Comprehensive Assessment of Genital Infections and Reproductive Health in Women Visiting a Fertility Clinic in Warangal, India-A Case-Control Study.

Naresh Patnaik: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, software, validation, visualization, writing – original draft, writing – review and editing. Uttam Sarkar: formal analysis, software, validation, visualization, writing – review and editing. Malathi Jojula: data curation, investigation, resources, validation, visualization. Hema Vaddiraju: data curation, investigation, resources, validation, visualization. Ruchi Jain Dey: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing – original draft, writing – review and editing.

A comprehensive questionnaire‐based case‐control, observational study is carried out to identify the most significant parameters associated with subfertility, infertility (primary and secondary), or conditions like pre‐term birth, spontaneous abortions, medical termination of pregnancy, and ectopic pregnancy in females. Out of the 100 patients assessed, 62 were categorized into the infertile group and 38 were healthy patients with no history of infertility or menstrual disorders (Figure  1 ). Amongst the infertile group, only‐female infertility (39%) with healthy and fertile male spouses, predominated the assessed population (Figure  2A , Table  1A ). Infertility due to only‐male factors with apparently healthy/fertile females represented mere 2% of the study population (Figure  2A , Table  1A ). Female infertility is seen in 60% of the total study population including 21% of cases contributed by both male and female infertility (Figure  2A , Table  1A ). Out of the total 62 infertility cases, 90% are diagnosed with primary infertility, rest 10% are cases of secondary infertility (Figure  2B , Table  1B ). Our study highlights an early onset of female infertility as young as ≤ 20 years of age (Figure  2C , Table  1B ), with predominance of both primary and secondary infertility in the age group of 21‐30 years (Figure  2C , Table  1B ). Among 62 infertility cases, ~74% failed to conceive even once, and a large proportion of ~23% suffered from spontaneous abortions or have undergone medical termination of pregnancy (MTP) (Figure  2D , Table  1C ). Ectopic pregnancies were observed in ~3% of cases, however, no pre‐term birth cases were observed in this study population (Figure  2D , Table  1C ). Age distribution of these failed pregnancies reveals an early occurrence in the age group of 21‐25 years (Figure  2E , Table  1C ). Population distribution and pattern analysis of female infertility and other reproductive conditions. (A) Pie‐chart shows the population distribution of infertility amongst the assessed patients ( n  = 100, 62 infertile couples and 38 healthy patients). Out of 62 infertile couples, 60 cases were found to be associated with female infertility (39 cases of only female infertility and 21 cases of both male and female infertility), while 2 cases were identified as only male infertility leading to failure in conception. (B) Histogram shows the percentage distribution of primary and secondary infertility amongst the 62 cases of infertility. (C) Histogram shows the age‐based population distribution (females) of primary and secondary infertility amongst 62 cases of infertility. (D) Pie chart shows percentage population of preterm birth, spontaneous abortions/medical termination of pregnancy (MTP), ectopic pregnancy, and no conception amongst infertile females ( n  = 62). (E) Age‐based population distribution of females ( n  = 62) experiencing preterm birth, spontaneous abortions/MTP, ectopic pregnancy, and no conception. Demographic variables of the study population. Among the various parameters assessed in females belonging to the infertility group, incidence of genital infections were thoroughly investigated in this study, using (a) most recurring symptoms of infections such as vaginitis (vaginal itching, burning and discharge), cervicitis (cervical inflammation), dyspareunia (painful intercourse), PID, and symptoms of UTI (painful micturition/burning sensation on micturition), and (b) laboratory confirmed cases of infections such as UTI (urine examination), TB positivity (based on tuberculin test/QuantiFERON‐TB Gold/TB molecular diagnosis using PCR or TB culture test) (Table  S1 and  S2A , Supporting Information  3 ). Surprisingly, a large proportion of ~27% of infertile females show one or more signs of infection‐related symptoms (Figure  3A , Table  S2A , Supporting Information  3 ). The major symptoms observed in these patients are vaginitis (~18%), PID (~26%), dyspareunia (~11%), and 2 confirmed cases of pulmonary TB (~3.2%) (Figure  3B , Table  S2A , Supporting Information  3 ). Interestingly, the analysis of questionnaire data reveals that confirmatory detection of infections has largely been overlooked and is not typically included in the routine check‐ups involved in infertility management. Hence, the rest ~73% of the patients were rendered ineligible for infection‐screening in the absence of classical signs of infection. As the diagnosis is simply based on visible signs of genital infections listed above, asymptomatic but chronic genital infections cases go unnoticed/undetected and hence, left untreated. Therefore, detection of genital infections may prove to be highly valuable for early management of infection‐associated female infertility. Contribution of genital infections to female infertility. (A) The Pie chart shows percentage of infertile females experiencing symptoms of genital infections or confirmed diagnosis of infection. Reports suggest tuberculosis infection (based on PPD tests) in 2 cases out of 62 patients, while other infections are not identified during diagnosis. (B) Histogram shows population distribution of various genital infection related symptoms in females belonging to infertile group ( n  = 62, all cases of infertility) and healthy group ( n  = 38). Healthy females do not show any symptoms of infection; hence their bar is represented as “0”. Menstrual irregularities appear to be the predominant symptoms observed in the case of females in the infertile group (~42%, Figure  4A , Table  S2A and  B , Supporting Information  3 ). Menstrual irregularities were further stratified into various categories such as menorrhagia (heavy and prolonged bleeding), dysmenorrhoea (painful periods with cramps), amenorrhoea (absence of periods), metrorrhagia (spot bleeding in between the periods), and oligomenorrhoea (irregular periods or inconsistent blood flow). Among these symptoms, amenorrhoea (~13%), metrorrhagia (~8%), and oligomenorrhoea (~15%) are evident only in the case of infertile patients (Figure  4B , Table  S2A and  B , Supporting Information  3 ). However, symptoms such as menorrhagia (~34% vs. ~24% in infertile vs. healthy) and dysmenorrhoea (~65% vs. ~29% in infertile vs. healthy) are observed both in healthy and infertile patients, though a larger proportion is observed in the infertile group (Figure  4B , Table  S2A and  B , Supporting Information  3 ). Menstrual disorders and anatomical abnormalities of the reproductive tract in female infertility. (A) Histogram shows the percentage population distribution of females with respect to their menstrual health based on the regularity of the menstrual cycle. (B) Histogram shows population distribution of various menstrual disorders among females belonging to infertile ( n  = 62, all cases of infertility) and healthy group ( n  = 38). (C) Histogram shows population distribution of various anatomical abnormalities of the female reproductive tract in infertile ( n  = 62, all cases of infertility) and healthy group ( n  = 38). Infertile [%] = [No. of patients with symptoms/Total infertility cases]*100. Healthy [%] = [No. of healthy patients with symptoms/Total no. of healthy patients]*100. We next assessed the spectrum of anomalies in the female reproductive tract in infertile patients. Among the various anomalies, PCOS and PCOD are the most prominent pathological sequelae observed in infertile patients (n = 19, ~31%) (Figure  4C , Table  S2A , Supporting Information  3 ). The other prominent symptoms include fallopian tube