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. 2024 Jan 16;17(1):e14403. doi: 10.1111/1751-7915.14403

Gut microbiota composition may be an indicator of erectile dysfunction

Yu Qiao 1,2, Jianhuai Chen 1, Yongsheng Jiang 1, Ziheng Zhang 1, Heng Wang 1, Tao Liu 1, Zhaoxu Yang 1, Guangbo Fu 3,✉, Yun Chen 1,✉
PMCID: PMC10832513  PMID: 38226944

Abstract

Erectile Dysfunction (ED) is considered a physical and mental illness. A variety of potential associations between gut microbiota and health or disease have been found. By comparing the gut microbiota of healthy controls and ED patients, our study investigated the relationship between ED and gut microbiota. The results revealed that the ED group exhibited a significantly higher relative abundance of Bacteroides, Fusobacterium, Lachnoclostridium, Escherichia‐Shigella and Megamonas, while showing a significantly lower relative abundance of Bifidobacterium compared to the control group. The dysbiosis of gut microbiota played a role in the onset and progression of ED by influencing the gut barrier, cardiovascular system and mental health, which provided a novel perspective on understanding the pathophysiology of ED. What is more, we had identified several key gut microbiota. By combining 16S rRNA sequencing with machine learning techniques, we were able to uncover the significant value and impact of gut microbiota in the early detection of ED.

Our study investigated the potential relationship between ED and gut microbiota. By combining 16S rRNA sequencing with machine learning techniques, we were able to uncover the significant value and impact of gut microbiota in the early detection of ED.

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INTRODUCTION

Sexual dysfunction (SD) is a disorder that affects sexual behaviour and sensation, resulting in abnormal or insufficient responses to sexual stimuli. Research suggests that approximately 52% of men between the ages of 40 and 70 experience some form of SD (Cho & Duffy, 2019). One of the most common types of male SD is ED, which is defined as the inability to achieve and maintain a sufficient penile erection for satisfactory sexual intercourse (Salonia et al., 2021). ED can be categorized into psychological ED, organic ED and mixed ED based on its aetiology. From both psychological and physiological perspectives, ED is considered a physical and mental illness. It can have detrimental effects on men's self‐esteem, self‐confidence, career development and interpersonal communication. Additionally, it can lead to infertility and disrupt the emotional harmony between spouses. There are various risk factors that can negatively impact male sexual function, including old age, obesity, smoking, physical inactivity, high blood pressure, cardiovascular disease (CVD), diabetes, hyperlipidaemia, metabolic syndrome, anxiety and depression (Geng et al., 2021; Russo et al., 2023). The International Index of Erectile Function‐5 (IIEF‐5) is a widely used self‐report screening tool for assessing male erectile function (Rosen et al., 1999). It has been proven to have high sensitivity and is recommended as the primary test for evaluating the severity of ED (Rhoden et al., 2002).

The human gut microbiota, considered as the largest ecosystem in the human body, is composed of a microbial community. Functioning as a ‘microbial organ’, the gut microbiota plays a crucial role in maintaining homeostasis, which is closely linked to both health and disease (Zhang et al., 2023). Dysbiosis refers to a change in the composition and function of the microbiota in individuals with a disease, as compared to healthy individuals. This change may involve a decrease in beneficial microorganisms and an increase in potentially harmful microbes (Tiffany & Bäumler, 2019). Numerous diseases, including inflammatory bowel disease, arthritis, cancer, neuropsychiatric disorders, cardiovascular disease, obesity, type 2 diabetes and infertility, have been linked to the gut microbiota (Badran et al., 2020; Fabozzi et al., 2022; Papadopoulos et al., 2022; Sonali et al., 2022; Zhou et al., 2020). Inflammatory response, cardiovascular disease and psychological disorders are significant causes and risk factors for ED among the diseases associated with the gut microbiota (Geng et al., 2021; Russo et al., 2023). Therefore, we hypothesize that there is a correlation between gut microbiota and the occurrence and development of ED. This study aims to explore this correlation by comparing the gut microbiota of healthy controls and ED patients using clinical data. Additionally, the study also investigates gut microbiota profiles as a potential diagnostic tool for preventing ED.

EXPERIMENTAL PROCEDURES

Recruitment of study subjects

We recruited a total of 53 male patients diagnosed with ED from the male department of the Affiliated Hospital of Nanjing University of Chinese Medicine and the department of reproductive centre of the Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University in 2023. In addition, we enrolled 32 men with normal sexual function as controls. The study was reviewed and approved by the Ethics Committees of the Affiliated Hospital of Nanjing University of Chinese Medicine and the Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University. All participants provided informed consent by signing consent forms. Demographic and clinical information was collected through questionnaires and electronic medical records. The participants underwent evaluations using the BDI, BAI and IIEF‐5 assessment tools. Serum and faecal samples were collected from all participants for analysis. The study's inclusion criteria for participants were as follows: ① individuals who had completed middle school or higher education; ② individuals who identified as heterosexual and had engaged in regular sexual activity with a single, stable partner for at least six months; ③ individuals with normal external genital development. Participants who met any of the following criteria were excluded from the study: ① individuals who had received antibiotics, probiotics, hormones, insulin, proton pump inhibitors or traditional Chinese medicine treatment within the past three months; ② individuals who had undergone radiotherapy, chemotherapy, gastrointestinal surgery or gastroenteroscopy within the past three months; ③ individuals who had autoimmune diseases, malignant tumours, chronic diarrhoea or constipation.

Determination of ED

The IIEF‐5 was used to evaluate the severity of ED. A score above 21 indicated normal erectile function, while a score at or below this cutoff indicated the presence of ED. The diagnosis was confirmed by at least two attending physicians.

Clinical characteristics statistical analysis

In this study, we utilized SPSS23.0 statistical software to process and analyse our data. To ensure accuracy, we conducted a normality test for each group of continuous variables. For normally distributed measurement data, we presented the results as X ± S and used the t‐test to compare between two groups. The measurement data of non‐normal distribution were presented using M (P25, P75). To compare between two groups, the non‐parametric Mann–Whitney U‐test was utilized. Categorical data were presented as frequencies. Proportions were compared using either χ 2 or Fisher exact tests. A p value of less than 0.05 was considered statistically significant.

Faecal sample collection

The study collected faecal samples from all participants using a sterile plastic spoon and stored them in sterile plastic tubes. The samples were promptly placed in a −20°C refrigerator and then transferred to a −80°C refrigerator within 24 h for preservation.

DNA extraction and PCR amplification

To obtain the total DNA of the gut microbiota from faecal samples, we used the E.Z.N.A.® stool DNA kit (Omega Bio‐tek, Norcross, GA, U.S.) following the manufacturer's instructions. We amplified the V3‐V4 variable regions of the bacterial 16S rRNA gene using primer pairs 338F (5′‐ACTCCTACGGGAGGCAGCAG‐3′) and 806R (5′‐GGACTACHVGGGTWTCTAAT‐3′) with an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The PCR cycling parameters used in this study were as follows: an initial denaturation step at 95°C for 3 min, followed by 27 cycles consisting of denaturation at 95°C for 30 s, annealing at 55°C for 30 s and extension at 72°C for 45 s. We performed a single extension step at 72°C for 10 min, followed by cooling to 4°C. The amplification mixture volume was 20 μL, containing 4 μL of 5 × FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of Forward Primer (5 μM), 0.8 μL of Reverse Primer (5 μM), 0.4 μL of FastPfu Polymerase, 0.2 μL of BSA and 10 ng of Template DNA.

Sequencing processing

Paired‐end sequencing was conducted using the Illumina Miseq PE300 platform (Illumina, San Diego, USA). Raw data underwent screening, with sequences shorter than 200 bp, low quality scores (≤20), ambiguous bases or those not matching primer sequences and barcode tags being removed. The qualified PE reads obtained from Illumina sequencing were merged using FLASH (version 1.2.7) based on their overlapping relationship. The sequences were grouped into operational taxonomic units (OTUs) based on a 97% similarity threshold and compared to the Silva database (v138).

