1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death

• Published in: Molecular systems biology Vol. 4; no. 1; pp. 167 - n/a
• Main Authors: Mäkinen, Ville‐Petteri, Soininen, Pasi, Forsblom, Carol, Parkkonen, Maija, Ingman, Petri, Kaski, Kimmo, Groop, Per‐Henrik, Ala‐Korpela, Mika
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 2008; John Wiley & Sons, Ltd; EMBO Press; Nature Publishing Group; Springer Nature
• Subjects: 1H NMR spectroscopy; Acetates - blood; Adult; Albuminuria - metabolism; Antihypertensive Agents - therapeutic use; Apolipoproteins B - blood; Biomarkers; Biomarkers - blood; Cholesterol - blood; Cholesterol, HDL - blood; Clustering; Complex variables; Computer applications; Cost analysis; Creatinine - blood; Diabetes; Diabetes Complications - mortality; Diabetes Complications - prevention & control; Diabetes mellitus; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - drug therapy; Diabetes Mellitus, Type 1 - metabolism; Diabetes Mellitus, Type 1 - mortality; Diabetes Mellitus, Type 1 - urine; Diabetic Nephropathies - blood; Diabetic Nephropathies - diagnosis; Diagnostic systems; Disease resistance; EMBO10; EMBO24; Gene expression; Glycated Hemoglobin A - metabolism; Humans; Hypoglycemic Agents - therapeutic use; Insulin; Insulin - therapeutic use; Kidney diseases; Lactic Acid - metabolism; Lipids; Lipoproteins; Metabolic disorders; Metabolic syndrome; Metabolic Syndrome - blood; Metabolism; Metabolites; metabonomics; Middle Aged; Mortality; NMR; Nuclear magnetic resonance; Nuclear Magnetic Resonance, Biomolecular - methods; Patients; Phenotypes; Protons; Reproducibility of Results; Risk assessment; serum; Triglycerides - blood; type I diabetes; Urea - blood; Variables; Vascular diseases; Finland; H NMR spectroscopy; metabonomics; serum; type I diabetes; biomarkers
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb4100205

Subtle metabolic changes precede and accompany chronic vascular complications, which are the primary causes of premature death in diabetes. To obtain a multimetabolite characterization of these high‐risk individuals, we measured proton nuclear magnetic resonance ( 1 H NMR) data from the serum of 613 patients with type I diabetes and a diverse spread of complications. We developed a new metabonomics framework to visualize and interpret the data and to link the metabolic profiles to the underlying diagnostic and biochemical variables. Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome. We illustrate how a single 1 H NMR protocol is able to identify the polydiagnostic metabolite manifold of type I diabetes and how its alterations translate to clinical phenotypes, clustering of micro‐ and macrovascular complications, and mortality during several years of follow‐up. This work demonstrates the diffuse nature of complex vascular diseases and the limitations of single diagnostic biomarkers. However, it also promises cost‐effective solutions through high‐throughput analytics and advanced computational methods, as applied here in a case that is representative of the real clinical situation. Synopsis People with diabetes are at high risk of dying from heart disease and stroke, and many patients also suffer from severe degradation of the kidneys, retina and nervous system. Diabetes‐related diseases reflect the imbalance of glucose metabolism: patients with type I diabetes completely lack the normal insulin response that makes glucose available for cellular processes. With insulin replacement therapy the acute symptoms can be cured, but the natural metabolic balance is nevertheless disturbed, which leads to chronic systemic stress. Diabetic kidney disease (DKD) is an important predictor of premature death in type I diabetes. Its absence does not, however, preclude other risk factors for heart disease. Furthermore, the clinical diagnosis is based on a single biomarker (excess protein in urine) and subject to large individual variation that makes the early stages difficult to detect. We are therefore developing new cost‐effective analytical and computational approaches that can augment the existing biomarkers and provide a quantitative multidimensional disease characterization. In this study, we measured the 1 H NMR spectra of blood serum for 613 patients with type I diabetes from the Finnish Diabetic Nephropathy Study. We chose 1 H NMR spectroscopy, as it can detect many of the important risk markers (such as cholesterol, triglycerides, glucose and creatinine) with a single standardized experimental procedure (Figure 1 ). Our starting point was exploratory—we did not try to predict urine protein excretion, but rather to identify the diverse and diffuse systemic metabolic states of the diabetic condition, as seen in serum. The complex molecular data cannot be used as such; we thus visualized the spectral features with a self‐organizing map (SOM). Simply speaking, the SOM is just a layout of patients on a 2D canvas in such a way that patients with similar spectra are placed close to each other. Consequently, the map can be colored according to locally averaged values for a particular variable, which reveals the differences in the metabolic profiles between specific map regions. We also developed a new method to estimate the statistical significance of the observed patterns and to normalize the colorings, so that different sources of information can be easily visualized and reliably compared (Figure 1 ). Our results show that in the study group (aged between 30 and 50 years) mortality during the next decade was over three times higher than in the same age group of the entire Finnish population (Figure 6 ). Most of the premature deaths were attributed to the combination of DKD and adverse serum profile (eightfold relative risk). Note that none of the patients was on dialysis, so they still had adequate kidney function. The spectral features for these patients revealed hallmarks of insulin resistance that are characteristic of additional disturbance in glucose metabolism besides the insulin deprivation. High concentration of triglycerides, elevated total cholesterol and a decrease in high‐density lipoprotein particles (HDL 2 ) were observable in the 1 H NMR spectra, along with an increase of creatinine, which is associated with reduced filtering capacity of the kidneys. Lactate and acetate were also different between the high‐ and low‐risk groups, which further indicates alterations in cellular glucose metabolism (Figure 1 ). In addition to the 1 H NMR spectra, we also had numerous biochemical measurements and extensive clinical information available for the study subjects. The coalescent nature of kidney disease and insulin resistance was confirmed by overlapping the SOM patterns for urine albumin excretion, weight‐adjusted insulin dose, glycosylated hemoglobin (measure of long‐term glucose control) and waist circumference. The ability of a single 1 H NMR measurement to reveal multiple features of the effects of diabetes was thus validated (Figure 6 ). This work is, to our knowledge, the first metabonomics study on premature death and vascular diseases in a large human cohort. We used only serum to characterize the patients, and yet the high‐risk metabolic features were easily observable. This is an encouraging result with respect to general applicability as, unlike type I diabetes, urine albumin (or any other single biomarker) does not have an equally critical role in type II diabetes, let alone in the nondiabetic population. Furthermore, our application of 1 H NMR metabonomics and statistical visualizations may improve the tracking of patients’ progress in the diabetic disease continuum in a way not attainable by traditional approaches. Hence, it may become possible to re‐route the multimetabolite path of a vulnerable patient away from adverse clinical endpoints and towards a more favorable phenotype before it is too late. The combined application of 1H NMR metabonomics of serum and self‐organizing maps resulted in a new holistic framework to visualize and interpret data and to link metabolic phenotypes to underlying diagnostics, biochemical variables and premature death Based solely on 1H NMR data of serum, up to a 7.8‐fold relative risk of premature death was observed for type 1 diabetic patients with an adverse metabolic profile. The metabolic phenotype with the highest mortality combined biochemical features from the metabolic syndrome (high triglycerides, low HDL2 cholesterol), insulin resistance (high lactate) and kidney disease (high creatinine, low albumin). The diffuse nature of micro‐ and macrovascular diseases was illustrated by subtle multimetabolite differences between the metabolic syndrome and diabetic kidney disease; a demonstration of the enhanced detection power of the metabonomics approach beyond single biomarkers and univariate statistics.