Effect of various types of gut microbiota in patients on the diminished ovarian reserve

doi:10.21203/rs.3.rs-2063027/v1

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Effect of various types of gut microbiota in patients on the diminished ovarian reserve

https://doi.org/10.21203/rs.3.rs-2063027/v1

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Background

Diminished ovarian reserve (DOR) is a type of reproductive endocrine disease associated with infertility and premature menopause due to the decline in the number and quality of oocytes. In recent years, studies have been conducted on the relationship between gut microbiota and reproductive health, our objective was to find a solid relationship between gut microbiota and DOR.

Methods

After rigorous inclusion criteria, 34 women with DOR and 30 normal ovarian reserve controls were recruited at the Reproductive Medical Center of Shengjing Hospital of China Medical University from March to December 2021. Serum levels of AMH, basal sex hormones, AFC, gut microbiota and short-chain fatty acids were measured in two groups.

Results

Quantitative polymerase chain reaction (qPCR) sequencing technology and gas chromatography was introduced to find a significant difference of the composition and metabolites of gut microbiota between DOR and healthy individuals. After careful comparison of Prevotella/Bacteroides frequencies, we found that individuals with Prevotella /Bacteroides > 0.01 had a higher possibility with DOR. In addition, receiver operating characteristic and spearman correlation analysis were applied to identify butyrate and isobutyrate as a mark to predict the risk of DOR. After canonical correlation analysis, we found that in patients with Prevotella enterotype, Akkermansia muciniphila and Enterobacteria could be considered as harmful bacteria. Based on the above results, an intervention was conducted on six patients who suffered from DOR. After 28 day intervention, the concentration of Enterobacteria and isobutyrate were significantly reduced, meanwhile the amounts of Bifidobacteria were significantly increased. Finally, the concentrations of antral follicle count (AFC) were increased and two thirds of them were successfully pregnant.

Conclusions

In summary, we defined population with Prevotella/Bacteroides > 0.01 as Prevotella enterotype and found that Prevotella enterotype was closely related to DOR. Akkermansia muciniphila, Enterobacteria, Bifidobacteria, butyrate, and isobutyrate could be used as biomarkers to predict the ovarian reserve in two enterotypes. And different dietary interventions for two enterotypes and have demonstrated significant clinical effects.

Bacteroides Enterotype

Diminished Ovarian Reserve

Gut Microbiota

Prevotella Enterotype

Short-Chain Fatty Acids

Diminished ovarian reserve (DOR) is the decrease in the number and quality of oocytes that can be recruited in the ovary, resulting in decreased fertility and even premature menopause. ~10% of infertile women are affected by DOR(1). The clinical manifestations of DOR include oligomenorrhea and even amenorrhea, a prolonged menstrual cycle, increased follicle-stimulating hormone (FSH) levels, low estradiol (E2) and Anti-Mullerian Hormone (AMH) levels. Most patients have perimenopausal symptoms like hyposexuality, facial flushing, hot flushes, and excessive sweating. However, the etiology is unclear. Genetic factors, iatrogenic factors (such as ovarian surgery, radiotherapy and chemotherapy), inflammation and nutrition are all associated with DOR (2). Hiroyuki Sasaki et al. found significant alternations in the diversity and composition of gut microbiota in chronic anovulation women (3).

Gut microbiota has been closely related to endocrine disease, metabolic disorder, and senescence. Psychological factors are one of the main factors of DOR and animal experiments confirmed that under chronic stress mice had the same biochemical changes as in DOR (4). A bidirectional communication is conducted between gut microbiota and central nervous system via the gut-brain axis(5). Gut microbiota can influence the level of central neurotransmitters, produce short-chain fatty acids (SCFAs), and change the blood brain barrier through the “leaky gut” to affect the brain function (6). Disturbance of the axis is associated with the onset of central nervous system diseases like anxiety and depression (7). Oxidative stress is related to DOR (8). The accumulation of reactive oxygen species in oocytes can affect oocyte maturation and lead to the decreased oocyte quality through mitochondrial dysfunction, endoplasmic reticulum stress, and meiotic maturation disorder(9). Gut dysbiosis has also been identified to be a cause of increased oxidative stress in vivo and prebiotics can play a role by restoring the balance of gut microbiota, reducing oxidative stress, and upregulating anti-inflammatory and antioxidant molecules (10). Prevotella and Bacteroidetes are the main neutral bacteria in human body, and Prevotella /Bacteroides has been used to define enterotype in many studies. Therefore, we speculated that there could be an association between enterotype and DOR. People with certain enterotype are more likely to develop DOR, and some harmful bacteria and metabolites in this enterotype accelerate this process.

