青春期发育研究需建立全过程多指标评估和预测体系

成果; 陶芳标

doi:10.16835/j.cnki.1000-9817.2022.07.001

青春期发育研究需建立全过程多指标评估和预测体系

doi: 10.16835/j.cnki.1000-9817.2022.07.001

成果¹,
陶芳标^2, ,

1.
四川大学华西第二医院西部妇幼医学研究院出生缺陷与相关妇儿疾病教育部重点实验室, 成都 610041
2.
安徽医科大学公共卫生学院儿少卫生与妇幼保健学

基金项目:

国家自然科学基金项目 82073578

详细信息

作者简介:
成果(1980-)，女，山西吕梁人，博士，教授，主要研究方向为儿童营养与生长发育

通讯作者:
陶芳标，E-mail: fbtao@126.com

利益冲突声明 所有作者声明无利益冲突。
中图分类号: R 179 G 478 R 193
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-13
- 修回日期: 2022-04-10
- 网络出版日期: 2022-07-27
- 刊出日期: 2022-07-25

Research on the puberty needs to establish a multi-index evaluation and prediction system

CHENG Guo¹,
TAO Fangbiao^{2
, ,}

1.
Key Laboratory of Birth Defects and Related Diseases of Women and Children, West China Institute of Women and Children's Health, West China Second University Hospital, Sichuan University, Chengdu (610041), China

摘要

摘要: 青春期的启动时间、发育速度和进程受遗传与环境交互影响, 并与终身健康相互关联。搭建适合评估中国儿童青春期启动和发育全过程的指标体系和建立多维度青春期发育轨迹预测模型, 是明晰中国儿童青春期发育全程特点及探索青春期发育对生命后期健康影响的基础。研究通过从青春期发育评估方法及预测体系两个方面对当前国内外青春期发育评估进行梳理和展望, 并对中国儿童青春期发育领域的重点研究方向提出建议。
- 青春期发育 /
- 研究 /
- 质量指标, 卫生保健 /
- 儿童
Abstract: The onset and progression of puberty development is affected by interaction of genes and environments, and is linked to lifelong health. A comprehensive understanding of pubertal development in Chinese children and its influence on health outcomes becomes an important public health concern. Therefore, building an evaluation index system for Chinese children to assess the puberty onset and the temporal sequences throughout the puberty stage, and establishing a multi-dimensional predictive model for puberty trajectory lay down the foundation for this issue. This article reviews the currently available evidence referring to the assessment system and the prediction model for puberty timing, and outlines directions for future research in this area.
- Adolescent development /
- Research /
- Quality indicators, health care /
- Child
注释:

1) 利益冲突声明 所有作者声明无利益冲突。

HTML全文

表 1 青春期发育评价方法

Table 1. Assessment of puberty

分类	评价方法	评价内容
第二性征发育评价	Tanner分期法	将女生乳房和阴毛、男生睾丸和阴毛发育分为5个阶段，包括青春期前期、第二性征的出现直至发育成熟
	青春期发育过程自我评定量表	由儿童对自己身高、体毛生长、皮肤改变、女生乳房发育和月经情况、男生变声和胡须生长情况做自评
体格发育评价	身高生长速度指标评估法	主要指标：身高突增开始年龄和身高速度高峰年龄等
	参数模型评估法	常见模型：PB1模型、JPA-2模型、Kernel回归模型
激素水平评价	性激素水平	促性腺激素释放激素(GnRH)、垂体卵泡刺激素(FSH)和黄体生成素(LH)

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参考文献(25)

