Sjogren’s Syndrome: Recent Updates

Primary Sjögren’s syndrome (SS) is a chronic systemic autoimmune disorder affecting primarily perimenopausal women [...]

Menopause and its management: A review

Menopausal transition (premenopause) is the phase in the ageing process of women representing the transition from the reproductive stage of life to the non-reproductive stage. With improvements in average life expectancy, women live an increased proportion of their lives in the postmenopause. The consequences of oestrogen loss are the early symptoms (psychological and vasomotor), the genitourinary syndrome (intermediate), as well as postmenopausal osteoporosis with increased risk of fractures and cardiovascular diseases (late). The diagnosis of climacteric syndrome is based on typical clinical symptoms. Perimenopausal women should understand physiological changes occurring in menopausal transition. They should be encouraged to live a healthy lifestyle. Menopausal hormone therapy is indicated for relief of the acute symptoms of menopause and for treatment of urogenital atrophy. It should be administered in the lowest effective dose for the shortest period of time. The treatment should be initiated before the age of 60 years or within 10 years after menopause in order to decrease its risks. The benefit/risk profile needs to be individually re-assessed every year.

Endometrial Cut Off Thickness as Predictor of Endometrial Pathology in Perimenopausal Women with Abnormal Uterine Bleeding: A Cross-Sectional Study

Purpose. We aim to determine the predictive value of endometrial thickness by transvaginal ultrasonography (TVS) in diagnosing endometrial pathology and to evaluate whether Doppler complements its diagnostic efficacy in perimenopausal women with abnormal uterine bleeding. Methods. This cross-sectional observational study was conducted among 70 perimenopausal women with AUB who underwent TVS measurement of endometrial thickness (ET) and Doppler flow indices followed by endometrial sampling and histopathological examination (HPE). Results. In HPE, 51 (73%) women had normal diagnosis while 19 (27%) women had neoplastic histology either benign or malignant. They were categorised into group I and group II, respectively. There was a significant difference in age ( P = 0.001 ) and incidence of obesity ( P = 0.01 ) between the two groups. The ETs measured in group I and group II were 7.89 ± 2.62 mm and 14.07 ± 3.96 mm, respectively, with significant difference ( P < 0.001 ). A TVS-ET of 10.5 mm had the highest sensitivity and specificity of 89.5% and 86.3%, respectively, PPV of 70.68%, NPV of 95.68%, LR+ of 6.52, and LR− of 0.12. Doppler flow velocimetric study of endometrial and uterine vessels did not demonstrate a significant difference. Conclusions. Women in perimenopause with AUB should be offered to undergo endometrial sampling for histopathological examination if TVS ET ≥10.5 mm. The coexisting risk factors especially higher age (>45 years) and obesity (BMI>30) significantly escalate the chances of developing endometrial pathology.

Joint effects of self-reported sleep and modifiable physical activity on risk of dyslipidaemia in women aged 45–55 years: a cross-sectional study

ObjectivesModifiable physical activity (PA) plays an important role in dyslipidaemia risk in middle-aged women with sleep problems, especially perimenopausal women. We aimed to explore the joint effects of sleep and PA on the risk of dyslipidaemia in women aged 45–55 years, and the extent to which PA moderated the effect of sleep on the risk of dyslipidaemia.DesignA cross-sectional study.SettingThis study was based on the survey of Chronic Disease and Nutrition Monitoring in Adults in Inner Mongolia in 2015.Participants721 women aged 45–55 years were included.Outcome measurementPA was measured by the Global Physical Activity Questionnaire. Sleep was measured by questionnaire formulated by the Chinese Center for Disease Control and Prevention. Multivariate logistic regression analyses were performed to determine the joint effects of sleep and PA on dyslipidaemia risk. OR and 95% CI were reported.ResultsAmong all participants, 60.6% had sleep problems, 29.0% had low PA and 41.1% had dyslipidaemia. Women with sleep problems had higher dyslipidaemia risk than women without sleep problems, irrespective of low, moderate or high PA, with OR (95% CI) of 4.24 (2.40 to 7.49), 3.14 (1.80 to 5.49) and 2.04 (1.20 to 3.48), respectively. PA could not completely attenuate the negative association between sleep and dyslipidaemia risk. With PA increased from low to high, the OR of dyslipidaemia decreased by 2.20. Women with sleep problems and low PA had higher risks of high total cholesterol, high triglyceride, low high-density lipoprotein cholesterol and high low-density lipoprotein cholesterol than women without sleep problems and high PA, with OR (95% CI) of 2.51 (1.18 to 5.35), 2.42 (1.23 to 4.74), 2.88 (1.44 to 5.74) and 2.52 (1.12 to 5.70), respectively.ConclusionsAmong women aged 45–55 years, the joint effects of self-reported sleep and PA on dyslipidaemia risk were more marked for sleep than for PA. Modifiable PA is a widely accessible and effective intervention to reduce the dyslipidaemia risk in women with sleep problems, particularly among perimenopausal women.

