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 tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Savolainen, O, Fagerberg, B, Lind, MV, Sandberg, AS, Ross, AB & Bergström, G 2017, 'Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers?', P L o S One, bind 12, nr. 7, e0177738. https://doi.org/10.1371/journal.pone.0177738

APA

Savolainen, O., Fagerberg, B., Lind, M. V., Sandberg, A. S., Ross, A. B., & Bergström, G. (2017). Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers? P L o S One, 12(7), [e0177738]. https://doi.org/10.1371/journal.pone.0177738

Vancouver

Savolainen O, Fagerberg B, Lind MV, Sandberg AS, Ross AB, Bergström G. Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers? P L o S One. 2017;12(7). e0177738. https://doi.org/10.1371/journal.pone.0177738

Author

Savolainen, Otto ; Fagerberg, Björn ; Lind, Mads Vendelbo ; Sandberg, Ann Sofie ; Ross, Alastair B ; Bergström, Göran. / Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers?. I: P L o S One. 2017 ; Bind 12, Nr. 7.

Bibtex

@article{1baea9c724bd4872b16926c3379d5f04,
title = "Biomarkers for predicting type 2 diabetes development — Can metabolomics improve on existing biomarkers?",
abstract = "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.",
author = "Otto Savolainen and Bj{\"o}rn Fagerberg and Lind, {Mads Vendelbo} and Sandberg, {Ann Sofie} and Ross, {Alastair B} and G{\"o}ran Bergstr{\"o}m",
note = "CURIS 2017 NEXS 292",
year = "2017",
doi = "10.1371/journal.pone.0177738",
language = "English",
volume = "12",
journal = "P L o S One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

RIS

TY - JOUR

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 - P L o S One

JF - P L o S One

SN - 1932-6203

IS - 7

M1 - e0177738

ER -

ID: 185183994