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| Name | Class |
|---|---|
| University Hospital, Bonn | OTHER |
| ETH Zurich | OTHER |
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Polyendocrine metabolic ovarian syndrome (PMOS), previously known as polycystic ovary syndrome (PCOS), is a common endocrine and metabolic condition affecting women of reproductive age. It is associated with hormonal imbalances, irregular menstrual cycles, elevated androgen levels, and metabolic disturbances such as insulin resistance. These metabolic changes can increase the risk of type 2 diabetes and cardiovascular disease.
Insulin resistance means that the body's cells respond less effectively to insulin, a hormone that regulates blood glucose. This leads to compensatory increases in insulin levels, which can further disrupt hormonal balance and contribute to the clinical features of PMOS.
This study aims to investigate how the bodies of women with PMOS respond dynamically to glucose intake compared with women without PMOS. A standard clinical test, the oral glucose tolerance test (oGTT), will be used. Participants consume a glucose solution, and blood samples are collected before and two hours afterward. This procedure is routinely used in clinical practice.
Women with PMOS will be compared with age- and body mass index (BMI)-matched control participants without PMOS. Blood and urine samples will be analyzed using advanced multi-omics technologies to measure proteins, metabolites, extracellular vesicles, and immune-related signals.
The main objective is to understand how metabolic, hormonal, and immune pathways respond over time to a glucose challenge and whether these responses differ in PMOS. Special attention is given to inter-organ communication and systemic metabolic regulation.
The study includes two visits. The first visit involves health assessments, questionnaires, and body composition measurements. The second visit includes the glucose tolerance test and blood sampling. In total, approximately 100 mL of blood will be collected across both visits.
Participation is voluntary, and participants may withdraw at any time without affecting their medical care. The procedures involve minimal risk and consist of standard clinical methods.
The results of this study may improve understanding of PMOS and contribute to better diagnostic and therapeutic strategies in the future.
Background and Rationale: Polyendocrine metabolic ovarian syndrome (PMOS), previously referred to as polycystic ovary syndrome (PCOS), is a common endocrine disorder in women of reproductive age. PMOS is characterized by a heterogeneous clinical phenotype involving reproductive, endocrine, and metabolic disturbances, including hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology.
A central feature of PMOS is insulin resistance. Compensatory hyperinsulinemia can enhance ovarian androgen production and reduce hepatic sex hormone-binding globulin synthesis, thereby increasing circulating free androgen levels. This endocrine-metabolic interaction contributes to the reproductive and metabolic manifestations of PMOS.
Beyond reproductive dysfunction, PMOS is associated with impaired glucose metabolism, dyslipidemia, low-grade inflammation, and an increased long-term risk of type 2 diabetes and cardiovascular disease. Metabolic dysfunction in PMOS may not be fully captured by fasting measurements alone, because fasting assessments provide only a static snapshot of systemic physiology.
The oral glucose tolerance test provides a standardized metabolic challenge for assessing dynamic glucose homeostasis. Glucose ingestion induces coordinated physiological responses involving pancreatic insulin secretion, hepatic glucose regulation, peripheral glucose uptake, endocrine adaptation, and immune-metabolic signaling. Dynamic molecular profiling during an oral glucose tolerance test may therefore reveal regulatory abnormalities that are not apparent under fasting conditions.
Plasma represents an integrative biological compartment reflecting metabolic, endocrine, inflammatory, and inter-organ communication processes. Advances in high-resolution molecular profiling now allow simultaneous characterization of circulating metabolites, plasma proteins, steroid hormones, inflammatory mediators, and extracellular vesicle-associated molecular cargo. Extracellular vesicles are of particular interest because extracellular vesicles may contribute to intercellular and inter-organ communication by transporting proteins, nucleic acids, lipids, and metabolites between tissues.
The study is based on the concept that dynamic perturbation testing can provide additional biological information compared with steady-state measurements. A standardized oral glucose tolerance test is used as a controlled metabolic perturbation to characterize systemic molecular response patterns in PMOS and matched control participants.
Study Design: The study is a single-center, exploratory, matched case-control study. Participants with PMOS and age- and body mass index-matched control participants undergo standardized clinical and metabolic assessments. The study is designed to generate high-dimensional molecular data describing dynamic systemic responses to acute glucose ingestion.
Study Procedures: Participants attend a baseline assessment visit and a metabolic challenge visit. The baseline assessment includes clinical characterization, anthropometric measurements, body composition assessment, questionnaire-based phenotyping, and gynecological assessment as applicable.
During the metabolic challenge visit, participants undergo a standardized 75 g oral glucose tolerance test after an overnight fasting period. Blood samples are collected in the fasting state and 2 hours after glucose ingestion. The timing of the metabolic challenge is standardized in the morning, and menstrual cycle timing is controlled where applicable.
