不同生长模式与7~17岁儿童青少年代谢综合征的关系

doi:10.19723/j.issn.1671-167X.2023.03.005

摘要/Abstract

摘要：

目的: 分析不同生长模式与7~17岁儿童青少年代谢综合征之间的关系，为我国儿童青少年代谢综合征的预防与控制提供依据。方法: 采用横断面研究设计，利用2012年卫生公益性行业科研专项“学生重大疾病防控技术和相关标准研制及应用”项目收集的数据，选择其中体格测量及血生化指标数据完整的10 176名7~17岁的中小学生作为研究对象。使用二元Logistic回归模型分析不同生长模式与7~17岁儿童青少年代谢综合征的关系。结果: 儿童青少年代谢综合征患病率为6.56%，其中男生为7.18%，女生为5.97%。迟缓性生长组发生代谢综合征的风险是正常性生长组的1.42倍(95%CI：1.19~1.69)，追赶性生长组发生代谢综合征的风险是正常性生长组的0.66倍(95%CI：0.53~0.82)；经性别、年龄等因素校正后，迟缓性生长组发生代谢综合征的风险为正常性生长组的1.25倍(95%CI：1.02~1.52)，追赶性生长组发生代谢综合征的风险与正常性生长组的差异无统计学意义(OR=0.79, 95%CI：0.62~1.01)；分层分析显示，不同生长模式与代谢综合征的关联在7~12岁年龄组、城市和汉族学生群体中具有统计学意义。结论: 不同生长模式与儿童青少年代谢综合征之间存在关联，迟缓性生长儿童青少年发生代谢综合征的风险高于正常性生长组，提示应当注意儿童青少年的生长发育，及时纠正其迟缓性生长，预防不良健康结局的发生。

关键词: 生长模式, 儿童, 青少年, 代谢综合征

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

中图分类号:

R725.8

崔孟杰,马奇,陈曼曼,马涛,王鑫鑫,刘婕妤,张奕,陈力,蒋家诺,袁雯,郭桐君,董彦会,马军,星一. 不同生长模式与7~17岁儿童青少年代谢综合征的关系[J]. 北京大学学报（医学版）, 2023, 55(3): 415-420.

Meng-jie CUI,Qi MA,Man-man CHEN,Tao MA,Xin-xin WANG,Jie-yu LIU,Yi ZHANG,Li CHEN,Jia-nuo JIANG,Wen YUAN,Tong-jun GUO,Yan-hui DONG,Jun MA,Yi XING. Association between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years[J]. Journal of Peking University (Health Sciences), 2023, 55(3): 415-420.

图/表 3

表1

不同人口统计学特征下的生长模式分布特点"

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

表1

表2

表3

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Growth patterns	n(%)^#	Mode 1^*		Model 2^*
Growth patterns	n(%)^#	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

Stratification factor	Catch-down growth		Catch-up growth
Stratification factor	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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