Metabolic features of adiposity and glucose homoeostasis among school-aged inuit children from Nunavik (Northern Quebec, Canada).

In contrast to most Indigenous people in Canada, Inuit appeared until recently to have been protected from type 2 diabetes (T2D) related to obesity. We assessed the associations of metabolites (amino acids, acylcarnitines) with adiposity and biomarkers of T2D in school-aged Inuit children of Nunavik (Canada). Concentrations of metabolite were measured in plasma samples from a cross-sectional analysis of 248 children (mean age = 10.8 years). We assessed associations of plasma metabolites with adiposity measures (BMI, skinfold thicknesses) and T2D markers (insulin, glucose, adiponectin). Plasma concentrations of valine and tyrosine were higher in obese and overweight children compared to those of normal weight children (P < 0.05). An increment of 1-SD in BMI (SD = 3.3 kg/m2) was statistically associated with an increment of 0.21 (95% CI: 0.08, 0.33) for valine, 0.15 (95% CI: 0.02, 0.27) for isoleucine and 0.17 (95% CI: 0.04, 0.29) for tyrosine. Insulin concentration increased with concentrations of all amino acids (P < 0.05) except methionine. None of the acylcarnitines measured were statistically significantly associated with adiposity or T2D biomarkers A signature of metabolites, particularly higher levels of branched-chain amino acids, might allow for early detection of T2D among school-aged Inuit children.

Risk of gestational diabetes mellitus in relation to plasma concentrations of amino acids and acylcarnitines: A nested case-control study.

AIMS: Gestational diabetes mellitus (GDM) affects between 5 and 10% of all pregnancies in Canada and can lead to adverse health outcomes in both the mother and fetus. Amino acids (AA) and acylcarnitines (AC) have been identified as early biomarkers of type 2 diabetes but their usefulness in screening for GDM has yet to be demonstrated. METHODS: We conducted a nested case-control study involving 50 controls and 50 GDM cases diagnosed between the 24th and 28th week of gestation. Heparinized plasma samples were obtained during the first and early second trimester of pregnancy. Case and controls were matched according to date of recruitment, maternal age, gestational age at blood sampling as well as pre-pregnancy body mass index. Eight AA and eight AC were quantified using an ultra-high pressure liquid-chromatography quadrupole time-of-flight mass spectrometry platform. Conditional regression analyses adjusted for matching factors and smoking habits during pregnancy were performed to identify plasma metabolites associated with GDM risk. RESULTS: Odds ratio (OR) and 95% confidence interval (CI) for the prediction of GDM per one standard deviation increase of AA or AC in plasma levels were 0.25 (0.08-0.79) for butyrylcarnitine, 0.31 (0.12-0.79) for glutamic acid, 2.5 (1.2-5.3) for acetylcarnitine, 2.9 (1.3-6.8) for isobutyrylcarnitine and 5.3 (1.7-17.0) for leucine. These five metabolites were selected by stepwise conditional logistic regression to create a predictive model with an OR of 2.7 (1.5-4.9). CONCLUSION: Whether the identified metabolites can predict the risk of developing GDM requires additional studies in a larger sample of pregnant women.

Is mercury exposure causing diabetes, metabolic syndrome and insulin resistance? A systematic review of the literature.

INTRODUCTION: Several populations are exposed to mercury (Hg) via their environment, occupation or diet. It is hypothesized that Hg exposure can lead to the development of diabetes mellitus (DM). Metabolic syndrome (MS) is also a possible outcome as its symptoms are closely linked to those of DM. METHOD: We conducted a systematic review of the literature by screening Web of Science, MEDLINE, SciFinder and Embase and we included original studies pertaining to the relationship of total Hg exposure (elemental, inorganic or organic) to DM, MS or insulin resistance. The studies were selected based on the PICOS (patients, intervention, comparator, outcomes and study design) criteria and their quality assessed using a nine-point scale. Study characteristics and results were extracted and presented in structured tables. We also extracted covariates entered as confounding factors to evaluate possible biases in selected studies. Finally, a weight of evidence approach was used to assess the causality of the relationship. RESULTS: A total of 34 studies were included in the present review. Epidemiological data assessment suggests a possible association between total Hg concentrations in different biological matrices and incidence of DM or MS, but the relationship is not consistent. In vivo and in vitro studies support the biological plausibility of the relation between Hg exposure and DM or MS. Five out of nine of Bradford Hill's criteria were fulfilled: strength, temporality, plausibility, coherence and analogy. CONCLUSION: Increased total Hg exposure may augment the risk of DM and MS, but the lack of consistency of the epidemiological evidence prevents inference of a causal relationship. Additional prospective cohort studies and careful consideration of confounding variables and interactions are required to conclude on the causal relationship of total Hg exposure on the development of DM or MS.

Quantitative analysis of amino acids and acylcarnitines combined with untargeted metabolomics using ultra-high performance liquid chromatography and quadrupole time-of-flight mass spectrometry.

Metabolomics is an "omic" technique being increasingly used in epidemiological and clinical studies. We developed a method combining untargeted metabolomics with the quantitative determination of eight amino acids (AA) and eight acylcarnitines (AC) in plasma using ultra-high pressure liquid chromatography (UHPLC), electrospray ionization (ESI) and quadrupole time-of-flight mass spectrometry (QTOFMS). Separation of metabolites is performed by ion-pair reverse phase UHPLC using a HSS T3 column (2.1×100mm, 100Å, 1.8µm particle size) and formic acid-ammonium acetate-heptafluorobutyric acid in water and formic acid-ammonium acetate in methanol as mobile phases. Metabolite identification and quantification are achieved using a QTOFMS operating in ESI-positive and full-scan mode along with MS(E) acquisition of fragmentation patterns. Targeted metabolites are quantified using the appropriate labeled standards and include branched-chain AA (leucine, isoleucine, valine), aromatic AA (phenylalanine, tyrosine) as well as acetylcarnitine and propionylcarnitine, which have been identified as biomarkers of future cardiometabolic disease risk. The inter-day precision (relative standard deviation) for the targeted method was <15% for all but one metabolite and accuracy (bias) of amino acids ranged from 0.5% to 13.9% using SRM 1950 as the external standard. Untargeted metabolomics in 30 plasma samples from the general Canadian population revealed 5018 features, of which 48 metabolites were identified using the MZmine 2.19 software including 23 by our in-house library that comprises 671 annotated metabolites. SRM 1950 analysis revealed 11,684 features, among which 154 metabolites were identified. Our method is currently applied in several epidemiological studies to better characterize cardiometabolic diseases and identify new biomarkers for disease prevention.