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FHS scientists: “New Cardiometabolic Risk Factors in the Framingham Heart Study”

FHS scientists: “New Cardiometabolic Risk Factors in the Framingham Heart Study”

Metabolic diseases present particular difficulty for clinicians because they are often present for years before becoming clinically apparent.  We investigated whether metabolite profiles can predict the development of diabetes in the Framingham Heart Study.  Five branched-chain and aromatic amino acids had highly-significant associations with future diabetes, while a combination of three amino acids strongly predicted future diabetes by up to 12 years (>5-fold increased risk for individuals in top quartile).  Our findings in over 1100 individuals underscore the potential importance of amino acid metabolism early in the pathogenesis of diabetes, and suggest that amino acid profiles could aid in diabetes risk assessment. 

In a “lipidomics” analysis in the Framingham Heart Study, we found that lipids of relatively lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk.  To explore potential mechanisms that modulate the distribution of plasma lipids, we also performed lipid profiling in the setting of perturbational experiments in MGH patients, including oral glucose tolerance testing, pharmacologic interventions such and acute exercise testing.  Lipids associated with increased diabetes risk (particularly triacylglycerols or TAGs) fell in response to insulin action; in turn, these TAGs were elevated in the setting of insulin resistance.  These studies identify a novel relationship between lipid acyl-chain content and diabetes risk, demonstrate how lipidomic profiling could also aid in clinical risk assessment beyond standard risk factors, and highlight enzymes including specific lipid elongases and desaturases for further exploration in the context of diabetes.

Most recently, we completed an analysis of “intermediate” metabolites (organic acids, purines, pyrimidines and other compounds) using a novel method.  The intermediate metabolite most strongly associated with incident DM was 2-aminoadipic acid (2-AAA). Individuals with elevated levels of 2-AAA had as much as a 4-fold higher risk of developing new-onset DM. We also replicated these findings in the Malmo Diet and Cancer Study. Of note, levels of 2-AAA were not correlated with other metabolite biomarkers of DM, such as BCAA and AroAA, suggesting they report on a distinct patho-physiological pathway. 2-AAA is a poorly characterized intermediary in lysine degradation, which can ultimately enter the TCA cycle via acetyl-CoA. In experimental studies, administration of 2-AAA lowered fasting plasma glucose levels in mice fed both standard chow and high fat diets. Further, 2-AAA treatment enhanced insulin secretion from both a pancreatic beta cell line as well as murine and human islets. These data highlight a novel metabolite not previously associated with T2D risk and a potential modulator of glucose homeostasis.


Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. Metabolite profiles and the risk of developing diabetes.  Nature Medicine. 2011 Mar 20. PMID:21423183

Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O’Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE.  Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. Journal of Clinical Investigation. 2011 Mar 14. pii: 44442. doi: 10.1172/JCI44442. PMID:21403394

Wang TJ, Ngo D, Psychogios N, Dejam A, Larson M, Ramachandran V, Ghorbani A, O’Sullivan J, Cheng S, Rhee EP, Sinha S, McCabe E, Fox C, O’Donnell C, Ho J, Florez J, Magnusson M, Pierce K, Souza A, Yu Y, Carter C, Light P, Melander O, Clish C, Gerszten RE. 2-Aminoadipic acid is a biomarker for diabetes risk. Journal of Clinical Investigation 2013 Oct 1;123(10):4309-17. PMID:24091325 PMCID:PMC3784523