The impact of nutrition and lifestyle on male fertility
Bibliographic record
Abstract
BACKGROUND AND AIMS: Male unexplained infertility has long been suspected to result from environmental, lifestyle and nutritional factors. However, the literature on the subject is still scarce, and clinical studies providing robust evidence are even scarcer. In addition, some similar studies come to different conclusions. Dietary pattern can influence spermatogenesis by its content of fatty acids and antioxidants. Yet, in an age of industrialized mass food production, human bodies become more exposed to the ingestion of xenobiotics, as well as chemicals used for production, preservation, transportation and taste enhancement of foods. We attempted in this paper to collect the available evidence to date on the effect of nutritional components on male fertility. MATERIAL AND METHODS: A systematic search of the relevant literature published in PubMed, ScienceDirect and Cochrane Central Register of Controlled Trials Database was conducted. Literature was evaluated according to the Newcastle-Ottawa- Scale. RESULTS: Epidemiological observations are concordant in demonstrating an association of low-quality sperm parameters with higher intake of red meat, processed and organ meat and fullfat dairy. On the contrary, better semen parameters were observed in subjects consuming a healthy diet, rich in fruit, vegetables, whole grains and fish. Evidences of the negative impact on male fertility of by-products of water disinfection, accumulation in food chain of persistent organochlorine pollutants, pesticides, phthalates from food and water containers and hormones used in breeding cattle have been reported. Clinical trials of the effects of micronutrients on semen parameters and outcomes of assisted fertilization are encouraging, although optimal modality of treatment should be established. CONCLUSIONS: Although quality of evidence should be ameliorated, it emerges that environmental factors can influence male fertility. Some nutrients may enhance fertility whereas others will worsen it. With diagnostic analysis on a molecular or even sub-molecular level, new interactions with micronutrients or molecular components of our daily ingested foods and leisure drugs may lead to a better understanding of so far suspected but as yet unexplained effects on male spermatogenesis and fertility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".