Modifiable and non-modifiable risk factors for poor sperm morphology
Bibliographic record
Abstract
STUDY QUESTION: Are common lifestyle factors associated with poor sperm morphology? SUMMARY ANSWER: Common lifestyle choices make little contribution to the risk of poor sperm morphology. WHAT IS KNOWN ALREADY: Although many studies have claimed that men's lifestyle can affect sperm morphology, the evidence is weak with studies often underpowered and poorly controlled. STUDY DESIGN, SIZE, DURATION: Unmatched case-referent study with 318 cases and 1652 referents. Cases had poor sperm morphology (<4% normal forms based on 200 sperm assessed). Exposures included self-reported exposures to alcohol, tobacco, recreational drugs as well as occupational and other factors. PARTICIPANTS/MATERIALS, SETTING, METHODS: Eligible men, aged 18 years or above, were part of a couple who had been attempting conception without success following at least 12 months of unprotected intercourse and also had no knowledge of any semen analysis before being enrolled. They were recruited from 14 fertility clinics across the UK during a 37-month period from 1 January 1999. MAIN RESULTS AND THE ROLE OF CHANCE: Risk factors for poor sperm morphology, after adjustment for centre and other risk factors, included: (i) sample production in summer [odds ratio (OR) = 1.99, 95% confidence interval (CI) 1.43-2.72]; and (ii) use of cannabis in the 3 months prior to sample collection in men aged ≤30 years (OR = 1.94, 95% CI 1.05-3.60). Men who produced a sample after 6 days abstinence were less likely to be a case (OR = 0.64, 95% CI 0.43-0.95). No significant association was found with body mass index, type of underwear, smoking or alcohol consumption or having a history of mumps. This suggests that an individual's lifestyle has very little impact on sperm morphology and that delaying assisted conception to make changes to lifestyle is unlikely to enhance conception. LIMITATIONS, REASONS FOR CAUTION: Data were collected blind to outcome and so exposure information should not have been subject to reporting bias. Less than half the men attending the various clinics met the study eligibility criteria and among those who did, two out of five did not participate. It is not known whether any of those who refused to take part did so because they had a lifestyle which they did not want subjected to investigation. Although the power of the study was sufficient to draw conclusions about common lifestyle choices, this is not the case for exposures that were rare or poorly reported. WIDER IMPLICATIONS OF THE FINDINGS: All participating clinics saw patients at no cost (under the UK National Health Service) and the study population may differ from those in countries without such provision. Even within the UK, low-income couples may choose not to undertake any investigation believing that they would subsequently be unable to afford treatment. Since a computer performed the measurements of sperm morphology, these results may not be comparable with studies where sperm morphology was assessed by other methods. STUDY FUNDING/COMPETING INTERESTS: The study was funded by the UK Health and Safety Executive, the UK Department of Environment, Transport and the Regions, the UK Department of Health (Grant Code DoH 1216760) and the European Chemical Industry Council (grant code EMSG19). No competing interests declared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".