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Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

Savolainen, Otto, 1982 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Fagerberg, Björn, 1943 (author)
Gothenburg University,Göteborgs universitet,Wallenberglaboratoriet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Wallenberg Laboratory,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg
Vendelbo Lind, Mads, 1988 (author)
Chalmers tekniska högskola,Chalmers University of Technology
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Sandberg, Ann-Sofie, 1951 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Ross, Alastair, 1976 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Bergström, Göran, 1964 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg
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 (creator_code:org_t)
2017-07-10
2017
English.
In: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 12:7
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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. Gas chromatography-tandem mass spectrometry metabolomics was used in a nested casecontrol 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. 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]). 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Näringslära (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Nutrition and Dietetics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Keyword

insulin-resistance
adiponectin levels
mass-spectrometry
risk
samples
models
women
acid
Science & Technology - Other Topics

Publication and Content Type

ref (subject category)
art (subject category)

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