In this study, serum levels of AMH, sex hormones, AFC, gut microbiota and SCFAs in DOR group and normal ovarian reserve group were measured. The aim of this study is to find the most suitable Prevotella /Bacteroides to define enterotype through frequency distribution analysis, thus we can distinguish healthy individuals and DOR individuals well, and establish interventions to reduce harmful bacteria and metabolites to improve the ovarian reserve.

2.1 Clinical parameters characteristic of participants

Data were available from 34 patients with DOR and 30 participants in the control group. No significant differences were found for basic demographic data (Table 1). As for biochemical indices, AFC, AMH and PRL were significantly higher in the control group (P < 0.05), while FSH was significantly higher in the DOR group (P < 0.05).

Table 1

Clinical parameters of control group and DOR group
	Control group	DOR group
Age(year)	32.33 ± 3.33	33.65 ± 2.75
Height(m)	1.61 ± 0.04	1.62 ± 0.05
Weight(kg)	60.10 ± 9.60	59.43 ± 9.99
BMI(kg/m2)	23.08 ± 3.27	22.76 ± 3.47
AMH	4.26 ± 1.66**	0.63 ± 0.60
AFC	13.03 ± 5.89**	1.88 ± 1.41
P(ng/ml)	0.60 ± 0.36	0.51 ± 0.39
PRL(ng/ml)	15.23 ± 6.55*	12.29 ± 0.58
T(ng/ml)	0.40 ± 0.15	0.36 ± 0.18
LH(mIU/ml)	4.91 ± 3.13	8.64 ± 12.13
FSH(mIU/ml)	7.18 ± 1.39	20.12 ± 21.31**
E2(pg/ml)	38.64 ± 16.23	61.49 ± 52.02
DOR: Diminished Ovarian Reserve; BMI: Body Mass Index; AMH: Anti-Müllerian Hormone; AFC: Antral Follicle Count; Prog: Progesterone; PRL: Prolactin; T: Testosterone; LH: Luteinizing Hormone; FSH: Follicle-Stimulating Hormone; E2: Estradiol

2.2 Alternation of gut microbiota and clinical indices in patients with DOR

Principal component analysis was performed using qPCR technology. In Fig. 2A, the first main component was 56.8%, the second main component was 20.9%, and the third main component was 11.5%. This finding indicated that the composition of gut microbiota from the control group was not significantly different from the DOR group (Fig. 2A).

The PICRUSt2 model was used to make a prediction with an accuracy of 76% in this study. Through the variation analysis via PICRUSt2, AMH, AFC, Bacteroides, PRL, BMI and height were significantly higher in the control group, whereas FSH, FSH/LH and Prevotella were significantly higher in the DOR group (P < 0.05). Bacteroides were significantly abundant in the control group, while Prevotella was significantly abundant in the DOR group (Fig. 2B).

2.3 Determination of enterotypes according to the P/B ratio

Gut microbiota analysis was performed on the stool samples of all participants (n = 64). The frequency plot of the relative abundance of log (P/B) (Fig. 3) demonstrated a bimodal distribution of separation between the two groups. These results suggested that the participants could be classified into two groups: Prevotella enterotype (P/B > 0.01) and Bacteroides enterotype (P/B < 0.01). All participants were divided into the control-Bacteroides (C-B) group, control-Prevotella (C-P) group, DOR-Bacteroides (DOR-B) group, and the DOR-Prevotella (DOR-P) group. The relationship between the clinical indices and SCFAs were separately studied.

To observe the differences among the four groups in different enterotype, variation analysis via PICRUSt2 was applied. AMH and AFC were significantly higher in the C-P group, whereas FSH, FSH/LH, isobutyrate, and isovalerateare were significantly higher in DOR-P group (P < 0.05) (Fig. 4A). It indicated that DOR was related to excessive SCFAs for the Prevotella enterotype. In addition, AMH, AFC, BMI and height were significantly higher in control-B group, whereas FSH was significantly higher in DOR-B group (P < 0.05) (Fig. 4B). It indicated that gut microbiota and SCFAs could not significantly differentiate the DOR group from control group with the Bacteroides enterotype.

2.4 Correlation of AMH, SCFAs and gut microbiota in DOR patients according to enterotype

After analysis via the Spearman method (Fig. 5), DOR was related to SCFAs. For the Prevotella enterotype, isobutyrate had a positive correlation with DOR (P < 0.05) (Fig. 5A). For the Bacteroides enterotype, butyrate had a significant positive correlation with DOR (P < 0.05) (Fig. 5B). Fusobacterium, Clostridium leptum and Lactobacillus were positively related to AMH, whereas Roseburia was negatively related to AMH (Fig. 6). For the Prevotella enterotype, Bifidobacterium, Akkermansia muciniphila(AKK) and Enterobacteriaceae were negatively related to AMH (Fig. 6A), which turned out to be the opposite for the Bacteroides enterotype (Fig. 6B).

