Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study.

BACKGROUND: The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.

OBJECTIVES: We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).

METHODS: We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.

RESULTS: The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m(2)) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).

CONCLUSIONS: Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.