Childhood overweight and obesity: age stratification contributes to the differences in metabolic characteristics
Jinxia Wu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorZhenchang Li
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorHongwei Zhu
Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
Search for more papers by this authorYajie Chang
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorQuanquan Li
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorCorresponding Author
Jing Chen
Department of Child Health, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
Correspondence
Jianghua Feng, Department of Electronic Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China.
Email: [email protected]
Jing Chen, Department of Child Health, Women and Children's Hospital, Xiamen University, 10 Zhenhai Road, Siming District, Xiamen, Fujian 361003, China.
Email: [email protected]
Search for more papers by this authorGuiping Shen
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorCorresponding Author
Jianghua Feng
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Correspondence
Jianghua Feng, Department of Electronic Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China.
Email: [email protected]
Jing Chen, Department of Child Health, Women and Children's Hospital, Xiamen University, 10 Zhenhai Road, Siming District, Xiamen, Fujian 361003, China.
Email: [email protected]
Search for more papers by this authorJinxia Wu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorZhenchang Li
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorHongwei Zhu
Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
Search for more papers by this authorYajie Chang
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorQuanquan Li
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorCorresponding Author
Jing Chen
Department of Child Health, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
Correspondence
Jianghua Feng, Department of Electronic Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China.
Email: [email protected]
Jing Chen, Department of Child Health, Women and Children's Hospital, Xiamen University, 10 Zhenhai Road, Siming District, Xiamen, Fujian 361003, China.
Email: [email protected]
Search for more papers by this authorGuiping Shen
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Search for more papers by this authorCorresponding Author
Jianghua Feng
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Correspondence
Jianghua Feng, Department of Electronic Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China.
Email: [email protected]
Jing Chen, Department of Child Health, Women and Children's Hospital, Xiamen University, 10 Zhenhai Road, Siming District, Xiamen, Fujian 361003, China.
Email: [email protected]
Search for more papers by this authorAbstract
Objective
The aim of this study was to identify the differential metabolic characteristics of children with overweight and obesity and understand their potential mechanism in different age stratifications.
Methods
Four hundred seventy-three children were recruited and divided into two age stratifications: >4 years (older children) and ≤4 years (younger children), and overweight and obesity were defined according to their BMI percentile. A one dimensional proton nuclear magnetic resonance (1H-NMR)–based metabolomics strategy combined with pattern recognition methods was used to identify the metabolic characteristics of childhood overweight and obesity.
Results
Four and sixteen potential biomarkers related to overweight and two and twenty potential biomarkers related to obesity were identified from younger and older children, respectively. Fluctuations in phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate co-occurred in children with obesity at two age stratifications. The disturbances in biosynthesis and metabolism of amino acids, lipid metabolism, and galactose metabolism disturbance were mainly involved in children with overweight and obesity.
Conclusions
The metabolic disturbances show a significant progression from overweight to obesity in children, and different metabolic characteristics were demonstrated in age stratifications. The changes in the levels of phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate were tracked with the persistence of childhood obesity. These findings will promote the mechanistic understanding of childhood overweight and obesity.
CONFLICT OF INTEREST STATEMENT
The authors declared no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
The study data are available on demand.
Supporting Information
Filename | Description |
---|---|
oby23964-sup-0001-Supinfo.docxWord 2007 document , 13.5 MB | FIGURE S1. Average 1H-NMR spectra of sera from the children clinical cohort. The spectral regions of δ6.00–8.60 (in the dashed box) were vertically expanded 20 times compared with those of δ0.50–6.00 for clarity, and the keys of the abbreviations on the spectra and the spectral information were represented in Table S1 in the supplemental materials. Con: control group; Ow: group with overweight; Ob: group with obesity. FIGURE S2. Serum metabolic differences among the children with overweight and obesity and control children in younger (≤4 year) and older (>4 year) cohort. Left panels: PLS-DA scores plots; Right panels: OPLS-DA scores plots and permutation test for pairwise comparison. TABLE S1. The identified metabolites from 1H-NMR spectra of serum samples of children. TABLE S2. Summary of the model quality parameters of the multivariate statistical analysis. |
oby23964-sup-0002-TableS3.xlsxExcel 2007 spreadsheet , 25 KB | TABLE S3. Supporting Information. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021; 9: 446-461.
- 2Wang L, Zhou B, Zhao Z, et al. Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004-18. Lancet. 2021; 398: 53-63.
- 3Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 2008; 9: 474-488.
