Unexplained infertility's determinants, correlation with bacterial vaginosis and anti-sperm antibodies among Congolese women: a case‒control study.

A retrospective, case‒control study was conducted at the Hôpital du Cinquantenaire de Kisangani in Kisangani city over 12 months, from August 2023 to July 2024. The centre was purposively selected due to its status as a provincial tertiary referral hospital that treats a large number of infertility cases. Additionally, it served as the referral hospital for women seeking medical assistance during the free gynaecological consultation campaign that began on 2 August and concluded on 2 October 2023. Since no previous studies—such as case-control, cohort, or analytical cross-sectional research—focusing on the key parameters of this study, namely antisperm antibodies and bacterial vaginosis in cases of unexplained infertility, had been conducted to determine the appropriate sample size, this pilot study was undertaken to collect initial experiences and data. This information can serve to design a more comprehensive future study with an estimated sample size. A total of 288 patients aged between 20 and 40 years participated during the study period. The case group comprised 144 women with unexplained infertility, selected through systematic random sampling from patients who had attempted to conceive for at least one year without success. Inclusion criteria required participants to have regular menstruation, a normal ovarian reserve test (10–15 Antral follicle counts in both ovaries during the first four days of the menstrual cycle, and 1–4 ng/ml of antimüllerian hormone), bilateral patent fallopian tubes, and a normal uterine cavity confirmed by hysterosalpingography. Hormonal assessments included antimüllerian hormone (AMH), prolactin, progesterone and thyroid hormones (T3 and T4) levels. AMH was measured between days 2 and 4 of the menstrual cycle, and progesterone on day 20. The timing of the AMH test was mainly for organisational reasons. Additionally, their partners underwent semen analysis with normal results [ 2 ]. The control group consisted of 144 fertile, non-pregnant women attending the postnatal consultation clinic for their child’s final vaccination, with the infants being scheduled for 15 months of age. All standard, approved infertility investigations were performed for case participants. In addition to the aforementioned paraclinical examinations conducted exclusively on case participants, further examinations were carried out on both case and control participants. These included: Vaginal swabs: For vaginal discharge, medical biologists confirmed that patients had not used antibiotics or vaginal medication in the past 14 days, or engaged in sexual intercourse or vaginal baths 24 h before collection. If any of these conditions were not met, collection was delayed. To ensure reliability [ 18 ], save time, and prevent patient distress, samples were collected without using a speculum. The swab was inserted deep into the vaginal cavity by a medical biotechnologist after parting the labia. Two vaginal swabs were taken: one for immediate examination and the other for Gram staining. The swabs for immediate testing were preserved with physiological serum in an isothermal cooler and sent for analysis of Candida albicans and Trichomonas vaginalis . The sample for Gram staining was applied to slides, heat fixed, placed in a slide holder at the patient’s consultation laboratory, and then sent to the main laboratory. A Gram-2 Kit (BioMérieux, Marcy-l’Etoile, France) was used for Gram staining, which involved four distinct steps. (a) The slides were stained with Gentian violet for one minute, then rinsed. (b) The slides were then treated with Lugol’s solution for one minute, then rinsed. (c) The slides were quickly flooded with a bleaching agent and alcohol for ten seconds, then rinsed. (d) The slides were counterstained with Fuchsine for one minute, then rinsed. The slides were examined under an optical microscope with a 100× magnification objective. The Nugent scoring system [ 19 ] was employed to diagnose bacterial vaginosis (BV) by scoring all Gram-stained slides. Five microscopic fields per slide were evaluated for the presence and quantity of Lactobacillus (gram-positive rods), Gardnerella vaginalis/Bacteroides (gram-variable coccobacilli), and Mobiluncus (gram-negative curved rods). The vaginal smear was classified based on the Nugent score, indicating a healthy vaginal microbiota (score 0–3), an intermediate microbiota (score 4–6), or BV (score 7–10). Two independent readers scored all slides in a single-blind manner. If there was a discrepancy in categorisation, the two reviewers re-evaluated the slide and discussed their findings. If consensus was not reached, a third reviewer was consulted to make the final decision. Antisperm Antibody (ASA): Two different tests were used to detect antisperm antibodies: a rapid serum test with Hightop ASA IgA and IgG Rapid Test (Qingdao HIGHTOP Biotech Co., Ltd., China) and a mixed antiglobulin reaction test (MAR test) with SpermMAr (FertiPro N.V., Conception Technology Inc.). The MAR test was performed if the serum test yielded a positive result. For the direct MAR test, partner’s sperm was used, while the indirect MAR test employed the patient’s serum. The tests were performed according to the manufacturer’s instructions. TORCH test: We used the Hightop TORCH lgM/lgG Rapid test (Qingdao HIGHTOP Biotech Co., Ltd., China) following