Maternal high-fat diet during pregnancy and lactation affects factors that regulate cell proliferation and apoptosis in the testis of adult progeny
Bibliographic record
Abstract
Context A maternal high-fat diet is thought to pose a risk to spermatogenesis in the progeny. Aims We tested whether a maternal high-fat diet would affect Sertoli cell expression of transcription factors (insulin-like growth factor I (IGF-I); glial-cell line-derived neurotrophic factor (GDNF); Ets variant 5 (ETV5)) and cell proliferation and apoptotic proteins, in the testis of adult offspring. Methods Pregnant rats were fed ad libitum with a standard diet (Control) or a high-fat diet (HFat) throughout pregnancy and lactation. After weaning, male pups were fed the standard diet until postnatal day 160. Males were monitored daily from postnatal day 34 to determine onset of puberty. On postnatal day 160, their testes were processed for morphometry and immunohistochemistry. Key results The HFat diet increased seminiferous-tubule diameter (P < 0.03), the numbers of Sertoli cells (P < 0.0001) and Ki-67-positive spermatogonia (P < 0.0006), and the areas immunostained for ETV5 (P < 0.0001), caspase-3 (P < 0.001) and Bcl-2 (P < 0.0001). By contrast, the HFat diet reduced the areas immunostained for IGF-I (P < 0.01) and GDNF (P < 0.0001). Conclusions A maternal high-fat diet alters the balance between spermatogonia proliferation and spermatid apoptosis. Implications A maternal high-fat diet seems to ‘program’ adult male fertility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".