Metabolomic Profiles of Overweight/Obesity Phenotypes During Adolescence: A Cross‐Sectional Study in Project Viva

Abstract

Original Article EPIDEMIOLOGY/GENETICS Study ImportanceWhat is already known? ► In adults, researchers have observed evidence of metabolically healthy obesity and metabolically unhealthy normal weight. ► Little is known regarding the existence of these phenotypes earlier in the life course, and the underlying biological pathways remain unclear.What does this study add?► In 524 adolescents, we found evidence of the existence of metabolically healthy overweight/obesity (OWOB) and metabolically unhealthy normal weight (non-OWOB), in addition to the usual phenotypes of metabolically unhealthy OWOB and metabolically healthy non-OWOB. ► Using untargeted metabolomics profiling of fasting plasma, we identified five metabolite patterns that differ with respect to OWOB status and high vs. low metabolic risk: long-chain fatty acids, branched-chain amino acids, diacylglycerols, steroid hormones, and long-chain acylcarnitines.Objective: The purpose of this article is to characterize metabolomic profiles of four overweight/obesity (OWOB) and metabolic risk (MetRisk) phenotypes among 524 adolescents aged approximately 13 years. Methods: A four-level phenotype variable (non-OWOB and low MetRisk, non-OWOB and high MetRisk, OWOB and low MetRisk, and OWOB and high MetRisk) was created using BMI percentile to define OWOB, and high versus low MetRisk was derived as the fourth versus first to third quartiles of a z score calculated as the average of five externally standardized z scores for waist circumference, homeostatic model assessment of insulin resistance, high-density lipoprotein, triglycerides, and systolic blood pressure. Then associations of nine metabolite patterns derived from principal components analysis with phenotype after accounting for age, sex, race, and pubertal status were evaluated. Results: Five metabolite patterns differed with respect to phenotype: factor 1 consisted of long-chain fatty acids and was lower among non-OWOB and high MetRisk (−0.90 [95% CI: −1.39 to −0.42]) versus non-OWOB and low MetRisk (referent); factors 5 (branched-chain amino acids), 8 (diacylglycerols), and 9 (steroid hormones) were highest among OWOB and high MetRisk; and factor 7 (long-chain acylcarnitines) was higher among non-OWOB and high MetRisk (0.47 [95% CI: 0.04 to 0.91]) and lower among OWOB and low MetRisk (−0.36 [95% CI: −0.68 to −0.04]).Conclusions: Long-chain fatty acids, branched-chain amino acids, acylcarnitines, diacylglycerols, and steroid hormones differed by weight status and metabolic phenotype.

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

“…This network comprised several androgen hormones including dehydroisoanderosterone sulfate (DHEA-S), androstenediol disulfate, and pregnenediol disulfate. We previously identified a similar metabolite profile during mid-childhood [34] and early adolescence [32] as a correlate of obesity and metabolic risk, which is consistent with findings in the present study, as this metabolite network was positively associated with MetS z-score. The steroid hormone composition of this pattern suggests that it likely represents increased androgen synthesis and a more rapid tempo of sexual maturation, which has implications for metabolic health [46].…”

“…Earlier age at BMI rebound associated with a higher score for ME11, which was composed entirely of diacylglycerols (DAGs) and resembles a metabolite profile previously identified as a correlate of overweight/obesity status in conjunction with high metabolic risk in this cohort [32]. This network was associated with higher metabolic risk, suggesting that ME8 is a marker of adverse metabolic processes, potentially reflecting diet given that the top metabolites in this network are of the palmitoyl-linoleoyl-glycerol moiety, which are common emulsifiers used in bakery products, shortening, whipped toppings, and other confections [50].…”

“…These networks comprised metabolites on androgen hormone (ME7), lysophospholipids (ME8), and diacylglycerol (ME11) metabolism pathways. In addition to having been previously identified as correlates of obesity sub-phenotypes in this cohort [32], individual compounds and/or pathways in the lysophospholipid and diacylglycerol patterns have been linked to cardiometabolic risk in other studies. For instance, some metabolites in the lysophospholipid pattern have been implicated in perturbed hepatic fat oxidation in young adults [43], and the diacylglycerol pattern captured several metabolites on the glycerophosphocholine pathway, which has been implicated in the development of insulin resistance and metabolic syndrome in non-human primates [44].…”

“…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.…”

Find the Needle in the Haystack, Then Find It Again: Replication and Validation in the ‘Omics Era

“…We note that in the absence of true replication, there are specific examples in the literature where certain metabolite profiles have emerged as being relevant to specific biological conditions across different study populations and age groups, and using different laboratory platforms and statistical techniques. One such example is that of metabolites involved in branched chain amino acid and acylcarnitine metabolic pathways which have been identified as correlates [ 10 , 11 , 12 , 13 , 14 ] and prospective risk factors for cardiometabolic biomarkers [ 15 ], as well as biomarkers that are evident up to a decade prior to development of obesity-related chronic disease like type 2 diabetes [ 16 , 17 ]. While these studies were conducted without the a priori intent of replication, consistency in findings within the broader literature provide confidence that such findings are not spurious.…”