Biological Sampling and Molecular Analyses: Blood-derived biospecimens are processed for clinical chemistry and molecular profiling. Plasma is used for metabolomic profiling, proteomic profiling, targeted steroid hormone analysis, inflammatory protein profiling, and extracellular vesicle characterization. Extracellular vesicle analyses include assessment of vesicle concentration, vesicle size distribution, and extracellular vesicle-associated molecular cargo.
Inflammatory and immune-related proteins are measured using a predefined multiplex protein panel. Buffy coat-derived or blood cell-associated material may be used for immune-related analyses, depending on the final laboratory workflow.
Metabolomic analyses focus on circulating small molecules involved in glucose metabolism, lipid metabolism, amino acid turnover, and energy homeostasis. Proteomic analyses focus on circulating proteins related to insulin signaling, inflammatory pathways, lipid metabolism, endocrine regulation, and inter-organ communication. Targeted steroid profiling is performed using mass spectrometry to characterize endocrine regulation in PMOS.
Analytical Approach: The primary analytical focus is the characterization of molecular changes between the fasting state and the post-glucose state. Molecular response patterns are compared between PMOS and matched control participants to identify dynamic differences in metabolic, endocrine, inflammatory, and extracellular vesicle-associated signaling pathways.
The study is exploratory and hypothesis-generating. Integrated analysis of clinical, biochemical, and molecular data is intended to identify systemic response patterns associated with PMOS, insulin resistance, and altered metabolic flexibility.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| PMOS group | Experimental | Woman diagnosed with PMOS, BMI 18.5-39.9 kg/m2 |
|
| Control group | Experimental | Control participants will be selected to match PMOS group participants with respect to age (±3 years) and BMI (≤ ±2 kg/m²). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Oral Clucose Tolerance Test (oGTT) | Procedure | After an overnight fasting period (≥8 hours), participants ingest a 75 g oral glucose solution. Venous blood samples are collected at predefined time points (fasting and typically 2 hours post-ingestion) to measure plasma glucose and insulin levels. The test evaluates whole-body glucose tolerance and insulin response under controlled metabolic conditions and is routinely used in clinical and research settings. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Normalized Relative Plasma Metabolite Abundance From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of dynamic changes in circulating metabolites in response to a standardized oral glucose tolerance test (oGTT). Metabolomic profiling includes targeted and untargeted analyses of plasma metabolites involved in glucose metabolism, lipid metabolism, amino acid turnover, and energy homeostasis. Longitudinal changes between fasting state and post-glucose challenge will be compared between PMOS participants and age- and BMI-matched controls. The metabolomic response is used as a central readout of systemic metabolic flexibility. | Baseline (fasting, Visit 2) and 2 hours post-glucose ingestion (Visit 2). |
| Change in Normalized Relative Plasma Protein Abundance From Baseline to 2 Hours Post-Glucose Ingestion | Quantification of dynamic changes in circulating plasma proteins in response to oGTT using nanoparticle-enhanced high-resolution proteomics. The analysis focuses on proteins involved in insulin signaling, inflammatory pathways, lipid metabolism, endocrine regulation, and inter-organ communication. Temporal protein abundance changes between fasting and post-glucose states will be assessed to characterize systemic proteomic adaptations and differences in metabolic flexibility between PMOS and controls. | Baseline (fasting, Visit 2) and 2 hours post-glucose ingestion (Visit 2). |
| Measure | Description | Time Frame |
|---|---|---|
| Change in oGTT-Derived Glucose and Insulin Response Indices From Baseline to 2 Hours Post-Glucose Ingestion | Integrated assessment of metabolic flexibility based on conventional oGTT parameters, including glucose and insulin dynamics. Derived indices (e.g., insulin sensitivity proxies, glucose clearance patterns) will be used in combination with clinical chemistry to evaluate systemic metabolic responsiveness. |
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Inclusion Criteria:
Age: 18-35 years
Body weight (BMI): between BMI 18.5-39.9 kg/m2
Ability to consent and to provide written informed consent
CG: History of regular MCs (21 to 35 days) 3 months prior to study enrollment
PCOS-G: Existing or new established diagnosis of PCOS. Diagnosis is verified in accordance with the ESHRE/ASRM Rotterdam consensus (2003)23, phenotype A (hyperandrogenism, oligo-/anovulation, and polycystic ovarian morphology). PCOS is diagnosed when at least the following three criteria are present, after exclusion of other etiologies:
Exclusion Criteria:
Eligibility is limited to participants whose gender identity corresponds to their biological sex (cisgender women). This restriction is necessary because the study investigates sex-specific physiological, hormonal, metabolic, and molecular responses, including analyses influenced by endogenous sex hormones and menstrual cycle characteristics.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jens Stepan, MD, PhD | Contact | +41 43 253 17 59 | jens.stepan@usz.ch |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital Zurich | Zurich | Canton of Zurich | 8091 | Switzerland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 14688154 | Background | Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2004 Jan;19(1):41-7. doi: 10.1093/humrep/deh098. | |
| 35275789 | Background | Ferdosi S, Tangeysh B, Brown TR, Everley PA, Figa M, McLean M, Elgierari EM, Zhao X, Garcia VJ, Wang T, Chang MEK, Riedesel K, Chu J, Mahoney M, Xia H, O'Brien ES, Stolarczyk C, Harris D, Platt TL, Ma P, Goldberg M, Langer R, Flory MR, Benz R, Tao W, Cuevas JC, Batzoglou S, Blume JE, Siddiqui A, Hornburg D, Farokhzad OC. Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions. Proc Natl Acad Sci U S A. 2022 Mar 15;119(11):e2106053119. doi: 10.1073/pnas.2106053119. Epub 2022 Mar 11. |
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De-identified individual participant data (IPD) underlying the results reported in publications may be shared with qualified researchers for scientific research purposes. Shared data may include demographic, physiological, questionnaire, laboratory, and multi-omics datasets collected as part of the study. The study protocol, statistical analysis plan, informed consent form, and data dictionary may also be made available.
