Enhancing Skin Health Through Omega-3 Fatty Acids and Antioxidant-Rich Diets: A Comprehensive Review Supported by Premium Doctors’ Medical Expertise
Bibliographic record
Abstract
Background: Skin health is an integrative marker of systemic well-being, and recent research increasingly recognizes nutrition as a modifiable determinant of skin physiology and aging. Among nutritional factors, omega-3 polyunsaturated fatty acids (PUFAs) and antioxidant-rich diets have received significant attention for their protective effects against inflammation, oxidative stress, and photodamage—key contributors to dermatological aging and disorders.Methods: This literature review systematically explores the scientific evidence on the roles of omega-3 fatty acids and dietary antioxidants in supporting and enhancing skin health. A total of 45 peer-reviewed articles published between 2015 and 2025 from databases including PubMed, Scopus, and Web of Science were analyzed.Results: Findings indicate that omega-3 fatty acids—primarily eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)—modulate inflammatory responses in the skin, reduce UV-induced damage, and improve skin barrier integrity. Antioxidants such as vitamins C and E, polyphenols, flavonoids, and carotenoids protect dermal structures from reactive oxygen species and support collagen preservation. A synergistic effect is evident when both omega-3 fatty acids and antioxidant-rich diets are combined, enhancing cutaneous repair, hydration, and elasticity while slowing extrinsic aging processes. Gaps remain in understanding precise dosages, bioavailability, and long-term dermatological outcomes.Conclusions: Omega-3 fatty acids and antioxidants represent a promising, non-invasive frontier in dermatology. Future research should focus on controlled clinical trials and nutrigenomic studies to develop targeted interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".