Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity
Oded Shaham1,2,3, Ru Wei1, Thomas J Wang4,5, Catherine Ricciardi6, Gregory D Lewis1,5, Ramachandran S Vasan4,7, Steven A Carr1, Ravi Thadhani6,8, Robert E Gerszten1,5,9,11 & Vamsi K Mootha1,3,10,11
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard-MIT Division of Health Sciences & Technology, Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Framingham Heart Study, National Heart Lung and Blood Institute and Boston University, Framingham, MA, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- General Clinical Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Sections of Cardiology and Preventive Medicine, and the Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA, USA
- Renal Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Immunology & Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Co-senior authors
Correspondence to: Vamsi K Mootha1,3,10,11 Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5-806, Boston, MA 02114, USA. Tel.: +1 617 643 3096; Fax: +1 617 643 2108; Email: vamsi@hms.harvard.edu
Received 18 March 2008; Accepted 30 June 2008; Published online 5 August 2008
Article highlights
- Ingesting glucose after an overnight fast affects the levels of 18 plasma metabolites in a reproducible manner, as revealed by metabolic profiling of two independent groups of healthy humans
- The profiling approach spotlighted physiologic changes not previously linked to glucose homeostasis, most notably a sustained elevation of bile acids
- The changes in plasma metabolites reflect insulin's action in four distinct physiologic axes: glucose metabolism (lactate), triglyceride breakdown (glycerol), synthesis of ketone bodies (beta-hydroxybutyrate) and protein breakdown (amino acids)
- Pre-diabetic individuals vary in their profile of sensitivity to insulin's actions: some are less sensitive to insulin's suppression of protein breakdown, whereas others are less sensitive to insulin's suppression of triglyceride breakdown.
Synopsis
The human body strives to control the concentration of blood glucose within a narrow range, by using counterbalancing hormones. When we ingest sugar, the surge in blood glucose triggers the release of the hormone insulin, which acts to dispose of the extra glucose. An inability to lower the glucose levels leads to diabetes, a disease whose prevalence is quickly rising. Insulin released in response to glucose ingestion also switches the body from a fasting state to a fed state, reducing the consumption of energy sources and triggering fuel storage. The processes involved in this transition are important to understand, as they determine the body's ability to adapt its metabolism to varying conditions.
Focused studies over the past few decades revealed much on the fasting–feeding transition, but a global analysis of the metabolic processes involved in this transition has not been possible. Today, new technologies permit the simultaneous monitoring of large collections of metabolites (small molecules participating in metabolism). In the current study, we applied such a profiling technology to measure metabolite changes in blood of healthy individuals in response to an oral glucose challenge. We identified 21 metabolites whose levels change significantly (Figure 1C), and with the exception of glucose, all those alterations were present 2 h after the ingestion. 18 of these 2-h metabolite changes also replicated reliably in an independent group of healthy individuals. Metabolites responding to glucose ingestion span multiple chemical classes and biological pathways, and interestingly, point to pathways that have not been previously linked to the maintenance of glucose levels, such as bile acid secretion, urea synthesis and nucleotide degradation.
Figure 1
Temporal response to an oral glucose challenge in individuals with normal glucose tolerance (MACS). (A) Kinetics of blood glucose and insulin in response to glucose ingestion (mean
s.e.m.). (B) Magnitude and significance of metabolite change over time. Dots represent the 97 metabolites detected in plasma. Change is with respect to the fasting metabolite levels. Significant (P<0.001) changes are colored red. (C) Significant metabolite changes. In total, 21 metabolites changed significantly (P<0.001) when compared to their fasting levels and showed a significantly (P<0.05) distinct response compared to control (water ingestion). Color intensity reflects the median fold change compared to the fasting levels. Metabolites are ordered according to the magnitude of change.
For insulin to drive the transition from a fasting state to a fed state, the processes involved in the transition must be sensitive to insulin's action. Disposal of blood glucose, for example, requires that the process of glucose uptake be sensitive to stimulation by insulin. Loss of sensitivity impairs the body's ability to adapt, and is an early sign of type II diabetes. While the sensitivity of glucose uptake to insulin has been studied extensively, less is known about the sensitivity of other processes. Through metabolic profiling of the response to glucose ingestion in healthy individuals, we have identified metabolite changes that reflect insulin's action in four distinct axes: suppression of fat breakdown, suppression of protein breakdown, suppression of ketone body synthesis and stimulation of glucose metabolism. We next asked how these metabolic responses are altered in pre-diabetic individuals. In all four axes, we found that individuals with lower sensitivity (as determined by elevated fasting insulin levels) exhibited a blunted metabolic response (Figure 5A). Interestingly, there were individuals with similar elevations of fasting insulin who exhibited different profiles: some were less sensitive to insulin's suppression of fat breakdown, whereas others were less sensitive to insulin's suppression of protein breakdown (Figure 5B). These findings suggest the existence of multiple, uncorrelated dimensions of insulin sensitivity within pre-diabetic populations.
Figure 5
Correlation between fasting insulin and 2-h metabolite changes in individuals with impaired glucose tolerance (FOS-IGT). (A) 2-h changes in markers of insulin action are correlated with fasting insulin concentration. Each dot corresponds to an individual. (B) A bivariate model explaining fasting insulin using the 2-h decline of Leu/Ile and glycerol. Each circle represents an individual, and the circle size is proportionate to fasting insulin levels. aA representative individual exhibiting a blunted decline in Leu/Ile (resistant to proteolysis suppression). bA representative individual exhibiting a blunted decline in glycerol (resistant to lipolysis suppression).
Full figure and legend (228K)Figures & Tables indexOur findings suggest that there might be value in measuring insulin sensitivity along several physiological dimensions—not only with respect to glucose metabolism. By coupling the measurement of only four metabolites to the common oral glucose tolerance test, 'an insulin sensitivity profile' could be readily defined for an individual. In future longitudinal studies, it would be interesting to explore the association between loss of sensitivity in a specific axis and the clinical outcome. Findings from such studies might guide the choice of therapy for pre-diabetic individuals and help predict future complications.
Acknowledgements
We thank Joseph Avruch, Ronald Kahn, Barbara Kahn, Sudha Biddinger, and Mark Herman for helpful comments on the paper; Toshimori Kitami for glucose uptake measurements; and Alice M McKenney for assistance in preparing figures. OS was supported by a training grant for Bioinformatics and Integrative Genomics from the National Human Genome Research Institute. TJW was supported by the National Institutes of Health (NIH) (R01-HL-086875 and R01-HL-083197). GDL was supported by the Heart Failure Society of America and the Harvard/MIT Clinical Investigator Training Program. REG was supported by the NIH (U01HL083141), the Donald W Reynolds Foundation, and Fondation Leducq. VKM was supported by a Burroughs Wellcome Career Award in the Biomedical Sciences and a Howard Hughes Medical Institute Early Career Physician Scientist Award. This study was supported by a generous grant from the Broad Institute Scientific Planning and Allocation of Resources Committee, a General Clinical Research Center grant awarded by the NIH to the Massachusetts Institute of Technology General Clinical Research Center (MO1-RR01066), and a NIH/National Heart Lung and Blood Institute contract supporting the Framingham Heart Study (N01-HC-25195).
