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Coin-sized Micro-needles Patch to deliver Insulin: An innovation by Shanghai University Scientists

Stem Cells Help Researchers Study The Effects of Pollution on Human Health

A Novel and Robust Muscle Activity Onset Detection Technique by Using an Unsupervised Electromyogram Learning Framework

Early Modern Humans and Neanderthals were close bedfellows

FANCD2 and REV1 Cooperate in the Protection of Nascent DNA Strands in Response to Replication Stress

Spiders' Foraging Strategies Have Cascading Effects on Litter Decomposition Rates

Yingli Announces Sale of 18.8 MW Solar Power Plant in the UK to NextEnergy Solar Fund

China's Award Winning Desertification Control in Kubiqi Desert

Earthquake Early Warning System for Nepal

Mechanical Coupling Mechanism of a Mechanical Force-sensing Channel Protein Discovered by IBP Scientists

Jiahui International Hospital and Brigham and Women's Hospital to Co-Develop Women's Health Center of Excellence in Shanghai

Big Grain1: The ‘Mr. BIG’ for Crops to Grow Bigger by Regulating Auxin Transport in Rice

When there is no Queen in the house, Asian Hive Bees Avoid Risky Foraging for Reproduction

UNICEF HK joins hands with Government to help Mothers sustain Breastfeeding

Fresenius Medical Care Springs into Action after Deadly Tianjin Explosion

Polycystic ovary syndrome (PCOS) is a common endocrinopathy in women of reproductive age. Although its essential clinical manifestation includes a plethora of symptoms and signs, which largely reflects the underlying hyperandrogenism, oligo/anovulation, and polycystic ovarian morphology, PCOS may also be associated with many metabolic derangements. These metabolic derangements happen to overlap with many of the core constituents of the metabolic syndrome (MBS)—increased insulin resistance, central obesity, and dyslipidemia. The two disorders also display similarly increased risks for certain metabolic and vascular diseases, such as type 2 diabetes mellitus, hypertension, and cardiovascular diseases. Due to the many similarities between metabolic syndrome and PCOS, this review aims to examine the evidence concerning the overlapping features, the risks for comorbidities, possible shared mechanisms, and treatment strategies in patients with coexisting PCOS and MBS.

Adverse climate change is more than an environmental concern. The are several consequential outcomes of climate change such as loss of oil wealth, depletion of scarce resources, decline in sources of livelihood, property loss, water shortages, and land scarcity. Secondary effects could include increased mortality, deteriorating health, hunger, poverty, inequality, and financial hardship. Studies over the years have shown that climate change could also precipitate conflicts and lead to displacements. For many women in conflict situations, it is difficult to access medical care, especially reproductive healthcare. Amid efforts to contain insecurity and its devastating effect, the government often loses sight of the vulnerabilities of women and girls, whose reproductive health challenges have been exacerbated by the effects of climate change. Men and women may encounter health challenges in climate change-induced conflict situations however, women may be affected more differently than men. Specific healthcare services, treatments, and commodities for women’s general and reproductive/sexual health are often ignored despite the need for healthcare, menstrual hygiene products, health education, and general health supplies in times of conflict. In addition to the associated conflict-related risk, women may face a heightened risk of rape and other sexual-related violence. The risk to women and girls may further increase if resources are diverted from sexual and reproductive health care to respond to the insecurity and crises or where the supply chain is affected by climate change-induced conflicts. This study examines the challenges that women face in climate change-induced conflict and post-conflict situations, particularly as it affects their reproductive health rights. It further argues that the realization that women’s reproductive and sexual health in a climate change-induced conflict setting is a human right that warrants concerted attention. Ultimately, it advocates for gender-sensitive responses to the reproductive and sexual health of women and girls as a matter of fundamental human right.

Women’s health is an often-overlooked aspect of medicine. The National Institutes of Health has emphasized the importance of investigating ‘sex as a biological variable’ in all new research grants. This has placed emphasis once again on the need for more nuanced studies that explore the role of sex as a biological variable on study outcomes. This session sought to elicit participation from researchers with strong backgrounds in women’s health and informatics to develop methods that harness big datasets and ‘big data techniques’ including machine learning and artificial intelligence and apply those tools to women’s health questions. Some important questions discussed in this section include Intimate Partner Violence (IPV) and the importance of early identification along with C-section deliveries and the importance of emergency vs. elective procedures.

