scholarly journals Metabolomic Profiling After a Meal Shows Greater Changes and Lower Metabolic Flexibility in Cardiometabolic Diseases

2020 ◽  
Vol 4 (11) ◽  
Author(s):  
Elaine A Yu ◽  
Tianwei Yu ◽  
Dean P Jones ◽  
Manuel Ramirez-Zea ◽  
Aryeh D Stein
Keyword(s):  
Liquid Chromatography ◽  
Reversed Phase ◽  
Metabolic Flexibility ◽  
Linear Regression Models ◽  
Acid Metabolite ◽  
Metabolomic Profiling ◽  
Peak Areas ◽  
Study Participants ◽  
Dual Column ◽  
Meal Challenge

Abstract Context Metabolic flexibility is the physiologic acclimatization to differing energy availability and requirement states. Effectively maintaining metabolic flexibility remains challenging, particularly since metabolic dysregulations in meal consumption during cardiometabolic disease (CMD) pathophysiology are incompletely understood. Objective We compared metabolic flexibility following consumption of a standardized meal challenge among adults with or without CMDs. Design, Setting, and Participants Study participants (n = 349; age 37-54 years, 55% female) received a standardized meal challenge (520 kcal, 67.4 g carbohydrates, 24.3 g fat, 8.0 g protein; 259 mL). Blood samples were collected at baseline and 2 hours postchallenge. Plasma samples were assayed by high-resolution, nontargeted metabolomics with dual-column liquid chromatography and ultrahigh-resolution mass spectrometry. Metabolome-wide associations between features and meal challenge timepoint were assessed in multivariable linear regression models. Results Sixty-five percent of participants had ≥1 of 4 CMDs: 33% were obese, 6% had diabetes, 39% had hypertension, and 50% had metabolic syndrome. Log2-normalized ratios of feature peak areas (postprandial:fasting) clustered separately among participants with versus without any CMDs. Among participants with CMDs, the meal challenge altered 1756 feature peak areas (1063 reversed-phase [C18], 693 hydrophilic interaction liquid chromatography [HILIC]; all q < 0.05). In individuals without CMDs, the meal challenge changed 1383 feature peak areas (875 C18; 508 HILIC; all q < 0.05). There were 108 features (60 C18; 48 HILIC) that differed by the meal challenge and CMD status, including dipeptides, carnitines, glycerophospholipids, and a bile acid metabolite (all P < 0.05). Conclusions Among adults with CMDs, more metabolomic features differed after a meal challenge, which reflected lower metabolic flexibility relative to individuals without CMDs.

scholarly journals Metabolomic Profile Responses to a Standardized Meal Challenge Differ by Cardiometabolic Disease Status

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 675-675
Author(s):  
Elaine Yu ◽  
Tianwei Yu ◽  
Dean Jones ◽  
Reynaldo Martorell ◽  
Manuel Ramirez-Zea ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Electrospray Ionization ◽  
High Resolution Mass Spectrometry ◽  
Metabolic Flexibility ◽  
Metabolomic Profile ◽  
Peak Areas ◽  
Meal Challenge ◽  
Meal Consumption

