Associations of childhood BMI, general and visceral fat mass with metabolite profiles at school-age.
Journal
International journal of obesity (2005)
ISSN: 1476-5497
Titre abrégé: Int J Obes (Lond)
Pays: England
ID NLM: 101256108
Informations de publication
Date de publication:
08 Jun 2024
08 Jun 2024
Historique:
received:
23
01
2024
accepted:
30
05
2024
revised:
22
05
2024
medline:
9
6
2024
pubmed:
9
6
2024
entrez:
8
6
2024
Statut:
aheadofprint
Résumé
Childhood obesity increases metabolic disease risk. Underlying mechanisms remain unknown. We examined associations of body mass index (BMI), total body fat mass, and visceral fat mass with serum metabolites at school-age, and explored whether identified metabolites improved the identification of children at risk of a metabolically unhealthy phenotype. We performed a cross-sectional analysis among 497 children with a mean age of 9.8 (95% range 9.1, 10.6) years, participating in a population-based cohort study. We measured BMI, total body fat mass using DXA, and visceral fat mass using MRI. Serum concentrations of amino-acids, non-esterified-fatty-acids, phospholipids, and carnitines were determined using LC-MS/MS. Children were categorized as metabolically healthy or metabolically unhealthy, according to BMI, blood pressure, lipids, glucose, and insulin levels. Higher BMI and total body fat mass were associated with altered concentrations of branched-chain amino-acids, essential amino-acids, and free carnitines. Higher BMI was also associated with higher concentrations of aromatic amino-acids and alkyl-lysophosphatidylcholines (FDR-corrected p-values < 0.05). The strongest associations were present for Lyso.PC.a.C14.0 and SM.a.C32.2 (FDR-corrected p-values < 0.01). Higher visceral fat mass was only associated with higher concentrations of 6 individual metabolites, particularly Lyso.PC.a.C14.0, PC.aa.C32.1, and SM.a.C32.2. We selected 15 metabolites that improved the prediction of a metabolically unhealthy phenotype, compared to BMI only (AUC: BMI: 0.59 [95% CI 0.47,0.71], BMI + Metabolites: 0.91 [95% CI 0.85,0.97]). An adverse childhood body fat profile, characterized by higher BMI and total body fat mass, is associated with metabolic alterations, particularly in amino acids, phospholipids, and carnitines. Fewer associations were present for visceral fat mass. We identified a metabolite profile that improved the identification of impaired cardiometabolic health in children, compared to BMI only.
Sections du résumé
BACKGROUND
BACKGROUND
Childhood obesity increases metabolic disease risk. Underlying mechanisms remain unknown. We examined associations of body mass index (BMI), total body fat mass, and visceral fat mass with serum metabolites at school-age, and explored whether identified metabolites improved the identification of children at risk of a metabolically unhealthy phenotype.
METHODS
METHODS
We performed a cross-sectional analysis among 497 children with a mean age of 9.8 (95% range 9.1, 10.6) years, participating in a population-based cohort study. We measured BMI, total body fat mass using DXA, and visceral fat mass using MRI. Serum concentrations of amino-acids, non-esterified-fatty-acids, phospholipids, and carnitines were determined using LC-MS/MS. Children were categorized as metabolically healthy or metabolically unhealthy, according to BMI, blood pressure, lipids, glucose, and insulin levels.
RESULTS
RESULTS
Higher BMI and total body fat mass were associated with altered concentrations of branched-chain amino-acids, essential amino-acids, and free carnitines. Higher BMI was also associated with higher concentrations of aromatic amino-acids and alkyl-lysophosphatidylcholines (FDR-corrected p-values < 0.05). The strongest associations were present for Lyso.PC.a.C14.0 and SM.a.C32.2 (FDR-corrected p-values < 0.01). Higher visceral fat mass was only associated with higher concentrations of 6 individual metabolites, particularly Lyso.PC.a.C14.0, PC.aa.C32.1, and SM.a.C32.2. We selected 15 metabolites that improved the prediction of a metabolically unhealthy phenotype, compared to BMI only (AUC: BMI: 0.59 [95% CI 0.47,0.71], BMI + Metabolites: 0.91 [95% CI 0.85,0.97]).
CONCLUSIONS
CONCLUSIONS
An adverse childhood body fat profile, characterized by higher BMI and total body fat mass, is associated with metabolic alterations, particularly in amino acids, phospholipids, and carnitines. Fewer associations were present for visceral fat mass. We identified a metabolite profile that improved the identification of impaired cardiometabolic health in children, compared to BMI only.
