Genetic Ancestry Is Associated With Systolic Blood Pressure and Glucose in Brazilian Children and Adolescents
Bibliographic record
Abstract
Background: Studies in admixed populations show that the prevalence of obesity and related diseases, such as type 2 diabetes and hypertension, may vary by ethnic group. The aim of this study was to investigate the relationship of genetic ancestry with phenotypes associated with obesity in a sample of school children and adolescents from Ouro Preto, Minas Gerais. Methods: We used data from genetic ancestry of 189 individuals previously determined by 15 ancestry informative markers (AIMs), and segregated individuals into three ancestral groups (predominantly African (PAFR), predominantly mixed (PMIX), and predominantly European (PEUR)) using the proportion of ancestry. The ancestral groups were compared with mean values of anthropometric, clinical, biochemical, and demographic variables. The simple linear regression analysis was used to test whether differences in mean values of the dependent variables (blood pressure and glucose) between the ancestral groups were dependent on the other variables. Results: Our results show that the proportions of African (F = 144.2, P < 0.001), Amerindian (F = 15.5, P < 0.001) and European (F = 184.9, P < 0.001) ancestry differed significantly (P < 0.001) among the three ancestral groups. PAFR individuals had higher mean blood pressure (P is less than or equal to 0.029) and glucose (P = 0.025) as compared to PEUR. In the linear regression model, the difference in systolic blood pressure (SBP) values remained significant in all models tested and independent of confounding variables (P is less than or equal to 0.041). The difference in diastolic blood pressure values observed in PAFR and PEUR groups did not remain significant when the metabolic profile was included in the tested model (P = 0.097). The difference in glucose values was significant only between PMIX and PEUR groups and independent of the settings (P is less than or equal to 0.037). Conclusion: The positive correlation between genetic ancestry and SBP and glucose in Brazilian children and adolescents suggests the need for special care in the subgroups of this population. J Endocrinol Metab. 2016;6(6):167-171 doi: https://doi.org/10.14740/jem383w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".