From sperm to offspring: Assessing the heritable genetic consequences of paternal smoking and potential public health impacts
Bibliographic record
Abstract
Individuals who smoke generally do so with the knowledge of potential consequences to their own health. What is rarely considered are the effects of smoking on their future children. The objective of this work was to review the scientific literature on the effects of paternal smoking on sperm and assess the consequences to offspring. A literature search identified over 200 studies with relevant data in humans and animal models. The available data were reviewed to assess the weight of evidence that tobacco smoke is a human germ cell mutagen and estimate effect sizes. These results were used to model the potential increase in genetic disease burden in offspring caused by paternal smoking, with specific focus on aneuploid syndromes and intellectual disability, and the socioeconomic impacts of such an effect. The review revealed strong evidence that tobacco smoking is associated with impaired male fertility, and increases in DNA damage, aneuploidies, and mutations in sperm. Studies support that these effects are heritable and adversely impact the offspring. Our model estimates that, with even a modest 25% increase in sperm mutation frequency caused by smoke-exposure, for each generation across the global population there will be millions of smoking-induced de novo mutations transmitted from fathers to offspring. Furthermore, paternal smoking is estimated to contribute to 1.3 million extra cases of aneuploid pregnancies per generation. Thus, the available evidence makes a compelling case that tobacco smoke is a human germ cell mutagen with serious public health and socio-economic implications. Increased public education should be encouraged to promote abstinence from smoking, well in advance of reproduction, to minimize the transmission of harmful mutations to the next-generation.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".