Network Identification of Hormonal Regulation

Vis, Daniël J.; Westerhuis, Johan A.; Hoefsloot, Huub C. J.; Roelfsema, Ferdinand; Greef, J. van der; Hendriks, Margriet M. W. B.; Smilde, Age K.

doi:10.1371/journal.pone.0096284

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…Cross-correlation estimates the temporal relationship between 2 hormonal concentrations. It is a common analysis performed with data of 2 simultaneously measured hormonal time series (Vis et al, 2014). Therefore, it could be of interest for researchers to know to which extent measurement error would affect the estimates, especially since this method might be sensitive to the presence of outliers that co-occur in different time series data, for example, due to the dilution of a sample.…”

Section: Methodsmentioning

confidence: 99%

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

et al. 2019

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Measurement errors commonly occur in 24-h hormonal data and may affect the outcomes of such studies. Measurement errors often appear as outliers in such data sets; however, no well-established method is available for their automatic detection. In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data. Hormones (glucose, insulin, thyroid-stimulating hormone, cortisol, and growth hormone) were measured in blood sampled every 10 min for 24 h in 38 participants of the Leiden Longevity Study. Four methods for detecting outliers were compared: (1) eyeballing, (2) Tukey’s fences, (3) stepwise approach, and (4) the expectation-maximization (EM) algorithm. Eyeballing detects outliers based on experts’ knowledge, and the stepwise approach incorporates physiological knowledge with a statistical algorithm. Tukey’s fences and the EM algorithm are data-driven methods, using interquartile range and a mathematical algorithm to identify the underlying distribution, respectively. The performance of the methods was evaluated based on the number of outliers detected and the change in statistical outcomes after removing detected outliers. Eyeballing resulted in the lowest number of outliers detected (1.0% of all data points), followed by Tukey’s fences (2.3%), the stepwise approach (2.7%), and the EM algorithm (11.0%). In all methods, the mean hormone levels did not change materially after removing outliers. However, their minima were affected by outlier removal. Although removing outliers affected the correlation between glucose and insulin on the individual level, when averaged over all participants, none of the 4 methods influenced the correlation. Based on our results, the EM algorithm is not recommended given the high number of outliers detected, even where data points are physiologically plausible. Since Tukey’s fences is not suitable for all types of data and eyeballing is time-consuming, we recommend the stepwise approach for outlier detection, which combines physiological knowledge and an automated process.

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Section: Methodsmentioning

confidence: 99%

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

et al. 2019

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“…However, methods are still in active development, and no gold standard has been achieved yet. For instance, network inference and model-free approaches may also be used to unravel hormonal regulations [51,52].…”

Section: 2) Computational Approaches Of the Gnrh And Lh Pulse Generatormentioning

confidence: 99%

Advances in computational modeling approaches of pituitary gonadotropin signaling

Yvinec

Crépieux

Reiter

et al. 2018

Expert Opinion on Drug Discovery

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Pituitary gonadotropins play an essential and pivotal role in the control of human and animal reproduction within the hypothalamic-pituitary-gonadal (HPG) axis. The computational modeling of pituitary gonadotropin signaling encompasses phenomena of different natures such as the dynamic encoding of gonadotropin secretion, and the intracellular cascades triggered by gonadotropin binding to their cognate receptors, resulting in a variety of biological outcomes. Areas covered: The authors provide an overview of the historical and ongoing issues in modeling and data analysis related to gonadotropin secretion in the field of both physiology and neuroendocrinology. They mention the different mathematical formalisms involved, their interest and limits. They also discuss open statistical questions in signal analysis associated with key endocrine issues and review recent advances in the modeling of the intracellular pathways activated by gonadotropins, which yields promising development for innovative approaches in drug discovery. Expert opinion: The greatest challenge to be tackled in computational modeling of pituitary gonadotropin signaling is the embedding of gonadotropin signaling within its natural multi-scale environment, from the single cell level, to the organic and whole HPG level. The development of modeling approaches of G protein-coupled receptor signaling, together with multicellular systems biology may lead to unexampled mechanistic understanding with critical expected fallouts in the therapeutic management of reproduction.

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“…bromokryptyna), analogami somatostatyny, dekstrotyroksyną oraz glikokortykoidami, choć te ostatnie nie są zalecane z powodu zbyt poważnych działań ubocznych [3,[38][39][40][41]. Bromokryptyna powoduje supresję tyreotropów w przysadce przez receptory dopaminowe [41,42]. Podobnie jak sama dopamina, redukuje amplitudę pulsów TSH, nie wpływając na ich częstotliwość [39].…”

Section: Leczenieunclassified

Resistance to thyroid hormone in an adolescent patient with signs of hyperthyroidism- efficient treatment with bromocriptine in a 2-yearfollow-up. A case report

Budzyńska¹,

Ben-Skowronek²

2016

Pediatr. Endocrinol.

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Resistance to thyroid hormone due to defective thyroid hormone receptor β (RTHβ) is characterized by markedly elevated thyroid hormone levels accompanied by an inappropriately normal or slightly elevated thyroid-stimulating hormone (TSH) level. The most typical clinical manifestations of RTHβ (frequent occurrence of tachycardia, goiter, and hyperkinetic behavior with not often seen mental retardation or short stature) results from euthyroid state in organs where the affected receptor is predominant and hyperthyroidism where thyroid hormones act with the wild type of thyroid receptor β (WT TRβ). We report here an adolescent patient with signs of hyperthyroidism associated with RTHβ (phenotypic pituitary resistance to thyroid hormone, PRTH) and identified known mutation in the TRβ gene (G344E) previously described in this syndrome. Due to the presented symptoms, she required therapy and finally a dopamine agonist (bromocriptine, BC) was used. A cardioselective β-bloker (atenolol) was associated to obtain heart rate normalization. This safe and efficient BC therapy for RTH in a 2-year follow-up seems to confirm our therapeutic choice.

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Network Identification of Hormonal Regulation

Cited by 7 publications

References 36 publications

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

Advances in computational modeling approaches of pituitary gonadotropin signaling

Resistance to thyroid hormone in an adolescent patient with signs of hyperthyroidism- efficient treatment with bromocriptine in a 2-yearfollow-up. A case report

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