Using a mass spectrometry-based metabolite profiling platform, we identified a panel of amino acids whose fasting levels at a routine examination predicted the future development of diabetes in otherwise healthy, normoglycemic individuals. Indeed, fasting concentrations of these amino acids were elevated up to 12 years prior to the onset of diabetes. The risk of future diabetes was elevated at least 4-fold in those with high plasma amino acid concentrations in both the discovery and replication samples.
A growing number of studies have used mass spectrometry as a tool for biomarker discovery18,19
, but these studies have been largely cross-sectional, providing limited information regarding the relation of metabolomic (or proteomic) biomarkers to the future
development of disease. Thus, an important strength of the current investigation is the use of two well-characterized prospective cohorts, one for derivation and one for replication, each with more than 3,000 participants who have been followed longitudinally for decades. All individuals in our study were free of diabetes at the time the blood samples were collected, and matching for BMI and fasting blood glucose in our study design minimized confounding from existing glucose intolerance. The long period of observation is a distinctive feature of our study, because it enabled us to demonstrate that circulating amino acid elevations can occur well before any alteration in insulin action is detectable using standard biochemical measures.
Our findings, which highlight five branched chain and aromatic amino acids from 61 metabolites profiled, are particularly noteworthy in the context of experimental and clinical data suggesting that certain amino acids may be both markers and effectors of insulin resistance14,15,18,20,21
. Several decades ago, Felig and colleagues studied 20 non-obese and obese individuals, and found that fasting concentrations of branched chain and aromatic amino acids correlated with obesity and serum insulin20
. Additionally, glucose loading lowered amino acid concentrations in insulin-sensitive, but not insulin-resistant individuals. Both sets of findings have been corroborated by more recent studies using LC-MS-based metabolomics platforms14-16,18
. Studies of branched chain amino acid supplementation in both animals14
indicate that circulating amino acids may directly promote insulin resistance, possibly via disruption of insulin signaling in skeletal muscle. The underlying cellular mechanisms may include activation of the mTOR, JUN and IRS1 signaling pathways in skeletal muscle14,21
. By contrast, others have demonstrated improved glucose homeostasis in animals fed a diet specifically enriched in leucine23
In addition to insulin resistance, impaired insulin secretion plays a critical role in the pathogenesis of type 2 diabetes. In this regard, it is noteworthy that multiple amino acids, particularly the branched chain amino acids, are modulators of insulin secretion24-26
. Thus, another possible mechanism by which hyperaminoacidemia could promote diabetes is via hyperinsulinemia leading to pancreatic α-cell exhaustion.
While circulating amino acids were correlated with standard biochemical measures of insulin resistance and α-cell function, amino acid concentrations were predictive even among individuals with similar fasting insulin and glucose levels. Furthermore, stimulation of the insulin axis with OGTT did not elicit differential amino acid changes between cases and controls. All of these findings support the notion that hyperaminoacidemia could be a very early manifestation of insulin resistance—one that presages the clinical onset of diabetes by years.
The ability to identify individuals prior to the onset of disease is particularly important for conditions such as diabetes, because proven, preventive therapies exist and end-organ complications accrue over time. Although traditional risk factors such as body mass index and fasting glucose provide important information about future diabetes risk, not all individuals who are obese develop diabetes. It is important to understand which “at-risk” individuals are most likely to progress to overt disease. There has been interest in genetic risk prediction, but the known diabetes polymorphisms add modestly to risk assessment27,28
. For instance, known polymorphisms are only associated with 5% to 37% increases in the relative risk of diabetes, compared with the 60% to 100% increases in risk that we observed with elevation in amino acids. Indeed, the relative risks associated with elevated amino acids were comparable to, or higher than, those associated with higher age, fasting glucose, or body mass index in prior population-based studies28
Additionally, our findings may provide insight regarding subgroups in which amino profiles could yield the most incremental information. Most of our analyses were based on “high risk” study samples, as a result of the matching scheme which paired cases with controls who had a high predicted risk of diabetes. In this setting, amino acid elevations were associated with very high relative risks for developing diabetes, and the amino acid profiles led to large improvements in model fit and discrimination (c-statistics). This result was noted in both the discovery and replication cohorts, attenuating concern for over-fitting the data. In a more heterogeneous study sample, obtained by looking at a random set of controls from the Framingham cohort, the relative risks associated with elevated amino acids were attenuated (though still significant, in the 2-fold range) and changes in c-statistics were modest. Baseline BMI and glucoses in the random cohort analysis were lower, on average, compared with the case-control analyses, and the distributions much broader. Most of the variation in diabetes risk in such a sample is attributable to variation in BMI and other standard diabetes risk factors. Overall, these findings suggest that amino acid profiling might have greater value in high-risk individuals, but confirmation in additional studies is needed.
Several limitations of the study deserve comment. We used a “targeted” approach that coupled liquid chromatography with a triple quadrupole tandem mass spectrometer. Although alternate LC-MS techniques or nuclear magnetic resonance spectroscopy can be used to acquire spectral data in a less “biased” manner, targeted LC-MS/MS provides much greater sensitivity, highly-specific identification of analytes, and the ability to quantify absolute analyte concentrations when appropriate standards are added. The platform used for the present study was geared toward small molecules such as amino acids, as well as urea cycle and nucleotide metabolites. This choice was informed by prior studies suggesting a cross-sectional association between insulin resistance and several metabolites18,20
, yet the absence of prospective data linking metabolite concentrations to future risk of diabetes. That we identified a set of five amino acids whose fasting levels strongly predicted the future development of diabetes does not preclude that other metabolites may also predict disease. Identification of novel biomarkers will no doubt accelerate as platforms expand their metabolite coverage.
In the Framingham Heart Study, close surveillance of the participants over serial examinations ensured reliable ascertainment of the development of diabetes over time. In the replication cohort (MDC), incident diabetes cases were identified through the use of three registries. Although this introduces the possibility of misclassification of diabetes status in MDC, such misclassification would be expected to bias the results toward the null. Indeed, the robustness of the findings in two longitudinal cohorts with widely different methods for ascertaining diabetes further increases confidence in the validity of the results. Lastly, individuals in both cohorts were predominantly white and of European descent. Further studies are needed to determine whether the findings extend to other racial/ethnic groups.
In summary, from a panel of >60 metabolites, branched chain and aromatic amino acids emerged as predictors of the future development of diabetes. A single, fasting measurement of these amino acids provided information incrementally over standard risk factors (such as BMI, dietary patterns, and fasting glucose). Further investigation is warranted to test whether plasma amino acid measurements might help identify candidates for interventions to reduce diabetes risk, and to elucidate the biological mechanisms by which selected amino acids might promote type 2 diabetes.