Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers?
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Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers? / Savolainen, Otto; Fagerberg, Björn; Lind, Mads Vendelbo; Sandberg, Ann Sofie; Ross, Alastair B; Bergström, Göran.
I: P L o S One, Bind 12, Nr. 7, e0177738, 2017.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
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T1 - Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers?
AU - Savolainen, Otto
AU - Fagerberg, Björn
AU - Lind, Mads Vendelbo
AU - Sandberg, Ann Sofie
AU - Ross, Alastair B
AU - Bergström, Göran
N1 - CURIS 2017 NEXS 292
PY - 2017
Y1 - 2017
N2 - Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Methods Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years followup. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Results Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]). Conclusions Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.
AB - Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Methods Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years followup. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Results Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]). Conclusions Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.
UR - http://www.scopus.com/inward/record.url?scp=85022329372&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0177738
DO - 10.1371/journal.pone.0177738
M3 - Journal article
C2 - 28692646
AN - SCOPUS:85022329372
VL - 12
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 7
M1 - e0177738
ER -
ID: 185183994