O-297 Seasonal trends in sperm quality across Denmark and Florida: a study of 15,125 semen samples
Notice bibliographique
Résumé
Abstract Study question Does seasonal variation affect the quality of ejaculates produced by candidate sperm donors in Denmark and Florida (US), and what factors contribute to these trends? Summary answer Sperm quality (grade A concentration) peaks in early summer, drops in winter, influenced by lifestyle and temperature, with unexplained factors remaining significant. What is known already Denmark has a temperate climate with four distinct seasons marked by warm summers and cool winters, whereas Florida (US), experiences a subtropical climate with hot, humid summers and mild winters. Spermatogenesis is temperature-sensitive, and heat exposure is known to affect both the concentration and motility of spermatozoa. Seasonal variation in semen has been studied previously with different methodological approaches and study designs. Existing studies have generally found the lowest sperm concentration and motility during summer (May-August) and the highest during winter (October-January). We suggest that some apparent trends in sperm concentration and motility may result from methodological anomalies introducing bias. Study design, size, duration Data on semen quality from 15,125 candidate sperm donors at the Cryos International sperm banks in the four largest cities of Denmark (Aarhus, Aalborg, Copenhagen, and Odense; 10,637 men) and in Orlando, Florida, USA (4,488 men) from 2018 to 2024 were collected from the sperm bank database and anonymized before analysis. All males were between 18 and 46 years old and lived in or near these cities. Participants/materials, setting, methods Ejaculates were analysed (at22oC) within one hour of production using the same protocols and CASA system in all years at each site. Semen parameters included ejaculate volume, total sperm concentration (106/mL), and the concentration of grade a and b spermatozoa. Semen parameters were analyzed with respect to year and month of production, the city where the ejaculate was produced, and monthly outdoor temperatures. We used longitudinal data from accepted donors to test for methodological biases. Main results and the role of chance While neither ejaculate volume nor total sperm concentration varied significantly across the months of the year in any city, the concentration of grade a spermatozoa (highest motility) varied seasonally in both Denmark and Florida. The same trends were seen in both countries although the monthly variation in Orlando was not statistically significant. Thus, sperm quality (measured as the concentration of grade spermatozoa) was lowest during the winter months (October-March) and highest during the early summer (May-July). The mean monthly temperature had a significant effect on the concentration of grade a sperm in Denmark but not in Orlando. Even accounting for temperature, the pattern in both countries remained unchanged, and unexplained. Results presented from this study are not congruent with previous studies showing that sperm motility is lowest during summertime, nor that sperm motility decreases as the outdoor temperature rises. It is also clear that other environmental factors influenced the seasonal variation in sperm motility in this study, including lifestyle, where winter clothing or the reduction in outdoor activities in winter create a warmer environment for the testes during sperm production, as well as a lower exposure to daylight in winter. Limitations, reasons for caution With the large sample size (15,125 study participants), statistical controls for potential confounds, and consistent protocols for analysing ejaculates across years, city and country, this study eliminated the potential biases in the sampling and analyses that have made previous studies of seasonal variation in sperm quality difficult to interpret. Wider implications of the findings These findings have implications for male fertility and the selection of sperm donors, where motile-sperm concentration and motility are crucial criteria. The results indicate that seasonal variations might have affected the initial semen samples of donor candidates, who could have been accepted had they applied a different time of year. Trial registration number No
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».