the manufacturer’s instructions. Chlamydia test: We used the Hightop chlamydia IgM and IgG rapid test (Qingdao HIGHTOP Biotech Co., Ltd., China) following the manufacturer’s instructions. HIV and syphilis: In accordance with the guidelines of the national HIV programme, all patients were counselled and screened at the initiative of the healthcare provider following a clinical examination. The test was performed with the patient’s consent. The SD HIV/Syphilis Duo (Standard Diagnostics, Inc., Korea) was used to detect the presence of HIV and syphilis. If the Duo test returned a positive result for HIV, two additional tests were carried out to confirm the diagnosis. The Uni-Gold™ Recombigen ® HIV-1/2 (Trinity Biotech Plc, Ireland) and VIKIA ® HIV 1/2 (Biomérieux, France) tests were employed. Women were excluded from the study if they had any of the following medical conditions: hypertension, diabetes, endocrine disorders, autoimmune or immunocompromised conditions, a history of genetic disease, a history of sexually transmitted infections within the past six months, or had received antibiotics in the last 14 days. Additionally, women with polycystic ovary syndrome (PCOS) were excluded. The Rotterdam criteria were used as the standard for diagnosing PCOS. The diagnosis of PCOS required the presence of at least two out of three key indicators: oligo-anovulation (irregular menstrual cycles or periods that are more than 35 days apart, or fewer than 21 days apart), clinical or biochemical hyperandrogenism (clinical signs of excess androgens, such as hirsutism, acne, or alopecia, or biochemical evidence of high androgen levels in the blood), and polycystic-appearing ovaries (having at least 20 follicles measuring between 2 and 9 mm in diameter and/or an ovarian volume greater than 10 mL) [ 20 ]. The biochemical evidence of high androgen levels in the blood was required only if a woman had no clinical hyperandrogenism but had oligo-anovulation or ultrasound signs of PCOS. Women were also excluded if they had taken anti-inflammatory medicines or hormonal contraception within the past six months. Finally, any woman who refused to sign the informed consent form or withdrew during the study was excluded. A simple matching method was employed to select the control group participants. The control participants were prospectively matched to the cases by age, as the rate of false positive diagnoses of unexplained infertility increased rapidly after 35 years of age [ 21 ]. Figure 1 illustrates how patients were selected. Fig. 1 Flowchart of patients included in the study Flowchart of patients included in the study A structured, pretested questionnaire was used. It gathered information on sociodemographic, anthropometric, and behavioural factors from the study participants. The questionnaire was initially drafted in French and then translated into the country’s main local languages, namely Lingala and Swahili. To ensure consistency, the translations were back-translated into French and reviewed by an independent researcher proficient in both languages. The principal investigator completed the questionnaires for the cases. An assistant, trained by the principal investigator and an expert in reproductive health, helped complete the questionnaires for the control group. Data completeness was checked daily at the end of each data collection session, and any incomplete data were identified and corrected. The interview guide used in this study was specifically developed for this research (Annex 1 Supplementary Material 1). Subsequently, the principal investigator also requested and collected information on the clinical and/or paraclinical parameters of patients and their partners. The follow-up of study participants was carried out by tracking their contact information (such as phone numbers of the participants and their partners) and making regular contact (reminders, updates) to reduce loss to follow-up. The research assistant and the principal investigator coordinated and supervised the overall follow-up and data collection processes. These are the data collected: Sociodemographic parameters: Age, address, marital status, number of unions to date, duration of current union, type of union, level of education, occupation (profession), socioeconomic status, BMI. Behavioural parameters: Coitarche, number of partners to date, sex with a casual partner in the last 12 months, use of a condom during sex with a casual partner, tabagism, alcohol intak, number of coitus per week, vaginal hygiene with antiseptics, type of antiseptic product used, use of indigenous products in the vagina, usual antibiotics self-medication. Clinical parameters: Menarche, last 3 months’ menstruation dates, parity, gravidity, number and type of abortions, curettage, contraception, type of contraception used and its duration. Unexplained infertility: After conducting the standard investigation of the most common causes of infertility, semen analysis, ovulation assessment, and tubal patency testing were performed. If no underlying cause could be identified, the condition was classified as unexplained [ 2 ]. Polygamous relationship: We define a relationship as polygamous when one partner (man or woman) has multiple permanent spouses. Education level: This variable indicates the level of education in these categories: none, primary (grades 1 to 8), secondary (grades 9 to 12), and more than secondary (a diploma or higher). Tabagism: This variable encompasses the use of cigarettes