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Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period

BACKGROUND: Although follicle stimulating hormone (FSH) is known to be predictive of age at final menstrual period (FMP), previous methods use FSH levels measured at time points that are defined relative to the age at FMP, and hence are not useful for prospective prediction purposes in clinical sett...

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Autores principales: Jiang, Bei, Sammel, Mary D., Freeman, Ellen W., Wang, Naisyin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683774/
https://www.ncbi.nlm.nih.gov/pubmed/26677844
http://dx.doi.org/10.1186/s12874-015-0101-3
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author Jiang, Bei
Sammel, Mary D.
Freeman, Ellen W.
Wang, Naisyin
author_facet Jiang, Bei
Sammel, Mary D.
Freeman, Ellen W.
Wang, Naisyin
author_sort Jiang, Bei
collection PubMed
description BACKGROUND: Although follicle stimulating hormone (FSH) is known to be predictive of age at final menstrual period (FMP), previous methods use FSH levels measured at time points that are defined relative to the age at FMP, and hence are not useful for prospective prediction purposes in clinical settings where age at FMP is an unknown outcome. This study is aimed at assessing whether FSH trajectory feature subgroups identified relative to chronological age can be used to improve the prediction of age at FMP. METHODS: We develop a Bayesian model to identify latent subgroups in longitudinal FSH trajectories, and study the relationship between subgroup membership and age at FMP. Data for our study is taken from the Penn Ovarian Aging study, 1996–2010. The proposed model utilizes mixture modeling and nonparametric smoothing methods to capture hypothesized latent subgroup features of the FSH longitudinal trajectory; and simultaneously studies the prognostic value of these latent subgroup features to predict age at FMP. RESULTS: The analysis identified two FSH trajectory subgroups that were significantly associated with FMP age: 1) early FSH class (15 %), which displayed initial increases in FSH shortly after age 40; and 2) late FSH class (85 %), which did not have a rise in FSH until after age 45. The use of FSH subgroup memberships, along with class-specific characteristics, i.e., level and rate of FSH change at class-specific pre-specified ages, improved prediction of FMP age by 20–22 % in comparison to the prediction based on previously identified risk factors (BMI, smoking and pre-menopausal levels of anti-mullerian hormone (AMH)). CONCLUSIONS: To the best of our knowledge, this work is the first in the area to demonstrate the existence of subgroups in FSH trajectory patterns relative to chronological age and the fact that such a subgroup membership possesses prediction power for age at FMP. Earlier ages at FMP were found in a subgroup of women with rise in FSH levels commencing shortly after age 40, in comparison to women who did not exhibit an increase in FSH until after 45 years of age. Periodic evaluations of FSH in these age ranges are potentially useful for predicting age at FMP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0101-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-46837742015-12-19 Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period Jiang, Bei Sammel, Mary D. Freeman, Ellen W. Wang, Naisyin BMC Med Res Methodol Research Article BACKGROUND: Although follicle stimulating hormone (FSH) is known to be predictive of age at final menstrual period (FMP), previous methods use FSH levels measured at time points that are defined relative to the age at FMP, and hence are not useful for prospective prediction purposes in clinical settings where age at FMP is an unknown outcome. This study is aimed at assessing whether FSH trajectory feature subgroups identified relative to chronological age can be used to improve the prediction of age at FMP. METHODS: We develop a Bayesian model to identify latent subgroups in longitudinal FSH trajectories, and study the relationship between subgroup membership and age at FMP. Data for our study is taken from the Penn Ovarian Aging study, 1996–2010. The proposed model utilizes mixture modeling and nonparametric smoothing methods to capture hypothesized latent subgroup features of the FSH longitudinal trajectory; and simultaneously studies the prognostic value of these latent subgroup features to predict age at FMP. RESULTS: The analysis identified two FSH trajectory subgroups that were significantly associated with FMP age: 1) early FSH class (15 %), which displayed initial increases in FSH shortly after age 40; and 2) late FSH class (85 %), which did not have a rise in FSH until after age 45. The use of FSH subgroup memberships, along with class-specific characteristics, i.e., level and rate of FSH change at class-specific pre-specified ages, improved prediction of FMP age by 20–22 % in comparison to the prediction based on previously identified risk factors (BMI, smoking and pre-menopausal levels of anti-mullerian hormone (AMH)). CONCLUSIONS: To the best of our knowledge, this work is the first in the area to demonstrate the existence of subgroups in FSH trajectory patterns relative to chronological age and the fact that such a subgroup membership possesses prediction power for age at FMP. Earlier ages at FMP were found in a subgroup of women with rise in FSH levels commencing shortly after age 40, in comparison to women who did not exhibit an increase in FSH until after 45 years of age. Periodic evaluations of FSH in these age ranges are potentially useful for predicting age at FMP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0101-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-18 /pmc/articles/PMC4683774/ /pubmed/26677844 http://dx.doi.org/10.1186/s12874-015-0101-3 Text en © Jiang et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Jiang, Bei
Sammel, Mary D.
Freeman, Ellen W.
Wang, Naisyin
Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title_full Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title_fullStr Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title_full_unstemmed Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title_short Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
title_sort bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683774/
https://www.ncbi.nlm.nih.gov/pubmed/26677844
http://dx.doi.org/10.1186/s12874-015-0101-3
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