blockage (n = 5, ~8%) and a variety of endometrial disorders, such as secretory/proliferative/thickened endometrium/hyperplasia/cysts ( n  = 4; ~7%), and endometriosis (n = 2, ~3%) (Figure  4C , Table  S2A , Supporting Information  3 ). Unlike these symptoms, only one case of anovulation and ovarian teratoma is observed in the infertile group (Figure  4C , Table  S2A , Supporting Information  3 ). The major objective of this analysis is to identify the most significant parameters (Table  S1 , Supporting Information  3 ) that impact fertility and reproductive health in females. We also wanted to assess if changes in these critical female parameters co‐occur/correlate with the reduced sperm/semen quality in males, which can happen as a consequence of male genital infection [ 39 , 40 ]. We stratified the parameters into various categories such as (a) menstrual disorders (Table  S2A and  B , Supporting Information  3 ), (b) reproductive tract anomalies such as endometriosis, other endometrial disorders (eg thickened/secretory/proliferative endometrium/hyperplasia/cysts), fallopian tube or uterine blockages, PCOS/PCOD, anovulation and ovarian cysts/teratoma (Table  S2A , Supporting Information  3 ), (c) genital infection associated symptoms/confirmed diagnosis of infections like TB/UTIs (Table  S2A , Supporting Information  3 ), (d) physiological parameters such as reproductive hormones (FSH, LH, Prolactin, AMH) (Table  S2D , Supporting Information  3 and  4 ), (e) physical parameters and lifestyles, such as weight, height, BMI and exercise routine (Table  S2A and  C , Supporting Information  3 and  4 ) and (f) male sperm and semen abnormalities (Table  S2A , Supporting Information  3 and  4 ). To derive the correlations between various parameters, we performed multivariate correlation analysis (Phi correlation) [ 41 , 42 , 43 ]. We observe a highly significant and very strong correlation between parameters related to genital infection‐associated symptoms (or confirmed diagnosis) and menstrual disorders such as oligomenorrhoea, amenorrhoea, metrorrhagia, and dysmenorrhoea. (Figures  5 and  6 ). Further, a very strong and significant correlation is observed between genital infections and perturbations in the female reproductive hormones such as LH (Figures  5 and  6 ). For more details on comparisons and categorization of the LH, FSH, AMH and prolactin hormonal profiles in the infertile patients, see Figure  S1A and  B in Supporting Information  4 . The presence of infection‐associated symptoms also is strongly correlated with the occurrence of PCOS and PCOD, uterine and tubal blockage, ovarian cysts, and endometrial disorders (Figures  5 and  6 ). Correlation heat map. The data gathered by questionnaire‐based assessment of female patients is used for finding out the correlation amongst the various parameters used for the assessment. A multivariate correlation values (Phi correlation with significance of correlation p  ≤ 0.05) used for generating this correlation heat map. (P1‐P45 are the various parameters assessed in this study; for description see Table  S1 ). Schematic diagram showing correlation between various fertility parameters and their association with the incidence of genital infections. The correlation between various parameters is depicted schematically with the help of bidirectional arrows of varying thickness corresponding to the strength of correlation between these parameters. The correlations are deduced from the multivariate correlation analysis (Phi correlation and the significance of the correlation). For details, refer to the correlation heat map shown in Figure  5 . We next assessed if female genital infection factors correlate/co‐occur with male infection factors, such as abnormalities in the semen and sperm profile. Our study suggests a very strong and significant correlation of female genital infections with the occurrence of teratozoospermia, pyospermia, and hyper viscosity of semen, and a strong correlation with asthenozoospermia. The abnormalities observed in male partners of infertile females in this study concur well with the