or other tobacco products. The types of tobacco include any pipe filled with tobacco, chewing tobacco, snuff (nasal), kreteks, cheroots or cigarillos, water pipe, snuff taken orally, betel quid with tobacco, and similar products. Alcohol consumption: This variable encompasses the regular use of traditional or modern alcohol, usually within one month prior to the data collection period. Body mass index (BMI): BMI is defined as a woman’s weight in kilograms divided by the square of her height in metres (W/H2). The result was then categorised based on the classification of the Centres for Disease Control and Prevention (CDC): <18.5 (underweight range), 18.5 to < 25 (healthy weight range), 25 to < 30 (overweight range), and 30 or higher (obesity range) [ 22 ]. Socioeconomic level: Economic status was determined by the household’s asset score relative to the adjusted poverty index (API). A score of 0 or 1 was assigned, respectively, to the absence or presence of an asset in the household. The index was calculated by summing these scores. The aforementioned index was used to categorise the patients as follows: A patient was classified as having a high API (score of 7) if they had access to household amenities such as running water, an internal toilet, electricity, and all four consumer goods (radio, television, refrigerator, and vehicle). A moderately high API (score from 2 to 6) was assigned if the patient had access to any kind of water source other than surface water, an internal toilet, electricity, or at least two of the four consumer goods (radio, television, refrigerator or vehicle). A medium API (score of 1) was assigned when the patient had a combination of water sources, toilet, electricity, and consumer goods that was more than that defined by the low API but less than that of the medium-high API. A low API (low level, score of 0) is defined as follows: the individual uses surface water for drinking and, for other purposes, has no access to a toilet, no electricity, or any consumer goods such as radio, television, refrigerator, or vehicle. A patient was classified as having a high API (score of 7) if they had access to household amenities such as running water, an internal toilet, electricity, and all four consumer goods (radio, television, refrigerator, and vehicle). A moderately high API (score from 2 to 6) was assigned if the patient had access to any kind of water source other than surface water, an internal toilet, electricity, or at least two of the four consumer goods (radio, television, refrigerator or vehicle). A medium API (score of 1) was assigned when the patient had a combination of water sources, toilet, electricity, and consumer goods that was more than that defined by the low API but less than that of the medium-high API. A low API (low level, score of 0) is defined as follows: the individual uses surface water for drinking and, for other purposes, has no access to a toilet, no electricity, or any consumer goods such as radio, television, refrigerator, or vehicle. The data was encoded in Excel and imported for analysis via R software. To describe the sample, frequencies and percentages were calculated for categorical variables, while means and standard deviations were determined for continuous variables. The significance of differences in proportions was assessed using Pearson’s chi-square test. When the assumptions of the Pearson’s chi-square test were not met, Fisher’s exact test was used as an alternative. Student’s t test compared the means between the groups mentioned earlier. Statistical significance was set at a p value of less than 0.05. A purposeful selection process identified the determinants of unexplained infertility. After univariate analysis of each variable, binary logistic regression was performed to identify independent predictors of unexplained infertility through both bivariable and multivariable approaches. Variables with a p value of less than 0.2 in the bivariable logistic regression were considered candidates for the multivariable model. The results are presented as crude odds ratios (ORs) and adjusted odds ratios (AORs) with their corresponding 95% confidence intervals. The crude odds ratio and its 95% confidence interval were calculated to assess the strength of the association. To control for potential confounding factors, conditional logistic regression was performed, and adjusted odds ratios were derived. The Phi (φ) coefficient was calculated to assess the relationship between two dichotomous variables. This study was conducted following the ethical principles of the Declaration of Helsinki (as per the version active during approval, 2013, updated in 2024). Ethical approval was obtained from the local ethics committee, Comité Provincial d’Ethique de la Santé de la Division Provinciale de Santé de la TSHOPO (Approval No. 701/FBL/DPS/TSHOPO/SEC/0173/2023), and from the national ethics committee, Comité National d’Ethique de la Santé (Approval No. 531/CNES/BN/PMMF/2024). Before their inclusion in the study, written informed consent was obtained from all participants. All patients were informed that the consultations were for research purposes and that the de-identified data collected would be used for scientific publications. This was done to obtain their consent. In the event of refusal, the patient was excluded. Consent for the publication of identifying images or other personal or clinical details that could compromise anonymity is not applicable for this study.

