Free fatty acid metabolic profile and biomarkers of isolated post-challenge diabetes and type 2 diabetes mellitus based on GC–MS and multivariate statistical analysis

• Published in: Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Vol. 878; no. 28; pp. 2817 - 2825
• Main Authors: Liu, Liyan, Li, Ying, Guan, Chunmei, Li, Kang, Wang, Cheng, Feng, Rennan, Sun, Changhao
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.10.2010; Elsevier
• Subjects: Adult; Aged; Analysis; Analysis of Variance; Analytical, structural and metabolic biochemistry; Biological and medical sciences; Biomarker; Biomarkers - blood; Case-Control Studies; Classification; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - blood; Diagnosis; Fasting; Fatty acids; Fatty Acids, Nonesterified - blood; Female; Free fatty acid; Fundamental and applied biological sciences. Psychology; Gas Chromatography-Mass Spectrometry; General pharmacology; Humans; Isolated post-challenge diabetes; Least-Squares Analysis; Linear Models; Male; Medical sciences; Middle Aged; Patients; Pharmacology. Drug treatments; Principal Component Analysis; Reproducibility of Results; Type 2 diabetes mellitus; Biomarker; Isolated post-challenge diabetes; Free fatty acid; Type 2 diabetes mellitus; Gas chromatography–mass spectrometry; Endocrinopathy; Type 2 diabetes; Statistical analysis; Metabolite; Biological marker; Lipids; Metabolic diseases; Gas chromatography-mass spectrometry; Multivariate analysis; Isolation; Mass spectrometry
• ISSN: 1570-0232; 1873-376X; 1873-376X
• DOI: 10.1016/j.jchromb.2010.08.035

Isolated post-challenge diabetes (IPD, 2h-PG ≥11.1 mmol/L and FPG <7.0 mmol/L) is often ignored in screening for diabetes by fasting plasma glucose (FPG) levels. The aim of this study was to investigate the metabolic profiles of serum free fatty acids (FFAs) and to identify biomarkers that can be used to distinguish patients with IPD from those with type 2 diabetes mellitus (T2DM) or healthy control individuals. FFA profiles of the subjects were investigated using gas chromatography–mass spectrometry (GC–MS). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used for classification and prediction among the three groups. The predictive correct rates were 92.86% for IPD and healthy control individuals and 90.70% for T2DM and healthy control individuals, indicating that PLS-DA could satisfactorily distinguish IPD individuals from healthy controls and those with T2DM. Finally, palmitic acid, stearic acid, oleic acid, linoleic acid and α-linolenic acid were identified as potential biomarkers for distinguishing IPD from healthy control and T2DM individuals. These potential biomarkers might be helpful for diagnosis and characterization of diabetes.