Sleep on a high heat capacity mattress increases conductive body heat loss and slow wave sleep.

Environmental temperature can strongly affect sleep. The habitual sleep phase is usually located between evening decline and morning rise of the circadian rhythm of core body temperature (CBT). However, the thermophysiological mechanisms promoting or disturbing sleep are not yet fully understood. The purpose of this study was to examine the effects of a high heat capacity mattress (HHCM) on CBT, skin temperatures and sleep in comparison to a conventional low heat capacity mattress (LHCM). Based on the higher heat capacity of HHCM an increase in conductive body heat loss enhances the nocturnal decline in CBT can be expected. Based on previous findings this may then be accompanied by an increase in slow wave sleep (SWS). The mattresses were studied in a randomized single-blind crossover design in fifteen healthy young men (Age: 26.9±2.1yr, BMI: 22.2±0.4kg/m2) by overnight in laboratory standard video-polysomnography in a temperature stabilized setting. CBT, room temperature, and skin and mattress surface temperatures were continuously recorded in order to get information about inner and outer body heat flow. Additionally, subjective sleep quality was estimated by visual analogue scale. In comparison to LHCM sleep on HHCM exhibited a selective increase in SWS (16%, p<0.05), increased subjective sleep quality and sleep stability [reduced cyclic alternating pattern (CAP) rate; 5.3%, p<0.01]. Additionally, analyses of the sleep stages showed in the second part of the night a significant increase in SWS and a decrease in REMS. In addition, HHCM induced a greater reduction in CBT (maximally by -0.28°C), reduced the increase in proximal skin temperatures on the back (PROBA; maximally by -0.98°C), and delayed the increase in mattress surface temperature (maximal difference LHCM-HHCM: 6.12°C). Thus, the CBT reduction can be explained by an increase in conductive heat loss to the mattress via proximal back skin regions. Regression analysis identified PROBA as the critical variable to predict inner conductive heat transfer from core to shell and SWS. In conclusion, the study expands the previous findings that a steeper nocturnal decline in CBT increases SWS and subjective sleep quality, whereas inner conductive heat transfer could be identified as the crucial thermophysiological variable, and not CBT.

OvAge: a new methodology to quantify ovarian reserve combining clinical, biochemical and 3D-ultrasonographic parameters.

BACKGROUND: In the last decade, both endocrine and ultrasound data have been tested to verify their usefulness for assessing ovarian reserve, but the ideal marker does not yet exist. The purpose of this study was to find, if any, a statistical advanced model able to identify a simple, easy to understand and intuitive modality for defining ovarian age by combining clinical, biochemical and 3D-ultrasonographic data. METHODS: This is a population-based observational study. From January 2012 to March 2014, we enrolled 652 healthy fertile women, 29 patients with clinical suspect of premature ovarian insufficiency (POI) and 29 patients with Polycystic Ovary syndrome (PCOS) at the Unit of Obstetrics & Gynecology of Magna Graecia University of Catanzaro (Italy). In all women we measured Anti Müllerian Hormone (AMH), Follicle Stimulating Hormone (FSH), Estradiol (E2), 3D Antral Follicle Count (AFC), ovarian volume, Vascular Index (VI) and Flow Index (FI) between days 1 and 4 of menstrual cycle. We applied the Generalized Linear Models (GzLM) for producing an equation combining these data to provide a ready to use information about women ovarian reserve, here called OvAge. To introduce this new variable, expression of ovarian reserve, we assumed that in healthy fertile women ovarian age is identical to chronological age. RESULTS: GzLM applied on the healthy fertile controls dataset produced the following equation OvAge = 48.05 - 3.14*AHM + 0.07*FSH - 0.77*AFC - 0.11*FI + 0.25*VI + 0.1*AMH*AFC + 0.02*FSH*AFC. This model showed a high statistical significance for each marker included in the equation. We applied the final equation on POI and PCOS datasets to test its ability of discovering significant deviation from normality and we obtained a mean of predicted ovarian age significantly different from the mean of chronological age in both groups. CONCLUSIONS: OvAge is one of the first reliable attempt to create a new method able to identify a simple, easy to understand and intuitive modality for defining ovarian reserve by combining clinical, biochemical and 3D-ultrasonographic data. Although design data prove a statistical high accuracy of the model, we are going to plan a clinical validation of model reliability in predicting reproductive prognosis and distance to menopause.