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Background: Although insulin resistance (IR) is among the most frequent and pathogenically relevant complications accompanying childhood obesity, its role in modulating and exacerbating obesity pathophysiology has not yet been completely clarified., Methods: To get deeper insights into the interplay between childhood obesity and IR, we leveraged a comprehensive experimental design based on a combination of observational data, in vivo challenge tests (i.e., oral glucose tolerance test), and ex vivo assays (i.e., incubation of erythrocytes with insulin) using a population comprising children with obesity and IR, children with obesity without IR, and healthy controls, from whom plasma and erythrocyte samples were collected for subsequent metabolomics analysis., Results: Children with concomitant IR showed exacerbated metabolic disturbances in the crosstalk between endogenous, microbial, and environmental determinants, including failures in energy homeostasis, amino acid metabolism, oxidative stress, synthesis of steroid hormones and bile acids, membrane lipid composition, as well as differences in exposome-related metabolites associated with diet, exposure to endocrine disruptors, and gut microbiota. Furthermore, challenge tests and ex vivo assays revealed a deleterious impact of IR on individuals' metabolic flexibility, as reflected in blunted capacity to regulate homeostasis in response to hyperinsulinemia, at both systemic and erythroid levels., Conclusions: Thus, we have demonstrated for the first time that metabolite alterations in erythrocytes represent reliable and sensitive biomarkers to disentangle the metabolic complexity of IR and childhood obesity. This study emphasizes the crucial need of addressing inter-individual variability factors, such as the presence of comorbidities, to obtain a more accurate understanding of obesity-related molecular mechanisms., (© 2024. The Author(s).)

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Life Sciences Institute.)

Background: Insulin resistance is a frequent precursor of typical obesity and metabolic syndrome complications. However, accurate diagnosis remains elusive because of its pathophysiological complexity and heterogeneity. Herein, we have explored the utility of insulin secretion dynamics in response to an oral glucose tolerance test as a surrogate marker to identify distinct metabotypes of disease severity., Methods: The study population consisted of children with obesity and insulin resistance, stratified according to the post-challenge insulin peak timing (i.e., early, middle, and late peak), from whom fasting and postprandial plasma and erythrocytes were collected for metabolomics analysis., Results: Children with late insulin peak manifested worse cardiometabolic health (i.e., higher blood pressure, glycemia, and HOMA-IR scores) than early responders. These subjects also showed more pronounced changes in metabolites mirroring failures in energy homeostasis, oxidative stress, metabolism of cholesterol and phospholipids, and adherence to unhealthy dietary habits. Furthermore, delayed insulin peak was associated with impaired metabolic flexibility, as reflected in compromised capacity to regulate mitochondrial energy pathways and the antioxidant defense in response to glucose overload., Conclusions: Altogether, these findings suggest that insulin resistance could encompass several phenotypic subtypes characterized by graded disturbances in distinctive metabolic derangements occurring in childhood obesity, which serve as severity predictive markers., (© 2024. The Author(s).)

Background: Diet is frequently associated with both the development and prevention of type 2 diabetes (T2D), but there is a lack of objective tools for assessing the relation between diet and T2D. Biomarkers of dietary intake are unconfounded by recall and reporting bias, and using multiple dietary biomarkers could help strengthen the link between a healthy diet and the prevention of T2D. Objective: The objective of this study was to explore how diet is related to glucose tolerance status (GTS) and to future development of T2D irrespective of common T2D and cardiovascular disease risk factors by using multiple dietary biomarkers. Design: Dietary biomarkers were measured in plasma from 64-y-old Swedish women with different GTS [normal glucose tolerance (NGT; n = 190), impaired glucose tolerance (IGT; n = 209), and diabetes ( n = 230)]. The same subjects were followed up after 5 y to determine changes in glucose tolerance ( n = 167 for NGT, n = 174 for IGT, and n = 159 for diabetes). ANCOVA and logistic regression were used to explore baseline data for associations between dietary biomarkers, GTS, and new T2D cases at follow-up ( n = 69). Results: Of the 10 dietary biomarkers analyzed, β-alanine (beef) ( P -raw < 0.001), alkylresorcinols C17 and C19 (whole-grain wheat and rye) ( P -raw = 0.003 and 0.011), eicosapentaenoic acid (fish) ( P -raw = 0.041), 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) (fish) ( P -raw = 0.002), linoleic acid ( P -raw < 0.001), oleic acid ( P -raw = 0.003), and α-tocopherol (margarine and vegetable oil) ( P -raw < 0.001) were associated with GTS, and CMPF (fish) (OR: 0.72; 95% CI: 0.56, 0.93; P -raw = 0.013) and α-tocopherol (OR: 0.71; 95% CI: 0.51, 0.98; P -raw = 0.041) were inversely associated with future T2D development. Conclusions: Several circulating dietary biomarkers were strongly associated with GTS after correction for known T2D risk factors, underlining the role of diet in the development and prevention of T2D. To our knowledge, this study is the first to use multiple dietary biomarkers to investigate the link between diet and disease risk., (© 2017 American Society for Nutrition.)

Aim: The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers., Methods: Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D., Results: Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711])., Conclusions: Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

Plasma alkylresorcinols are increasingly analyzed in cohort studies to improve estimates of whole grain intake and their relationship with disease incidence. Current methods require large volumes of solvent (>10 ml/sample) and have relatively low daily sample throughput. We tested five different supported extraction methods for extracting alkylresorcinols from plasma and improved a normal-phase liquid chromatography coupled to a tandem mass spectrometer method to reduce sample analysis time. The method was validated and compared with gas chromatography–mass spectrometry analysis. Sample preparation with HybridSPE supported extraction was most effective for alkylresorcinol extraction, with recoveries of 77–82% from 100 μl of plasma. The use of 96-well plates allowed extraction of 160 samples per day. Using a 5-cm NH 2 column and heptane reduced run times to 3 min. The new method had a limit of detection and limit of quantification equivalent to 1.1–1.8 nmol/L and 3.5–6.1 nmol/L plasma, respectively, for the different alkylresorcinol homologues. Accuracy was 93–105%, and intra- and inter-batch precision values were 4–18% across different plasma concentrations. This method makes it possible to quantify plasma alkylresorcinols in 100 μl of plasma at a rate of at least 160 samples per day without the need for large volumes of organic solvents. [ABSTRACT FROM AUTHOR]

Quinoa is a pseudocereal grown in the Andean region of South America that is of increasing interest worldwide as an alternative staple food. We have detected a complex mixture of both odd- and even-alkyl chain alkylresorcinols (AR), branched-chain alkylresorcinols (bcAR) and methylalkylresorcinols (mAR) in ethyl acetate extracts of quinoa. We quantified the content of AR in 17 commercial samples of quinoa, and found that the mean ± SD content of AR was 58 ± 16 μg/g, bcAR was 182 ± 52 μg/g, and mAR was 136 ± 40 μg/g. AR from quinoa could also be detected in plasma after eating quinoa, indicating that some of these unique AR could be used as biomarkers of quinoa intake in humans. Further work is required to understand the role of these ARs in the quinoa plant and whether any of the novel ARs may be of particular interest in human nutrition. [ABSTRACT FROM AUTHOR]