青少年生命历程BMI和身高发育轨迹与青春早期多维度健康结局的相关性

Association of life-course BMI and height developmental trajectories with multidimensional health outcomes in early adolescence

摘要

目的 探讨青少年生命历程身体质量指数(BMI)和身高发育轨迹与青春早期多维度健康结局之间的关联,为早期识别高风险健康受损青少年群体提供依据。方法 基于2002—2016年在中国西部农村开展的孕期微量营养素干预随机对照试验所建立的出生队列,纳入在婴幼儿期(12、24月龄)、学龄早期(6～10岁)、青春早期(10～14岁)完成随访并具有身高体重等体格数据的研究对象。采用基于组的轨迹模型(group-based trajectory model, GBTM),构建出生到青春早期BMI和身高的发育轨迹；整合青春早期的行为问题得分、认知得分、肺活量、BMI、心率、收缩压等指标,利用熵权法求青春早期多维度健康结局的综合得分,K-means聚类分析以识别青春早期的多维度健康结局模式。通过多因素线性回归和Logistic回归模型,分析发育轨迹与青春早期多维度健康结局的关联,并控制相关混杂因素。结果 共有1 037名研究对象纳入分析,其中男性634名(61.1%)。GBTM建立4类BMI轨迹:正常组(36.6%)、持续偏高组(14.1%)、早期偏高组(30.6%)、偏低组(18.7%)；以及4类身高轨迹:平均组(51.0%)、偏高组(22.6%)、快速增高组(14.1%)、偏低组(12.3%)。Logistic回归显示:与BMI正常组相比,BMI持续偏高组出现青春早期行为问题(OR=2.30,95% CI:1.20～4.43)与代谢问题(OR=3.54,95% CI:2.12～5.91)的风险更高,而BMI偏低组出现青春早期行为问题(OR=0.46,95% CI:0.27～0.81)与代谢问题(OR=0.39,95% CI:0.25～0.60)的风险显著下降。与平均身高组相比,身高快速增高组青少年出现行为问题(OR=2.46,95% CI:1.15～5.27)与代谢问题(OR=3.17,95% CI:1.70～5.89)的风险更高。结论 青少年生命历程BMI和身高发育轨迹可能预示青春早期的多维健康问题,利用连续、易得的体格指标轨迹,可在资源有限场景下,实现对青少年多维健康风险结局的早期识别与干预。

Abstract

Objective To explore the association of body mass index (BMI) and height development trajectories from birth to adolescence with multidimensional health outcomes in early adolescence, so as to provide a basis for the early identification in high-risk child population. Methods This study utilized a birth cohort established from a randomized controlled trial on prenatal micronutrient supplementation conducted in rural western China from 2002 to 2016. Participants included children who had completed follow-ups and had measurements such as height and weight taken during infancy (12, 24 months), early school age (6-10 years), and early adolescence (10-14 years). Group-based trajectory modeling (GBTM) was used to model BMI and height development trajectories from birth to early adolescence. Indicators of health outcomes in early adolescence, including behavioral problem scores, cognitive scores, lung capacity, BMI, heart rate, and systolic blood pressure, were integrated. The entropy weighting method was used to calculate a comprehensive score for early adolescent health outcomes; K-means cluster analysis was employed to identify multidimensional health outcome patterns in early adolescence. Multivariable linear regression and Logistic regression models were used to analyze the association between developmental trajectories and early adolescent health outcomes, controlling for relevant confounding factors. Results A total of 1 037 children, including 634 boys (61.1%), were included in the analysis. GBTM identified four BMI trajectories: normal group (36.6%), persistently high group (14.1%), early high group (30.6%), and low group (18.7%); and four height trajectories: average group (51.0%), high group (22.6%), rapid increase group (14.1%), and low group (12.3%). Logistic regression analysis showed that compared to the BMI normal group, the BMI persistently high group had a higher risk of behavioral problems (OR=2.30, 95% CI: 1.20-4.43) and metabolic problems (OR=3.54, 95% CI: 2.12-5.91) in early adolescence. Compared to the BMI normal group, the BMI low group had a significantly lower risk of behavioral problems (OR=0.46, 95% CI: 0.27-0.81) and metabolic problems (OR=0.39, 95% CI: 0.25-0.60). Compared to the average height group, the rapid increase height group had a higher risk of behavioral problems (OR=2.46, 95% CI: 1.15-5.27) and metabolic problems (OR=3.17, 95% CI: 1.70-5.89). Conclusion BMI and height development trajectories during childhood and adolescence may predict multidimensional health problems in early adolescence. Utilizing continuous, easily obtainable physical measurement trajectories can enable the early identification and intervention of multidimensional health risks in adolescents, particularly in resource-limited settings.

关键词

基于组的轨迹分析 / 出生队列 / 体格发育 / 青少年 / 多维度健康结局

Key words

group-based trajectory analysis / birth cohort / physical growth / adolescent / multidimensional health outcome

参考文献

[1] 袁金娜, 金冰涵, 斯淑婷, 等 . 2009至2019年6～15岁中国儿童超重和肥胖趋势分析[J]. 中华儿科杂志, 2021, 59(11): 935-941.

