NUTRIGENETICS AND PERSONALIZED NUTRITION IN PEDIATRIC METABOLIC DISORDERS: GENETIC DETERMINANTS OF OBESITY AND DIABETES MELLITUS
DOI:
https://doi.org/10.21272/eumj.2026;14(1);28-38Keywords:
child, genetic polymorphism, metabolic syndrome, obesity, diabetes mellitus, nutrigenetics, nutrigenomics, personalized nutritionAbstract
Introduction. In recent years, the prevalence of metabolic diseases, particularly obesity and diabetes mellitus, in the pediatric population has been steadily increasing. Modern scientific research proves that the formation of metabolic disorders is due to the complex interaction of genetic, epigenetic mechanisms, as well as environmental factors. A special role in this process is played by genetic polymorphisms that affect appetite regulation, energy metabolism, insulin sensitivity and lipid metabolism. Identification of genetic determinants of obesity and diabetes mellitus opens up new opportunities for the implementation of personalized preventive and therapeutic strategies.
Materials and methods. The study was conducted as an analytical review of the scientific literature in order to summarize and critically analyze current data on the nutrigenetic mechanisms of obesity and type 1 and type 2 diabetes mellitus in children. For this purpose, a systematic search and analysis of publications in international scientific databases, including PubMed, Google Scholar, Scopus and Web of Science for the period from 2017 to 2025, was conducted.
Results. A review of current studies confirmed the significant role of genetic factors in the development of obesity and diabetes in children. The most stable associations with increased body mass index were demonstrated for variants of the FTO and MC4R genes, which affect appetite regulation and energy balance. For type 1 diabetes, immunogenetic predisposition is the leading one, while in the development of type 2 diabetes in adolescents, the combination of polygenic predisposition (in particular, TCF7L2, KCNJ11 variants) with obesity and environmental factors is important. It has been shown that polygenic risk scores can be used for early risk assessment, but their predictive value depends on population characteristics and lifestyle. The integration of genetic, epigenetic and metabolic data opens up prospects for personalization of nutrition in pediatrics, but requires further clinical validation and standardization.
Conclusions. Nutrigenetics deepens the understanding of the individual predisposition of children to obesity and diabetes, and explains how nutrition and lifestyle interact with genetic variants in shaping metabolic disorders. At the same time, most genetic markers are characterized by a moderate effect, which is significantly modified by environmental factors. Further development of the direction requires standardization of methodology, accumulation of high-quality pediatric studies and development of clear clinical recommendations for the substantiated integration of nutrigenetic data in the prevention and treatment of metabolic diseases in children.
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References
Lister NB, Wabitsch M. Child and adolescent obesity. Nat Rev Dis Primers. 2023;9(1):24. https://doi.org/10.1038/s41572-023-00435-4
Norris JM, Johnson RK, Stene LC. Type 1 diabetes—early life origins and changing epidemiology. Lancet Diabetes Endocrinol. 2020;8(3):226-238. https://doi.org/10.1016/S2213-8587(19)30412-7
Dabelea D; TODAY Study Group. Accelerating epidemic of type 2 diabetes in youth: The TODAY2 study. N Engl J Med. 2022;386(15):1407-1418. https://doi.org/10.1056/NEJMoa2205600
Huang LO, Felix JF. Do genetic risk scores for childhood adiposity operate similarly across early life? Int J Obes (Lond). 2021;45(9):1873-1883. https://doi.org/10.1038/s41366-021-00869-4
Wang F, et al. Personalized nutrition: Genotype-based dietary recommendations—A review. Nutrients. 2022;14(19):4034. https://doi.org/10.3390/nu14194034
Blaak EE, et al. Personalized nutrition: Current status and perspectives. Front Nutr. 2021;8:669307. https://doi.org/10.3389/fnut.2021.669307
Simakhina HO, Naumenko NV. Novi mozhlyvosti ozdorovchoho kharchuvannia z pozytsii nutrigenetyky ta nutrigenomiky. Naukovi pratsi Natsionalnoho universytetu kharchovykh tekhnolohiy. 2025;31(1):145-156.
Madeo G, Donato K, Micheletti C, Cristoni S, Miertus S, Miertus J, Veselenyiova D, Iaconelli A, Aquilanti B, Matera G, Connelly ST, Bertelli M. Nutrigenomics: SNPs correlated to lipid and carbohydrate metabolism. Clin Ter. 2023;174(Suppl 2(6)):200-208. https://doi.org/10.7417/CT.2023.2488
Farzand A, Rohin MAK, Awan SJ, Ahmad AMR, Akram H, Saleem T, Imran MM. Nutrigenomics of obesity: Integrating genomics, epigenetics, and diet–microbiome interactions for precision nutrition. Life. 2025;15:1658. https://doi.org/10.3390/life15111658
Park SH, Choi HK, Park JH, Hwang JT. Current insights into genome-based personalized nutrition technology: a patent review. Front Nutr. 2024;11:1346144. https://doi.org/10.3389/fnut.2024.1346144
Meral G, Tunaligil V. Advancing public health through nutrigenetics and microbiota in early life: initiating longevity in childhood. Characterization of a case. Am J Pedia Neonat. 2025;1(1):29-38.
