Does genetic heterogeneity account for the divergent risk of type 2 diabetes in South Asian and white European populations?
Bibliographic record
Abstract
AIMS/HYPOTHESIS: South Asians are up to four times more likely to develop type 2 diabetes than white Europeans. It is postulated that the higher prevalence results from greater genetic risk. To evaluate this hypothesis, we: (1) systematically reviewed the literature for single nucleotide polymorphisms (SNPs) predisposing to type 2 diabetes in South Asians; (2) compared risk estimates, risk alleles and risk allele frequencies of predisposing SNPs between South Asians and white Europeans; and (3) tested the association of novel SNPs discovered from South Asians in white Europeans. METHODS: MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and the Cochrane registry were searched for studies of genetic variants associated with type 2 diabetes in South Asians. Meta-analysis estimates for common and novel bi-allelic SNPs in South Asians were compared with white Europeans from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. The population burden from predisposing SNPs was assessed using a genotype score. RESULTS: Twenty-four SNPs from 21 loci were associated with type 2 diabetes in South Asians after meta-analysis. The majority of SNPs increase odds of the disorder by 15-35% per risk allele. No substantial differences appear to exist in risk estimates between South Asians and white Europeans from SNPs common to both groups, and the population burden also does not differ. Eight of the 24 are novel SNPs discovered from South Asian genome-wide association studies, some of which show nominal associations with type 2 diabetes in white Europeans. CONCLUSIONS/INTERPRETATION: Based on current literature there is no strong evidence to indicate that South Asians possess a greater genetic risk of type 2 diabetes than white Europeans.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".