Maternal Polycystic Ovary Syndrome and the Risk of Early Childhood Obesity in Female Offspring: A Nationwide Population-Based Study

Background@#To investigate the relationship between polycystic ovary syndrome (PCOS) in Korean women and childhood growth and obesity of their offspring. @*Methods@#This longitudinal case-control study using the Korean National Health Insurance claims database and the National Health Screening Program for Infants and Children database included women who delivered singletons between January 2007 and December 2008. Offspring’s body mass index (BMI) measurements taken between 42 and 80 months of age were compared according to a maternal history of PCOS. @*Results@#Among a total of 131,805 participants, 1,213 women had a history of PCOS and 130,592 women did not. Female offspring aged 66–80 months born to women with PCOS had significantly higher BMI than those born to women without PCOS; there was no significant difference in that of male offspring regardless of maternal PCOS. In the generalized estimating equation and multivariable logistic regression analyses, the female offspring born to women with PCOS had a significantly higher risk of obesity during the age of 42–54 and 66–80 months (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.09–2.21 and OR, 1.5; 95% CI, 1.05–2.15, respectively), than those born to women without PCOS, after adjusting for several confounding factors. @*Conclusion@#Maternal PCOS is independently associated with an increased incidence of childhood obesity in female offspring among Korean women. Women with PCOS should consider the risk of early childhood obesity in their daughters, even if they maintain a healthy weight themselves.

Socioeconomic Factors and Abortive Outcomes of Clinical Pregnancy After Embryo Transfer in the Setting of Universal Health Insurance Coverage of IVF

Background@#In vitro fertilization-embryo transfer (IVF-ET), an expensive option for infertile couples, started to be fully covered by the National Health Insurance (NHI) from October 2017 in South Korea. We investigated the association between woman’s socioeconomic status (SES) and abortive outcomes in pregnancies after IVF-ET in the setting of universal coverage of the treatment. @*Methods@#Using the NHI database in South Korea, we conducted a retrospective cohort study of all women who achieved clinical pregnancy after ET between October 2017 and February 2019. A total of 44,038 clinical pregnancy episodes of 29,847 women who underwent ET were analyzed. We used employment status, income in percentiles, and living in the Seoul capital area as indicators of SES. Relative risks (RRs) for abortive pregnancy outcomes were calculated for each socioeconomic stratum, using log-binomial regression models included woman’s age, body mass index, fasting blood glucose, fresh ET, month of ET, and history of smoking. @*Results@#While most pregnancy outcomes were live births (n = 30,783, 69.9%), 11,215 (25.5%) cycles ended with abortion or early pregnancy loss, 1,779 (4.0%) cycles were ectopic pregnancy, 45 (0.1%) were coded as molar pregnancy, and 224 (0.5%) were fetal death in utero or stillbirth. The risk of overall abortive outcomes was higher when a woman was unemployed (adjusted RR, 1.08; 95% confidence interval [CI], 1.05–1.11) or living in a nonSeoul capital area (1.11; 95% CI, 1.08–1.14). The association between relative income level and abortive outcomes was close to null. Living outside Seoul capital area was associated with the greater risk of abortive outcomes especially in younger women. @*Conclusion@#Unemployment and living in non-capital areas were associated with a higher risk of abortive outcomes among pregnancies after ET, even in the setting of universal coverage of IVF-ET. This suggests potential impact of socioeconomic position on the IVF-ET pregnancy.

