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Lipid profiling following intake of the omega 3 fatty acid DHA identifies the peroxidized metabolites F4-neuroprostanes as the best predictors of atherosclerosis prevention.

Gladine C, Newman JW, Durand T, Pedersen TL, Galano JM, Demougeot C, Berdeaux O, Pujos-Guillot E, Mazur A, Comte B - PLoS ONE (2014)

Bottom Line: A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increased oxidative stress associated with plaque formation.Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was strongly correlated with the hepatic DHA level.While these may contribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contention and to decipher the molecular mechanisms of action.

View Article: PubMed Central - PubMed

Affiliation: UMR1019 Unité de Nutrition Humaine (UNH), INRA, CRNH Auvergne, Clermont Université, Université d'Auvergne, Clermont-Ferrand, France.

ABSTRACT
The anti-atherogenic effects of omega 3 fatty acids, namely eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) are well recognized but the impact of dietary intake on bioactive lipid mediator profiles remains unclear. Such a profiling effort may offer novel targets for future studies into the mechanism of action of omega 3 fatty acids. The present study aimed to determine the impact of DHA supplementation on the profiles of polyunsaturated fatty acids (PUFA) oxygenated metabolites and to investigate their contribution to atherosclerosis prevention. A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increased oxidative stress associated with plaque formation. Atherosclerosis prone mice (LDLR(-/-)) received increasing doses of DHA (0, 0.1, 1 or 2% of energy) during 20 weeks leading to a dose-dependent reduction of atherosclerosis (R(2) = 0.97, p = 0.02), triglyceridemia (R(2) = 0.97, p = 0.01) and cholesterolemia (R(2) = 0.96, p<0.01). Targeted lipidomic analyses revealed that both the profiles of EPA and DHA and their corresponding oxygenated metabolites were substantially modulated in plasma and liver. Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was strongly correlated with the hepatic DHA level. Moreover, unbiased statistical analysis including correlation analyses, hierarchical cluster and projection to latent structure discriminate analysis revealed that the hepatic level of F4-neuroprostanes was the variable most negatively correlated with the plaque extent (p<0.001) and along with plasma EPA-derived diols was an important mathematical positive predictor of atherosclerosis prevention. Thus, oxygenated n-3 PUFAs, and F4-neuroprostanes in particular, are potential biomarkers of DHA-associated atherosclerosis prevention. While these may contribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contention and to decipher the molecular mechanisms of action.

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Hierarchical cluster and Partial Least Squares Discrimination Analysis (PLS-DA).(A) Complete data sets (n = 10/group) were segregated into 7 unique clusters of variables (C1–C7) by hierarchical cluster analysis. Clusters were assigned unique colors and used to highlight variables in PLS-DA. Plasma and liver variables are indicated by yellow triangles and orange inverted triangles, respectively. Dominant variables for feeding group discrimination are identified by asterisks (*). Dominant variables for plaque area discrimination are identified by open circles (○). (B) Animals eating each dietary mixture with complete data sets (n = 10/group) were partially segregated by PLS-DA. Mice from DHA1 group were indistinguishable from Controls (p<0.05), while DHA2 (p<0.0001) and DHA3 (p<1.5E-07) were significantly different in this model. (C) Plasma (yellow triangle) and liver (orange triangle) metabolites belonging to clusters C2, C4, and C6 as identified in (A) were included in this model. Predictive variables are labeled with their point size, indicating relative selection frequency of 20–80% in 10 models constructed using a Pearson’s-correlation variable selection filter which out performed other filters in terms of the minimum root mean squared error of prediction (RMSEP = 3.4±1). Analytes appearing in ≥30% of models were retained for the final predictive model construction.
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pone-0089393-g006: Hierarchical cluster and Partial Least Squares Discrimination Analysis (PLS-DA).(A) Complete data sets (n = 10/group) were segregated into 7 unique clusters of variables (C1–C7) by hierarchical cluster analysis. Clusters were assigned unique colors and used to highlight variables in PLS-DA. Plasma and liver variables are indicated by yellow triangles and orange inverted triangles, respectively. Dominant variables for feeding group discrimination are identified by asterisks (*). Dominant variables for plaque area discrimination are identified by open circles (○). (B) Animals eating each dietary mixture with complete data sets (n = 10/group) were partially segregated by PLS-DA. Mice from DHA1 group were indistinguishable from Controls (p<0.05), while DHA2 (p<0.0001) and DHA3 (p<1.5E-07) were significantly different in this model. (C) Plasma (yellow triangle) and liver (orange triangle) metabolites belonging to clusters C2, C4, and C6 as identified in (A) were included in this model. Predictive variables are labeled with their point size, indicating relative selection frequency of 20–80% in 10 models constructed using a Pearson’s-correlation variable selection filter which out performed other filters in terms of the minimum root mean squared error of prediction (RMSEP = 3.4±1). Analytes appearing in ≥30% of models were retained for the final predictive model construction.

