Association between dietary niacin and retinal nerve fibre layer thickness in healthy eyes of different ages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it