Bioinformatics analysis

To assess the α‐diversity, we utilized the Shannon index, Ace index, Chao index and Simpson index. For the examination of β‐diversity, we employed principal coordinates analysis (PCoA) and non‐metric multidimensional scaling (NMDS). The difference between the two groups on the phylum and genus level was explored using the Wilcoxon rank‐sum test. To identify differentially abundant bacteria between the two groups, we employed the linear discriminant analysis (LDA) effect size (LEfSe) with an LDA threshold of 3.5. We analysed the correlation between the serum level of IL‐6 and the total scores for IIEF‐5, beck depression inventory (BDI) and beck anxiety inventory (BAI). We utilized a Spearman correlation heatmap to illustrate the connection between gut microbiota and clinical indicators.

Random forest classifier construction

The classification model was constructed using the random forest method in the randomForest R package. The entire cohort was randomly divided into a training cohort and a validation cohort. We identified the variables that showed the top 15 highest accuracy decreases between these two groups. The prediction performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve and the area under curve (AUC) was calculated.

RESULTS

Participant's demographic and clinical data

There were no significant differences in demographic characteristics, such as age, body mass index (BMI), hypertension and diabetes mellitus, between the control group and ED group, as shown in Table S1. Additionally, we compared the serum levels of sex hormones (T and prolactin (PRL)) and biochemical indicators between the two groups. The serum levels of PRL, aspartate transaminase (AST), alanine aminotransferase (ALT), ureophil (UREA), creatinine (CREA), total cholesterol (TC), triglyceride (TG), Interleukin‐1β (IL‐1β), Tumour necrosis factor‐α (TNF‐α), Trimetlylamine oxide (TMAO) and 5‐Hydroxytryptamine (5‐HT) showed no significant differences between the two groups. However, the control group had a higher serum level of T compared to the ED group, while the level of IL‐6 and the uric acid (UA) in the control group were lower than those in the ED group (Table S1). In Table S1, it was found that the total scores for BDI and BAI were significantly higher in the ED group compared to the control group. On the other hand, the total score for IIEF‐5 was significantly lower in the ED group compared to the control group.

Gut microbial composition, α‐diversity and β‐diversity between the control group and ED group

A Venn diagram was used to display the overlapping OTU data between the two groups. The analysis indicated that 619 OTUs were shared by all samples. Specifically, approximately 140 OTUs were identified in the samples of the ED group, while around 56 OTUs were found in the samples of the control group (Figure 1A).

FIGURE 1.

FIGURE 1

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Comparison of α‐diversity and β‐diversity between the control group and ED group. (A) The Venn diagram illustrated the overlap of OTUs in gut microbiota between the two groups. (B–E) Shannon index was higher in the ED group compared with the control group, p = 0.04082. There were no significant difference in Ace index, Chao index and Simpson index between the two groups. (F,G) PCoA and NMDS showed that the distribution of the microbial community in the control group was different from that of the ED group. (p = 0.009, p = 0.009, respectively). ED, erectile dysfunction; NMDS, non‐metric multidimensional scaling; OTUs, operational taxonomic units; PCoA, principal coordinates analysis.

The alpha diversity of the gut microbiota composition was analysed using the Shannon index, Ace index, Chao index and Simpson index. The results were presented in Figure 1B–E. Our findings indicated that the Shannon index was higher in the ED group compared with the control group (p = 0.04082).

The indices of β‐diversity was displayed in Figure 1F,G. To visualize multidimensional data based on Bray–Curtis distance and obtain principal coordinates, a PCoA was conducted. The results of the PCoA indicated a significant difference in bacterial communities between the control group and ED group. NMDS also showed that the distribution of the microbial community in the control group was different from that of the ED group.

Figure 2A illustrated the relative abundance of the major gut microbiota on phylum level of the two groups. The results revealed significant differences between the two groups. Specifically, the ED group had a significantly higher relative abundance of Bacteroidota, Proteobacteria, Fusobacteriota, Desulfobacterota and Verrucomicrobiota compared to the control group, as shown in Figure 2B.

