Home Type 2 Multi-ancestry polygenic mechanisms of type 2 diabetes

Multi-ancestry polygenic mechanisms of type 2 diabetes

by Aaron J. Deutsch
0 comment
  • Tobias, D. K. et al. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. 29, 2438–2457 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Misra, S. et al. Precision subclassification of type 2 diabetes: a systematic review. Commun. Med. (Lond.) 3, 138 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, H. et al. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 66, 495–507 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Suzuki, K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature https://doi.org/10.1038/s41586-024-07019-6 (2024).

  • Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • BasuRay, S., Wang, Y., Smagris, E., Cohen, J. C. & Hobbs, H. H. Accumulation of PNPLA3 on lipid droplets is the basis of associated hepatic steatosis. Proc. Natl Acad. Sci. USA 116, 9521–9526 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, S. M., Muratalla, J., Sierra-Cruz, M. & Cordoba-Chacon, J. Role of hepatic peroxisome proliferator-activated receptor γ in non-alcoholic fatty liver disease. J. Endocrinol. 257, e220155 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Getz, G. S. & Reardon, C. A. Apoprotein E and reverse cholesterol transport. Int. J. Mol. Sci. 19, 3479 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, K. et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article 
    PubMed Central 

    Google Scholar
     

  • Caleyachetty, R. et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol. 9, 419–426 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yaghootkar, H., Whitcher, B., Bell, J. D. & Thomas, E. L. Ethnic differences in adiposity and diabetes risk—insights from genetic studies. J. Intern. Med. 288, 271–283 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ntuk, U. E., Gill, J. M. R., Mackay, D. F., Sattar, N. & Pell, J. P. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK Biobank participants. Diabetes Care 37, 2500–2507 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Hsu, W. C., Araneta, M. R. G., Kanaya, A. M., Chiang, J. L. & Fujimoto, W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care 38, 150–158 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Rodriguez, L. A. et al. Examining if the relationship between BMI and incident type 2 diabetes among middle-older aged adults varies by race/ethnicity: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Diabet. Med. 38, e14377 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Aggarwal, R. et al. Diabetes screening by race and ethnicity in the United States: equivalent body mass index and age thresholds. Ann. Intern. Med. 175, 765–773 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157–163 (2004).

    Article 

    Google Scholar
     

  • Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goodarzi, M. O. & Rotter, J. I. Genetics insights in the relationship between type 2 diabetes and coronary heart disease. Circ. Res. 126, 1526–1548 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sattar, N. et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375, 735–742 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • González-Lleó, A. M., Sánchez-Hernández, R. M., Boronat, M. & Wägner, A. M. Diabetes and familial hypercholesterolemia: interplay between lipid and glucose metabolism. Nutrients 14, 1503 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wei, Y. et al. Associations between serum total bilirubin, obesity and type 2 diabetes. Diabetol. Metab. Syndr. 13, 143 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hansen, M. et al. Bile acid sequestrants for glycemic control in patients with type 2 diabetes: a systematic review with meta-analysis of randomized controlled trials. J. Diabetes Complications 31, 918–927 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Glunk, V. et al. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat. Metab. 5, 861–879 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fathzadeh, M. et al. FAM13A affects body fat distribution and adipocyte function. Nat. Commun. 11, 1465 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, B.-T. et al. Disruption of the ERLIN–TM6SF2–APOB complex destabilizes APOB and contributes to non-alcoholic fatty liver disease. PLoS Genet. 16, e1008955 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • ElSayed, N. A. et al. 2. Classification and diagnosis of diabetes: Standards of Care in Diabetes—2023. Diabetes Care 46, S19–S40 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Vyas, D. A., Eisenstein, L. G. & Jones, D. S. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. N. Engl. J. Med. 383, 874–882 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Narayan, K. M. V. et al. Incidence and pathophysiology of diabetes in South Asian adults living in India and Pakistan compared with US blacks and whites. BMJ Open Diabetes Res. Care 9, e001927 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Narayan, K. M. V. & Kanaya, A. M. Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 63, 1103–1109 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • All of Us Research Program Investigators et al. The ‘All of Us’ Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Article 

    Google Scholar
     

  • Castro, V. M. et al. The mass general brigham biobank portal: an i2b2-based data repository linking disparate and high-dimensional patient data to support multimodal analytics. J. Am. Med. Inform. Assoc. 29, 643–651 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Kho, A. N. et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J. Am. Med. Inform. Assoc. 19, 212–218 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Szczerbinski, L. et al. Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores—a new resource for diabetes precision medicine. Preprint at bioRxiv https://doi.org/10.1101/2023.09.05.23295061 (2023).

  • Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Crawford, S. L. Correlation and regression. Circulation 114, 2083–2088 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • DiCorpo, D. et al. Type 2 diabetes partitioned polygenic scores associate with disease outcomes in 454,193 individuals across 13 cohorts. Diabetes Care 45, 674–683 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Patel, A. P. et al. Association of rare pathogenic DNA variants for familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and lynch syndrome with disease risk in adults according to family history. JAMA Netw. Open 3, e203959 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Magudia, K. et al. Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves. Radiology 298, 319–329 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Bridge, C. P. et al. A fully automated deep learning pipeline for multi-vertebral level quantification and characterization of muscle and adipose tissue on chest CT scans. Radio. Artif. Intell. 4, e210080 (2022).

    Article 

    Google Scholar
     

  • You may also like

    Leave a Comment

    1

    Today’s Diabetes News, your ultimate destination for up-to-date and insightful information on diabetes, health tips, and living a fulfilling life with diabetes. Our mission is to empower and support individuals with diabetes, their loved ones, and the wider community by providing reliable, relevant, and engaging content that fosters a healthier and happier life.

    Interesting Topics

    Most Viewed Articles

    Latest Articles

    Copyright MatchingDonors.com©️ 2024 All rights reserved.

    Are you sure want to unlock this post?
    Unlock left : 0
    Are you sure want to cancel subscription?