Current risk factors for male infertility and semen parameters: an umbrella review of systematic reviews and meta-analyses

Abstract

Male infertility poses a substantial healthcare challenge and severely impacts the lives of patients. We aimed to investigate the risk factors for infertility and abnormal semen parameters. We conducted a comprehensive search of the articles published in Web of Science, MEDLINE, and Embase databases from January 2000 to February 2025. Infertility, semen volume, sperm concentration, sperm count, sperm morphology, sperm motility, and sperm progressive motility were used as endpoints to evaluate the relevance of risk factors. A total of 43 studies were included, covering 67 risk factors associated with infertility and abnormal sperm parameters. A total of 249 effect sizes were scored individually using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool, of which 136 (54.6%) were classified as “very low”, 59 (23.7%) as “low”, and 54 (21.7%) as “moderate”. Suffering from type 1 diabetes, metabolic syndrome, hyperthyroidism, systemic lupus erythematosus, chronic prostatitis, and leukocytospermia may increase the risk of abnormal semen parameters. Poor lifestyle habits (obesity, sleep disorders, and smoking), exposure to pollutants and various compounds (carbon disulfide, organophosphates, and lead), the use of medications (sulfasalazine, mesalazine, and selective serotonin reuptake inhibitors), and even some viral infections (severe acute respiratory syndrome coronavirus 2, human papillomavirus, and hepatitis viruses) were associated with decreased semen quality. Regular physical exercise, nut consumption, and adherence to a healthy dietary pattern may reverse this process. An increasing number of factors are associated with infertility; however, some of the aforementioned studies lack verification of causal relationships. Future studies need to be well designed to further confirm these relationships.

INTRODUCTION

Clinical infertility refers to the condition in which a couple is unable to achieve conception despite engaging in consistent, unprotected intercourse for a period of 12 months or more.1 Approximately 15% of couples of reproductive age experience infertility, with approximately 50% attributed to male infertility.2 Various diseases and factors can contribute to male infertility, including pretesticular factors (endocrine abnormalities), testicular factors (congenital abnormalities or acquired damage), and posttesticular factors (structural or functional abnormalities of the vas deferens).3,4 Routine semen analysis plays a crucial role in further diagnosing the specific type of infertility by assessing parameters such as semen volume, sperm count, and sperm motility.5,6 One of the causes of male infertility is sexually transmitted infections, among which the most common is human papillomavirus (HPV) infection. HPV infection can lead to natural conception and assisted reproductive failure through various means, primarily by damaging sperm DNA integrity, sperm motility, and sperm morphology, and HPV infection may also induce the production of antisperm antibodies (ASAs).7 A study evaluating semen samples from 117 patients undergoing in vitro fertilization also revealed that patients with an HPV-positive status had deteriorated sperm motility and morphology and that high-risk HPV types specifically affected the integrity of sperm DNA.8

In an umbrella review, a comprehensive literature search on a predetermined topic is conducted to gather relevant systematic reviews and meta-analyses (secondary evidence) to provide a more comprehensive evaluation of the latest research advancements pertaining to a disease, mechanism, or risk factor.9 This approach also offers an extensive overview of disease intervention, diagnosis, prognosis, prevalence, and risk factors to aid in disease prevention and medical decision-making.10,11,12,13 As few umbrella reviews have been published on the risk factors affecting sperm quality, this study integrates all available meta-analyses on this subject to assist patients in preventing the occurrence of infertility early. Additionally, it serves as a reference for researchers conducting new studies related to secondary research evidence.

Therefore, this review aims to summarize and provide a general assessment of the current risk factors associated with male infertility and decreased sperm quality and to assess the quality of the evidence and possible biases.

MATERIALS AND METHODS

This review is reported following the generally accepted Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.14

Eligibility criteria

Meta-analyses via the PRISMA guidelines were considered for inclusion in this review. All meta-analyses that included male infertility and sperm quality parameters (such as semen volume, sperm count, and sperm motility) as main outcome variables were eligible for inclusion. Outcome effects mainly included analyses of the prevalence of infertility, including odds ratios (ORs) and relative risks (RRs), and intergroup correlation effects of semen quality, including observational meta-analyses of the mean difference (MD) and standard mean difference (SMD). Original clinical trials, editorials, conference abstracts, studies lacking outcome measures for male semen factors, and systematic reviews without meta-analyses were excluded.

Search strategy

A comprehensive search was carried out on articles published from January 2000 to February 2025. The primary databases searched included Web of Science, MEDLINE, and Embase. The search terms used were “infertility” (OR “male infertility”, OR “semen quality”) and “systematic review” (OR “meta-analysis”, OR “meta analysis”, OR “meta-analyses”). Two investigators (QHW and JJY) initially screened the titles, keywords, and abstracts of all the retrieved studies to determine their compliance with the eligibility criteria. If a study substantially met the inclusion criteria or if it was inconclusive on the basis of the aforementioned assessment, its full text was obtained. QHW and JJY independently assessed the full texts for inclusion, resolving any discrepancies through discussion. Finally, QHW and JJY manually searched the introduction sections and reference lists of all identified studies to maximize the collection of relevant meta-analyses pertaining to this study’s topic. Any disagreements were resolved by consensus following discussion with a third investigator (ZYC).

Data collection

The data extraction process was conducted by two researchers (QHW and JJY). The following information was extracted from each meta-analysis: the type and quantity of included studies, investigators of the meta-analysis, year of publication, number of participants, outcome indicators, effect sizes of interest and 95% confidence interval (95% CI), utilization of fixed or random effects models, assessment of study heterogeneity, tests for publication bias, and criteria utilized to evaluate study quality.

Methodological and evidence quality

The review included two measures to assess study quality. Assessment of Multiple Systematic Reviews (AMSTAR),15 a reliable and effective tool for evaluating the quality of meta-analysis reporting, has been proven to be useful in assessing the quality of systematic reviews and meta-analyses of observational studies. AMSTAR consists of 11 items that are rated as “Yes”, “No”, “Cannot answer”, or “Not applicable”. These items are used to evaluate the search quality, description of individual studies, assessment of publication bias, utility of appropriate statistical methods, evaluation of the risk of bias in individual studies, and aspects related to reporting, funding sources, and conflicts of interest. We also utilized the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guideline16 to assign an overall rating of the strength of evidence.

The GRADE guideline categorizes evidence as “high” (indicating that the true effect closely aligns with the estimated effect), “moderate” (suggesting a substantial possibility that the true effect is close to the estimate, but not certain), “low” (reflecting limited confidence in the effect estimate and potential deviation from it), or “very low” (signifying little confidence in both the effect estimate and its proximity to reality, where substantial differences are likely). Randomized controlled trials initially receive a “high” score, whereas observational studies start with a “low” score. Scores are then adjusted on the basis of various factors, such as controlling for confounding variables, assessing dose-response effects, evaluating bias risk, and measuring effect size.

Data analyses

The effect sizes and 95% CIs included in this review were directly derived from published meta-analyses. The selection of meta-analysis effects with common themes was guided by the following principles. First, to avoid misleading estimates, we refrained from pooling average effects on the basis of individual meta-analyses that addressed the same topic. Therefore, when multiple meta-analyses addressed the same research question, we chose the most updated meta-analysis for inclusion in this study. Moreover, in cases where multiple meta-analyses assessing the same research topic were published within a similar time frame (within 2 years of each other), we selected the meta-analysis with the highest AMSTAR score. Additionally, meta-analyses typically provide effect sizes for specific study subgroups. When adjusted and unadjusted estimates were reported separately, we prioritized selecting effect sizes that had been adjusted for important confounders. If separate pooled effect estimates were presented for studies categorized as high or low quality, our preference was to use the pooled effect estimate specifically for high-quality studies over those representing the entire body of evidence.

We conducted a reanalysis of meta-analyses that reported weighted mean differences to obtain standardized mean effects. The I² statistic serves as an estimation of the overall variation attributed to heterogeneity rather than chance across studies within the meta-analysis.17,18 Additionally, information regarding the impact of small studies (publication bias) was initially extracted through assessing the significance value of either the Egger regression asymmetry test or Begg’s test.19,20 A small study effect was indicated when P < 0.05. Furthermore, we also assessed whether meta-analyses with a smaller number of studies tended to yield higher risk estimates using Egger’s test in a similar manner. Publication bias can occur alongside small study effects, and significant asymmetry (either visually observed in a funnel plot or indicated by a statistical regression P value) may lead to an exaggeration or underestimation of the risk estimate, thus reducing its reliability. The results were visualized using the software package R (http://www.R-project.org; The R Foundation, Boston, MA, USA).

RESULTS

Characteristics of the included studies

The process of article screening and inclusion is illustrated in Figure 1. A total of 5134 records were identified by searching three electronic databases, and 17 additional records were supplemented by manually searching the relevant articles mentioned in the “Introduction” section and the “Reference” list of the included articles. After titles, abstracts, and keywords were carefully reviewed, the full texts of 112 studies were obtained and assessed for eligibility. Sixty-nine studies were excluded because they did not meet the inclusion criteria outlined in this article, resulting in the final inclusion of 43 meta-analyses on observational studies pertaining to influencing factors.21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63 These 43 studies included 67 risk factors categorized into four groups: “Lifestyle”, “Chemical Substances”, “Infection”, and “Disease”. The main outcome measures included infertility, semen volume, sperm concentration, sperm count, sperm morphology, sperm motility, and sperm progressive motility.

Figure 1.

Flowchart pertaining to the process of literature search and screening.

Risk factors for infertility

A total of 16 infertility-related factors were retrieved from 9 studies, and detailed information is provided in Supplementary Table 1. These factors can be categorized into three main areas: lifestyle, infection, and disease. Figure 2 provides a comprehensive overview of each meta-analysis, detailing the effect size, participant count, quality assessment, and degree of heterogeneity among the studies. Regardless of bacterial infection (OR: 3.31; 95% CI: 2.60 to 4.23) or viral infection (OR: 2.24; 95% CI: 1.09 to 4.59), an overall elevated risk of infertility was observed. The representative sources of infection related to infertility included HPV (OR: 3.02; 95%: 2.11 to 4.33), Escherichia coli (OR: 3.31; 95% CI: 2.44 to 4.50), Mycoplasma genitalium (OR: 3.44; 95% CI: 1.78 to 6.64), Ureaplasma urealyticum (OR: 3.28; 95% CI: 2.07 to 5.18), and Chlamydia trachomatis (OR: 2.28; 95% CI: 1.90 to 2.72). Nevertheless, no statistically significant differences were observed in relation to cytomegalovirus infections or Ureaplasma parvum infections (P > 0.05). Furthermore, the results indicated a significant correlation between hypertension and an increased incidence of male infertility (RR: 1.08; 95% CI: 1.02 to 1.14; P < 0.05). The three forms of impaired intrauterine growth, small for gestational age, low birth weight, and very low birth weight, were associated with an increased risk of male infertility (OR: 1.10; 95% CI: 1.07 to 1.12; OR: 1.16; 95% CI: 1.05 to 1.27; and OR: 1.75; 95% CI: 1.23 to 2.49, respectively). However, cannabis use (RR: 1.16; 95% CI: 0.84 to 1.60) and multiple sclerosis (OR: 1.87; 95% CI: 0.89 to 3.94) did not significantly increase the risk of infertility (both P > 0.05).

Supplementary Table 1.

