Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study
Abstract Background Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors. Methods Nuclear magnetic resonance (NMR) metabolomic profiling w...
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BMC
2022-05-01
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| Series: | BMC Medicine |
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| Online Access: | https://doi.org/10.1186/s12916-022-02354-9 |
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| author | Fiona Bragg Eirini Trichia Diego Aguilar-Ramirez Jelena Bešević Sarah Lewington Jonathan Emberson |
| author_facet | Fiona Bragg Eirini Trichia Diego Aguilar-Ramirez Jelena Bešević Sarah Lewington Jonathan Emberson |
| author_sort | Fiona Bragg |
| collection | DOAJ |
| description | Abstract Background Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors. Methods Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken 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 (sociodemographic characteristics, parental history of diabetes, lifestyle—including dietary—factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction 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. Results During median 11.9 (IQR 11.1–12.6) 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 p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, 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 [95% CI 0.791–0.812] to 0.830 [0.822–0.841]), continuous NRI (0.44 [0.38–0.49]) and relative (15.0% [10.5–20.4%]) and absolute (1.5 [1.0–1.9]) 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 [0.819–0.838] to 0.837 [0.831–0.848]; continuous NRI, 0.22 [0.17–0.28]; relative IDI, 6.3% [4.1–9.8%]; absolute IDI, 0.7 [0.4–1.1]). Conclusions When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction. |
| first_indexed | 2024-12-12T01:21:57Z |
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| language | English |
| last_indexed | 2024-12-12T01:21:57Z |
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| spelling | doaj.art-399d5c5970fa4e25a189922bfe5cf0522022-12-22T00:43:12ZengBMCBMC Medicine1741-70152022-05-0120111210.1186/s12916-022-02354-9Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank studyFiona Bragg0Eirini Trichia1Diego Aguilar-Ramirez2Jelena Bešević3Sarah Lewington4Jonathan Emberson5MRC Population Health Research Unit, Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordMRC Population Health Research Unit, Nuffield Department of Population Health, University of OxfordMRC Population Health Research Unit, Nuffield Department of Population Health, University of OxfordAbstract Background Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors. Methods Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken 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 (sociodemographic characteristics, parental history of diabetes, lifestyle—including dietary—factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction 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. Results During median 11.9 (IQR 11.1–12.6) 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 p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, 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 [95% CI 0.791–0.812] to 0.830 [0.822–0.841]), continuous NRI (0.44 [0.38–0.49]) and relative (15.0% [10.5–20.4%]) and absolute (1.5 [1.0–1.9]) 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 [0.819–0.838] to 0.837 [0.831–0.848]; continuous NRI, 0.22 [0.17–0.28]; relative IDI, 6.3% [4.1–9.8%]; absolute IDI, 0.7 [0.4–1.1]). Conclusions When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.https://doi.org/10.1186/s12916-022-02354-9BiomarkersDiabetesMetabolomicsRisk prediction |
| spellingShingle | Fiona Bragg Eirini Trichia Diego Aguilar-Ramirez Jelena Bešević Sarah Lewington Jonathan Emberson Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study BMC Medicine Biomarkers Diabetes Metabolomics Risk prediction |
| title | Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study |
| title_full | Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study |
| title_fullStr | Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study |
| title_full_unstemmed | Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study |
| title_short | Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study |
| title_sort | predictive value of circulating nmr metabolic biomarkers for type 2 diabetes risk in the uk biobank study |
| topic | Biomarkers Diabetes Metabolomics Risk prediction |
| url | https://doi.org/10.1186/s12916-022-02354-9 |
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