Abstract
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。
Keywords: 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.
Keywords: Growth pattern, Children, Adolescents, Metabolic syndrome
代谢综合征(metabolic syndrome, MS)是一组以高血压、高血糖、血脂异常和肥胖等为主要特征的代谢性疾病[1]，是2型糖尿病(diabetes mellitus type 2，T2DM)和心血管疾病(cardiovascular diseases, CVDs)等不良健康结局的重要危险因素[2-3]。2020年全球儿童(6~12岁)MS患病率为2.8%，青少年(13~18岁)为4.8%[4]。2017年中国内地10~17岁儿童青少年代谢综合征MS患病率为4.3%，且在经济较为发达的地区患病率更高[5]。儿童青少年的生长发育是一个动态的连续过程，存在生长轨迹现象[6]。当营养不足、心理应激和疾病等不利因素出现时，其生长发育的连续性被打断，出现生长迟缓；而一旦这些不利因素解除，机体将表现出向原有的正常轨迹靠拢并加速生长的迹象，即追赶性生长[7]。而机体在儿童青少年时期的不同生长发育状态将显著影响成年期后的健康[8]，尤其是心脑血管疾病和代谢结局[9]。研究表明，膳食习惯、生活方式、心理状态、社会适应以及遗传等因素对代谢综合征的发病具有一定影响[3, 10-13]，但不同生长模式与儿童青少年代谢综合征关系的研究较少见。因此，本研究旨在分析不同生长模式与儿童青少年代谢综合征的关系，为儿童青少年代谢综合征的预防与控制提供依据。
1. 资料与方法
1.1. 数据来源与对象选取
使用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”项目数据[14]，采用分层整群随机抽样的方法在广东、湖南、辽宁、上海、重庆、天津和宁夏7个省/直辖市/自治区中共抽取93所中小学校65 347名学生，考虑到经费预算，随机选择25%的学生收集血样。本研究纳入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。本研究开始前已经北京大学生物医学伦理委员会审查批准(IRB0000105213034)，所有研究对象的监护人和本人(小学1~3年级除外)均签署知情同意书。
1.2. 信息收集与质量控制
(1) 身高和体质量测量：按照《2010年中国学生体质与健康调研工作手册》的操作规范，对学生的身高和体质量进行测量；(2)血压测量：使用统一且经过校准的电子血压计进行血压的测量，以mmHg为单位，由经过统一培训的专业人员测量2次并记录；(3)出生身长、民族、城乡和父母文化程度等指标均由父母填写问卷获得；(4)血样采集与处理：由具备资质的专业人员抽取学生空腹(>8 h)静脉血5 mL于真空促凝管中，2 h内分离血清后冷冻保存，并统一冷冻运送至项目中心，由项目中心集中送至临床检验公司进行血生化指标检测；(5)血糖与血脂检测：采用酶比色法检测所有血样的血糖、甘油三酯和高密度脂蛋白等；(6)质量控制：所有测量仪器均在检定有效期内且经过严格校准，测量和检测人员均经过统一培训具备相应资质。数据录入采用双人模式，并且及时进行逻辑检查，同时本课题组组织专家对各基地进行现场督导，及时发现和解决现场技术等问题。有关项目实施、信息收集和质量控制参照本课题组既往研究[14]。
1.3. 指标定义标准
(1) 超重与肥胖：采用2018年发布的WS/T 586—2018《学龄儿童青少年超重与肥胖筛查》进行判定。(2)代谢综合征：儿童青少年代谢综合征是根据Cook等[2]修改的《美国国家胆固醇教育计划成人治疗小组第3版指南(The National Cholesterol Education Program Adult Treatment Panel Ⅲ Guidelines，NCEP-ATP Ⅲ)》判定，符合下列5项指标中的3项或3项以上者则为代谢综合征：①中心性肥胖：采用2018年发布的WS/T 611—2018《7岁~18岁儿童青少年高腰围筛查界值》进行判定，腰围(waist circumference，WC)>各年龄/性别第90百分位数为偏高；②血压偏高：采用2018年发布的WS/T 610—2018《7岁~18岁儿童青少年血压偏高筛查界值》进行判定，收缩压(systolic blood pressure，SBP)或舒张压(diastolic blood pressure，DBP)>各性别/年龄/身高的第90百分位数为偏高；③甘油三酯(triglyceride，TG)偏高：TG≥1.24 mmol/L；④高密度脂蛋白(high-density lipoprotein，HDL)偏低：HDL-C≤1.03 mmol/L)；⑤血糖偏高[15]：空腹血糖(fasting blood-glucose，FBG)≥5.6 mmol/L。(3)生长模式：出生身长和身高按照美国2000年CDC标准[16]进行Z标准化评分(Z-score)，以评估儿童青少年生长发育状况，差值=身高Z-score-出生身长的Z-score，差值≥0.67为追赶性生长；差值在-0.67~0.67为正常性生长；差值≤-0.67为迟缓性生长。
1.4. 统计学分析
采用Epidata 3.1软件进行数据录入，经数据核查及清理后，采用SPSS 26.0软件进行数据分析与统计检验。使用卡方检验比较不同人口统计学特征下的生长模式分布差异；计量资料(出生体质量、腰围以及血生化指标)以均数±标准差表示，用方差分析比较不同组间的差异；采用多因素二元Logistic回归分析不同生长模式与代谢综合征之间的关系。检验水准α=0.05，均为双侧检验。
2. 结果
2.1. 不同人口统计学特征下的生长模式分布
研究共纳入10 176名7~17岁儿童青少年，平均年龄为(11.81±3.12)岁。其中，男生占比49.44%，女生占比50.56%；城市学生占比52.20%，乡村占比47.80%；非超重肥胖儿童青少年占比82.55%，超重肥胖占比17.45%。MS总体患病率为6.56%，其中男生为7.18%，女生为5.97%。
按照儿童青少年生长模式不同将其分为迟缓性生长、正常性生长和追赶性生长3组，各占30.47%、41.60%和27.93%。男女不同性别间，生长模式分布的差异无统计学意义(P>0.05，表 1)；不同年龄亚组间，13~17岁组迟缓性生长占比略高于7~12岁组；不同营养状况亚组间，超重肥胖组迟缓性生长占比略高于非超重肥胖组。不同城乡、民族、父母受教育程度亚组之间，各生长模式分布的差异性具有统计学意义(P < 0.01)。此外，除出生体质量外，3种生长模式之间体重指数(body mass index，BMI)、腰围、收缩压、舒张压、甘油三酯、高密度脂蛋白和空腹血糖之间的差异同样具有统计学意义(P < 0.01)。
表 1.
不同人口统计学特征下的生长模式分布特点
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 |
| BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; FBG, fasting blood-glucose. | |||||
| 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，x±s | 3.27±0.52 | 3.28±0.47 | 3.30±0.48 | 10 176 | 0.050 |
| BMI/(kg/m2)，x±s | 19.66±3.99 | 18.77±3.83 | 18.11±3.60 | 10 176 | < 0.001 |
| WC/cm，x±s | 69.31±10.99 | 65.79±10.48 | 62.82±10.04 | 10 176 | < 0.001 |
| SBP/mmHg，x±s | 107.03±12.21 | 104.49±11.74 | 102.59±11.73 | 10 176 | < 0.001 |
| DBP/mmHg，x±s | 67.77±8.88 | 65.99±8.68 | 65.13±8.78 | 10 176 | < 0.001 |
| TG/(mmol/L)，x±s | 0.95±0.49 | 0.92±0.46 | 0.92±0.46 | 10 176 | 0.030 |
| HDL-C/(mmol/L)，x±s | 1.34±0.32 | 1.37±0.34 | 1.38±0.32 | 10 176 | < 0.001 |
| FBG/(mmol/L)，x±s | 4.74±0.67 | 4.76±0.62 | 4.70±0.61 | 10 176 | 0.001 |
2.2. 不同生长模式与MS关系的Logistic回归分析
以是否患有MS为结局变量，不同生长模式为自变量进行Logistic回归分析，迟缓性生长组发生MS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MS的风险是正常性生长组的0.66倍(95%CI：0.53~0.82)；当以性别、年龄等为协变量进行调整时，发现相比于正常性生长组，迟缓性生长组发生MS的OR(95%CI)值为1.25(1.02~1.52)，而追赶性生长组发生MS的风险与正常性生长组相比差异不具有统计学意义(OR=0.79, 95%CI：0.62~1.01，表 2)。
表 2.
不同生长模式与MS关系的Logistic回归分析
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 | |||
| # meant the prevalence of MS. * Model 1 did not adjust for any variables, while model 2 adjusted for gender, age, urban and rural areas, nation, birth weight, nutrition status and parental education levels. | ||||||
| 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 | |
2.3. 不同生长模式与MS关系的多因素分层Logistic回归分析
分层分析发现，年龄亚组间，7~12岁迟缓性生长组和追赶性生长组发生代谢综合征的风险分别是正常性生长组的1.41倍(95%CI：1.07~1.86)和0.66倍(95%CI：0.46~0.95)；城乡亚组间，城市迟缓性生长组和追赶性生长组发生代谢综合征的风险分别是正常性生长组的1.38倍(95%CI：1.04~1.84)和0.66倍(95%CI：0.44~0.98)；民族亚组间，汉族迟缓性生长组发生代谢综合征的风险是正常性生长组的1.24倍(95%CI：1.10~1.52)；而在不同性别和营养状况间，迟缓性生长组和追赶性生长组发生MS的风险与正常性生长组相比，差异无统计学意义(P>0.05，表 3)。
表 3.
