Metabolomic Determinants of Metabolic Risk in Mexican Adolescents

Abstract

Objective To identify metabolites associated with metabolic risk, separately by sex, in Mexican adolescents. Methods We carried out untargeted metabolomic profiling on fasting serum of 238 youth age 8–14 years, and identified metabolites associated with a metabolic syndrome risk z-score (MetRisk z-score), separately for boys and girls using the simulation and extrapolation (SIMEX) algorithm. We examined associations of each metabolite with MetRisk z-score using linear regression models that accounted for maternal education, child’s age, and pubertal status. Results Of the 938 features identified in metabolomics analysis, 7 named compounds (of 27 identified metabolites) were associated with MetRisk z-score in girls, and 3 named compounds (of 14 identified) were associated with MetRisk z-score in boys. In girls, diacylglycerol (DG) 16:0/16:0, 1,3-dielaidin, myo-inositol, and urate corresponded with higher MetRisk z-score, whereas N-acetylglycine, thymine, and dodecenedioic acid were associated with lower MetRisk z-score. For example, each z-score increment in DG 16:0/16:0 corresponded with 0.60 (0.47, 0.74). In boys, we found positive associations of DG 16:0/16:0, tyrosine, and 5′-methylthioadenosine with MetRisk z-score. Conclusions Metabolites on lipid, amino acid, and carbohydrate metabolism pathways are associated with metabolic risk in girls. Compounds on lipid and DNA pathways correspond with metabolic risk in boys.

Maternal intake of omega-3 and omega-6 polyunsaturated fatty acids during mid-pregnancy is inversely associated with linear growth

“…Because the intraclass correlation (ICC) between the measurements was high (ICC SBP = 0.95; ICC DBP = 0.89), we used the average of the five measurements in the analysis, as previously done in this population. 21 In addition to examining individual biomarkers, we also calculated a metabolic syndrome risk z-score (MetS z-score) modified from one proposed by Viitasalo et al 24 and previously used in this population 25 as an indicator of metabolic risk. We took the average of five age-and sex-specific z-score for fasting glucose, fasting C-peptide (in lieu of fasting insulin), WC, TG/ HDL ratio and the average of SBP and DBP.…”

Association of blood leukocyte DNA methylation at LINE-1 and growth-related candidate genes with pubertal onset and progression

“…One more peri-pubertal visit (lateteen visit) was completed approximately five years later (n = 549, with 223 having also participated in the early-teen visit). Fasting blood, pubertal status and anthropometry were collected at both teen visits [58] (Figure 1).…”

Metabolite Profiles of the Relationship between Body Mass Index (BMI) Milestones and Metabolic Risk during Early Adolescence

“…One key strength of this study was that the relatively large sample size (especially in comparison to other studies of metabolomics in youth where N < 300 [33][34][35][36][37][38]) enabled us to conduct sex-specific analyses, which is important given that previous findings identified sex differences in the evolution of metabolic risk biomarkers from late childhood to early adolescence in this cohort [30], and there is an established literature indicating that puberty is a time of sex-specific differences in multiple aspects of metabolic risk, including fat distribution [67,68], glucose-insulin homeostasis [48], and lipid profile [69]. Additional strengths include the prospective design with respect to the association between early growth and the metabolite networks, which provided temporal separation to avoid reverse causation bias; the implementation of a data-driven multivariate technique (WGCNA) to generate networks of metabolites assayed on an untargeted platform, which is an ideal approach for discovery of novel features related a biological trait [70]; use of the "meeting-in-the-middle" analytical strategy to identify metabolite profiles of interest (i.e., those that mark the relationship of early growth with future metabolic risk), which was recently shown to reveal novel high-dimensional biomarkers that may link exposures to disease in cohort studies [71]; the multi-ethnic study population, which may enhance the generalizability; and our ability to account for key covariates, such as pubertal status, that contribute to variability in metabolism.…”

“…Finally, of the metabolite profiles of interest (i.e., those related to the BMI milestones), our third objective was to hone in on those that are also associated with MetS z-score during early adolescence. Given the existing literature on the relevance of amino acid, acylcarnitine, lipid, and androgen hormone pathways to early growth and development [31], as well as obesity-related conditions in children and adolescents [32][33][34][35][36][37][38], we hypothesized that we would identify metabolites involved in some or all of these pathways in the present study. Key terms and concepts pertinent to this study are listed in Table 1.…”