Silao CLT. A child's nutrition and epigenetics. Nestle Nutr Inst Workshop Ser. 2023;97:1-10. https://doi.org/10.1159/000528991
Omelchuk ST, Velyka NV, Zalesky VM. Rol’ henetychnykh ta epigenetychnykh chynnykiv u formuvanni ozhyrinnia i kardiometabolichnykh porushen u ditey. Ukrainskyi zhurnal pediatrii. 2020;4:45-56.
Li H, Chen X, Zhang Y, et al. Genetic etiology and clinical features of non syndromic pediatric obesity in the Chinese population: a large cohort study. BMC Pediatr. 2025;25:102. https://doi.org/10.1186/s12887-025-05702-9
Do Rosario NAD, Gokhale D, Gore M. Exploring the future of nutrigenomics: dietitians’ perceptions on integration in Indian practice. Genes Nutr. 2025;20:7. https://doi.org/10.1186/s12263-024-00760-7
Abaturov OY, Nikulina AO. Fenotypy ozhyreniia u ditei, klinichni proiavy i henetychni asosiiatsii [Obesity phenotypes in children, clinical manifestations, and genetic associations]. Zdorov’ia dytyny. 2020;15(4):238-251.
Rohani P, Rasoulizadeh Z, Tavakoli S, et al. Effect of a carbohydrate-restricted diet on weight loss in overweight and obese pediatric population: a meta-analysis of randomized controlled trials. Diabetol Metab Syndr. 2024;16(1):210. https://doi.org/10.1186/s13098-024-01458-x
Perry MH, Lovegrove JA. Gene–diet interactions in childhood obesity: a systematic review. Nutr Rev. 2023;81(5):543-558. https://doi.org/10.1093/nutrit/nuac105
Wu AJ, Oken E. Developmental contributions to obesity: nutritional exposures in the first thousand days. Gastroenterol Clin North Am. 2023;52(2):333-345. https://doi.org/10.1016/j.gtc.2023.02.006
Wang S, Sun G. Rs12970134 near MC4R is associated with appetite and beverage intake in overweight and obese children. PLoS One. 2017;12(5):e0177983. https://doi.org/10.1371/journal.pone.0177983
Jiang Z, Wang T. FTO rs9939609 and dietary fibre intake with obesity in early adolescents. Obes Res Clin Pract. 2019;13(3):257-263. https://doi.org/10.1016/j.orcp.2019.03.003
Bradfield JP, Loos RJF. A genome-wide association meta-analysis identifies new childhood obesity loci. Int J Obes (Lond). 2021;45(6):1234-1243. https://doi.org/10.1038/s41366-021-00782-y
Vogelezang S, Felix JF. Novel genetic loci associated with childhood BMI and cross-ancestry analyses identify new pathways. Nat Commun. 2020;11:4423. https://doi.org/10.1038/s41467-020-18272-0
Felix JF, Jaddoe VWV. Genome-wide association study identifies loci associated with childhood adiposity trajectories. JAMA Pediatr. 2023;177(2):150-159.
Brandkvist M, Vie GÅ. Separating the genetics of childhood and adult obesity: a validation study of genetic scores in adolescence. Hum Mol Genet. 2020;29(24):3966-3973. https://doi.org/10.1093/hmg/ddaa256
Khera AV, Kathiresan S. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177(3):587-596. https://doi.org/10.1016/j.cell.2019.03.028
Viljakainen H, Pahkala K. Interaction between genetic susceptibility to obesity and diet in children. Sci Rep. 2023;13:15027. https://doi.org/10.1038/s41598-023-42430-5
Song Y, Li S, Liu H, Liu X, Li J, Wang Y, Yang J. Higher risk of metabolic syndrome in children and adolescents and polymorphisms in the fat mass and obesity-associated gene: a systematic review and meta-analysis. Pediatr Res. 2025 Apr 1. https://doi.org/10.1038/s41390-025-04020-1.
Bryl E, Szczesniewska P, Dutkiewicz A, et al. FTO and MC4R polymorphisms, and selected pre-, peri- and postnatal factors as determinants of body mass index and fatness in children: a thorough analysis of the associations. J Physiol Anthropol. 2023;42:29. https://doi.org/10.1186/s40101-023-00344-1
Azzam SK, Alsafar H, Sajini AA. FTO m6A demethylase in obesity and cancer: implications and underlying molecular mechanisms. Int J Mol Sci. 2022;23(7):3800. https://doi.org/10.3390/ijms23073800.
Shaker A, Shekari S, Zeinalabedini M, et al. Role of rs9939506 polymorphism of FTO gene in resistance to eating in male adolescents. BMC Pediatr. 2023;23:486. https://doi.org/10.1186/s12887-023-04310-9
Kilpeläinen TO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 2011;8(11):e1001116. https://doi.org/10.1371/journal.pmed.1001116.
