Pub Date : 2024-11-26eCollection Date: 2024-01-01DOI: 10.1093/hropen/hoae071
Yangyi Fang, Zhe Zhang, Yinchu Cheng, Zhigao Huang, Jiayuan Pan, Zixuan Xue, Yidong Chen, Vera Y Chung, Li Zhang, Kai Hong
<p><strong>Study question: </strong>Which independent factors influence ICSI outcomes in patients with complete azoospermia factor c (AZFc) microdeletions?</p><p><strong>Summary answer: </strong>In patients with complete AZFc microdeletions, the sperm source, male LH, the type of infertility in women, and maternal age are the independent factors associated with ICSI outcomes.</p><p><strong>What is known already: </strong>AZF microdeletions are the second most prevalent factor contributing to infertility in men, with AZFc microdeletions being the most frequently affected locus, accounting for 60-70% of all cases. The primary clinical phenotypes are oligoasthenozoospermia and azoospermia in patients with complete AZFc microdeletions. These patients can achieve paternity through ICSI using either testicular (T-S) or ejaculated (E-S) spermatozoa. With aging in men with AZFc microdeletions, oligoasthenozoospermia or severe oligozoospermia may gradually progress to azoospermia.</p><p><strong>Study design size duration: </strong>In this retrospective cohort study, the independent factors associated with the outcomes of 634 ICSI cycles in 634 couples with the transfer of 1005 embryos between February 2015 and December 2023 were evaluated. The analysis included 398 ICSI cycles in 398 couples using E-S and 236 ICSI cycles in 236 couples using T-S; all men had complete AZFc microdeletions.</p><p><strong>Participants/materials setting methods: </strong>The inclusion criteria were as follows: (i) men had complete AZFc microdeletions and (ii) the couple underwent ICSI treatment using T-S or E-S. The exclusion criteria were as follows: (i) cycles involving frozen-thawed oocytes; (ii) cycles in which all fresh embryos were frozen and not transferred; (iii) cycles lost to follow-up; and (iv) multiple ICSI cycles, apart from the first cycle for each couple. The primary outcome was the cumulative live birth rate per ICSI cycle, whereas the secondary outcomes were the clinical pregnancy rate per ICSI cycle, fertilization rate, and the no-embryo-suitable-for-transfer cycle rate (NESTR). Moreover, the maternal and neonatal outcomes were analyzed. Continuous variables showing non-normal distributions were expressed as median and interquartile range and were analyzed using the Kruskal-Wallis test. Categorical variables were expressed as percentages and were analyzed using the χ<sup>2</sup> or Fisher's exact test. Linear and logistic regression models were constructed to assess the independent factors associated with ICSI outcomes.</p><p><strong>Main results and the role of chance: </strong>The T-S group exhibited inferior ICSI outcomes than the E-S group, marked by significantly reduced rates of cumulative live birth, clinical pregnancy, fertilization, high-quality embryos, blastocyst formation, and implantation, with higher NESTRs. However, the miscarriage rate and neonatal outcomes did not significantly differ between the groups. Multivariate linear regression analysi
{"title":"Independent factors associated with intracytoplasmic sperm injection outcomes in patients with complete azoospermia factor c microdeletions.","authors":"Yangyi Fang, Zhe Zhang, Yinchu Cheng, Zhigao Huang, Jiayuan Pan, Zixuan Xue, Yidong Chen, Vera Y Chung, Li Zhang, Kai Hong","doi":"10.1093/hropen/hoae071","DOIUrl":"10.1093/hropen/hoae071","url":null,"abstract":"<p><strong>Study question: </strong>Which independent factors influence ICSI outcomes in patients with complete azoospermia factor c (AZFc) microdeletions?</p><p><strong>Summary answer: </strong>In patients with complete AZFc microdeletions, the sperm source, male LH, the type of infertility in women, and maternal age are the independent factors associated with ICSI outcomes.</p><p><strong>What is known already: </strong>AZF microdeletions are the second most prevalent factor contributing to infertility in men, with AZFc microdeletions being the most frequently affected locus, accounting for 60-70% of all cases. The primary clinical phenotypes are oligoasthenozoospermia and azoospermia in patients with complete AZFc microdeletions. These patients can achieve paternity through ICSI using either testicular (T-S) or ejaculated (E-S) spermatozoa. With aging in men with AZFc microdeletions, oligoasthenozoospermia or severe oligozoospermia may gradually progress to azoospermia.</p><p><strong>Study design size duration: </strong>In this retrospective cohort study, the independent factors associated with the outcomes of 634 ICSI cycles in 634 couples with the transfer of 1005 embryos between February 2015 and December 2023 were evaluated. The analysis included 398 ICSI cycles in 398 couples using E-S and 236 ICSI cycles in 236 couples using T-S; all men had complete AZFc microdeletions.</p><p><strong>Participants/materials setting methods: </strong>The inclusion criteria were as follows: (i) men had complete AZFc microdeletions and (ii) the couple underwent ICSI treatment using T-S or E-S. The exclusion criteria were as follows: (i) cycles involving frozen-thawed oocytes; (ii) cycles in which all fresh embryos were frozen and not transferred; (iii) cycles lost to follow-up; and (iv) multiple ICSI cycles, apart from the first cycle for each couple. The primary outcome was the cumulative live birth rate per ICSI cycle, whereas the secondary outcomes were the clinical pregnancy rate per ICSI cycle, fertilization rate, and the no-embryo-suitable-for-transfer cycle rate (NESTR). Moreover, the maternal and neonatal outcomes were analyzed. Continuous variables showing non-normal distributions were expressed as median and interquartile range and were analyzed using the Kruskal-Wallis test. Categorical variables were expressed as percentages and were analyzed using the χ<sup>2</sup> or Fisher's exact test. Linear and logistic regression models were constructed to assess the independent factors associated with ICSI outcomes.</p><p><strong>Main results and the role of chance: </strong>The T-S group exhibited inferior ICSI outcomes than the E-S group, marked by significantly reduced rates of cumulative live birth, clinical pregnancy, fertilization, high-quality embryos, blastocyst formation, and implantation, with higher NESTRs. However, the miscarriage rate and neonatal outcomes did not significantly differ between the groups. Multivariate linear regression analysi","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae071"},"PeriodicalIF":8.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11652272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21eCollection Date: 2025-01-01DOI: 10.1093/hropen/hoae070
Hossein Jamalirad, Mahdie Jajroudi, Bahareh Khajehpour, Mohammad Ali Sadighi Gilani, Saeid Eslami, Marjan Sabbaghian, Hassan Vakili Arki
<p><strong>Study question: </strong>How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?</p><p><strong>Summary answer: </strong>AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.</p><p><strong>What is known already: </strong>Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.</p><p><strong>Study design size duration: </strong>A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to 15 May 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed's 'similar articles' and 'cited by' features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature.</p><p><strong>Participants/materials setting methods: </strong>The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations, and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigor.</p><p><strong>Main results and the role of chance: </strong>Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal, and biological factors. However, limitations of the studies included small sample sizes, legal barriers, and challenges in generalizability and validation. While some studies featured larger, multicenter designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied.</p><p><strong>Limitations reasons for caution: </strong>The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PubMed and Scopus), which may limit the scope of the findings. Additionally, the absence of a meta-analysis prevents quantit
{"title":"AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.","authors":"Hossein Jamalirad, Mahdie Jajroudi, Bahareh Khajehpour, Mohammad Ali Sadighi Gilani, Saeid Eslami, Marjan Sabbaghian, Hassan Vakili Arki","doi":"10.1093/hropen/hoae070","DOIUrl":"10.1093/hropen/hoae070","url":null,"abstract":"<p><strong>Study question: </strong>How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?</p><p><strong>Summary answer: </strong>AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.</p><p><strong>What is known already: </strong>Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.</p><p><strong>Study design size duration: </strong>A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to 15 May 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed's 'similar articles' and 'cited by' features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature.</p><p><strong>Participants/materials setting methods: </strong>The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations, and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigor.</p><p><strong>Main results and the role of chance: </strong>Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal, and biological factors. However, limitations of the studies included small sample sizes, legal barriers, and challenges in generalizability and validation. While some studies featured larger, multicenter designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied.</p><p><strong>Limitations reasons for caution: </strong>The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PubMed and Scopus), which may limit the scope of the findings. Additionally, the absence of a meta-analysis prevents quantit","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2025 1","pages":"hoae070"},"PeriodicalIF":8.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11700607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13eCollection Date: 2024-01-01DOI: 10.1093/hropen/hoae069
Andrea Guzmán-Jiménez, Sara González-Muñoz, Miriam Cerván-Martín, Nicolás Garrido, José A Castilla, M Carmen Gonzalvo, Ana Clavero, Marta Molina, Saturnino Luján, Samuel Santos-Ribeiro, Miguel Ángel Vilches, Andrea Espuch, Vicente Maldonado, Noelia Galiano-Gutiérrez, Esther Santamaría-López, Cristina González-Ravina, Fernando Quintana-Ferraz, Susana Gómez, David Amorós, Luis Martínez-Granados, Yanira Ortega-González, Miguel Burgos, Iris Pereira-Caetano, Ozgur Bulbul, Stefano Castellano, Massimo Romano, Elena Albani, Lluís Bassas, Susana Seixas, João Gonçalves, Alexandra M Lopes, Sara Larriba, Rogelio J Palomino-Morales, F David Carmona, Lara Bossini-Castillo