Table 1 displays the anthropometric and sociodemographic characteristics of the participants. Concerning participants’ occupation, businesswomen were significantly more likely to experience unexplained infertility (18.75% versus 5.56%, p  = 0.001), with a fourfold higher odds after adjustment (AOR = 4.21, 95% CI: 1.81–9.77). Likewise, participants described as resourceful had a sevenfold increased odds of unexplained infertility (AOR = 7.32, 95% CI: 1.59–33.77], Fisher’s exact test = 0.017). No other profession showed a significant association with unexplained infertility ( p  > 0.05). The participants’ address was not linked to unexplained infertility ( p  > 0.05). Education level was found to be a significant factor. Primary education was associated with lower odds of UI (AOR = 0.35, 95% CI: 0.15–0.82, p  = 0.016), while university education was associated with higher odds (AOR = 1.88, 95% CI: 1.02–3.46, p  = 0.011) compared to secondary education. Regarding socioeconomic status and marital status, there were no significant differences between cases and controls ( p  > 0.05). In terms of union type, women in polygamous unions had higher odds of UI than those in monogamous unions (AOR = 2.49, 95% CI: 1.16–5.37, p  = 0.008). When considering body mass index (BMI), only obese participants (BMI ≥ 30 kg/m²) showed a strong association with unexplained infertility (AOR = 2.21, 95% CI: 1.34–3.65, p  = 0.004). Patients with unexplained infertility had a significantly higher mean BMI (Student’s t = 0.005) than controls (26.47 ± 5.1 kg/m² versus 24.76 ± 5.3 kg/m²). No significant differences were observed for other parameters such as place of residence, socioeconomic level, or marital status. Table 1 Characteristics of participants and unexplained infertility Cases ( N  = 144) Controls ( N  = 144) p value OR [CI] AOR[CI] n(%) n(%) n(%) n(%) Age  < 35 years 75(52.08) 75(52.08) -  ≥ 35 years 69(47.42) 69(47.42) 1 1[0.62–1.58]  Mean 33.03 ± 5.65 33.03 ± 5.65 NA Occupation  Civil servants 21(14.58) 31(21.53) 0.128 0.62[0.33–1.14]  Private sector employees 2(1.39) 3(2.08) 0.643* 0.65[0.11–3.96]  Business 27(18.75) 8(5.56) 0.001 3.89[1.70–8.90] 4.21[1.81–9.77]  Resourceful 12(8.33) 2(1.39) 0.017* 6.36[1.39–28.97] 7.32[1.59–33.77]  Soldier/Policeman 1(0.14) 6(0.86) 0.989* 0.98[0.13–7.09]  Health workers 7(4.86) 2(1.39) 0.116* 3.57[0.73–17.52] 3.73[0.74–18.74]  Housewives 71(49.31) 87(60.42) 1 - Address  City center 62(43.06) 70(48.61) 0.344 0.79[0.50–1.27]  Suburbs 82(56.54) 74(51.39) 1 Education level  Primary school 9(9.25) 22(15.28) 0.016 0.37[0.16–0.83] 0.35[0.15–0.82]  Secondary school 96(66.67) 101(70.14) 1  University 39(27.08) 21(14.58) 0.011 2.15[1.195–3.89] 1.88[1.02–3.46] Socio-economic status  Low 28(18.06) 25(17.36) 0.877 1.05[0.57–1.92]  Medium 74(51.39) 82(56.94) 1 -  Medium-high 21(14.58) 22(15.28) 0.849 0.93[0.49–1.79]  High 23(15.97) 15(10.42) 0.173 1.62[0.81–3.25] 1.66[0.81–3.39] Marital status  Married 120(83.33) 111(78.08) 1  Single 24(16.67) 33(22.92) 0.183 0.67[0.37–1.21] 0.65[0.36–1.2] Type of union  Monogamous 118(81.94) 113(92.36) 1  Polygamous 26(18.06) 11(7.64) 0.008 2,66[1.26–5.62] 2.49[1.16–5.37] BMI  < 18.5 7(4.86) 9(6.25) 0.608 0.76[0.2862.12]  18.5–24.9 58(40.28) 77(53.47) 1  25–29.9.9 10(6.94) 14(9.72) 0.376 0.68[0.29361.59]  ≥ 30 69(47.92) 44(30.36) 0.004 2.05[1.2663.32] 2.21[1.3463.65]  Mean of BMI 26,47 ± 5.1 24,76 ± 5.3 0,005 ¥ *Fisher’s exact test, ¥ Student’s t test Characteristics of participants and unexplained infertility *Fisher’s exact test, ¥ Student’s t test Table 2 analyses and summarises patient behaviours as potential factors associated with unexplained infertility. Compared with the controls, reporting sexual intercourse with a casual partner in the last 12 months was strongly associated with UI (AOR = 10.21, 95% CI: 3.45–30.17, p  < 0.001). Alcohol consumption (AOR = 1.83, 95% CI: 1.13–2.96, p  = 0.006) and the use of indigenous products for vaginal hygiene (AOR = 2.41, 95% CI: 1.01–5.76, p  = 0.039) were also associated with significantly higher odds of UI. Other behaviours, such as smoking (8.33% of cases versus 3.47% of controls) and vaginal hygiene with antiseptics (12.50% of cases versus 6.94% of controls), showed trends but no statistically significant associations. Table 2 Determinants of unexplained infertility linked to patient behavior Cases ( N  = 144) Controls ( N  = 144) p value OR [CI] AOR[CI] n(%) n(%) n(%) n(%) Coitarche  At < 18 years 80(55.56) 91(63.19) 0.186 0.72[0.45–1.18] 0.748[0.46–1.22]  At ≥ 18 years 64(44.44) 53(36.81) 1 - Sex with a casual partner in the last 12 months 44(30.56) 16(11.11) 0.000 3.52[1.87–6.60] 10.21[3.45–30.17] Vaginal hygiene with antiseptics 18(12.50) 10(6.94) 0.111 1.91[0.85–4.31] 2.06[0.90–4.71] Tabagism 12(8.33) 5(3.47) 0.080 2.52[0.86–7.36] 2.46[0.837.34] Alcohol consumption 87(60.42) 64(44.44) 0.006 1.91[1.19–3.05] 1.83[1.13–2.96] Use of indigenous products in the vagina 18(12.50) 8(5.56) 0.039 2.42[1.02–5.78] 2.41[1.01–5.76] Determinants of unexplained infertility linked to patient behavior Table 3 explores medical and surgical history as potential factors for unexplained infertility. Among the factors evaluated in the participants’ medical histories, only a history of early menarche (< 10 years) (AOR = 3.59, 95% CI: 1.14–11.37, p  = 0.050) and unsafe abortion (AOR = 5.93, 95% CI: 2.01–17.48, p  = 0.025) were associated with significantly higher odds of UI. No significant links were identified for history of abdominopelvic surgery, genital infection, allergy, or contraception. Table 3 Medical and surgical history and unexplained infertility Cases ( N  = 144) Controls ( N  = 144) p value OR [CI] AOR[CI] n(%) n(%) n(%) n(%) Menarche  < 10 years 13(9.03) 5(3.47) 0.050 2.78[0.96–8.06] 3.59[1.136–11.37]  10–14 years 112(77.78) 120(83.33) 1 -  ≥ 15 years 19(13.19) 19(13.19) 0.843 1.07[0.53–2.12 Previous abdominopelvic surgery ¥ 88(61.11) 87(60.42) 0.903 1.02[0.64–1.65] GTI 72(50.00) 61(42.36) 0.193 1.36[0.85–2.16] 1.85[0.79–4.32] Allergy 16(11.11) 12(8.33) 0.428 1.37[0.626–3.02] Unsafe abortion 58(40.28) 40(27.78) 0.025 1.75[1.07–2.87] 5.93[2.01–17.48] Contraception 23(15.97) 13(9.03) 0.074 1.91[0.92–3.94] 1.87[0.91–3.8] ¥Surgery for appendicitis, ovarian cyst, fibroid, cesarean section, ectopic pregnancy, tubal abscess, hydrosalpinx. Medical and surgical history and unexplained infertility ¥Surgery for appendicitis, ovarian cyst, fibroid, cesarean section, ectopic pregnancy, tubal abscess, hydrosalpinx. The interrelationships between infections and unexplained infertility are outlined in Table 4 . Intermediate flora appeared protective (AOR = 0.05, 95% CI: 0.01–0.2, p  < 0.001), while bacterial vaginosis (BV) was associated with markedly higher odds of UI (AOR = 8.91, 95% CI: 4.83–16.44, p  < 0.001). Regarding TORCH infections, Seropositivity for CMV (AOR = 0.54, 95% CI: 0.31–0.96, p  = 0.027) and toxoplasmosis (AOR = 0.55, 95% CI: 0.31–0.98, p  = 0.034) were associated with lower odds of UI. No significant differences were observed for the other infections. Table 4 Relationships between unexplained infertility and infections Cases ( N  = 144) Controls ( N  = 144) p value OR [CI] AOR[CI] n(%) n(%) n(%) n(%) Vaginal flora  Normal flora 19(13.19) 46(31.94) 1 -  Intermediate flora 2(1.39) 34(23.61) 0.000 0.04[0.01–0.19] 0.05[0.01–0.2]  Bacterial vaginosis 123(85.42) 64(44.44) 0.000 7.32[4.15–12.91] 8.91[4.83–16.44]   C. trachomatis 24(16.67) 18(12.50) 0.316 1.4[0.72–2.71] TORCH diseases positive serology  CMV 27(17.75) 43(29.86) 0.027 0.54[0.31–0.93] 0.54[0.31–0.96]  HSV I 21(14.58) 19(13.19) 0.733 1.12[0.57–2.19]  HSV II 21(14.58) 22(15.28) 0.868 0.94[0.49–1.81]  Rubella 70(48.61) 54(37.50) 0.056 1.57[0.98–2.52] 1.53[0.95–2.49]  Toxoplasmosis 25(17.36) 40(27.78) 0.034 0.54[0.31–0.96] 0.55[0.31–0.98]  Syphilis 10(6.94) 8(5.56) 0.626 1.26[0.48–3.31]  HIV 5(3.47) 8(5.56) 0.393 0,61[0.19–1.91] Relationships between unexplained infertility and infections When the nature of the vaginal flora was linked to unexplained infertility, bacterial vaginosis was significantly positively associated with it (φ coefficient = 0.38, p  < 0.001), and intermediate flora was moderately negatively associated with it (φ coefficient = 0.33, p  < 0.001) (Table 5 ). Table 5 Correlations between vaginal flora and unexplained infertility Vaginal flora Cases Controls p value Chi 2 φ coefficient n(%) n(%) Normal flora 19(13.19) 46(31.94) 1 Intermediate flora 2(1.39) 34(23.61) 0.000 10.89 0.33 Bacterial vaginosis 123(85.42) 64(44.44) 0.000 36.74 0.38 Total 144 144 Correlations between vaginal flora and unexplained infertility Analysis of the presence of anti-sperm antibodies as a factor in unexplained infertility showed that the presence of IgA (AOR = 4.28, 95% CI: 1.88–9.74, p  = 0.001), IgG (AOR = 10.69, 95% CI: 6.16–18.56, p  < 0.001), or either antibody (AOR = 14.9, 95% CI: 8.18–27.13, p  < 0.001) was associated with significantly higher odds of UI (Table 6 ). Strong positive phi correlation coefficients further supported these associations (Table 7 ). Table 6 Relationships between unexplained infertility and anti-sperm antibody (ASA) levels Cases ( N  = 144) Controls ( N  = 144) p value OR [CI] AOR[CI] Type of ASA n(%) n(%) n(%) n(%)  Ig G 115(79.86) 39(27.08) 0.000 10.67[6.16–18.47] 10.69[6.16–18.56]  Ig A 29(20.14) 8(5.56) 0.001 4,29[1.9–9.74] 4.28[1.88–9.74]  Ig G and/or Ig A 125(86.81) 44(30.56) 0.000 14.95[8.22–27.21] 14.9[8.18–27.13] Relationships between unexplained infertility and anti-sperm antibody (ASA) levels Table 7 Correlation between anti-sperm antibody (ASA) and unexplained infertility Cases ( N  = 144) Controls ( N  = 144) p value Chi 2 φ coefficient Type of ASA n(%) n(%)  Ig G 115(79.86) 39(27.08) 0.000 84.37 0.54  Ig A 29(20.14) 8(5.56) 0.001 75.14 0.51  Ig G and/or Ig A 125(86.81) 44(30.56) 0.000 71.77 0.50 Correlation between anti-sperm antibody (ASA) and unexplained infertility