Data will be available beginning 12 months after publication of the primary study results and for up to 10 years thereafter. Access will be granted upon reasonable request, following review and approval of a scientifically sound research proposal by the study investigators and sponsoring institution. Any data sharing will be subject to approval by the responsible ethics committee, where required, and compliance with applicable data protection regulations. Data sharing will further require execution of an appropriate data sharing or transfer agreement to ensure partici
12 months after publication until 10 years after publication
Upon reasonable request and approva
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|
| Baseline and 2 hours post-glucose ingestion (Visit 2). |
| Change in Concentration of Plasma Extracellular Vesicles From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of extracellular vesicle concentration in plasma at baseline and 2 hours after oral glucose ingestion. EV abundance will be reported as the number of particles per milliliter of plasma (Particles/mL). | Baseline and 2 hours post-glucose ingestion (Visit 2). |
| Change in Median Diameter of Plasma Extracellular Vesicles From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of extracellular vesicle size distribution in plasma at baseline and 2 hours after oral glucose ingestion. EV size will be reported as median vesicle diameter in nanometers (nm). | Baseline and 2 hours post-glucose ingestion (Visit 2). |
| Change in Normalized Relative Abundance of Extracellular Vesicle-Associated Proteins From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of extracellular vesicle-associated protein abundance in plasma at baseline and 2 hours after oral glucose ingestion. EV protein cargo will be assessed using proteomic profiling and reported as normalized relative protein abundance values. | Baseline and 2 hours post-glucose ingestion (Visit 2). |
| Change in Normalized Relative Abundance of Extracellular Vesicle-Associated Metabolites From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of extracellular vesicle-associated metabolite abundance in plasma at baseline and 2 hours after oral glucose ingestion. EV metabolite cargo will be assessed using metabolomic profiling and reported as normalized relative metabolite abundance values. | aseline and 2 hours post-glucose ingestion (Visit 2). |
| Change in Normalized Relative Abundance of Plasma Inflammatory Proteins From Baseline to 2 Hours Post-Glucose Ingestion | Assessment of inflammatory and immune-related proteins in plasma using a predefined multiplex protein panel. The panel will include cytokines, chemokines, and other immune-related proteins relevant to inflammatory and metabolic responses. Results will be reported as normalized relative protein abundance values. | Baseline and 2 hours post-glucose ingestion (Visit 2). |
| Concentrations of Circulating Steroid Hormones from Baseline to 2 Hours Post-Glucose Ingestion | Targeted quantification of circulating steroid hormones (androgens, estrogens, progestogens, adrenal steroids) using mass spectrometry to assess endocrine regulation in PMOS. | Baseline (fasting state, Visit 1 or Visit 2). |
| Total Body Bone Mineral Density Measured by DXA at Baseline | Assessment of bodybone density using DXA scanning. | Baseline assessment (Visit 1). |
| Total Body Fat Mass Measured by DXA at Baseline | Assessment of body composition using DXA scanning. | Baseline assessment (Visit 1). |
| Score on the State-Trait Anxiety Inventory at Baseline | State-Trait Anxiety Inventory (STAI), range 20-80; higher scores indicate greater anxiety symptoms (worse outcome). | Baseline assessment (Visit 1). |
| Score on the Beck Depression Inventory-II at Baseline | Beck Depression Inventory-II (BDI-II), range 0-63; higher scores indicate more severe depressive symptoms (worse outcome). | Baseline assessment (Visit 1). |
| 29101596 | Background | Mansournia MA, Jewell NP, Greenland S. Case-control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018 Jan;33(1):5-14. doi: 10.1007/s10654-017-0325-0. Epub 2017 Nov 3. |
