Bioscience Evidence 2024, Vol.14, No.1, 11-15 http://bioscipublisher.com/index.php/bm 11 Scientific Review Open Access Unveiling the Genetic Heterogeneity of Type 2 Diabetes: From Multi-ethnic Studies to Personalized Medicine Mengting Luo Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China Corresponding author email: juliemtluo@gmail.com Bioscience Evidence, 2024, Vol.15, No.1 doi: 10.5376/be.2024.14.0002 Received: 01 Jan., 2024 Accepted: 02 Feb., 2024 Published: 13 Feb., 2024 Copyright © 2024 Luo, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Luo M.T., 2024, Unveiling the genetic heterogeneity of type 2 diabetes: from multi-ethnic studies to personalized medicine, Bioscience Method, 15(1): 11-15 (doi: 10.5376/be.2024.14.0002) The paper titled “Genetic drivers of heterogeneity in type 2 diabetes pathophysiology” published in Nature on February 19, 2024, authored by Ken Suzuki, Konstantinos Hatzikotoulas, Lorraine Southam, Henry J. Taylor, Xianyong Yin, Kim M. Lorenz, Ravi Mandla, et al., originates from institutions including the University of Manchester, UK, and the University of Tokyo, Japan. The study amalgamated genome-wide association study (GWAS) data from 2,535,601 individuals, 39.7% of whom were of non-European descent, including 428,452 cases of type 2 diabetes (T2D), aiming to characterize the genetic contributions to the development of T2D. It identified 1,289 independent association signals mapped to 611 loci, with 145 being novel discoveries. By defining cluster-based polygenic risk scores and examining their association with vascular outcomes related to T2D, the study highlighted the significant role of obesity-related processes in the development of vascular outcomes. Integrating and analyzing large-scale, multi-ethnic GWAS data, the study substantially expanded our understanding of the genetic diversity of T2D. Its results not only enhance our knowledge of the genetic architecture of T2D but also provide new directions for future research, especially in the development of customized treatment plans for T2D patients with specific genetic backgrounds. The findings of this study signify a step towards more personalized care for diabetes, emphasizing the importance of considering genetic heterogeneity in public health strategies and therapeutic interventions. 1 Experimental Data Analysis This study has successfully identified 1,289 independent signals closely associated with Type 2 Diabetes (T2D), distributed across 611 genetic loci and encompassing a diverse range of ethnicities, including non-European ancestries. This underscores the importance of conducting genetic research in globally diverse populations. The integration with single-cell epigenomic data has provided deeper insights into the genetic diversity and complexity underlying T2D. These findings not only offer a new perspective on the genetic mechanisms driving T2D but also direct future research, particularly in the exploration of personalized treatment and management strategies, demonstrating the substantial potential of precision medicine. Figure 1 presents a heatmap of the associations between 37 cardiometabolic phenotypes and 8 clusters of index SNVs related to T2D. Each column corresponds to a cluster, and each row corresponds to a cardiometabolic phenotype. The 'temperature' of each cell in the heatmap indicates the association's z-score for the phenotype with index SNVs assigned to a specific cluster (adjusted for the T2D risk allele). For instance, glucose-related phenotypes (such as fasting glucose and glycated hemoglobin) show a strong positive correlation with the "Beta cell +PI" and "Beta cell -PI" clusters, while showing a negative correlation with the "Obesity" cluster. After adjusting for body mass index, a complex relationship between different metabolic phenotypes and genetic pathways in the development of T2D is observed, which is essential for understanding the etiology of T2D and its interaction with cardiometabolic states.
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