HormoneBayes: a novel Bayesian framework for the analysis of pulsatile hormone dynamics
HormoneBayes: a novel Bayesian framework for the analysis of pulsatile hormone dynamics
复制标题
HormoneBayes：一种用于分析脉动激素动力学的新颖贝叶斯框架
DOI：
10.1101/2022.03.14.22272000
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Voliotis M
中科院分区：
文献类型：
--
作者：
Voliotis M
下丘脑是生殖激素分泌的中枢调节器。促性腺激素释放激素（GnRH）的脉冲式分泌是生理刺激垂体释放促黄体生成激素（LH）和促卵泡激素（FSH）的基础。此外，在常见的生殖疾病如多囊卵巢综合征（PCOS）和下丘脑性闭经（HA）中，GnRH脉动性也会改变。LH在临床实践中使用自动荧光免疫测定法常规测量，并且是GnRH的金标准替代标记物。LH可以以频繁的间隔测量（例如，10分钟），以评估GnRH/LH脉动性。然而，这在临床实践中很少进行，因为它是资源密集型的，并且没有开放访问的图形界面软件用于临床医生可用的LH数据的计算分析。在这里，我们presenthormoneBayes，一种新的开放访问贝叶斯框架，可以很容易地应用于可靠地分析系列LH测量，以评估LH脉动。该框架利用简约模型来模拟驱动LH动态的下丘脑信号，以及最先进的（顺序）蒙特-卡罗方法来推断关键参数和潜在的下丘脑动态。我们表明，这种方法提供了估计的关键脉冲参数，包括脉冲间的间隔，分泌和清除率，并确定LH脉冲线广泛使用的反卷积方法。我们表明，这些参数可以在不同的临床背景下区分LH脉动，包括男性和女性的生殖健康和疾病（例如，健康男性、绝经前后的健康女性、患有HA或PCOS的女性）。贝叶斯的另一个优点是，我们的数学方法提供了一个量化的不确定性估计。我们的框架将补充实现实时体内激素监测的方法，因此有可能帮助翻译个性化，数据驱动，临床护理的患者呈现生殖激素功能障碍的条件。
The hypothalamus is the central regulator of reproductive hormone secretion. Pulsatile secretion of gonadotropin releasing hormone (GnRH) is fundamental to physiological stimulation of the pituitary gland to release luteinizing hormone (LH) and follicle stimulating hormone (FSH). Furthermore, GnRH pulsatility is altered in common reproductive disorders such as polycystic ovary syndrome (PCOS) and hypothalamic amenorrhea (HA). LH is measured routinely in clinical practice using an automated chemiluminescent immunoassay method and is the gold standard surrogate marker of GnRH. LH can be measured at frequent intervals (e.g., 10 minutely) to assess GnRH/LH pulsatility. However, this is rarely done in clinical practice because it is resource intensive, and there is no open-access, graphical interface software for computational analysis of the LH data available to clinicians. Here we presenthormoneBayes, a novel open-access Bayesian framework that can be easily applied to reliably analyze serial LH measurements to assess LH pulsatility. The framework utilizes parsimonious models to simulate hypothalamic signals that drive LH dynamics, together with state-of-the-art (sequential) Monte-Carlo methods to infer key parameters and latent hypothalamic dynamics. We show that this method provides estimates for key pulse parameters including inter-pulse interval, secretion and clearance rates and identifies LH pulses in line with the widely used deconvolution method. We show that these parameters can distinguish LH pulsatility in different clinical contexts including in reproductive health and disease in men and women (e.g., healthy men, healthy women before and after menopause, women with HA or PCOS). A further advantage ofhormoneBayesis that our mathematical approach provides a quantified estimation of uncertainty. Our framework will complement methods enabling real-timein-vivohormone monitoring and therefore has the potential to assist translation of personalized, data-driven, clinical care of patients presenting with conditions of reproductive hormone dysfunction.