Identifiants
pubmed: 38851839
doi: 10.1038/s41366-024-01558-8
pii: 10.1038/s41366-024-01558-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 2019;7:231–40.
pubmed: 30704950
pmcid: 7360432
doi: 10.1016/S2213-8587(19)30026-9
Weihrauch-Blüher S, Schwarz P, Klusmann JH. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood. Metabolism. 2019;92:147–52.
pubmed: 30529454
doi: 10.1016/j.metabol.2018.12.001
Vukovic R, Dos Santos TJ, Ybarra M, Atar M. Children with metabolically healthy obesity: a review. Front Endocrinol (Lausanne). 2019;10:865.
pubmed: 31920976
doi: 10.3389/fendo.2019.00865
Handakas E, Lau CH, Alfano R, Chatzi VL, Plusquin M, Vineis P, et al. A systematic review of metabolomic studies of childhood obesity: state of the evidence for metabolic determinants and consequences. Obes Rev. 2022;23:e13384.
pubmed: 34797026
doi: 10.1111/obr.13384
Prentice AM, Jebb SA. Beyond body mass index. Obes Rev. 2001;2:141–7.
pubmed: 12120099
doi: 10.1046/j.1467-789x.2001.00031.x
Kelishadi R, Mirmoghtadaee P, Najafi H, Keikha M. Systematic review on the association of abdominal obesity in children and adolescents with cardio-metabolic risk factors. J Res Med Sci. 2015;20:294–307.
pubmed: 26109978
pmcid: 4468236
doi: 10.4103/1735-1995.156179
Syme C, Czajkowski S, Shin J, Abrahamowicz M, Leonard G, Perron M, et al. Glycerophosphocholine metabolites and cardiovascular disease risk factors in adolescents: a cohort study. Circulation. 2016;134:1629–36.
pubmed: 27756781
doi: 10.1161/CIRCULATIONAHA.116.022993
Kooijman MN, Kruithof CJ, van Duijn CM, Duijts L, Franco OH, van IMH, et al. The generation R study: design and cohort update 2017. Eur J Epidemiol. 2016;31:1243–64.
pubmed: 28070760
doi: 10.1007/s10654-016-0224-9
Fredriks AM, van Buuren S, Wit JM, Verloove-Vanhorick SP. Body index measurements in 1996-7 compared with 1980. Arch Dis Child. 2000;82:107–12.
pubmed: 10648362
pmcid: 1718204
doi: 10.1136/adc.82.2.107
Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012;7:284–94.
pubmed: 22715120
doi: 10.1111/j.2047-6310.2012.00064.x
Gishti O, Gaillard R, Manniesing R, Abrahamse-Berkeveld M, van der Beek EM, Heppe DH, et al. Fetal and infant growth patterns associated with total and abdominal fat distribution in school-age children. J Clin Endocrinol Metab. 2014;99:2557–66.
pubmed: 24712569
doi: 10.1210/jc.2013-4345
Santos S, Monnereau C, Felix JF, Duijts L, Gaillard R, Jaddoe VWV. Maternal body mass index, gestational weight gain, and childhood abdominal, pericardial, and liver fat assessed by magnetic resonance imaging. Int J Obes (Lond). 2019;43:581–93.
pubmed: 30232419
doi: 10.1038/s41366-018-0186-y
VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 1990;52:953–9.
pubmed: 2239792
doi: 10.1093/ajcn/52.6.953
Wells JC, Cole TJ, steam As. Adjustment of fat-free mass and fat mass for height in children aged 8 y. Int J Obes Relat Metab Disord. 2002;26:947–52.
pubmed: 12080448
doi: 10.1038/sj.ijo.0802027
Kruithof CJ, Kooijman MN, van Duijn CM, Franco OH, de Jongste JC, Klaver CC, et al. The generation R study: Biobank update 2015. Eur J Epidemiol. 2014;29:911–27.
pubmed: 25527369
doi: 10.1007/s10654-014-9980-6
Voerman E, Jaddoe VWV, Uhl O, Shokry E, Horak J, Felix JF, et al. A population-based resource for intergenerational metabolomics analyses in pregnant women and their children: the Generation R Study. Metabolomics. 2020;16:43.
pubmed: 32206914
pmcid: 7089886
doi: 10.1007/s11306-020-01667-1
Hellmuth C, Uhl O, Standl M, Demmelmair H, Heinrich J, Koletzko B, et al. Cord blood metabolome is highly associated with birth weight, but less predictive for later weight development. Obes Facts. 2017;10:85–100.
pubmed: 28376503
pmcid: 5644937
doi: 10.1159/000453001
IUPAC-IUB Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature and symbolism for amino acids and peptides. Recommendations 1983. Eur J Biochem. 1984;138:9–37.
doi: 10.1111/j.1432-1033.1984.tb07877.x
Uhl O, Fleddermann M, Hellmuth C, Demmelmair H, Koletzko B. Phospholipid species in newborn and 4-month-old infants after consumption of different formulas or breast milk. PLoS One. 2016;11:e0162040.
pubmed: 27571269
pmcid: 5003354
doi: 10.1371/journal.pone.0162040
Hellmuth C, Weber M, Koletzko B, Peissner W. Nonesterified fatty acid determination for functional lipidomics: comprehensive ultrahigh performance liquid chromatography-tandem mass spectrometry quantitation, qualification, and parameter prediction. Anal Chem. 2012;84:1483–90.
pubmed: 22224852
doi: 10.1021/ac202602u
Wei R, Wang J, Su M, Jia E, Chen S, Chen T, et al. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci Rep. 2018;8:663.
pubmed: 29330539
pmcid: 5766532
doi: 10.1038/s41598-017-19120-0
Wong SN, Tz Sung RY, Leung LC. Validation of three oscillometric blood pressure devices against auscultatory mercury sphygmomanometer in children. Blood Press Monit. 2006;11:281–91.
pubmed: 16932037
doi: 10.1097/01.mbp.0000209082.09623.b4
Damanhoury S, Newton AS, Rashid M, Hartling L, Byrne JLS, Ball GDC. Defining metabolically healthy obesity in children: a scoping review. Obes Rev. 2018;19:1476–91.
pubmed: 30156016
doi: 10.1111/obr.12721
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.
pubmed: 3203132
doi: 10.2307/2531595
Mansell T, Magnussen CG, Nuotio J, Laitinen TT, Harcourt BE, Bekkering S, et al. Decreasing severity of obesity from early to late adolescence and young adulthood associates with longitudinal metabolomic changes implicated in lower cardiometabolic disease risk. Int J Obes (Lond). 2022;46:646–54.
pubmed: 34987202
doi: 10.1038/s41366-021-01034-7
De 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.
pubmed: 34742239
pmcid: 8571978
doi: 10.1186/s10020-021-00394-0
Peters L, Kuebler WM, Simmons S. Sphingolipids in atherosclerosis: chimeras in structure and function. Int J Mol Sci. 2022;23:11948.
pubmed: 36233252
pmcid: 9570378
doi: 10.3390/ijms231911948
Schmitz G, Ruebsaamen K. Metabolism and atherogenic disease association of lysophosphatidylcholine. Atherosclerosis. 2010;208:10–8.
pubmed: 19570538
doi: 10.1016/j.atherosclerosis.2009.05.029
Liu J, Fox CS, Hickson D, Bidulescu A, Carr JJ, Taylor HA. Fatty liver, abdominal visceral fat, and cardiometabolic risk factors: the Jackson Heart Study. Arterioscler Thromb Vasc Biol. 2011;31:2715–22.
pubmed: 21885852
pmcid: 3228266
doi: 10.1161/ATVBAHA.111.234062
Preis SR, Massaro JM, Robins SJ, Hoffmann U, Vasan RS, Irlbeck T, et al. Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study. Obes (Silver Spring). 2010;18:2191–8.
doi: 10.1038/oby.2010.59
Hu HH, Nayak KS, Goran MI. Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging. Obes Rev. 2011;12:e504–15.
pubmed: 21348916
pmcid: 3079791
doi: 10.1111/j.1467-789X.2010.00824.x
Gishti O, Gaillard R, Durmus B, Abrahamse M, van der Beek EM, Hofman A, et al. BMI, total and abdominal fat distribution, and cardiovascular risk factors in school-age children. Pediatr Res. 2015;77:710–8.
pubmed: 25665058
doi: 10.1038/pr.2015.29
Hiuge-Shimizu A, Kishida K, Funahashi T, Ishizaka Y, Oka R, Okada M, et al. Absolute value of visceral fat area measured on computed tomography scans and obesity-related cardiovascular risk factors in large-scale Japanese general population (the VACATION-J study). Ann Med. 2012;44:82–92.
pubmed: 20964583
doi: 10.3109/07853890.2010.526138
Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48.
pubmed: 17576866
doi: 10.1161/CIRCULATIONAHA.106.675355
Papandreou C, García-Gavilán J, Camacho-Barcia L, Hansen TT, Sjödin A, Harrold JA, et al. Circulating metabolites associated with body fat and lean mass in adults with overweight/obesity. Metabolites. 2021;11:317.
pubmed: 34068443
pmcid: 8153621
doi: 10.3390/metabo11050317
Boulet MM, Chevrier G, Grenier-Larouche T, Pelletier M, Nadeau M, Scarpa J, et al. Alterations of plasma metabolite profiles related to adipose tissue distribution and cardiometabolic risk. Am J Physiol Endocrinol Metab. 2015;309:E736–46.
pubmed: 26306599
doi: 10.1152/ajpendo.00231.2015
Boone S, Mook-Kanamori D, Rosendaal F, den Heijer M, Lamb H, de Roos A, et al. Metabolomics: a search for biomarkers of visceral fat and liver fat content. Metabolomics. 2019;15:139.
pubmed: 31587110
pmcid: 6778586
doi: 10.1007/s11306-019-1599-x
Martin FP, Montoliu I, Collino S, Scherer M, Guy P, Tavazzi I, et al. Topographical body fat distribution links to amino acid and lipid metabolism in healthy obese women [corrected]. PLoS One. 2013;8:e73445.
pubmed: 24039943
pmcid: 3770640
doi: 10.1371/journal.pone.0073445
Rodríguez-Carmona Y, Meijer JL, Zhou Y, Jansen EC, Perng W, Banker M, et al. Metabolomics reveals sex-specific pathways associated with changes in adiposity and muscle mass in a cohort of Mexican adolescents. Pediatr Obes. 2022;17:e12887.
pubmed: 35023314
doi: 10.1111/ijpo.12887
van Beijsterveldt I, Snowden SG, Myers PN, de Fluiter KS, van de Heijning B, Brix S, et al. Metabolomics in early life and the association with body composition at age 2 years. Pediatr Obes. 2022;17:e12859.
pubmed: 34644810
doi: 10.1111/ijpo.12859
do Prado WL, Josephson S, Cosentino RG, Churilla JR, Hossain J, Balagopal PB. Preliminary evidence of glycine as a biomarker of cardiovascular disease risk in children with obesity. Int J Obes (Lond). 2023;47:1023–6.
pubmed: 37516817
doi: 10.1038/s41366-023-01354-w
Cosentino RG, Churilla JR, Josephson S, Molle-Rios Z, Hossain MJ, Prado WL, et al. Branched-chain amino acids and relationship with inflammation in youth with obesity: a randomized controlled intervention study. J Clin Endocrinol Metab. 2021;106:3129–39.
pubmed: 34286837
doi: 10.1210/clinem/dgab538
Cole LK, Vance JE, Vance DE. Phosphatidylcholine biosynthesis and lipoprotein metabolism. Biochim Biophys Acta. 2012;1821:754–61.
pubmed: 21979151
doi: 10.1016/j.bbalip.2011.09.009
Furse S, de Kroon AI. Phosphatidylcholine’s functions beyond that of a membrane brick. Mol Membr Biol. 2015;32:117–9.
pubmed: 26306852
doi: 10.3109/09687688.2015.1066894
Weber DR, Leonard MB, Zemel BS. Body composition analysis in the pediatric population. Pediatr Endocrinol Rev. 2012;10:130–9.
pubmed: 23469390
pmcid: 4154503
Loomba-Albrecht LA, Styne DM. Effect of puberty on body composition. Curr Opin Endocrinol Diabetes Obes. 2009;16:10–5.
pubmed: 19115520
doi: 10.1097/MED.0b013e328320d54c
Goulding A, Taylor RW, Gold E, Lewis-Barned NJ. Regional body fat distribution in relation to pubertal stage: a dual-energy X-ray absorptiometry study of New Zealand girls and young women. Am J Clin Nutr. 1996;64:546–51.
pubmed: 8839498
doi: 10.1093/ajcn/64.4.546
Roemmich JN, Clark PA, Lusk M, Friel A, Weltman A, Epstein LH, et al. Pubertal alterations in growth and body composition. VI. Pubertal insulin resistance: relation to adiposity, body fat distribution and hormone release. Int J Obes Relat Metab Disord. 2002;26:701–9.
pubmed: 12032756
doi: 10.1038/sj.ijo.0801975
Levy-Marchal C, Arslanian S, Cutfield W, Sinaiko A, Druet C, Marcovecchio ML, et al. Insulin resistance in children: consensus, perspective, and future directions. J Clin Endocrinol Metab. 2010;95:5189–98.
