Voliotis Margaritis, Abbara Ali, Prague Julia K, Veldhuis Johannes D, Dhillo Waljit S, Tsaneva-Atanasova Krasimira
Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, London, United Kingdom.
PLoS Comput Biol. 2024 Feb 29;20(2):e1011928. doi: 10.1371/journal.pcbi.1011928. eCollection 2024 Feb.
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 present hormoneBayes, 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 of hormoneBayes is that our mathematical approach provides a quantified estimation of uncertainty. Our framework will complement methods enabling real-time in-vivo hormone monitoring and therefore has the potential to assist translation of personalized, data-driven, clinical care of patients presenting with conditions of reproductive hormone dysfunction.
下丘脑是生殖激素分泌的中枢调节者。促性腺激素释放激素（GnRH）的脉冲式分泌是垂体释放黄体生成素（LH）和卵泡刺激素（FSH）的生理刺激的基础。此外，GnRH脉冲性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）中会发生改变。在临床实践中，LH通常使用自动化化学发光免疫测定法进行常规测量，并且是GnRH的金标准替代标志物。可以频繁间隔（例如，每10分钟）测量LH以评估GnRH/LH脉冲性。然而，这在临床实践中很少进行，因为它资源密集，并且没有可供临床医生用于LH数据计算分析的开放获取的图形界面软件。在此，我们展示了hormoneBayes，这是一个新颖的开放获取贝叶斯框架，可轻松应用于可靠地分析连续LH测量值以评估LH脉冲性。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，该方法提供了关键脉冲参数的估计值，包括脉冲间期、分泌和清除率，并与广泛使用的松卷积方法一致地识别LH脉冲。我们表明，这些参数可以区分不同临床背景下的LH脉冲性，包括男性和女性的生殖健康与疾病（例如，健康男性、绝经前后的健康女性、患有HA或PCOS的女性）。hormoneBayes的另一个优点是我们的数学方法提供了不确定性的量化估计。我们的框架将补充能够进行实时体内激素监测的方法，因此有可能有助于将个性化、数据驱动的临床护理转化应用于患有生殖激素功能障碍的患者。
