Estrogen receptor polymorphism predicts the onset of natural and surgical menopause

Age at menopause and risk of hysterectomy have strong genetic components, but the genes involved remain ill defined. We investigated whether genetic variation at the estrogen receptor (ER) gene contributes to the variability in the onset of menopause in 900 postmenopausal women, aged 55-80 yr, of the Rotterdam Study, a population-based cohort study in The Netherlands. Gynecological information was obtained, and if women reported surgical menopause, validation of type and indication of surgery was accomplished by checking medical records. The ER genotypes (PP, Pp, and pp) were assessed by PCR using the PvuII endonuclease. Compared with women carrying the pp genotype, homozygous PP women had a 1.1-yr (P &lt; 0.02) earlier onset of menopause. Furthermore, an allele dose effect was observed, corresponding to a 0.5-yr (P &lt; 0.02) earlier onset of menopause per copy of the P allele. The risk of surgical menopause was 2.4 (95% confidence interval, 1.5-3.8) times higher for women carrying the PP genotype compared to those in the pp group, with the most prominent effect in women who underwent hysterectomy due to fibroids or menorrhagia. We conclude that genetic variations of the ER gene are related to the onset of natural menopause and the risk of surgical menopause, especially hysterectomy.</p

The Rotterdam Study: objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in the Netherlands. The study targets cardiovascular, neurological, ophthalmological and endocrine diseases. As of 2008 about 15,000 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in some 600 research articles and reports (see http://www.epib.nl/rotterdamstudy). This article gives the reasons for the study and its design. It also presents a summary of the major findings and an update of the objectives and methods

COVID outcome prediction in the emergency department (COPE)

Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model - COVID outcome prediction in the emergency department (COPE) - with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.</p