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. 2010 Aug;43(12):948-56.
doi: 10.1016/j.clinbiochem.2010.04.075. Epub 2010 May 16.

Perturbations in the lipid profile of individuals with newly diagnosed type 1 diabetes mellitus: lipidomics analysis of a Diabetes Antibody Standardization Program sample subset

Christina M Sorensen  1 , Jie Ding, Qibin Zhang, Thierry Alquier, Rui Zhao, Patricia W Mueller, Richard D Smith, Thomas O Metz
Affiliations

Perturbations in the lipid profile of individuals with newly diagnosed type 1 diabetes mellitus: lipidomics analysis of a Diabetes Antibody Standardization Program sample subset

Christina M Sorensen et al. Clin Biochem. 2010 Aug.

Abstract

Objectives: To characterize the lipid profile of individuals with newly diagnosed type 1 diabetes mellitus using LC-MS-based lipidomics and the accurate mass and time (AMT) tag approach.

Design and methods: Lipids were extracted from plasma and sera of 10 subjects from the Diabetes Antibody Standardization Program (years 2000-2005) and 10 non-diabetic subjects and analyzed by capillary liquid chromatography coupled with a hybrid ion-trap-Fourier transform ion cyclotron resonance mass spectrometer. Lipids were identified and quantified using the AMT tag approach.

Results: Five hundred fifty-nine lipid features differentiated (q<0.05) diabetic from healthy individuals in a partial least-squares analysis, characterizing individuals with recently diagnosed type 1 diabetes mellitus.

Conclusions: A lipid profile associated with newly diagnosed type 1 diabetes may aid in further characterization of biochemical pathways involved in lipid regulation or mobilization.

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Figures

Figure 1
Figure 1. Representative LC-FTICR base peak ion chromatograms
Shown are chromatograms of lipid extracts from control (top) and patient (bottom) individuals.
Figure 2
Figure 2. Box plots of detected lipid feature abundances
Relative abundances of detected lipid features (log2 scale) are shown for 59 samples analyzed by LC-FTICR. Each box in the plot describes the abundance distribution (log2 scale) of an average of 4441 individual features based on five-number summaries: the smallest observation, lower quartile, median, upper quartile, and largest observation. Replicate B for Patient 10 is not shown and was removed as an outlier prior to normalization. (A) Before normalization. (B) After central tendency normalization. C: control; P: patient.
Figure 3
Figure 3. Partial least squares (PLS) Score plot based on significantly different lipid features
Five hundred sixty lipid features (both AMT tag database matched and unmatched) determined to be significantly different (q < 0.05) by ANOVA were used in a PLS analysis in an attempt to identify natural clustering of the samples. PC 1: principal component 1; PC 3: principal component 3; Inset: % variability in data captured by the principal components.
Figure 4
Figure 4. Partial least squares (PLS) Score plot based on significantly different lipids
Sixty-three lipids determined to be significantly different (q < 0.05) by ANOVA and matching entries in the lipid AMT tag database were used in a PLS analysis in an attempt to identify natural clustering of the samples. PC 1: principal component 1; PC 3: principal component 3; Inset: % variability in data captured by the principal components.
Figure 5
Figure 5. Vertical scatter plots of significantly different SM species
Ten SM species determined to be significantly different (q < 0.05) by ANOVA are plotted.
See this image and copyright information in PMC

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