Age at Menarche and Menopause, Reproductive Lifespan, and Risk of Cardiovascular Events Among Chinese Postmenopausal Women: Results From a Large National Representative Cohort Study

Association between reproductive lifespan and multimorbidity among Chinese postmenopausal women

OBJECTIVE

Although menopause is considered a risk factor for multimorbidity, few studies have explored the association between reproductive lifespan and multimorbidity. This study aimed to explore the association between reproductive lifespan and multimorbidity in postmenopausal Chinese women.

METHODS

This cross-sectional study selected postmenopausal women as study participants. The reproductive lifespan refers to the interval between menarche and menopause. Multimorbidity refers to having two or more self-reported chronic diseases. We used a logistic regression model to explore potential associations based on the adjustment of a set of covariates.

RESULTS

In total, 1,310 postmenopausal women with an average reproductive lifespan of 34 years were included in this study. The prevalence of multimorbidity was 22.2% (291/1,310) in postmenopausal women. Our findings showed that compared with postmenopausal women with the Q1 of reproductive lifespan (≤32 reproductive years), those with Q3 (35-37 reproductive years) and Q4 (≥38 reproductive years) were less likely to have multimorbidity (ORQ3 = 0.529, 95% CIQ3 = 0.347-0.805, ORQ4 = 0.510, 95% CIQ4 = 0.308-0.842), whereas those with Q2 (33-34 reproductive years) were not (OR = 0.700, 95% CI = 0.446-1.098). This study also revealed a linear trend in the association between the reproductive lifespan and multimorbidity; that is, the longer the reproductive lifespan, the lower the risk of multimorbidity.

CONCLUSIONS

In postmenopausal Chinese women, a longer reproductive lifespan was associated with a lower prevalence of multimorbidity. This study suggests that for the prevention and intervention of multimorbidity in postmenopausal women, healthcare professionals should screen and assess reproductive factors to identify high-risk individuals.

Early Menarche as a Protective Factor Against Cardiovascular Events: A Systematic Review and Meta-analysis

BACKGROUND

Women are often neglected in cardiovascular health prevention. Age at menarche (AAM) has been linked to cardiovascular (CVD) disease in women and is potentially identified as one of the significant CVD risk factor. However, there is still limited comprehensive evidence addressing this issue. This systematic review and meta-analysis aimed to investigate how early menarche affects the outcome of all-cause mortality, CVD mortality, total cardiovascular disease event, stroke (ischemic, hemorrhagic, and total stroke), and coronary heart disease (CHD).

METHOD

The Cochrane Library, MEDLINE, Embase, ScienceDirect, and Google Scholar databases were searched from March 2013 to March 2023 for cohorts investigating the effect of early onset of menarche on CVD events with a minimum follow-up period of 5 years. Studies that observed specific population and/or included women with a history of CVD at baseline were excluded. The Newcastle-Ottawa scale was used for risk of bias assessment for each cohort included. The data were presented as dichotomous measure using risk ratios. I2 statistics were utilized to evaluate the heterogeneity of presented data.

RESULTS

Thirteen cohorts included 18 626 799 female patients with ages ranging from 43 to 62.6 years. These reported 6 estimates each for CHD (5 483 298 patients) and all-cause mortality (1 595 878 patients), 5 estimates each for total stroke (2 941 321 patients) and CVD mortality (1 706 742 patients), 4 estimates each for total CVD events (3 988 311 patients) and ischemic stroke (2 434 580 patients), and 1 estimate for hemorrhagic stroke (66 104 patients). Our study found that events of CHD were significantly lower in early menarche (RR 0.57; 95% CI 0.41-0.78; P <.00001), as well as total stroke (RR 0.51; 95% CI 0.35-0.73; P =.0003), CVD mortality (RR 0.47; 95% CI 0.22-0.98; P =.04), total CVD events (RR 0.44; 95% CI 0.25-0.76; P =.003), ischemic stroke (RR 0.31; 95% CI 0.15-0.61; P <.0008), and hemorrhagic stroke (RR 0.12; 95% CI 0.07-0.20; P <.00001); and insignificantly higher in all-cause mortality (RR 0.90, 95% CI 0.76-1.06, P =.20).

CONCLUSION

In our study, cardiovascular events are lower in women with early menarche; hence, the later age of menarche is a potential risk factor to be considered when assessing CVD risk in a patient. However, our sample characteristics were heterogenous, and we did not consider other female hormonal factors that might potentially contribute to the CVD outcomes observed; thus, further studies are needed to clarify.

Italian Association of Hospital Cardiologists Position Paper 'Gender discrepancy: time to implement gender-based clinical management'

It has been well assessed that women have been widely under-represented in cardiovascular clinical trials. Moreover, a significant discrepancy in pharmacological and interventional strategies has been reported. Therefore, poor outcomes and more significant mortality have been shown in many diseases. Pharmacokinetic and pharmacodynamic differences in drug metabolism have also been described so that effectiveness could be different according to sex. However, awareness about the gender gap remains too scarce. Consequently, gender-specific guidelines are lacking, and the need for a sex-specific approach has become more evident in the last few years. This paper aims to evaluate different therapeutic approaches to managing the most common women's diseases.