FIGURE 2.

FIGURE 2

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Comparisons of the relative abundances of the major gut microbiota between the control group and ED group. (A) The relative abundance of gut microbiota on phylum level was different between two groups. (B) The column chart of top 6 species with significant difference on phylum level. The X‐axis of the left part represented the average relative abundance of a species in the two groups, the Y‐axis of the left part represented the top 6 species with significant difference on phylum level in the two groups and the right part represented the confidence interval and p value. (C) The relative abundance of gut microbiota on genus level was different between two groups. (D) The X‐axis of the left part represented the average relative abundance of a species in the two groups, the Y‐axis of the left part represented the top 10 species with significant difference on genus level in the two groups and the right part represented the confidence interval and p value. (E) Histogram of the LDA scores for differentially abundant species between the two groups. The LDA scores >3.5 were listed. (F) Cladogram showing the most differentially abundant bacterial taxa and their relationship identified by LEfSe from the phylum to the genus level between the two groups. Red bars indicated taxa were enrichment in the control group, while blue bars indicated taxa were enrichment in the ED group. *p < 0.05; **p < 0.01. ED, erectile dysfunction; LDA, linear discriminant analysis; LEfSe, linear discriminant analysis effect size.

Figure 2C shows the relative abundance of the major gut microbiota on genus level of the two groups. It was found that the control group had a significantly higher relative abundance of Bifidobacterium compared to the ED group. Additionally, the control group showed a significant decrease in the relative abundance of various bacteria, including Bacteroides, Escherichia‐Shigella, Lachnoclostridium, Roseburia, Megamonas, unclassified_f__Lachnospiraceae, Coprococcus, Anaerostipes, Butyricicoccus, Fusobacterium, Eubacterium_ventriosum_group, CAG‐56, Parabacteroides and norank_f__Ruminococcaceae, compared to the ED group (Figure 2D).

To identify the bacterial taxa and predominant bacteria associated with ED, we conducted LEfSe analysis. Our findings revealed that the ED group exhibited an increase in the relative abundance of Bacteroides, Megamonas, Escherichia‐Shigella, Lachnoclostridium, Roseburia, Fusobacterium and Coprococcus when compared to the control group, as depicted in Figure 2E,F.

Associations of the serum level of IL‐6 and gut microbiota with clinical indicators

The serum level of IL‐6 showed a negative correlation with the total score of IIEF‐5, but a positive correlation with the total scores of BDI and BAI. The correlation with BDI was found to be statistically significant (Figure 3A–C).

FIGURE 3.

FIGURE 3

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Associations of the serum level of IL‐6 and gut microbiota with clinical indicators. (A–C) The correlation between the serum level of IL‐6 and the total scores for IIEF‐5, BDI and BAI. (D) The spearman correlation heatmap between clinical indicators and the top 40 genus level of gut microbiota in relative abundance.*p < 0.05; **p < 0.01; ***p < 0.001. BAI, beck anxiety inventory; BDI, beck depression inventory; IIEF‐5, international index of erectile function‐5; IL‐6, interleukin‐6.

To investigate the relationship between the gut microbiota and clinical indicators such as sex hormone levels, biochemical indexes and scale scores, we conducted a spearman correlation heatmap analysis on genus level (Figure 3D).

The analysis revealed that the serum levels of IL‐1β and IL‐6 were negatively correlated with the relative abundance of Bifidobacterium, while positively correlated with the relative abundance of Bacteroides. Furthermore, there was a positive correlation between the serum TMAO level and the relative abundance of Lachnoclostridium and Bacteroides. Additionally, the total score of BDI showed a statistically significant negative association with the relative abundance of Bifidobacterium, while it was positively correlated with the relative abundance of Bacteroides, Escherichia‐Shigella, Lachnoclostridium and Megamonas.