previously reported sperm abnormalities during male genital infections [ 39 , 40 ], however, no confirmatory diagnostic reports were available to verify the nature of infections in these males (Figures  5 and  6 , Figure  S2 A and B in Supporting Information  4 ). Another key finding is a very strong and significant correlation between male semen/sperm abnormalities characteristic of infections, such as low sperm DNA integrity, asthenozoospermia, teratozoospermia, pyospermia and hyper viscosity with female disorders such as occurrence of PCOS and PCOD, uterine and tubal blockage, oligomenorrhoea, amenorrhoea, metrorrhagia & dysmenorrhoea (Figures  5 and  6 , Figure  S2A and  B in Supporting Information  4 ). These observations highlight that transmission of genital infections between sexual partners could potentially influence female menstrual health and fertility in both males and females. Male factors are also strongly correlated with hormonal perturbations in infertile females such as FSH, LH, and AMH (Figures  5 and  6 ). Among other parameters, we observe a strong and significant correlation between reproductive tract anomalies such as PCOS and PCOD with the occurrence of menstrual disorders such as amenorrhoea (Figures  5 and  6 ). We also observe endometrial disorders to impact menstrual health and are significantly correlated with menorrhagia. We next assessed if the reproductive tract anomalies are linked with reproductive hormones. We observe a strong and significant correlation between ovarian cysts with AMH and anovulation with FSH, LH, and prolactin (Figures  5 and  6 ). In addition, uterine and tubal blockage are strongly correlated with prolactin, LH, and AMH levels (Figures  5 and  6 ). However, one limitation of the data collected was the presence of reports corresponding to female reproductive hormones only in a limited number of patients (Figure  S1A and  B , Supporting Information  4 and Table  S2D , Supporting Information  3 ). Peculiarly, we observe a very strong and significant correlation between BMI in females and genital infection‐associated symptoms (such as dyspareunia/cervicitis/vaginitis), menstrual disorders (such as menorrhagia), and reproductive hormones (such as LH) (Figures  5 and  6 ). For more details on contribution of lifestyle and BMI see Figure  S1 C and D in Supporting Information  4 . The multivariate logistic regression analysis identified several predictors associated with infertility, with likelihood ratios calculated for each. Additionally, regression coefficient values (β) were determined to indicate the direction and strength of association. Parameters with a likelihood ratio greater than 1 and positive regression coefficients included: TB positivity, confirmed UTI or visible symptoms of UTI, vaginitis and oligospermia (Table  2 ). Pyospermia in the male spouse showed a positive regression coefficient value as well (Table  2 ). However, for these parameters, p ‐values were found to be greater than 0.9, suggesting that while there is a positive predictive association with infertility (as indicated by β > 0), these results are not statistically significant in this analysis (Table  2 ). The following parameters also showed a significant positive association with infertility, indicated by likelihood ratios greater than 1, such as menstrual irregularities ‐oligomenorrhea ( p  < 0.05) and dysmenorrhea ( p  < 0.05), anovulation, endometrial abnormalities, ovarian cysts, PID ( p  < 0.05), hormonal markers (prolactin and AMH) and abnormal BMI ( p   10, p  < 0.05), demonstrating a very strong association with infertility, were PCOS/PCOD, repeated abortions/MTP and abnormal FSH levels (Table  2 ). Multivariate logistic regression analysis of the various parameters associated with infertility in females. Abbreviations: NR, No results obtained in the statistical analysis; NS, No significance. These findings suggest that while multiple factors are associated with infertility, certain conditions—particularly PCOS/PCOD, a history of abortion/MTP, and abnormal FSH—exhibit the strongest association in this study population.