The J shaped association of age at menarche and cardiovascular events: systematic review and meta-analysis

This study aimed to evaluate the association between age at menarche and cardiovascular (CV) events through a systematic review and meta-analysis of observational studies. A comprehensive literature search covering studies published from January 1, 2000, to October 31, 2023, was conducted in PubMed, MEDLINE, Embase, and Scopus. Twenty-nine observational studies involving 4,931,160 adult women aged 18 years or older were included. The meta-analysis revealed a J-shaped association between age at menarche and CV events. Individuals with menarche at 12-13 years exhibited the lowest risk, while those with younger (≤ 11 years) or older ages (14-15 years and ≥ 16 years) showed an increased risk. Notably, individuals with age at menarche of 16 years and older had the highest risk of CV events. The pooled odds of CV mortality in age at menarche categories 14-15 years and ≥ 16 years were 37% (OR: 1.37, 95% CI 1.14-1.64, I2: 76.9%) and 64% (OR: 1.64, 95% CI 1.20-2.24, I2: 87%) higher than referent age at menarche 12-13 years. No statistically significant difference was found in CV mortality risk between individuals with age at menarche ≤ 11 years and those with age at menarche 12-13 years. The ORs for coronary heart disease were significantly higher for age at menarche ≥ 16 years (35% increase), while no significant difference was found for age at menarche ≤ 11 years or 14-15 years compared to age at menarche 12-13 years. Regarding stroke, the ORs for age at menarche ≤ 11, 14-15, and ≥ 16 years were significantly higher (7%, 24%, and 94% increase, respectively) compared to age at menarche 12-13 years. Dose-response meta-analysis and one-stage random-effect cubic spline models confirmed the J-shaped risk pattern. Meta-regression indicated that age and BMI were not significant sources of heterogeneity. Sensitivity analyses and the absence of publication bias further supported the robustness of the findings. This study concludes that age at menarche is independently associated with CV events, with a J-shaped pattern. The findings underscore the significance of considering menarche age as an independent risk factor for CV events. Further research is warranted to validate these findings and explore potential underlying mechanisms.

A mini-review of the evidence for cerebrovascular changes following gender-affirming hormone replacement therapy and a call for increased focus on cerebrovascular transgender health

Gender-affirming hormone replacement therapy (gaHRT) is an important step for many in the gender diverse community, associated with increased quality-of-life and lower self-reported scores of depression and anxiety. However, considering the interactions that the involved sex hormones have on vasculature (with oestrogen and testosterone demonstrating vasodilatory and vasoconstricting properties, respectively), it is important for transgender healthcare research to examine how the manipulation of these hormones interact with cerebrovascular structure and functioning. There is a stark lack of research in this area. This mini-review outlines the research suggesting a vascular impact of these sex hormones using evidence from a range of cohorts (e.g., menopause, polycystic ovary syndrome) and discusses the work that has been done into cerebrovascular changes following gaHRT. Finally, recommendations for future research into cerebrovascular health in transgender cohorts following gaHRT are outlined.

Fisetin attenuates doxorubicin-induced cardiotoxicity by inhibiting the insulin-like growth factor II receptor apoptotic pathway through estrogen receptor-α/-β activation

Doxorubicin (DOX), an effective chemotherapeutic drug, has been used to treat various cancers; however, its cardiotoxic side effects restrict its therapeutic efficacy. Fisetin, a flavonoid phytoestrogen derived from a range of fruits and vegetables, has been reported to exert cardioprotective effects against DOX-induced cardiotoxicity; however, the underlying mechanisms remain unclear. This study investigated fisetin's cardioprotective role and mechanism against DOX-induced cardiotoxicity in H9c2 cardiomyoblasts and ovariectomized (OVX) rat models. MTT assay revealed that fisetin treatment noticeably rescued DOX-induced cell death in a dose-dependent manner. Moreover, western blotting and TUNEL-DAPI staining showed that fisetin significantly attenuated DOX-induced cardiotoxicity in vitro and in vivo by inhibiting the insulin-like growth factor II receptor (IGF-IIR) apoptotic pathway through estrogen receptor (ER)-α/-β activation. The echocardiography, biochemical assay, and H&E staining results demonstrated that fisetin reduced DOX-induced cardiotoxicity by alleviating cardiac dysfunction, myocardial injury, oxidative stress, and histopathological damage. These findings imply that fisetin has a significant therapeutic potential against DOX-induced cardiotoxicity.

Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction

Background

Several studies have shown that women have a higher mortality rate than do men from ST-segment elevation myocardial infarction (STEMI). The present study was aimed at developing a new risk-prediction model for all-cause in-hospital mortality in women with STEMI, using predictors that can be obtained at the time of initial evaluation.

Methods

We enrolled 8158 patients who were admitted with STEMI to the Tianjin Chest Hospital and divided them into two groups according to hospital outcomes. The patient data were randomly split into a training set (75%) and a testing set (25%), and the training set was preprocessed by adaptive synthetic (ADASYN) sampling. Four commonly used machine-learning (ML) algorithms were selected for the development of models; the models were optimized by 10-fold cross-validation and grid search. The performance of all-population-derived models and female-specific models in predicting in-hospital mortality in women with STEMI was compared by several metrics, including accuracy, specificity, sensitivity, G-mean, and area under the curve (AUC). Finally, the SHapley Additive exPlanations (SHAP) value was applied to explain the models.

Results

The performance of models was significantly improved by ADASYN. In the overall population, the support vector machine (SVM) combined with ADASYN achieved the best performance. However, it performed poorly in women with STEMI. Conversely, the proposed female-specific models performed well in women with STEMI, and the best performing model achieved 72.25% accuracy, 82.14% sensitivity, 71.69% specificity, 76.74% G-mean and 79.26% AUC. The accuracy and G-mean of the female-specific model were greater than the all-population-derived model by 34.64% and 9.07%, respectively.

Conclusions

A machine-learning-based female-specific model can conveniently and effectively identify high-risk female STEMI patients who often suffer from an incorrect or delayed management.