Type 2 diabetes associated changes in the plasma non-esterified fatty acids, oxylipins and endocannabinoids

Dmitry Grapov, Sean H Adams, Theresa L Pedersen, W Timothy Garvey, John W Newman, Dmitry Grapov, Sean H Adams, Theresa L Pedersen, W Timothy Garvey, John W Newman

Abstract

Type 2 diabetes has profound effects on metabolism that can be detected in plasma. While increases in circulating non-esterified fatty acids (NEFA) are well-described in diabetes, effects on signaling lipids have received little attention. Oxylipins and endocannabinoids are classes of bioactive fatty acid metabolites with many structural members that influence insulin signaling, adipose function and inflammation through autocrine, paracrine and endocrine mechanisms. To link diabetes-associated changes in plasma NEFA and signaling lipids, we quantitatively targeted >150 plasma lipidome components in age- and body mass index-matched, overweight to obese, non-diabetic (n = 12) and type 2 diabetic (n = 43) African-American women. Diabetes related NEFA patterns indicated ∼60% increase in steroyl-CoA desaturase activity and ∼40% decrease in very long chain polyunsaturated fatty acid chain shortening, patterns previously associated with the development of nonalcoholic fatty liver disease. Further, epoxides and ketones of eighteen carbon polyunsaturated fatty acids were elevated >80% in diabetes and strongly correlated with changes in NEFA, consistent with their liberation during adipose lipolysis. Endocannabinoid behavior differed by class with diabetes increasing an array of N-acylethanolamides which were positively correlated with pro-inflammatory 5-lipooxygenase-derived metabolites, while monoacylglycerols were negatively correlated with body mass. These results clearly show that diabetes not only results in an increase in plasma NEFA, but shifts the plasma lipidomic profiles in ways that reflect the biochemical and physiological changes of this pathological state which are independent of obesity associated changes.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. The type 2 diabetes-associated lipidomic…
Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.
Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p

Figure 2. An OPLS-DA model built from…

Figure 2. An OPLS-DA model built from 15 plasma lipids discriminates non-diabetic and diabetic cohorts.

Figure 2. An OPLS-DA model built from 15 plasma lipids discriminates non-diabetic and diabetic cohorts.
Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).

Figure 3. Analysis of correlations among all…

Figure 3. Analysis of correlations among all measured variables and estimated enzyme activities in non-diabetic…

Figure 3. Analysis of correlations among all measured variables and estimated enzyme activities in non-diabetic and type 2 diabetic African-American women.
Significant (p

Figure 4. Parameter connectivity networks of metabolites…

Figure 4. Parameter connectivity networks of metabolites and clinical parameters in African-American women with and…

Figure 4. Parameter connectivity networks of metabolites and clinical parameters in African-American women with and without type 2 diabetes.
Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).
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References
    1. Bergman RN, Ader M (2000) Free fatty acids and pathogenesis of type 2 diabetes mellitus. Trends Endocrinol Metab 11: 351–356. - PubMed
    1. Matthews DR, Naylor BA, Jones RG, Ward GM, Turner RC (1983) Pulsatile insulin has greater hypoglycemic effect than continuous delivery. Diabetes 32: 617–621. - PubMed
    1. Roden M, Stingl H, Chandramouli V, Schumann WC, Hofer A, et al. (2000) Effects of free fatty acid elevation on postabsorptive endogenous glucose production and gluconeogenesis in humans. Diabetes 49: 701–707. - PubMed
    1. Komjati M, Bratusch-Marrain P, Waldhausl W (1986) Superior efficacy of pulsatile versus continuous hormone exposure on hepatic glucose production in vitro. Endocrinology 118: 312–319. - PubMed
    1. Paolisso G, Scheen AJ, Giugliano D, Sgambato S, Albert A, et al. (1991) Pulsatile insulin delivery has greater metabolic effects than continuous hormone administration in man: importance of pulse frequency. J Clin Endocrinol Metab 72: 607–615. - PubMed
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Figure 2. An OPLS-DA model built from…
Figure 2. An OPLS-DA model built from 15 plasma lipids discriminates non-diabetic and diabetic cohorts.
Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).
Figure 3. Analysis of correlations among all…
Figure 3. Analysis of correlations among all measured variables and estimated enzyme activities in non-diabetic and type 2 diabetic African-American women.
Significant (p

Figure 4. Parameter connectivity networks of metabolites…

Figure 4. Parameter connectivity networks of metabolites and clinical parameters in African-American women with and…

Figure 4. Parameter connectivity networks of metabolites and clinical parameters in African-American women with and without type 2 diabetes.
Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).
Figure 4. Parameter connectivity networks of metabolites…
Figure 4. Parameter connectivity networks of metabolites and clinical parameters in African-American women with and without type 2 diabetes.
Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).

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