Association between dietary niacin and retinal nerve fibre layer thickness in healthy eyes of different ages
Bibliographic record
Abstract
BACKGROUND: ) and retinal nerve fibre layer (RNFL) thickness in healthy eyes. METHODS: This cross-sectional study examined the association between daily niacin intake and RNFL thickness in three large population-based cohorts with varied age differences. RNFL thickness was extracted from optical coherence tomography data; energy-adjusted niacin intake was estimated from food frequency questionnaires. Linear mixed-effects models were utilised to examine the association between RNFL thickness and energy-adjusted niacin intake. Three separate analyses were conducted, with niacin treated as a continuous, a categorical (quartiles) or a dichotomous (above/below Australian recommended daily intake) variable. RESULTS: In total, 4937 subjects were included in the study [Raine Study Gen2, n = 1204, median age 20; Busselton Healthy Ageing Study (BHAS), n = 1791, median age 64; TwinsUK, n = 1942, median age 64). When analysed as a continuous variable, there was no association between RNFL thickness and niacin intake in any of the three cohorts (95% CI β: Raine Study Gen 2, -0.174 to 0.074; BHAS, -0.066 to 0.078; TwinsUK -0.435 to 0.350). Similar findings were observed with quartiles of niacin intake and for niacin intakes above or below Australian recommended daily intake levels in all three cohorts. CONCLUSIONS: Dietary intake of niacin from a standard diet does not appear to be associated with age-related RNFL thinning in healthy eyes. Supraphysiological doses of niacin may be required for therapeutic effect in the retina.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".