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A distinct metabolic signature predicts development of fasting plasma glucose.

Hische M, Larhlimi A, Schwarz F, Fischer-Rosinský A, Bobbert T, Assmann A, Catchpole GS, Pfeiffer AF, Willmitzer L, Selbig J, Spranger J - J Clin Bioinforma (2012)

Bottom Line: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression.We also showed that adding established risk markers did not improve the model accuracy.This result could only be captured by application of multivariate statistical approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinic of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. joachim.spranger@charite.de.

ABSTRACT

Background: High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.

Methods: We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.

Results: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.

Conclusions: We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.

No MeSH data available.


Related in: MedlinePlus

Correlation matrix. This correlation matrix visualises not only the significant correlations between Δ glucose and the metabolites (first row/column) but also the correlation among the metabolites. The colour intensity and tile size indicate the strength of correlation. Positive correlation are marked blue, negative correlation are marked red.
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Figure 1: Correlation matrix. This correlation matrix visualises not only the significant correlations between Δ glucose and the metabolites (first row/column) but also the correlation among the metabolites. The colour intensity and tile size indicate the strength of correlation. Positive correlation are marked blue, negative correlation are marked red.

Mentions: We used the Spearman correlation as a filter and included all metabolites significantly correlated with Δglucose to build a linear regression model. The accuracy (i.e. the Pearson correlation between true and estimated Δglucose) using all samples was 0.57, the median accuracy after tenfold cross-validation was 0.22. This accuracy is unsatisfactorily poor. Despite the weak accuracy of the linear model, it is known that multicollinearity often leads to an unstable model and affects the correct calculation of coefficients of linear regression models [20]. The calculated condition number of the correlation matrix K > >1000 indicated the strong collinearity within the chosen metabolites. However, high correlation within a metabolite matrix is not surprising, since the observed metabolites are not independent but connected via metabolism. The detected multicollinearity is illustrated with the correlation matrix in Figure 1. Hence, we had to find a regression model that is robust against multicollinearity. This characteristic is given for Random Forest models [21].


A distinct metabolic signature predicts development of fasting plasma glucose.

Hische M, Larhlimi A, Schwarz F, Fischer-Rosinský A, Bobbert T, Assmann A, Catchpole GS, Pfeiffer AF, Willmitzer L, Selbig J, Spranger J - J Clin Bioinforma (2012)

Correlation matrix. This correlation matrix visualises not only the significant correlations between Δ glucose and the metabolites (first row/column) but also the correlation among the metabolites. The colour intensity and tile size indicate the strength of correlation. Positive correlation are marked blue, negative correlation are marked red.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3298809&req=5

Figure 1: Correlation matrix. This correlation matrix visualises not only the significant correlations between Δ glucose and the metabolites (first row/column) but also the correlation among the metabolites. The colour intensity and tile size indicate the strength of correlation. Positive correlation are marked blue, negative correlation are marked red.
Mentions: We used the Spearman correlation as a filter and included all metabolites significantly correlated with Δglucose to build a linear regression model. The accuracy (i.e. the Pearson correlation between true and estimated Δglucose) using all samples was 0.57, the median accuracy after tenfold cross-validation was 0.22. This accuracy is unsatisfactorily poor. Despite the weak accuracy of the linear model, it is known that multicollinearity often leads to an unstable model and affects the correct calculation of coefficients of linear regression models [20]. The calculated condition number of the correlation matrix K > >1000 indicated the strong collinearity within the chosen metabolites. However, high correlation within a metabolite matrix is not surprising, since the observed metabolites are not independent but connected via metabolism. The detected multicollinearity is illustrated with the correlation matrix in Figure 1. Hence, we had to find a regression model that is robust against multicollinearity. This characteristic is given for Random Forest models [21].

Bottom Line: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression.We also showed that adding established risk markers did not improve the model accuracy.This result could only be captured by application of multivariate statistical approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinic of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. joachim.spranger@charite.de.

ABSTRACT

Background: High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.

Methods: We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.

Results: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.

Conclusions: We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.

No MeSH data available.


Related in: MedlinePlus