Abstract Objectives Metabolic flexibility is the physiologic adaptation to differing energy availability and requirement states, such as during meal consumption. Metabolic inflexibility is definitional of some cardiometabolic diseases (CMDs), however the metabolomic profile response (metabolomic flexibility) after meal consumption, relative to a fasting state, in CMDs remains unclear. We compared metabolomic flexibility following consumption of a standardized meal challenge among adults with or without CMDs. Methods Study participants (n = 349; 37–54 years, 55% female) received a standardized meal challenge (520 kcal, 67.4 g carbohydrates, 24.3 g fat, 8.0 g protein; 259 mL). Blood samples were collected at baseline and two hours post-challenge. Plasma samples were assayed by high-resolution metabolomic profiling with dual column liquid chromatography (carbon 18 (C18], negative electrospray ionization; hydrophilic interaction liquid chromatography [HILIC], positive electrospray ionization) coupled to ultra-high-resolution mass spectrometry. Participants were categorized by obesity, hypertension, diabetes and metabolic syndrome. Metabolome-wide associations between features and meal challenge timepoint were assessed in feature-by-feature multivariate generalized linear regression models, including disease profile, age, and sex. Results Two hundred and twenty-six participants (65%) had at least one of the four CMDs: 33% were considered obese, 6% had diabetes, 39% had hypertension, and 50% had metabolic syndrome. Log2-normalized ratios of feature peak areas (post-prandial: fasting) clustered separately among participants with versus without any CMDs. Among participants with CMDs, the meal challenge altered >1700 feature peak areas (all q < 0.05). In individuals without CMDs, the meal challenge changed >1100 feature peak areas (all q < 0.05). The response to the meal challenge of > 100 feature peak areas (C18, HILIC) differed by CMD status (all P < 0.05). These features included dipeptides, lipids (carnitines, glycerophospholipids), and a bile acid metabolite (all P < 0.05). Conclusions Compared to individuals without CMDs, macronutrient metabolism following a standardized meal challenge differed among those with CMDs, reflecting altered metabolic flexibility. Funding Sources National Institutes of Health.

Determination of Curcuminoids by Liquid Chromatography with Diode Array Detection: Application to the Characterization of Turmeric and Curry Samples

2020 ◽  
Vol 16 (2) ◽  
pp. 95-105 ◽  
Author(s):  
Òscar Vidal ◽  
Xavier Castilla ◽  
Núria Aliaga-Alcalde ◽  
Ana M. López-Periago ◽  
Concepción Domingo ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Reversed Phase ◽  
Diode Array ◽  
Diode Array Detection ◽  
Experimental Conditions ◽  
Gradient Based ◽  
Peak Areas ◽  
Array Detection

Background: A simple, rapid and efficient method for the determination of curcumin and other polyphenols in turmeric and curry samples was here developed. The method relied on sample extraction with methanol and extract analysis by liquid chromatography with diode array detection (HPLC-DAD). Methods: The separation of components was carried out in reversed-phase mode using an elution gradient based on 0.1% (v/v) formic acid aqueous solution and acetonitrile as the components of the mobile phase. Chromatograms were recorded at 420 nm for specific monitoring of curcumin and related compounds. Results: Extraction and separation conditions were optimized by experimental design and multicriteria response functions. Figures of merit were established under the selected experimental conditions. In general, repeatability of peak areas were better than 0.4%, detection limits were below 0.006 mg L-1 and quantitative recoveries expressed as a percentage were about 100 ± 2. The method was applied to quantify curcuminoids in commercial samples. It was found that apart from curcumin, demethoxycurcumin and bisdemethoxycurcumin, other related molecules also occurred in the samples. In this regard, a tentative elucidation of possible unknown curcuminoids was attempted by liquid chromatography coupled to mass spectrometry. Conclusion: Differences in the compositional profiles among samples were encountered to be relevant, so that the resulting HPLC-DAD data was exploited for chemometric characterization of turmeric and curry samples. Samples were successfully discriminated according to matrix types, species varieties and origins.

Rapid assay for amino acids in serum or urine by pre-column derivatization and reversed-phase liquid chromatography.

1982 ◽  
Vol 28 (3) ◽  
pp. 527-531 ◽  
Author(s):  
D C Turnell ◽  
J D Cooper
Keyword(s):  
Amino Acids ◽  
Liquid Chromatography ◽  
Limit Of Detection ◽  
Internal Standard ◽  
Reversed Phase ◽  
Homocysteic Acid ◽  
Coefficients Of Variation ◽  
Peak Areas ◽  
Phase Liquid ◽  
The Mean

Abstract This method for estimating clinically important amino acids in serum or urine within 40 min involves o-phthalaldehyde/2-mercaptoethanol derivatization and reversed-phase "high-pressure" liquid chromatography. Homocysteic acid is an internal standard, and homoserine and norvaline are reference peaks. For all the amino acids estimated, the between-run coefficients of variation ranged from 2.0 to 13.5%, and the mean analytical recoveries from both serum and urine samples was 101%. Peak areas vary linearly with concentration up to 1500 mumol/L for all the amino acids assayed. The limit of detection for each amino acid was estimated to be 38 fmol.

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