Clinical trial NCT03843736: The Role of Dysbiosis of Gut Microbiota in the Pathogenesis of PCOS.
Official title: The Role of Dysbiosis of Gut Microbiota in the Pathogenesis of Polycystic Ovary Syndrome.
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: PHASE3
Conditions: Polycystic Ovary Syndrome
Keywords: Polycystic Ovary Syndrome
Metagenomics
Metabonomics
Lead sponsor: Peking Union Medical College Hospital
Sponsor class: OTHER
Collaborators: name: Peking Union Medical College
First posted: 2019-02-18
Enrollment: 200
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: This study will collect 50 healthy participants and 150 PCOS participants. At the same time, 150 patients with PCOS will be randomly divided into the lifestyle intervention group, lifestyle interventions + Probiotic Agent group, lifestyle intervention + oral contraceptive group by random number table method.
Primary purpose: TREATMENT

Brief summary: Polycystic ovary syndrome (PCOS) has a significant impact on women's health, but its pathogenesis is not yet clear. Dysbiosis of gut microbiota may play a role in the pathological change of PCOS. Most of the current researches are still limited to the use of amplicon sequencing to compare the basic taxonomic differences of gut microbiota between PCOS patients and normal controls. Overall analysis of microbiome species, genes, function, metabolism, and immunity in PCOS is still lacked. In this research, we would perform metagenomic sequencing to find the characteristics of gut microbiota of PCOS and to explore their correlations with metabolic, immune, and clinical symptoms. Finally, different interventions (lifestyle interventions, lifestyle interventions + oral probiotic, lifestyle interventions+ compound oral contraceptives) would be used to explore the change of gut microbiome in PCOS patients. This research will not only help the understanding of the pathophysiology of PCOS, but also provide a reference for the selection of clinical treatment options.

Detailed description: 1. Data quality assurance: ① all inspections and measurements will be performed by either the hospital or the sequencing company personnel according to standard operating procedures (SOPs), except for saliva and stool samples, which will be self-collected by patients. For sample collection, we will provide text descriptions of the SOPs as well as video instruction. Designated staff will be assigned for support and can be contacted if participants have any queries concerning sample collection; ② a case report form (CRF) will be prepared according to the current SOPs, and detailed instructions will be provided to ensure consistency in data collection. At the same time, each CRF will be properly stored at least 5 years for verification and backtracking; ③ all experimental data will be logged into the database to ensure information accuracy based on the existing data; ④ we will keep the contact information of each participant, remind them of precautions during participation, and conduct regular follow-ups.
2. Sample size determination: The number of participants is based on comparable sample sizes in the literature. In this trial, there will be 50 healthy individuals (control group) and 150 PCOS (polycystic ovary syndrome) patients. The 150 PCOS patients will be randomly assigned to three intervention groups. This sample size accounts for a plausible insufficiency of data caused by patient dropouts and withdrawals before the study is completed. The participation cycle is of approximately four months, followed by a 2-year follow-up.
3. Metagenomic sequencing technology Metagenomic sequencing is the main technique used in this study. Metagenomics, also known as economics, was first proposed by Handelman and studies the molecular composition of microbial populations, their interactions, and gene functions.

   In medicine, metagenomics compares the structural and functional changes of human microbial communities under normal and disease states. It can analyze the diversity and the functional differences of microbial communities from healthy individuals and from patients with diseases, thus determine how diseases relate to changes in the microbial communities and in their respective metabolic networks. Therefore, metagenomics provides theoretical evidence for disease prevention, detection, and treatment. At present, the internationally renowned Human Microbiome Project (HMP, http://www.hmpdacc.org/) and the Metagenomics of the Human Intestinal Tract (MetaHIT) are typical applications of metagenomics in medicine.

   \[Metagenomic species, genes, and functional annotation\]

   ① Data quality control: the sequenced raw data will contain a certain amount of low-quality data, so quality control must be performed. Only high-quality data can correctly reflect the actual occurrence of microorganisms in the sample.

   ② Metagenome assembly: based on Clean Data, individual samples will be assembled separately at first, then reads that do not participate in the assembling above will be combined and mixed for assembly. This will increase the sequencing depth of low-abundance species in each sample and provide more sequencing information for each species.

   ③ Gene prediction: MetaGeneMark will be used for gene prediction based on single samples and mixed-assembled scaftigs. The redundancy of all predicted genes will be reduced to obtain a Uniq gene set. Then, the Clean Data of each sample will be compared to the gene set and the abundance of the gene set will be determined for each sample.

   ④ Species annotation: Clean Data will be used for quality control. It will be compared with an annotated according to reference genome databases of bacteria, archaea, viruses, and fungi from NCBI. A species abundance table will be obtained for each sample at different classification levels.

   ⑤ Functional annotation: functional annotation and abundance statistics will be based on the Uniq gene set and the KEGG database.

Eligibility criteria: Inclusion Criteria:

1. Conforms to the 2003 Rotterdam classic PCOS diagnostic criteria.

   1. sparse ovulation or anovulation;
   2. clinical manifestations of high androgen and/or hyperandrogenism;
   3. ovarian polycystic changes: ultrasound suggests one or both sides of the ovary with a diameter of 2-9 mm follicles ≥ 12, and / or ovarian volume ≥ 10 ml;

      2 out of 3 items, and exclude other high androgen causes, such as congenital adrenal hyperplasia, Cushing's syndrome, and androgen-secreting tumors;
2. Age: 18-45 years old.

Exclusion Criteria:

1. pregnancy;
2. menopause;
3. adrenal abnormalities;
4. thyroid dysfunction;
5. taking antibiotics for the past 3 months;
6. is taking oral contraceptive treatment;
7. basic diseases of the gastrointestinal tract (ulcerative colitis, Crohn's disease, inflammatory bowel disease, etc.);
8. history of smoking;
9. BMI\<18kg/m2.

Primary outcomes:
measure: Diversity analysis of genes and species
timeFrame: Through study completion, an average of 12 weeks
measure: Analysis of differences in intestinal microbiota between PCOS patients and the control group
timeFrame: Through study completion, an average of 12 weeks
measure: Analysis of functional differences in the intestinal microbiota of PCOS patients in comparison to the control group
timeFrame: Through study completion, an average of 12 weeks
measure: Correlation analysis between biomarkers and clinical indicators
timeFrame: Through study completion, an average of 12 weeks
Secondary outcomes:
measure: Insulin resistance
timeFrame: Through study completion, an average of 12 weeks
measure: Androgen level
timeFrame: Through study completion, an average of 12 weeks
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking Union Medical College Hospital
Study officials:
affiliation: Beijing Union Medical College Hospital
name: Rong Chen, Ph. D.
role: STUDY_DIRECTOR
References:
citation: Alvarez-Blasco F, Luque-Ramirez M, Escobar-Morreale HF. Diet composition and physical activity in overweight and obese premenopausal women with or without polycystic ovary syndrome. Gynecol Endocrinol. 2011 Dec;27(12):978-81. doi: 10.3109/09513590.2011.579658. Epub 2011 May 24.
citation: Tremellen K, Pearce K. Dysbiosis of Gut Microbiota (DOGMA)--a novel theory for the development of Polycystic Ovarian Syndrome. Med Hypotheses. 2012 Jul;79(1):104-12. doi: 10.1016/j.mehy.2012.04.016. Epub 2012 Apr 27.
citation: Liu R, Zhang C, Shi Y, Zhang F, Li L, Wang X, Ling Y, Fu H, Dong W, Shen J, Reeves A, Greenberg AS, Zhao L, Peng Y, Ding X. Dysbiosis of Gut Microbiota Associated with Clinical Parameters in Polycystic Ovary Syndrome. Front Microbiol. 2017 Feb 28;8:324. doi: 10.3389/fmicb.2017.00324. eCollection 2017.
citation: Guo Y, Qi Y, Yang X, Zhao L, Wen S, Liu Y, Tang L. Association between Polycystic Ovary Syndrome and Gut Microbiota. PLoS One. 2016 Apr 19;11(4):e0153196. doi: 10.1371/journal.pone.0153196. eCollection 2016.
citation: Torres PJ, Siakowska M, Banaszewska B, Pawelczyk L, Duleba AJ, Kelley ST, Thackray VG. Gut Microbial Diversity in Women With Polycystic Ovary Syndrome Correlates With Hyperandrogenism. J Clin Endocrinol Metab. 2018 Apr 1;103(4):1502-1511. doi: 10.1210/jc.2017-02153.