Diagnostic potential of ED based on the gut microbial markers

We utilized the random forest algorithm to develop a response‐prediction classifier for patients with ED. To create a training dataset, we employed 10‐fold cross‐validation to select specific genera markers. The random forest classifier was then trained by determining the feature importance of 15 genera, which were ranked based on their contribution to the model. The significant genera identified by the model were described using the mean decrease accuracy (Figure 4A). The AUC obtained from the training datasets was 0.719 (95% confidence interval (CI), 0.509–0.929) (Figure 4B). In the test set, the AUC was 1.000 (95% CI, 1.000–1.000) (Figure 4C), indicating a successful distinction between patients with ED and healthy individuals.

FIGURE 4.

FIGURE 4

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The gut microbiota classifier for ED. Establishment of a predictive model for diagnosing ED based on the gut microbiota profiles. (A) The bacterial genera that could significantly discriminate between the control group and the ED group were presented in descending order. (B) The ROC curve for the training cohort based on the random forest classifier constructed by microbial variables only. The AUC was 71.9% (95% CI, 50.9%–92.9%). (C) The ROC curve for the test cohort based on the random forest classifier constructed by microbial variables only. The AUC was 100% (95% CI, 100%–100%). AUC, area under curve; CI, confidence interval; ED, erectile dysfunction; ROC, receiver operating characteristic.

DISCUSSION

We observed differences in the gut microbiota between the control group and the ED group. The Venn diagram revealed unique taxa in each group, highlighting distinct gut microbiota. Our findings demonstrated that the α‐diversity (Shannon index) was higher in the ED group compared to the control group, contradicting a previous study (Geng et al., 2021). This could be attributed to a decrease in beneficial bacteria and an increase in potentially harmful bacteria. Furthermore, our study revealed differences in the microbial community structure (β‐diversity) between the two groups, indicating a clear separation between the control and ED groups. LEfSe analysis showed a disruption in the intestinal microecology of ED patients. Specifically, the relative abundance of Bifidobacterium was higher in the control group, while the ED group exhibited higher relative abundance of Bacteroides, Megamonas, Escherichia‐Shigella, Lachnoclostridium, Roseburia, Fusobacterium and Coprococcus.

The gut barrier serves as protection against harmful substances and microorganisms in the gut, which is crucial for maintaining human health (Martel et al., 2022). Normal gut microbiota and their metabolites play a vital role in preserving intestinal integrity and permeability (Papadopoulos et al., 2022). An imbalance in the intestinal microecology can lead to damage to the gut barrier, allowing toxins like lipopolysaccharide (LPS) to leak into the circulatory system. This leakage triggers systemic inflammation by recruiting monocytes and activating Toll‐like receptors (Badran et al., 2020; Sun et al., 2016). The inflammatory response is considered one of the pathogenic mechanisms of ED (Vlachopoulos et al., 2007). In this study, we compared the levels of inflammatory cytokines between two groups. We observed that IL‐6 levels were notably higher in the ED group compared to the control group. Furthermore, the correlation analysis indicated a negative association between the serum level of IL‐6 and the total score for IIEF‐5. Additionally, the levels of IL‐1β and TNF‐α were also higher in the ED group, although the difference was not statistically significant. IL‐6 was directly associated with cytokine storms and was elevated during endothelial cell dysfunction. IL‐6 could serve as a marker of ED (Sivritepe et al., 2022). The serum levels of IL‐1β and IL‐6 were found to have a negative correlation with the relative abundance of Bifidobacterium, while having a positive correlation with the relative abundance of Bacteroides. Bacteroides, which is an opportunistic pathogen, showed a shift from the gut to cause inflammation and bacteraemia when the gut barrier was compromised (Lim & Shin, 2020). Fusobacterium has also been identified as an opportunistic pathogen that can cause opportunistic infections and induce the expression of pro‐inflammatory cytokines (Richarte et al., 2021). On the other hand, Bifidobacterium has the ability to regulate the immune system by inhibiting pro‐inflammatory cytokines like IL‐6 and inducing anti‐inflammatory cytokines such as IL‐10, thereby playing a role in reducing inflammation and preventing infections (Brennan & Garrett, 2019). Short chain fatty acids (SCFAs) are produced through the fermentation of dietary fibre by gut microbiota, which includes acetic acid, propionic acid, butyric acid and valerate. SCFAs play a vital role in regulating blood pressure, preventing fat accumulation, providing anti‐inflammatory effects and maintaining the integrity of the gut barrier (Badran et al., 2020; Papadopoulos et al., 2022). It is important to note that gut microbiota dysbiosis can lead to a decrease in SCFA‐producing bacteria. Bifidobacterium is known to be involved in the production of SCFAs (Abdi et al., 2022).

