American Diabetes Association
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Nontargeted and targeted metabolomic profiling reveals novel metabolite biomarkers of incident diabetes in African Americans

posted on 2022-08-23, 18:45 authored by Zsu-Zsu Chen, Julian Avila Pacheco, Yan Gao, Shuliang Deng, Bennet Peterson, Xu Shi, Shuning Zheng, Usman A. Tahir, Daniel H. Katz, Daniel E. Cruz, Debby Ngo, Mark D. Benson, Jeremy M. Robbins, Xiuqing Guo, Magdalena del Rocio Sevilla Gonzalez, Alisa Manning, Adolfo Correa, James B. Meigs, Kent D. Taylor, Stephen S. Rich, Mark O. Goodarzi, Jerome I. Rotter, James G. Wilson, Clary B. Clish, Robert E. Gerszten

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes associated metabolites, we leveraged a method that measures 305 targeted or “known” and 2,342 nontargeted or “unknown” compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)—a community cohort of self-identified African Americans (AAs), who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate (FDR) <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose (FPG). Of these, 144 and 68 associations, respectively, replicated in a multi-ethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ=0.29) and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.


Research in this manuscript was supported by the NIDDK with K23DK127073 to Z.C. and R01DK081572 to R.E.G. and J.G.W. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute 1 and the National Institute for Minority Health and Health Disparities (NIMHD). MESA and the MESA SHARe projects are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420. Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1, contract HHSN268201800002I) (Broad RNA Seq, Proteomics HHSN268201600034I, UW RNA Seq HHSN268201600032I, USC DNA Methylation HHSN268201600034I, Broad Metabolomics HHSN268201600038I). Phenotype harmonization, data management, sample-identity QC, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL-120393; U01HL-120393; contract HHSN268180001I). The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR00