Menopause

Summary

Menopause, also known as the climacteric, is the time when menstrual periods permanently cease, marking the end of reproduction. It typically occurs between the ages of 45 and 55, although the exact timing can vary. Menopause is usually a natural change. It can occur earlier in those who smoke tobacco. Other causes include surgery that removes both ovaries or some types of chemotherapy. At the physiological level, menopause happens because of a decrease in the ovaries' production of the hormones estrogen and progesterone. While typically not needed, a diagnosis of menopause can be confirmed by measuring hormone levels in the blood or urine. Menopause is the opposite of menarche, the time when a girl's periods start. In the years before menopause, a woman's periods typically become irregular, which means that periods may be longer or shorter in duration or be lighter or heavier in the amount of flow.

Official source

https://en.wikipedia.org/wiki/Menopause

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We will define the concept of homeostasis and principles of hormone action and the molecular mechanisms underlying them. Interactions with the environment and pertinent public health issues will be analyzed and preventative strategies will be discussed.

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The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021

Differential Roles of MAPK Kinases MKK3 and MKK6 in Osteoclastogenesis and Bone Loss

Mario Michael Zaiss

Bone mass is maintained by osteoclasts that resorb bone and osteoblasts that promote matrix deposition and mineralization. Bone homeostasis is altered in chronic inflammation as well as in post-menopausal loss of estrogen, which favors osteoclast activity that leads to osteoporosis. The MAPK p38 alpha is a key regulator of bone loss and p38 inhibitors preserve bone mass by inhibiting osteoclastogenesis. p38 function is regulated by two upstream MAPK kinases, namely MKK3 and MKK6. The goal of this study was to assess the effect of MKK3- or MKK6-deficiency on osteoclastogenesis in vitro and on bone loss in ovariectomy-induced osteoporosis in mice. We demonstrated that MKK3 but not MKK6, regulates osteoclast differentiation from bone marrow cells in vitro. Expression of NFATc1, a master transcription factor in osteoclastogenesis, is decreased in cells lacking MKK3 but not MKK6. Expression of osteoclast-specific genes Cathepsin K, osteoclast-associated receptor and MMP9, was inhibited in MKK3-/- cells. The effect of MKK-deficiency on ovariectomy-induced bone loss was then evaluated in female WT, MKK3-/- and MKK6-/- mice by micro-CT analysis. Bone loss was partially inhibited in MKK3-/- as well as MKK6-/- mice, despite normal osteoclastogenesis in MKK6-/- cells. This correlated with the lower osteoclast numbers in the MKK-deficient ovariectomized mice. These studies suggest that MKK3 and MKK6 differentially regulate bone loss due to estrogen withdrawal. MKK3 directly mediates osteoclastogenesis while MKK6 likely contributes to pro-inflammatory cytokine production that promotes osteoclast formation.

Public Library of Science2014

, ,

IMPORTANCE There is a high prevalence of obesity in psychiatric patients, possibly leading to metabolic complications and reducing life expectancy. The CREB-regulated transcription coactivator 1 (CRTC1) gene is involved in energy balance and obesity in animal models, but its role in human obesity is unknown. OBJECTIVE To determine whether polymorphisms within the CRTC1 gene are associated with adiposity markers in psychiatric patients and the general population. DESIGN, SETTING, AND PARTICIPANTS Retrospective and prospective data analysis and population-based samples at Lausanne and Geneva university hospitals in Switzerland and a private clinic in Lausanne, Switzerland. The effect of 3 CRTC1 polymorphisms on body mass index (BMI) and/or fat mass was investigated in a discovery cohort of psychiatric outpatients taking weight gain-inducing psychotropic drugs (sample 1, n = 152). The CRTC1 variant that was significantly associated with BMI and survived Bonferroni corrections for multiple comparison was then replicated in 2 independent psychiatric samples (sample 2, n = 174 and sample 3, n = 118) and 2 white population-based samples (sample 4, n = 5338 and sample 5, n = 123 865). INTERVENTION Noninterventional studies. MAIN OUTCOME AND MEASURE Difference in BMI and/or fat mass between CRTC1 genotype groups. RESULTS Among the CRTC1 variants tested in the first psychiatric sample, only rs3746266A>G was associated with BMI (P-adjusted = .003). In the 3 psychiatric samples, carriers of the rs3746266 G allele had a lower BMI than noncarriers (AA genotype) (sample 1, P = .001; sample 2, P = .05; and sample 3, P = .0003). In the combined analysis, excluding patients taking other weight gain-inducing drugs, G allele carriers (n = 98) had a 1.81-kg/m(2) lower BMI than noncarriers (n = 226; P < .0001). The strongest association was observed in women younger than 45 years, with a 3.87-kg/m(2) lower BMI in G allele carriers (n = 25) compared with noncarriers (n = 48; P < .0001), explaining 9% of BMI variance. In the population-based samples, the T allele of rs6510997C>T (a proxy of the rs3746266 G allele; r(2) = 0.7) was associated with lower BMI (sample 5, n = 123 865; P = .01) and fat mass (sample 4, n = 5338; P = .03). The strongest association with fat mass was observed in premenopausal women (n = 1192; P = .02). CONCLUSIONS AND RELEVANCE These findings suggest that CRTC1 contributes to the genetics of human obesity in psychiatric patients and the general population. Identification of high-risk subjects could contribute to a better individualization of the pharmacological treatment in psychiatry.

Amer Medical Assoc2013

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