CVD has been widely recognized as a cause of ED. An imbalance in gut microbiota has also been linked to the development and progression of CVD (Cai et al., 2022; Papadopoulos et al., 2022). One specific metabolite derived from the gut microbiota, known as TMAO, has been found to play a significant role in the formation of atherosclerotic plaque, inflammation in the vascular wall, generation of reactive oxygen species and inhibition of cholesterol reverse transport (Sun et al., 2016). Increased levels of TMAO have been identified as an independent risk factor for atherosclerosis and have been positively associated with the occurrence and progression of CVD (Cai et al., 2022; Chen et al., 2021). The elevation of serum TMAO levels can lead to vascular inflammation and damage to the endothelium and smooth muscle cells of the cavernosa, ultimately contributing to the development of ED (Wang & Xie, 2022). Interestingly, it has been observed that Lachnoclostridium efficiently converts choline to TMA, which serves as a precursor to TMAO. Furthermore, Lachnoclostridium has been found to be more abundant in patients with atherosclerosis compared to individuals without the condition (Cai et al., 2022). Bacteroides has been identified as a potential pathogen of CVD (Liu et al., 2019). Additionally, a positive correlation has been observed between Bacteroides and plasma TMA (Wang et al., 2022). The abundance of Bacteroides has been found to increase in patients with a high‐fat diet or diabetes (Qin et al., 2012; Wan et al., 2019). In our study, we also discovered a positive correlation between the serum TMAO level and the relative abundance of Lachnoclostridium and Bacteroides.

The gut microbiota can communicate with the brain through various pathways, such as neural, immune and metabolic pathways. This communication is referred to as the microbiota‐gut‐brain axis. The axis plays a crucial role in influencing the cognitive, mood and behavioural functions of the host. Additionally, it may contribute to the development of psychosomatic diseases. Furthermore, psychological factors may also play a role in ED. To assess the levels of depression and anxiety, the BDI and BAI scales are commonly used. Higher scores on these scales indicate more severe depression and anxiety. The study revealed that the total scores of BDI and BAI were significantly higher in the ED group compared to the control group (p < 0.05). Additionally, a negative correlation was found between the total BDI score and the relative abundance of Bifidobacterium. Depressed patients exhibited lower levels of Bifidobacterium, which is essential for synthesizing riboflavin, nicotine and folate. Previous studies have shown a negative correlation between folate levels and the severity of depressive symptoms (Barandouzi et al., 2020; Beydoun et al., 2010; Kwak et al., 2016). Research has indicated an increased relative abundance of Bacteroides in male patients with major depressive disorder (Chen et al., 2018). Other studies have also demonstrated that the depressive group had higher levels of Bacteroides, Escherichia‐Shigella and Lachnoclostridium compared to non‐depressive individuals (Liang et al., 2023; Radjabzadeh et al., 2022). Moreover, a higher level of Megamonas has been observed in some psychiatric conditions such as attention‐deficit/hyperactivity disorder, autism spectrum disorder and depression (Zafar & Saier, 2021; Zou et al., 2020). Our study also found a positive correlation between the total BDI score and the relative abundance of Bacteroides, Escherichia‐Shigella, Lachnoclostridium and Megamonas.

Previous studies have shown a negative correlation between Lactobacillus and Bifidobacterium with ED, while Actinomyces has been found to be positively correlated with ED (Andersen et al., 2023; Kang et al., 2023). In our study, we observed more differences in the relative abundance of various microbiota, but no differences were found in the relative abundance of Lactobacillus and Actinomyces between ED patients and healthy individuals. This discrepancy may be attributed to variations in race, diet, geographic location and individual differences.

Although there are various causes of ED, the diagnosis and treatment methods are currently limited. It is important to note that there is constant bidirectional communication between the gut microbiota and the host. The gut microbiome can both trigger host health problems and react to them (Turjeman & Koren, 2022; Zhang et al., 2023). Compared to traditional examinations, analysing the gut microbiota provides a more comprehensive and non‐invasive approach to assessing the health status of patients with ED, offering significant advantages and promising prospects. Our study developed a predictive classifier for diagnosing ED based on gut microbiota profiles. We found that 15 genera were identified as predictors for the development of ED, with an AUC value of 71.9% in the training cohort and 100% in the validation cohort. Our results suggested that gut microbial markers had the potential to predict the development of ED and could be utilized as effective tools for preventing ED. On the other hand, regulating gut microbiota may be a potential approach for treating or improving ED. One possible method is to extract the functional flora from the faeces of men with normal erectile function and transplant them into the intestines of ED patients. This can be done through colonoscopy or oral capsules after mating culture. By doing so, it is possible to restore the intestinal microecosystem of patients and potentially provide therapeutic benefits.

Several limitations of our study should be acknowledged. Firstly, the sample size was small, and further validation of the research findings is necessary using a larger multicentre sample. Secondly, we did not detect certain intestinal microbial metabolites such as SCFAs, tryptophan‐derived metabolites and bile acids. Future experiments should be conducted to address this gap. Thirdly, it is recommended to conduct animal experiments on faecal microbiota transplantation (FMT) to further investigate the role of gut microbiota in the onset and progression of ED.

CONCLUSIONS

Our study had revealed a potential link between gut microbiota and ED. The dysbiosis of gut microbiota played a role in the onset and progression of ED by influencing the gut barrier, cardiovascular system and mental health. In this study, we had identified several key gut microbiota that were related to ED, providing new insights and a scientific foundation for the diagnosis and treatment of patients with ED. In the future, enhancing the intestinal microecology could potentially serve as a novel approach to both prevent and treat ED.

AUTHOR CONTRIBUTIONS

Yu Qiao: Conceptualization (equal); methodology (equal); project administration (equal); writing – original draft (equal); writing – review and editing (equal). Jianhuai Chen: Data curation (equal); project administration (equal). Yongsheng Jiang: Data curation (supporting). Ziheng Zhang: Writing – review and editing (equal). Heng Wang: Writing – review and editing (equal). Tao Liu: Writing – review and editing (equal). Zhaoxu Yang: Writing – review and editing (equal). Guangbo Fu: Supervision (equal). Yun Chen: Funding acquisition (lead); supervision (equal).

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interest that pertain to this work.

Supporting information

Table S1. Clinical characteristics of enrolled patients and healthy controls.

Click here for additional data file. (17.3KB, docx)

ACKNOWLEDGEMENTS

The authors funding from Jiangsu Province Hospital of Chinese Medicine Project (Y21008), Key project of Jiangsu Provincial Health Commission (ZDA2020025), National Natural Science Foundation of China (81871154), Natural Science Foundation of Nanjing University of Chinese Medicine (XZR2020003), Special Project of Innovation and Development Fund of Jiangsu Province Hospital of Chinese Medicine (Y2021CX24) for carrying out the research, Huaian Health Research Project (HAWJ202007).

Qiao, Y. , Chen, J. , Jiang, Y. , Zhang, Z. , Wang, H. , Liu, T. et al. (2024) Gut microbiota composition may be an indicator of erectile dysfunction. Microbial Biotechnology, 17, e14403. Available from: 10.1111/1751-7915.14403

Yu Qiao and Jianhuai Chen are the co‐first authors.

Contributor Information

Guangbo Fu, Email: fgb200@vip.163.com.

Yun Chen, Email: chenyunnju@163.com.

DATA AVAILABILITY STATEMENT

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA‐Human: HRA005526) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa‐human.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Clinical characteristics of enrolled patients and healthy controls.

Click here for additional data file. (17.3KB, docx)

Data Availability Statement

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA‐Human: HRA005526) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa‐human.

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