Women's health study (WENDY) - a protocol of a population-based study assessing gynecological and metabolic health in women in their mid-30s.

The Women's Health Study (WENDY) was conducted to improve insights into women's health and health burden. It provides a unique, comprehensive data source that can be broadly utilised to understand gynaecological symptoms, diseases, and their relation to metabolic and overall health more deeply in a population-based setting. The study was conducted in Finland from May 2020 to October 2022. It included 1,918 women (33-37 years old) who were born in Northern Finland between July 1985 and December 1987. Data collection comprised one three-to-four-hour study visit that included clinical measurements, biological samples, ultrasound examinations and an extensive questionnaire on gynaecological and reproductive history, physical and mental health, quality of life, lifestyles, current life situations, health awareness and opinions. The study also included a menstrual cycle follow-up and cognitive testing up to three months via a mobile application. Given that all participants' data can be linked to all Finnish national registers, and the NFBC participants' data can be linked to the birth cohort data set collected from gestational week 24 onwards, WENDY study forms one of the largest datasets worldwide to investigate gynecological and metabolic health burden in women.

The role of Interleukin-32 in autoimmunity.

Interleukin-32 (IL-32) is a pro-inflammatory cytokine that induces other cytokines involved in inflammation, including tumour necrosis factor (TNF)-α, IL-6 and IL-1ß. Recent evidence suggests that IL-32 has a crucial role in host defence against pathogens, as well as in the pathogenesis of chronic inflammation. Abnormal IL-32 expression has been linked to several autoimmune diseases, such as rheumatoid arthritis and inflammatory bowel diseases, and a recent study suggested the importance of IL-32 in the pathogenesis of type 1 diabetes. However, despite accumulating evidence, many molecular characteristics of this cytokine, including the secretory route and the receptor for IL-32, remain largely unknown. In addition, the IL-32 gene is found in higher mammals but not in rodents. In this review, we outline the current knowledge of IL-32 biological functions, properties, and its role in autoimmune diseases. We particularly highlight the role of IL-32 in rheumatoid arthritis and type 1 diabetes.

Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium.

Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists' assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women's samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF).

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138- cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138- and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36-0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.

AMH as part of the diagnostic PCOS workup in large epidemiological studies.

OBJECTIVES: Previous studies have shown good correlation between polycystic ovarian morphology (PCOM) and serum anti-Müllerian hormone (AMH) levels. We evaluated the utility of AMH as a surrogate for PCOM as a part of the polycystic ovary syndrome (PCOS) diagnosis by describing how the use of different AMH cut-off values would change the prevalence of PCOS. METHODS: A general population-based birth cohort study. Anti-Müllerian hormone concentrations were measured from serum samples taken at age 31 years (n = 2917) using the electrochemiluminescence immunoassay (Elecsys). Anti-Müllerian hormone data were combined with data on oligo/amenorrhoea and hyperandrogenism to identify women with PCOS. RESULTS: The addition of AMH as a surrogate marker for PCOM increased the number of women fulfilling at least two PCOS features in accordance with the Rotterdam criteria. The prevalence of PCOS was 5.9% when using the AMH cut-off based on the 97.5% quartile (10.35 ng/mL) and 13.6% when using the recently proposed cut-off of 3.2 ng/mL. When using the latter cut-off value, the distribution of PCOS phenotypes A, B, C, and D was 23.9%, 4.7%, 36.6%, and 34.8%, respectively. Compared with the controls, all PCOS groups with different AMH concentration cut-offs showed significantly elevated testosterone (T), free androgen index (FAI), luteinizing hormone (LH), LH/follicle-stimulating hormone (FSH) ratio, body mass index (BMI), waist circumference, and homoeostatic model assessment of insulin resistance (HOMA-IR) values, as well as significantly decreased sex hormone-binding globulin (SHBG) values. CONCLUSIONS: Anti-Müllerian hormone could be useful surrogate for PCOM in large data sets, where transvaginal ultrasound is not feasible, to aid the capturing of women with typical PCOS characteristics. Anti-Müllerian hormone measurement from archived samples enables retrospective PCOS diagnosis when combined with oligo/amenorrhoea or hyperandrogenism.

Artificial intelligence deep learning model assessment of leukocyte counts and proliferation in endometrium from women with and without polycystic ovary syndrome.

