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Why Diabetic Kidney Disease (DKD)? Leadership Theme 1: Role of Altered Lactate Metabolism in DN Theme 2: Sub-phenotyping of Asians with T2DM to stratify risk in DN Theme 3: Retinal microvasculature as a “window” to study mechanisms and pathways in DN Theme 4: Human genetics of kidney and metabolic dysfunction in DN Theme 5: Systems genetics approaches to DN pathogenesis Publications Contact Us
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DYNAMO was created to address the problem of diabetic nephropathy (DN), the leading cause of end-stage kidney disease (ESKD) in Singapore where the incidence of DN-associated ESKD is among the highest in the world. Despite its prominent global prevalence, the molecular pathogenesis of DN is not clearly defined. The major objective of DYNAMO is to identify the root causes of DN to provide a basis for new approaches to prevention, risk stratification, and treatment.

 

DYNAMO I first received its National Medical Research Council (NMRC)’s Open-Fund Large Collaborative Grant (OF-LCG) from July 2017 – Jun 2023 of S$25M.

 

In July 2023, DYNAMO II received another S$25M funding from NMRC’s OF-LCG for another 5 years till Jun 2028.

 

Overall Goals

  • Address major unmet needs by identifying new mechanistic pathways in Diabetic Nephropathy
  • Discovering and validating potential new targets for treatment
  • Defining novel biomarkers and strategies allowing early stratification of risk for DN within the larger population of people with type 2 diabetes

RESEARCH FOCUS

Representative Publications

  1. Inflammation and immunity pathways regulate genetic susceptibility to diabetic nephropathy.
    Gurley, Susan B., Sujoy Ghosh, Stacy A. Johnson, Kengo Azushima, Rashidah Binte Sakban, Simi E. George, Momoe Maeda, Timothy W. Meyer, and Thomas M. Coffman.
    Diabetes 2018;67(10):2096-106

  2. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.
    Sabanayagam C, Xu D, Ting DS, Nusinovici S, Banu R, Hamzah H, Lim C, Tham YC, Cheung CY, Tai ES, Wang YX. 
    The Lancet Digital Health. 2020 Jun 1;2(6):e295-302.

  3. Whole-genome sequencing of Finnish type 1 diabetic siblings discordant for kidney disease reveals DNA variants associated with diabetic nephropathy.
    Guo J, Rackham OJ, Sandholm N, He B, Österholm AM, Valo E, Harjutsalo V, Forsblom C, Toppila I, Parkkonen M, Li Q. 
    Journal of the American Society of Nephrology: JASN. 2020 Jan 9;31(2):309.

  4. Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes.
    Wang J, Liu JJ, Gurung RL, Liu S, Lee J, Yiamunaa M, Ang K, Shao YM, Tang JI, Benke PI, Torta F.
    Diabetologia. 2022 Dec;65(12):2176-.

  5. Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.
    Sabanayagam C, He F, Nusinovici S, Li J, Lim C, Tan G, Cheng CY. 
    elife. 2023 Sep 14;12:e81878.

  6. Abnormal lactate metabolism is linked to albuminuria and kidney injury in diabetic nephropathy.
    Azushima K, Kovalik JP, Yamaji T, Ching J, Chng TW, Guo J, Liu JJ, Nguyen M, Sakban RB, George SE, Tan PH. 
    Kidney international. 2023 Dec 1;104(6):1135-49.

  7. Metabolomics profiling in multi-ancestral individuals with type 2 diabetes in Singapore identified metabolites associated with renal function decline.
    Chen Y, Torta F, Koh HW, Benke PI, Gurung RL, Liu JJ, Ang K, Shao YM, Chan GC, Choo JC, Ching J. 
    Diabetologia. 2025 Mar;68(3):557-75.

  8. Precision Approaches to Kidney Disease in Asia: Perspectives from Singapore.
    Chen H, Seow SW, Coffman T, Tan P, Petretto E. 
    Journal of the American Society of Nephrology. 2026 Jul 22.

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