Early metabolic markers identify potential targets for the prevention of type 2 diabetes
Peddinti, Gopal ; Cobb, Jeff ; Yengo, Loic ; Froguel, Philippe ; Kravic, Jasmina LU ; Balkau, Beverley ; Tuomi, Tiinamaija LU
; Aittokallio, Tero
and Groop, Leif
LU
(2017)
In Diabetologia
p.1-11
- Abstract
Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10... (More)
Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong’s p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
(Less)
- author
- Peddinti, Gopal
; Cobb, Jeff
; Yengo, Loic
; Froguel, Philippe
; Kravic, Jasmina
LU
; Balkau, Beverley
; Tuomi, Tiinamaija
LU
; Aittokallio, Tero
and Groop, Leif
LU
- organization
- publishing date
- 2017-06-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biomarkers, Early prediction, Kallikrein–kinin system, Machine learning, Metabolomics, Multivariate models, Prevention, Risk classification
- in
- Diabetologia
- pages
- 11 pages
- publisher
- Springer
- external identifiers
-
- pmid:28597074
- wos:000407446700021
- scopus:85020459515
- ISSN
- 0012-186X
- DOI
- 10.1007/s00125-017-4325-0
- language
- English
- LU publication?
- yes
- id
- 5e1d884a-03b6-49c9-bebc-026eaef3f322
- date added to LUP
- 2017-08-11 16:07:08
- date last changed
- 2024-03-31 12:38:20
@article{5e1d884a-03b6-49c9-bebc-026eaef3f322,
abstract = {{<p>Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong’s p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.</p>}},
author = {{Peddinti, Gopal and Cobb, Jeff and Yengo, Loic and Froguel, Philippe and Kravic, Jasmina and Balkau, Beverley and Tuomi, Tiinamaija and Aittokallio, Tero and Groop, Leif}},
issn = {{0012-186X}},
keywords = {{Biomarkers; Early prediction; Kallikrein–kinin system; Machine learning; Metabolomics; Multivariate models; Prevention; Risk classification}},
language = {{eng}},
month = {{06}},
pages = {{1--11}},
publisher = {{Springer}},
series = {{Diabetologia}},
title = {{Early metabolic markers identify potential targets for the prevention of type 2 diabetes}},
url = {{http://dx.doi.org/10.1007/s00125-017-4325-0}},
doi = {{10.1007/s00125-017-4325-0}},
year = {{2017}},
}