Mentions: To determine the covariant behavior of the measured variables, a hierarchical cluster analysis and a PLS-DA (Figure 6) were performed to identify variables which best discriminate plaque area or dietary groups (Figure S5 in File S1). Variables were separated into 7 unique variable clusters (Figure 6A). Clusters 1 and 2 were dominated by plasma and liver n-3 PUFAs and n-3 oxylipins, but also contained liver saturated fat, with cluster 2 containing 13 of 15 variables able to segregate the relative dose of the diet groups (see * symbol in Figure 6A). In contrast, clusters 2, 4, 5 and 6 each contained variables responsible for plaque size prediction (see ○ symbol in Figure 6A). It should be noted that the liver F4-NeuroPs concentrations appear in both lists of discriminant variables. The PLS discrimination of mice according to their plaque area (Figure 6B) clearly shows the multi-dimensional overlap of the Control and DHA-1 mice, as well as those from the Control and DHA-2 groups, in accordance with the results shown in Figure 2. Moreover, as shown in Figure 6C, groups were discriminated using only 12 of the 152 variables collected, representing 3 variable clusters. Of these, liver F4-NeuroPs and the plasma 14,15-diol metabolite of EPA (14,15-DiHETE) were the strongest negative predictors of plaque size, while plasma MUFAs and liver TC were the best positive predictors of plaque size in this LDLR−/− model.


Lipid profiling following intake of the omega 3 fatty acid DHA identifies the peroxidized metabolites F4-neuroprostanes as the best predictors of atherosclerosis prevention.

Gladine C, Newman JW, Durand T, Pedersen TL, Galano JM, Demougeot C, Berdeaux O, Pujos-Guillot E, Mazur A, Comte B - PLoS ONE (2014)

Hierarchical cluster and Partial Least Squares Discrimination Analysis (PLS-DA).(A) Complete data sets (n = 10/group) were segregated into 7 unique clusters of variables (C1–C7) by hierarchical cluster analysis. Clusters were assigned unique colors and used to highlight variables in PLS-DA. Plasma and liver variables are indicated by yellow triangles and orange inverted triangles, respectively. Dominant variables for feeding group discrimination are identified by asterisks (*). Dominant variables for plaque area discrimination are identified by open circles (○). (B) Animals eating each dietary mixture with complete data sets (n = 10/group) were partially segregated by PLS-DA. Mice from DHA1 group were indistinguishable from Controls (p<0.05), while DHA2 (p<0.0001) and DHA3 (p<1.5E-07) were significantly different in this model. (C) Plasma (yellow triangle) and liver (orange triangle) metabolites belonging to clusters C2, C4, and C6 as identified in (A) were included in this model. Predictive variables are labeled with their point size, indicating relative selection frequency of 20–80% in 10 models constructed using a Pearson’s-correlation variable selection filter which out performed other filters in terms of the minimum root mean squared error of prediction (RMSEP = 3.4±1). Analytes appearing in ≥30% of models were retained for the final predictive model construction.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3928438&req=5

pone-0089393-g006: Hierarchical cluster and Partial Least Squares Discrimination Analysis (PLS-DA).(A) Complete data sets (n = 10/group) were segregated into 7 unique clusters of variables (C1–C7) by hierarchical cluster analysis. Clusters were assigned unique colors and used to highlight variables in PLS-DA. Plasma and liver variables are indicated by yellow triangles and orange inverted triangles, respectively. Dominant variables for feeding group discrimination are identified by asterisks (*). Dominant variables for plaque area discrimination are identified by open circles (○). (B) Animals eating each dietary mixture with complete data sets (n = 10/group) were partially segregated by PLS-DA. Mice from DHA1 group were indistinguishable from Controls (p<0.05), while DHA2 (p<0.0001) and DHA3 (p<1.5E-07) were significantly different in this model. (C) Plasma (yellow triangle) and liver (orange triangle) metabolites belonging to clusters C2, C4, and C6 as identified in (A) were included in this model. Predictive variables are labeled with their point size, indicating relative selection frequency of 20–80% in 10 models constructed using a Pearson’s-correlation variable selection filter which out performed other filters in terms of the minimum root mean squared error of prediction (RMSEP = 3.4±1). Analytes appearing in ≥30% of models were retained for the final predictive model construction.
Mentions: To determine the covariant behavior of the measured variables, a hierarchical cluster analysis and a PLS-DA (Figure 6) were performed to identify variables which best discriminate plaque area or dietary groups (Figure S5 in File S1). Variables were separated into 7 unique variable clusters (Figure 6A). Clusters 1 and 2 were dominated by plasma and liver n-3 PUFAs and n-3 oxylipins, but also contained liver saturated fat, with cluster 2 containing 13 of 15 variables able to segregate the relative dose of the diet groups (see * symbol in Figure 6A). In contrast, clusters 2, 4, 5 and 6 each contained variables responsible for plaque size prediction (see ○ symbol in Figure 6A). It should be noted that the liver F4-NeuroPs concentrations appear in both lists of discriminant variables. The PLS discrimination of mice according to their plaque area (Figure 6B) clearly shows the multi-dimensional overlap of the Control and DHA-1 mice, as well as those from the Control and DHA-2 groups, in accordance with the results shown in Figure 2. Moreover, as shown in Figure 6C, groups were discriminated using only 12 of the 152 variables collected, representing 3 variable clusters. Of these, liver F4-NeuroPs and the plasma 14,15-diol metabolite of EPA (14,15-DiHETE) were the strongest negative predictors of plaque size, while plasma MUFAs and liver TC were the best positive predictors of plaque size in this LDLR−/− model.

Bottom Line: A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increased oxidative stress associated with plaque formation.Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was strongly correlated with the hepatic DHA level.While these may contribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contention and to decipher the molecular mechanisms of action.

View Article: PubMed Central - PubMed

Affiliation: UMR1019 Unité de Nutrition Humaine (UNH), INRA, CRNH Auvergne, Clermont Université, Université d'Auvergne, Clermont-Ferrand, France.

ABSTRACT
The anti-atherogenic effects of omega 3 fatty acids, namely eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) are well recognized but the impact of dietary intake on bioactive lipid mediator profiles remains unclear. Such a profiling effort may offer novel targets for future studies into the mechanism of action of omega 3 fatty acids. The present study aimed to determine the impact of DHA supplementation on the profiles of polyunsaturated fatty acids (PUFA) oxygenated metabolites and to investigate their contribution to atherosclerosis prevention. A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increased oxidative stress associated with plaque formation. Atherosclerosis prone mice (LDLR(-/-)) received increasing doses of DHA (0, 0.1, 1 or 2% of energy) during 20 weeks leading to a dose-dependent reduction of atherosclerosis (R(2) = 0.97, p = 0.02), triglyceridemia (R(2) = 0.97, p = 0.01) and cholesterolemia (R(2) = 0.96, p<0.01). Targeted lipidomic analyses revealed that both the profiles of EPA and DHA and their corresponding oxygenated metabolites were substantially modulated in plasma and liver. Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was strongly correlated with the hepatic DHA level. Moreover, unbiased statistical analysis including correlation analyses, hierarchical cluster and projection to latent structure discriminate analysis revealed that the hepatic level of F4-neuroprostanes was the variable most negatively correlated with the plaque extent (p<0.001) and along with plasma EPA-derived diols was an important mathematical positive predictor of atherosclerosis prevention. Thus, oxygenated n-3 PUFAs, and F4-neuroprostanes in particular, are potential biomarkers of DHA-associated atherosclerosis prevention. While these may contribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contention and to decipher the molecular mechanisms of action.

Show MeSH
Related in: MedlinePlus