Nocturnal Asthma and the Importance of Race/Ethnicity and Genetic Ancestry
Bibliographic record
Abstract
RATIONALE: Nocturnal asthma is a common presentation and is associated with a more severe form of the disease. However, there are few epidemiologic studies of nocturnal asthma, particularly in minority populations. OBJECTIVES: To identify factors associated with nocturnal asthma, including the contribution of self-identified race/ethnicity and genetic ancestry. METHODS: The analysis included individuals from the Study for Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) cohort. Nocturnal asthma symptoms were assessed by questionnaire. Genome-wide genotype data were used to estimate genetic ancestry in a subset of African American participants. Logistic regression was used evaluate the association of various factors with nocturnal asthma, such as self-identified race/ethnicity and genetic ancestry. MEASUREMENT AND MAIN RESULTS: The study comprised 3,380 African American and 1,818 European Americans individuals with asthma. After adjusting for other potential explanatory variables, including controller medication use, African Americans were more than twice as likely (odds ratio, 2.56; 95% confidence interval, 2.24-2.93) to report nocturnal asthma when compared with European American individuals. Among the subset of African American participants with genome-wide genotype data (n = 1,040), estimated proportion of African ancestry was also associated with an increased risk of nocturnal asthma (P = 0.007). Differences in lung function explained a small, but statistically significant (P = 0.02), proportion of the relationship between genetic ancestry and nocturnal asthma symptoms. CONCLUSIONS: Both self-identified race/ethnicity and African ancestry appear to be independent predictors of nocturnal asthma. The mechanism by which genetic ancestry contributes to population-level differences in nocturnal asthma appears to be largely independent of lung function.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".