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How can two biological variables have opposing secular trends, yet be positively related? A demonstration using timing of puberty and adult height.
Annals of Human Biology ( IF 1.2 ) Pub Date : 2020-08-05 , DOI: 10.1080/03014460.2020.1795256
Liina Mansukoski 1 , William Johnson 2
Affiliation  

Abstract

Timing of puberty and adult height have opposing secular trends yet are positively associated in individuals. We demonstrate this using data from a single sample and discuss possible statistical and epidemiological reasons behind it. The sample comprised 365 females from Fels Longitudinal Study born 1929–1992. We used Super-Imposition by Translation and Rotation (SITAR) to estimate individual age at peak height velocity (PHV) and PHV from serial height data (8149 observations between 5 and 24 years). General linear regression was used to investigate the association between height and age at PHV, and secular trends in height, age at PHV and PHV. Although adult height increased 0.42 (95% CI: 0.08, 0.77) cm per decade, and age at PHV decreased 1.14 (−3.74, 1.45) weeks per decade, adult height increased by 2.44 (1.78, 3.10) cm per year higher age at PHV. We found tentative evidence of the positive association between age at PHV and adult height strengthened 0.25 (−0.09, 0.59) cm each decade. Secular trends in related variables may differ if the between-individual and between-cohort associations are different. To understand if a secular trend in one variable has contributed to a trend in another, each needs to be modelled over time, together with the changing association between them.



中文翻译:

两个生物学变量如何具有相反的长期趋势,却又呈正相关?使用青春期和成人身高的时间进行的演示。

摘要

青春期的时间和成年人的身高具有相反的长期趋势,但在个体中却呈正相关。我们使用来自单个样本的数据证明了这一点,并讨论了其背后可能的统计和流行病学原因。该样本包括来自Fels纵向研究的1929–1992年出生的365位女性。我们使用平移和旋转叠加法(SITAR)从序列高度数据(5149年至24年之间的8149次观测)中,估计了处于峰值高度速度(PHV)和PHV的个体年龄。一般线性回归被用来研究身高和年龄在PHV,以及身高,年龄在PHV和PHV之间的长期趋势之间的关系。尽管成年身高每十年增加0.42(95%CI:0.08,0.77)cm,PHV年龄每十年减少1.14(−3.74,1.45)周,但成年身高增加2.44(1.78,3。在PHV时,年龄要高出10厘米/年。我们发现暂定证据表明,每十年,PHV年龄与成年身高之间的正相关性会增强0.25(-0.09,0.59)cm。如果个体之间和群体之间的关联不同,则相关变量的长期趋势可能会不同。为了了解一个变量的长期趋势是否导致了另一变量的趋势,每个变量都需要随着时间的流逝以及它们之间不断变化的关联进行建模。

更新日期:2020-08-05
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