13 research outputs found
Specific Dietary Preferences Are Linked to Differing Gut Microbial Metabolic Activity in Response to Dark Chocolate Intake
Systems biology approaches are providing novel insights
into the
role of nutrition for the management of health and disease. In the
present study, we investigated if dietary preference for dark chocolate
in healthy subjects may lead to different metabolic response to daily
chocolate consumption. Using NMR- and MS-based metabolic profiling
of blood plasma and urine, we monitored the metabolic response of
10 participants stratified as chocolate desiring and eating regularly
dark chocolate (CD) and 10 participants stratified as chocolate indifferent
and eating rarely dark chocolate (CI) to a daily consumption of 50
g of dark chocolate as part of a standardized diet over a one week
period. We demonstrated that preference for chocolate leads to different
metabolic response to chocolate consumption. Daily intake of dark
chocolate significantly increased HDL cholesterol by 6% and decreased
polyunsaturated acyl ether phospholipids. Dark chocolate intake could
also induce an improvement in the metabolism of long chain fatty acid,
as noted by a compositional change in plasma fatty acyl carnitines.
Moreover, a relationship between regular long-term dietary exposure
to a small amount of dark chocolate, gut microbiota, and phenolics
was highlighted, providing novel insights into biological processes
associated with cocoa bioactives
Metabolic Signatures of Extreme Longevity in Northern Italian Centenarians Reveal a Complex Remodeling of Lipids, Amino Acids, and Gut Microbiota Metabolism
<div><p>The aging phenotype in humans has been thoroughly studied but a detailed metabolic profiling capable of shading light on the underpinning biological processes of longevity is still missing. Here using a combined metabonomics approach compromising holistic <sup>1</sup>H-NMR profiling and targeted MS approaches, we report for the first time the metabolic phenotype of longevity in a well characterized human aging cohort compromising mostly female centenarians, elderly, and young individuals. With increasing age, targeted MS profiling of blood serum displayed a marked decrease in tryptophan concentration, while an unique alteration of specific glycerophospholipids and sphingolipids are seen in the longevity phenotype. We hypothesized that the overall lipidome changes specific to longevity putatively reflect centenarians' unique capacity to adapt/respond to the accumulating oxidative and chronic inflammatory conditions characteristic of their extreme aging phenotype. Our data in centenarians support promotion of cellular detoxification mechanisms through specific modulation of the arachidonic acid metabolic cascade as we underpinned increased concentration of 8,9-EpETrE, suggesting enhanced cytochrome P450 (CYP) enzyme activity. Such effective mechanism might result in the activation of an anti-oxidative response, as displayed by decreased circulating levels of 9-HODE and 9-oxoODE, markers of lipid peroxidation and oxidative products of linoleic acid. Lastly, we also revealed that the longevity process deeply affects the structure and composition of the human gut microbiota as shown by the increased extrection of phenylacetylglutamine (PAG) and p-cresol sulfate (PCS) in urine of centenarians. Together, our novel approach in this representative Italian longevity cohort support the hypothesis that a complex remodeling of lipid, amino acid metabolism, and of gut microbiota functionality are key regulatory processes marking exceptional longevity in humans.</p> </div
Markers of longevity as per <sup>1</sup>H-NMR urine profiling.
<p>Bar plots indicating mean (relative concentration) ±standard error. PAGâ=âPhenylacetylglutamine, PCSâ=âp-cresol-sulfate, 2HBâ=â2-hydroxybenzoate. All significantly regulated metabolites and statistical changes are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056564#pone.0056564.s014" target="_blank">Table S12</a>. Significant differences were assessed by Mann-Whitney U test where ***p<0.001.</p
Differences in metabolic profiles as displayed by LC/MS-MS targeted approach between centenarian's offspring (46 subjects average age 68.4 yrs) and offspring of non long-lived parents (42 subjects average age 70.7 yrs).
<p>Bar plots indicating mean (”M) ±standard error. All significantly regulated metabolites and statistical changes are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056564#pone.0056564.s011" target="_blank">Table S9</a>. Significant differences were assessed by Mann-Whitney U test where *p<0.05., **p<0.01, ***p<0.001.</p
Demographic characteristics of the recruited age cohorts.