OBJECTIVE: To study whether artificial intelligence (AI) technology can be used to discern quantitative differences in endometrial immune cells between cycle phases and between samples from women with polycystic ovary syndrome (PCOS) and non-PCOS controls. Only a few studies have analyzed endometrial histology using AI technology, and especially, studies of the PCOS endometrium are lacking, partly because of the technically challenging analysis and unavailability of well-phenotyped samples. Novel AI technologies can overcome this problem. DESIGN: Case-control study. SETTING: University hospital-based research laboratory. PATIENT(S): Forty-eight women with PCOS and 43 controls. Proliferative phase samples (26 control and 23 PCOS) and luteinizing hormone (LH) surge timed LH+ 7-9 (10 control and 16 PCOS) and LH+ 10-12 (7 control and 9 PCOS) secretory endometrial samples were collected during 2014-2019. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Endometrial samples were stained with antibodies for CD8+ T cells, CD56+ uterine natural killer cells, CD68+ macrophages, and proliferation marker Ki67. Scanned whole slide images were analyzed with an AI deep learning model. Cycle phase differences in leukocyte counts, proliferation rate, and endometrial thickness were measured within the study populations and between the PCOS and control samples. A subanalysis of anovulatory PCOS samples (n = 11) vs. proliferative phase controls (n = 18) was also performed. RESULT(S): Automated cell counting with a deep learning model performs well for the human endometrium. The leukocyte numbers and proliferation in the endometrium fluctuate with the menstrual cycle. Differences in leukocyte counts were not observed between the whole PCOS population and controls. However, anovulatory women with PCOS presented with a higher number of CD68+ cells in the epithelium (controls vs. PCOS, median [interquartile range], 0.92 [0.75-1.51] vs. 1.97 [1.12-2.68]) and fewer leukocytes in the stroma (CD8%, 3.72 [2.18-4.20] vs. 1.44 [0.77-3.03]; CD56%, 6.36 [4.43-7.43] vs. 2.07 [0.65-4.99]; CD68%, 4.57 [3.92-5.70] vs. 3.07 [1.73-4.59], respectively) compared with the controls. The endometrial thickness and proliferation rate were comparable between the PCOS and control groups in all cycle phases. CONCLUSION(S): Artificial intelligence technology provides a powerful tool for endometrial research because it is objective and can efficiently analyze endometrial compartments separately. Ovulatory endometrium from women with PCOS did not differ remarkably from the controls, which may indicate that gaining ovulatory cycles normalizes the PCOS endometrium and enables normalization of leukocyte environment before implantation. Deviant endometrial leukocyte populations observed in anovulatory women with PCOS could be interrelated with the altered endometrial function observed in these women.

Protein interactome of the Cancerous Inhibitor of protein phosphatase 2A (CIP2A) in Th17 cells.

Cancerous inhibitor of protein phosphatase 2A (CIP2A) is involved in immune response, cancer progression, and Alzheimer's disease. However, an understanding of the mechanistic basis of its function in this wide spectrum of physiological and pathological processes is limited due to its poorly characterized interaction networks. Here we present the first systematic characterization of the CIP2A interactome by affinity-purification mass spectrometry combined with validation by selected reaction monitoring targeted mass spectrometry (SRM-MS) analysis in T helper (Th) 17 (Th17) cells. In addition to the known regulatory subunits of protein phosphatase 2A (PP2A), the catalytic subunits of protein PP2A were found to be interacting with CIP2A. Furthermore, the regulatory (PPP1R18, and PPP1R12A) and catalytic (PPP1CA) subunits of phosphatase PP1 were identified among the top novel CIP2A interactors. Evaluation of the ontologies associated with the proteins in this interactome revealed that they were linked with RNA metabolic processing and splicing, protein traffic, cytoskeleton regulation and ubiquitin-mediated protein degradation processes. Taken together, this network of protein-protein interactions will be important for understanding and further exploring the biological processes and mechanisms regulated by CIP2A both in physiological and pathological conditions.

Estradiol Valerate in COC Has More Favorable Inflammatory Profile Than Synthetic Ethinyl Estradiol: A Randomized Trial.

CONTEXT: Combined oral contraceptives (COCs) alter inflammatory status and lipid metabolism. Whether different estrogens have different effects is poorly understood. OBJECTIVE: We compared the effects of COCs containing ethinyl estradiol (EE) or estradiol valerate (EV) and dienogest (DNG) with those containing DNG only on inflammation and lipid metabolism. DESIGN: Randomized, controlled, open-label clinical trial. SETTING: Two-center study in Helsinki and Oulu University Hospitals. PARTICIPANTS: Fifty-nine healthy, young, nonsmoking women with regular menstrual cycles. Age, body mass index, and waist-to-hip ratio were comparable in all study groups at the beginning. Fifty-six women completed the study (EV + DNG, n = 20; EE + DNG, n = 19; DNG only, n = 17). INTERVENTIONS: Nine-week continuous use of COCs containing either EV + DNG or EE + DNG, or DNG only as control. MAIN OUTCOME MEASURES: Parameters of chronic inflammation (high-sensitivity C-reactive protein [hs-CRP], and pentraxin 3 [PTX-3]) and lipid profile (high-density lipoprotein [HDL], low-density lipoprotein [LDL], triglycerides, and total cholesterol). RESULTS: Serum hs-CRP increased after 9-week use of EE + DNG (mean change ± standard deviation 1.10 ± 2.11 mg/L) compared with EV + DNG (-0.06 ± 0.97 mg/L, P = 0.001) or DNG only (0.13 ± 0.68 mg/L, P = 0.021). Also, PTX-3 increased in the EE + DNG group compared with EV + DNG and DNG-only groups (P = 0.017 and P = 0.003, respectively). In the EE + DNG group, HDL and triglycerides increased compared with other groups (HDL: EE + DNG 0.20 ± 0.24 mmol/L vs EV + DNG 0.02 ± 0.20 mmol/L [P = 0.002] vs DNG 0.02 ± 0.18 mmol/L [P = 0.002]; triglycerides: EE + DNG 0.45 ± 0.21 mmol/L vs EV + DNG 0.18 ± 0.36 mmol/L [P = 0.003] vs DNG 0.06 ± 0.18 mmol/L [P < 0.001]). CONCLUSIONS: EV + DNG and DNG only had a neutral effect on inflammation and lipids, while EE + DNG increased both hs-CRP and PTX-3 levels as well as triglycerides and HDL. TRIAL REGISTRATION: ClinicalTrials.gov NCT02352090.

CIP2A Constrains Th17 Differentiation by Modulating STAT3 Signaling.

Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) is an oncogene and a potential cancer therapy target protein. Accordingly, a better understanding of the physiological function of CIP2A, especially in the context of immune cells, is a prerequisite for its exploitation in cancer therapy. Here, we report that CIP2A negatively regulates interleukin (IL)-17 production by Th17 cells in human and mouse. Interestingly, concomitant with increased IL-17 production, CIP2A-deficient Th17 cells had increased strength and duration of STAT3 phosphorylation. We analyzed the interactome of phosphorylated STAT3 in CIP2A-deficient and CIP2A-sufficient Th17 cells and indicated together with genome-wide gene expression profiling, a role of Acylglycerol Kinase (AGK) in the regulation of Th17 differentiation by CIP2A. We demonstrated that CIP2A regulates the strength of the interaction between AGK and STAT3, and thereby modulates STAT3 phosphorylation and expression of IL-17 in Th17 cells.

Quantitative Proteomics Reveals the Dynamic Protein Landscape during Initiation of Human Th17 Cell Polarization.

Th17 cells contribute to the pathogenesis of inflammatory and autoimmune diseases and cancer. To reveal the Th17 cell-specific proteomic signature regulating Th17 cell differentiation and function in humans, we used a label-free mass spectrometry-based approach. Furthermore, a comprehensive analysis of the proteome and transcriptome of cells during human Th17 differentiation revealed a high degree of overlap between the datasets. However, when compared with corresponding published mouse data, we found very limited overlap between the proteins differentially regulated in response to Th17 differentiation. Validations were made for a panel of selected proteins with known and unknown functions. Finally, using RNA interference, we showed that SATB1 negatively regulates human Th17 cell differentiation. Overall, the current study illustrates a comprehensive picture of the global protein landscape during early human Th17 cell differentiation. Poor overlap with mouse data underlines the importance of human studies for translational research.

Early Detection of Peripheral Blood Cell Signature in Children Developing ß-Cell Autoimmunity at a Young Age.

The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding autoantibody positivity or reflect progressive ß-cell destruction. Here we report the mRNA sequencing-based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4-CD8- cell fractions and unfractionated peripheral blood mononuclear cell samples longitudinally collected from seven children who developed ß-cell autoimmunity (case subjects) at a young age and matched control subjects. We identified transcripts, including interleukin 32 (IL32), that were upregulated before T1D-associated autoantibodies appeared. Single-cell RNA sequencing studies revealed that high IL32 in case samples was contributed mainly by activated T cells and NK cells. Further, we showed that IL32 expression can be induced by a virus and cytokines in pancreatic islets and ß-cells, respectively. The results provide a basis for early detection of aberrations in the immune system function before T1D and suggest a potential role for IL32 in the pathogenesis of T1D.