Melanin levels in relation to vitamin D among first-generation immigrants from different ethnic groups and origins: A comparative national Canadian cross-sectional study
Bibliographic record
Abstract
Introduction Immigrants to Western countries tend to have darker skin than native-born populations. We examined the relationship between skin melanin and serum vitamin D (vitD) [S-25(OH)D] levels and explored whether melanin levels explained S-25(OH)D variations between immigrants and native-born Canadians. This study offers novel findings as no such study has been conducted. Methods We used a national cross-sectional population-based design with data from the Canadian Health Measures Survey (CHMS). Skin melanin levels among first-generation immigrants based on their ethnicity and origin/country of birth were compared with white and native-born populations. We assessed the association between S-25(OH)D and melanin after adjusting for independent variables related to S-25(OH)D, melanin level, and immigration status. Results Of 11,579 participants, 21.9% were immigrants aged 3–79 years (mean age 39.23 years). Compared with non-immigrants, immigrants had lower S-25(OH)D levels (mean: 51.23 vs. 62.72; 95% CI: 8.37, 14.62; P < 0.001) but higher melanin levels (mean [ SE ]: 17.08 [0.25] vs. 16.29 [0.29]; 95% CI: −1.29, −0.281; P = 0.004). Melanin did not differ by length of stay in Canada but was weakly positively correlated ( r = 0.088, P < 0.001) with S-25(OH)D. Sex (male), age (≥18 years), summer/fall seasons, sunlight exposure, sunscreen non-use, smoking, and alcohol consumption were associated with higher melanin levels, whereas indoor tanning use was not. Conclusion Skin melanin levels were associated with sociodemographic and behavioral characteristics. Immigrants had higher melanin levels, but melanin did not differ by length of stay in Canada. The weak positive correlation between melanin and S-25(OH)D suggested confounding factors may impact the relationship between melanin levels, S-25(OH)D, and immigration status.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".