The lead author Ruchi Jain Dey affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Our pilot‐scale investigation revealed that genital infections are often overlooked during fertility assessment in women of reproductive age, indicating a glaring gap in current healthcare practices and a significant lack of robust POC diagnostic tools in rural areas for detecting genital infections, including TB. Hence, infertile women are typically not treated for infections unless there are visible signs. As a result, asymptomatic females remain undiagnosed for infections and do not receive appropriate health care promptly. This prolonged lack of diagnosis and treatment can lead to chronic infections and consequential pathological sequelae, ultimately resulting in infertility. Our study reveals an early onset of infertility in females between 18 and 25 years of age, suggesting possibly occurrence of undiagnosed and untreated chronic infections postpuberty. Importantly, a substantial portion of these patients exhibited classical signs of infection (27%), while 73% were rendered ineligible for confirmatory diagnostic tests due to a lack of classical symptoms of infections. This underscores the urgent need for more comprehensive screening strategies. We observed in our study that infertile females present heterogenous symptoms such as, menstrual disorders, PCOS/PCOD, tubal blockage, endometriosis, anovulation, uterine fibroids, and ovarian cysts, making it extremely challenging for the clinicians to establish a clear link between these reproductive disorders and genital infections. Furthermore, we observed a very strong and significant correlation of genital infections with physiological parameters (hormonal profiles and BMI) and pathological impact on female reproductive health (reproductive anomalies and menstrual disorders) and sperm/semen quality in the case of their respective spouses [ 44 , 45 ]. While causation between pelvic infections and PCOS remains unestablished and requires further experimental validation in animal models, existing literature suggests a plausible indirect relationship mediated by inflammation, insulin resistance, hormonal imbalances, and menstrual disorders. González et al. demonstrated that chronic low‐grade inflammation, characterized by elevated cytokines like TNF‐α and adipocytokines, significantly contributes to insulin resistance and PCOS pathogenesis [ 46 ]. Supporting this, Juber et al. reported higher infection susceptibility among women with PCOS compared to those without the condition [ 47 ]. Furthermore, studies such as Azat and Rasim highlight the role of pelvic infections in disrupting hormonal balance, altering estrogen‐to‐androgen ratios, and contributing to menstrual irregularities and infertility, with partial recovery of ovarian function observed following antibiotic treatment [ 48 ]. The heterogeneity in the impact of pelvic infections on PCOS and menstrual disorders suggests a complex interplay, where factors like diabetes and obesity may predispose certain women to these conditions leading to dysbiosis and higher risk of infection with pathogenic species [ 49 ]. Hence, regular screening and treating these infections is crucial not only for improving female reproductive health but also for enhancing the success rate of fertility treatments, including assisted reproductive technologies (ART). Thus, our study emphasizes on the necessity of a POC for the detection of genital infections as a primary regular screening tool, especially in the rural settings, wherein, it is often challenging to diagnose asymptomatic and the regular culture tests may fail in efficiently diagnosing such cases [ 14 , 21 , 22 , 23 , 24 , 25 , 26 , 50 ]. A comprehensive questionnaire‐based assessment followed by POC molecular diagnosis screening methods may aid in validating the clinical suspicion of genital infections and may further address the gap in reproductive health care [ 21 , 22 , 23 , 51 , 52 ]. The statistical analysis provides a comprehensive view of the factors associated with infertility. TB positivity, UTI symptoms, vaginitis, pyospermia, and oligospermia were found to have positive regression coefficients (β > 0), suggesting a directionally positive association with infertility. However, with p ‐values greater than 0.9 for these parameters, these findings are statistically nonsignificant in this study. Although these factors could theoretically influence reproductive health, the lack of statistical significance indicates that future studies with larger sample sizes could clarify these associations further. The study also reaffirms the significance of menstrual irregularities, endometrial abnormalities, and ovarian factors as contributors to infertility. The strong likelihood ratios for PCOS/PCOD, repeated abortions, and abnormal FSH levels highlight their critical role in the etiology of infertility. PCOS/PCOD remains a prominent risk factor due to its hormonal and metabolic implications, which disrupt normal ovarian function [ 53 ]. The high likelihood ratio for repeated abortions/MTP with secondary infertility observed in our study could possibly originate from uterine scarring, sepsis, peritonitis, visceral injuries, and hormonal effects incurred during these surgical procedures as shown previously [ 54 ]. Abnormal FSH has also been previously found to be the biological basis of infertility due to consequent diminished ovarian reserve [ 55 ], which corroborates with our findings and underscores the indirect effect of infections with hypothalamic secretions and effect on fertility [ 15 ]. In addition, elevated prolactin and AMH levels observed in infertile patients in our study also highlight the role of hormonal dysregulation in fertility issues, aligning with existing knowledge about their role in reproductive health and increase during infections [ 56 , 57 , 58 , 59 ].

In conclusion, this study highlights the significant association between genital infections and infertility, emphasizing the critical need for improved diagnostic and management approaches, particularly in rural areas. Our findings reveal a high prevalence of genital infections among infertile women and underscore the potential benefits of integrating self‐assessment questionnaires and point‐of‐care (POC) molecular diagnostic tools to facilitate timely diagnosis and intervention. Such measures could improve reproductive health outcomes and increase the success rates of fertility treatments. In addition to infectious factors, the study confirms that hormonal and pathological abnormalities—particularly PCOS/PCOD, repeated abortions/MTP, and abnormal FSH levels—are strong predictors of infertility, reinforcing the importance of a multifactorial approach in infertility risk assessment. Future research should further explore the links between infections and reproductive health, focusing on tailored interventions to address both infectious and noninfectious contributors to infertility.

Infertility and menstrual disorders are significant global health concerns, with an estimated 17.5% of women affected by infertility according to the WHO [ 2 , 3 , 4 ]. This issue spans low‐, middle‐, and high‐income countries, underscoring the urgency of expanding reproductive healthcare and research [ 3 , 4 ]. Infertility, defined as the inability to conceive after a year of unprotected sexual intercourse, affects both genders due to genetic, environmental, infectious, and lifestyle factors [ 3 , 4 , 5 ]. Limited access to care and societal stigma result in delayed diagnosis and treatment, causing emotional and financial stress [ 3 ]. High medical costs can lead to healthcare‐induced poverty during infertility treatments [ 3 , 6 , 7 , 8 ]. In regions like India, where infectious diseases, such as tuberculosis, are prevalent, infectious agents play a prominent role in infertility [ 6 , 9 , 10 , 11 ]. Genital infections may cause a wide spectrum of conditions/symptoms like vaginitis, cervicitis, dyspareunia, and PID [ 12 , 13 ], endometriosis and other endometrial disorders [ 6 , 14 ], PCOS/PCOD, ovarian atresia or cysts, and anovulation [ 15 , 16 ]. These infections become chronic if left undiagnosed and untreated, resulting in long‐lasting inflammation and irreversible reproductive tract scarring, leading to sub‐fertility, infertility, and various adverse outcomes [ 6 , 10 , 11 ]. Female genital tuberculosis (FGTB) alone is responsible for a significant proportion of female infertility cases in India [ 6 , 17 , 18 , 19 , 20 ]. The success rate of the anti‐TB drug regimens in improving fertility (~30%) suggests that early detection and effective antimicrobials, along with adjunct therapies to reduce inflammation may thus help in reversing reproductive tract damage and restoring fertility [ 21 , 22 , 23 ]. Bacterial vaginosis (BV) is also linked to PID and endometritis, further contributing to infertility [ 12 , 14 ], reducing pregnancy chances by 40% in women with subclinical PID [ 13 ]. In addition, genital infections with Chlamydia trachomatis and Ureaplasma urealyticum and its serovars have also been identified to be associated with women infertility in 13.5% and 20% cases, respectively [ 24 , 25 , 26 ]. Current diagnostic approaches for asymptomatic or subclinical genital infections rely on advanced methodologies such as molecular diagnostics (e.g., NAATs, qPCR) [ 27 , 28 , 29 ], serological tests [ 30 ], and microbial cultures [ 29 ], which are highly sensitive and effective for pathogen detection. Techniques like endometrial biopsy combined with NAAT [ 31 ] or next‐generation sequencing (NGS) [ 32 ] have further enhanced the detection of infections and dysbiosis [ 33 , 34 ] that are often missed by conventional methods and could potentially be linked to pre‐term births, recurrent abortions, or infertility. Imaging methods, including transvaginal ultrasound, laparoscopy, hysterosalpingography (HSG) [ 35 ], magnetic resonance imaging (MRI) [ 35 ], and pelvic doppler ultrasound, are currently used in identifying subclinical PID, pelvic infections, abscesses, or tubal damage that may contribute to unexplained infertility [ 35 ]. However, these techniques are often inaccessible in resource‐limited settings, presenting a huge challenge to diagnose asymptomatic women. Over the past few years, our lab has concentrated on developing rapid point‐of‐care diagnostic tools. However, during clinical validation, we observed that genital infections in women can be low in bacterial count and persist chronically without symptoms, often remaining undetected and untreated. This oversight can lead to severe complications and infertility. Remarkably, without obvious signs of infection, these women aren't routinely screened for genital infections. This prompted us to conduct a comprehensive case‐control, observational study among infertile patients in rural India, where challenges such as poor hygiene and limited access to diagnostic services exacerbate the issue. To explore infertility and the impact of genital infections on female reproductive health, we devised a detailed questionnaire and conducted a one‐time survey among infertile women in Warangal, India. The major goal was to develop a method essentially to stratify women based on detailed analysis of menstrual and reproductive anomalies, identifying those with subtle indications of infection. This systematic stratification can help prioritize women at‐risk for advanced diagnostic testing, thereby bridging the diagnostic gap and improving outcomes in fertility management.