不同生长模式与MS关系的分层分析
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 | ||
| The control group was the normal growth group. The model adjusted for gender, age, district, nation, birth weight, nutrition status and parental education levels. | |||||
| 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 | |
3. 讨论
目前国际上尚未见关于MS的统一定义或者标准[1, 4, 17]，本研究中MS的判定在Cook标准的基础上结合了国内的相关腰围和血压国家标准，适用性可能更强。研究发现，BMI、腰围、收缩压和舒张压的平均水平均为迟缓性生长组最高，正常性生长组次之，追赶性生长组最低，两两比较差异具有统计学意义(P < 0.05)。这与Danese等[18]的研究结果相符，即生命早期的不良生活经历会导致儿童青少年持久的情绪、免疫和代谢异常，增加相关疾病风险。
经Logistic回归分析，发现相比于正常性生长组，迟缓性生长儿童青少年患MS的危险性更高，这可能与他们本身所面临的不利环境因素有关。Su等[19]的一项纵向研究也显示，与正常组相比，暴露于不利环境因素下的儿童青少年在其成年早期的血压水平更高。其机制可能是由于儿童青少年时期神经系统、内分泌系统和免疫系统等尚未发育完全，过多的不良环境因素会导致正常发育受阻，产生不利结果[20]。也有研究表明，早期不良环境因素可能会引起血浆内皮素-1(endothelin-1, ET-1)水平升高，而ET-1途径可能是血压升高的基础[20]。追赶性生长组发生MS的风险与正常性生长组相比，差异不具有统计学意义(P>0.05)。Kramer等[21]的研究结果也显示，相对于正常性生长的儿童，追赶性生长并没有增加肥胖的风险。
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。13~17岁不同生长模式儿童青少年发生MS风险的差异不具有统计学意义的原因可能是该阶段处于青春发育期，体内激素水平变化较大且作用明显[22]，产生了干扰作用。另外，分层之后观察到追赶性生长组儿童青少年发生MS的风险要低于正常性生长组(P < 0.05)。Crowther等[23]的一项研究结果显示，未发生追赶性生长的低出生体质量儿童的血糖和胰岛素原指数水平最高，提示追赶性生长对低出生体质量儿童发生MS具有保护作用，这可能有助于解释本研究的结果。但也有研究表明，过快的追赶性生长也会增加成年期高血压和肥胖的风险[23-26]，即追赶性生长儿童青少年脂肪和体质量的快速增加会导致机体内游离脂肪酸和炎症介质水平的增加，进而造成微血管功能障碍，通过功能性和/或结构性小动脉和毛细血管脱落以及小动脉收缩，增加外周阻力，从而使血压升高[27]。
国内外相关研究多着眼于不同生长模式对血压等MS单项指标的影响，有关生长模式对总的MS患病风险影响的研究较少见。此外，关于生长模式的判断，不同研究所依据的指标不同。一些研究以体质量的变化作为不同生长模式的判断指标[7, 28-30]，该类研究多着重于探索体质量的追赶所带来的健康结局，时间跨度较短，一般为几周或几个月。相比于体质量，身高更能反映个体长期的营养水平及发育状况[31]。本研究儿童青少年出生时和数据收集时的时间跨度较大(7年以上)，因此以身高来判断生长模式更符合背景和实际。
本研究采用分层随机抽样的方法，在全国7个省份中共抽取93所中小学校学生进行分析，结果具有一定代表性。但本研究也存在局限性：首先，不同生长模式与MS的关系结果是基于横断面分析得出的，无法确定其因果关联；其次，在数据收集的过程中，可能存在回忆偏倚；第三，MS的发生受到很多因素的影响，而有些因素是本研究尚未纳入和处理的，需要进一步探讨。
综上所述，相比于正常性生长组儿童来说，迟缓性生长组发生MS的风险更高，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。追赶性生长对儿童青少年发生MS的影响尚无法确认，需要进一步的纵向研究来解释追赶性生长是否会降低或增加MS发病风险。
Funding Statement
国家自然科学基金(91846302)和科技部重点研发项目(2016YFA0501604)
Supported by the National Natural Science Foundation of China (91846302) and the Key R & D Projects of the Ministry of Science and Technology (2016YFA0501604)
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