Based on the enterotype study, the ROC curve (Fig. 7) indicated that isobutyrate (0.7632), Bifidobacterium (0.6513), Lactobacillus (0.6382), and Fusobacterium (0.6053) have good diagnostic performance in DOR patients with the Prevotella enterotype (Fig. 7A). Butyrate (0.7333), Fusobacterium (0.6697), Clostridium leptum(0.6394), and Roseburia (0.6333) were good diagnostics for DOR patients with the Bacteroides enterotype (Fig. 7B).

2.5 Intervention results for DOR patients with different enterotypes

According to this study, different intervention plans for the different enterotypes were designed. Patients with the Prevotella enterotype were given polysaccharide for 14 days. After that, the protein intake was adjusted, giving them easily absorbed protein with a digestive enzyme. Patients with the Bacteroides enterotype were continuously given Bifidobacterium. After the intervention, AFC of six patients was significantly higher than before and entered the hyperstimulation cycle (Fig. 8). Among them, four patients successfully became pregnant after the embryo transfer.

Enterotype refers to clusters composed of gut microbial communities with similar bacteria composition, was first proposed in 2011(11), which is stratification of human gut microbiota essentially. Previous studies have found that the Bacteroides enterotype was an independent risk factor for type 2 diabetes (12). Due to the lack of a unified standard for the method of enterotype analysis, researchers should choose the method suitable for their own samples. In this study, the composition of gut microbiota can be simply described as Bacteroides enterotype and Prevotella enterotype. But not as before, in this study participants were defined with Prevotella/Bacteroides > 0.01 as Prevotella enterotype, which has a high relationship with DOR. Dietary habit is strongly linked with enterotype, other factors such as lifestyle, environmental pressure, age, and antibiotics are also related with enterotype (13). As mentioned earlier, we gave Bifidobacteria to individuals with Bacteroides enterotype. For individuals with Prevotella enterotype, we supplemented with polysaccharide to reduce Enterobacteriaceae and improved the indigestion of protein to reduce butyrate. The ovarian reserve function in both groups was significantly improved.

Gut dysbiosis has been shown to be associated with a variety of diseases, including inflammatory bowel disease, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, obesity, diabetes and so on(14). In this study, Prevotella was the dominant taxa in DOR group, while Bacteroides was significantly abundant in the normal group. These results are consistent with Hiroyuki Sasaki et al. They found that Prevotella-enriched microbiota was present in the irregular menstrual cycles group, whereas Bacteroidales S24-7 was significantly less abundant(3).

DOR affects ~ 10% of infertile women with a decreased number and quality of oocytes, low oocyte retrieval, poor embryo quality, high cycle cancellation rate, and low clinical pregnancy rate during in vitro fertilization-embryo transfer (IVF-ET) treatment. Total mural granulosa cell apoptosis, which may be used as a marker to indicate oocyte quality, is increased in women with DOR, resulting in worse ovarian response and fewer oocytes (15). Mutation of GDF9 gene plays an important role in DOR by disturbing ovarian hormone secretion such as inhibin B, affecting folliculogenesis and granulosa cells proliferation (16). The increase of inflammatory cytokines such as TNF-α and IL-6, and the decrease of anti-inflammatory cytokines such as IL-4, IL-10 play important roles in premature ovarian insufficiency (17). In addition, Coulam et al. found that pathologic biopsy of part of the ovarian tissue of premature ovarian failure patients showed follicular inflammatory cell infiltration (18). However, none of these studies provided early prediction of DOR, which continues to confound reproductive experts. This study is first to comprehensively compare gut microbiota between DOR patients and healthy individuals. In this study, gut microbiota was thought to participate in DOR through the following mechanism. Gut dysbiosis enhances the permeability of the intestinal barrier, releases endotoxin into the blood, and promotes the production of pro-inflammatory cytokines. Disorder of digestion and absorption of nutrients produces harmful metabolites, which are involved in the development of disease. Gut microbiota is involved in the development of DOR by regulating the gut-brain axis and interfering with the Hypothalamus-Pituitary-Ovary axis by producing SCFAs. The results confirmed the hypothesis that gut dysbiosis contributes to the occurrence and development of DOR through the decreased beneficial bacteria and increased disease-related bacteria.

In DOR patients, Clostridium leptum and Lactobacillus were both positively correlated with AMH, which is a commonly used clinical indicator to reflect ovarian reserve function. He et al. discovered that administration of Lactobacillus as prebiotics could not only alleviate ovary abnormalities of letrozole-treated polycystic ovary syndrome (PCOS) rats, restore LH, FSH and testosterone levels, but also improve gut dysbiosis such as decreases the abundance of Prevotella and increases the abundance of AKK(19). The current study also yielded consistent results, Clostridium leptum and Lactobacillus are beneficial bacteria could exhibit health-promoting effects through participating in the maintenance of gut microbiota balance and producing SCFAs which can improve gut health, increase the intestinal barrier function, and reduce the translocation of bacterial endotoxins(20).

In DOR patients with the Bacteroides enterotype, AKK and Bifidobacteria were both positively related to AMH. Zhang et al. found that Bifidobacteria, as a kind of probiotics, could significantly reduce the total testosterone level in women with PCOS, which might be related to the reconstruction of gut microbiota, the enhanced digestion, and absorption of nutrients in the diet and the interaction in the gut-brain axis(21). AKK attached to the intestinal mucosa, using the mucin as a source of energy, inhibiting the colonization of pathogens through competition effect (22) and reducing endotoxin levels(23). These two beneficial bacteria can prevent the development of DOR in population with Bacteroides enterotype by improving the integrity of the intestinal barrier, reducing the level of endotoxin (24), and reducing the expression of inflammatory genes in intestinal epithelial cells (25). Among SCFAs, butyrate, as the preferred energy source of intestinal epithelial cells (26), has received particular attention for its beneficial effects. It can increase colon motility and promote water and sodium absorption. In an animal model, butyrate enhances the intestinal barrier function by regulating tight junction proteins (27). However, this study had contradictory results since butyrate was positively correlated with DOR. This discrepancy may be due to the different concentrations caused the opposite effect of butyrate. High circulating concentrations of propionate and butyrate were observed in acidaemic disease in humans (28), indicating that SCFAs might exhibit toxicity at high concentrations. Some studies have shown that butyrate can induce the development of obesity through involving in glycolipid metabolism signal pathways. Metabolic syndrome was one of the possible mechanisms of DOR(29).

In DOR patients with Prevotella enterotype, AKK and Enterobacteriaceae were negatively related with AMH. Some scholars hypothesized that AKK can transform mucin into beneficial substances to maintain the balance of host intestinal microorganisms(30). There was no evidence proving that AKK alone had a special pathogenicity in people. It might be pathogenic in the presence of other pathogenic bacteria or in susceptible people, which was consistent with the findings obtained in this study. Enterobacteriaceae as harmful bacteria, whose abnormal elevation may damage the intestinal barrier and increase intestinal permeability by stimulating the secretion of pro-inflammatory cytokines, directly interfering with the tight junctions of epithelial cells, or triggering neutrophils to migrate through the intestinal epithelium(31). Taken together, the dominant Prevotella bacteria and the abnormally elevated Enterobacteriaceae are both Gram-negative bacteria, whose lipopolysaccharide could enter circulation through the damaged intestinal mucosa, promoting the development of metabolic endotoxemia and inflammatory responses(32). In summary, in the existence of abnormally elevated Enterobacteriaceae, high levels of AKK showed adverse health effects, which may explain the opposite effect of AKK in the two enterotypes.

In this study, isobutyrate, as by-products of protein degradation, was markedly higher in patients with the Prevotella enterotype. Healthy individuals displayed a lower amount of isobutyrate compared to colorectal cancer patients(33). In our daily clinical practice, we found that patients with the Prevotella enterotype, the harmful bacteria was significantly increased after taking protein powder. However, the harmful bacteria decreased after stopping taking protein powder for a period of time. Because of the diet preference of high carbohydrate and fiber, people with the Prevotella enterotype could not digest protein easily. Hence, a high-protein diet for a long time might be one of the predispositions to DOR in people with the Prevotella enterotype through producing overmuch isobutyrate. However, after identification of enterotypes for patients, different food supplementation was offered. Enterobacteriaceae and isobutyrate decreased in patients with the Prevotella enterotype, and Bifidobacteria increased in patients with Bacteroides enterotype. The AFC of six patients was significantly higher than before and entered the hyperstimulation cycle after the intervention.

This study revealed the connection between gut microbiota and DOR and helped to establish the subsequent intervention plan. At present, most studies on gut microbiota used 16S rDNA sequencing technology, whereas this study used qPCR technology. Compared with 16S rDNA technology, qPCR technology could not accurately determine the microbiota to the species level. qPCR technology is more suitable for clinical use because of its convenience and low price. It can also be quantitative, which can be used for judging the process of disease and evaluating the clinical intervention effect. There are still some limitations in this study: (1) Sample taken from a rectal swab can also be different from a stool sample and (2) Small sample size. And although diet is the main determinant of gut microbiota, the onset of DOR is a combination of gene, environment and lifestyle. Therefore diet is also an important part and needs to be taken into account, there is no need to strip away the effects of diet. Despite the above limitations, this study revealed the potential relationship between DOR and gut microbiota, and different intervention plans were designed for different enterotypes with significant effects. Further large-scale prospective cohort studies will provide increased insights into this problem. Further work is needed to assess the potential relationship and its mechanism.

In summary, we defined population with Prevotella/Bacteroides > 0.01 as Prevotella enterotype and found that Prevotella enterotype was closely related to DOR. AKK, Enterobacteria, Bifidobacteria, butyrate, and isobutyrate could be used as biomarkers to predict the ovarian reserve in two enterotypes. We have designed different interventions for two enterotypes and have demonstrated significant clinical effects. Patients with the Prevotella enterotype were given polysaccharide to reduce Enterobacteria and adjusted the form and amount of protein intake. Patients with the Bacteroides enterotype were given Bifidobacterium to increase beneficial bacteria. After the intervention, the number of AFC was significantly increased and ovarian reserve was significantly improved. More studies on whether the diet-gut microbiota-host metabolic axis could be manipulated to prevent the occurrence and development of disease should be performed in the future.

5.1 Participants

In the beginning, 80 people with ovarian dysfunction and 70 people due to male factors were enrolled at the Reproductive Medical Center of Shengjing Hospital of China Medical University from March to December 2021. However, female fertility problems are the results of the interaction of many diseases, so strict inclusion criteria was utilized excluding women with other reproductive endocrine diseases. Finally, 34 women with DOR and 30 normal ovarian reserve controls were recruited. The inclusion process is shown in Fig. 1 and the inclusion criteria were as follows: (1) age < 40 years, (2) duration of subfertility > 1 year, and (3) DOR diagnosis criteria including the following two or more items such as 25 IU/L > FSH > 10 IU/L, E2 > 80 pg/ml, AMH < 1.1 ng/ml and AFC ≤ 5 on day 2–3 of a spontaneous cycle. The exclusion criteria were shown as follows: (1) combine with other reproductive endocrine diseases, such as recurrent spontaneous abortion, repeated implanting failure and so on, (2) having used hormone therapy in the past three months, (3) antibiotics used in the past three months, (4) intake of probiotics or prebiotics in the last four weeks, (5) history of ovarian surgery or radiotherapy or chemotherapy, (6) combined with liver and kidney dysfunction, malignant tumors, immune system diseases and etc, (7) alcoholism (drinking more than five times a week, an average of two glasses of white wine, half a bottle of rice wine, or five bottles of beer); (8) severe mental illness in the past six months, (9) inflammatory bowel disease and other confirmed gastrointestinal diseases, (10) the investigator believed that it was not suitable for participants in this clinical trial. All participants were given written informed consent. The study was performed in accordance with relevant guidelines and approved by the ethical review committee of Shengjing Hospital of China Medical University.

5.2 Sampling

Blood samples were obtained from each patient during day second to day forth of menstrual cycle. The samples were sent to the laboratory on the same day for testing. FSH, E2, luteinizing hormone (LH), total testosterone, prolactin (PRL) and progestin were measured by chemiluminescence immunoassay. Serum AMH was measured using an enzyme immunoassay. All participants were instructed to use a stool sampling tube to collect stool samples. The fecal samples were stored at − 20°C and transported to the research center on dry ice within 24 hours of collection where they were stored at − 80°C until DNA was extracted.

5.3 AFC collection

The number of sinus follicles with a diameter of 2–9 mm, namely AFC, was measured by transvaginal ultrasonography on day 2–4 of the menstrual period by the same sonographer.

5.4 Anthropometric Parameters Measurements

Body weight measurements were performed using the Tsinghua Tong-fang Human Body Composition Analyzer with light clothing after eight hours of fasting. Height measurement was performed with a stadiometer. Body mass index (BMI) was calculated as body weight in kilograms divided by body height squared in meters.

5.5 Extracting bacterial DNA from the fecal samples

Bacterial DNA extraction in 64 individual fecal samples was performed using the TIANamp Stool DNA Kit (Tiangen biotech Beijing Co., LTD.) according to the manufacturer’s instructions.

