Subpopulations with Iron Deficiency, Liver Disease, or HFE Mutations Revealed by Statistical Mixture Modeling of Transferrin Saturation and Serum Ferritin Concentration in Asians, African Americans, Hispanics, and Whites.
Bibliographic record
Abstract
Abstract In previous investigations, we modeled the distribution of transferrin saturation (TS) in Caucasians and demonstrated a strong association between HFE genotype and TS subpopulations. Extending this approach, we now have analyzed joint population distributions of TS and serum ferritin concentration (SF) measured in the multi-ethnic Hemochromatosis and Iron Overload Screening (HEIRS) Study and examined the association of these distributions with the presence of HFE C282Y and H63D mutations, self-reported liver disease, and iron deficiency (defined as SF <15 μg/L). Based on separate models for each race/ethnicity by gender, four components with successively increasing age-adjusted means for TS and SF were identified in data from 26,832 African Americans, 12,620 Asians, 12,264 Hispanics, and 43,254 Whites. Fig. 1 illustrates age-adjusted values from 16,662 White men. Superimposed 95% confidence ellipses reflect component probability densities and show separation of the 1st and 4th components that had the lowest and highest means for TS and SF, respectively. Table 1 presents the range of estimates from individual models and indicates that the 2nd (largest) component had TS means of 22–26% for women (29–30% for men) and SF means of 43–82 μg/L for women (165–242 μg/L for men). The 3rd and 4th components had progressively smaller proportions and higher mean values of TS and SF, while the 1st component in each model had mean TS <16% for women (<20% for men), and mean SF <28 μg/L for women (<47 μg/L for men). Compared to the 2nd component: adjusted odds of iron deficiency were significantly higher in the 1st component (15–48 for women, 61–3530 for men); adjusted odds of self-reported liver disease were significantly higher in the 3rd and 4th components for African-American women and all men; and adjusted odds of any HFE mutation were increased in the 3rd component (1.4–1.8 for women, 1.2–1.9 for men) and in the 4th component for Hispanic and White women (1.5, 5.2, respectively) and men (2.8, 4.7, respectively). Joint mixture modeling identifies one component with lower mean SF and TS at risk for iron deficiency and two components with higher mean SF and TS at risk for liver disorders and HFE mutations. This approach permits characterization of the aggregate effects of hereditary or acquired factors that influence these serum iron measures in populations, and complements and enhances genetic and phenotypic testing for assessment of disease characteristics. Table 1 Range of estimates from models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".