- 4Park MH, Falconer C, Viner RM, Kinra S. The impact of childhood obesity on morbidity and mortality in adulthood: a systematic review. Obes Rev. 2012; 13: 985-1000.
- 5Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev. 2017; 18: 742-754.
- 6Rangel-Huerta OD, Pastor-Villaescusa B, Gil A. Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies. Metabolomics. 2019; 15: 93.
- 7Zhao X, Gang X, Liu Y, Sun C, Han Q, Wang G. Using metabolomic profiles as biomarkers for insulin resistance in childhood obesity: a systematic review. J Diabetes Res. 2016; 2016:8160545.
- 8Bervoets L, Massa G, Guedens W, Reekmans G, Noben JP, Adriaensens P. Identification of metabolic phenotypes in childhood obesity by (1)H NMR metabolomics of blood plasma. Future Sci OA. 2018; 4: FSO310.
- 9Szczerbinski L, Wojciechowska G, Olichwier A, et al. Untargeted metabolomics analysis of the serum metabolic signature of childhood obesity. Nutrients. 2022; 14: 214.
- 10Suzuki Y, Kido J, Matsumoto S, Shimizu K, Nakamura K. Associations among amino acid, lipid, and glucose metabolic profiles in childhood obesity. BMC Pediatr. 2019; 19: 273.
- 11Gumus Balikcioglu P, Jachthuber Trub C, Balikcioglu M, et al. Branched-chain α-keto acids and glutamate/glutamine: biomarkers of insulin resistance in childhood obesity. Endocrinol Diabetes Metab. 2023; 6:e388.
- 12Zhang Y, Yuan X, Yang X, et al. Associations of obesity with growth and puberty in children: a cross-sectional study in Fuzhou. Int J Public Health. 2023; 68:1605433.
- 13Basu S, Duren W, Evans CR, Burant CF, Michailidis G, Karnovsky A. Sparse network modeling and Metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics. 2017; 33: 1545-1553.
- 14Monasor-Ortola D, Quesada-Rico JA, Nso-Roca AP, et al. Degree of accuracy of the BMI Z-score to determine excess fat mass using DXA in children and adolescents. Int J Environ Res Public Health. 2021; 18: 12114.
- 15Michael N, Gupta V, Fogel A, et al. Longitudinal characterization of determinants associated with obesogenic growth patterns in early childhood. Int J Epidemiol. 2022; 52: 426-439.
- 16Short KR, Chadwick JQ, Teague AM, et al. Effect of obesity and exercise training on plasma amino acids and amino metabolites in American Indian adolescents. J Clin Endocrinol Metab. 2019; 104: 3249-3261.
- 17Butte NF, Liu Y, Zakeri IF, et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am J Clin Nutr. 2015; 102: 256-267.
- 18Ajoolabady A, Liu S, Klionsky DJ, et al. ER stress in obesity pathogenesis and management. Trends Pharmacol Sci. 2022; 43: 97-109.
- 19Htun KT, Pan J, Pasanta D, et al. Identification of metabolic phenotypes in young adults with obesity by (1)H NMR metabolomics of blood serum. Life (Basel). 2021; 11: 574.
- 20Wahl S, Yu Z, Kleber M, et al. Childhood obesity is associated with changes in the serum metabolite profile. Obes Facts. 2012; 5: 660-670.
- 21Vanweert F, Schrauwen P, Phielix E. Role of branched-chain amino acid metabolism in the pathogenesis of obesity and type 2 diabetes-related metabolic disturbances BCAA metabolism in type 2 diabetes. Nutr Diabetes. 2022; 12: 35.
- 22Lau CE, Siskos AP, Maitre L, et al. Determinants of the urinary and serum metabolome in children from six European populations. BMC Med. 2018; 16: 202.
- 23Bernard JR, Liao YH, Hara D, et al. An amino acid mixture improves glucose tolerance and insulin signaling in Sprague-Dawley rats. Am J Physiol Endocrinol Metab. 2011; 300: E752-E760.
- 24Amari S, Shahrook S, Namba F, Ota E, Mori R. Branched-chain amino acid supplementation for improving growth and development in term and preterm neonates. Cochrane Database Syst Rev. 2020; 10:Cd012273.
- 25Saner C, Harcourt BE, Pandey A, et al. Sex and puberty-related differences in metabolomic profiles associated with adiposity measures in youth with obesity. Metabolomics. 2019; 15: 75.