5.6 Quantitative PCR amplification

The quantitative polymerase chain reaction (qPCR) experiment was conducted using Direct Detect Nine Family/Genus/Species Gut Microbes Detection Kit (PCR-Fluorescence Probe, Beijing Tiangen Technology Biochemical co., LTD.). Amplification program: 5min at 95°C, 45 cycles of 15 s at 95°C and 40 s at 56°C. Quantitative PCR was carried out using an ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, USA). Sequences of primers and TaqMan probes are provided in Additional file 1.

5.7 Determination of short-chain fatty acids in the faecal samples by gas chromatography

The amount of short-chain fatty acids in each stool sample was determined. A certain amount (0.8000 ± 0.010g) of feces was placed in a fecal sample box, treated with a HALO-F100 fecal treatment device (Hailu Biotechnology Co., Ltd., Suzhou, China), and a 10% fecal suspension was prepared. 2ml was extracted into a new 2ml sterilization centrifuge tube and centrifuged at 10,000 rpm, 4°C for 5 min (Thermo Fisher Scientific Co., Ltd., Massachusetts, America). The extracted supernatant was added to centrifuge tube and mixed well. 500µl of sample extract liquid was put into a 1.5mL centrifuge tube, and 100µl of short-chain fatty acid pretreatment solution was added, then the sample was frozen at − 30°C for 24h. The sample was centrifuged at 10,000rpm, 4°C for 3min after thawing, the supernatant was extracted and filtered with a 0.22µm filter. The 100 µL sample extract liquid was transferred to the tube in the gas phase sample bottle, covered tightly with the cap to remove the bubbles, then analyzed (Fuli 9720, Taizhou, China). The gas chromatography conditions were: the column was an FFAP elastic quartz capillary column (30 m × 0.25 mm × 0.25 µm), The column temperature program was 180°C (1 min). The heating rate was 20°C/min→ 220°C (1 min). The heating rate was 50°C/min. The carrier gas was high purity nitrogen with a purity ≥ 99.999%. The carrier gas flow rate was 2.5 mL/min (6.5min), then rising to 2.8mL/min (2min). The inlet temperature was 250°C. The injection mode was no split injection with an injection volume of 1.0 µL and the detector temperature (FID) was 250°C.

5.8 Intervention plan for DOR patients with different enterotypes

According to previous results, patients were identified as various enterotypes. Patients with the Prevotella enterotype were given 9.8 g polysaccharide daily for 14 days to reduce the number of Enterobacteriaceae. Combined with previous studies, patients with the Prevotella enterotype might have protein malabsorption. After 14 days, the amount and form of protein intake was adjusted and supplemented with 0.8-1.0g/kg/d protein, 90% of which was provided by polypeptides. They also took a DAGEs digestive enzyme with meals to improve the problem of protein malabsorption. It lowered the isobutyrate. Patients with Bacteroides enterotype were given Bifidobacteria quadruple live tablets three times a day, 1.5 g each time, under the condition of normal diet to increase the number of Bifidobacteria.

5.9 Statistical methods

The data in the table are expressed as means ± SD and were subjected to rigorous statistical analysis using two-tailed unpaired non-parametric Mann–Whitney analyses via SPSS 23.0 (IBM, Armonk, NY, USA). Sample parameter correlations and significance tests were constructed using Spearman analysis. Principal component analysis and canonical correlation analysis were performed on the qPCR sequencing results using MATLAB (v8.1.0.430) (MathWorks Inc., Northampton, NY, USA). Variation analysis was performed using PICRUSt2. Receiver operating characteristic (ROC) curves were generated with SPSS 23.0. The false discovery rate (FDR) analysis of the Benjamini and Hochberg method was applied to reduce the false positive rate for a large amount of data. A value of P < 0.05 were considered statistically significant. A value of P was represented as follows in the figures: ∗ <0.05; ∗∗ <0.01.

DOR diminished ovarian reserve;

qPCR quantitative Polymerase Chain Reaction;

SCFAs short-chain fatty acids;

AMH Anti-Mullerian Hormone;

FSH follicle-stimulating hormone;

E2 estradiol;

AFC antral follicle count;

LH luteinizing hormone;

PRL prolactin;

BMI body mass index;

ROC receiver operating characteristic;

FDR false discovery rate;

AKK Akkermansia muciniphila;

PCOS polycystic ovary syndrome;

IVF-ET in vitro fertilization-embryo transfer;

Acknowledgments

We are grateful to PhD Shuyi Jiang for antral follicle count collection and all the nurses at the Reproductive Medical Center of Shengjing Hospital of China Medical University for blood samples collection.