Uterine leiomyomata, or fibroids, are common gynecological tumors causing pelvic and menstrual symptoms that can negatively affect quality of life and child-bearing desires. As fibroids grow, symptoms can intensify and lead to invasive treatments that are less likely to preserve fertility. Identifying individuals at highest risk for fibroids can aid in access to earlier diagnoses. Polygenic risk scores (PRS) quantify genetic risk to identify those at highest risk for disease. Utilizing the PRS software PRS-CSx and publicly available genome-wide association study (GWAS) summary statistics from FinnGen and Biobank Japan, we constructed a multi-ancestry (META) PRS for fibroids. We validated the META PRS in two cross-ancestry cohorts. In the cross-ancestry Electronic Medical Record and Genomics (eMERGE) Network cohort, the META PRS was significantly associated with fibroid status and exhibited 1.11 greater odds for fibroids per standard deviation increase in PRS (95% confidence interval [CI]: 1.05 – 1.17, p = 5.21x10⁻⁵). The META PRS was validated in two BioVU cohorts: one using ICD9/ICD10 codes and one requiring imaging confirmation of fibroid status. In the ICD cohort, a standard deviation increase in the META PRS increased the odds of fibroids by 1.23 (95% CI: 1.15 – 1.32, p = 9.68x10⁻⁹), while in the imaging cohort, the odds increased by 1.26 (95% CI: 1.18 – 1.35, p = 2.40x10⁻¹¹). We subsequently constructed single ancestry PRS for FinnGen (European ancestry [EUR]) and Biobank Japan (East Asian ancestry [EAS]) using PRS-CS and discovered a nominally significant association in the eMERGE cohort within fibroids and EAS PRS but not EUR PRS (95% CI: 1.09 – 1.20, p = 1.64x10⁻⁷). These findings highlight the strong predictive power of multi-ancestry PRS over single ancestry PRS. This study underscores the necessity of diverse population inclusion in genetic research to ensure precision medicine benefits all individuals equitably.

Uterine leiomyomata (fibroids, UFs) are common, benign tumors in females, having an estimated prevalence of up to 80%. They are fibrous masses growing within the myometrium leading to chronic symptoms like dysmenorrhea, abnormal uterine bleeding, anemia, severe pelvic pain, and infertility. Hypertension (HTN) is a common risk factor for UFs, though less prevalent in premenopausal individuals. While observational studies have indicated strong associations between UFs and HTN, the biological mechanisms linking the two conditions remain unclear. Understanding the relationship between HTN and UFs is crucial because UFs and HTN lead to substantial comorbidities adversely impacting female health. Identifying the common underlying biological mechanisms can improve treatment strategies for both conditions. To clarify the genetic and causal relationships between UFs and BP, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis and evaluated the genetic correlations across BP traits and UFs. We used data from a multi-ancestry genome-wide association study (GWAS) meta-analysis of UFs (44,205 cases and 356,552 controls), and data from a cross-ancestry GWAS meta-analysis of BP phenotypes (diastolic BP [DBP], systolic BP [SBP], and pulse pressure [PP], N=447,758). We evaluated genetic correlation of BP phenotypes and UFs with linkage disequilibrium score regression (LDSC). LDSC results indicated a positive genetic correlation between DBP and UFs (Rg=0.132, p<5.0x10^-5), and SBP and UFs (Rg=0.063, p<2.5x10^-2). MR using UFs as the exposure and BP traits as outcomes indicated a relationship where UFs increases DBP (odds ratio [OR]=1.20, p<2.7x10^-3). Having BP traits as exposures and UFs as the outcome showed that DBP and SBP increase risk for UFs (OR =1.04, p<2.2x10^-3; OR=1.00, p<4.0x10^-2; respectively). Our results provide evidence of shared genetic architecture and pleiotropy between HTN and UFs, suggesting common biological pathways driving their etiologies. Based on these findings, DBP appears to be a stronger risk factor for UFs compared to SBP and PP.

Women’s health conditions are influenced by both genetic and environmental factors. Understanding these factors individually and their interactions is crucial for implementing preventative, personalized medicine. However, since genetics and environmental exposures, particularly social determinants of health (SDoH), are correlated with race and ancestry, risk models without careful consideration of these measures can exacerbate health disparities. We focused on seven women’s health disorders in the All of Us Research Program: breast cancer, cervical cancer, endometriosis, ovarian cancer, preeclampsia, uterine cancer, and uterine fibroids. We computed polygenic risk scores (PRSs) from publicly available weights and tested the effect of the PRSs on their respective phenotypes as well as any effects of genetic risk on age at diagnosis. We next tested the effects of environmental risk factors (BMI, lifestyle measures, and SDoH) on age at diagnosis. Finally, we examined the impact of environmental exposures in modulating genetic risk by stratified logistic regressions for different tertiles of the environment variables, comparing the effect size of the PRS. Of the twelve sets of weights for the seven conditions, nine were significantly and positively associated with their respective phenotypes. None of the PRSs was associated with different ages at diagnoses in the time-to-event analyses. The highest environmental risk group tended to be diagnosed earlier than the low and medium-risk groups. For example, the cases of breast cancer, ovarian cancer, uterine cancer, and uterine fibroids in highest BMI tertile were diagnosed significantly earlier than the low and medium BMI groups, respectively). PRS regression coefficients were often the largest in the highest environment risk groups, showing increased susceptibility to genetic risk. This study’s strengths include the diversity of the All of Us study cohort, the consideration of SDoH themes, and the examination of key risk factors and their interrelationships. These elements collectively underscore the importance of integrating genetic and environmental data to develop more precise risk models, enhance personalized medicine, and ultimately reduce health disparities.