- 26Feng RN, Niu YC, Sun XW, et al. Histidine supplementation improves insulin resistance through suppressed inflammation in obese women with the metabolic syndrome: a randomised controlled trial. Diabetologia. 2013; 56: 985-994.
- 27Xu Y, Shi T, Cui X, et al. Asparagine reinforces mTORC1 signaling to boost thermogenesis and glycolysis in adipose tissues. EMBO J. 2021; 40:e108069.
- 28Rao Y, Kuang Z, Li C, et al. Gut Akkermansia muciniphila ameliorates metabolic dysfunction-associated fatty liver disease by regulating the metabolism of L-aspartate via gut-liver axis. Gut Microbes. 2021; 13: 1-19.
- 29Matthews DE. Review of lysine metabolism with a focus on humans. J Nutr. 2020; 150: 2548s-2555s.
- 30Kim B, Choi KM, Yim HS, Park HT, Yim JH, Lee MG. Adipogenic and lipolytic effects of ascorbic acid in ovariectomized rats. Yonsei Med J. 2018; 59: 85-91.
- 31Gwanyanya A, Godsmark CN, Kelly-Laubscher R. Ethanolamine: a potential Promoiety with additional effects on the brain. CNS Neurol Disord Drug Targets. 2022; 21: 108-117.
- 32Patel D, Witt SN. Ethanolamine and phosphatidylethanolamine: Partners in health and disease. Oxid Med Cell Longev. 2017; 2017:4829180.
- 33Kiss Z, Crilly KS, Anderson WH. Extracellular sphingosine 1-phosphate stimulates formation of ethanolamine from phosphatidylethanolamine: modulation of sphingosine 1-phosphate-induced mitogenesis by ethanolamine. Biochem J. 1997; 328(Pt 2): 383-391.
- 34Choi J, Yin T, Shinozaki K, et al. Comprehensive analysis of phospholipids in the brain, heart, kidney, and liver: brain phospholipids are least enriched with polyunsaturated fatty acids. Mol Cell Biochem. 2018; 442: 187-201.
- 35Rauschert S, Uhl O, Koletzko B, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab. 2016; 101: 871-879.
- 36Pietiläinen KH, Sysi-Aho M, Rissanen A, et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects—a monozygotic twin study. PloS One. 2007; 2:e218.
- 37Sherriff JL, O'Sullivan TA, Properzi C, Oddo JL, Adams LA. Choline, its potential role in nonalcoholic fatty liver disease, and the case for human and bacterial genes. Adv Nutr. 2016; 7: 5-13.
- 38Lever M, McEntyre CJ, George PM, Chambers ST. Is N,N-dimethylglycine N-oxide a choline and betaine metabolite? Biol Chem. 2017; 398: 775-784.
- 39Otvos JD, Shalaurova I, Wolak-Dinsmore J, et al. GlycA: a composite nuclear magnetic resonance biomarker of systemic inflammation. Clin Chem. 2015; 61: 714-723.
- 40De Spiegeleer M, De Paepe E, Van Meulebroek L, Gies I, De Schepper J, Vanhaecke L. Paediatric obesity: a systematic review and pathway mapping of metabolic alterations underlying early disease processes. Mol Med. 2021; 27: 145.
- 41Cabrera-Cruz H, Oróstica L, Plaza-Parrochia F, Torres-Pinto I, Romero C, Vega M. The insulin-sensitizing mechanism of myo-inositol is associated with AMPK activation and GLUT-4 expression in human endometrial cells exposed to a PCOS environment. Am J Physiol Endocrinol Metab. 2020; 318: E237-e248.
- 42L'Abbate S, Nicolini G, Forini F, et al. Myo-inositol and d-chiro-inositol oral supplementation ameliorate cardiac dysfunction and remodeling in a mouse model of diet-induced obesity. Pharmacol Res. 2020; 159:105047.
- 43Hosking J, Pinkney J, Jeffery A, et al. Insulin resistance during normal child growth and development is associated with a distinct blood metabolic phenotype (Earlybird 72). Pediatr Diabetes. 2019; 20: 832-841.
- 44Carrière A, Jeanson Y, Berger-Müller S, et al. Browning of white adipose cells by intermediate metabolites: an adaptive mechanism to alleviate redox pressure. Diabetes. 2014; 63: 3253-3265.
- 45Mills EL, Pierce KA, Jedrychowski MP, et al. Accumulation of succinate controls activation of adipose tissue thermogenesis. Nature. 2018; 560: 102-106.