Metabolomic profiles and development of metabolic risk during the pubertal transition: a prospective study in the ELEMENT Project

Prediction of postoperative delirium by blood metabolome analysis

No established blood markers can preoperatively predict postoperative delirium. Blood concentrations of amino acid catabolites and dipeptides, including those secreted extracellularly during T-lymphocyte activation, were investigated as predictors of postoperative delirium using metabolomic analyses to ascertain whether preoperative blood metabolites could predict postoperative delirium. Eighteen and 24 participants were included in the delirium and non-delirium groups, respectively. Higher preoperative levels of amino acid (tryptophan) catabolites, via the indoleamine 2,3-dioxygenase pathway, were observed in the delirium group and identified as potential predictors of postoperative delirium in this study. The delirium group had preoperatively elevated levels of tryptophan catabolites and only a limited increase postoperatively, suggesting that the tryptophan catabolic pathway may be activated preoperatively in patients at high risk of delirium. Non-targeted metabolomic analysis found a set of preoperatively elevated γ-glutamyl dipeptides as potential predictors of postoperative delirium. In vitro experiments showed that T-cell-receptor stimulation increases tryptophan metabolism and specific γ-glutamyl dipeptide secretion, offering a possible explanation for the increased levels of metabolites in postoperative delirium. This study showed that levels of amino acid metabolites associated with blood immune activity may have the potential to predict postoperative delirium.

Serum branched-chain amino acids are mainly associated with body mass index and waist circumference

BACKGROUND AND AIMS

To assess the associations between serum concentrations of branched-chain amino acids (BCAAs)-valine, leucine, and isoleucine-and different anthropometric markers, including leptin and adiponectin levels, as well as body composition.

METHODS AND RESULTS

This cross-sectional study used data from the CoLaus|PsyCoLaus and the OsteoLaus studies in Lausanne, Switzerland. Anthropometric markers included the conicity index (CI), body roundness index (BRI), a body shape index (ABSI), body mass index (BMI), and waist circumference, among others. Grip strength was used as a proxy for muscle mass. Bivariate analysis revealed that most anthropometric markers were positively correlated with BCAA, while adiponectin levels was negatively correlated with BCAA. These correlations were generally stronger in males than in females, with the exceptions of CI, ABSI, and adiponectin. After multivariable analysis, weight and BMI showed the strongest association coefficients with BCAA in males, while in females the strongest associations were found for waist circumference and waist-to-height ratio. No significant associations were found between the ABSI and BCAA levels in males, or between grip strength and BCAA levels in females. Stepwise linear regression identified BMI in males, and waist circumference in females as the anthropometric markers most strongly positively associated with BCAA.

CONCLUSION

We observed a significant difference in the association between BCAA levels and anthropometric markers by sex. BMI displayed the strongest positive association with BCAA levels in males while in females, waist circumference exhibited the strongest association.

The Potential of Metabolomics as a Tool for Identifying Biomarkers Associated with Obesity and Its Complications: A Scoping Review

Obesity and its related diseases, such as type 2 diabetes (T2DM), cardiovascular disease (CVD), and metabolic fatty liver disease (MAFLD), require new diagnostic markers for earlier detection and intervention. The aim of this study is to demonstrate the potential of metabolomics as a tool for identifying biomarkers associated with obesity and its comorbidities in every age group. The presented systematic review makes an important contribution to the understanding of the potential of metabolomics in identifying biomarkers of obesity and its complications, especially considering the influence of branched-chain amino acids (BCAAs), amino acids (AAs) and adipokines on the development of T2DM, MAFLD, and CVD. The unique element of this study is the combination of research results from the last decade in different age groups and a wide demographic range. The review was based on the PubMed and Science Direct databases, and the inclusion criterion was English-language original studies conducted in humans between 2014 and 2024 and focusing on the influence of BCAAs, AAs or adipokines on the above-mentioned obesity complications. Based on the PRISMA protocol, a total of 21 papers were qualified for the review and then assigned to a specific disease entity. The collected data reveal that elevated levels of BCAAs and some AAs strongly correlate with insulin resistance, leading to T2DM, MAFLD, and CVD and often preceding conventional clinical markers. Valine and tyrosine emerge as potential markers of MAFLD progression, while BCAAs are primarily associated with insulin resistance in various demographic groups. Adipokines, although less studied, offer hope for elucidating the metabolic consequences of obesity. The review showed that in the case of CVDs, there is still a lack of studies in children and adolescents, who are increasingly affected by these diseases. Moreover, despite the knowledge that adipokines play an important role in the pathogenesis of obesity, there are no precise findings regarding the correlation between individual adipokines and T2DM, MAFLD, or CVD. In order to be able to introduce metabolites into the basic diagnostics of obesity-related diseases, it is necessary to develop panels of biochemical tests that will combine them with classical markers of selected diseases.

Modifiable factors associated with cognitive performance in Chinese adolescents: a national environment-wide association study

Growing evidence exists about the candidate factors of childhood cognitive performance, but mainly limited to single-exposure studies. We sought to systematically and simultaneously identify and validate a wide range of potential modifiable factors for childhood cognitive performance. We used data from five waves of data from the China Family Panel Studies (CFPS-2010, 2012, 2014, 2016 and 2018). Our analytical sample was restricted to those children aged 2-5 at baseline with valid exposure information. A total of 80 modifiable factors were identified. Childhood cognitive performance was assessed using vocabulary and mathematics test at wave 5. We used an environment-wide association study (EnWAS) to screen all exposure-outcome associations independently and used the least absolute shrinkage and selection operator (LASSO) variable selection algorithm to identify factors associated with cognitive performance. Multivariable linear model was then used to evaluate causal relationships between identified factors and cognitive performance. Of the 1305 participants included in the study (mean ± SD, 3.5 ± 1.1 years age at baseline, 45.1% girls). Eight factors were retained in the LASSO regression analysis. Six factors across community characteristics (percentage of poverty in the community; percentage of children in the community), household characteristics (family size), child health and behaviors (mobile internet access), parenting behaviors and cognitive enrichment (parental involvement in child' s education), and parental wellbeing (paternal happiness) domains were significantly associated with childhood cognition. Using a three-stage approach, this study validates several actionable targets for improving childhood cognitive performance.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

The Role of Molecular and Hormonal Factors in Obesity and the Effects of Physical Activity in Children

Obesity and overweight are defined as abnormal fat accumulations. Adipose tissue consists of more than merely adipocytes; each adipocyte is closely coupled with the extracellular matrix. Adipose tissue stores excess energy through expansion. Obesity is caused by the abnormal expansion of adipose tissue as a result of adipocyte hypertrophy and hyperplasia. The process of obesity is controlled by several molecules, such as integrins, kindlins, or matrix metalloproteinases. In children with obesity, metabolomics studies have provided insight into the existence of unique metabolic profiles. As a result of low-grade inflammation in the system, abnormalities were observed in several metabolites associated with lipid, carbohydrate, and amino acid pathways. In addition, obesity and related hormones, such as leptin, play an instrumental role in regulating food intake and contributing to childhood obesity. The World Health Organization states that physical activity benefits the heart, the body, and the mind. Several noncommunicable diseases, such as cardiovascular disease, cancer, and diabetes, can be prevented and managed through physical activity. In this work, we reviewed pediatric studies that examined the molecular and hormonal control of obesity and the influence of physical activity on children with obesity or overweight. The purpose of this review was to examine some orchestrators involved in this disease and how they are related to pediatric populations. A larger number of randomized clinical trials with larger sample sizes and long-term studies could lead to the discovery of new key molecules as well as the detection of significant factors in the coming years. In order to improve the health of the pediatric population, omics analyses and machine learning techniques can be combined in order to improve treatment decisions.

Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts

Here, we seek to identify metabolite predictors of dysglycemia in youth. In the discovery analysis among 391 youth in the Exploring Perinatal Outcomes among CHildren (EPOCH) cohort, we used reduced rank regression (RRR) to identify sex-specific metabolite predictors of impaired fasting glucose (IFG) and elevated fasting glucose (EFG: Q4 vs. Q1 fasting glucose) 6 years later and compared the predictive capacity of four models: Model 1: ethnicity, parental diabetes, in utero exposure to diabetes, and body mass index (BMI); Model 2: Model 1 covariates + baseline waist circumference, insulin, lipids, and Tanner stage; Model 3: Model 2 + baseline fasting glucose; Model 4: Model 3 + baseline metabolite concentrations. RRR identified 19 metabolite predictors of fasting glucose in boys and 14 metabolite predictors in girls. Most compounds were on lipid, amino acid, and carbohydrate metabolism pathways. In boys, no improvement in aurea under the receiver operating characteristics curve AUC occurred until the inclusion of metabolites in Model 4, which increased the AUC for prediction of IFG (7.1%) from 0.81 to 0.97 (p = 0.002). In girls, %IFG was too low for regression analysis (3.1%), but we found similar results for EFG. We replicated the results among 265 youth in the Project Viva cohort, focusing on EFG due to low %IFG, suggesting that the metabolite profiles identified herein have the potential to improve the prediction of glycemia in youth.

Paediatric obesity: a systematic review and pathway mapping of metabolic alterations underlying early disease processes

BACKGROUND

The alarming trend of paediatric obesity deserves our greatest awareness to hinder the early onset of metabolic complications impacting growth and functionality. Presently, insight into molecular mechanisms of childhood obesity and associated metabolic comorbidities is limited. This systematic review aimed at scrutinising what has been reported on putative metabolites distinctive for metabolic abnormalities manifesting at young age by searching three literature databases (Web of Science, Pubmed and EMBASE) during the last 6 years (January 2015-January 2021). Global metabolomic profiling of paediatric obesity was performed (multiple biological matrices: blood, urine, saliva and adipose tissue) to enable overarching pathway analysis and network mapping. Among 2792 screened Q1 articles, 40 met the eligibility criteria and were included to build a database on metabolite markers involved in the spectrum of childhood obesity. Differential alterations in multiple pathways linked to lipid, carbohydrate and amino acid metabolisms were observed. High levels of lactate, pyruvate, alanine and acetate marked a pronounced shift towards hypoxic conditions in children with obesity, and, together with distinct alterations in lipid metabolism, pointed towards dysbiosis and immunometabolism occurring early in life. Additionally, aberrant levels of several amino acids, most notably belonging to tryptophan metabolism including the kynurenine pathway and its relation to histidine, phenylalanine and purine metabolism were displayed. Moreover, branched-chain amino acids were linked to lipid, carbohydrate, amino acid and microbial metabolism, inferring a key role in obesity-associated insulin resistance.

CONCLUSIONS

This systematic review revealed that the main metabolites at the crossroad of dysregulated metabolic pathways underlying childhood obesity could be tracked down to one central disturbance, i.e. impending insulin resistance for which reference values and standardised measures still are lacking. In essence, glycolytic metabolism was evinced as driving energy source, coupled to impaired Krebs cycle flux and ß-oxidation. Applying metabolomics enabled to retrieve distinct metabolite alterations in childhood obesity(-related insulin resistance) and associated pathways at early age and thus could provide a timely indication of risk by elucidating early-stage biomarkers as hallmarks of future metabolically unhealthy phenotypes.

Personalized nutrition approach in pediatrics: a narrative review

Dietary habits represent the main determinant of health. Although extensive research has been conducted to modify unhealthy dietary behaviors across the lifespan, obesity and obesity-associated comorbidities are increasingly observed worldwide. Individually tailored interventions are nowadays considered a promising frontier for nutritional research. In this narrative review, the technologies of importance in a pediatric clinical setting are discussed. The first determinant of the dietary balance is represented by energy intakes matching individual needs. Most emerging studies highlight the opportunity to reconsider the widely used prediction equations of resting energy expenditure. Artificial Neural Network approaches may help to disentangle the role of single contributors to energy expenditure. Artificial intelligence is also useful in the prediction of the glycemic response, based on the individual microbiome. Other factors further concurring to define individually tailored nutritional needs are metabolomics and nutrigenomic. Since most available data come from studies in adult groups, new efforts should now be addressed to integrate all these aspects to develop comprehensive and-above all-effective interventions for children. IMPACT: Personalized dietary advice, specific to individuals, should be more effective in the prevention of chronic diseases than general recommendations about diet. Artificial Neural Networks algorithms are technologies of importance in a pediatric setting that may help practitioners to provide personalized nutrition. Other approaches to personalized nutrition, while promising in adults and for basic research, are still far from practical application in pediatrics.

