The Phytoestrogen Genistein Produces Similar Effects as 17β-Estradiol on Anxiety-Like Behavior in Rats at 12 Weeks after Ovariectomy

The phytoestrogen genistein produces anxiolytic-like effects in ovariectomized rats, which highlights its potential therapeutic effect in ameliorating anxiety in surgical menopausal women. However, no studies have directly compared the effects of identical doses of genistein and 17β-estradiol, the main estrogen used in hormone replacement therapy in menopausal women. The present study evaluated the anxiolytic-like effects of identical doses of genistein and 17β-estradiol (0.045, 0.09, and 0.18 mg/kg/7 days, s.c.) in a surgical menopause model in rats in the elevated plus maze and locomotor activity tests at 12 weeks after ovariectomy. Additionally, the participation of estrogen receptor-β in the anxiolytic-like effect of genistein and 17β-estradiol was explored by previous administration of the 5 mg/kg tamoxifen antagonist. Genistein and 17β-estradiol (0.09 and 0.18 mg/kg) similarly reduced anxiety-like behavior in the elevated plus maze and also increased the time spent grooming and rearing, without affecting crossing in locomotor activity test. These effects were blocked by tamoxifen. Present results indicate that the phytoestrogen genistein has a similar behavioral profile as 17β-estradiol in rats at 12 weeks after ovariectomy through action at the estrogen receptor-β. Thus genistein has potential for reducing anxiety-like behavior associated with low concentrations of ovarian hormones, which normally occurs during natural and surgical menopause

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications