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Record W4381620100 · doi:10.1093/humrep/dead093.249

P-088 The Paternal Clock: Shedding Light on the Relationship Between Paternal Age and Sperm DNA Fragmentation

2023· article· en· W4381620100 on OpenAlexaboutno aff
Eva Kadoch, A Tadevsoyan, Armand Zini, S. Phillips, F. Bissonnette, Isaac-Jacques Kadoch

Bibliographic record

VenueHuman Reproduction · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSpermSemenDNA fragmentationOffspringPopulationFertilitySemen analysisMedicineInfertilityGynecologyMale infertilityBiologyAndrologyDemographyPregnancyGenetics

Abstract

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Abstract Study question What is the impact of advanced paternal age (APA) on sperm DNA fragmentation index (DFI)? Summary answer Sperm DFI levels remain relatively stable until the age of 35 and increase progressively beyond that age. What is known already APA can have a negative impact on male fertility and the health of offspring. Previous studies have shown that APA is linked to poor conventional sperm parameters, including decreased semen volume, sperm count, motility, morphology, as well as poor sperm DNA integrity. Additionally, APA has been associated with reduced natural or assisted reproduction and perinatal outcomes and a higher risk of genetic and chromosomal abnormalities in the offspring. Study design, size, duration A retrospective cohort study of 4250 consecutive semen samples from men undergoing infertility evaluation at the OVO clinic, in Montreal, Canada, between April 2016 and December 2022. Participants were stratified into seven age groups: <26 (n = 36; 0,8%), 26-30 (n = 500; 11,8%), 31-35 (n = 1269; 29,9%), 36-40 (n = 1268; 29,8%), 41-45 (n = 732; 17,2%), 46-50 (n = 304; 7,2%), >50 years (n = 141; 3,3%). The mean age was 37.4 ± 6.4 years (range 18-71 years). Participants/materials, setting, methods The study was population-based and included male patients from all ages, ethnicities, and medical histories. Semen samples were collected after 2-3 days of abstinence. For patients who underwent more than one DFI testing, only the first sample was included, and any duplicates were excluded. DFI was evaluated by flow-cytometry based TUNEL assay using the APO-Direct Kit. Data were analyzed using one-way ANOVA and T3 Dunnett post-hoc multiple comparison test, as well as Pearson’s correlation coefficient. Main results and the role of chance The results show a significant positive correlation between %DFI and age (r = 0.19, p < 0.001). Mean %DFI levels were relatively stable in men aged <26 to 35 years (17.9%, 18.1% and 18.1% in men aged <26, 26-30 and 31-35, respectively), with %DFI increasing progressively beyond age 35 (20.7%, 22.5%, 25.7% and 27.9% in men aged 36-40, 41-45, 46-50 and >50, respectively). The mean %DFI in the 36-40 age group was significantly higher than in the 31-35 age group (p < 0,001). Our study has uncovered that %DFI follows an exponential curve starting at age 35, indicating that the %DFI accelerates significantly as men age beyond their mid-30s. Limitations, reasons for caution This retrospective analysis has inherent limitations that may introduce confounding variables. The patient clinical background, such as medical history and lifestyle factors was not assessed. Also, the studied cohort consisted of a population under investigation for infertility and may not be representative of the general male population. Wider implications of the findings The study demonstrates the age-related increase in sperm %DFI and suggests that there may be an age cut-off below which sperm %DFI is stable and beyond which sperm %DFI increases. This information may be useful to medical specialists that offer sperm DNA testing to infertile couples. Trial registration number not applicable

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.340
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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