Title: <sup>1</sup> H NMR metabonomics approach to the disease continuum of diabetic complications and premature death
Abstract: Article12 February 2008Open Access 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death Ville-Petteri Mäkinen Ville-Petteri Mäkinen Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Pasi Soininen Pasi Soininen Laboratory of Chemistry, Department of Biosciences, University of Kuopio, Finland Search for more papers by this author Carol Forsblom Carol Forsblom FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Maija Parkkonen Maija Parkkonen FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Petri Ingman Petri Ingman Instrument Centre, Department of Chemistry, University of Turku, Finland Search for more papers by this author Kimmo Kaski Kimmo Kaski Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland Search for more papers by this author Per-Henrik Groop Corresponding Author Per-Henrik Groop FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Mika Ala-Korpela Corresponding Author Mika Ala-Korpela Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland Search for more papers by this author Ville-Petteri Mäkinen Ville-Petteri Mäkinen Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Pasi Soininen Pasi Soininen Laboratory of Chemistry, Department of Biosciences, University of Kuopio, Finland Search for more papers by this author Carol Forsblom Carol Forsblom FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Maija Parkkonen Maija Parkkonen FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Petri Ingman Petri Ingman Instrument Centre, Department of Chemistry, University of Turku, Finland Search for more papers by this author Kimmo Kaski Kimmo Kaski Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland Search for more papers by this author Per-Henrik Groop Corresponding Author Per-Henrik Groop FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland Search for more papers by this author Mika Ala-Korpela Corresponding Author Mika Ala-Korpela Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland Search for more papers by this author Author Information Ville-Petteri Mäkinen1,2,3, Pasi Soininen4, Carol Forsblom2,3, Maija Parkkonen2,3, Petri Ingman5, Kimmo Kaski1, Per-Henrik Groop 2,3 and Mika Ala-Korpela 1 1Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland 2FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, Finland 3Division of Nephrology, Department of Medicine, Helsinki University Hospital, Finland 4Laboratory of Chemistry, Department of Biosciences, University of Kuopio, Finland 5Instrument Centre, Department of Chemistry, University of Turku, Finland *Corresponding authors. FinnDiane Study Group, Folkhälsan Research Center, Folkhälsan Insititute of Genetics, Biomedicum Helsinki, University of Helsinki, PO Box 63, Helsinki FI-00014, Finland. Tel.: +358919125459; Fax: +358919125452; E-mail: [email protected] Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, PO Box 9203, Helsinki FI-02015 HUT, Finland. Tel.: +358503535457; Fax: +35894514833; E-mail: [email protected] Molecular Systems Biology (2008)4:167https://doi.org/10.1038/msb4100205 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info 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 (1H 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 1H 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 1H NMR spectra of blood serum for 613 patients with type I diabetes from the Finnish Diabetic Nephropathy Study. We chose 1H 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 (HDL2) were observable in the 1H 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 1H 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 1H 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 1H 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. Introduction Type I diabetes is caused by an autoimmune reaction against the insulin-producing pancreatic β-cells and subsequent disturbance of normal blood glucose metabolism. Insulin replacement therapy cures the acute symptoms, but is not able to match the natural response to rising or falling glucose levels. This persistent metabolic imbalance is linked to high incidence of vascular complications such as diabetic kidney disease (DKD) (Finne et al, 2005), diabetic retinal disease (DRD) (Roy et al, 2004) and macrovascular diseases (MVDs) (Libby et al, 2005), all of which are co-occurring in vulnerable patients (Groop et al, 2005; Thorn et al, 2005; Ala-Korpela, 2007). The diagnosis, risk assessment and treatment of these conditions are currently determined by a number of biochemical and clinical variables, although none of these are conclusive on its own (Soedamah-Muthu et al, 2004; Stadler et al, 2006). Furthermore, the simultaneous clustering of complications and metabolic risk factors has not been studied by high-throughput analytical techniques that could reveal the multidimensional metabolic state of an individual more effectively. The standard differential diagnostics in medicine may not be sufficient in detecting complex perturbations of biological systems (Zenker et al, 2007). Conditions such as insulin resistance and atherosclerosis stem from nonlinear interactive pathways between the genes (Hakonarson et al, 2007), gene expression (Sieberts and Schadt, 2007), metabolic environment (Goodacre, 2007) and the symbiotic microflora (Martin