Association between vasomotor symptoms and sarcopenia assessed by L3 skeletal muscle index among Korean menopausal women

Objective@#To evaluate the association between vasomotor symptoms, skeletal muscle index, and sarcopenia in menopausal women. @*Methods@#This cross-sectional study included 295 Korean menopausal women aged 40–65 years who underwent abdominal computed tomography during routine health checkups between January 2014 and May 2016. The cross-sectional areas of adipose and skeletal muscles were measured at the L3 level using computed tomography. The skeletal muscle index is defined as the sum of the skeletal muscle area (cm2 )/height 2 (m2). Sarcopenia was identified by a skeletal muscle index of ＜ 34.9 cm2 /m2 . Vasomotor symptoms were assessed using the Menopause Rating Scale. @*Results@#The mean age of the participants was 54.93 ± 6.20 years. Vasomotor symptoms were reported in 160 women (54.2%). Sarcopenia was more prevalent in women without vasomotor symptoms (18.5%) than in those with (6.9%). Multivariate logistic regression showed that the prevalence of sarcopenia was inversely associated with the prevalence of vasomotor symptoms (odds ratio, 0.32; 95% confidence interval, 0.15–0.67). Moreover, the paraspinal muscle index was positively associated with the prevalence of vasomotor symptoms (odds ratio, 1.06; 95% confidence interval, 1.01–1.11) after adjusting for age, body mass index, waist circumference, adipose tissue area, history of hormone therapy, systolic and diastolic blood pressures, total cholesterol, insulin resistance, alcohol intake, and exercise. @*Conclusions@#Vasomotor symptoms are less common in women with sarcopenia than in those without, and are positively associated with paraspinal muscle mass in Korean menopausal women. Further longitudinal studies are required to investigate the causal relationships and underlying mechanisms.

Decision support system for the prognostication of sarcopenia in adult women: Machine learning analysis using Korean National Health and Nutrition Examination Survey data

Background@#We used machine learning and population-based data for analyzing the determinants of sarcopenia in adult women and developing its decision support systems for various subgroups. @*Methods@#All data was acquired from the Korea National Health and Nutrition Examination Survey, and women 18 years and older were included in this research. The variables were selected based on female characteristics and the ability to be acquired in a survey format, and were ranked by importance using Random Forest. From this ranking, four main variables were selected, age, menopause age, menarche age and number of pregnancy. A decision supporting system was constructed based on a tree randomly selected from Random Forest. @*Results@#We defined sarcopenia as -2SD below the appendicular skeletal mass (ASM) index reference of 0.5136, and 89.87% (n = 8,610) were found non-sarcopenic and 10.13% (n = 971) were found sarcopenic. The subjects were divided into 6 groups based on menopausal status and BMI. The obese postmenopausal women had the highest number of sarcopenia, whereas the non-obese premenopausal women had the least number of sarcopenic subjects. In non-obese premenopausal women, which was considered to be at the lowest risk for sarcopenia, the most determining variable was the menarche age, followed by age and number of pregnancies. In obese and postmenopausal women, which was considered to be at the highest risk for sarcopenia, the most influential factor was the menopausal age, followed by age and menarche age. @*Conclusions@#We identified the major determinants of sarcopenia using machine learning and population-based data. This study demonstrated the strengths of the random forest as an effective decision support system for each stratified subgroup to find its own optimal cut-off points for the major variables of sarcopenia.

Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data

Background@#To analyze the factors associated with women's vasomotor symptoms (VMS) using machine learning. @*Methods@#Data on 3,298 women, aged 40–80 years, who attended their general health check-up from January 2010 to December 2012 were obtained from Korea University Anam Hospital in Seoul, Korea. Five machine learning methods were applied and compared for the prediction of VMS, measured by the Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying the major factors associated with VMS. @*Results@#In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two, and three hidden layers (1.5576, 1.5184, and 1.5833, respectively). Based on the variable importance from the random forest, the most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19-9, C-reactive protein, and low-density lipoprotein cholesterol levels. Indeed, the following variables were ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in 1 second, height, homeostatic model assessment for insulin resistance, and carcinoembryonic antigen. @*Conclusion@#Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.

Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data

Background@#To analyze the factors associated with women's vasomotor symptoms (VMS) using machine learning. @*Methods@#Data on 3,298 women, aged 40–80 years, who attended their general health check-up from January 2010 to December 2012 were obtained from Korea University Anam Hospital in Seoul, Korea. Five machine learning methods were applied and compared for the prediction of VMS, measured by the Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying the major factors associated with VMS. @*Results@#In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two, and three hidden layers (1.5576, 1.5184, and 1.5833, respectively). Based on the variable importance from the random forest, the most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19-9, C-reactive protein, and low-density lipoprotein cholesterol levels. Indeed, the following variables were ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in 1 second, height, homeostatic model assessment for insulin resistance, and carcinoembryonic antigen. @*Conclusion@#Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.

Vasomotor Symptoms: More Than Temporary Menopausal Symptoms

Vasomotor symptoms (VMS), such as hot flashes and night sweating, are classic menopausal symptoms experienced by a majority of perimenopausal and postmenopausal women. VMS have received a great deal of attention due to their relationship with cardiometabolic risk. Further, accumulating evidence indicates that VMS are associated with an increased risk of several chronic diseases, including metabolic syndrome, type 2 diabetes mellitus, nonalcoholic fatty liver diseases, and osteoporosis in perimenopausal and postmenopausal women. These findings suggest VMS as biomarkers of impaired cardiometabolic conditions rather than just temporary symptoms in menopausal women, warranting further studies to confirm the casual relationship of VMS with these diseases and the exact underlying mechanism in this context.

Vasomotor Symptoms: More Than Temporary Menopausal Symptoms

Vasomotor symptoms (VMS), such as hot flashes and night sweating, are classic menopausal symptoms experienced by a majority of perimenopausal and postmenopausal women. VMS have received a great deal of attention due to their relationship with cardiometabolic risk. Further, accumulating evidence indicates that VMS are associated with an increased risk of several chronic diseases, including metabolic syndrome, type 2 diabetes mellitus, nonalcoholic fatty liver diseases, and osteoporosis in perimenopausal and postmenopausal women. These findings suggest VMS as biomarkers of impaired cardiometabolic conditions rather than just temporary symptoms in menopausal women, warranting further studies to confirm the casual relationship of VMS with these diseases and the exact underlying mechanism in this context.

Comparison of the Efficacy of Tibolone and Transdermal Estrogen in Treating Menopausal Symptoms in Postmenopausal Women

OBJECTIVES: This study aimed to compare the efficacy of tibolone and transdermal estrogen in treating menopausal symptoms in postmenopausal women with an intact uterus.METHODS: Overall, 26 women consumed tibolone orally and 31 women received transdermal estrogen gel mixed with progestogen. The menopause rating scale (MRS) was used to assess their menopausal symptoms at their first outpatient visit and 6 months later.RESULTS: The transdermal estrogen group showed significant improvements in more items of the MRS questionnaire. There was a favorable change in body weight in the transdermal estrogen group compared with that in the tibolone group. Depressive mood, irritability, physical and mental exhaustion, sexual and bladder problems, and joint and muscular discomfort improved only in the transdermal estrogen group, whereas heart discomfort and vaginal dryness improved only in the tibolone group. Nevertheless, the intergroup differences in each item were insignificant after adjusting for body mass index and hypertension, which differed before treatment.CONCLUSIONS: Both the therapeutic options improved menopausal symptoms within 6 months of use. However, transdermal estrogen appeared to be more effective in preventing weight gain in menopausal women than tibolone.

Vasomotor symptoms and the homeostatic model assessment of insulin-resistance in Korean postmenopausal women

The aim of this cross-sectional study was to evaluate the association between vasomotor symptoms (VMS) and insulin resistance, which can be postulated by the homeostatic model assessment (HOMA) index. This study involved 1,547 Korean postmenopausal women (age, 45 to 65 years) attending a routine health check-up at a single institution in Korea from January 2010 to December 2012. A menopause rating scale questionnaire was used to assess the severity of VMS. The mean age of participants was 55.22+/-4.8 years and 885 (57.2%) reported VMS in some degree. The mean HOMA index was 1.79+/-0.96, and the HOMA index increased with an increase in severity of VMS (none, mild, moderate and severe) in logistic regression analysis (beta=0.068, t=2.665, P =0.008). Insulin resistance needs to be considered to understand the linkage between VMS and cardiometabolic disorders.