Metabolites are powerful diagnostic indicators (biomarkers) of current and future health status. Treatments aimed at suppressing specific predictive risk metabolites can improve clinical outcome and prevent disease, even when patients are otherwise asymptomatic. For example, while high plasma cholesterol is not a disease, it is an important risk factor for myocardial infarction and stroke, and therapies aimed at reducing plasma cholesterol reduce incidence of these diseases. The aim of metabolomics is to comprehensively and unbiasedly detect, identify and quantify the full complement of small molecules found in cells, tissues or biological fluids. By providing a comprehensive metabolic snapshot of an organism within a particular context, metabolomics is able to capture complex metabolic signatures. This global perspective has already allowed doctors to better stratify patient groups (1), and is expected to provide researchers with critical mechanistic insight into the etiology of multifactorial diseases (2). However, since metabolism is a dynamic process, dynamic metabolic signatures can appear transiently within precise temporal and spatial windows that are difficult to know a priori. These dynamic metabolic signatures thus escape detection when sampling a single tissue, or a single time point.
We recently showed that a circadian perspective of metabolite dynamics within and across different tissues captures complex metabolic signatures and relationships under a typical range of physiological conditions, including feeding-fasting, and rest-activity cycles. We achieved this by sampling blood and tissues at high temporal resolution, and by comparing metabolite dynamics across 24 hours under different nutritional, pharmacological or genetic interventions (3,4). These temporal and spatial perspectives provide a deeper understanding of how and why particular metabolic signatures arise in different tissues, highlight the specific conditions under which they are most evident, and suggest their pathophysiological relevance. Since skeletal muscle is a major site of insulin-stimulated glucose disposal (5) and muscle insulin resistance is considered to be a major determining factor in the pathogenesis of type 2 diabetes (6), we will focus on identifying biomarkers for prediabetes and type 2 diabetes in skeletal muscle that are also reflected in blood.
Circadian Metabolomics to Identify Novel Predictive Biomarkers for Prediabetes and Type 2 Diabetes
To identify novel metabolic signatures for prediabetes and type 2 diabetes, we will perform comparative 24-hour global metabolite profiling of blood (serum) and skeletal muscles (gastrocnemius) from a series of murine cohorts under basal conditions, and after established interventions known to cause prediabetes and type 2 diabetes. Prior to metabolite profiling, we will characterize and verify each experimental group according to a panel of established clinical hallmarks for prediabetes and type 2 diabetes (glucose and insulin tolerance, hemoglobin A1c, serum triglycerides, free fatty acids and cholesterol profile). After circadian metabolomics profiling, cohorts will be stratified and classified according to their common and unique metabolic signatures. We will focus especially on identifying the particular conditions (time-of-day, feeding/fasting, rest/activity) when these unique biomarkers are most evident. In partnership with The Metabolomics Innovation Centre of the University of Alberta, we will validate our novel biomarker hits in biobank samples from prediabetics and type 2 diabetics, along with age, sex, and BMI matched nondiabetic controls collected under the appropriate conditions.
Impact: This project will provide specific metabolic signatures and prediction tools for prediabetes, type 2 diabetes and its complications, and enable development of new projects and precision therapeutics based on these principles. The result will be more accurate, more sensitive, and more specific sets of biomarkers to better define health status and disease trajectory, to inform more personalized interventions, and to track therapeutic progress.
1. Nicholson JK. Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol 2: 52. (2006).
2. Ramautar R, et al., Human metabolomics: strategies to understand biology. Curr Opin Chem Biol 17: 841-846. (2013).
3. Dyar KA, et al., Atlas of Circadian Metabolism Reveals System-wide Coordination and Communication between Clocks. Cell 174(6):1571-85. (2018).
4. Dyar KA, et al., Transcriptional programming of lipid and amino acid metabolism by the skeletal muscle circadian clock. PLoS Biol 16: e2005886. (2018).
5. DeFronzo, RA, et al., The effect of insulin on the disposal of intravenous glucose. Results from indirect calorimetry and hepatic and femoral venous catheterization. Diabetes 30:1000–1007. (1981).
6. DeFronzo RA, & Tripathy D. Skeletal muscle insulin resistance is the primary defect in type 2 diabetes. Diabetes Care. 32 Suppl 2:S157-63. (2009).