Fundamental details of the incorporated infertility-related studies

Exposure	Author	Year	Primary studies	Included patients	Semen parameters	Risk estimate	Value	Lower CI	Upper CI	Effects model	I2	P for I2	Begg’s/Egger’s test	P for Egger’s/Begg’s test	AMSATR	GRADE
Exposure to viruses	Gholami	2022	6	940	Infertility	OR	2.24	1.09	4.59	Random	31	0.218	Egger’s test	0.698	7	Moderate
Cytomegalovirus	Gholami	2022	3	600	Infertility	OR	2.03	0.91	4.54	Random	42	0.181	NA	NA	7	Low
Exposure to bacteria	Gholami	2022	26	596	Infertility	OR	3.31	2.60	4.23	Random	16	0.23	Egger’s test	0.431	7	Moderate
Chlamydia trachomatis	Gholami	2022	2	13	Infertility	OR	10.56	1.91	58.44	Random	0	0.551	NA	NA	7	Very low
Escherichia coli	Gholami	2022	3	188	Infertility	OR	3.31	2.44	4.50	Random	0	0.467	NA	NA	7	Low
HPV infection	Moreno	2020	9	1635	Infertility	OR	3.02	2.11	4.33	Random	96	<0.001	Begg’s test	0.174	10	Moderate
Mycoplasma genitalium	Cheng	2023	7	720	Infertility	OR	3.44	1.78	6.64	Fixed	0	0.456	Begg’s/Egger’s test	1.000/0.165	8	Moderate
Mycoplasma hominis	Cheng	2023	9	884	Infertility	OR	1.84	1.01	3.34	Random	77	<0.001	Begg’s/Egger’s test	0.602/0.907	8	Moderate
Ureaplasma urealyticum	Cheng	2023	14	3099	Infertility	OR	3.28	2.07	5.18	Random	85	<0.001	Begg’s/Egger’s test	0.584/0.252	8	Moderate
Ureaplasma parvum	Cheng	2023	4	246	Infertility	OR	1.67	0.95	2.95	Random	51	0.107	Begg’s/Egger’s test	0.734/0.902	8	Moderate
Chlamydia trachomatis infection	Keikha	2023	12	931	Infertility	OR	2.28	1.90	2.72	Random	82	0.01	Begg’s/Egger’s test	0.73/0.61	8	Moderate
Hypertension	Li	2022	7	102 152	Infertility	RR	1.08	1.02	1.14	Random	50	0.064	Begg’s/Egger’s test	0.133/0.056	9	Moderate
Multiple sclerosis	Shahraki	2023	3	3 897 652	Infertility	OR	1.87	0.89	3.94	Random	86.1	0.001	NA	NA	5	Very low
Small for gestational age	Meng	2024	5	1 526 653	Infertility	OR	0.91	0.89	0.93	Fixed	0	0.437	NA	NA	8	Low
Low birth weight	Meng	2024	4	1 595 679	Infertility	OR	0.86	0.78	0.94	Random	88.4	<0.001	NA	NA	8	Low
Very low birth weight	Meng	2024	5	735 755	Infertility	OR	0.57	0.4	0.81	Random	75.7	0.002	NA	NA	8	Low

NA: not applicable; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; OR: odds ratio; RR: relative risk

Figure 2.

Summary estimates for association studies of male infertility. AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; CI: confidence interval; CMV: cytomegalovirus.

Risk factors for semen quality

The remaining 34 meta-analyses reported 51 risk factors associated with semen quality (Figure 3 and 4, Supplementary Figure 1 ^{(262.6KB, tif)} –Supplementary Figure 4 ^{(207.2KB, tif)} , and Supplementary Table 2). The following section analyzes the influence of different risk factors on various semen quality evaluation indicators from four aspects: lifestyle, chemical exposure, infection-related factors, and disease conditions.

Figure 3.

Summary estimates for association studies of semen volume. COVID-19: coronavirus disease 2019; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; CI: confidence interval; NA: not applicable; PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; PFHxS: perfluorohexane sulfonate; CBP: chronic bacterial prostatitis; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; EDs: endocrine disruptors; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SCH: subclinical hyperthyroidism; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus.

Figure 4.

Summary estimates for association studies of sperm concentration. PFOA: perfluorooctanoic acid; COVID-19: coronavirus disease 2019; CI: confidence interval; NA: not applicable; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; PFHxS: perfluorohexane sulfonate; CBP: chronic bacterial prostatitis; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; EDs: endocrine disruptors; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SCH: subclinical hyperthyroidism; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus.

Supplementary Table 2.

Fundamental details of the incorporated semen quality-related studies

Exposure	Author	Year	Primary studies	Included patients	Semen parameters	Risk estimate	Value	Lower CI	Upper CI	Effects model	I2	P for I2	Begg’s/Egger’s test	P for Egger’s/Begg’s test	AMSATR	GRADE
Obese	Santi	2023	21	39 604	Semen volume	SMD	−0.22	−0.28	−0.15	Random	NA	NA	NA	NA	9	Moderate
Obese	Santi	2023	21	40 361	Sperm count	SMD	−0.33	−0.44	−0.21	Random	88	<0.0001	Begg’s test	0.19	9	Moderate
Obese	Santi	2023	21	40 092	Sperm concentration	SMD	−0.33	−0.44	−0.21	Random	NA	NA	NA	NA	9	Moderate
Obese	Santi	2023	20	15 645	Sperm morphology	SMD	−0.16	−0.25	−0.06	Random	NA	NA	NA	NA	9	Moderate
Obese	Santi	2023	12	37 698	Sperm motility	SMD	−0.22	−0.36	−0.07	Random	NA	NA	NA	NA	9	Moderate
Obese	Santi	2023	18	40 366	Sperm progressive motility	SMD	−0.24	−0.36	−0.13	Random	NA	NA	NA	NA	9	Moderate
Overweight	Santi	2023	14	51 185	Semen volume	SMD	−0.11	−0.20	−0.02	Random	NA	NA	NA	NA	9	Moderate
Overweight	Santi	2023	12	49 393	Sperm count	SMD	−0.06	−0.11	0.00	Random	63	0.002	Begg’s test	0.45	9	Moderate
Overweight	Santi	2023	18	53 186	Sperm concentration	SMD	−0.04	−0.09	0.02	Random	NA	NA	NA	NA	9	Moderate
Overweight	Santi	2023	13	19 740	Sperm morphology	SMD	−0.03	−0.14	0.07	Random	NA	NA	NA	NA	9	Moderate
Overweight	Santi	2023	6	45 889	Sperm motility	SMD	−0.04	−0.08	0.01	Random	NA	NA	NA	NA	9	Moderate
Overweight	Santi	2023	13	50 489	Sperm progressive motility	SMD	−0.15	−0.23	−0.06	Random	NA	NA	NA	NA	9	Moderate
Organophosphates	Giulioni	2021	6	493	Semen volume	MD	−0.47	−0.69	−0.25	Random	0	0.49	NA	NA	7	Low
Organophosphates	Giulioni	2021	6	493	Sperm count	MD	−40.03	−66.81	−13.25	Random	0.37	0.16	NA	NA	7	Low
Organophosphates	Giulioni	2021	5	433	Sperm concentration	MD	−13.69	−23.27	−4.12	Random	21	0.28	NA	NA	7	Low
Organophosphates	Giulioni	2021	3	196	Sperm motility	MD	−12.53	−23.30	−1.75	Random	82	0.004	NA	NA	7	Low
Organophosphates	Giulioni	2021	3	297	Sperm progressive motility	MD	−5.70	−12.89	1.50	Random	79	0.008	NA	NA	7	Low
Organophosphates	Giulioni	2021	5	400	Sperm morphology	MD	−2.40	−7.37	2.57	Random	84	<0.0001	NA	NA	7	Low
Pyrethroids	Giulioni	2021	2	61	Sperm morphology	MD	−7.61	−11.92	−3.30	Random	5	0.30	NA	NA	7	Very low
Pyrethroids	Giulioni	2021	3	101	Semen volume	MD	0.19	−0.73	1.11	Random	56	0.1	NA	NA	7	Very low
Pyrethroids	Giulioni	2021	3	320	Sperm count	MD	81.63	−112.60	275.86	Random	97	<0.0001	NA	NA	7	Very low
Pyrethroids	Giulioni	2021	4	141	Sperm concentration	MD	−1.93	−10.47	6.62	Random	0	0.54	NA	NA	7	Very low
Pyrethroids	Giulioni	2021	4	141	Sperm motility	MD	−1.04	−6.31	4.24	Random	0	1	NA	NA	7	Very low
Cannabis	Belladelli	2020	3	3332	Sperm morphology	RR	0.90	0.56	1.45	random	85	0.0016	NA	NA	8	Very low
Cigarette	Sharma	2016	20	4587	Sperm count	MD	−8.92	−12.40	−5.44	Random	90	<0.0001	NA	NA	8	Low
Cigarette	Sharma	2016	15	6357	Sperm motility	MD	−3.48	−5.53	−1.44	Random	78	<0.0001	NA	NA	8	Low
Cigarette	Sharma	2016	10	2626	Sperm morphology	MD	−1.37	−2.63	−0.11	Random	87	<0.0001	NA	NA	8	Low
Cigarette	Sharma	2016	13	3004	Semen volume	MD	−0.16	−0.33	0.01	Random	85	<0.0001	NA	NA	8	Low
PFOA	Wang	2022	4	978	Sperm progressive motility	β value	−1.38	−2.44	0.32	Fixed	3	0.38	NA	NA	7	Low
PFOA	Wang	2022	7	2190	Sperm concentration	β value	0.06	0.00	0.11	Fixed	30	0.167	NA	NA	7	Very low
PFOA	Wang	2022	5	1279	Sperm motility	β value	0.13	−0.41	0.66	Random	65	0.006	NA	NA	7	Very low
PFOA	Wang	2022	7	2190	Semen volume	β value	0.00	−0.02	0.02	Fixed	0	0.595	NA	NA	7	Very low
PFOA	Wang	2022	6	1934	Sperm count	β value	0.06	−0.02	0.13	Fixed	43	0.083	NA	NA	7	Very low
PFOA	Wang	2022	6	1934	Sperm morphology	β value	0.00	−0.01	0.01	Fixed	10	0.353	NA	NA	7	Very low
PFOA (subgroup: Asian countries)	Wang	2022	2	597	Sperm concentration	β value	0.15	0.06	0.25	Fixed	0	0.55	NA	NA	7	Very low
PFOA (subgroup: Asian countries)	Wang	2022	2	597	Sperm count	β value	0.23	0.08	0.38	Fixed	0	0.966	NA	NA	7	Very low
PFOA (subgroup: n>200)	Wang	2022	4	1430	Sperm concentration	β value	0.08	0.02	0.14	Fixed	34	0.194	NA	NA	7	Very low
PFNA	Wang	2022	3	978	Sperm progressive motility	β value	−1.31	−2.35	−0.26	Fixed	37	0.188	NA	NA	7	Very low
PFNA	Wang	2022	4	1566	Sperm concentration	β value	0.05	−0.03	0.12	Fixed	0	0.442	NA	NA	7	Very low
PFNA	Wang	2022	4	1566	Semen volume	β value	0.00	−0.03	0.02	Fixed	17	0.305	NA	NA	7	Very low
PFNA	Wang	2022	4	1566	Sperm morphology	β value	0.06	−0.05	0.18	Fixed	0	0.673	NA	NA	7	Very low
PFNA	Wang	2022	4	1566	Sperm count	β value	−0.03	−0.08	0.02	Fixed	34	0.208	NA	NA	7	Very low
PFNA (subgroup: n>200)	Wang	2022	4	911	Sperm morphology	β value	−0.14	−0.26	−0.01	Fixed	0	0.522	NA	NA	7	Very low
PFOS	Wang	2022	7	2190	Sperm concentration	β value	−0.01	−0.03	0.01	Fixed	11	0.342	NA	NA	7	Very low
PFOS	Wang	2022	3	978	Sperm progressive motility	β value	−0.63	−1.90	0.65	Random	56	0.078	NA	NA	7	Very low
PFOS	Wang	2022	5	1279	Sperm motility	β value	−0.01	−0.02	0.01	Fixed	0	0.948	NA	NA	7	Very low
PFOS	Wang	2022	7	2190	Semen volume	β value	0.01	0.00	0.01	Fixed	0	0.517	NA	NA	7	Very low
PFOS	Wang	2022	6	1934	Sperm count	β value	0.00	−0.03	0.02	Fixed	27	0.203	NA	NA	7	Very low
PFOS	Wang	2022	6	1934	Sperm morphology	β value	0.00	0.00	0.01	Fixed	0	0.547	NA	NA	7	Very low
PFOS (subgroup: n>200)	Wang	2022	3	1174	Sperm count	β value	0.06	0.01	0.11	Fixed	0	0.584	NA	NA	7	Very low
PFDA	Wang	2022	3	978	Sperm concentration	β value	−0.01	−0.08	0.06	Fixed	1	0.386	NA	NA	7	Very low
PFDA	Wang	2022	3	978	Sperm progressive motility	β value	−0.18	−2.78	2.42	Random	69.6	0.02	NA	NA	7	Very low
PFDA	Wang	2022	3	978	Sperm morphology	β value	0.01	−0.01	0.04	Fixed	0.428	0.155	NA	NA	7	Very low
PFDA	Wang	2022	3	978	Sperm count	β value	0.00	−0.11	0.12	Fixed	0	0.445	NA	NA	7	Very low
PFDA	Wang	2022	3	978	Semen volume	β value	−0.04	−0.12	0.03	Fixed	0	0.565	NA	NA	7	Very low
PFHxS	Wang	2022	3	902	Sperm concentration	β value	7.23	−11.94	26.40	Random	74	0.021	NA	NA	7	Very low
PFHxS	Wang	2022	3	902	Semen volume	β value	0.04	−0.02	0.11	Fixed	0	0.503	NA	NA	7	Very low
PFHxS	Wang	2022	3	902	Sperm count	β value	0.21	−0.03	0.45	Fixed	44	0.168	NA	NA	7	Very low
PFHxS	Wang	2022	3	902	Sperm morphology	β value	0.05	−0.11	0.21	Random	68	0.043	NA	NA	7	Very low
SASP or 5-ASA	Banerjee	2019	4	123	Sperm motility	MD	−0.39	−0.40	−0.37	Fixed	76	0.005	NA	NA	7	Low
SASP or 5-ASA	Banerjee	2019	4	107	Sperm count	MD	−28.31	−31.30	−25.32	Fixed	0	0.56	NA	NA	7	Low
Sleep disorder	Zhong	2022	6	4199	Sperm count	MD	−27.91	−37.82	−18.01	Random	72	0.0002	Begg’s/Egger’s test	1.8926/0.6642	8	Moderate
Sleep disorder	Zhong	2022	7	5595	Sperm concentration	MD	−5.16	−9.67	−0.65	Random	71	0.0003	Begg’s/Egger’s test	1.6289/0.1639	8	Moderate
Sleep disorder	Zhong	2022	7	5856	Sperm progressive motility	MD	−2.94	−5.28	−0.59	Random	81	<0.0001	Begg’s/Egger’s test	1.7871/0.2992	8	Moderate
Sleep disorder	Zhong	2022	5	3022	Sperm morphology	MD	−0.52	−0.80	−0.24	Random	0	0.74	Begg’s/Egger’s test	0.9170/0.8881	8	Moderate
Sleep disorder	Zhong	2022	5	4247	Semen volume	MD	−0.04	−0.14	0.05	Fixed	0	0.55	NA	NA	8	Moderate
Air pollution	Qian	2021	5	3206	Sperm concentration	SMD	−0.17	−0.20	−0.13	Fixed	48	0.061	NA	NA	7	Very low
Air pollution	Qian	2021	6	3370	Sperm count	SMD	−0.05	−0.08	−0.02	Fixed	46	0.064	NA	NA	7	Very low
Air pollution	Qian	2021	4	3089	Sperm motility	SMD	−0.33	−0.54	−0.11	Random	97	<0.0001	NA	NA	7	Very low
Air pollution	Qian	2021	3	2925	Sperm progressive motility	SMD	0.00	−0.13	0.12	Random	91	0	NA	NA	7	Very low
Pollutants	Pizzol	2020	13	2098	Semen volume	SMD	−0.28	−0.37	−0.20	Random	91	NA	Egger’s test	> 0.05	8	Low
Pollutants	Pizzol	2020	11	1743	Sperm count	SMD	−0.42	−0.52	−0.32	Random	96	NA	Egger’s test	> 0.05	8	Low
Pollutants	Pizzol	2020	16	2365	Sperm concentration	SMD	−0.25	−0.33	−0.16	Random	89	NA	Egger’s test	> 0.05	8	Low
Pollutants	Pizzol	2020	16	2339	Sperm motility	SMD	−0.53	−0.62	−0.43	Random	98	NA	Egger’s test	> 0.05	8	Low
Environment pollution	Pizzol	2020	2	905	Semen volume	SMD	−0.05	−0.18	0.08	Random	0	NA	Egger’s test	> 0.05	8	Low
Environment pollution	Pizzol	2020	3	949	Sperm count	SMD	0.02	−0.11	0.14	Random	0	NA	Egger’s test	> 0.05	8	Low
Environment pollution	Pizzol	2020	3	949	Sperm concentration	SMD	0.02	−0.11	0.15	Random	0	NA	Egger’s test	> 0.05	8	Low
Environment pollution	Pizzol	2020	3	949	Sperm motility	SMD	−0.12	−0.25	0.01	Random	0	NA	Egger’s test	> 0.05	8	Low
Environment pollution	Pizzol	2020	2	154	Sperm morphology	SMD	−0.21	−0.54	0.12	Random	0	NA	Egger’s test	> 0.05	8	Low
Traffic pollution	Pizzol	2020	2	243	Semen volume	SMD	−1.26	−1.55	−0.98	Random	97	NA	Egger’s test	> 0.05	8	Low
Traffic pollution	Pizzol	2020	4	475	Sperm concentration	SMD	−1.07	−1.26	−0.87	Random	89	NA	Egger’s test	> 0.05	8	Low
Traffic pollution	Pizzol	2020	4	475	Sperm motility	SMD	−2.36	−2.71	−2.00	Random	99	NA	Egger’s test	> 0.05	8	Low
Carbon disulfide	Pizzol	2020	3	355	Semen volume	SMD	−0.39	−0.62	−0.17	Random	83	NA	Egger’s test	> 0.05	8	Low
Carbon disulfide	Pizzol	2020	2	248	Sperm count	SMD	−1.91	−2.21	−1.61	Random	0	NA	Egger’s test	> 0.05	8	Low
Carbon disulfide	Pizzol	2020	3	355	Sperm concentration	SMD	−0.05	−0.27	0.18	Random	82	NA	Egger’s test	> 0.05	8	Low
Carbon disulfide	Pizzol	2020	2	248	Sperm morphology	SMD	1.67	1.38	1.97	Random	6	NA	Egger’s test	> 0.05	8	Low
HPV infection	Moreno	2020	10	4157	Sperm progressive motility	MD	−10.35	−13.75	−6.96	Random	90	<0.0001	NA	NA	10	Low
HPV infection	Moreno	2020	8	3498	Sperm morphology	MD	−2.46	−3.83	−1.08	Random	72	<0.0001	NA	NA	10	Low
HPV infection	Moreno	2020	9	3346	Semen volume	MD	−0.17	−0.37	0.03	Random	45	0.05	NA	NA	10	Low
HPV infection	Moreno	2020	10	3062	Sperm concentration	MD	−8.51	−18.96	1.94	Random	96	<0.0001	NA	NA	10	Low
HPV infection	Weinberg	2020	8	1726	Sperm count	MD	−17.68	−24.42	−11.93	Fixed	0	0.67	NA	NA	10	Moderate
COVID-19	Che	2022	5	282	Semen volume	MD	−0.08	−0.37	0.21	Fixed	0	0.425	Begg’s/Egger’s test	0.81/0.54	9	Low
COVID-19	Che	2022	5	282	Sperm concentration	MD	−6.77	−11.31	−2.23	Fixed	0	0.445	Begg’s/Egger’s test	0.81/0.63	9	Low
COVID-19	Che	2022	4	254	Sperm count	MD	−22.06	−43.45	−0.66	Fixed	46	0.133	Begg’s/Egger’s test	0.73/0.82	9	Low
COVID-19	Che	2022	5	282	Sperm progressive motility	MD	−5.10	−7.55	−2.64	Fixed	50	0.091	Begg’s/Egger’s test	1.00/0.97	9	Low
COVID-19	Che	2022	5	282	Sperm motility	MD	−6.39	−12.71	−0.07	Random	76	0.002	Begg’s/Egger’s test	0.81/0.64	9	Low
COVID-19	Che	2022	2	124	Sperm morphology	MD	−0.74	−1.16	−0.32	Fixed	0	0.554	Begg’s test	1.00	9	Low
T1DM	Facondo	2021	6	856	Sperm morphology	MD	−0.36	−0.66	−0.06	Random	NA	NA	NA	NA	7	Very low
T1DM	Facondo	2021	2	190	Sperm progressive motility	MD	−33.62	−39.13	−28.11	Random	NA	NA	NA	NA	7	Very low
T1DM	Facondo	2021	8	1049	Semen volume	MD	−0.51	−1.03	0.02	Random	NA	NA	NA	NA	7	Very low
T1DM	Facondo	2021	5	785	Sperm count	MD	−9.50	−100.99	81.99	Random	NA	NA	NA	NA	7	Very low
T1DM	Facondo	2021	8	1049	Sperm concentration	MD	5.04	−18.69	8.61	Random	71	0.001	Begg’s test	0.71	7	Very low
CPB + CP/CPPS	Condorelli	2017	10	1016	Semen volume	SMD	0.13	−0.23	0.49	Random	83	<0.0001	Egger’s test	0.070	8	Very low
CPB + CP/CPPS	Condorelli	2017	10	1086	Sperm concentration	SMD	−0.65	−1.08	−0.22	Random	89	<0.0001	Egger’s test	0.160	8	Very low
CPB + CP/CPPS	Condorelli	2017	5	367	Sperm motility	SMD	−0.37	−0.88	0.13	Random	77	0.007	NA	NA	8	Very low
CPB + CP/CPPS	Condorelli	2017	11	1228	Sperm progressive motility	SMD	−0.62	−0.94	−0.30	Random	82	<0.0001	<0.05	NA	8	Very low
CPB	Condorelli	2017	7	402	Semen volume	SMD	−0.22	−0.59	0.15	Random	63	0.01	NA	NA	8	Very low
CPB	Condorelli	2017	4	266	Sperm concentration	SMD	−0.12	−0.43	0.19	Random	28	0.24	NA	NA	8	Very low
CPB	Condorelli	2017	4	157	Sperm motility	SMD	−0.63	−0.99	−0.26	Random	0	0.63	NA	NA	8	Very low
CPB	Condorelli	2017	4	221	Sperm progressive motility	SMD	−0.41	−0.70	−0.12	Random	0	0.86	NA	NA	8	Very low
CP/CPPS	Condorelli	2017	5	614	Semen volume	SMD	0.59	0.04	1.14	Random	86	<0.0001	NA	NA	8	Very low
CP/CPPS	Condorelli	2017	7	820	Sperm concentration	SMD	−1.00	−1.63	−0.37	Random	92	<0.0001	NA	NA	8	Very low
CP/CPPS	Condorelli	2017	2	210	Sperm motility	SMD	0.02	−0.85	0.88	Random	87	0.005	NA	NA	8	Very low
CP/CPPS	Condorelli	2017	8	1007	Sperm progressive motility	SMD	−0.72	−1.16	−0.28	Random	88	<0.0001	NA	NA	8	Very low
CP/CPPS	Condorelli	2017	6	743	Sperm morphology	SMD	−1.81	−2.77	−0.84	Random	96	<0.0001	NA	NA	8	Very low
Metabolic syndrome	Zhao	2020	10	5076	Semen volume	SMD	−0.46	−2.30	1.37	Random	1	<0.0001	Egger’s test	0.122	8	Low
Metabolic syndrome	Zhao	2020	5	4341	Sperm count	SMD	−0.94	−1.58	−0.31	Random	97	<0.0001	Egger’s test	0.200	8	Low
Metabolic syndrome	Zhao	2020	11	13 471	Sperm concentration	SMD	−1.13	−1.85	−0.41	Random	99	<0.0001	Egger’s test	0.185	8	Low
Metabolic syndrome	Zhao	2020	9	13 347	Sperm morphology	SMD	−0.61	−1.01	−0.21	Random	97	<0.0001	Egger’s test	0.400	8	Low
Metabolic syndrome	Zhao	2020	6	11 479	Sperm motility	SMD	−0.68	−1.39	0.02	Random	99	<0.0001	Egger’s test	0.659	8	Low
Metabolic syndrome	Zhao	2020	9	10 591	Sperm progressive motility	SMD	−0.58	−1.00	−0.17	Random	94	<0.0001	Egger’s test	0.120	8	Low
Leukocytospermia	Castellini	2019	28	6176	sperm concentration	SMD	−0.14	−0.28	−0.01	Random	71	<0.00001	NA	NA	9	Low
Leukocytospermia	Castellini	2019	27	6104	Sperm progressive motility	SMD	−0.18	−0.29	−0.06	Random	59	<0.0001	NA	NA	9	Low
Leukocytospermia	Castellini	2019	NA	4836	Semen volume	SMD	−0.03	−0.11	0.05	Random	0	0.45	NA	NA	9	Low
Leukocytospermia	Castellini	2019	NA	3046	Sperm count	SMD	−0.11	−0.26	0.03	Random	37	0.14	NA	NA	9	Low
Leukocytospermia	Castellini	2019	NA	5915	Sperm morphology	SMD	−0.08	−0.23	0.06	Random	73	<0.00001	NA	NA	9	Low
EDs	Bliatka	2020	3	286	Sperm progressive motility	SMD	−0.45	−0.77	−0.13	Random	38	0.2	Egger’s test	> 0.05	7	Very low
EDs	Bliatka	2020	8	1102	Sperm morphology	SMD	−0.50	−0.85	−0.14	Random	87	<0.01	Egger’s test	> 0.05	7	Very low
EDs	Bliatka	2020	9	1178	Semen volume	SMD	−0.18	−0.46	0.11	Random	81	<0.01	Egger’s test	> 0.05	7	Very low
EDs	Bliatka	2020	9	1178	Sperm concentration	SMD	−0.29	−0.62	0.04	Random	86	<0.01	Egger’s test	> 0.05	7	Very low
EDs	Bliatka	2020	6	808	Sperm motility	SMD	−0.67	−1.46	0.11	Random	96	<0.01	Egger’s test	> 0.05	7	Very low
Virus	Guo	2024	60	9103	Semen volume	MD	−0.29	−0.58	0.00	Random	95	NA	Begg’s/Egger’s test	< 0.005/0.3909	9	Moderate
Virus	Guo	2024	48	6789	Sperm count	MD	−29.93	−45.87	−13.99	Random	100	NA	Begg’s/Egger’s test	< 0.005/0.3909	9	Moderate
Virus	Guo	2024	67	12 019	Sperm concentration	MD	−9.87	−14.70	−5.04	Random	97	NA	Begg’s/Egger’s test	< 0.005/0.3909	9	Moderate
Virus	Guo	2024	52	7637	Sperm motility	MD	−10.14	−14.07	−6.20	Random	99	NA	Begg’s/Egger’s test	< 0.005/0.3909	9	Moderate
Virus	Guo	2024	62	12 005	Sperm morphology	MD	−2.75	−4.47	−1.03	Random	97	NA	Begg’s/Egger’s test	< 0.005/0.3909	9	Moderate
HBV	Guo	2024	10	720	Semen volume	MD	−0.23	−0.42	−0.03	Random	83	NA	NA	NA	9	Moderate
HBV	Guo	2024	3	460	Sperm count	MD	−41.20	−82.89	0.49	Random	86	NA	NA	NA	9	Moderate
HBV	Guo	2024	13	492	Sperm concentration	MD	−18.13	−27.57	−8.68	Random	99	NA	NA	NA	9	Moderate
HBV	Guo	2024	5	627	Sperm motility	MD	−22.27	−45.96	1.43	Random	99	NA	NA	NA	9	Moderate
HBV	Guo	2024	13	436	Sperm morphology	MD	−5.11	−11.73	1.51	Random	99	NA	NA	NA	9	Moderate
HCV	Guo	2024	7	2117	Semen volume	MD	−1.28	−3.62	1.07	Random	99	NA	NA	NA	9	Moderate
HCV	Guo	2024	4	410	Sperm count	MD	−107.43	−157.36	−57.50	Random	97	NA	NA	NA	9	Moderate
HCV	Guo	2024	6	4433	Sperm concentration	MD	−14.81	−30.65	1.04	Random	98	NA	NA	NA	9	Moderate
HCV	Guo	2024	6	2320	Sperm motility	MD	−16.91	−28.76	−5.06	Random	97	NA	NA	NA	9	Moderate
HCV	Guo	2024	5	4569	Sperm morphology	MD	−10.38	−15.67	−5.09	Random	99	NA	NA	NA	9	Moderate
HIV	Guo	2024	10	1803	Semen volume	MD	−0.39	−0.70	−0.07	Random	98	NA	NA	NA	9	Moderate
HIV	Guo	2024	7	1376	Sperm count	MD	−88.38	−154.40	−23.26	Random	98	NA	NA	NA	9	Moderate
HIV	Guo	2024	13	2143	Sperm concentration	MD	−13.14	−28.87	2.60	Random	97	NA	NA	NA	9	Moderate
HIV	Guo	2024	9	1120	Sperm motility	MD	−10.73	−19.47	−1.99	Random	98	NA	NA	NA	9	Moderate
HIV	Guo	2024	7	1602	Sperm morphology	MD	−1.28	−5.51	2.96	Random	96	NA	NA	NA	9	Moderate
Herpes virus	Guo	2024	2	113	Semen volume	MD	0.09	−0.78	0.96	Random	49	NA	NA	NA	9	Moderate
Herpes virus	Guo	2024	9	1199	Sperm count	MD	2.60	−8.93	14.14	Random	98	NA	NA	NA	9	Moderate
Herpes virus	Guo	2024	6	602	Sperm concentration	MD	−3.75	−21.39	13.90	Random	77	NA	NA	NA	9	Moderate
Herpes virus	Guo	2024	11	1548	Sperm motility	MD	0.25	−2.37	2.87	Random	89	NA	NA	NA	9	Moderate
Herpes virus	Guo	2024	8	682	Sperm morphology	MD	1.05	−1.19	3.29	Random	83	NA	NA	NA	9	Moderate
SCH	Bahreiny	2024	8	NA	Semen volume	SMD	−0.99	−1.43	−0.55	Random	82.46	0.09	Egger’s test	NA	8	Low
SCH	Bahreiny	2024	6	NA	Sperm concentration	SMD	−0.51	−0.77	0.26	Fixed	8.2	0.36	Egger’s test	NA	8	Low
SCH	Bahreiny	2024	6	NA	Sperm count	SMD	−3.10	−4.50	−1.70	Random	91.2	0.06	Egger’s test	NA	8	Low
SCH	Bahreiny	2024	9	NA	Sperm morphology	SMD	−0.86	−1.45	−0.27	Random	61.17	0.27	Egger’s test	NA	8	Low
SCH	Bahreiny	2024	9	NA	Sperm progressive motility	SMD	−1.91	−3.09	−0.73	Random	95.05	<0.0001	Egger’s test	NA	8	Low
Nut Consumption	Cardoso	2024	2	223	Sperm motility	SMD	0.51	0.24	0.78	Random	0	0.95	NA	NA	8	Moderate
Nut Consumption	Cardoso	2024	2	223	Sperm morphology	SMD	0.54	0.24	0.83	Random	16	0.28	NA	NA	8	Moderate
Nut Consumption	Cardoso	2024	2	223	Sperm concentration	SMD	0.26	−0.00	0.52	Random	0	0.71	NA	NA	8	Moderate
Bariatric surgery	Gao	2022	9	436	Semen volume	SMD	−0.34	−0.86	0.20	Random	77	<0.0001	Egger’s test	0.216	8	Very low
Bariatric surgery	Gao	2022	7	252	Sperm concentration	SMD	−0.17	−0.60	0.25	Random	62	0.01	Egger’s test	0.674	8	Very low
Bariatric surgery	Gao	2022	5	294	Sperm count	SMD	0.21	−0.27	0.70	Random	75	0.003	NA	NA	8	Very low
Bariatric surgery	Gao	2022	7	336	Sperm motility	SMD	0.15	−0.07	0.36	Fixed	9	0.36	Egger’s test	0.268	8	Very low
Bariatric surgery	Gao	2022	6	312	Sperm progressive motility	SMD	−0.41	−0.91	0.08	Random	78	0.004	Egger’s test	0.599	8	Very low
Bariatric surgery	Gao	2022	9	436	Sperm morphology	SMD	−0.31	−0.80	0.19	Random	83	<0.00001	Egger’s test	0.48	8	Very low
Healthy dietary pattern	Cao	2022	6	708	Sperm concentration	MD	6.88×106 ml−1	1.26×106 ml−1	1.26×106 ml−1	Fixed	0.6	>0.1	Egger’s test	0.854	8	Low
Healthy dietary pattern	Cao	2022	5	614	Sperm count	MD	16.70×106	2.37×106	31.03×106	Fixed	48.5	>0.1	Egger’s test	0.279	8	Low
Healthy dietary pattern	Cao	2022	5	530	Sperm morphology	MD	0.28%	−0.33%	0.90%	Fixed	28.3	>0.1	Egger’s test	0.744	8	Low
Healthy dietary pattern	Cao	2022	4	436	Sperm motility	MD	6.86%	−0.25%	13.96%	Fixed	78.6	0.003	Egger’s test	0.074	8	Low
Healthy dietary pattern	Cao	2022	4	425	Sperm progressive motility	MD	5.85%	2.59%	9.12%	Fixed	0.3	>0.1	Egger’s test	0.193	8	Low
Healthy dietary pattern	Cao	2022	5	614	Semen volume	MD	0.04 ml	−0.20 ml	0.28 ml	Fixed	0	>0.1	Egger’s test	0.89	8	Low
Abstinence time	Du	2023	13	362	Semen volume	MD	−0.95	−1.16	−0.74	Random	93.83	NA	NA	NA	6	Very low
Abstinence time	Du	2023	8	207	Sperm count	MD	−102.45	−117.98	−86.91	Random	60.01	NA	NA	NA	6	Very low
Abstinence time	Du	2023	9	211	Sperm concentration	MD	−11.88	−18.96	−4.80	Random	75.99	NA	NA	NA	6	Very low
Abstinence time	Du	2023	10	281	Sperm progressive motility	MD	0.78	−2.18	3.74	Random	88.28	NA	NA	NA	6	Very low
Abstinence time	Du	2023	8	280	Sperm morphology	MD	−0.17	−0.95	0.61	Random	73.37	NA	NA	NA	6	Very low
Alcohol	Ricci	2016	13	13 299	Semen volume	MD	−0.25	−0.42	−0.07	Random	81	<0.00001	Egger’s test	> 0.05	5	Very low
Alcohol	Ricci	2016	13	14 120	Sperm concentration	MD	−0.50	−4.06	3.05	Random	73	<0.0001	Egger’s test	> 0.05	5	Very low
Alcohol	Ricci	2016	15	16 395	Sperm motility	MD	−1.53	−3.84	0.77	Random	90	<0.00001	Egger’s test	> 0.05	5	Very low
Alcohol	Ricci	2016	11	14 652	Sperm morphology	MD	−1.87	−2.88	−0.86	Random	87	<0.00001	Egger’s test	> 0.05	5	Very low
Bisphenol A	Castellini	2022	9	2399	Sperm concentration	β -coefficient	0.00	−0.14	0.14	Random	0	0.55	NA	NA	9	Very low
Bisphenol A	Castellini	2022	7	2083	Sperm count	β -coefficient	−0.0005	−0.17	0.17	Random	0	0.37	NA	NA	9	Very low
Bisphenol A	Castellini	2022	9	1897	Sperm morphology	β -coefficient	−0.02	−0.19	0.14	Random	0	0.79	NA	NA	9	Very low
Bisphenol A	Castellini	2022	7	2397	Sperm motility	β -coefficient	−0.82	−1.51	−0.12	Random	42.9	0.1	NA	NA	9	Very low
Lead	Giulioni	2023	7	818	Semen volume	SMD	−1.42	−2.29	−0.55	Random	96	<0.00001	NA	NA	6	Very low
Lead	Giulioni	2023	10	939	Sperm concentration	SMD	−1.29	−2.04	−0.54	Random	96	<0.00001	NA	NA	6	Very low
Lead	Giulioni	2023	4	262	Sperm count	SMD	−1.94	−3.77	−0.11	Random	96	<0.00001	NA	NA	6	Very low
Lead	Giulioni	2023	7	686	Sperm motility	SMD	−1.77	−2.88	−0.66	Random	97	<0.00001	NA	NA	6	Very low
Lead	Giulioni	2023	3	492	Sperm progressive motility	SMD	−0.35	−0.78	0.09	Random	75	0.02	NA	NA	6	Very low
Lead	Giulioni	2023	7	670	Sperm morphology	SMD	−1.25	−2.20	−0.29	Random	96	<0.00001	NA	NA	6	Very low
Triclosan	Adegbola	2024	2	8049	Semen volume	SMD	0.24	0.04	0.45	Random	88	0.004	NA	NA	10	Low
Triclosan	Adegbola	2024	3	11 729	Sperm count	SMD	−0.16	−0.52	0.20	Random	96	P<0.00001	NA	NA	10	Low
Triclosan	Adegbola	2024	5	19 267	Sperm concentration	SMD	−0.42	−0.75	−0.10	Random	97	P<0.00001	NA	NA	10	Low
Triclosan	Adegbola	2024	4	15 237	Sperm motility	SMD	−1.30	−2.26	−0.34	Random	100	P<0.00001	NA	NA	10	Low
Triclosan	Adegbola	2024	3	10 713	Sperm progressive motility	SMD	−1.61	−3.24	0.02	Random	100	P<0.00001	NA	NA	10	Low
Triclosan	Adegbola	2024	2	7365	Sperm morphology	SMD	1.37	−1.88	4.63	Random	100	P<0.00001	NA	NA	10	Low
Stress	Li	2010	6	1013	Semen volume	MD	−0.03	−0.37	0.32	Random	0	0.95	NA	NA	6	Low
Stress	Li	2010	6	1875	Sperm motility	MD	−6.49	−10.20	−2.78	Random	31	0.21	NA	NA	6	Low
Arsenic	Akhigbe	2024	2	453	Semen volume	MD	−0.30	−0.54	−0.05	Fixed	25	0.25	NA	NA	8	Very low
Arsenic	Akhigbe	2024	2	453	Sperm concentration	MD	−25.04	−95.50	45.42	Random	99	P<0.00001	NA	NA	8	Very low
Arsenic	Akhigbe	2024	2	453	Sperm motility	MD	−22.89	−59.94	14.15	Random	99	P<0.00001	NA	NA	8	Very low
Mobile telephone	Adams	2014	9	1448	Sperm motility	MD	−8.1	−13.1	−3.20	Random	89.5	P<0.0001	NA	NA	7	Low
Mobile telephone	Adams	2014	6	1376	Sperm concentration	MD	−3.19	−16.60	10.22	Random	89.1	P<0.0001	NA	NA	7	Low
Vegetarian diet	Samimisedeh	2023	4	544	Sperm concentration	SMD	−0.20	−0.50	0.11	Fixed	38	0.19	Egger’s test	> 0.05	7	Very low
Vegetarian diet	Samimisedeh	2023	3	534	Sperm morphology	SMD	−0.06	−0.38	0.25	Fixed	0	0.6	Egger’s test	> 0.05	7	Very low
Vegetarian diet	Samimisedeh	2023	3	534	Sperm motility	SMD	0.02	−1.32	1.36	Random	91	<0.01	Egger’s test	> 0.05	7	Very low
Vegetarian diet	Samimisedeh	2023	4	544	Sperm progressive motility	SMD	−0.47	−1.20	0.25	Random	70	0.02	Egger’s test	> 0.05	7	Very low
Vegetarian diet	Samimisedeh	2023	3	504	Sperm count	SMD	0.28	−0.66	1.23	Random	74	0.02	Egger’s test	> 0.05	7	Very low
Physical activity	Giudice	2024	2	280	Sperm concentration	SMD	0.28	0.05	0.52	Fixed	0	0.69	NA	NA	9	Moderate
Physical activity	Giudice	2024	NA	NA	Semen volume	SMD	0.15	0.06	0.36	Fixed	0	0.6	NA	NA	9	Moderate
Physical activity	Giudice	2024	3	340	Sperm motility	SMD	0.63	0.41	0.85	Fixed	60	0.08	NA	NA	9	Moderate
Physical activity	Giudice	2024	2	99	Sperm count	SMD	0.62	0.19	1.05	Fixed	0	0.99	NA	NA	9	Moderate
Physical activity	Giudice	2024	2	323	Sperm morphology	SMD	0.56	0.34	0.78	Fixed	75	0.05	NA	NA	9	Moderate
COVID-19 vaccination	Huang	2022	9	1352	Semen volume	MD	0.18 ml	−0.02	0.38	Random	46	0.06	Egger’s test	> 0.05	8	Low
COVID-19 vaccination	Huang	2022	10	1414	Sperm concentration	MD	1.16 million/ml	−1.34	3.66	Random	0	0.82	Egger’s test	> 0.05	8	Low
COVID-19 vaccination	Huang	2022	8	1156	Sperm motility	MD	−0.14%	−2.84	2.56	Random	70	0.001	Egger’s test	> 0.05	8	Low
COVID-19 vaccination	Huang	2022	8	1196	Sperm progressive motility	MD	−1.06%	−2.88	0.77	Random	0	0.76	Egger’s test	> 0.05	8	Low
COVID-19 vaccination	Huang	2022	6	1028	Sperm count	MD	5.92 million	−10.22	22.05	Random	46	0.1	Egger’s test	> 0.05	8	Low
COVID-19 vaccination	Huang	2022	6	850	Sperm morphology	MD	0.07%	−0.84	0.97	Random	88	<0.00001	Egger’s test	> 0.05	8	Low
SSRIs	Xu	2022	5	316	Sperm morphology	MD	−10.03	−16.29	−3.77	Random	98	<0.00001	NA	NA	7	Very low
SSRIs	Xu	2022	5	316	Sperm concentration	MD	−24.03	−43.88	−4.18	Random	94	<0.00001	NA	NA	7	Very low
SSRIs	Xu	2022	5	316	Sperm motility	MD	−11.96	−23.46	−0.47	Random	99	<0.00001	NA	NA	7	Very low
SSRIs	Xu	2022	3	216	Semen volume	MD	−0.05	−0.75	0.65	Random	88	0.0003	NA	NA	7	Very low
SLE	Zhu	2025	3	162	Sperm concentration	MD	−38.71	−106.13	28.70	Random	75	0.02	NA	NA	9	Very low
SLE	Zhu	2025	3	162	Semen volume	MD	0.20	−1.07	1.47	Random	87	0.0004	NA	NA	9	Very low
SLE	Zhu	2025	3	162	Sperm count	MD	−0.54	−0.86	−0.22	Fixed	30	0.24	NA	NA	9	Very low
SLE	Zhu	2025	3	162	Sperm motility	MD	−6.48	−14.99	2.02	Random	51	0.13	NA	NA	9	Very low
SLE	Zhu	2025	3	162	Sperm morphology	MD	−2.87	−6.18	0.44	Random	86	0.0008	NA	NA	9	Very low

NA: not applicable; RR: relative risk; MD: mean difference; SMD: standard mean difference; CMV: cytomegalovirus; PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; PFHxS: perfluorohexane sulfonate; SASP: sulphasalazine; 5-ASA: mesalazine; CBP: chronic bacterial prostatitis; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; T1DM: diabetes mellitus type 1; EDs: endocrine disruptors; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SCH: subclinical hyperthyroidism; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus; CI: Confidence interval; AMSTAR: Assessment of Multiple Systematic Review; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; COVID-19: coronavirus disease 2019

Impact of lifestyle on sperm quality

First, for lifestyle factors, obesity was significantly associated with decreased semen volume (SMD: −0.22; 95% CI: −0.22 to −0.15), sperm concentration (SMD: −0.33; 95% CI: −0.44 to −0.21), sperm count (SMD: −0.33; 95% CI: −0.44 to −0.21), sperm morphology (SMD: −0.16; 95% CI: −0.25 to −0.06), sperm motility (SMD: −0.22; 95% CI: −0.36 to −0.07), and sperm progressive motility (SMD: −0.24; 95% CI: −0.36 to −0.13; all P < 0.05), whereas overweight increased only the risk of decreased semen volume (SMD: −0.11; 95% CI: −0.20 to −0.02) and sperm progressive motility (SMD: −0.15; 95% CI: 0.23 to −0.06). Another study indicated that bariatric surgery fails to increase semen quality in obese patients. Smoking was also linked to a decreased sperm count (MD: −8.92 × 10⁶ ml⁻¹; 95% CI: −12.40 × 10⁶ ml⁻¹ to −5.44 × 10⁶ ml⁻¹), abnormal sperm morphology (MD: −1.37%; 95% CI: −2.63% to −3.35%), and impaired sperm motility (MD: −3.48%; 95% CI: −5.53% to −1.44%). Furthermore, individuals who abstain from alcohol consumption and those with low alcohol intake presented greater semen volume (MD: 0.25 ml; 95% CI: 0.07 ml to 0.42 ml) and superior sperm morphology (MD: 1.87%; 95% CI: 0.86% to 2.88%). Additionally, a study revealed that subjects adhering to a healthier dietary pattern exhibited significantly higher semen concentrations (MD: 6.88 × 10⁶ ml⁻¹; 95% CI: 1.26 × 10⁶ ml⁻¹ to 12.49 × 10⁶ ml⁻¹), sperm counts (MD: 16.70 × 10⁶; 95% CI: 2.37 × 10⁶ ml⁻¹ to 31.03 × 10⁶ ml⁻¹), and sperm progressive motility (MD: 5.85%; 95% CI: 2.59% to 9.12%) than subjects in the control group (all P < 0.05). Daily consumption of nuts has also been shown to mildly improve specific sperm parameters, including sperm motility (SMD: 0.51; 95% CI: 0.24 to 0.78) and sperm morphology (SMD: 0.54; 95% CI: 0.24 to 0.83). However, a follow-up study demonstrated no definitive correlation between a vegetarian diet and semen quality (P > 0.05).

Exposure to various pollutants was associated with significant decreases in semen volume, sperm concentration, sperm count, and sperm motility (all P > 0.05). Air pollution exposure was associated with decreased sperm concentration (SMD: −0.17; 95% CI: −0.20 to −0.13), sperm count (SMD: −0.05; 95% CI: −0.08 to −0.02), and sperm motility (SMD: −0.33; 95% CI: −0.54 to −0.11), whereas traffic pollution exposure was associated with decreased semen volume (SMD: −1.26; 95% CI: −1.55 to −0.98), sperm concentration (SMD: −1.07; 95% CI: −1.26 to −0.87), and sperm motility (SMD: −2.36; 95% CI: −2.71 to −2.00). Exposure to carbon disulfide, as a pollutant, showed different effects than the other exposure factors, with a positive effect on sperm morphology (SMD: 1.67; 95% CI: 1.38 to 1.97); however, it had a negative effect on semen volume (SMD: −0.39; 95% CI: −0.62 to −0.17) and sperm count (SMD: −1.91; 95% CI: −2.21 to −1.61).

In addition, sleep disorders were associated with decreased sperm concentration (MD: −5.16; 95% CI: −9.67 to −0.56), sperm count (MD: −27.91; 95% CI: −37.82 to −18.01), sperm morphology (MD: −0.52; 95% CI: −0.80 to −0.24), and sperm progressive motility (MD: −2.94; 95% CI: −5.28 to −0.59). Psychological stress also had a detrimental effect on sperm motility (MD: −6.49; 95% CI: −10.2 to −2.78). Another study indicated that mobile phone usage was associated with a reduction in sperm motility (MD: −8.10%; 95% CI: −13.1 to −3.20) relative to other semen parameters. Compared with short-term abstinence, long-term abstinence led to decreases in semen volume (MD: −0.95 ml; 95% CI: −1.16 ml to −0.74 ml), sperm count (MD: −102.45 × 10⁶; 95% CI: −117.98 × 10⁶ to −86.91 × 10⁶), and sperm concentration (MD: −11.88 × 10⁶ ml⁻¹; 95% CI: −18.96 × 10⁶ ml⁻¹ to −4.8 × 10⁶ ml⁻¹) in healthy males. Compared with the adverse effects on semen quality caused by other lifestyle factors, a recent study demonstrated that consistent engagement in sports activities improved parameters such as sperm concentration (SMD: 0.28; 95% CI: 0.05 to 0.52), semen volume (SMD: 1.67; 95% CI: 0.06 to 0.36), sperm motility (SMD: 0.63; 95% CI: 0.41 to 0.85), sperm count (SMD: 0.62; 95% CI: 0.19 to 1.05), and sperm morphology (SMD: 0.56; 95% CI: 0.34 to 0.78).

Impact of chemical exposure on sperm quality

The effects of chemical exposure on semen quality are mainly due to drugs and compounds in the environment. Organophosphates were positively correlated with decreased semen volume (MD: −0.47 ml; 95% CI: −0.69 ml to −0.25 ml), sperm concentration (MD: −13.69 × 10⁶ ml⁻¹; 95% CI: −23.27 × 10⁶ ml⁻¹ to −4.12 × 10⁶ ml⁻¹), sperm count (MD: −40.03 × 10⁶; 95% CI: −66.81 × 10⁶ to −13.25 × 10⁶), and sperm motility (MD: −12.53%; 95% CI: −23.30% to −1.75%). Similarly, pyrethroids had an adverse effect on sperm morphology (MD: −7.61%; 95% CI: −11.92% to −3.30%). Postpartum exposure to endocrine disruptors may lead to a decline in sperm motility (SMD: −0.45; 95% CI: −0.77 to −0.13) and a reduction in the number of sperm with a normal morphology (SMD: −0.50; 95% CI: −0.85 to −0.14). Similarly, a study investigating environmental disruptors revealed a negative correlation between urinary bisphenol A levels and sperm motility (β coefficient: −0.82; 95% CI: −1.51 to −0.12). Daily exposure to triclosan may impair semen quality by reducing sperm concentration (SMD: −0.42; 95% CI: −0.75 to −0.10) and sperm motility (SMD: −1.30; 95% CI: −2.26 to −0.34).

Moreover, there was significant heterogeneity in the effects of fluoride exposure on sperm quality. A significant negative correlation was observed between perfluorononanoic acid (PFNA) exposure and both sperm morphology (β value: –0.14; 95% CI: –0.26 to –0.01) and progressive motility (β value: -1.31; 95% CI: –2.35 to –0.26) in studies with sample sizes over 200. Meanwhile, perfluorooctanoic acid (PFOA) exposure showed a positive association with sperm concentration (β value: 0.15; 95% CI: 0.06 to 0.25) and total sperm count (β value: 0.23; 95% CI: 0.08 to 0.38) in the subgroup of Asian countries. Conversely, compared with the unexposed group, the perfluorooctane sulfonate (PFOS) subgroup presented an increased sperm count (β value: 0.06; 95% CI: 0.01 to 0.11) in studies with sample sizes over 200. Furthermore, exposure to arsenic led to a slight decrease in semen volume (MD: −0.30 ml; 95% CI: −0.05 ml to −0.54 ml). Lead is a prevalent chemical substance in daily life, and research has demonstrated that exposure to lead is associated with adverse effects on various semen parameters, including semen volume (SMD: −1.42; 95% CI: −2.29 to −0.55), sperm concentration (SMD: −1.29; 95% CI: −2.04 to −0.54), sperm count (SMD: −1.94; 95% CI: −3.77 to −0.11), sperm motility (SMD: −1.77; 95% CI: −2.88 to −0.66), and sperm morphology (SMD: −1.25; 95% CI: −2.20 to −0.29).

Several additional studies have examined the effects of specific therapeutic drugs on semen quality. Significant differences were observed in the reduction in sperm count (MD: −28.31 × 10⁶; 95% CI: −31.30 × 10⁶ to −25.32 × 10⁶) and sperm motility (MD: −0.39%; 95% CI: −0.40 to −0.37) with inflammatory bowel disease medications (sulfasalazine or mesalazine). Furthermore, selective serotonin reuptake inhibitors (SSRIs) are extensively utilized across a broad spectrum of clinical conditions. A recent study revealed an association between SSRIs use and a decrease in several semen parameters, including sperm morphology (MD: −10.03%; 95% CI: −16.29% to −3.77%), sperm concentration (MD: −24.03 × 10⁶ ml⁻¹; 95% CI: −43.88 × 10⁶ ml⁻¹ to −4.18 × 10⁶ ml⁻¹), and sperm motility (MD: −11.96%; 95% CI: −23.46% to −0.47%). Specifically, coronavirus disease 2019 (COVID-19) vaccination had no detrimental effect on semen quality.

Impact of infection and disease condition on sperm quality

COVID-19 infection was associated with a decreased sperm concentration (MD: −6.77 × 10⁶ ml⁻¹; 95% CI: −11.31 × 10⁶ ml⁻¹ to −2.23 × 10⁶ ml⁻¹), sperm count (MD: −22.06 × 10⁶; 95% CI: −43.45 × 10⁶ to −0.66 × 10⁶), sperm morphology (MD: −0.74%; 95% CI: −1.16% to −0.32%), sperm motility (MD: −6.39%; 95% CI: −12.71% to −0.07%), and sperm progressive motility (MD: −5.10%; 95% CI: −7.55% to −2.64%), whereas HPV infection was negatively associated with sperm count (MD: −17.68 × 10⁶; 95% CI: −24.42 × 10⁶ to −11.93 × 10⁶), sperm morphology (MD: −2.46%; 95% CI: −3.83% to −1.08%), and sperm progressive motility (MD: −10.35%; 95% CI: −13.75% to −6.96%). A separate investigation examined the influence of coexisting viral infections on seminal parameters. Overall, viral infections have the potential to exert detrimental effects on key parameters of semen quality, including sperm count (MD: −29.93 × 10⁶; 95% CI: −45.87 × 10⁶ to −13.99 × 10⁶), sperm concentration (MD: −9.87 × 10⁶ ml⁻¹; 95% CI: −14.7 × 10⁶ ml⁻¹ to −5.04 × 10⁶ ml⁻¹), sperm motility (MD: −10.14%; 95% CI: −14.07% to −6.20%), and morphological characteristics (MD: −2.75; 95% CI: −4.47 to −1.03). Among these, hepatitis virus infections can induce varying degrees of damage to semen parameters. Specifically, hepatitis B virus (HBV) infection may result in a reduced semen volume (MD: −0.23; 95% CI: −0.42 to −0.03) and sperm concentration (MD: −18.13 × 10⁶ ml⁻¹; 95% CI: −27.57 × 10⁶ ml⁻¹ to −8.68 × 10⁶ ml⁻¹), whereas hepatitis C virus (HCV) infection can lead to a decline in sperm count (MD: −107.43 × 10⁶; 95% CI: −157.36 × 10⁶ to −57.50 × 10⁶), sperm motility (MD: −16.91%; 95% CI: −28.76% to −5.06%), and sperm morphology (MD: −10.38%; 95% CI: −15.67% to −5.09%). Moreover, human immunodeficiency virus (HIV) infection can also result in a reduction in semen volume (MD: −0.39 ml; 95% CI: −0.70 ml to −0.07 ml), sperm concentration (MD: −88.38 × 10⁶ ml⁻¹; 95% CI: −154.40 × 10⁶ ml⁻¹ to −23.26 × 10⁶ ml⁻¹), and sperm motility (MD: −10.73%; 95% CI: −19.47% to −1.99%). Conversely, herpes virus infection had no apparent significant effect on semen quality parameters.

In addition, endocrine-related metabolic disorders can also damage semen quality to a significant extent. The decreases in sperm morphology (MD: −0.36%; 95% CI: −0.66% to −0.06%) and sperm progressive motility (MD: −33.62%; 95% CI: −39.13% to −28.11%) were more pronounced in people with type 1 diabetes mellitus. In addition, people with metabolic syndrome had decreased sperm concentration (SMD: −1.13; 95% CI: −1.85 to −0.41), sperm count (SMD: −0.94; 95% CI: −1.58 to −0.31), sperm morphology (SMD: −0.61; 95% CI: −1.01 to −0.21), and sperm progressive motility (SMD: −0.58; 95% CI: −1.00 to −0.17). A subsequent study confirmed that compared with the normal population, patients with subclinical hyperthyroidism (SCH) had decreased semen volume (SMD: −0.99; 95% CI: −1.43 to −0.55), sperm count (SMD: −3.10; 95% CI: −4.50 to −1.70), sperm morphology (SMD: −0.86; 95% CI: −1.45 to −0.27), and sperm progressive mobility (SMD: −1.91; 95% CI: −3.09 to −0.73). For prostatitis, chronic bacterial prostatitis (CBP) has been shown to be negatively correlated with sperm motility (SMD: −0.63; 95% CI: −0.99 to −0.26) and sperm progressive motility (SMD: −0.41; 95% CI: −0.70 to −0.12). Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) leads to a decrease in sperm concentration (SMD: −1.00; 95% CI: −1.63 to −0.37), sperm morphology (SMD: −1.81; 95% CI: −2.77 to −0.84), and sperm progressive motility (SMD: −0.72; 95% CI: −1.16 to −0.28). Specifically, an increased volume of semen (SMD: 0.59; 95% CI: 0.04 to 1.14) has been observed within the CP/CPPS population. In addition, the sperm concentration (SMD: −0.14; 95% CI: −0.28 to −0.01) and sperm progressive motility (SMD: −0.18; 95% CI: −0.29 to −0.06) of patients with leukocytospermia also decreased significantly. For patients with certain autoimmune diseases, such as systemic lupus erythematosus (SLE), studies have demonstrated a slight decrease in sperm count (SMD: −0.54; 95% CI: −0.86 to −0.22), whereas no significant changes were observed in other semen parameters.

Publication bias and research quality

Supplementary Table 3 provides detailed data on the main findings as well as summary tables presenting the AMSTAR scores for each of the 43 meta-analyses. The quality of meta-analysis report writing was generally found to be high (AMSTAR, mean: 7.7, range: 5–10). Among the meta-analyses included in this study,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63 publication bias was assessed for 28 out of 67 (41.8%) risk factors using Egger’s test or Begg’s test. Other studies assessed publication bias by visually inspecting the symmetry of funnel plots, except for 12 articles that were not evaluated. The lack of reporting on publication bias in most cases could be attributed to the small number of main effects (n < 10), where estimates were deemed unreliable. The GRADE tool was used to individually score the 249 effect sizes. A total of 136 (54.6%) effect sizes were classified as “very low”, 59 (23.7%) as “low”, and 54 (21.7%) as “moderate”. A low GRADE score was typically due to all included studies being observational, resulting in an initial “low” score, whereas downgraded scores were influenced primarily by poor control over bias or significant heterogeneity among articles. There were 31 effects that displayed substantial heterogeneity (I²: 50%–75%) and 108 effects that exhibited very high heterogeneity (I² > 75%). Heterogeneity is often accounted for by moderating factors such as geographic location, demographic characteristics (e.g., age, weight, and sex), study quality assessments, and sample sizes.

Supplementary Table 3.

Assessments of assessment of multiple systematic review scores for studies included

Risk factors	Author	Year	A priori design provided	Duplicate study selection and data extraction	At least two electronic databases searched	Status of publication used as an inclusion criterion	List of included and excluded studies provided	Characteristics of included studies provided	Scientific quality of included studies assessed	Scientific quality of the included studies used appropriately to form conclusions	Appropriate methods to combine studies	Publication bias assessed	Conflict of interest included	Total AMSTAR score
Weight	Santi	2023	1	1	1	0	1	1	1	0	1	1	1	9
Pyrethroids or organophosphates	Giulioni	2021	0	1	1	0	1	1	1	0	1	0	1	7
Cannabis	Belladelli	2020	0	1	1	0	1	1	1	0	1	1	1	8
Cigarette	Sharma	2016	0	1	1	0	1	1	1	0	1	1	1	8
PFAS	Wang	2023	0	1	1	0	1	1	1	0	1	0	1	7
SASP and 5-ASA	Banerjee	2019	0	1	1	0	1	1	1	0	1	0	1	7
Sleep disorders	Zhong	2022	0	1	1	0	1	1	1	0	1	1	1	8
Air pollution	Qian	2021	0	1	1	0	1	1	1	0	1	0	1	7
Pollutants	Pizzol	2020	0	1	1	0	1	1	1	0	1	1	1	8
Zinc levels in seminal plasma	Zhao	2016	0	1	1	0	1	1	0	0	1	1	1	7
Cadmium content in semen	Zhang	2018	0	0	1	0	1	1	1	0	1	1	1	7
Bacterial and viral	Gholami	2022	0	0	1	0	1	1	1	0	1	1	1	7
HPV1	Moreno	2020	1	1	1	0	1	1	1	1	1	1	1	10
HPV2	Weinberg	2020	1	1	1	0	1	1	1	1	1	1	1	10
Genital mycoplasma	Chen	2023	0	1	1	0	1	1	1	0	1	1	1	8
COVID-19	Che	2023	1	1	1	0	1	1	1	0	1	1	1	9
Chlamydia trachomatis	Masoud	2023	0	0	1	0	1	1	1	0	1	1	1	7
Diabetes mellitus type 1	Facondo	2021	0	1	0	0	1	1	1	0	1	1	1	7
Hypertension	Li	2022	0	1	1	0	1	1	1	1	1	1	1	9
Chronic prostatitis	Condorelli	2017	0	1	1	0	1	1	1	0	1	1	1	8
Metabolic Syndrome	Zhao	2020	0	1	1	0	1	1	1	0	1	1	1	8
Leukocytospermia	Castellini	2019	1	1	1	0	1	1	1	0	1	1	1	9
Endocrine disruptors	Bliatka	2020	0	1	1	0	1	1	0	0	1	1	1	7
Virus	Guo	2024	1	1	1	0	1	1	1	0	1	1	1	9
Multiple sclerosis	Shahraki	2023	0	0	1	0	1	1	0	0	1	0	1	5
Impaired intrauterine growth	Meng	2024	1	1	1	0	1	1	1	0	1	0	1	8
Subclinical hyperthyroidism	Bahreiny	2024	1	1	1	0	1	1	1	0	1	0	1	8
Nut consumption	Cardoso	2024	1	1	1	0	1	1	1	0	1	0	1	8
Bariatric surgery	Gao	2022	0	1	1	0	1	1	1	0	1	1	1	8
Healthy dietary pattern	Cao	2022	0	1	1	0	1	1	1	0	1	1	1	8
Abstinence time	Du	2023	1	1	1	0	1	0	0	0	1	0	1	6
Alcohol	Ricci	2016	0	1	1	0	0	1	0	0	0	1	1	5
Bisphenol A	Castellini	2022	1	1	1	0	1	1	1	0	1	1	1	9
Lead	Giulioni	2023	1	1	1	0	0	1	1	0	0	0	1	6
Triclosan	Adegbola	2024	1	1	1	0	1	1	1	1	1	1	1	10
Stress	Li	2010	0	0	1	0	1	1	1	0	1	0	1	6
Arsenic	Akhigbe	2024	0	1	1	0	0	1	1	1	1	1	1	8
Mobile telephones	Adams	2014	0	1	1	0	1	1	0	0	1	1	1	7
Vegetarian diet	Samimisedeh	2023	0	1	1	0	1	1	0	0	1	1	1	7
Physical activity	Giudice	2024	1	1	1	0	1	1	1	0	1	1	1	9
COVIDcel vaccination	Huang	2022	0	1	1	0	1	1	1	0	1	1	1	8
SSRIs	Xu	2022	0	1	1	0	1	1	1	0	1	0	1	7
SLE	Zhu	2025	1	1	1	0	1	1	1	0	1	1	1	9

COVID-19: coronavirus disease 2019; AMSTAR: Assessment of Multiple Systematic Review; CMV: cytomegalovirus; PFAS: perfluoroalkyl and polyfluoroalkyl substance; SASP: sulphasalazine; 5-ASA: mesalazine; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus; HPV: human papillomavirus

DISCUSSION

This review provides a broad overview of the available evidence on the relevance of lifestyle factors, chemical substances, infections, and diseases to male infertility. The methodological quality of the included meta-analyses and the quality of evidence and risk of bias for extracting associations were assessed.

This umbrella review included a total of 43 published meta-analyses with 249 estimated summary effects for 67 risk factors for male infertility. The methodological quality of the included meta-analyses was generally high (the mean AMSTAR score was 7.7). None of the estimated combined effects of the interventions were rated high in terms of evidence quality, with moderate, low, and very low evidence quality accounting for approximately 21.7%, 23.7%, and 54.6%, respectively. High blood pressure, impaired intrauterine growth, and bacterial and viral infections are considered risk factors for infertility. These findings suggest that reducing exposure to these risk factors or implementing timely interventions may be an effective way to prevent male infertility. Numerous studies have indicated that a variety of factors are linked to irregularities in semen quality parameters. These factors include weight gain, sleep disturbances, and stress. Additionally, the frequent use of mobile telephones has been suggested to have adverse effects due to potential electromagnetic radiation exposure. Lifestyle choices such as smoking and drinking alcohol are also known to impair semen quality. Environmental factors such as traffic pollution and exposure to chemicals such as carbon disulfide, organophosphates, and pyrethroids can introduce harmful substances that affect sperm health. Furthermore, exposure to fluoride, endocrine disruptors (EDs), bisphenol A, arsenic, and triclosan, as well as heavy metals such as lead, is associated with decreased semen quality. The use of certain medications, including COVID-19 vaccines, SSRIs, SASP (sulfasalazine), and 5-ASA (mesalazine), has also been linked to abnormal semen parameters. Additionally, contracting SARS-CoV-2 itself, along with other viral infections, such as those caused by HPV, HBV, HCV, and HIV, can lead to reproductive issues. Chronic health conditions such as diabetes, metabolic syndrome, subclinical hyperthyroidism (SCH), systemic lupus erythematosus (SLE), and prostatitis are also associated with abnormal semen quality, as they can contribute to inflammation and hormonal imbalances that impair male reproductive function. The consumption of nuts, adherence to a healthy dietary pattern, and engagement in appropriate physical exercise can potentially enhance semen quality. Therefore, men of reproductive age should endeavor to minimize their exposure to these potential risk factors. Furthermore, the pathological mechanisms underlying male infertility and the alterations in semen quality parameters resulting from exposure to various risk and protective factors remain incompletely understood. The subsequent sections of this article delve deeper into the associated molecular mechanisms.

Regarding the influence of lifestyle on semen quality, our primary focus is on certain conventional detrimental habits, such as smoking and alcohol consumption. Sharma et al.38 suggested that smoking was associated with a reduction in sperm count, motility, and morphology, while the impact on semen volume remained inconclusive. The precise mechanism by which smoking impacts semen parameters remains incompletely understood; however, chemicals derived from cigarettes, namely, nicotine and cotinine, have been demonstrated to exert detrimental effects on the development of male germ cells.64 Additionally, this study further substantiated the inverse relationship between daily cigarette consumption and semen parameters. Nevertheless, no existing study has specifically assessed the temporal aspects or the effects of smoking cessation on semen characteristics. Moreover, a meta-analysis involving 15 cross-sectional studies demonstrated that excessive alcohol consumption can lead to a decrease in semen volume and a reduction in the proportion of morphologically normal sperm.52 Animal experiments have revealed oxidative damage in the testicles and epididymides of animals exposed to alcohol, which in turn leads to sperm DNA breakage, thereby increasing sperm DNA fragmentation or degeneration and inducing cell apoptosis.65 The impact of air pollution on male semen quality has been extensively studied, and research indicates that particulate matter 2.5 (PM2.5), as a key parameter for assessing air pollution, can transport various toxic substances and trace elements, leading to the induction of oxidative stress. Additionally, it can induce DNA damage and histopathological changes in the testes of mice, resulting in abnormal semen parameters.66,67,68,69 In addition to other factors, the effects of PM2.5 exposure on semen quality reduction may also involve the hypothalamic-pituitary-gonadal (HPG) axis, which affects hormone levels.70 In addition to the aforementioned description of air pollution, further studies have demonstrated that toxic pollutants can induce alterations in the fluidity and electrochemical potential of the sperm membrane, thereby affecting sperm quality.71 The proposed mechanisms underlying sperm movement disabilities include the initiation of a cascade culminating in sperm apoptosis, perturbations in mitochondrial functionality, and the upregulation of mitochondrially associated apoptosis-promoting genes.67,72

With the advancement of modern society and the hastened pace of daily life, the detrimental impact of obesity, along with its myriad underlying causes, on health has become increasingly pronounced. Santi et al.41 demonstrated a “dose-dependent” relationship between weight gain and deterioration of semen parameters. Specifically, when the body mass index (BMI) falls within the range of 25.0 kg m⁻² to 29.9 kg m⁻², only a limited number of abnormal semen parameters are observed; however, when the BMI exceeds 30 kg m⁻², all semen parameters significantly decrease. To date, three different mechanisms have been proposed to explain this phenomenon: (1) obesity-related leptin resistance restrains the HPG axis, thereby reducing the physiological stimulation of the testis; (2) inflammation at the hypothalamic level impairs HPG axis function owing to decreased expression of the kisspeptin-1 receptor; and (3) overweight may cause changes in gonadal function, such as spermatogenic interstitial compartment damage, leading to decreased semen quality.73,74,75 A subsequent study demonstrated that bariatric surgery did not significantly improve semen quality in obese men, likely because of the irreversible effects of long-term leptin resistance on semen parameters, which cannot be fully addressed by bariatric surgery alone.49 Existing animal experiments have demonstrated that compared to sedentary animals, exercising animals have higher serum testosterone levels and increased levels of mitochondrial superoxide dismutase 1 (SOD1) and SOD2,76 suggesting a greater cellular capacity for recovery from stress-induced states. There are also aggregated clinical studies that have demonstrated a significant correlation between physical activity and improvements in semen parameters.60 In addition, adhering to a healthy diet and maintaining a daily intake of nuts can improve semen quality to a certain extent, whereas a vegetarian diet has not been found to have any positive effect on semen parameters.48,50,59 Reactive oxygen species (ROS) are intermediate products of oxygen-derived molecules formed during cellular metabolism, which can affect sperm membranes and alter sperm DNA at high concentrations.77 Individuals adhering to a healthy diet typically consume larger proportions of antioxidant vitamins and folic acid. These compounds, which act as ROS scavengers, can mitigate the detrimental effects of elevated ROS levels on semen quality. Furthermore, nuts are rich in omega-3 fatty acids, and increased intake of omega-3 fatty acids has been shown to potentially improve sperm quality by reducing the levels of proinflammatory cytokines and adhesion molecules.78,79 Studies also indicate that omega-3 fatty acids have a positive regulatory effect on the antioxidant response of sperm.78,79

With the increasing prevalence of mobile phone usage, researchers have begun to investigate its impact on semen quality. Several studies have indicated an association between mobile phone use and a decline in sperm motility. The effects of mobile phone exposure on sperm quality can be attributed to both thermal and nonthermal mechanisms.80 Nonthermal effects are hypothesized to increase the production of ROS, leading to DNA damage. An in vitro study has demonstrated that exposure to frequencies similar to those emitted by mobile phones can increase mitochondrial ROS levels in sperm, thereby diminishing their viability.81 Thermal effects may arise from the proximity of mobile phones to reproductive organs when stored in pockets, potentially increasing the testicular temperature and impeding sperm production, although no statistically significant changes in sperm count have been observed.58,82 The results of a meta-analysis conducted in 2022 demonstrated a significant association between sleep disorders and various parameters of sperm quality, including total sperm count, sperm concentration, sperm progressive motility, and normal sperm morphology.35 However, no statistically significant correlation was found between reproductive hormones and semen volume. However, the underlying mechanism of the relationship between sleep disorders and semen quality remains unclear. Existing studies suggest that sleep disorders may adversely affect male reproductive health through mechanisms such as reducing testosterone levels, disrupting circadian rhythms, and increasing inflammation or proinflammatory cytokines.83,84,85 There is ongoing debate regarding the impact of sleep disorders on androgen levels. Both animal and human studies have demonstrated that sleep deprivation leads to a reduction in testosterone levels within the body.73,86 This effect may be attributed to the induction of oxidative stress through alterations in inducible nitric oxide synthase (iNOS) expression, the upregulation of 5-hydroxytryptamine expression, and the inhibition of steroidogenic acute regulatory protein (stAR) activity, thereby suppressing testosterone synthesis.87 Disruption of circadian clock gene expression caused by poor sleep habits has a detrimental effect on male fertility.84,87,88 Zhang et al.35 conducted gene sequencing analysis on testicular specimens from four patients diagnosed with nonobstructive azoospermia (NOA) and compared them with those of four patients who exhibited normal spermatogenesis. The results revealed significant downregulation of circadian clock genes (period 1 [Per1], Per2, cryptochrome 2 [Cry2], nuclear receptor Rev-erbalpha [Nr1d1], and neuronal PAS domain protein 2 [Npas2]) in the patients with NOA. Zhang et al.89 reported significantly lower expression levels of circadian clock genes (brain and muscle ARNT-like protein-1 [BMAL1], circadian locomotor output cycles kaput [CLOCK], CRY1, PER1, and PER2) in the sperm of infertile asthenospermic men than in those of normal individuals. Although there is no targeted research to prove the mechanism underlying the negative effects of stress on sperm motility at present, we speculate that the mechanism may be related to a reduction in testosterone levels and an increase in inflammation or proinflammatory cytokines.

SSRIs are common drugs that are widely used to treat depression/anxiety disorders and posttraumatic stress disorder. One study showed that SSRIs can affect sperm morphology, sperm concentration, sperm motility, and sperm DNA integrity without measurable effects on semen volume.62 Many sperm are produced in the seminiferous tubules of the testes, whereas seminal plasma is produced mainly by the epididymis, prostate, seminal vesicles, and urethral glands.90 Therefore, SSRIs may affect only the production of sperm in testicles without affecting the production of epididymal fluid, prostatic fluid, seminal vesicle fluid, or urethral gland fluid. This could explain why there was no significant change in semen volume after the use of SSRIs. In an animal experiment, 15 rats were randomly assigned to receive three different oral doses of fluoxetine three times for five consecutive days. The results revealed that the semen parameters of the experimental group deteriorated significantly in a dose-dependent manner.91 Researchers believe that the use of SSRIs may lead to the production of indoleamine 2,3-dioxygenase (IDO), dysregulation of tryptophan metabolism, and oxidative stress-induced abnormalities in semen quality.92,93 Another study reported that, after discontinuing SSRI treatment, semen quality reverted to the baseline level.94 Therefore, during treatment with SSRIs, patients can temporarily postpone their attempts to conceive until they stop using SSRIs. A study conducted by Banerjee et al.36 compared the effects of SASP and 5-ASA on sperm count and motility in patients with inflammatory bowel disease (IBD). Compared with 5-ASA treatment, SASP treatment resulted in reduced sperm count and motility. However, considering the lower incidence of side effects, especially for young men of reproductive age, 5-ASA should be considered the initial treatment for IBD. Recently, the impact of COVID-19 vaccination on reproductive capacity has drawn much attention. However, studies have shown that COVID-19 vaccination has no adverse effect on semen quality, regardless of the type of vaccine used.61

In contrast, a study confirmed that the use of pesticides, including pyrethroids and organophosphates (OPs), has an impact on male semen quality.40 The effects of pyrethroids on normal sperm morphology can be observed through abnormalities in sperm head morphology, increased disomic and sex chromosome rates, and a greater percentage of DNA in the tail.95,96 Research has shown that OP can affect sperm quality through the following mechanisms. First, OP induces the production of ROS, changes the physiological function of the blood-testis barrier, and creates covalent bonds with occludens zone 2 (ZO2).97,98 Upon entry into the nucleus, OP modifies the methylation of some gene promoters, such as nuclear factor erythroid-2-related factor 2 (NRF2) and 8-oxoguanine DNA glycosylase-1 (OGG1), by altering their expression, resulting in dysregulation of their antioxidant effects and DNA repair function.99 The addition of OP also leads to a decrease in chromatin condensation and compromises DNA integrity.100 Previous meta-analyses have revealed that exposure to common chemical substances found in other environments, such as endocrine disruptors, bisphenol A, and triclosan, is correlated with reduced sperm motility.43,53,55 The potential molecular mechanism involves triggering a series of oxidative reactions that lead to the accumulation of ROS, which in turn causes mitochondrial dysfunction and the upregulated expression of proapoptotic genes associated with mitochondria. A meta-analysis conducted in 2023 revealed an inverse association of the concentrations of PFOA and PFNA with sperm progressive motility.37 Subgroup analysis further revealed that PFNA exposure was associated with abnormal sperm morphology in studies with sample sizes greater than 200. Animal experiments have demonstrated the role of several perfluorocompounds, including PFOS, PFOA and PFNA, in the pathogenesis of male infertility and impaired semen quality. In mice exposed to PFOA, testosterone levels decreased in a dose-dependent manner, while seminiferous tubules were destroyed and sperm quality was reduced.101,102 The findings of another study suggested that exposure to low levels of PFOA can increase StAR expression by inhibiting H3K9me1/3, thereby promoting the synthesis of steroid hormones in the testes of rats.103 Exposure to PFNA has been shown to adversely affect the specific secretory function of Sertoli cells in rats. Additionally, PFNA has been found to induce impaired testosterone biosynthesis and heightened oxidative stress in mice.104 Similarly, exposure to lead can damage various semen parameters, including sperm count, sperm motility, and sperm morphology.54 At the cellular level, after exposure to lead, vacuolization of the sperm cytoplasm, apoptosis of heterochromatin nuclei, and increased deposition of collagen in the basement membrane can be observed by electron microscopy.105 An increase in the percentage of DNA breaks106 and chromatin structure damage can also be observed within cells. Furthermore, a higher seminal lead concentration is associated with higher levels of the proapoptotic protein p53 and lower levels of the prosurvival protein, protein kinase B (Akt).107 However, exposure to arsenic has a relatively limited effect on semen quality.57

