Genomic ancestry and education level independently influence abdominal fat distributions in a Brazilian admixed population
Bibliographic record
Abstract
We aimed to identify the independent associations of genomic ancestry and education level with abdominal fat distributions in the 1982 Pelotas birth cohort study, Brazil. In 2,890 participants (1,409 men and 1,481 women), genomic ancestry was assessed using genotype data on 370,539 genome-wide variants to quantify ancestral proportions in each individual. Years of completed education was used to indicate socio-economic position. Visceral fat depth and subcutaneous abdominal fat thickness were measured by ultrasound at age 29-31y; these measures were adjusted for BMI to indicate abdominal fat distributions. Linear regression models were performed, separately by sex. Admixture was observed between European (median proportion 85.3), African (6.6), and Native American (6.3) ancestries, with a strong inverse correlation between the African and European ancestry scores (ρ = -0.93; p<0.001). Independent of education level, African ancestry was inversely associated with both visceral and subcutaneous abdominal fat distributions in men (both P = 0.001), and inversely associated with subcutaneous abdominal fat distribution in women (p = 0.009). Independent of genomic ancestry, higher education level was associated with lower visceral fat, but higher subcutaneous fat, in both men and women (all p<0.001). Our findings, from an admixed population, indicate that both genomic ancestry and education level were independently associated with abdominal fat distribution in adults. African ancestry appeared to lower abdominal fat distributions, particularly in men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".