The authors declare no conflicts of interest.

This study was conducted as a case‐control, observational survey, adhering to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) (Supporting Information  1 ) [ 1 , 36 ] guidelines for reporting epidemiological studies. The study was carried out in a fertility clinic (Curewell Hospital) located in a rural setting in Warangal, India. The study was a one‐time assessment, conducted at a single time‐point for each participant. The study included female patients aged 18‐45 years who were seeking infertility treatment at the clinic, along with their male partners. Patient recruitment for the study was done based on the gynaecologist's assessment. Patients were enrolled in the study based on the inclusion/exclusion (I/E) criteria. In this study, female patients (18‐45 years) visiting the clinic for infertility treatment (along with their male partners) were enrolled. Patients (couples) with complaints of infertility (not getting conceived for a duration of ≥ 12 months) were classified into the infertile group. Female patients outside the age group of 18‐45 years were excluded from this study. Female patients with history of HIV/HCV were excluded. The sample size (n = 97) was determined by the help of formula n = [DEFF * Np(1‐p)]/[(d 2 /Z 2 1‐α/2 *(N‐1) + p(1‐p)], where n = sample size, DEFF = design effect, N = population size (10 7 ), p = estimated proportion (6.7% ± 5), q = 1‐p, d = desired absolute precision (5%) or absolute level of precision, achieving a confidence level of 95% [ 37 , 38 ]. A total of 100 female patients were enrolled based on the inclusion/exclusion criteria and provided informed consent. The patients were categorised into case and control based on their health assessment done by the concerned gynaecologist using a questionnaire designed by the researchers. Infertile patients were further stratified into primary (no live birth) and secondary infertility cases (at least one live birth). Females with regular menstrual cycles and no recent history of infertility or genital infection were classified into the healthy control group. A total of 100 female patients were enrolled for this study based on I/E criteria defined above, following their informed consent. These patients were further allocated into groups (infertile and healthy) based on their fertility status (Figure  1 for study outline). The patients in each group were assessed at a single time‐point using a detailed questionnaire. The questionnaire (Supporting Information  2 ), designed to collect a comprehensive data on the female patients, includes a variety of 45 parameters (Table  S1 , Supporting Information  3 ). The study solely assessed the existing information provided by the patients (including previous test reports verified by the gynaecologist) and did not involve collection of any new samples or performance of any new tests. Study design flowchart. Figure depicts an outline of the case control survey conducted in infertile and healthy women to understand various parameters influencing female fertility. In this study, 45 parameters (listed in Table  S1 ) were assessed to determine frequency and correlation between different parameters (as described in Table  S2A‐D and Figure  5 , respectively). This study was performed in line with the principles of the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines (Supporting Information  1 ) [ 1 , 36 ]. The study was performed following an ethical approval of the Institutional Human Ethics Committee (IHEC approval number: BITS‐HYD/IHEC/2022/01). All participants were informed about the “aims, methods, sources of funding, possible conflicts of interest, institutional affiliations of the researcher, the anticipated benefits and potential risks of the study, and the discomfort it may involve”. Participants were enrolled only after obtaining their informed consent (See Supporting Information  2 for patient consent form). Data was collected using a questionnaire and digitally recorded in Microsoft Excel sheet using a Google Form. For the purpose of analysis, the data sets were simplified into a multinomial format (Yes/No/No data). Data analysis was performed using Microsoft Excel and IBM SPSS Statistics software (Version 25.0, IBM Corp., Chicago, IL). Analytical graphs were plotted using Microsoft Excel. Statistical analyses such as frequency distribution, likelihood ratio test, multivariate logistic regression, and multivariate correlation analysis (Phi coefficient) were conducted to assess the relationships between various parameters. The analysis followed the STROBE guidelines for reporting (Supporting Information  1 ) [ 1 , 36 ].

