Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes

O’Sullivan, John; Morningstar, Jordan; Yang, Qiong; Zheng, Baohui; Gao, Yan; Jeanfavre, Sarah; Scott, Justine A; Fernandez, Céline; Zheng, Hui; O’Connor, Sean Timothy Francis; Cohen, Paul; Vasan, Ramachandran S.; Long, Michelle T.; Wilson, James G.; Melander, Olle; Wang, Thomas J.; Fox, Caroline S.; Peterson, Randall T.; Clish, Clary B.; Corey, Kathleen E.; Gerszten, Robert E.

doi:10.1172/jci95995

Cited by 124 publications

(106 citation statements)

References 43 publications

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“…An elegant study was reported containing several large clinical cohorts containing biopsy-proven NASH patients that also had liver fat determined by CT. Serum metabolomics identified the top metabolite associated with liver fat as a mass of 202.1185 + . Databases contained a large number of hits for this mass and so a GWAS strategy was adopted yielding SNPs for the AGXT2 gene whose expressed enzyme produces a metabolite, dimethylguanidino valeric acid (DMGV), which matched this mass [191]. In terms of a biomarker for NASH or NAFL, DMGV displayed a wide overlap between control and NASH with approx.…”

Section: Nafl and Nashmentioning

confidence: 99%

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

Beyoğlu

Idle

2020

Metabolites

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In recent years, there has been a plethora of attempts to discover biomarkers that are more reliable than α-fetoprotein for the early prediction and prognosis of hepatocellular carcinoma (HCC). Efforts have involved such fields as genomics, transcriptomics, epigenetics, microRNA, exosomes, proteomics, glycoproteomics, and metabolomics. HCC arises against a background of inflammation, steatosis, and cirrhosis, due mainly to hepatic insults caused by alcohol abuse, hepatitis B and C virus infection, adiposity, and diabetes. Metabolomics offers an opportunity, without recourse to liver biopsy, to discover biomarkers for premalignant liver disease, thereby alerting the potential of impending HCC. We have reviewed metabolomic studies in alcoholic liver disease (ALD), cholestasis, fibrosis, cirrhosis, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH). Specificity was our major criterion in proposing clinical evaluation of indole-3-lactic acid, phenyllactic acid, N-lauroylglycine, decatrienoate, N-acetyltaurine for ALD, urinary sulfated bile acids for cholestasis, cervonoyl ethanolamide for fibrosis, 16α-hydroxyestrone for cirrhosis, and the pattern of acyl carnitines for NAFL and NASH. These examples derive from a large body of published metabolomic observations in various liver diseases in adults, adolescents, and children, together with animal models. Many other options have been tabulated. Metabolomic biomarkers for premalignant liver disease may help reduce the incidence of HCC.

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Section: Nafl and Nashmentioning

confidence: 99%

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

Beyoğlu

Idle

2020

Metabolites

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“…Urea cycle metabolites (arginine, citrulline, ornithine), and methylarginines (ADMA, SDMA, NMMA) were analysed using a targeted approach (Broad Institute). Raw data were obtained and processed using liquid chromatography tandem mass spectrometry, as previously described . The coefficient of variation for each metabolite was: arginine, 2.5%; ornithine, 2.4%; citrulline, 2.1%; ADMA, 7.2%; SDMA, 4.5%; NMMA, 5.5%.…”

Section: Methodsmentioning

confidence: 99%

“…Raw data were obtained and processed using liquid chromatography tandem mass spectrometry, as previously described. 5 The coefficient of variation for each metabolite was: arginine, 2.5%; ornithine, 2.4%; citrulline, 2.1%; ADMA, 7.2%; SDMA, 4.5%; NMMA, 5.5%.…”

Section: Blood Specimens and Metabolite Profilingmentioning

confidence: 95%

Changes in arginine are inversely associated with type 2 diabetes: A case‐cohort study in the PREDIMED trial

Ruíz-Canela

Razquín

et al. 2018

Diabetes Obesity Metabolism

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The associations between arginine-based metabolites and incident type 2 diabetes (T2D) are unknown. We employed a case-cohort design, nested within the PREDIMED trial, to examine six plasma metabolites (arginine, citrulline, ornithine, asymmetric dimethylarginine [ADMA], symmetric dimethylarginine [SDMA] and N-monomethyl-l-arginine [NMMA]) among 892 individuals (251 cases) for associations with incident T2D and insulin resistance. Weighted Cox models with robust variance were used. The 1-year changes in arginine (adjusted hazard ratio [HR] per SD 0.68, 95% confidence interval [CI] 0.49, 0.95; Q4 vs. Q1 0.46, 95% CI 0.21, 1.04; P trend = 0.02) and arginine/ADMA ratio (adjusted HR per SD 0.73, 95% CI 0.51, 1.04; Q4 vs. Q1 0.52, 95% CI 0.22, 1.25; P trend = 0.04) were associated with a lower risk of T2D. Positive changes of citrulline and ornithine, and negative changes in SDMA and arginine/(ornithine + citrulline) were associated with concurrent 1-year changes in homeostatic model assessment of insulin resistance. Individuals in the low-fat-diet group had a higher risk of T2D for 1-year changes in NMMA than individuals in Mediterranean-diet groups (P interaction = 0.02). We conclude that arginine bioavailability is important in T2D pathophysiology.

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“…These approaches developed by bioinformaticians utilize modern computational developments in network analytics and machine learning algorithms and have the potential to uncover biological networks and association that may not have been identifiable using traditional candidate approaches . O'Sullivan et al have demonstrated the integration of nontargeted metabolomics, genetics, and detailed human phenotyping in biomarker discovery. Using this approach, they identified dimethylguanidino valeric acid as an independent biomarker of nonalcoholic fatty liver disease in the offspring cohort of the Framingham Heart Study …”

Section: Unbiased “Omics” Approachesmentioning

confidence: 99%

“…O'Sullivan et al have demonstrated the integration of nontargeted metabolomics, genetics, and detailed human phenotyping in biomarker discovery. Using this approach, they identified dimethylguanidino valeric acid as an independent biomarker of nonalcoholic fatty liver disease in the offspring cohort of the Framingham Heart Study …”

Section: Unbiased “Omics” Approachesmentioning

confidence: 99%

Utilizing state‐of‐the‐art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease

et al. 2018

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Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)-diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking-has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large-scale multi-omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.

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Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes

Cited by 124 publications

References 43 publications

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

Changes in arginine are inversely associated with type 2 diabetes: A case‐cohort study in the PREDIMED trial

Utilizing state‐of‐the‐art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease

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