Funding

This study was supported by Shengjing Free Researcher Foundation of Shenjing hospital(NO: 201902), Science and Technology Plan Project of Shenyang(NO: 202054006), LY21C200007, LQ20C200010, 19-110-4-23 and 20-205-4-006. No competing interests were declared.

Author contribution

GY, JJ and XW are the principal researchers, take primary responsibility for the paper. GY, JJ, XZ and XW contributed to the conception, design and coordination of the research. SD, LF, NZ, WG and DL recruited the patients. GY, LM, WY and TF contributed to the collection and experimental processing. GY, JJ, SX and WL analyzed the data. GY, JJ, XZ and XW contributed to writing the manuscript. The final version was approved by all the authors.

Data Availability Statement

The datasets generated and analyzed during the current study are available in the figshare repository, 10.6084/m9.figshare.20980864.

Competing interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Consent for publication

Written informed consents for the publication of related indices had been obtained from the individuals.

Ethics approval and consent to participate

All procedures and sample collection methods were approved by the ethical review committee of Shengjing Hospital of China Medical University(2021PS016F). A written informed consent was obtained from all recruited individuals.

Greene AD, Patounakis G, Segars JH. Genetic associations with diminished ovarian reserve: a systematic review of the literature. J Assist Reprod Genet. 2014;31(8):935–46.
Silva CA, Yamakami LY, Aikawa NE, Araujo DB, Carvalho JF, Bonfá E. Autoimmune primary ovarian insufficiency. Autoimmun Rev. 2014;13(4–5):427–30.
Sasaki H, Kawamura K, Kawamura T, Odamaki T, Katsumata N, Xiao JZ, et al. Distinctive subpopulations of the intestinal microbiota are present in women with unexplained chronic anovulation. Reprod Biomed Online. 2019;38(4):570–8.
Gao L, Zhao F, Zhang Y, Wang W, Cao Q. Diminished ovarian reserve induced by chronic unpredictable stress in C57BL/6 mice. Gynecol Endocrinol. 2020;36(1):49–54.
Kelsey CM, Prescott S, McCulloch JA, Trinchieri G, Valladares TL, Dreisbach C, et al. Gut microbiota composition is associated with newborn functional brain connectivity and behavioral temperament. Brain Behav Immun. 2021;91:472–86.
Tetel MJ, de Vries GJ, Melcangi RC, Panzica G, O'Mahony SM. Steroids, stress and the gut microbiome-brain axis. J Neuroendocrinol. 2018;30(2).
Vuotto C, Battistini L, Caltagirone C, Borsellino G. Gut Microbiota and Disorders of the Central Nervous System. Neuroscientist. 2020;26(5–6):487–502.
Zou W, Wang Z, Xia J, Yang J. Retinol-binding protein 4 (RBP4) and high sensitivity C-reactive protein (hs-CRP) levels in patients with diminished ovarian reserve (DOR): a cross-sectional study. Reprod Biol Endocrinol. 2020;18(1):111.
Snider AP, Wood JR. Obesity induces ovarian inflammation and reduces oocyte quality. Reproduction. 2019;158(3):R79-r90.
Vasquez EC, Pereira TMC, Peotta VA, Baldo MP, Campos-Toimil M. Probiotics as Beneficial Dietary Supplements to Prevent and Treat Cardiovascular Diseases: Uncovering Their Impact on Oxidative Stress. Oxid Med Cell Longev. 2019;2019:3086270.
Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174–80.
Wang J, Li W, Wang C, Wang L, He T, Hu H, et al. Enterotype Bacteroides Is Associated with a High Risk in Patients with Diabetes: A Pilot Study. J Diabetes Res. 2020;2020:6047145.
Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334(6052):105–8.
Buford TW. (Dis)Trust your gut: the gut microbiome in age-related inflammation, health, and disease. Microbiome. 2017;5(1):80.
Fan Y, Chang Y, Wei L, Chen J, Li J, Goldsmith S, et al. Apoptosis of mural granulosa cells is increased in women with diminished ovarian reserve. J Assist Reprod Genet. 2019;36(6):1225–35.
Wang TT, Ke ZH, Song Y, Chen LT, Chen XJ, Feng C, et al. Identification of a mutation in GDF9 as a novel cause of diminished ovarian reserve in young women. Hum Reprod. 2013;28(9):2473–81.
Huang Y, Hu C, Ye H, Luo R, Fu X, Li X, et al. Inflamm-Aging: A New Mechanism Affecting Premature Ovarian Insufficiency. J Immunol Res. 2019;2019:8069898.
Sun S, Chen H, Zheng X, Ma C, Yue R. Analysis on the level of IL-6, IL-21, AMH in patients with auto-immunity premature ovarian failure and study of correlation. Exp Ther Med. 2018;16(4):3395–8.
He Y, Wang Q, Li X, Wang G, Zhao J, Zhang H, et al. Lactic acid bacteria alleviate polycystic ovarian syndrome by regulating sex hormone related gut microbiota. Food Funct. 2020;11(6):5192–204.
Canfora EE, Jocken JW, Blaak EE. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 2015;11(10):577–91.
Zhang J, Sun Z, Jiang S, Bai X, Ma C, Peng Q, et al. Probiotic Bifidobacterium lactis V9 Regulates the Secretion of Sex Hormones in Polycystic Ovary Syndrome Patients through the Gut-Brain Axis. mSystems. 2019;4(2).
Wang J, Liu S, Wang H, Xiao S, Li C, Li Y, et al. Xanthophyllomyces dendrorhous-Derived Astaxanthin Regulates Lipid Metabolism and Gut Microbiota in Obese Mice Induced by A High-Fat Diet. Mar Drugs. 2019;17(6).
Robertson RC, Kaliannan K, Strain CR, Ross RP, Stanton C, Kang JX. Maternal omega-3 fatty acids regulate offspring obesity through persistent modulation of gut microbiota. Microbiome. 2018;6(1):95.
Yang C, Fujita Y, Ren Q, Ma M, Dong C, Hashimoto K. Bifidobacterium in the gut microbiota confer resilience to chronic social defeat stress in mice. Sci Rep. 2017;7:45942.
Sanctuary MR, Kain JN, Chen SY, Kalanetra K, Lemay DG, Rose DR, et al. Pilot study of probiotic/colostrum supplementation on gut function in children with autism and gastrointestinal symptoms. PLoS ONE. 2019;14(1):e0210064.
Mrozinska S, Kapusta P, Gosiewski T, Sroka-Oleksiak A, Ludwig-Słomczyńska AH, Matejko B, et al. The Gut Microbiota Profile According to Glycemic Control in Type 1 Diabetes Patients Treated with Personal Insulin Pumps. Microorganisms. 2021;9(1).
Wang HB, Wang PY, Wang X, Wan YL, Liu YC. Butyrate enhances intestinal epithelial barrier function via up-regulation of tight junction protein Claudin-1 transcription. Dig Dis Sci. 2012;57(12):3126–35.
Morrison DJ, Preston T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes. 2016;7(3):189–200.
Liu H, Wang J, He T, Becker S, Zhang G, Li D, et al. Butyrate: A Double-Edged Sword for Health? Adv Nutr. 2018;9(1):21–9.
Gómez-Gallego C, Pohl S, Salminen S, De Vos WM, Kneifel W. Akkermansia muciniphila: a novel functional microbe with probiotic properties. Benef Microbes. 2016;7(4):571–84.
Pawłowska B, Sobieszczańska BM. Intestinal epithelial barrier: The target for pathogenic Escherichia coli. Adv Clin Exp Med. 2017;26(9):1437–45.
Wu S, Hu R, Nakano H, Chen K, Liu M, He X, et al. Modulation of Gut Microbiota by Lonicera caerulea L. Berry Polyphenols in a Mouse Model of Fatty Liver Induced by High Fat Diet. Molecules. 2018;23(12).
Niccolai E, Baldi S, Ricci F, Russo E, Nannini G, Menicatti M, et al. Evaluation and comparison of short chain fatty acids composition in gut diseases. World J Gastroenterol. 2019;25(36):5543–58.

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Effect of various types of gut microbiota in patients on the diminished ovarian reserve

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Abstract

Background

Methods

Results

Conclusions

Figures

1 Background

2 Results

2.1 Clinical parameters characteristic of participants

2.2 Alternation of gut microbiota and clinical indices in patients with DOR

2.3 Determination of enterotypes according to the P/B ratio

2.4 Correlation of AMH, SCFAs and gut microbiota in DOR patients according to enterotype

2.5 Intervention results for DOR patients with different enterotypes

3 Discussion

4 Conclusion

5 Methods

5.1 Participants

5.2 Sampling

5.3 AFC collection

5.4 Anthropometric Parameters Measurements

5.5 Extracting bacterial DNA from the fecal samples

5.6 Quantitative PCR amplification

5.7 Determination of short-chain fatty acids in the faecal samples by gas chromatography

5.8 Intervention plan for DOR patients with different enterotypes

5.9 Statistical methods

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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