Integrative Analysis of Gene-Specific DNA Methylation and Untargeted Metabolomics Data from the ELEMENT Cohort

Epigenetic modifications, such as DNA methylation, influence gene expression and cardiometabolic phenotypes that are manifest in developmental periods in later life, including adolescence. Untargeted metabolomics analysis provide a comprehensive snapshot of physiological processes and metabolism and have been related to DNA methylation in adults, offering insights into the regulatory networks that influence cellular processes. We analyzed the cross-sectional correlation of blood leukocyte DNA methylation with 3758 serum metabolite features (574 of which are identifiable) in 238 children (ages 8-14 years) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) study. Associations between these features and percent DNA methylation in adolescent blood leukocytes at LINE-1 repetitive elements and genes that regulate early life growth (IGF2, H19, HSD11B2) were assessed by mixed effects models, adjusting for sex, age, and puberty status. After false discovery rate correction (FDR q < 0.05), 76 metabolites were significantly associated with LINE-1 DNA methylation, 27 with HSD11B2, 103 with H19, and 4 with IGF2. The ten identifiable metabolites included dicarboxylic fatty acids (five associated with LINE-1 or H19 methylation at q < 0.05) and 1-octadecanoyl-rac-glycerol (q < 0.0001 for association with H19 and q = 0.04 for association with LINE-1). We then assessed the association between these ten known metabolites and adiposity 3 years later. Two metabolites, dicarboxylic fatty acid 17:3 and 5-oxo-7-octenoic acid, were inversely associated with measures of adiposity (P < .05) assessed approximately 3 years later in adolescence. In stratified analyses, sex-specific and puberty-stage specific (Tanner stage = 2 to 5 vs Tanner stage = 1) associations were observed. Most notably, hundreds of statistically significant associations were observed between H19 and LINE-1 DNA methylation and metabolites among children who had initiated puberty. Understanding relationships between subclinical molecular biomarkers (DNA methylation and metabolites) may increase our understanding of genes and biological pathways contributing to metabolic changes that underlie the development of adiposity during adolescence.

Branched-chain amino acids, history of gestational diabetes, and breastfeeding: The Bogalusa Heart Study

BACKGROUND AND AIMS

To examine the associations between history of gestational diabetes mellitus (GDM) and breastfeeding with branched-chain amino acids (BCAA) and their metabolites in later life.

METHODS AND RESULTS

638 women (mean age 48.0 y) who had participated in the Bogalusa Heart Study and substudies of pregnancy history had untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy conducted by Metabolon© on serum samples. Metabolites were identified that were BCAA or associated with BCAA metabolic pathways. History of GDM at any pregnancy (self-reported, confirmed with medical records when possible) as well as breastfeeding were examined as predictors of BCAA using linear models, controlling for age, race, BMI, waist circumference, and menopausal status. None of the BCAA differed statistically by history of either GDM or breastfeeding, although absolute levels of each of the BCAA were higher with GDM and lower with breastfeeding. Of the 27 metabolites on the leucine, isoleucine and valine metabolism subpathway, 1-carboxyethylleucine, 1-carboxyethyvaline, and 3-hydroxy-2-ethylpropionate were higher in women with a history of GDM, but lower in women in women with a history of breastfeeding. Similar results were found for alpha-hydroxyisocaproate, 1-carboxyethylisoleucine, and N-acetylleucine.

CONCLUSIONS

GDM and breastfeeding are associated in opposite directions with several metabolites on the BCAA metabolic pathway.

Mitochondrial Nutrient Utilization Underlying the Association Between Metabolites and Insulin Resistance in Adolescents

CONTEXT

A person's intrinsic metabolism, reflected in the metabolome, may describe the relationship between nutrient intake and metabolic health.

OBJECTIVES

Untargeted metabolomics was used to identify metabolites associated with metabolic health. Path analysis classified how habitual dietary intake influences body mass index z-score (BMIz) and insulin resistance (IR) through changes in the metabolome.

DESIGN

Data on anthropometry, fasting metabolites, C-peptide, and dietary intake were collected from 108 girls and 98 boys aged 8 to 14 years. Sex-stratified linear regression identified metabolites associated with BMIz and homeostatic model assessment of IR using C-peptide (HOMA-CP), accounting for puberty, age, and muscle and fat area. Path analysis identified clusters of metabolites that underlie the relationship between energy-adjusted macronutrient intake with BMIz and HOMA-CP.

RESULTS

Metabolites associated with BMIz include positive associations with diglycerides among girls and positive associations with branched chain and aromatic amino acids in boys. Intermediates in fatty acid metabolism, including medium-chain acylcarnitines (AC), were inversely associated with HOMA-CP. Carbohydrate intake is positively associated with HOMA-CP through decreases in levels of AC, products of β-oxidation. Approaching significance, fat intake is positively associated with HOMA-CP through increases in levels of dicarboxylic fatty acids, products of omega-oxidation.

CONCLUSIONS

This cross-sectional analysis suggests that IR in children is associated with reduced fatty acid oxidation capacity. When consuming more grams of fat, there is evidence for increased extramitochondrial fatty acid metabolism, while higher carbohydrate intake appears to lead to decreases in intermediates of β-oxidation. Thus, biomarkers of IR and mitochondrial oxidative capacity may depend on macronutrient intake.

Precision Nutrition and Childhood Obesity: A Scoping Review

Environmental exposures such as nutrition during life stages with high developmental plasticity-in particular, the in utero period, infancy, childhood, and puberty-may have long-lasting influences on risk of chronic diseases, including obesity-related conditions that manifest as early as childhood. Yet, specific mechanisms underlying these relationships remain unclear. Here, we consider the study of 'omics mechanisms, including nutrigenomics, epigenetics/epigenomics, and metabolomics, within a life course epidemiological framework to accomplish three objectives. First, we carried out a scoping review of population-based literature with a focus on studies that include 'omics analyses during three sensitive periods during early life: in utero, infancy, and childhood. We elected to conduct a scoping review because the application of multi-'omics and/or precision nutrition in childhood obesity prevention and treatment is relatively recent, and identifying knowledge gaps can expedite future research. Second, concomitant with the literature review, we discuss the relevance and plausibility of biological mechanisms that may underlie early origins of childhood obesity identified by studies to date. Finally, we identify current research limitations and future opportunities for application of multi-'omics in precision nutrition/health practice.

Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents

Background: Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of healthy children and adolescents who have been subjected to deep phenotyping, including collection of biospecimens for metabolomic analysis. The present study considered whether amino acid (AA) and acylcarnitine (AC) concentrations are associated with age, sex, body mass index (BMI), and puberty during childhood and adolescence. It also investigated whether there are associations between amino acids (AAs) and acylcarnitines (ACs) and laboratory parameters of glucose and lipid metabolism, as well as liver, kidney, and thyroid parameters. Methods: A total of 3989 dried whole blood samples collected from 2191 healthy participants, aged 3 months to 18 years, from the LIFE Child cohort (Leipzig, Germany) were analyzed using liquid chromatography tandem mass spectrometry to detect levels of 23 AAs, 6 ACs, and free carnitine (C0). Age- and sex-related percentiles were estimated for each metabolite. In addition, correlations between laboratory parameters and levels of the selected AAs and ACs were calculated using hierarchical models. Results: Four different age-dependent profile types were identified for AAs and ACs. Investigating the association with puberty, we mainly identified peak metabolite levels at Tanner stages 2 to 3 in girls and stages 3 to 5 in boys. Significant correlations were observed between BMI standard deviation score (BMI-SDS) and certain metabolites, among them, branched-chain (leucine/isoleucine, valine) and aromatic (phenylalanine, tyrosine) amino acids. Most of the metabolites correlated significantly with absolute concentrations of glucose, glycated hemoglobin (HbA1c), triglycerides, cystatin C (CysC), and creatinine. After age adjustment, significant correlations were observed between most metabolites and CysC, as well as HbA1c. Conclusions: During childhood, several AA and AC levels are related to age, sex, BMI, and puberty. Moreover, our data verified known associations but also revealed new correlations between AAs/ACs and specific key markers of metabolic function.