Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling.
OBJECTIVE
Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions.


METHODS
We developed predictive models of type 2 diabetes using two strategies. One strategy focused on the probability of incidence only and was based on logistic regression (MRS1); the other strategy accounted for the age at diagnosis of diabetes and was based on Cox regression (MRS2). We assessed 293 metabolites using non-targeted metabolomics in fasting plasma samples of 1,044 participants (including 231 incident cases over 9 years) used as training population; and fasting serum samples of 128 participants (64 incident cases versus 64 controls) used as validation population. We applied a LASSO-based variable selection aiming at maximizing the out-of-sample area under the receiver operating characteristic curve (AROC) and integrated AROC.


RESULTS
Sixteen and 17 metabolites were selected for MRS1 and MRS2, respectively, with AROC = 90% and 73% in the training and validation populations, respectively for MRS1. MRS2 had a similar performance and was significantly associated with a younger age of onset of type 2 diabetes (β = -3.44 years per MRS2 SD in the training population, p = 1.56 × 10(-7); β = -4.73 years per MRS2 SD in the validation population, p = 4.04 × 10(-3)).


CONCLUSIONS
Overall, this study illustrates that metabolomics improves prediction of type 2 diabetes incidence of 4.5% on top of known clinical and biological markers, reaching 90% in total AROC, which is considered the threshold for clinical validity, suggesting it may be used in targeting interventions to prevent type 2 diabetes.
CNRS UMR8199, Pasteur Institute of Lille, Lille, France; European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France; Lille University, France.
Loic
L
Yengo
Qatar Biomedical Research Institute, Doha, Qatar.
Abdelilah
A
Arredouani
INSERM, U1138 (équipe 2: Pathophysiology and Therapeutics of Vascular and Renal Diseases Related to Diabetes, Centre de Recherches des Cordeliers), Paris, France; University Paris 7 Denis Diderot, Sorbonne Paris Cité, France; AP-HP, DHU FIRE, Department of Endocrinology, Diabetology, Nutrition, and Metabolic Diseases, Bichat Claude Bernard Hospital, Paris, France.
Michel
M
Marre
INSERM, U1138 (équipe 2: Pathophysiology and Therapeutics of Vascular and Renal Diseases Related to Diabetes, Centre de Recherches des Cordeliers), Paris, France; University Paris 7 Denis Diderot, Sorbonne Paris Cité, France; AP-HP, DHU FIRE, Department of Endocrinology, Diabetology, Nutrition, and Metabolic Diseases, Bichat Claude Bernard Hospital, Paris, France.
Ronan
R
Roussel
CNRS UMR8199, Pasteur Institute of Lille, Lille, France; European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France; Lille University, France.
Martine
M
Vaxillaire
Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK.
Mario
M
Falchi
Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Abdelali
A
Haoudi
IRSA, La Riche, France.
Jean
J
Tichet
INSERM U-1018, CESP, Renal and Cardiovascular Epidemiology, UVSQ-UPS, Villejuif, France.
Beverley
B
Balkau
CNRS UMR8199, Pasteur Institute of Lille, Lille, France; European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France; Lille University, France.
Amélie
A
Bonnefond
CNRS UMR8199, Pasteur Institute of Lille, Lille, France; European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France; Lille University, France; Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK. Electronic address: p.froguel@imperial.ac.uk.
Philippe
P
Froguel
High dimensional regression; LASSO; Metabolomics; Risk prediction; Type 2 diabetes
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