.79 (0.55-1.12) 0.19
  Overweight or above 1.25 (0.96-1.62) 0.09 0.78 (0.55-1.11) 0.17
Education level of father
  Junior high school or below 1.60 (1.18-2.17) 0.002 1.02 (0.71-1.44) 0.93
  Senior high school or equivalent 1.00 (0.70-1.43) 1 0.57 (0.36-0.89) 0.01
  College or above 1.04 (0.69-1.55) 0.86 0.74 (0.42-1.29) 0.28
Education level of mother
  Junior high school or below 1.27 (0.96-1.70) 0.10 0.92 (0.67-1.27) 0.61
  Senior high school or equivalent 1.43 (0.97-2.11) 0.07 0.52 (0.29-0.93) 0.03
  College or above 1.05 (0.71-1.56) 0.80 0.75 (0.44-1.28) 0.29
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