Genetic diversity and the risk for dysglycemia
Bibliographic record
Abstract
Background: Type 2 diabetes affects approximately 8% of the world’s population. Individuals of South Asian ancestry tend to develop metabolic abnormalities, leading to diabetes, at lower measures of absolute obesity and approximately 10 years earlier than white Caucasians. Current literature is unclear on the source of this ethnic heterogeneity; the variation in risk cannot be explained by lifestyle factors alone. The overarching aim of this thesis is to explore the role of genetic variants and epigenetic differences to explain the greater risk for type 2 diabetes among South Asians. Methods: We first conducted a systematic review of the literature to ascertain the genetic risk from known single nucleotide polymorphisms (SNPs) among South Asians. We then compared these risk estimates to those from white Caucasians in a cohort of 69,033 individuals. Second, using the EpiDREAM prospective cohort study of individuals at high-risk for diabetes, we assessed the impact of genetic burden for impaired pancreatic beta-cell function alone and together with abdominal obesity on glucose traits. Ethnic heterogeneity in this interaction was also studied. Lastly, using data from two Canadian birth cohorts of South Asian and white Caucasian ancestry, we investigated ethnic differences in the epigenetic architecture for genes known to be implicated birth weight and length, as both are associated with the future risk of adult diabetes. Results: The systematic review identified 15 SNPs robustly associated with type 2 diabetes in both South Asians and white Caucasians. The magnitude of risk and allele frequency of these genetic variants did not differ between the ethnic groups. Additionally, we identified 8 novel polymorphisms implicated in diabetes only among South Asians. Second, using data from the EpiDREAM study, we identified an interaction between cumulative genetic burden of beta-cell impairment, measured using an un-weighted genotype score, and abdominal obesity on glucose traits in South Asians, but not white Caucasians. Third, our investigation of differential DNA methylation between the ethnic groups revealed seven CpG sites for which changes in methylation corresponded to alterations in birth weight among white Caucasians, but not South Asians. An independent agnostic genome-wide search identified methylation levels at three CpG sites that appear to uniquely modulate birth weight in South Asians. Conclusions: Overall, our results indicate that the greater risk for metabolic traits in South Asians likely does not result from common genetic variants shared by both South Asians and white Caucasians. Rather, differences in risk may be additionally influenced by unique risk variants in South Asians. Furthermore, it appears that the risk from a genetic impairment in South Asians may be magnified by abdominal obesity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".