Vitamin D-rich marine Inuit diet and markers of inflammation – a population-based survey in Greenland
Bibliographic record
Abstract
The traditional Inuit diet in Greenland consists mainly of fish and marine mammals, rich in vitamin D. Vitamin D has anti-inflammatory capacity but markers of inflammation have been found to be high in Inuit living on a marine diet. Yet, the effect of vitamin D on inflammation in Inuit remains unsettled. This led us to investigate the association between vitamin D and markers of inflammation in a population with a high intake of a marine diet. We studied 535 Inuit and non-Inuit living in West and East Greenland. Information concerning dietary habits was obtained by interview-based FFQ. Blood samples were drawn for analysis of 25-hydroxyvitamin D, high-sensitivity C-reactive protein (hsCRP) and chitinase-3-like protein 1(YKL-40). Participants were divided into three groups based on degree of intake of the traditional Inuit diet. The diet groups (Inuit diet/mixed diet/imported foods) were associated with vitamin D levels in serum (74·2, 69·8 and 52·9 nm; P < 0·001), hsCRP (1·6, 1·4 and 1·3 mg/l; P = 0·002) and YKL-40 (130, 95 and 61 ng/ml; P < 0·001), respectively. YKL-40 level decreased with rising vitamin D level in Inuit (Inuit diet P = 0·002; mixed diet P = 0·011). YKL-40 was lower in groups with higher vitamin D levels after adjusting for other factors known to influence inflammation (P < 0·001). This was not seen for hsCRP. In conclusion, vitamin D and markers of inflammation vary in parallel with the intake of the marine Inuit diet. Vitamin D levels were inversely associated with YKL-40 levels, but no association with hsCRP was found. The hypothesised anti-inflammatory effect of vitamin D was not supported. Other factors in the marine diet may be speculated to influence inflammation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".