This study identified a multifactorial range of sociodemographic, behavioural, and clinical factors linked to unexplained infertility among Congolese women. The findings emphasise that UI is a complex condition with potentially various contributing factors. Notably, bacterial vaginosis and anti-sperm antibodies showed the strongest associations with UI in this cohort. These results highlight the clinical importance of including routine screening and management of bacterial vaginosis and testing for anti-sperm antibodies as part of the standard diagnostic process for infertility in this setting. Future research should focus on longitudinal studies to determine temporality and explore whether targeted treatments for BV and ASA can enhance fertility outcomes in women with UI. This study has several limitations that should be acknowledged when interpreting the results. First, the case-control design is inherently prone to recall and social desirability bias, as data on sensitive behaviours (e.g., sexual history) were collected via questionnaire. This may have resulted in underreporting and an underestimation of the true associations for these factors. Second, the sample size was not determined based on a prior power calculation, which may have restricted the statistical power to detect smaller, yet clinically important, associations (e.g., for smoking) and increased the risk of selection bias. Consequently, the findings might not be entirely generalisable to the wider population and should be confirmed in larger, prospective studies. Third, although we adjusted for several confounders in our analysis, the potential for residual or unmeasured confounding cannot be excluded. Finally, the cross-sectional measurement of exposures such as BV and ASA complicates the establishment of a temporal relationship between these factors and the diagnosis of infertility.

The findings of this study highlight several factors observed in women with unexplained infertility compared to fertile controls. However, since this was a pilot case-control study, these results should be regarded as preliminary rather than definitive evidence of causation. Furthermore, longitudinal and mechanistic studies are needed to determine whether these factors directly contribute to unexplained infertility. Although our analysis provides odds ratios (ORs) for these factors, we recognise that in case-control studies of common conditions like unexplained infertility, ORs might not accurately reflect the true risk (RR). Therefore, these associations should be interpreted with caution, and larger future studies could offer more precise risk estimates. The aetiology of unexplained infertility can stem from endocrinological, immunological, genetic, and reproductive physiological disturbances. Current knowledge of reproductive assessment remains incomplete. Many aspects are not routinely evaluated or are unavailable. Limitations are present in both male and female fertility assessments [ 7 ]. This renders unexplained infertility a complex scenario for couples and clinicians. This study aimed to identify the predictive factors of unexplained infertility in Congolese women through logistic regression and to examine the association between unexplained infertility, bacterial vaginosis, and anti-sperm antibodies. Although no significant associations between risk factors and unexplained infertility were found when assessing anthropometric parameters and other sociodemographic characteristics in a study conducted by Jašinskienė et al. [ 23 ], this research identified several factors linked to unexplained infertility. Concerning patients’ occupations, the current study found that businesswomen had a fourfold higher risk of unexplained infertility after adjustment. Similarly, participants described as resourceful had a sevenfold increased odds of unexplained infertility. These individuals face unexplained infertility due to their specific lifestyles. Such professions often involve irregular routines, long working hours, and greater exposure to stressful environments. Stress is well known to impact ovulation. The kisspeptinergic system connects stress, reproductive signals, and nutrition. During chronic stress, this system can influence hypothalamic-pituitary-gonadal function and GnRH pulsatility. Stress hormones, such as catecholamines, affect ovulation by interacting with hormones like gonadotropin-releasing hormone (GnRH), prolactin, LH, and FSH [ 24 ]. Women’s education is recognised as the most important factor affecting fertility rates in developing countries [ 25 ]. In this study, education level significantly influenced unexplained infertility. Participants with primary education had a protective effect against unexplained infertility, while those with university education faced a twofold increased odds. However, contrary to this study’s findings, other researchers have reported either no link between infertility and education level [ 8 ] or a connection with lower education levels [ 26 ]. The results suggest that educated women in sub-Saharan Africa tend to marry later, use contraception more effectively, and have greater autonomy over reproductive choices, leading to lower fertility and smaller desired family sizes [ 25 ], whereas women with less education tend to marry earlier and are less exposed to infertility risks [ 27 ]. This study found no link between socioeconomic status and unexplained infertility. The connection between socioeconomic status and infertility is complicated and has shown mixed results [ 27 , 28 ]. This suggests that there may be other confounding factors that have not been fully acknowledged to explain this difference. In this study, no