<p><strong>Study question: </strong>Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility?</p><p><strong>Summary answer: </strong>Our findings revealed a significant association between SPGF and the <i>SHOC1</i> gene and identified three novel genes (<i>PCSK4</i>, <i>AP3B1</i>, and <i>DLK1</i>) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition.</p><p><strong>What is known already: </strong>SPGF is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWASs) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort.</p><p><strong>Study design size duration: </strong>We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 20 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics.</p><p><strong>Participants/materials setting methods: </strong>This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (i) variant-wise association analyses using logistic regression models, (ii) gene-wise association analyses using combined multivariate and collapsing burden tests, and (iii) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients.</p><p><strong>Main results and the role of chance: </strong>The variant-wise analyses revealed an association between SPGF and <i>SHOC1</i>-rs12347237 (<i>P </i>=<i> </i>4.15E-06, odds ratio = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (<i>PCSK4</i>, <i>AP3B1</i>, and <i>DLK1</i>) were identified as novel relevan
{"title":"A comprehensive study of common and rare genetic variants in spermatogenesis-related loci identifies new risk factors for idiopathic severe spermatogenic failure.","authors":"Andrea Guzmán-Jiménez, Sara González-Muñoz, Miriam Cerván-Martín, Nicolás Garrido, José A Castilla, M Carmen Gonzalvo, Ana Clavero, Marta Molina, Saturnino Luján, Samuel Santos-Ribeiro, Miguel Ángel Vilches, Andrea Espuch, Vicente Maldonado, Noelia Galiano-Gutiérrez, Esther Santamaría-López, Cristina González-Ravina, Fernando Quintana-Ferraz, Susana Gómez, David Amorós, Luis Martínez-Granados, Yanira Ortega-González, Miguel Burgos, Iris Pereira-Caetano, Ozgur Bulbul, Stefano Castellano, Massimo Romano, Elena Albani, Lluís Bassas, Susana Seixas, João Gonçalves, Alexandra M Lopes, Sara Larriba, Rogelio J Palomino-Morales, F David Carmona, Lara Bossini-Castillo","doi":"10.1093/hropen/hoae069","DOIUrl":"10.1093/hropen/hoae069","url":null,"abstract":"<p><strong>Study question: </strong>Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility?</p><p><strong>Summary answer: </strong>Our findings revealed a significant association between SPGF and the <i>SHOC1</i> gene and identified three novel genes (<i>PCSK4</i>, <i>AP3B1</i>, and <i>DLK1</i>) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition.</p><p><strong>What is known already: </strong>SPGF is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWASs) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort.</p><p><strong>Study design size duration: </strong>We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 20 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics.</p><p><strong>Participants/materials setting methods: </strong>This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (i) variant-wise association analyses using logistic regression models, (ii) gene-wise association analyses using combined multivariate and collapsing burden tests, and (iii) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients.</p><p><strong>Main results and the role of chance: </strong>The variant-wise analyses revealed an association between SPGF and <i>SHOC1</i>-rs12347237 (<i>P </i>=<i> </i>4.15E-06, odds ratio = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (<i>PCSK4</i>, <i>AP3B1</i>, and <i>DLK1</i>) were identified as novel relevan","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae069"},"PeriodicalIF":8.3,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Study question: </strong>Can semen parameters predict long-term health outcomes in men?</p><p><strong>Summary answer: </strong>There is a lack of evidence to suggest a higher risk of comorbidities in men with poor semen concentration.</p><p><strong>What is known already: </strong>Male infertility has been long associated with a higher mortality risk and possibly higher chance of developing comorbidities but there has been less focus on semen analysis as a potential predictive factor.</p><p><strong>Study design size duration: </strong>We searched PubMed/MEDLINE, EMBASE, and EBM databases from inception to December 2023. MESH term strategy: heading 1 ('OR', semen analysis, sperm count, sperm parameter*, male infertility, azoospermia, aspermia, oligospermia, teratozoospermia, asthenozoospermia) 'AND' heading 2 ('OR', morbidity, mortality, diabetes, cancer, cardiovascular, death, hypertension, stroke, long-term health). We included all studies that analyzed the risk of mortality and/or future development of comorbidities in men with at least one semen analysis. Case series and reviews were excluded.</p><p><strong>Participants/materials setting methods: </strong>A narrative synthesis was done for all studies and meta-analysis where possible. Odds ratio (ORs) (95% CI, <i>P</i>-value) were calculated for all men with one suboptimal semen parameter and associated with the risk of a particular outcome. The risk of bias was assessed with QUADAS-2.</p><p><strong>Main results and the role of chance: </strong>Twenty-one studies were finally included. There was either a high or unclear risk of bias in all studies. The results only allowed for meta-analysis on categories of sperm concentration. We found a 2-fold increase in mortality risk in azoospermic men compared to oligospermic (OR 1.96, 95% CI: 1.29-2.96) and normozoospermic (OR 2.00, 95% CI: 1.23-3.25) groups, but not in oligospermic compared to normozoospermic (OR 1.04, 95% CI: 0.52-2.09). There was no difference in risk of cardiovascular disease in any of the sperm concentration groups (azoospermic-oligospermic OR 0.94, 95% CI: 0.74-1.20, azoospermic-normozoospermic OR 1.11, 95% CI: 0.71-1.75, and oligospermic-normozoospermic OR 1.12, 95% CI: 0.80-1.55). OR for diabetes in azoospermic men was higher only compared to oligospermic (OR 2.16, 95% CI: 1.55-3.01). The risk of all-site cancer was higher in azoospermic men compared to oligospermic (OR 2.16, 95% CI: 1.55-3.01) and normozoospermic (OR 2.18, 95% CI: 1.20-3.96). Only azoospermic men might be at higher risk of testicular cancer when compared to men with normal sperm concentration (OR 1.80, 95% CI: 1.12-2.89).</p><p><strong>Limitations reasons for caution: </strong>Although our pooled analysis shows an increased risk of mortality and all-site cancer risk in azoospermic men, the results show a lack of evidence to suggest a higher risk of comorbidities in men with poor semen concentration. Given the limited available data, the nature of the
{"title":"Lab-based semen parameters as predictors of long-term health in men-a systematic review.","authors":"Silvia Nedelcu, Srisailesh Vitthala, Abha Maheshwari","doi":"10.1093/hropen/hoae066","DOIUrl":"10.1093/hropen/hoae066","url":null,"abstract":"<p><strong>Study question: </strong>Can semen parameters predict long-term health outcomes in men?</p><p><strong>Summary answer: </strong>There is a lack of evidence to suggest a higher risk of comorbidities in men with poor semen concentration.</p><p><strong>What is known already: </strong>Male infertility has been long associated with a higher mortality risk and possibly higher chance of developing comorbidities but there has been less focus on semen analysis as a potential predictive factor.</p><p><strong>Study design size duration: </strong>We searched PubMed/MEDLINE, EMBASE, and EBM databases from inception to December 2023. MESH term strategy: heading 1 ('OR', semen analysis, sperm count, sperm parameter*, male infertility, azoospermia, aspermia, oligospermia, teratozoospermia, asthenozoospermia) 'AND' heading 2 ('OR', morbidity, mortality, diabetes, cancer, cardiovascular, death, hypertension, stroke, long-term health). We included all studies that analyzed the risk of mortality and/or future development of comorbidities in men with at least one semen analysis. Case series and reviews were excluded.</p><p><strong>Participants/materials setting methods: </strong>A narrative synthesis was done for all studies and meta-analysis where possible. Odds ratio (ORs) (95% CI, <i>P</i>-value) were calculated for all men with one suboptimal semen parameter and associated with the risk of a particular outcome. The risk of bias was assessed with QUADAS-2.</p><p><strong>Main results and the role of chance: </strong>Twenty-one studies were finally included. There was either a high or unclear risk of bias in all studies. The results only allowed for meta-analysis on categories of sperm concentration. We found a 2-fold increase in mortality risk in azoospermic men compared to oligospermic (OR 1.96, 95% CI: 1.29-2.96) and normozoospermic (OR 2.00, 95% CI: 1.23-3.25) groups, but not in oligospermic compared to normozoospermic (OR 1.04, 95% CI: 0.52-2.09). There was no difference in risk of cardiovascular disease in any of the sperm concentration groups (azoospermic-oligospermic OR 0.94, 95% CI: 0.74-1.20, azoospermic-normozoospermic OR 1.11, 95% CI: 0.71-1.75, and oligospermic-normozoospermic OR 1.12, 95% CI: 0.80-1.55). OR for diabetes in azoospermic men was higher only compared to oligospermic (OR 2.16, 95% CI: 1.55-3.01). The risk of all-site cancer was higher in azoospermic men compared to oligospermic (OR 2.16, 95% CI: 1.55-3.01) and normozoospermic (OR 2.18, 95% CI: 1.20-3.96). Only azoospermic men might be at higher risk of testicular cancer when compared to men with normal sperm concentration (OR 1.80, 95% CI: 1.12-2.89).</p><p><strong>Limitations reasons for caution: </strong>Although our pooled analysis shows an increased risk of mortality and all-site cancer risk in azoospermic men, the results show a lack of evidence to suggest a higher risk of comorbidities in men with poor semen concentration. Given the limited available data, the nature of the ","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae066"},"PeriodicalIF":8.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07eCollection Date: 2024-01-01DOI: 10.1093/hropen/hoae064
Jill Browning, Magda Ghanim, William Jagoe, Jennifer Cullinane, Louise E Glover, Mary Wingfield, Vincent P Kelly
