Evidence mapPaperPMID 38422116Full record

ArticlePLoS computational biology2024

HormoneBayes: A novel Bayesian framework for the analysis of pulsatile hormone dynamics.

Margaritis Voliotis, Ali Abbara, Julia K Prague, Johannes D Veldhuis, Waljit S Dhillo, Krasimira Tsaneva-Atanasova

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact, top 97% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Quantitative approaches in clinical reproductive endocrinology.Current opinion in endocrine and metabolic research · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Margaritis VoliotisDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0000-0001-6488-7198
Ali AbbaraDepartment of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, London, United Kingdom.
Julia K PragueDepartment of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, London, United Kingdom.
Johannes D VeldhuisEmeritus Mayo Clinic, Rochester, Michigan, United States of America.
Waljit S DhilloDepartment of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, London, United Kingdom.
Krasimira Tsaneva-AtanasovaDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0000-0002-6294-7051
University of Exeter · GBHammersmith Hospital · GBMichigan United · US

Funding

Medical Research Council MR/M024954/1
6 · The paper itself

Abstract

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.

Indexed as

Gonadotropin-Releasing HormoneLuteinizing HormoneBayes TheoremFemaleFollicle Stimulating HormoneHumansHypothalamusMaleFollicle Stimulating HormoneGonadotropin-Releasing HormoneLuteinizing Hormone

Identifiers

PMID38422116
PMCPMC10931492
OpenAlexW4392282880

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.