pubmed: 20829185
pmcid: 3206517
doi: 10.1210/jc.2010-1047
Cheng D, Zhao X, Yang S, Cui H, Wang G. Metabolomic signature between metabolically healthy overweight/obese and metabolically unhealthy overweight/obese: a systematic review. Diabetes Metab Syndr Obes. 2021;14:991–1010.
pubmed: 33692630
pmcid: 7939496
doi: 10.2147/DMSO.S294894
Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O, et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008;7:45–56.
pubmed: 18177724
doi: 10.1016/j.cmet.2007.10.013
Fatima S, Hu X, Gong RH, Huang C, Chen M, Wong HLX, et al. Palmitic acid is an intracellular signaling molecule involved in disease development. Cell Mol Life Sci. 2019;76:2547–57.
pubmed: 30968170
pmcid: 11105207
doi: 10.1007/s00018-019-03092-7
Zhao X, Han Q, Liu Y, Sun C, Gang X, Wang G. The relationship between branched-chain amino acid-related metabolomic signature and insulin resistance: a systematic review. J Diabetes Res. 2016;2016:2794591.
pubmed: 27642608
pmcid: 5014958
doi: 10.1155/2016/2794591
Rutkowsky JM, Knotts TA, Ono-Moore KD, McCoin CS, Huang S, Schneider D, et al. Acylcarnitines activate proinflammatory signaling pathways. Am J Physiol Endocrinol Metab. 2014;306:E1378–87.
pubmed: 24760988
pmcid: 4059985
doi: 10.1152/ajpendo.00656.2013
Hellmuth C, Kirchberg FF, Brandt S, Moß A, Walter V, Rothenbacher D, et al. An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children. Sci Rep. 2019;9:5053.
pubmed: 30911015
pmcid: 6433919
doi: 10.1038/s41598-019-41449-x
Rzehak P, Hellmuth C, Uhl O, Kirchberg FF, Peissner W, Harder U, et al. Rapid growth and childhood obesity are strongly associated with lysoPC(14:0). Ann Nutr Metab. 2014;64:294–303.
pubmed: 25300273
doi: 10.1159/000365037
Rauschert S, Uhl O, Koletzko B, Kirchberg F, Mori TA, Huang RC, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab. 2016;101:871–9.
pubmed: 26709969
doi: 10.1210/jc.2015-3525
van Valkengoed IGM, Argmann C, Ghauharali-van der Vlugt K, Aerts J, Brewster LM, Peters RJG, et al. Ethnic differences in metabolite signatures and type 2 diabetes: a nested case-control analysis among people of South Asian, African and European origin. Nutr Diabetes. 2017;7:300.
pubmed: 29259157
pmcid: 5865542
doi: 10.1038/s41387-017-0003-z
Li-Gao R, Hughes DA, le Cessie S, de Mutsert R, den Heijer M, Rosendaal FR, et al. Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy. PLoS One. 2019;14:e0218549.
pubmed: 31220183
pmcid: 6586348
doi: 10.1371/journal.pone.0218549
Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E, et al. Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points-a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. Eur Heart J. 2016;37:1944–58.
pubmed: 27122601
pmcid: 4929379
doi: 10.1093/eurheartj/ehw152
Keirns BH, Sciarrillo CM, Koemel NA, Emerson SR. Fasting non-fasting and postprandial triglycerides for screening cardiometabolic risk. J Nutr Sci. 2021;10:e75.
pubmed: 34589207
pmcid: 8453457
doi: 10.1017/jns.2021.73
Benn M, Tybjaerg-Hansen A, McCarthy MI, Jensen GB, Grande P, Nordestgaard BG. Nonfasting glucose, ischemic heart disease, and myocardial infarction: a Mendelian randomization study. J Am Coll Cardiol. 2012;59:2356–65.
pubmed: 22698489
pmcid: 4606982
doi: 10.1016/j.jacc.2012.02.043
DeBoer MD, Filipp SL, Gurka MJ. Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals. Nutr Metab Cardiovasc Dis. 2020;30:92–8.
pubmed: 31662283
doi: 10.1016/j.numecd.2019.08.010
Vajravelu ME, Hirschfeld E, Gebremariam A, Burant CF, Herman WH, Peterson KE, et al. Prospective test performance of nonfasting biomarkers to identify dysglycemia in children and adolescents. Horm Res Paediatr. 2023;96:316–24.
pubmed: 36380614
doi: 10.1159/000528043