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

Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, Ingman P, Kaski K, Groop PH, FinnDiane Study GroupAla-Korpela M - Mol. Syst. Biol. (2008)

Bottom Line: Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland.

ABSTRACT
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.

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Summary of clinical and metabolic characteristics. (A–F) Statistics for a selection of non-NMR variables for patient groups defined by six districts on the SOM. The map was constructed based on the 1H NMR spectra for 613 type I diabetic patients. The percentages of cases with respect to the total number of patients in a given district and for the whole population (ALL) are listed for 10-year mortality (normalized by follow-up time), DKD, the MetS, MVD, DRD and male gender. Relative risk of death (RR) was defined as the ratio of the observed mortality in type I diabetic patients against the entire Finnish population. The MetS was defined as present if the score was three or more. Median values are listed for the continuous variables, with the full statistics available for the non-NMR data in Table 3 in Supplementary data 2.
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f6: Summary of clinical and metabolic characteristics. (A–F) Statistics for a selection of non-NMR variables for patient groups defined by six districts on the SOM. The map was constructed based on the 1H NMR spectra for 613 type I diabetic patients. The percentages of cases with respect to the total number of patients in a given district and for the whole population (ALL) are listed for 10-year mortality (normalized by follow-up time), DKD, the MetS, MVD, DRD and male gender. Relative risk of death (RR) was defined as the ratio of the observed mortality in type I diabetic patients against the entire Finnish population. The MetS was defined as present if the score was three or more. Median values are listed for the continuous variables, with the full statistics available for the non-NMR data in Table 3 in Supplementary data 2.

Mentions: In the final stage, we merged map units into larger districts and collected the regional characteristics into tabular format to create a summary of the metabolic characteristics (Figure 6; Table 3 in Supplementary data 2). For instance, the southwest district (Figure 6A) is populated by patients with high relative risk due to DKD and the MetS (7.8 versus 2.0–2.1), compared with the districts in the north and northeast (Figure 6E and F). Biochemically, these groups differ significantly: triglycerides (2.8 versus 0.84 mmol/l), cholesterol (5.8 versus 4.5–4.9 mmol/l), serum creatinine (116 versus 87–88 μmol/l) and 24 h-urine albumin (356 versus 13–16 mg) are high, whereas HDL-subfractions are low in the MetS district. Patients in the MetS corner have also poor glycemic control (HbA1c 9.2 versus 8.2%) and larger waist circumference (94 versus 81–83 cm).


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

Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, Ingman P, Kaski K, Groop PH, FinnDiane Study GroupAla-Korpela M - Mol. Syst. Biol. (2008)

Summary of clinical and metabolic characteristics. (A–F) Statistics for a selection of non-NMR variables for patient groups defined by six districts on the SOM. The map was constructed based on the 1H NMR spectra for 613 type I diabetic patients. The percentages of cases with respect to the total number of patients in a given district and for the whole population (ALL) are listed for 10-year mortality (normalized by follow-up time), DKD, the MetS, MVD, DRD and male gender. Relative risk of death (RR) was defined as the ratio of the observed mortality in type I diabetic patients against the entire Finnish population. The MetS was defined as present if the score was three or more. Median values are listed for the continuous variables, with the full statistics available for the non-NMR data in Table 3 in Supplementary data 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2267737&req=5

f6: Summary of clinical and metabolic characteristics. (A–F) Statistics for a selection of non-NMR variables for patient groups defined by six districts on the SOM. The map was constructed based on the 1H NMR spectra for 613 type I diabetic patients. The percentages of cases with respect to the total number of patients in a given district and for the whole population (ALL) are listed for 10-year mortality (normalized by follow-up time), DKD, the MetS, MVD, DRD and male gender. Relative risk of death (RR) was defined as the ratio of the observed mortality in type I diabetic patients against the entire Finnish population. The MetS was defined as present if the score was three or more. Median values are listed for the continuous variables, with the full statistics available for the non-NMR data in Table 3 in Supplementary data 2.
Mentions: In the final stage, we merged map units into larger districts and collected the regional characteristics into tabular format to create a summary of the metabolic characteristics (Figure 6; Table 3 in Supplementary data 2). For instance, the southwest district (Figure 6A) is populated by patients with high relative risk due to DKD and the MetS (7.8 versus 2.0–2.1), compared with the districts in the north and northeast (Figure 6E and F). Biochemically, these groups differ significantly: triglycerides (2.8 versus 0.84 mmol/l), cholesterol (5.8 versus 4.5–4.9 mmol/l), serum creatinine (116 versus 87–88 μmol/l) and 24 h-urine albumin (356 versus 13–16 mg) are high, whereas HDL-subfractions are low in the MetS district. Patients in the MetS corner have also poor glycemic control (HbA1c 9.2 versus 8.2%) and larger waist circumference (94 versus 81–83 cm).

Bottom Line: Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Medicine Research Group, Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus