Development and Application of a Stable Isotope Dilution Analysis for the Quantitation of Advanced Glycation End Products of Creatinine in Biofluids of Type 2 Diabetic Patients and Healthy Volunteers

<i>N</i>-(1-Methyl-4-oxoimidazolidin-2-ylidene) α-amino acids were recently identified in roasted meat as so far unknown advanced glycation end products (AGEs) of creatinine. For the first time, this paper reports on the preparation of ¹³C-labeled twin molecules of six <i>N</i>-(1-methyl-4-oxoimidazolidin-2-ylidene) α-amino acids and the development of a stable isotope dilution analysis (SIDA) for their simultaneous quantitation in meat, plasma, and urine samples by means of HPLC-MS/MS. Method validation demonstrated good precision (<14% RSD) and accuracy (97–118%) for all analytes and a lower limit of quantitation of 1 pg injected onto the column. The SIDA was applied to monitor plasma appearance and urinary excretion of these AGEs in type 2 diabetes mellitus patients (DM, <i>n</i> = 7) and healthy controls (<i>n</i> = 10) prior to and after ingestion of a bolus of processed beef meat. Interestingly, the basal concentration of <i>N</i>-(1-methyl-4-oxoimidazolidin-2-ylidene) aminopropionic acid was elevated in plasma and urine of DM patients compared to healthy individuals. Further, ingestion of processed meat led to a significantly higher concentration of this AGE in biofluids from DM patients when compared to healthy controls. These findings suggest a favored in vivo formation, as demonstrated by physiological model incubations of creatinine and carbohydrates (37 °C, pH 7.4), or a more efficient dietary up-take of <i>N</i>-(1-methyl-4-oxoimidazolidin-2-ylidene) α-amino acids in hyperglycemic diabetes patients

Data_Sheet_1_Dynamic patterns of postprandial metabolic responses to three dietary challenges.pdf

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.</p

Data_Sheet_2_Dynamic patterns of postprandial metabolic responses to three dietary challenges.xlsx

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.</p