Circulating NMR metabolic biomarkers may enhance risk prediction of type 2 diabetes

Published On 2022-05-25 02:30 GMT   |   Update On 2022-05-25 02:34 GMT

UK: Circulating nuclear magnetic resonance (NMR)-based metabolic biomarkers when added to conventional risk factors modestly enhanced the risk prediction of type 2 diabetes, a recent study concludes. Accurate prediction of disease risk is dependent on the effective targeted prevention of type 2 diabetes (T2D). Fiona Bragg, University of Oxford, Old Road Campus, Oxford, UK, and...

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UK: Circulating nuclear magnetic resonance (NMR)-based metabolic biomarkers when added to conventional risk factors modestly enhanced the risk prediction of type 2 diabetes, a recent study concludes. 

Accurate prediction of disease risk is dependent on the effective targeted prevention of type 2 diabetes (T2D). Fiona Bragg, University of Oxford, Old Road Campus, Oxford, UK, and colleagues aimed to assess the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors in the study published in the journal BMC Medicine. 

For this purpose, the researchers undertook NMR metabolomic profiling on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication.

Among a subset of 50,519 participants with data available on all relevant co-variates (parental history of diabetes, sociodemographic characteristics, lifestyle -- including dietary—factors, anthropometric measures, and fasting time), Cox regression yielded hazard ratios for the associations of 143 individual metabolic biomarkers and 11 metabolic biomarker principal components (PCs) with incident T2D. These 11 PCs were added to established models for the risk prediction of T2D among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed. 

The study led to the following findings:

· During median 11.9 years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled).

· Overall, 8 metabolic biomarker PCs were independently associated with T2D.

· Among the full study population of 65,684 participants, of whom 1719 developed T2D, the addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index, and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 to 0.830), continuous NRI (0.44) and relative (15.0%) and absolute (1.5) IDI.

· More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure, and plasma lipid concentrations (c-statistic, 0.829 to 0.837; continuous NRI, 0.22; relative IDI, 6.3%; absolute IDI, 0.7).

To conclude, this study provides large-scale evidence of the incremental predictive value of metabolomic profiling for the prediction of T2D risk.

Reference:

Bragg, F., Trichia, E., Aguilar-Ramirez, D. et al. Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study. BMC Med 20, 159 (2022). https://doi.org/10.1186/s12916-022-02354-9

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Article Source : BMC Medicine

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