<p>Values are presented as mean ±SD with the range in parentheses.</p
Metabolic signature of aging and longevity in serum as per LC/MS eicosanoids profiling.
<p>Reported is median value in ng/100 ”l serum among the three age groups. Blue denotes negative/decreased concentration, orange denotes positive/increased correlation, black denotes no changes. All significantly regulated metabolites are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056564#pone.0056564.s008" target="_blank">Table S6</a>.</p
Spearman correlation map between urine markers of longevity (PAGâ=âphenylacetylglutamine, PCSâ=âp-cresol sulfate, 3-HBâ=â3-hydroxybenzoate) and order/genus-like bacterial phylogroups.
<p>Blue denotes negative correlation, orange denotes positive correlation, and black denotes no correlation.</p
Bar plots describing metabolite variations in the study population stratified in four quartiles according to visceral fat adiposity (intraperitoneal fat) at V2.
<p>Statistical significance is reported in Table S3. Key: PC-O, 1-O-alkyl-2- acylglycerophosphocholines. Assignment of PC-O species is made on the assumption that only even numbered carbon chains are present. A potential overlap between PC species containing odd-chain fatty acids and even-chained PC-O species cannot be excluded with low mass resolution.</p
Plot describing metabolite importance and robustness in predicting visceral fat adiposity as assessed by Random forest analysis using metabolic data collected at V0 and V2.
<p>Visceral adiposity was associated with increasing concentrations of amino acids (glutamine, leucine/isoleucine, phenylalanine and tyrosine), lysophosphatidylcholine LPC 24â¶0 and diacyl phospholipids (PC 30â¶0, PC 34â¶4). In addition, visceral adiposity was marked by a depletion in ether lipid species PC<i>-O</i> 36â¶3, PC<i>-O</i> 40â¶3, PC<i>-O</i> 40â¶4, PC<i>-O</i> 40â¶6, PC<i>-O</i> 42â¶2, PC<i>-O</i> 42â¶3, PC<i>-O</i> 42â¶4, PC<i>-O</i> 44â¶3, PC<i>-O</i> 44â¶4, PC<i>-O</i> 44â¶6, and two diacyl phosphocholines (PC 42â¶0 and PC 42â¶2). To reflect the weight of the selected biomarkers in the classification of visceral adiposity, a pooled mean decrease of accuracy for each compound was calculated from 10000 forest generations. Higher variable importance corresponds to higher values of pooled mean decrease in accuracy. Key: IPVF, intraperitoneal fat volume; LPC, Lysophosphatidylcholines; PC, Phosphatidylcholines; PC-O, 1-O-alkyl-2- acylglycerophosphocholines; Ratio1, intraperitoneal/subcutaneous fat ratio; Ratio 2, intraperitoneal/abdominal fat ratio. Assignment of PC-O species is made on the assumption that only even numbered carbon chains are present. A potential overlap between PC species containing odd-chain fatty acids and even-chained PC-O species cannot be excluded with low mass resolution.</p
Metabolite variations across subjects stratified according to intraperitoneal/abdominal fat ratio.
<p>NB: Blood plasma metabolites highlighted by multivariate analyses are reported as mean values ± SD. Key: Qi: data for population quartile i according to intraperitoneal/abdominal fat ratio. 12-HETE, 12-hydroxy-eicosatetraenoic acid; 15-HETE, 12-hydroxy-eicosatetraenoic acid; 9-HODE, 9-Hydroxy-10,12-octadecadienoic acid; AA, arachidonic acid; LPC, Lysophosphatidylcholines; PC, Phosphatidylcholines; PC-O, 1-O-alkyl-2- acylglycerophosphocholines; SM, Sphingomyelines; SM-OH, Hydroxy-Sphingomyelin.</p>*<p>Assignment of PC-O species is made on the assumption that only even numbered carbon chains are present. A potential overlap between PC species containing odd-chain fatty acids and even-chained PC-O species cannot be excluded with low mass resolution.</p