et al, 2007). To pinpoint the nodes and their roles in the disease networks requires a large number of samples with multidimensional quantitative data—a direct consequence of the curse of dimensionality. The genome-wide association studies have shown that this can be achieved at the DNA level (Frayling, 2007; Wellcome Trust Case Control Consortium, 2007). However, for personalized risk assessment and treatment the genetic approach is limited, as it does not take into account the dynamic environment, unlike the metabonomics approach (Nicholson and Wilson, 2003; Clayton et al, 2006), which has gained popularity as analytical technologies are evolving (Nicholson, 2006; Ala-Korpela, 2007; Salek et al, 2007). Diabetic complications pose a difficult challenge to public health care, as populations grow older and life style becomes more sedentary and energy-rich (Reunanen et al, 2000). For this reason, we are aiming at new screening methods and metabolic characterization tools to find the vulnerable patients at an early stage when preventive treatment is still effective (Tenenbaum et al, 2004; Gross et al, 2005). Mass spectrometry and proton (1H) nuclear magnetic resonance (NMR) spectroscopy are the two key experimental methods in the area of 'global biochemistry' (Fernie et al, 2004). 1H NMR, in particular, is advantageous for screening, as it can efficiently extract detailed molecular information on a large number of metabolites in various biofluids (Tang et al, 2004; Beckonert et al, 2007; Ala-Korpela, 2008). The earliest experiments with plasma have already demonstrated this in type II diabetes (Nicholson et al, 1984). Recently, we have shown that DKD can be detected by 1H NMR of serum (Mäkinen et al, 2006) and that the metabolic syndrome (MetS) can be distinguished by multivariate methods and 1H NMR spectroscopy (Suna et al, 2007). A similar approach has been applied to cardiovascular disease, but with limited success (Brindle et al, 2002; Kirschenlohr et al, 2006). The metabolic changes in type II diabetes have also been studied by chromatographic methods (Yang et al, 2004; Wang et al, 2006). Animal models have provided encouraging results and further justification for the metabonomic NMR approach (Williams et al, 2005; Clayton et al, 2006; Salek et al, 2007), but more experience from human populations is needed (Griffin and Nicholls, 2006; Ala-Korpela, 2007). In this study, the emphasis is on the metabolic continuum that underlies the slow and often elusive development of chronic complications. We focus first on DKD due to its high significance in the treatment and prognosis of diabetic patients (Gross et al, 2005). Our main goal, however, is to extract a metabolite manifold that highlights not only DKD, but also other important clinical and biochemical characteristics and their complex relationships (Fernie et al, 2004). The combination of 1H NMR of serum and metabonomic mapping provides the necessary insight: neural network analysis and statistically verified visualizations of both the spectroscopic and clinical data will not only help decision making in clinical environment, but will also increase the knowledge of multifactorial disease states that are difficult to pinpoint by reductionist approaches (Sams-Dodd, 2005; Weckwerth and Morgenthal, 2005; Loscalzo et al, 2007). The new source of information can then be used in personalized risk assessment as a cost-effective high-throughput alternative to a collection of specific biomarker assays (Lindon et al, 2006; Ala-Korpela, 2008). Results Molecular windows to metabolism We obtained serum samples from the FinnDiane study to measure two molecular windows for 613 patients with type I diabetes. A typical 1H NMR spectrum of human serum is characterized by broad resonances from the lipid molecules of lipoprotein particles, such as the −CH3 group of triglycerides, cholesterol compounds and phospholipids (Figure 1C and D). This so-called lipoprotein lipids (LIPO) window is a complex mixture of the aforementioned lipid signals, serum albumin and albumin-bound fatty acid resonances across the aliphatic region, and the less intense signals from smaller molecules such as creatinine, lactate and glucose (Ala-Korpela, 1995, 2008). Figure 1.1H NMR spectral profile of diabetic kidney disease. (A) The SOM of 613 × 2 1H NMR spectra of serum, colored according to the percentage estimate of DKD within a given map region. Each hexagonal map unit defines a specific model spectrum and a corresponding subset of patients, the spectra of which best match the aforementioned model. (B) The low molecular weight metabolites (LMWM) model spectrum and (C) the lipoprotein lipid and albumin (LIPO) model spectrum for a patient subset within the map unit with the lowest percentage of DKD. The colored curve segments indicate the current model, whereas the solid black curve indicates the mean spectrum over all data, thus serving as a constant reference. The colored areas below the model spectra represent the proportional differences of the unit-specific model and the mean model. (D) The LIPO model and (E) the LMWM model spectrum for patients within a map unit of the highest DKD percentage. An interactive presentation of the model spectra is available in Supplementary data 3. Download figure Download PowerPoint To reveal the resonances from smaller molecules, the spectrometer settings can