association was found between marital status and unexplained infertility. Conversely, Abdullah et al. [ 8 ] in Sudan reported that married women have a higher risk of unexplained infertility. Cultural differences may explain these contrasting outcomes. In Sudan, infertility is seen as a burdensome fate believed to be caused by divine will. This view may lead men to engage in high-risk sexual behaviours, including polygamy and extramarital relationships, in their attempt to conceive. Such actions increase the likelihood of sexually transmitted infections [ 8 ]. STIs can contribute to unexplained infertility through various mechanisms previously described [ 7 ]. In the DRC, there is a strong tendency to seek advice from healthcare professionals, both traditional and modern [ 29 ]. The fact that 99.1% of unexplained infertility cases occurred among married women may support Abdullah et al.‘s findings [ 8 ]. This study shows that people in polygamous unions face a higher odds of unexplained infertility than those in monogamous unions. This increased odds is linked to common factors in polygamous relationships, such as irregular sexual contact, greater STI risk, and limited healthcare resources [ 30 ]. On the other hand, research in West Africa found no significant fertility difference between women in polygamous unions and their monogamous counterparts [ 31 ]. Considering body mass index (BMI), this study revealed that obese participants (BMI ≥ 30 kg/m²) were strongly associated with unexplained infertility, and patients with unexplained infertility had a significantly higher mean BMI. This is because, during obesity, excess body fat can influence the production of gonadotropin-releasing hormone (GnRH), which is vital for ovulation. Obesity is also linked to inflammation, which can affect hormones, proteins, and soluble factors that regulate reproductive organs [ 32 ]. Several studies worldwide have reported an increased odds of infertility associated with obesity [ 33 – 35 ]. This study revealed that participants who admitted to having sexual intercourse with a casual partner in the last 12 months or who used indigenous products in the vagina were significantly more likely to experience unexplained infertility. Those using antiseptics for vaginal hygiene showed positive tendencies, but the associations were not statistically significant. These factors disturb the protective vaginal flora, thereby increasing the risk of sexually transmitted infections (STIs) and bacterial vaginosis. These infections can ultimately cause infertility through various inflammatory and immunological mechanisms, which may differ in complexity [ 7 , 36 ]. Participants who consumed alcohol had a higher odds of unexplained infertility. Smoking followed a similar trend, but there was no statistically significant link with unexplained infertility. What does this statement mean? In their study, Abdullah et al. did not find any connection between alcohol consumption, smoking, and unexplained infertility in Sudan [ 8 ]. The most consistent evidence for the harmful effects of smoking and alcohol on specific aspects of female reproductive function comes from experimental studies in animals. Overall, clinical studies indicate that smoking is associated with reduced fertility, although causality still needs to be conclusively established. Research on the impact of alcohol consumption on female fertility has yielded mixed results, though most studies report no association [ 37 , 38 ]. Among the factors examined in the participants’ medical histories, only early menarche (before the age of 10) and unsafe abortions were linked to a risk of unexplained infertility. In their cohort study, Warp et al. observed a lower likelihood of conceiving during any given menstrual cycle up to 12 cycles in women with early-stage disease. This is because early menarche is associated with an increased odds of ovulatory dysfunction, which can negatively impact fertility [ 39 ]. Some researchers have identified a significant link between a history of induced abortion and the subsequent development of infertility [ 40 – 42 ]. After an abortion, infertility can result from various factors, including infectious complications, endometrial damage, and subsequent endometriosis-related issues [ 36 , 42 ]. No significant differences were observed for a history of contraception and unexplained infertility. Abdullah et al. [ 8 ] also reported no association between contraception and unexplained infertility. Although abdominopelvic surgery can increase the risk of infertility due to adhesions causing tubal occlusion, ovarian encapsulation, etc [ 43 ]., its impact on unexplained infertility is less likely, as it causes evident reasons for infertility. Although there is a potential link between allergic disorders and infertility, as both involve the hormonal and immune systems, research indicates that lower levels of VEGF in the endometrial lining may contribute to decreased fertility in asthmatic patients [ 44 ]. This might explain why this study did not find a relationship between allergies and unexplained infertility. Regarding vaginal flora, bacterial vaginosis was linked with a higher odds and