The “Consolidated Standards of Reporting Trails (CONSORT)” document provides a checklist which emphases on the documenting and reporting of the observational survey‐based trial design, analysis, and interpretation. Patient consent form and questionnaire. Table S1. List of parameters. Table enlists the details of parameters used for the questionnaire‐based assessment of the patients. Each parameter is provided with a variable name (ID), starting from P1 to P45. The IDs of the variable/parameter are assigned for the ease of statistical analysis – frequency distribution and Phi correlation. Table S2. Frequency distribution analysis of parameters in infertile and healthy groups. The table represents frequency distribution of individuals in each population group (healthy and infertile) with respect to parameters. A. Pathological sequelae‐ menstrual disorders (P1, P3‐6) and reproductive tract anomalies (P2, P7, P10‐14 and P16), genital infection associated symptoms or confirmed diagnosis of infections (P8, P9, P15, P17‐21), female treatment (P22‐26), exercise (P35), male reproductive semen/sperm abnormalities (P36‐44) and male treatment (P45); B. Details of menstrual flow (P33); C. Body Mass Index (BMI ‐ P34); and D. Female reproductive hormones (P27‐30)‐ FSH, LH, Prolactin and AMH. The percentage frequencies for each parameter (or variable) are calculated with respect to the total frequencies in the respective group (infertility group: 62 and healthy control group: 38). Frequency distribution in all these parameters are analyzed on the basis of multinomial response “Yes” (represented as 1) and “No” (represented as 0) and absence of response by the patients for any parameter is recorded as “No data”. P31 and P32 are excluded from the frequency analysis as the responses for these parameters are continuous in nature. Figure S1. (A and B) Female infertility and hormonal perturbations. The histogram shows population distribution of females categorized under infertile group (n = 62) experiencing abnormal (high/low) levels of female reproductive hormones such as A. FSH and LH; B. Prolactin and AMH. Hormone profiles of healthy patients were not available and hence not considered during analysis. FSH [Normal: 4‐12 IU/L; high > 12 IU/L, low  15 IU/L, low  25 ng/ml]; AMH [Normal:  3 ng/ml]. (C and D) Contribution of life style (exercise) and body mass index (BMI) to female infertility. C. Histogram shows the population distribution of all females (healthy and infertile) based on their exercise routine. D. Histogram depicts the population distribution of all females (healthy and infertile) with respect to their BMI. (Normal BMI:18‐25; Low BMI:  25). Figure S2. Male infertility and semen abnormalities. A. The histogram shows that male infertility is primarily diagnosed with abnormal semen analysis in the infertility group (n = 62) and healthy group (n = 38). B. Histogram shows the various semen abnormalities prevalent in male infertile patients in the study. Infertile [%] = [No. of male patients with symptoms/Total infertility cases]*100. Healthy [%] = [No. of healthy patients with symptoms/Total no. of healthy patients]*100. Healthy males do not show any semen abnormalities; hence their bar is represented as “0”.