be altered to suppress most of the broad resonances while still enabling the detection of the more mobile low molecular weight molecules (LMWM). The LMWM window is dominated by the numerous glucose resonances between 3.1 and 3.9 p.p.m., although some lipid signals still remain (Figure 1B and E). The spectral shapes from both windows share a common axis of chemical shift and are superimposable, except for the intensity scaling constant. For example, lactate creates a strong doublet signal around 1.28 p.p.m. in the LMWM window, but only small shapes on top of the wider lipid and albumin resonances in the LIPO window. On the other hand, most of the molecules with the −CH3 group contribute to the prominent signal around 0.8 p.p.m. in the LIPO window, but only the more mobile species can be detected in the LMWM window. Spectral profile of type I diabetes A self-organizing map (SOM) (Kohonen, 2000) was constructed from the 1H NMR data (Figure 1A). The SOM was the result of reducing the 613 experimental spectra into 9 × 9=81 representative spectral models, each of which was assigned to a unique hexagonal unit on the map grid. Subsequently, a best-matching model was determined for each experiment, thus each patient had a best-matching unit or a 'place of residence' on the map. Localized similarity is the fundamental idea behind the SOM, that is, neighboring units or the patients therein are more similar to each other than those from the opposite sides of the map. In this case, similarity was defined by the arithmetic multidimensional difference between the two spectral models; thus, any two neighbors shared more metabolic characteristics (their spectra looked the same) than two randomly picked patients, on average. As the SOM is analogous to a geographic map in all but the way the patients' coordinates are assigned, it is possible to use ordinary demographic methods to visualize the properties of patients in different metabolic neighborhoods (Supplementary data 1). Here, we started by coloring the units based on the percentage of DKD patients within a local population (Figure 1A). The highest value of 70% can be seen on the western edge of the map, on the unit at row 5 and column 1 (5,1). The unit at (2,9) near the northeast corner has the lowest percentage of DKD (16%), and is located far from (5,1). In fact, when looking at the overall coloring, the DKD patients are clustered on the western side, whereas the patients with fewer complications are concentrated on the northeast corner of the map. The typical spectral profile of DKD was examined by comparing the spectral models at (5,1) and (2,9) to see if any of the metabolite resonances differed. Each of the spectral plots, such as Figure 1C, consists of three components. First, the mean model over all data is depicted as a solid black line to serve as a constant reference to which the spectral models can be compared. The second curve alongside the reference is the unit-specific spectral model, which was split into orange or blue segments depending on where the model exceeded or was less intense than the reference. As the first two curves are close to each other in terms of absolute intensity, a third curve that depicts the proportional differences is helpful in revealing any significant changes in intensity. In Figure 1C for instance, the third curve was drawn below the two absolute intensity curves and painted similarly to the unit-specific model. The lipoprotein lipid resonances at around 0.81, 1.23 and 1.95 p.p.m. show reduced values from the mean, whereas the albumin background is increased. In Figure 1E, the two creatinine peaks at 2.98 and 3.99 p.p.m. are higher for the DKD region (5,1). However, looking at just two map locations is not enough for accurate interpretation; a more global perspective is required. Diabetic kidney disease, the metabolic syndrome and mortality A majority of the patients in this study had either micro- (22%) or macroalbuminuria (37%), the spatial distributions of which are revealed by class-specific colorings in Figure 2A and B. The macroalbuminuric group (clinically diagnosed with DKD) is concentrated on the western side of the map (P=1.2 × 10−8), whereas the diagnostically intermediate microalbuminuric group does not form any statistically significant pattern (P=0.077). While the DKD status was determined by urine albumin excretion, the map was constructed solely based on the 1H NMR spectra of serum, thus illustrating the systemic biological connection between the two biofluids. Figure 2.Statistical colorings of albuminuria, the MetS, MVD and mortality. (A–C) Demographic properties of patients on the SOM that was constructed from 613 × 2 1H NMR spectra of serum. The three upper plots depict the clustering of (A) microalbuminuria, (B) macroalbuminuria and (C) 10-year mortality in patients with type I diabetes. The color of each hexagonal map unit indicates the estimated proportion of cases with respect to the total number of patients who reside on the unit in question. For mortality, the estimates were normalized by follow-up time. (D–H) Five grades of the MetS according to the NCEP ATP III recommendations and (I) the distribution of patients with a history of macrovascular events. Empirical P-values for each plot as a whole are shown below the colorings. Download figure Download PowerPoint All-cause mortality in Figure 2C (P=0.00057) was estimated based on 8.2±0.6 years of follow-up and scaled to the percentage of deaths in a decade (number of deaths per 1000 patient years). As expected, there is a clear connection between DKD and increased mortality, and the highest value of 25% is observed at (7,1) close to the highest DKD percentage at (5,1). Additional details are available in Figure 1 in Supplementary data 2. The MetS (NCEP, 2002; Eckel et al, 2005) represents a binary classification according to a clinical scoring system (a score of 3 or more is considered positive) that combines several components of insulin resistance and obesity (Figure 2D–H). Patients with the lowest score 1 reside on the northern part of the SOM with a 42% (P=1.1 × 10−5) occupancy at (1,8), and those with a score above 3 are tightly concentrated on the southwestern corner, with hardly any overlap with the first group (P=1.8 × 10−8 for score 4, P=1.3 × 10−7 for score 5). The SOM colorings indicate strong associations between DKD, mortality and the MetS, but with subtle differences. For instance, the first two MetS categories split the normoalbuminuric northeastern side, rather than spread evenly to mirror the DKD group (Figure 2A, B, D and E). The highest percentage of DKD at (5,1) does not coincide with the highest MetS scores at (9,1). Interestingly, a history of macrovascular complications in these patients seem to be related more to the MetS than to DKD (Figure 2I), although the numbers are too small for statistical significance (P=0.035 for the MVD pattern). Finally, the highest 10-year mortality of 25% is observed at (7,1) in Figure 2C, where the MetS and DKD overlap the most. Confounding factors and treatments The colorings for age (mean±s.d. 40±11 years), type I diabetes duration (27±10 years) and gender (311 males, 302 females) show only minor spatial clustering and weak statistical significance (Figure 3A–C). Furthermore, the observed patterns show little similarity to DKD, the MetS or MVD in Figure 2, suggesting that the major chronological and physiological determinants of risk do not confound the biochemical characteristics. Figure 3.Statistical colorings of confounding factors and treatments. (A–F) Clinical characteristics of patients on the SOM of 1H NMR spectra. The colors of the map units indicate the estimates for the average (A) age and (B) diabetes duration within the patient subset on a particular map region. (C) The gender distributions on the map units. The color of each hexagonal map unit indicates the percentage of male gender with respect to the total number of patients that reside on the unit in question. (D, E) Blood pressure and (F) waist circumference for the patient subsets within each map unit. The percentages of (G) antihypertensive treatment, (H) DRD and (I) lipid-lowering treatment were obtained as described above. Download figure Download PowerPoint Most of the patients were on medication or had undergone laser treatment for DRD. Antihypertensive treatment (P=2.7 × 10−5) is most common (up to 75%) in those areas on the western side of the map (Figure 3G), which have a high percentage of DKD in Figure 2B and elevated blood pressure in Figure 3D and E, as expected. Furthermore, the same areas have a high proportion of DRD (P=0.00027), although the pattern is more widely dispersed on the southwestern half of the SOM (Figure 3H). Lastly, patients with the highest MetS scores have also the widest waist (94 cm) in Figure 3F and the highest percentage (30%) of lipid-lowering treatment (Figure 3I). Biochemical backdrop of diabetic complications To create a more comprehensive metabolic picture than that in Figure 1, we colored the map according to estimates from regression models and spectral features that quantify key biochemical variables directly from the 1H NMR spectra (Mäkinen et al, 2006). The previously used null hypothesis of no dependence between the map and a target variable could not be used here, as both the map and the coloring were derived from the spectra. The dynamic range of statistical fluctuations was nevertheless estimated to determine a suitable color scale (Supplementary data 1). Triglyceride concentration is a part of the MetS definition, and the highest unit-specific value (3.6 mmol/l) can therefore be seen at the southwest corner of the map, where also the MetS is most severe (Figure 4A). Total serum cholesterol is only partially linked to triglycerides, as it produces an ascending north-south pattern on the SOM (Figure 4B). Nevertheless, the highest value (6.0 mmol/l) coincides with that of triglycerides at (1,9). HDL2 cholesterol exhibits a more complicated pattern (Figure 4C), with the highest value (0.64 mmol/l) located near the southeast corner, and the lower values (0.41–0.47 mmol/l) located on the western side. Figure 4.Statistical colorings of 1H NMR estimates of biochemical variables. (A–I) Quantitative estimates of biochemical variables based on the statistical modeling of the 1H NMR spectra, and visualized on the SOM that was obtained previously from the same data. Regression model estimates for (A) serum triglyceride concentration, (B) cholesterol level, (C) HDL2 cholesterol and (D) serum creatinine. The colors of the map units indicate the averaged estimates for patients who reside in a given region. (E) Concentrations of serum urea were obtained by direct peak integration around 5.