Bayesian analysis improves pulse secretion characterization in reproductive hormones. (2nd January 2018)

• Record Type: Journal Article
• Authors: Liu, Huayu; Polotsky, Alex J.; Grunwald, Gary K.; Carlson, Nichole E.
• Is Part Of: Systems biology in reproductive medicine. Volume 64:Number 1(2018:Feb.)
• Page Start: 80
• Page End: 91
• Publication Date: 2018-01-02
• Subjects: Bayesian -- deconvolution -- luteinizing hormone -- pulsing hormone -- reproductive physiology; Systems biology -- Periodicals; Andrology -- Periodicals; Generative organs, Male -- Diseases -- Periodicals; Biological systems -- Periodicals; Reproductive health -- Periodicals; Human reproduction -- Periodicals; 612.61
• DOI: 10.1080/19396368.2017.1411541
• Languages: English
• ISSNs: 1939-6368

Abstract

ABSTRACT: Pulsatile secretion of hormones in the hypothalamic-pituitary-gonadal axis is critical for normal functioning of the reproductive system. Thus, appropriate characterization of pulsatile secretion is important for identifying the (patho)physiology of reproductive conditions. Existing analysis methods often fail to adequately characterize pulsatility, especially when the signal-to-noise ratio is low. Newer Bayesian analysis methods for pulsatile hormones may offer improved secretion quantification in noisier data. The objective of this study was to extensively validate a Bayesian analysis approach for analyzing pulsatile hormones in settings that occur in reproductive studies. An investigative approach was chosen so that clinical research teams will have the knowledge to adopt this newer analysis approach in practice. Three experimental conditions were investigated: luteinizing hormone (LH) profiles in ovariectomized ewes (N=6; high signal-to-noise setting), LH profiles in young ovulating women (N=12; lower signal-to-noise setting), and computer-simulated scenarios (N=200). For each experimental condition, differences in luteinizing hormone pulse outcomes (pulse number, average pulse size, hormone half-life, and non-pulse secretion) were obtained and compared between non-Bayesian and Bayesian analysis pulse analysis methods. For the ewe model, the estimated pulse number and mass were comparable between the Bayesian and non-Bayesian analyses. For the human model, only 4 of 12 subjects could be fitted with the non-Bayesian analysis compared to 10 of the 12 with Bayesian analysis. In general, the Bayesian analysis had lower false negative rates (<4.5%) compared to the non-Bayesian analysis while maintaining a high specificity (false positive rate <2.5%). The Bayesian analysis also had less biased estimates of all pulse features. In conclusion, Bayesian analysis provides a more reliable pulse characterization in low signal-to-noise experiments and should be used for the analysis of reproductive physiology studies of pulsatile hormones. Software is available atwww.github.com/BayesPulse . Abbreviations : LH: luteinizing hormone; FSH: follicle stimulating hormone; GnRH: gonadotropin-releasing hormone; FP: false positive; FN: false negative