ArticleCommunications biology2026
Decomposing population differences in type 2 diabetes through a high-resolution GWAS of ~600,000 individuals with East Asian ancestry.
Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
Abstract
Type 2 diabetes (T2D) is a highly heterogeneous metabolic trait, with a higher prevalence in East Asians. Despite extensive genetic studies focusing mainly on Europeans, how genetic risk for T2D is distributed across biological pathways in East Asians in comparison with Europeans remains incompletely understood. Here, we show results from what is, to our knowledge, the largest genome-wide association study in East Asians, including 596,778 individuals, with a direct comparison to a European study of comparable sample size. We identified 196 loci, including three previously unreported loci. Subsequent multi-ancestry fine-mapping resolved 521 independent causal signals with high precision, revealing that 74% are shared while the remainder are strictly ancestry-specific. Beyond this shared architecture, we observed population differences in regulatory and pathway-level contributions, particularly involving pancreatic regulation and lipid- or adipose-related pathways. Furthermore, constructing a polygenic risk score using the cross-ancestry meta-analysis yielded the most robust predictive performance (AUC = 0.788). These findings clarify how T2D genetic risk is organized across populations, demonstrating that while multi-ancestry integration maximizes clinical predictive power, well-powered single-ancestry studies remain indispensable for uncovering population-specific biological mechanisms.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.