Dietary Docosahexaenoic Acid Supplementation Prevents Age-Related Functional Losses and A2E Accumulation in the Retina
Bibliographic record
Abstract
PURPOSE: With age, retina function progressively declines and A2E, a constituent of the toxin lipofuscin, accumulates in retinal pigment epithelial (RPE) cells. Both events are typically exacerbated in age-related retina diseases. We studied the effect of dietary docosahexaenoic acid (DHA, C22:6n-3) supplementation on these events, using a transgenic mouse model (mutant human ELOVL4; E4) displaying extensive age-related retina dysfunction and massive A2E accumulation. METHODS: Retina function was assessed with the electroretinogram (ERG) and A2E levels were measured in E4 and wildtype (WT) mice. Dietary DHA was manipulated from 1 to 3, 1 to 6, 6 to 12, and 12 to 18 months: 1% DHA over total fatty acids (E4+, WT+) or similar diet without DHA (E4-, WT-). RESULTS: Increased omega-3/6 ratios (DHA/arachidonic acid) in E4+ and WT+ retinas were confirmed for the 1- to 3-month and 1- to 6-month trials. Although 1- to 3-month intervention had no effects, when prolonged to 1 to 6 months, RPE function (ERG c-wave) was preserved in E4+ and WT+. Intervention from 6 to 12 months led to maintained outer and inner retina function (ERG a- and b-wave, respectively) in E4+. At 12 to 18 months, a similar beneficial effect on retina function occurred in WT+; A2E levels were reduced in E4+ and WT+. CONCLUSIONS: DHA supplementation was associated with: preserved retina function at mid-degenerative stages in E4 mice; prevention of age-related functional losses in WT mice; and reduced A2E levels in E4 and WT mice at the oldest age examined. These findings imply that dietary DHA could have broad preventative therapeutic applications (acting on pathologic and normal age-related ocular processes).
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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) | 0.001 | 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 | 0.001 |
| 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".