Pterostilbene Targets Hallmarks of Aging in the Gene Expression Landscape in Blood of Healthy Rats
Bibliographic record
Abstract
SCOPE: Polyphenols from the phytoestrogen group, including pterostilbene (PTS), are known for their antioxidant, anti-inflammatory, and anti-cancer effects. In recent reports, phytoestrogens attenuate age-related diseases; however, their pro-longevity effects in healthy models in mammals remain unknown. As longevity research demonstrates age-related transcriptomic signatures in human blood, the current study hypothesizes that phytoestrogen-supplemented diet may induce changes in gene expression that ultimately confer pro-longevity benefits. METHODS AND RESULTS: In the present study, RNA sequencing is conducted to determine transcriptome-wide changes in gene expression in whole blood of healthy rats consuming diets supplemented with phytoestrogens. Ortholog cell deconvolution is applied to analyze the omics data. The study discovered that PTS leads to changes in the gene expression landscape and PTS-target genes are associated with functions counteracting hallmarks of aging, including genomic instability, epigenetic alterations, compromised autophagy, mitochondrial dysfunction, deregulated nutrient sensing, altered intercellular interaction, and loss of proteostasis. These functions bridge together under anti-inflammatory effects through multiple pathways, including immunometabolism, where changes in cellular metabolism (e.g., ribosome biogenesis) impact the immune system. CONCLUSION: The findings provide a rationale for pre-clinical and clinical longevity studies and encourage investigations on PTS in maintaining cellular homeostasis, decelerating the process of aging, and improving conditions with chronic inflammation.
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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".