Risk factors and prognosis of metabolic syndrome in perimenopausal women with hypothyroidism

One of the main problems is that the metabolic syndrome (MS) is not clinically manifested until thesignificant carbohydrate and lipid metabolism violations, that initiate a pathological range of symptoms, leading to cardiovascular disease, diabetes mellitus, non-alcoholic steatohepatosis, impairment of reproductive function and other diseases. Conco­mitant endocrine disorders create a background on which dyshormonal and metabolic factors are superimposed. Therefore, an urgent and important task is the correct, and most importantly timely, interpretation of existing risk factors for metabolic disorders in women of perimenopausal age with concomitant hypothyroidism (HT), purposed on their correction to prevent or slowdown the development of complications.Aim — to develop a mathematical model for assessing the prognostic value of risk factors for investigation ofMSdevelopment and progression in perimenopausal women with HT.Materials and methods. 146 perimenopausal women with HT were examined to predict the MS development. Multiple regression analysis was used to construct prognostic model of MS risk.Results. With the help of logistic regression analysis, the following significant multicollinear risk factors for MS have been identified: triglycerides, thyroid-stimulating hormone, vitamin D, and waist circumference. A correlation matrix with the calculation of regression coefficients and coefficient of determination was constructed, and mathematical model was created to determine the coefficient of the MSprogression. The developed mathematical model for predicting of MSdevelopment had a high predictive accuracy, which confirms the compliance of the predicted results with the theoretically expected. The probability that patients will develop MS with a previously predicted risk of MS was 96.1 % (p < 0.05).Conclusions. The developed mathematical model of the predictionof metabolic syndromedevelopment against the background of hypothyroidism is highly informative and allows to determine in advance the contingent of women with high probability of MS development for the timely implementation of appropriate preventive measures.

Steroid Hormone Secretion Over the Course of the Perimenopause: Findings From the Swiss Perimenopause Study

Background: Perimenopause is characterized by a decline in the steroid hormones, estradiol, and progesterone. By contrast, the steroid hormone cortisol, a marker of the hypothalamic–pituitary–adrenal (HPA) axis, increases. Recent longitudinal studies reported fluctuations in steroid hormone levels during perimenopause, and even increases in estradiol levels. To understand these confounding results, it is necessary to conduct a longitudinal, highly standardized assessment of steroid hormone secretion patterns in perimenopausal women.Methods: This longitudinal study investigated 127 perimenopausal women aged 40–56 years for 13 months. Estradiol, progesterone, and cortisol were assessed using saliva samples, which were collected for two (during months 2 and 12 for estradiol and progesterone) or three (during months 2, 7, and 12 for cortisol) non-consecutive months over the course of the study. A total of 14 saliva samples per participant were analyzed to investigate the courses of estradiol and progesterone. Cortisol awakening response and fluctuations of cortisol throughout the day were measured using a total of 11 saliva samples per participant (on awakening, +30 min, +60 min, at 12:00 p.m., and before going to bed) for months 2, 7, and 12.Results: Multilevel analyses revealed variance in intercept and slope across participants for estradiol [intercept: SD = 5.16 (95% CI: 4.28, 6.21), slope: SD = 0.50 (95% CI: 0.39, 0.64)], progesterone [intercept: SD = 34.77 (95% CI: 25.55, 47.31), slope: SD = 4.17 (95% CI: 2.91, 5.99)], and cortisol (intercept: SD = 0.18 (95% CI: 0.14, 0.23), slope: SD = 0.02 (95% CI: 0.01, 0.02)]. Time predicted cortisol levels [b = −0.02, t(979) = −6.63, p &lt; 0.0001]. Perimenopausal status (early vs. late) did not predict estradiol [b = −0.36, t(1608) = −0.84, p = 0.400], progesterone [b = −4.55, t(1723) = −0.87, p = 0.385], or cortisol [b = 0.01, t(1124) = 0.61, p = 0.542] scores over time.Discussion: Our results are consistent with previous findings emphasizing highly individual fluctuations of estradiol and progesterone levels during perimenopause. However, our findings do not suggest a continuous decline during the observed transition phase, implying relatively stable periods of fluctuating hormone levels. Furthermore, given the lack of significant group differences, it may not be necessary to differentiate between early and late perimenopause from the standpoint of hormonal progression.