| 26967288 | Background | Tkach M, Thery C. Communication by Extracellular Vesicles: Where We Are and Where We Need to Go. Cell. 2016 Mar 10;164(6):1226-1232. doi: 10.1016/j.cell.2016.01.043. |
| 22424236 | Background | Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O'Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco MA, Greenberg PL, Snyder P, Klein TE, Altman RB, Butte AJ, Ashley EA, Gerstein M, Nadeau KC, Tang H, Snyder M. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307. doi: 10.1016/j.cell.2012.02.009. |
| 41225634 | Background | Ozer OF, Ibrahimoglu AZ, Gul AZ, Demirel M, Ates S, Taha HS, Ibrahimoglu M, Selek S. Mass spectrometry-based untargeted metabolomics study of polycystic ovary syndrome. J Ovarian Res. 2025 Nov 12;18(1):255. doi: 10.1186/s13048-025-01842-9. |
| 38180081 | Background | Stener-Victorin E, Eriksson G, Mohan Shrestha M, Rodriguez Paris V, Lu H, Banks J, Samad M, Perian C, Jude B, Engman V, Boi R, Nilsson E, Ling C, Nystrom J, Wernstedt Asterholm I, Turner N, Lanner J, Benrick A. Proteomic analysis shows decreased type I fibers and ectopic fat accumulation in skeletal muscle from women with PCOS. Elife. 2024 Jan 5;12:RP87592. doi: 10.7554/eLife.87592. |
| 32659951 | Background | Rajska A, Buszewska-Forajta M, Rachon D, Markuszewski MJ. Metabolomic Insight into Polycystic Ovary Syndrome-An Overview. Int J Mol Sci. 2020 Jul 9;21(14):4853. doi: 10.3390/ijms21144853. |
| 2670645 | Background | Dunaif A, Segal KR, Futterweit W, Dobrjansky A. Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome. Diabetes. 1989 Sep;38(9):1165-74. doi: 10.2337/diab.38.9.1165. |
| 30067154 | Background | Petersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018 Oct 1;98(4):2133-2223. doi: 10.1152/physrev.00063.2017. |
| 19288076 | Background | Henquin JC. Regulation of insulin secretion: a matter of phase control and amplitude modulation. Diabetologia. 2009 May;52(5):739-51. doi: 10.1007/s00125-009-1314-y. Epub 2009 Mar 14. |
| 42119588 | Background | Teede HJ, Khomami MB, Morman R, Laven JSE, Joham AE, Costello MF, Patil M, Rees DA, Berry L, Cree MG, Zhao H, Norman RJ, Dokras A, Piltonen T; Global Name Change Consortium. Polyendocrine metabolic ovarian syndrome, the new name for polycystic ovary syndrome: a multistep global consensus process. Lancet. 2026 Jun 6;407(10545):2329-2339. doi: 10.1016/S0140-6736(26)00717-8. Epub 2026 May 12. |
| 23300589 | Background | Gast KB, Tjeerdema N, Stijnen T, Smit JW, Dekkers OM. Insulin resistance and risk of incident cardiovascular events in adults without diabetes: meta-analysis. PLoS One. 2012;7(12):e52036. doi: 10.1371/journal.pone.0052036. Epub 2012 Dec 28. |
| 38637590 | Background | Stener-Victorin E, Teede H, Norman RJ, Legro R, Goodarzi MO, Dokras A, Laven J, Hoeger K, Piltonen TT. Polycystic ovary syndrome. Nat Rev Dis Primers. 2024 Apr 18;10(1):27. doi: 10.1038/s41572-024-00511-3. |
| 19910321 | Background | March WA, Moore VM, Willson KJ, Phillips DI, Norman RJ, Davies MJ. The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria. Hum Reprod. 2010 Feb;25(2):544-51. doi: 10.1093/humrep/dep399. Epub 2009 Nov 12. |
| ID | Term |
|---|---|
| D011085 | Polycystic Ovary Syndrome |
| D050177 | Overweight |
| D007249 | Inflammation |
| ID | Term |
|---|---|
| D010048 | Ovarian Cysts |
| D003560 | Cysts |
| D009369 | Neoplasms |
| D010049 | Ovarian Diseases |
| D000291 | Adnexal Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D000091662 | Genital Diseases |
| D006058 | Gonadal Disorders |
| D004700 | Endocrine System Diseases |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D010335 | Pathologic Processes |
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| ID | Term |
|---|---|
| D005951 | Glucose Tolerance Test |
| ID | Term |
|---|---|
| D001774 | Blood Chemical Analysis |
| D019963 | Clinical Chemistry Tests |
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D003940 | Diagnostic Techniques, Endocrine |
| D008919 | Investigative Techniques |
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