showed a moderate positive correlation with unexplained infertility. In a systematic review and meta-analysis, Van Oostrum et al. [ 14 ] reported that BV was significantly more prevalent in women with infertility compared to those who were pregnant in the same population [OR: 3.32, 95% CI 1.53–7.20]. The cause of BV-related infertility is likely multifactorial, involving factors such as inflammation, immune targeting of sperm antigens, the presence of bacterial toxins, and an increased risk of sexually transmitted infections [ 15 ]. Regarding TORCH infections, only CMV and toxoplasmosis were significantly associated with unexplained infertility. These associations had a protective effect on unexplained infertility. The other infections did not show significant differences. In contrast, a study conducted in northwestern China by Ren et al. reported that the risk of acquiring a primary infection by a TORCH pathogen remained elevated among infertile women of childbearing age [ 45 ]. Considering primary (IgM) TORCH infections and older (IgG) individuals in this study may explain the discrepancy with the findings of studies conducted in China. Analysis of the presence of anti-sperm antibodies as a factor in unexplained infertility revealed that the presence of IgA alone, IgG alone, or both IgA and/or IgG was significantly associated with unexplained infertility. These factors showed a strong positive correlation with the condition. This is because ASAs targeting the sperm tail can reduce sperm motility, while antibodies against the sperm head may hinder sperm penetration of cervical mucus and the sperm‒egg interaction [ 16 ]. Mahdi et al. [ 17 ] reported that anti-sperm antibodies were detected in the cervicovaginal secretions (62.2%) and sera (64.4%) of infertile women, with levels significantly higher ( p < 0.001) than in the control group (3.3% and 3.3%, respectively).

Unlike many other medical conditions, infertility is a disease influenced by an individual’s desire or lack of desire to conceive. Unlike other illnesses that require treatment regardless of personal choice, infertility is recognised as a medical issue only when a couple struggles to conceive despite their wish to do so [ 1 ]. When healthcare professionals are consulted about infertility, the couple’s main aim is to identify the underlying cause of their inability to conceive and to explore potential solutions. To ensure a logical and systematic process, guidelines exist for standard investigations that should be initially conducted. Unexplained infertility (UI) is a diagnosis (or lack of one) given to infertile couples in whom all standard and accepted infertility tests, such as ovulation tests, tubal patency, and semen analysis, yield normal results. These are the key standards in investigations mandated by the European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM). Rather than being a true diagnosis, unexplained infertility can be considered as the exclusion of confirmed pathology [ 2 ]. The diagnosis of infertility can be a very distressing experience for couples, potentially leading to a range of psychological issues. Additionally, when the cause of infertility remains unexplained, it can be particularly devastating for the couple, as they often perceive this as indicating that no effective treatment is available [ 3 , 4 ]. A recent systematic review highlighted the significant challenges caused by the wide variety of definitions currently used for unexplained infertility, which has resulted in a broad range of reported prevalence figures [ 2 ]. It is estimated that 15% to 30% of infertile couples worldwide have unexplained infertility [ 5 ]. In Africa, its prevalence varies considerably, reaching 37% in some regions [ 6 ]. The high prevalence of unexplained infertility suggests that the current assessment of the human reproductive system is inadequate and requires substantial improvements in diagnostic tools. Therefore, it is necessary to identify additional causes of female infertility to develop more effective treatment options for patients [ 7 ]. Several studies have been conducted to identify various risk factors linked to infertility and unexplained infertility. The factors examined include sociodemographic and anthropometric variables; environmental influences, such as alcohol consumption and tobacco use; dietary habits; obstetric history, including contraception and previous abortions; and other relevant variables [ 8 – 11 ]. A thorough review of existing literature on the risk factors associated with unexplained female infertility shows a lack of discussion on the role of infections and anti-sperm antibodies. Despite the high prevalence of these factors among infertile patients, the potential link between their presence and unexplained infertility remains insufficiently explored [ 12 – 17 ]. This study was conducted to expand the limited knowledge on unexplained female infertility. It aimed to identify the risk factors for unexplained female infertility among Congolese women and to explore the relationship between unexplained infertility, bacterial vaginosis, and anti-sperm antibodies.

Supplementary Material 1. Supplementary Material 1.