Brigitte Leeners

BACKGROUND: Obesity is caused by an imbalance between energy intake, i.e. eating and energy expenditure (EE). Severe obesity is more prevalent in women than men worldwide, and obesity pathophysiology and the resultant obesity-related disease risks differ in women and men. The underlying mechanisms are largely unknown. Pre-clinical and clinical research indicate that ovarian hormones may play a major role. OBJECTIVE AND RATIONALE: We systematically reviewed the clinical and pre-clinical literature on the effects of ovarian hormones on the physiology of adipose tissue (AT) and the regulation of AT mass by energy intake and EE. SEARCH METHODS: Articles in English indexed in PubMed through January 2016 were searched using keywords related to: (i) reproductive hormones, (ii) weight regulation and (iii) central nervous system. We sought to identify emerging research foci with clinical translational potential rather than to provide a comprehensive review. OUTCOMES: We find that estrogens play a leading role in the causes and consequences of female obesity. With respect to adiposity, estrogens synergize with AT genes to increase gluteofemoral subcutaneous AT mass and decrease central AT mass in reproductive-age women, which leads to protective cardiometabolic effects. Loss of estrogens after menopause, independent of aging, increases total AT mass and decreases lean body mass, so that there is little net effect on body weight. Menopause also partially reverses women's protective AT distribution. These effects can be counteracted by estrogen treatment. With respect to eating, increasing estrogen levels progressively decrease eating during the follicular and peri-ovulatory phases of the menstrual cycle. Progestin levels are associated with eating during the luteal phase, but there does not appear to be a causal relationship. Progestins may increase binge eating and eating stimulated by negative emotional states during the luteal phase. Pre-clinical research indicates that one mechanism for the pre-ovulatory decrease in eating is a central action of estrogens to increase the satiating potency of the gastrointestinal hormone cholecystokinin. Another mechanism involves a decrease in the preference for sweet foods during the follicular phase. Genetic defects in brain α-melanocycte-stimulating hormone-melanocortin receptor (melanocortin 4 receptor, MC4R) signaling lead to a syndrome of overeating and obesity that is particularly pronounced in women and in female animals. The syndrome appears around puberty in mice with genetic deletions of MC4R, suggesting a role of ovarian hormones. Emerging functional brain-imaging data indicates that fluctuations in ovarian hormones affect eating by influencing striatal dopaminergic processing of flavor hedonics and lateral prefrontal cortex processing of cognitive inhibitory controls of eating. There is a dearth of research on the neuroendocrine control of eating after menopause. There is also comparatively little research on the effects of ovarian hormones on EE, although changes in ovarian hormone levels during the menstrual cycle do affect resting EE. WIDER IMPLICATIONS: The markedly greater obesity burden in women makes understanding the diverse effects of ovarian hormones on eating, EE and body adiposity urgent research challenges. A variety of research modalities can be used to investigate these effects in women, and most of the mechanisms reviewed are accessible in animal models. Therefore, human and translational research on the roles of ovarian hormones in women's obesity and its causes should be intensified to gain further mechanistic insights that may ultimately be translated into novel anti-obesity therapies and thereby improve women's health.

STUDY QUESTION: Is fatigue a frequent symptom of endometriosis? SUMMARY ANSWER: Fatigue is an underestimated symptom of endometriosis as it affects the majority of women with endometriosis, but it is not widely discussed in literature. WHAT IS KNOWN ALREADY: Fatigue can be a symptom of endometriosis causing major distress impacting the daily activities and quality of life of women with endometriosis. However, few studies with large sample sizes have investigated fatigue as a symptom of endometriosis. STUDY DESIGN, SIZE, DURATION: The study was designed as a multi-center matched case-control study. Recruitment took place at hospitals and private practices in Switzerland, Germany and Austria between 2010 and 2016. Data was collected from 1120 women, 560 of them with endometriosis. The women with endometriosis were matched to 560 control women in regard to age ±3 years and ethnic background. PARTICIPANTS/MATERIALS, SETTING, METHODS: Diagnosis of women with endometriosis had to be surgically and histologically confirmed. Surgical exclusion or absence of any endometriosis-identifying symptoms was required for control subjects. Materials included surgical and histological reports as well as data retrieved from a self-administered questionnaire. This study focused on the symptom fatigue in endometriosis. Relationships of variables were established by regression analysis and associations were quantified as odds ratios. MAIN RESULTS AND THE ROLE OF CHANCE: Frequent fatigue was experienced by a majority of women diagnosed with endometriosis (50.7% versus 22.4% in control women, P < 0.001). Fatigue in endometriosis was associated with insomnia (OR: 7.31, CI: 4.62-11.56, P < 0.001), depression (OR: 4.45, CI: 2.76-7.19, P < 0.001), pain (OR: 2.22, CI: 1.52-3.23, P < 0.001), and occupational stress (OR: 1.45, CI: 1.02-2.07, P = 0.037), but was independent of age, time since first diagnosis and stage of the disease. LIMITATIONS, REASONS FOR CAUTION: Women with asymptomatic endometriosis cannot be excluded in the control group which would lead to underestimation of our results. The study's design allows no evaluation of causal effects. WIDER IMPLICATIONS OF THE FINDINGS: As fatigue is experienced by numerous women with endometriosis, it needs to be addressed in the discussion of management and treatment of the disease. In addition to treating endometriosis, it would be beneficial to reduce insomnia, depression, pain and occupational stress in order to better manage fatigue. STUDY FUNDING/COMPETING INTEREST(S): There was no additional funding received for this study and no conflict of interest. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT02511626.

BACKGROUND: Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. OBJECTIVE: In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. METHODS: We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. RESULTS: We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). CONCLUSIONS: Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests that taking one's daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone (LH) test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82%), with the lowest cycle temperature occurring in the fertile window 41% of the time. Most temporal shifts (307/357, 86%) occurred on ovulation day (OV) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, the present study suggests that WST could be a promising, convenient parameter for future multiparameter fertility awareness methods.