Reuter EM, Reuter CP, de Castro Silveira JF, et al. The genetic predisposition increases the chances of schoolchildren maintaining higher adiposity levels after three years. BMC Pediatr. 2023;23:57. https://doi.org/10.1186/s12887-023-03846-0
Abaturov O, Nikulina A. Association of genetic variants of the Fat Mass and Obesity (FTO) gene and obesity in children. Port J Pediatr. 2024;55(2):89-97.
Dastgheib SA, Bahrami R, Setayesh S, et al. Evidence from a meta-analysis for association of MC4R rs17782313 and FTO rs9939609 polymorphisms with susceptibility to obesity in children. Diabetes Metab Syndr. 2021;15(5):102234. https://doi.org/10.1016/j.dsx.2021.102234.
Carrasco-Luna J, Navarro-Solera M, Gombert M, et al. Association of the rs17782313, rs17773430 and rs34114122 polymorphisms of/near MC4R gene with obesity-related biomarkers in a Spanish pediatric cohort. Children (Basel). 2023;10(7):1221. https://doi.org/10.3390/children10071221
Song JY, Song QY, Wang S, Ma J, Wang HJ. Physical activity and sedentary behaviors modify the association between melanocortin 4 receptor gene variant and obesity in Chinese children and adolescents. PLoS One. 2017;12(1):e0170062. https://doi.org/10.1371/journal.pone.0170062.
Morell-Azanza L, Ojeda-Rodriguez A, Giuranna J, et al. Melanocortin-4 receptor and lipocalin 2 gene variants in Spanish children with abdominal obesity: effects on BMI-SDS after a lifestyle intervention. Nutrients. 2019;11(5):960. https://doi.org/10.3390/nu11050960.
Aykut A, Ozen S, Goksen D, et al. Melanocortin 4 receptor (MC4R) gene variants in children and adolescents having familial early-onset obesity: genetic and clinical characteristics. Eur J Pediatr. 2020;179(9):1445-1452. https://doi.org/10.1007/s00431-020-03630-7. Epub 2020 Mar 18.
Lampousi AM, Carlsson S, Lofvenborg JE. Dietary factors and risk of islet autoimmunity and type 1 diabetes: systematic review & meta-analysis. EClinicalMedicine. 2021;33:100773. https://doi.org/10.1016/j.eclinm.2021.100773
Hakola L, Virtanen SM, Ivarsson SA. Cereal, gluten, and dietary fiber intake with type 1 diabetes risk in children. JAMA Pediatr. 2019;173(10):953–960. https://doi.org/10.1001/jamapediatrics.2019.2564
Antvorskov JC, Halldorsson TI, Husby S. Maternal gluten intake during pregnancy and type 1 diabetes risk in offspring. BMJ. 2018;362:k3547. https://doi.org/10.1136/bmj.k3547
Lindgren M, Ivarsson SA. Cumulative type 1 diabetes incidence in cohorts with different infant feeding practices. Diabetologia. 2023;66:2480–2491. https://doi.org/10.1007/s00125-023-05906-8
Writing Group for the TRIGR Study, Knip M, et al. Effect of hydrolyzed vs conventional infant formula on type 1 diabetes risk: TRIGR randomized clinical trial. JAMA. 2018;319(1):38–48. https://doi.org/10.1001/jama.2017.19826
Kwak SH, Flannick J, et al. Genetic loci associated with type 2 diabetes in early life. Nat Metab. 2024;6:1206–1216. https://doi.org/10.1038/s42255-024-01039-3
Nadeau KJ, Zeitler PS. Youth-onset type 2 diabetes—what we’ve learned. Diabetes Care. 2025;48(7):1020–1042. https://doi.org/10.2337/dci24-0023
Sekar P, Ventura EF, Dhanapal ACTA, et al. Gene–diet interactions on metabolic disease-related outcomes in Southeast Asian populations: a systematic review. Nutrients. 2023;15(13):2948. https://doi.org/10.3390/nu15132948
Ling C, Ronn T. Epigenetics and nutrition in type 2 diabetes: implications for early-life interventions. Cell Metab. 2022;34(2):201–215. https://doi.org/10.1016/j.cmet.2021.12.002
Rozga M, Handu D. Nutrition interventions for pediatric obesity prevention: an umbrella review of systematic reviews. Nutrients. 2023;15(24):5097. https://doi.org/10.3390/nu15245097
Kohlmeier M, et al. Genotype-informed nutrition counselling in clinical practice. BMJ Nutr Prev Health. 2023;6(2):407–417.
Nourazarain A, Vaziri Y. Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies. Genes Nutr. 2025. https://doi.org/10.1186/s12263-025-00790-9
Livingstone KM, Celis-Morales C, Navas-Carretero S, et al. Precision nutrition: a review of current approaches and future challenges. Clin Nutr. 2022;41(11):3016–3027.
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Copyright (c) 2026 Andrii Loboda, Viktoriia Petrashenko, Yuliia Man’ko, Olexander Smiyan, Olena Vasylieva, Petro Sichnenko, Viktoriia Horbas, Kateryna Goliuk, Yuriy Reznichenko, Galyna Reznichenko, Tatiana Aleksakhina
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