Benefits, Potential Harms, and Optimal Use of Nutritional Supplementation for Preventing Progression of Age-Related Macular Degeneration
Bibliographic record
Abstract
OBJECTIVE: To briefly review age-related macular degeneration (AMD), the main findings from the Age Related Eye Disease Study (AREDS) report number 8 on the use of nutritional supplements for AMD, and to focus on data suggesting that supplement use should be guided using genetic testing of AMD risk genes. DATA SOURCES: A literature search (January 2001 through October 26, 2016) was conducted using MEDLINE and the following MeSH terms: Antioxidants/therapeutic use, Genotype, Macular Degeneration/drug therapy, Macular degeneration/genetics, Dietary Supplements, Proteins/genetics, and Zinc Compounds/therapeutic use. Bibliographies of publications identified were also reviewed. STUDY SELECTION AND DATA EXTRACTION: English-language studies assessing AREDS supplement response in patients with AMD in relation to complement factor H gene ( CFH) and age-related maculopathy susceptibility 2 gene ( ARMS2) risk alleles were evaluated. DATA SYNTHESIS: Three of the 4 studies demonstrated a treatment interaction between ARMS2 and CFH genotypes and a differential response to supplements. The fourth study documented an interaction for the CFH genotype only. Reported response interactions included attenuated response, no response, and good response, whereas a subset showed increased progression of AMD. Conversely, one study reported no interactions between CFH and ARMS2 risk alleles and response to supplements. CONCLUSIONS: The weight of the evidence supports using genetic testing to guide selection of ocular vitamin use. This approach will avoid using supplements that could speed the progression of AMD in vulnerable patients, avoid using supplements that will have little to no effect in others, and result in appropriately using supplements in those that are likely to derive meaningful benefits.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".