Ultradian Rhythms of Body Temperature Are Associated with Temperament in Greenfinch (Chloris chloris, Fringillidae, Aves)

2021 ◽  
Vol 499 (1) ◽  
pp. 93-96
Author(s):  
M. E. Diatroptov ◽  
A. S. Opaev ◽  
A. V. Surov
2020 ◽  
Author(s):  
Azure D. Grant ◽  
Mark Newman ◽  
Lance J. Kriegsfeld

AbstractThe human menstrual cycle is characterized by predictable patterns of physiological change across timescales, yet non-invasive anticipation of key events is not yet possible at individual resolution. Although patterns of reproductive hormones across the menstrual cycle have been well characterized, monitoring these measures repeatedly to anticipate the preovulatory luteinizing hormone (LH) surge is not practical for fertility awareness. In the present study, we explored whether non-invasive and high frequency measures of distal body temperature (DBT), sleeping heart rate (HR), sleeping heart rate variability (HRV), and sleep timing could be used to anticipate the preovulatory LH surge in women. To test this possibility, we used signal processing to examine these measures across the menstrual cycle. Cycles were examined from both pre- (n=45 cycles) and perimenopausal (n=10 cycles) women using days of supra-surge threshold LH and dates of menstruation for all cycles. For a subset of cycles, urinary estradiol and progesterone metabolites were measured daily around the time of the LH surge. Wavelet analysis revealed a consistent inflection point of ultradian rhythm (2-5 h) power of DBT and HRV that enabled anticipation of the LH surge at least 2 days prior to its onset in 100% of individuals. In contrast, the power of ultradian rhythms in heart rate, circadian rhythms in body temperature, and metrics of sleep duration and sleep timing were not predictive of the LH surge. Together, the present findings reveal fluctuations in distal body temperature and heart rate variability that consistently anticipate the LH surge and may aid in fertility awareness.Key PointsUltradian (2-5 h) rhythm power of distal body temperature and heart rate variability (RMSSD) exhibits a stereotyped inflection point and peak in the days leading up to the LH surge in premenopausal women.Circadian rhythms of distal body temperature and single time-point/day metrics do not permit anticipation of the LH surge.Measurement of continuous metabolic and autonomic outputs, enabling assessment of ultradian rhythms, may be of value to the fertility awareness method.


2019 ◽  
Vol 168 (2) ◽  
pp. 291-294
Author(s):  
M. E. Diatroptov ◽  
M. V. Rutovskaya ◽  
E. V. Kuznetsova ◽  
M. A. Diatroptova ◽  
A. M. Kosyreva ◽  
...  

2018 ◽  
Vol 33 (5) ◽  
pp. 475-496 ◽  
Author(s):  
Azure D. Grant ◽  
Kathryn Wilsterman ◽  
Benjamin L. Smarr ◽  
Lance J. Kriegsfeld

Whereas long-period temporal structures in endocrine dynamics have been well studied, endocrine rhythms on the scale of hours are relatively unexplored. The study of these ultradian rhythms (URs) has remained nascent, in part, because a theoretical framework unifying ultradian patterns across systems has not been established. The present overview proposes a conceptual coupled oscillator network model of URs in which oscillating hormonal outputs, or nodes, are connected by edges representing the strength of node-node coupling. We propose that variable-strength coupling exists both within and across classic hormonal axes. Because coupled oscillators synchronize, such a model implies that changes across hormonal systems could be inferred by surveying accessible nodes in the network. This implication would at once simplify the study of URs and open new avenues of exploration into conditions affecting coupling. In support of this proposed framework, we review mammalian evidence for (1) URs of the gut-brain axis and the hypothalamo-pituitary-thyroid, -adrenal, and -gonadal axes, (2) UR coupling within and across these axes; and (3) the relation of these URs to body temperature. URs across these systems exhibit behavior broadly consistent with a coupled oscillator network, maintaining both consistent URs and coupling within and across axes. This model may aid the exploration of mammalian physiology at high temporal resolution and improve the understanding of endocrine system dynamics within individuals.


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