<p><strong>Study question: </strong>Does receptor for advanced glycation end products (RAGE) on the surface membrane of the sperm cell function as a biomarker of low-quality sperm?</p><p><strong>Summary answer: </strong>Membrane-bound RAGE at a cellular level directly correlates with low sperm motility, high cell permeability, decreased mitochondrial function, DNA fragmentation, and higher levels of apoptosis.</p><p><strong>What is known already: </strong>RAGE has previously been measured by ELISA in low-quality sperm in diabetic men and has been shown to correlate with DNA fragmentation (terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay).</p><p><strong>Study design size duration: </strong>Semen samples were recovered from 60 non-obese, non-diabetic and non-smoking subjects, washed with fresh media, and analysed directly or purified further by differential gradient centrifugation (DGC) or fractionated by direct swim-up before being analysed for sperm motility and molecular health parameters, including cell membrane permeability, cell death, mitochondrial membrane potential, DNA fragmentation, and RAGE protein expression.</p><p><strong>Participants/materials setting methods: </strong>Sperm motility assessments were carried out by computer-assisted sperm analysis (CASA) on 1000 spermatozoa for washed samples and 300 spermatozoa for purified samples. Molecular sperm health parameters were evaluated using flow cytometry with the use of the following markers: DAPI for cell membrane permeability, Annexin V/DAPI for cell death (apoptosis and necrosis), MitoTracker<sup>®</sup> Red CMXRos for mitochondrial membrane potential, TUNEL assay for DNA fragmentation and 8-hydroxy-2-deoxyguanosine for identification of oxidative damage to sperm DNA, and contrasted to membrane-bound RAGE expression levels, which were evaluated using an anti-RAGE monoclonal mouse antibody.</p><p><strong>Main results and the role of chance: </strong>RAGE protein was shown to be present on the acrosomal and equatorial regions of sperm, with the levels of membrane bound receptor strongly correlating with poor sperm health across all parameters tested; motility (<i>R</i> <sup>2</sup> = 0.5441, <i>P</i> < 0.0001) and mitochondrial membrane potential (<i>R</i> <sup>2</sup> = 0.6181, <i>P</i> < 0.0001) being of particular note. The analysis was performed at a single cell level thereby removing confounding complications from soluble forms of the RAGE protein that can be found in seminal plasma. The expression of the RAGE protein was shown to be stable over time and its levels are therefore not subject to variation in sample handling or preparation time.</p><p><strong>Large scale data: </strong>N/A.</p><p><strong>Limitations reasons for caution: </strong>Inclusion criteria for this study were non-diabetic, non-obese and non-smoking participants to assess the distribution of RAGE expression in the general population, thereby excluding disease conditions that may inc
研究问题：精子细胞表面膜上的高级糖化终产物受体（RAGE）是否可作为低质量精子的生物标志物？在细胞水平上，膜结合的 RAGE 与精子活力低、细胞通透性高、线粒体功能下降、DNA 断裂和细胞凋亡水平升高直接相关：研究设计的规模和持续时间：从60名非肥胖、非糖尿病和非吸烟的受试者中采集精液样本，用新鲜培养基洗涤，直接分析或通过差分梯度离心法（DGC）进一步纯化，或通过直接泳升法分馏，然后分析精子活力和分子健康参数，包括细胞膜通透性、细胞死亡、线粒体膜电位、DNA碎片和RAGE蛋白表达：精子活力评估采用计算机辅助精子分析法（CASA）进行，水洗样本的精子数量为 1000 个，纯化样本的精子数量为 300 个。分子精子健康参数采用流式细胞术进行评估，并使用以下标记物：DAPI 检测细胞膜通透性，Annexin V/DAPI 检测细胞死亡（凋亡和坏死），MitoTracker® Red CMXRos 检测线粒体膜电位，TUNEL 检测 DNA 断裂，8-hydroxy-2-deoxyguanosine 检测精子 DNA 氧化损伤，并与膜结合 RAGE 表达水平进行对比，后者使用抗 RAGE 单克隆小鼠抗体进行评估：主要结果和偶然因素：RAGE 蛋白存在于精子的顶体和赤道区域，膜结合受体的水平与精子在所有测试参数中的健康状况密切相关；活力（R 2 = 0.5441，P R 2 = 0.6181，P 大比例数据：不适用：本研究的纳入标准是非糖尿病、非肥胖和非吸烟的参与者，以评估普通人群中 RAGE 表达的分布情况，从而排除可能增加精子中 RAGE 表达或导致精子质量低下的疾病。该研究并未涉及与男性不育症相关的其他患者亚群或疾病状态对 RAGE 表达的影响。流式细胞术精子分析法不适于研究精子数量少的男性：该研究结果表明，RAGE表达是精子细胞健康的分子标志物，可通过清除RAGE表达的精子改善辅助生殖，并通过将其作为男性不育症的生物标志物促进不明原因不育症的诊断：该研究由爱尔兰研究委员会根据爱尔兰政府计划（GOIPG/2015/3729）和爱尔兰企业创新合作伙伴计划（IP-2020-0952）资助。所有作者声明不存在利益冲突。
{"title":"Membrane-bound receptor for advanced glycation end products (RAGE) is a stable biomarker of low-quality sperm.","authors":"Jill Browning, Magda Ghanim, William Jagoe, Jennifer Cullinane, Louise E Glover, Mary Wingfield, Vincent P Kelly","doi":"10.1093/hropen/hoae064","DOIUrl":"10.1093/hropen/hoae064","url":null,"abstract":"<p><strong>Study question: </strong>Does receptor for advanced glycation end products (RAGE) on the surface membrane of the sperm cell function as a biomarker of low-quality sperm?</p><p><strong>Summary answer: </strong>Membrane-bound RAGE at a cellular level directly correlates with low sperm motility, high cell permeability, decreased mitochondrial function, DNA fragmentation, and higher levels of apoptosis.</p><p><strong>What is known already: </strong>RAGE has previously been measured by ELISA in low-quality sperm in diabetic men and has been shown to correlate with DNA fragmentation (terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay).</p><p><strong>Study design size duration: </strong>Semen samples were recovered from 60 non-obese, non-diabetic and non-smoking subjects, washed with fresh media, and analysed directly or purified further by differential gradient centrifugation (DGC) or fractionated by direct swim-up before being analysed for sperm motility and molecular health parameters, including cell membrane permeability, cell death, mitochondrial membrane potential, DNA fragmentation, and RAGE protein expression.</p><p><strong>Participants/materials setting methods: </strong>Sperm motility assessments were carried out by computer-assisted sperm analysis (CASA) on 1000 spermatozoa for washed samples and 300 spermatozoa for purified samples. Molecular sperm health parameters were evaluated using flow cytometry with the use of the following markers: DAPI for cell membrane permeability, Annexin V/DAPI for cell death (apoptosis and necrosis), MitoTracker<sup>®</sup> Red CMXRos for mitochondrial membrane potential, TUNEL assay for DNA fragmentation and 8-hydroxy-2-deoxyguanosine for identification of oxidative damage to sperm DNA, and contrasted to membrane-bound RAGE expression levels, which were evaluated using an anti-RAGE monoclonal mouse antibody.</p><p><strong>Main results and the role of chance: </strong>RAGE protein was shown to be present on the acrosomal and equatorial regions of sperm, with the levels of membrane bound receptor strongly correlating with poor sperm health across all parameters tested; motility (<i>R</i> <sup>2</sup> = 0.5441, <i>P</i> < 0.0001) and mitochondrial membrane potential (<i>R</i> <sup>2</sup> = 0.6181, <i>P</i> < 0.0001) being of particular note. The analysis was performed at a single cell level thereby removing confounding complications from soluble forms of the RAGE protein that can be found in seminal plasma. The expression of the RAGE protein was shown to be stable over time and its levels are therefore not subject to variation in sample handling or preparation time.</p><p><strong>Large scale data: </strong>N/A.</p><p><strong>Limitations reasons for caution: </strong>Inclusion criteria for this study were non-diabetic, non-obese and non-smoking participants to assess the distribution of RAGE expression in the general population, thereby excluding disease conditions that may inc","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae064"},"PeriodicalIF":8.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28eCollection Date: 2024-01-01DOI: 10.1093/hropen/hoae063
Yuqi Zeng, Yali Liu, Yunhan Nie, Xi Shen, Tiantian Wang, Yanping Kuang, Li Wang
<p><strong>Study question: </strong>Which specific groups of women would not benefit from repeated frozen embryo transfers (FETs)?</p><p><strong>Summary answer: </strong>Women over 45 years of age should stop treatment after three FET attempts due to the absence of further benefits, while women aged 40-45 years and those with a diminished ovarian reserve and other causes of infertility have a lower chance of improving their cumulative live birth rate (CLBR) within five FET cycles and experience fewer advantages from repeated transfers.</p><p><strong>What is known already: </strong>In real-life scenarios of ART, women who fail to achieve a live birth often choose to undergo repeated FETs via the freeze-all strategy.</p><p><strong>Study design size duration: </strong>This retrospective study included 43 972 women who underwent 86 496 oocyte retrieval cycles and 82 022 FET cycles between January 2010 and March 2023 under the freeze-all strategy.</p><p><strong>Participants/materials setting methods: </strong>We categorized the population based on the female's age at the first oocyte pick-up (OPU) cycle (Groups 1-6: <30, 30-34, 35-39, 40-42, 43-44, and ≥45 years of age), number of retrieved oocytes at the first OPU cycle (Groups 1-5: 1-5, 6-10, 11-15, 16-20, and >20 oocytes), and causes of infertility (Groups 1-9: tubal factor, male factor, polycystic ovary syndrome, diminished ovarian reserve, endometriosis, other uterine factors, combined factors, unexplained infertility, and other infertility) to analyse their CLBRs within different FET cycles via Kaplan-Meier analysis (optimistic method) and the competing risk method (conservative method). We utilized multivariate Cox and Fine-Gray models to examine the associations between the CLBR and age, the number of retrieved oocytes, and nine causes of infertility.</p><p><strong>Main results and the role of chance: </strong>The CLBR decreased with increasing female age over five FET cycles (Groups 1-6: optimistic method: 96.4%, 94.2%, 86.0%, 50.2%, 23.1%, and 10.1%; conservative method: 87.1%, 82.0%, 67.8%, 33.9%, 13.8%, and 3.5%, respectively). Moreover, there was an increasing trend in the number of retrieved oocytes (Groups 1-5: optimistic method: 82.5%, 91.7%, 93.6%, 94.1%, and 96.2%; conservative method: 58.6%, 76.7%, 84.8%, 88.0%, and 92.5%, respectively). Furthermore, the CLBR varied across different causes of infertility (Groups 1-9: optimistic method: 91.7%, 93.1%, 96.6%, 79.2%, 89.9%, 76.1%, 90.0%, 92.9%, and 35.4%; conservative method: 77.3%, 79.4%, 88.9%, 46.7%, 72.7%, 62.1%, 74.4%, 78.8%, and 20.1%, respectively).</p><p><strong>Limitations reasons for caution: </strong>Calculating the actual CLBR for each person is difficult because some patients have remaining embryos that have not been transferred; additionally, the current statistical methodology uses both optimistic and conservative methods to calculate the CLBR, and in real life, the CLBR falls between the optimistic and conservative curve
{"title":"Women may not benefit from repeated frozen embryo transfers: a retrospective analysis of the cumulative live birth rate of 43 972 women.","authors":"Yuqi Zeng, Yali Liu, Yunhan Nie, Xi Shen, Tiantian Wang, Yanping Kuang, Li Wang","doi":"10.1093/hropen/hoae063","DOIUrl":"10.1093/hropen/hoae063","url":null,"abstract":"<p><strong>Study question: </strong>Which specific groups of women would not benefit from repeated frozen embryo transfers (FETs)?</p><p><strong>Summary answer: </strong>Women over 45 years of age should stop treatment after three FET attempts due to the absence of further benefits, while women aged 40-45 years and those with a diminished ovarian reserve and other causes of infertility have a lower chance of improving their cumulative live birth rate (CLBR) within five FET cycles and experience fewer advantages from repeated transfers.</p><p><strong>What is known already: </strong>In real-life scenarios of ART, women who fail to achieve a live birth often choose to undergo repeated FETs via the freeze-all strategy.</p><p><strong>Study design size duration: </strong>This retrospective study included 43 972 women who underwent 86 496 oocyte retrieval cycles and 82 022 FET cycles between January 2010 and March 2023 under the freeze-all strategy.</p><p><strong>Participants/materials setting methods: </strong>We categorized the population based on the female's age at the first oocyte pick-up (OPU) cycle (Groups 1-6: <30, 30-34, 35-39, 40-42, 43-44, and ≥45 years of age), number of retrieved oocytes at the first OPU cycle (Groups 1-5: 1-5, 6-10, 11-15, 16-20, and >20 oocytes), and causes of infertility (Groups 1-9: tubal factor, male factor, polycystic ovary syndrome, diminished ovarian reserve, endometriosis, other uterine factors, combined factors, unexplained infertility, and other infertility) to analyse their CLBRs within different FET cycles via Kaplan-Meier analysis (optimistic method) and the competing risk method (conservative method). We utilized multivariate Cox and Fine-Gray models to examine the associations between the CLBR and age, the number of retrieved oocytes, and nine causes of infertility.</p><p><strong>Main results and the role of chance: </strong>The CLBR decreased with increasing female age over five FET cycles (Groups 1-6: optimistic method: 96.4%, 94.2%, 86.0%, 50.2%, 23.1%, and 10.1%; conservative method: 87.1%, 82.0%, 67.8%, 33.9%, 13.8%, and 3.5%, respectively). Moreover, there was an increasing trend in the number of retrieved oocytes (Groups 1-5: optimistic method: 82.5%, 91.7%, 93.6%, 94.1%, and 96.2%; conservative method: 58.6%, 76.7%, 84.8%, 88.0%, and 92.5%, respectively). Furthermore, the CLBR varied across different causes of infertility (Groups 1-9: optimistic method: 91.7%, 93.1%, 96.6%, 79.2%, 89.9%, 76.1%, 90.0%, 92.9%, and 35.4%; conservative method: 77.3%, 79.4%, 88.9%, 46.7%, 72.7%, 62.1%, 74.4%, 78.8%, and 20.1%, respectively).</p><p><strong>Limitations reasons for caution: </strong>Calculating the actual CLBR for each person is difficult because some patients have remaining embryos that have not been transferred; additionally, the current statistical methodology uses both optimistic and conservative methods to calculate the CLBR, and in real life, the CLBR falls between the optimistic and conservative curve","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae063"},"PeriodicalIF":8.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14eCollection Date: 2024-01-01DOI: 10.1093/hropen/hoae062
María Fernández de la Puente, Cristina Valle-Hita, Albert Salas-Huetos, María Ángeles Martínez, Elena Sánchez-Resino, Silvia Canudas, Daniel Torres-Oteros, Joana Relat, Nancy Babio, Jordi Salas-Salvadó
<p><strong>Study question: </strong>Could sperm and leukocyte telomere length (TL) be associated with sperm quality parameters and reproductive health in men from the general population?</p><p><strong>Summary answer: </strong>A positive association between sperm and leukocyte TL with sperm concentration and total count has been demonstrated.</p><p><strong>What is known already: </strong>Male factors account for almost half of cases of couple infertility, and shorter TLs have been observed in sperm from men with impaired sperm parameters. However, evidence in men from the general population is limited.</p><p><strong>Study design size duration: </strong>A total of 200 volunteers of reproductive age were recruited between February 2021 and April 2023 to participate in the Lifestyle and Environmental Determinants of Seminogram and Other Male Fertility-Related Parameters (Led-Fertyl) cross-sectional study.</p><p><strong>Participants/materials setting methods: </strong>TLs in sperm and leukocytes were measured using quantitative polymerase chain reaction (qPCR) in 168 and 194 participants, respectively. Sperm parameters, including concentration, total count, motility, vitality, and morphology, were analyzed using a computer-assisted sperm analysis (CASA) SCA<sup>®</sup> system according to the World Health Organization (WHO) 2010 guidelines. Multivariable regression models were performed to assess the associations between sperm and leukocyte TL, either in tertiles or as continuous variables, and sperm quality parameters while adjusting for potential confounders.</p><p><strong>Main results and the role of chance: </strong>Participants in tertiles 2 (T2) and 3 (T3) of sperm TL showed a higher sperm concentration (β: 1.09; 95% CI: 0.09-2.09 and β: 2.06; 95% CI: 1.04-3.09 for T2 and T3, respectively; <i>P</i>-trend < 0.001), compared to those in the reference tertile (T1). Participants in the highest tertile of sperm TL showed higher total sperm count (β: 3.83; 95% CI: 2.08-5.58 for T3 vs T1; <i>P</i>-trend < 0.001). Participants in the top tertile of leukocyte TL showed higher sperm concentration (β: 1.49; 95% CI: 0.44-2.54 for T3 vs T1; <i>P</i>-trend = 0.004), and total count (β: 3.49; 95% CI: 1.62-5.35 for T3 vs T1; <i>P</i>-trend < 0.001) compared with participants in T1. These results remained consistent when sperm and leukocyte TL were modelled as continuous variables.</p><p><strong>Limitations reasons for caution: </strong>One limitation is the impossibility of establishing a cause-effect relationship due to the cross-sectional study design. Additionally, the sample size of the study cannot be considered large.</p><p><strong>Wider implications of the findings: </strong>Sperm and leukocyte TLs are associated with sperm quality parameters in the general population. Additional determinations and further studies with larger sample sizes are needed to clarify the mechanisms underlying these associations and to investigate the further implications.</p><p
研究问题精子和白细胞端粒长度（TL）与普通人群中男性的精子质量参数和生殖健康是否相关？已证实精子和白细胞端粒长度与精子浓度和总计数呈正相关：男性因素几乎占夫妇不育症病例的一半，在精子参数受损的男性精子中观察到较短的TL。然而，来自普通人群的男性的证据却很有限：在 2021 年 2 月至 2023 年 4 月期间，共招募了 200 名育龄志愿者参与精液图和其他男性生育能力相关参数的生活方式和环境决定因素（Led-Fertyl）横断面研究：使用定量聚合酶链反应（qPCR）分别测量了 168 名和 194 名参与者精子和白细胞中的 TLs。根据世界卫生组织（WHO）2010 年指南，使用计算机辅助精子分析（CASA）SCA® 系统分析精子参数，包括浓度、总计数、活力、生命力和形态。在对潜在混杂因素进行调整后，采用多变量回归模型评估精子和白细胞TL（以三等分或连续变量表示）与精子质量参数之间的关系：主要结果和偶然性的作用：精子TL为2分层（T2）和3分层（T3）的参与者精子浓度较高（β：1.09; 95% CI: 0.09-2.09 和 β: 2.06; 95% CI: 1.04-3.09; P-trend P-trend P-trend = 0.004）和总计数（β：3.49; 95% CI: 1.62-5.35 for T3 vs T1; P-trend 局限性 需谨慎的原因：局限性之一是横断面研究设计无法确定因果关系。此外，该研究的样本量也不能算大：研究结果的广泛意义：在普通人群中，精子和白细胞TL与精子质量参数有关。需要进行更多的测定和样本量更大的进一步研究，以明确这些关联的机制，并探讨进一步的影响：Led-Fertyl研究得到了西班牙政府生物医学研究官方资助机构卡洛斯三世健康研究所（ISCIII）通过健康研究基金（FIS）提供的支持，并由欧盟ERDF/ESF "创造欧洲的方式"/"投资你的未来"（PI21/01447）和塔拉戈纳省议会（2021/11-No.Exp. 8004330008-2021-0022642）共同资助。J.S.-S.是本研究的第一作者，他的部分研究经费由 ICREA 学术项目提供。M.F.d.l.P. 获得了 Rovira i Virgili 大学和塔拉戈纳省议会的博士前期资助（2020-PMF-PIPF-8）。C.V.-H.获得了加泰罗尼亚自治区政府（2022 FI_B100108）的博士前期资助。M.Á.M. 获得了 Sara Borrell 博士后奖学金（CD21/00045-Instituto de Salud Carlos III (ISCIII)）。所有作者声明不存在利益冲突：不适用。
{"title":"Sperm and leukocyte telomere length are related to sperm quality parameters in healthy men from the Led-Fertyl study.","authors":"María Fernández de la Puente, Cristina Valle-Hita, Albert Salas-Huetos, María Ángeles Martínez, Elena Sánchez-Resino, Silvia Canudas, Daniel Torres-Oteros, Joana Relat, Nancy Babio, Jordi Salas-Salvadó","doi":"10.1093/hropen/hoae062","DOIUrl":"https://doi.org/10.1093/hropen/hoae062","url":null,"abstract":"<p><strong>Study question: </strong>Could sperm and leukocyte telomere length (TL) be associated with sperm quality parameters and reproductive health in men from the general population?</p><p><strong>Summary answer: </strong>A positive association between sperm and leukocyte TL with sperm concentration and total count has been demonstrated.</p><p><strong>What is known already: </strong>Male factors account for almost half of cases of couple infertility, and shorter TLs have been observed in sperm from men with impaired sperm parameters. However, evidence in men from the general population is limited.</p><p><strong>Study design size duration: </strong>A total of 200 volunteers of reproductive age were recruited between February 2021 and April 2023 to participate in the Lifestyle and Environmental Determinants of Seminogram and Other Male Fertility-Related Parameters (Led-Fertyl) cross-sectional study.</p><p><strong>Participants/materials setting methods: </strong>TLs in sperm and leukocytes were measured using quantitative polymerase chain reaction (qPCR) in 168 and 194 participants, respectively. Sperm parameters, including concentration, total count, motility, vitality, and morphology, were analyzed using a computer-assisted sperm analysis (CASA) SCA<sup>®</sup> system according to the World Health Organization (WHO) 2010 guidelines. Multivariable regression models were performed to assess the associations between sperm and leukocyte TL, either in tertiles or as continuous variables, and sperm quality parameters while adjusting for potential confounders.</p><p><strong>Main results and the role of chance: </strong>Participants in tertiles 2 (T2) and 3 (T3) of sperm TL showed a higher sperm concentration (β: 1.09; 95% CI: 0.09-2.09 and β: 2.06; 95% CI: 1.04-3.09 for T2 and T3, respectively; <i>P</i>-trend < 0.001), compared to those in the reference tertile (T1). Participants in the highest tertile of sperm TL showed higher total sperm count (β: 3.83; 95% CI: 2.08-5.58 for T3 vs T1; <i>P</i>-trend < 0.001). Participants in the top tertile of leukocyte TL showed higher sperm concentration (β: 1.49; 95% CI: 0.44-2.54 for T3 vs T1; <i>P</i>-trend = 0.004), and total count (β: 3.49; 95% CI: 1.62-5.35 for T3 vs T1; <i>P</i>-trend < 0.001) compared with participants in T1. These results remained consistent when sperm and leukocyte TL were modelled as continuous variables.</p><p><strong>Limitations reasons for caution: </strong>One limitation is the impossibility of establishing a cause-effect relationship due to the cross-sectional study design. Additionally, the sample size of the study cannot be considered large.</p><p><strong>Wider implications of the findings: </strong>Sperm and leukocyte TLs are associated with sperm quality parameters in the general population. Additional determinations and further studies with larger sample sizes are needed to clarify the mechanisms underlying these associations and to investigate the further implications.</p><p","PeriodicalId":73264,"journal":{"name":"Human reproduction open","volume":"2024 4","pages":"hoae062"},"PeriodicalIF":8.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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