Submitted to: Journal of Clinical Endocrinology and Metabolism
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/19/2024
Publication Date: 11/20/2024
Citation: Jiang, X., Zhu, F., Graca, G., Du, X., Ran, J., Ahmadizar, F., Wood, A.C., Zhou, Y., Scholtens, D.M., Farzaneh, A., Ikram, M.A., Kuang, A., Le Roux, C., Gadgil, M.D., Cornelis, M.C., Taylor, K.D., Guo, X., Ghanbari, M., Rasmussen-Torvik, L.J., Tracy, R.P., Bertoni, A.G., Rotter, J.I., Herrington, D.M., Greenland, P., Kavousi, M., Zhong, V.W. 2024. Serum metabolomic profiling of incident type 2 diabetes mellitus in the Multi-Ethnic Study of Atherosclerosis and Rotterdam Study. Journal of Clinical Endocrinology and Metabolism. https://doi.org/10.1210/clinem/dgae812.
DOI: https://doi.org/10.1210/clinem/dgae812

Interpretive Summary: This study explored the role of specific blood metabolites that different between patients with type 2 diabetes (T2DM) and patients without T2DM. Researchers analyzed over 23,000 metabolites and identified 19 strongly linked to T2DM. These metabolites were tied to processes like amino acid and glucose metabolism, implicating dietary intake in the development of T2DM. Adding these metabolites to standard diabetes risk tools improved prediction accuracy, but only to a small extent. Overall, the study highlighted how metabolic changes, linked to dietary intake, contribute to T2DM but found that any benefit for risk prediction with metabolite profiles would require additional study, when compared to traditional methods.

Technical Abstract: This study aimed to investigate serum metabolomic biomarkers associated with incident type 2 diabetes mellitus (T2DM) and evaluate their performance in improving T2DM risk prediction. Untargeted proton nuclear magnetic resonance (1H NMR) spectroscopy-based metabolomics analyses were conducted in the Multi-Ethnic Study of Atherosclerosis (MESA; n=3460; discovery cohort) and Rotterdam Study (RS; n=1556; replication cohort). Multivariable cause-specific hazards models were used to analyze the associations between 23,571 serum metabolomic spectral variables and incident T2DM. Replicated metabolites required an FDR-adjusted P<0.01 in MESA, P<0.05 in RS, and consistent direction of association. Pathway and network analyses were conducted to elucidate biological mechanisms underlying T2DM development. Utility of the replicated metabolites in improving T2DM risk prediction was assessed based on the Framingham Diabetes Risk Score. A 2-sample Mendelian randomization was conducted to assess causal associations. Nineteen metabolites were significantly associated with incident T2DM. Pathway analyses revealed disturbances in aminoacyl-tRNA biosynthesis, metabolism of branched-chain amino acids (BCAAs), glycolysis/gluconeogenesis, and glycerolipid metabolism. Network analyses identified interactions with upstream regulators including p38 MAPK, c-JNK, and mTOR signaling pathways. Adding replicated metabolites to the Framingham Diabetes Risk Score showed modest to moderate improvements in prediction performance in MESA and RS, with ' c-statistic of 0.05 (95% CI, 0.04-0.07) in MESA and 0.03 (95% CI, 0.01-0.05) in RS. Genetically increased BCAAs and mannose were associated with T2DM. 1H NMR measured metabolites involved in aminoacyl-tRNA biosynthesis, BCAA metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism were significantly associated with incident T2DM and provided modest to moderate predictive utility beyond traditional risk factors.