[1]	SMITH C E, BIRO F M. Pubertal development: what's normal/what's not [J]. Clin Obstet Gynecol, 2020, 63(3): 491-503. doi: 10.1097/GRF.0000000000000537
[2]	WU X, BAO L, DU Z, et al. Secular trends of age at menarche and the effect of famine exposure on age at menarche in rural Chinese women[J]. Ann Hum Biol, 2022, 49(1): 35-40. doi: 10.1080/03014460.2022.2041092
[3]	MENG X, LI S, DUAN W, et al. Secular trend of age at menarche in Chinese adolescents born from 1973 to 2004 [J]. Pediatrics, 2017, 140(2): e20170085. doi: 10.1542/peds.2017-0085
[4]	MA Q, LI R, WANG L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019[J]. Lancet Public Health, 2021, 6(12): e897-e906. doi: 10.1016/S2468-2667(21)00228-0
[5]	CHEN X, LIU Y, SUN X, et al. Age at menarche and risk of all-cause and cardiovascular mortality: a systematic review and dose-response Meta-analysis [J]. Menopause (New York, NY), 2018, 26(6): 670-676.
[6]	WALKER I V, SMITH C R, DAVIES J H, et al. Methods for determining pubertal status in research studies: literature review and opinions of experts and adolescents[J]. J Dev Orig Health Dis, 2020, 11(2): 168-187. doi: 10.1017/S2040174419000254
[7]	MARSHALL W A, TANNER J M. Growth and physiological development during adolescence [J]. Ann Rev Med, 1968, 19: 283-300. doi: 10.1146/annurev.me.19.020168.001435
[8]	CAMPISI S C, MARCHAND J D, SIDDIQUI F J, et al. Can we rely on adolescents to self-assess puberty stage? A systematic review and Meta-analysis [J]. J Clin Endocr Metab, 2020, 105(8): 2846–2856. doi: 10.1210/clinem/dgaa135
[9]	李丹, 史慧静, 张越, 等. 青春期性发育自评方法的有效性和适用性研究. 中华预防医学会儿少卫生分会第九届学术交流会、中国教育学会体育与卫生分会第一届学校卫生学术交流会、中国健康促进与教育协会学校分会第三届学术交流会论文集. 厦门: 中华预防医学会, 2011: 111-116. LI D, SHI H J, ZHANG Y, et al. The validity and applicability of self-assessment methods of adolescent sexual development. The ninth Academic Exchange Conference of the Child and Adolescent Health Section of Chinese Preventive Medicine Association, The first Academic Exchange Conference of Sports and Health Section of the Chinese Society of Education and the third Academic Exchange Conference of China Association of Health Promotion and Education. Fujian: Chinese Preventive Medicine Association, 2011: 111-116.
[10]	CHAVARRO J E, WATKINS D J, AFEICHE M C, et al. Validity of self-assessed sexual maturation against physician assessments and hormone levels[J]. J Pediatr, 2017, 186(3): 172-178.
[11]	DURDA-MASNY M, HANC T, CZAPLA Z, et al. BMI at menarche and timing of growth spurt and puberty in Polish girls-longitudinal study[J]. Anthropol Anz, 2019, 76(1): 37-47. doi: 10.1127/anthranz/2019/0920
[12]	BANIK S D, SALEHABADI S M, DICKINSON F. Preece-baines model 1 to estimate height and knee height growth in boys and girls from Merida, Mexico[J]. Food Nutr Bull, 2017, 38(2): 182-195. doi: 10.1177/0379572117700270
[13]	DEL PINO M, FANO V, ADAMO P. Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model[J]. J Pediatr Endocr Met, 2020, 33(12): 1589-1595. doi: 10.1515/jpem-2020-0298
[14]	ZHANG W, CHEN Z, LIU A, et al. A weighted kernel machine regression approach to environmental pollutants and infertility[J]. Stat Med, 2019, 38(5): 809-827. doi: 10.1002/sim.8003
[15]	GOLDBERG M, CIESIELSKI JONES A J, MCGRATH J A, et al. Urinary and salivary endocrine measurements to complement tanner staging in studies of pubertal development[J]. PLoS One, 2021, 16(5): e0251598. doi: 10.1371/journal.pone.0251598
[16]	黄心依. 机器学习在数据挖掘中的应用研究[J]. 信息记录材料, 2021, 22(8): 121-123. HUANG X Y. Research on the application of machine learning in data mining [J]. Inform Rec Mater, 2021, 22(8): 121-123.
[17]	JANG W Y, AHN K S, OH S, et al. Difference between bone age at the hand and elbow at the onset of puberty[J]. Medicine (Baltimore), 2022, 101(1): e28516. doi: 10.1097/MD.0000000000028516
[18]	TANNER J M, GIBBONS R D. Automatic bone age measurement using computerized image analysis[J]. J Pediatr Endocrinol, 1994, 7(2): 141-145.
[19]	SPAMPINATO C, PALAZZO S, GIORDANO D, et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Med Image Anal, 2017, 36: 41-51. doi: 10.1016/j.media.2016.10.010
[20]	HAO P Y, CHOKUWA S, XIE X H, et al. Skeletal bone age assessments for young children based on regression convolutional neural networks[J]. Math Biosci Eng, 2019, 16(6): 6454-6466. doi: 10.3934/mbe.2019323
[21]	BUI T D, LEE J J, SHIN J. Incorporated region detection and classification using deep convolutional networks for bone age assessment[J]. Artif Intell Med, 2019, 97: 1-8. doi: 10.1016/j.artmed.2019.04.005
[22]	LIU Y, ZHANG C, CHENG J, et al. A multi-scale data fusion framework for bone age assessment with convolutional neural networks[J]. Comput Biol Med, 2019, 108: 161-173. doi: 10.1016/j.compbiomed.2019.03.015
[23]	PAN L, LIU G, MAO X, et al. Development of prediction models using machine learning algorithms for girls with suspected central precocious puberty: retrospective study[J]. JMIR Med Inform, 2019, 7(1): e11728. doi: 10.2196/11728
[24]	WANG F, GU X, CHEN S, et al. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development[J]. Peer J, 2020, 8: e8854. doi: 10.7717/peerj.8854
[25]	XU Y, LIU X, PAN L, et al. Explainable dynamic multimodal variational autoencoder for the prediction of patients with suspected central precocious puberty[J]. IEEE J Biomed Health Inform, 2022, 26(3): 1362-1373. doi: 10.1109/JBHI.2021.3103271