Decreased Medial Prefrontal Cortex Glutamate Levels in Perimenopausal Women

Objective: There is an increased risk of experiencing depression during perimenopause (PM), a period of rapidly changing female hormone concentrations. Women at particular risk of developing major depression (MD) during PM are those with history of mood sensitivity to female hormone fluctuations i.e., women with a history of premenstrual dysphoric disorder (PMDD) and/or post-partum depression (PPD). Depressive symptomology has been associated with fluctuations of glutamate (Glu) levels in the medial prefrontal cortex (MPFC) in MD patients as well as PMDD and PPD patients. The objective of the study was to compare MPFC Glu levels in healthy perimenopausal and reproductive-aged (RD) women.Methods: Medial prefrontal cortex Glu levels in healthy perimenopausal (n = 15) and healthy RD women (n = 16) were compared via Magnetic Resonance Spectroscopy (MRS) scan using a 3 Tesla (T) magnet. Absence of depressive symptomology and psychiatric comorbidity was confirmed via semi-structured interview. Participants were scanned during the early follicular phase (FP) of the menstrual cycle (MC).Results: Mean MPFC Glu concentrations were decreased in the PM group compared to RD group (PM mean = 0.57 ± 0.03, RD mean = 0.63 ± 0.06, t = −3.84, df = 23.97, p = 0.001).Conclusion: Perimenopause is associated with decreases in MPFC Glu levels. This decrease may be contributing to the increased risk of experiencing depression during PM. Further research should assess MPFC Glu levels in perimenopausal women suffering from MD.

An Observational Study on Coping and Quality of Life among Perimenopausal Women

Background: Perimenopause is a natural phenomenon signaling the reduction of ovarian function. Worldwide, the age at which natural menopause occurs is between 45 to55 years. Many women during the premenopausal age group may experience menopausal symptoms such as physiological changes, psychological changes, urogenital changes, sexual changes and vasomotor changes. These changes and symptoms the women may have negative impact on Quality of Life among perimenopausal women. The main aim of the study was to assess the coping and quality of life among perimenopausal women. Materials and Methods: The researcher has used a quantitative research approach and convenient sampling technique was adapted to select 165 perimenopausal women with the age group of 40 to 55 years. Researcher assessed the perimenopausal symptoms, observed the coping and quality of life among perimenopausal women by Greene climacteric scales, modified cope inventory scale and Utian quality of life scale respectively. The study was conducted in Poonjeri, Kadampadi, and Perumalari villages from Chengalpattu District, for the period of four weeks. Results: There was a positive correlation between cope and quality of life among perimenopausal women. There is no association was observed between the monthly income and quality of life of the perimenopausal women, as the Chi-square test was statistically significant at p<0.05. Conclusion: The study concludes that the perimenopausal women who are participated in this study have moderate symptoms, moderate coping, and moderate quality of life. The monthly income was effective in influencing the perimenopausal women’s quality of life.