Yulia Medvedeva1* and Ekaterina Markelova2
1MBZUAI
2Research Center of Biotechnology, Institute of Bioengineering, Russian Academy of Sciences, Moscow, Russia
ju.medvedeva [at] gmail.com
Abstract
Type 2 diabetes (T2D) is a highly polygenic disease involving multiple pathways. Genetic ancestry influences the predominant mechanisms driving T2D, as evidenced by population-specific risk alleles and metabolic adaptations. However, research remains heavily biased toward Western populations, leaving a profound knowledge gap regarding the immune, genetic, and epigenetic architecture of T2D in most ancestries worldwide, with diverse Russian ethnic groups—Turkic, North Caucasian, and East Siberian—remaining absent from multi-omic profiling despite the central role of inflammation and cluster-specific mechanisms in T2D pathogenesis. Understanding how genetic background shapes T2D risk is crucial for personalized prevention and treatment. To directly address this gap, we personally collected and generated a unified multi-omic dataset comprising genetics, scRNA-seq, and scATAC-seq on PBMCs from individuals with T2D and matched controls across multiple understudied Russian ancestries, including Chechens, Tatars, and Yakuts. We analyzed ancestry-specific T2D mechanisms by integrating genetic data with cluster-specific polygenic scores and single-cell chromatin accessibility and transcriptional profiles to assess T2D-associated genetic clusters across ancestry groups. This unique, first-hand unified dataset enabled high-resolution, multi-omic characterization of ancestry-associated immune states unobtainable from any published resource, mapping population-specific transcriptional signatures, epigenetic landscapes, and cell-type-specific perturbations. Furthermore, cluster-specific polygenic scores varied significantly between populations. Yakuts exhibited higher scores for β-cell dysfunction, hyperinsulin secretion, and lipid metabolism alterations, whereas Chechens and Tatars had higher scores for obesity-related mechanisms. Our findings provide the first description of genetic, transcriptional, and epigenetic immune diversity in ancestrally distinct Russian populations, establishing a critical multi-omic reference for global diabetes research. Predominant T2D mechanisms differ across populations, with some groups showing greater susceptibility to β-cell dysfunction while others show obesity-driven pathways. These ancestry-specific differences should guide public health recommendations and personalized medicine, underscoring the necessity of ancestry-aware, primary multi-omic data collection.
Keywords: T2D, multi-omics, PRS, personalized medicine