The impact of diseases on semen quality can be categorized into three aspects: infectious diseases, chronic diseases, and urology-related conditions. First, numerous studies have demonstrated a negative correlation between infectious diseases such as COVID-19 and HPV and various semen parameters, with the exception of semen volume. Additionally, SARS-CoV has been found to induce testicular inflammation, resulting in testicular damage and spermatogenic defects among patients. The resemblance between the receptor-binding domain of SARS-CoV-2 and that of SARS-CoV implies that angiotensin-converting enzyme 2 (ACE2) may serve as a cellular receptor for SARS-CoV-2, with its highest expression observed in the testes of men in their reproductive years.108 The infection of SARS-CoV-2 involves transmembrane serine protease 2 (TMPRSS2), which facilitates protease-mediated priming and binding to host cell receptors, thereby cleaving the ACE2 receptor and facilitating viral entry into host cells.109,110 These findings suggest that testicles may also serve as target organs for SARS-CoV-2. The mechanism by which HPV infection impairs sperm quality or embryonic development remains unclear; however, scholars have made the following observations: (1) HPV induces sperm DNA rupture111 and (2) HPV hinders the capacity of sperm to effectively bind with and penetrate oocytes.112 HPV types 16, 18, 31, and 33 are associated with high-risk anogenital cancers, whereas types 6 and 11 are linked to low-risk genital warts. Researchers have found DNA fragments in sperm infected with HPV types 16 and 31, indicating that the DNA of HPV causes cellular DNA breaks, characterized by the induction of sperm apoptosis. Moreover, a decrease in sperm velocity parameters was observed in sperm exposed to HPV, suggesting impaired sperm function. Following HPV infection of human sperm, the virus can localize to the equatorial segment of the sperm head via the interaction between the HPV capsid protein L1 and syndecan-1. In vitro studies have demonstrated that sperm transfected with the HPV E6/E7 genes, as well as those exposed to the HPV L1 capsid protein, are capable of penetrating oocytes and transferring HPV DNA into them. However, evidence suggests that this fertilization process may be compromised. Furthermore, a meta-analysis analyzed the impact of various viruses, including HBV, HCV, HIV, and herpes viruses, as well as their combined effects, on semen quality.45 The findings indicated that, with the exception of herpes viruses, infections caused by other viruses had varying degrees of adverse effects on different semen parameters. The underlying mechanisms are analogous to those observed in COVID-19 and HPV infections.

A meta-analysis of eight studies revealed that men with diabetes mellitus type 1 (DM1) presented significantly reduced sperm morphology, sperm motility, and semen volume. Clinical studies investigating the inhibitory effect of DM1 on the hypothalamic-pituitary-testicular axis or hypogonadism have displayed high heterogeneity, making a direct association between DM1 and hypogonadism inconclusive.113,114 In addition, the specific mechanism by which DM1-induced damage affects male fertility remains unclear. Animal and in vitro studies have demonstrated that insulin promotes spermatogenesis and sustains sperm function; however, no in vivo investigations regarding the impact of insulin treatment on semen parameters have been reported.115,116 The pathogenesis of male infertility has been linked to metabolic syndrome, which is characterized by abdominal obesity, dyslipidemia, hypertension, and insulin resistance.117 The exact mechanism by which metabolic syndrome leads to a decline in semen quality remains unclear; however, numerous researchers posit that insulin resistance plays a pivotal role in pathogenic effects on semen quality. Several studies have demonstrated that the utilization of medications not only aids in blood sugar regulation but also enhances both semen quality and testosterone levels.118,119 Reproductive-age diabetic males also demonstrate increased vulnerability to accessory gland inflammation and infection, consequently leading to an increased incidence of in vitro fertilization failure.120,121 The decreased semen quality observed in patients with obesity, dyslipidemia, or hypertension may be attributed to the concurrent presence of oxidative stress and inflammation, as well as compromised sperm antioxidant capacity.122,123 The impact of SCH on semen quality is also significant and mainly involves the direct and indirect effects of thyroid hormones on the HPG axis, testicular function, and semen components, which are mediated by complex interactions at the cellular and molecular levels.47 While SLE has been proven to cause only a slight decrease in sperm count, it has no statistically significant effect on sperm morphology or motility.63

The presence of CBP is associated with a decrease in sperm concentration, sperm motility, total sperm count, and sperm progressive motility. Conversely, CP/CPPS is linked to reduced semen volume, sperm concentration, sperm progressive motility, and normal sperm morphology. Potential mechanisms underlying these associations include oxidative stress in sperm cells, the presence of inflammatory cytokines and immune reactions, and excessive ROS production.124 Cytokines play a pivotal role in orchestrating the inflammatory response. Several proinflammatory cytokines, namely interleukin-1 (IL-1), IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α), were significantly elevated in the seminal plasma of patients with CP/CPPS.125,126 The expression of IL-8 has been observed to be upregulated in prostatic secretions obtained from patients diagnosed with benign prostatic hyperplasia (BPH), CPPS, and CBP, which suggests that IL-8 plays a significant role as a mediator in the generation of inflammatory responses.127,128,129 The integrity of the blood-testis barrier and reproductive urinary tract remains unaffected by immune system attack during spermatogenesis. However, once this integrity is compromised, it can result in the generation of ASA and subsequent impairment of male reproductive function.130 Another infection associated with the urinary system, specifically leukocytospermia, can also adversely affect sperm concentration and forward motility.42 This impact may be attributed to the actions of ROS and inflammatory mediators released by white blood cells. Further in vitro studies and animal experiments are warranted to validate this hypothesis.

Collectively, our findings demonstrate that among the six semen parameters examined, decreased sperm progressive motility was associated with the fewest risk factors, whereas decreased sperm concentration was associated with the greatest number of risk factors. Risk factors associated with abnormal semen parameters include unhealthy lifestyle habits such as obesity, smoking, and sleep disorders, as well as certain chronic diseases such as diabetes, metabolic syndrome, hyperthyroidism, SLE, and chronic prostatitis. Additionally, it is crucial to avoid exposure to pollutants and chemicals such as pesticides, fluoride, and lead to effectively prevent damage to semen quality. Moreover, certain viral infections, such as SARS-CoV-2, HPV, HBV, HCV, and HIV, can also induce alterations in semen parameters. Most of the mechanisms underlying semen quality impairment have not been fully elucidated. However, existing evidence suggests that these risk factors may lead to a decrease in sperm count and concentration by inducing inflammation at the hypothalamic level and inhibiting the HPG axis. Simultaneously, oxidative stress reactions occur within the body, resulting in elevated levels of proinflammatory cytokines (such as IL-1, IL-6, IL-8, IL-10, and TNF-α), which can impair gonad function. Furthermore, alterations in gene promoter expression (e.g., NRF2 and OGG1) modify DNA methylation, leading to impaired DNA repair functionality and, subsequently, abnormal semen parameters. Additionally, it is possible that sperm motility disorders arise from activation of the apoptotic cascade within sperm cells, causing mitochondrial dysfunction and ultimately resulting in loss of motility.

Strengths and limitations

Several limitations must be noted as follows. First, the methodological constraints inherent in retrospective studies, particularly the paucity of eligible studies and restricted sample sizes, substantially diminish the statistical power of synthesized evidence and result in a lower level of evidence. Second, the validity of pooled estimates may be compromised by unrecognized methodological biases such as publication bias and analytical discrepancies in sperm quality evaluation, compounded by substantial interstudy heterogeneity. The clinical heterogeneity observed across studies primarily arises from demographic heterogeneity (ethnic/regional variations) and divergent selection criteria for seminal parameter reference standards. A random-effects model was adopted to account for statistical heterogeneity when synthesizing the data, thereby mitigating confounding effects on weighted mean differences. Future meta-analytical endeavors could further reduce heterogeneity through enhancing methodological consistency through strict adherence to the same version of the World Health Organization semen analysis guidelines across all included studies. Third, while this investigation systematically catalogs discernible risk parameters pertinent to male infertility, it must be underscored that these associations are principally correlative. Nevertheless, definitive causal inferences require prospective cohort studies employing standardized exposure assessments and comparator groups, complemented by mechanistic investigations elucidating the pathophysiological mechanisms underlying seminal parameter alterations. Furthermore, while our investigation synthesizes existing evidence regarding discernible associations between risk determinants and seminal parameters, it is imperative to acknowledge that these observational correlations do not substantiate direct etiological pathways to clinical infertility manifestations, given the multifactorial nature of reproductive pathophysiology. Notably, while epidemiological evidence suggests that lifestyle modifications such as increased physical activity and Mediterranean-style dietary patterns may beneficially modulate seminal parameters, the clinical relevance of these improvements in reversing andrological infertility remains unclear, necessitating cautious interpretation.

Despite these limitations, this investigation provides a comprehensive synthesis of modifiable risk factors impacting male fertility potential, intentionally avoiding hierarchical ranking, a methodological strength distinguishing traditional from network meta-analytical approaches. This synthesized evidence establishes a crucial foundation for developing personalized risk stratification models and targeted therapeutic interventions in reproductive medicine. The six clinically validated endpoints employed represent the most robust evaluation indicators for quantifying the effects of risk and protective factors on semen quality, ensuring preserved clinical and scientific merit.

CONCLUSION

The present review provides a comprehensive summary estimate of 67 risk factors. Notably, meta-analyses of risk factors often fail to account for significant confounding variables. On the basis of our research, risk factors including poor lifestyle, exposure to chemicals, bacterial or viral infections, and chronic diseases, can lead to an increased risk of infertility and decreased sperm quality. However, the quality of evidence is poor because of the small sample size and the characteristics of retrospective studies. Therefore, we recommend further meta-analyses of risk factors for male infertility and poor semen quality including more prospective studies and excluding low-quality studies.

AUTHOR CONTRIBUTIONS

JJY, QHW, QW, and YGB designed the manuscript. YGB, LRL, and QW provided administrative support for the manuscript. ZYC, XT, and CCZ provided materials. ZYC, LZ, KC, and QHW collected and assembled the data. ZYC, XYL, QHW, and JJY analyzed and interpreted the data. All authors read and approved the final manuscript.

COMPETING INTERESTS

All authors declare no competing interests.

Supplementary Figure 1

Summary estimates for association studies of sperm count. PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; PFHxS: perfluorohexane sulfonate; K: study number; A: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SCH: subclinical hyperthyroidism; SLE: systemic lupus erythematosus; SASP: sulphasalazine; 5-ASA: mesalazine.

Supplementary Figure 2

Summary estimates for association studies of sperm morphology. PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; PFHxS: perfluorohexane sulfonate; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; EDs: endocrine disruptors; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SCH: subclinical hyperthyroidism; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus.

Supplementary Figure 3

Summary estimates for association studies of sperm motility. PFOA: perfluorooctanoic acid; PFOS: perfluorooctane sulfonate; CBP: chronic bacterial prostatitis; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; EDs: endocrine disruptors; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; SSRIs: selective serotonin reuptake inhibitors; SLE: systemic lupus erythematosus; SASP: sulphasalazine; 5-ASA: mesalazine.

Supplementary Figure 4

Summary estimates for association studies of sperm progressive motility. PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFOS: perfluorooctane sulfonate; PFDA: perfluorodecanoic acid; AMSTAR: Assessment of Multiple Systematic Reviews; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; CBP: chronic bacterial prostatitis; CP/CPPS: chronic prostatitis/chronic pelvic pain syndrome; EDs: endocrine disruptors; SCH: subclinical hyperthyroidism.