[2] YUAN J N, JIN B H, SI S T, et al. Changing prevalence of overweight and obesity among Chinese children aged 6—15 from 2009—2019[J]. Chin J Pediatr, 2021, 59(11): 935-941.

[3] MIRANDA H, ANGELA B, SANDRA M, et al. Early adolescents' physical activity and nutrition beliefs and behaviours[J]. Int J Qual Stud Health Well—Being, 2022, 17(1): 2050523.

[4] MATSUMOTO N, KUBO T, NAKAMURA K, et al. Trajectory of body mass index and height changes from childhood to adolescence: a nationwide birth cohort in Japan[J]. Sci Rep, 2021, 11(1): 23004.

[5] 崔孟杰, 马奇, 陈曼曼, 等 . 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.

[6] CUI M J, MA Q, CHEN M M, et al. Association between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years[J]. J Peking Univ (Health Sci), 2023, 55(3): 415-420.

[7] SAGAR R, GUPTA T. Psychological aspects of obesity in children and adolescents[J]. Indian J Pediatr, 2018, 85(7): 554-559.

[8] ZHANG Y, HUANG X N, YANG Y N, et al. Double burden of malnutrition among children under 5 in poor areas of China[J]. PLoS One, 2018, 13(9): e0204142.

[9] PIERNAS C, WANG D, DU S, et al. The double burden of under—and overnutrition and nutrient adequacy among Chinese preschool and school—aged children in 2009—2011[J]. Eur J Clin Nutr, 2015, 69(12): 1323-1329.

[10] 李超, 高利旺, 吴妍, 等 . 中国西部贫困地区学龄儿童生长发育及营养状况与社会经济状况关联分析[J]. 中国儿童保健杂志, 2023, 31(10): 1058-1064.

[11] LI C, GAO L W, WU Y, et al. Correlation of growth development and nutritional status with socioeconomic status in school—age children from poor areas of western China[J]. Chin J Child Health Care, 2023, 31(10): 1058-1064.

[12] NAGIN D S. Group—based trajectory modeling: an overview[J]. Ann Nutr Metab, 2014, 65(2/3): 205-210.

[13] MATTSSON M, MAHER G M, BOLAND F, et al. Group—based trajectory modelling for BMI trajectories in childhood: a systematic review[J]. Obes Rev, 2019, 20(7): 998-1015.

[14] ZENG L X, DONG S N, CHENG Y, et al. Impact of micronutrient supplementation during pregnancy on birth weight, duration of gestation, and perinatal mortality in rural western China: double blind cluster randomised controlled trial[J]. BMJ, 2008, 337: a2001.

[15] VILLAR J, ISMAIL L C, VICTORA C G, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the newborn cross—sectional study of the INTERGROWTH—21st Project[J]. Lancet, 2014, 384(9946): 857-868.

[16] WHO. Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old[EB/OL]. (2019—05—28) [2025—05—30]. https://www.who.int/publications/i/item/9789241515559.

[17] MARSHALL W A, TANNER J M. Variations in pattern of pubertal changes in girls[J]. Arch Dis Child, 1969, 44(235): 291-303.

[18] MARSHALL W A, TANNER J M. Variations in the pattern of pubertal changes in boys[J]. Arch Dis Child, 1970, 45(239): 13-23.

[19] LEUNG P W, KWONG S L, TANG C P, et al. Test—retest reliability and criterion validity of the Chinese version of CBCL, TRF, and YSR[J]. J Child Psychol Psychiatry, 2006, 47(9): 970-973.

[20] 张厚粲. 韦氏儿童智力量表第四版(WISC—IV)中文版的修订[J]. 心理科学, 2009, 32(5): 1177-1179.

[21] ZHANG H C. Revision of the Chinese version of the Wechsler Intelligence Scale for Children, Fourth Edition (WISC—IV)[J]. Psychol Sci, 2009, 32(5): 1177-1179.

[22] 曾令霞, 颜虹, 陈志军. 农村家庭生活水平检测及围产期保健与财富指标的关系分析[J]. 卫生研究, 2008(6): 714-717.

[23] ZENG L X, YAN H, CHEN Z J. Measurement of the living standards of family in rural area and relationship between wealth index and perinatal care status[J]. J Hyg Res, 2008(6): 714-717.

[24] 冯国双, 于石成, 胡跃华. 轨迹分析模型在流行病学研究中的应用[J]. 中华流行病学杂志, 2014, 35(7): 865-867.

[25] FENG G S, YU S C, HU Y H. The application of trajectory model in epidemiological research[J]. Chin J Epidemiol, 2014, 35(7): 865-867.

[26] WEI J, LIU S, CHENG Y, et al. Association of infant physical development and rapid growth with pubertal onset among girls in rural China[J]. JAMA Netw Open, 2021, 4(5): e216831.

基金资助

国家自然科学基金资助项目(82103867)

中央高校基本科研业务费专项资金资助项目(xxj032025066)

陕西省重点研发计划项目(2024SF-YBXM-310)

陕西省创新能力支撑计划项目(2023-CX-PT-47)