Significance of single-nucleotide variants of anorexigenic hormone genes in childhood obesity

A.O.  Nikulina

doi:10.26641/2307-0404.2024.1.300508

Authors

A.O. Nikulina Dnipro State Medical University, Volodymyra Vernadskoho str., 9, Dnipro, 49044, Ukraine http://orcid.org/0000-0002-8617-9341

DOI:

https://doi.org/10.26641/2307-0404.2024.1.300508

Keywords:

single nucleotide variants of genes, genes of anorexigenic hormones, metabolically healthy obesity, metabolically unhealthy obesity

Abstract

Obesity-induced dysregulation of hypothalamic neurons is not completely eliminated by restoring body weight, therefore the most urgent task of modern precision medicine is to predict the trajectory of development of metabolic disorders associated with obesity in children. The aim of the study was to determine the level of association of single-nucleotide variants of genes that determine eating behavior – Neuronal growth regulator 1, Fat mass and obesity associated gene, Glucagon-like peptide-1 receptor, ghrelin, leptin receptor, cholecystokinin, in the development of metabolically unhealthy obesity. 252 obese children aged 6-18 years were examined. The main group (n=152) consisted of children with metabolically unhealthy obesity (MUO) according to Identification and prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants 2014 criteria. The control group (n=100) consisted of children with metabolically healthy obesity (MHO). All children underwent a general clinical, immunobiochemical examination at the Synevo laboratory (Ukraine). Whole-genome sequencing (CeGat, Germany) was performed in 31 children of the primary and 21 children of the control group. Static analysis: variance analysis ANOVA, method of estimating data dispersion, ROC-analysis, method of testing statistical hypotheses. The level of single nucleotide variants association of anorexigenic hormone genes with MUO that exceeded the threshold accepted by 75% of the available data was, respectively, in ascending order: leptin receptor (LEPR) rs1137101 (40.38%), Glucagon-like peptide-1 receptor (GLP1R) rs1126476 (40.38%), GLP1R rs2235868 (42.31%), GLP1R rs1042044 (42.31%), LEPR rs3790435 (48.08%), cholecystokinin (CCK) rs754635 (50%), LEPR rs2186248 (55.76%), GLP1R rs6918287 (55.76%). Genotypes of the GLP1R gene, such as CC rs10305421 determine insulin resistance (F=5.6); GA/AA rs3765468 – meta-inflammation (F=5.8); AA rs6918287 – basal hyperglycemia (F=6.3) and triglyceridemia (F=51.3), p<0.05. Single-nucleotide variants of the gene GLP1R rs6918287, LEPR rs2186248, CCK rs754635 of the anorexic hormones that control eating behavior are highly associated with the presence of metabolically unhealthy obesity in children.

References

Jebeile H, Kelly AS, O'Malley G, et al. Obesity in children and adolescents: epidemiology, causes, asses-sment, and management. Lancet Diabetes Endocrinol. 2022;10(5):351-65. doi: https://doi.org/10.1016/S2213-8587(22)00047-X

Wang H, Akbari-Alavijeh S, Parhar RS, et al. Partners in diabetes epidemic: A global perspective. World J Diabetes. 2023;14(10):1463-77. doi: https://doi.org/10.4239/wjd.v14.i10.1463

Chen J, Spracklen CN, Marenne G, et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 2021;53:840-60. doi: https://doi.org/10.1038/s41588-021-00852-9

Vujkovic M, Keaton JM, Lynch JA, et al. Disco-very of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52(7):680-91. doi: https://doi.org/10.1038/s41588-020-0637-y

Reisinger C, Nkeh-Chungag BN, Fredriksen PM, et al. The prevalence of pediatric metabolic syndrome—a critical look on the discrepancies between definitions and its clinical importance. Int J Obes. 2021;45:12-24. doi: https://doi.org/10.1038/s41366-020-00713-1

Crovesy L, Rosado EL. Interaction between genes involved in energy intake regulation and diet in obesity. Nutrition. 2019;67-68:110547. doi: https://doi.org/10.1016/j.nut.2019.06.027

Lister NB, Baur LA, Felix JF, et al. Child and adolescent obesity. Nat Rev Dis Primers. 2023;9(1):24. doi: https://doi.org/10.1038/s41572-023-00435-4

Beutler LR, Corpuz TV, Ahn JS, et al. Obesity causes selective and long-lasting desensitization of AgRP neurons to dietary fat. Elife. 2020;9:e55909. doi: https://doi.org/10.7554/eLife.55909

Flores-Dorantes MT, Díaz-López YE, Gutiérrez-Aguilar R. Environment and Gene Association With Obe-sity and Their Impact on Neurodegenerative and Neurodevelopmental Diseases. Front Neurosci. 2020;14:863. doi: https://doi.org/10.3389/fnins.2020.00863

Draznin B, Aroda VR, Bakris G, et al. American Diabetes Association Professional Practice Committee. 6. Glycemic targets: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022;45(Suppl 1):83-96. doi: https://doi.org/10.2337/dc22-S006

Santaliestra-Pasías AM, González-Gil EM, Pa-la V, et al. Predictive associations between lifestyle behaviours and dairy consumption: The IDEFICS study. Nutr Metab Cardiovasc Dis. 2020;30(3):514-22. doi: https://doi.org/10.1016/j.numecd.2019.10.006

Tagi VM, Samvelyan S, Chiarelli F. An update of the consensus statement on insulin resistance in children 2010. Front Endocrinol (Lausanne). 2022 Nov 16;13:1061524. doi: https://doi.org/10.3389/fendo.2022.1061524

Elkins C, Fruh Sh, Jones L, et al. Clinical Practice Recommendations for Pediatric Dyslipidemia. Journal of Pediatric Health Care. 2019;33(4):494-504. doi: https://doi.org/10.1016/j.pedhc.2019.02.009

Flynn JT, Kaelber DC, Baker-Smith CM, et al. Subcommittee on Screening and Management of High Blood Pressure in Children. Clinical Practice Guideline for Scree¬ning and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3):e20171904. Pediatrics. 2018;142(3):e20181739. doi: https://doi.org/10.1542/peds.2018-1739

Ahrens W, Moreno LA, Marild S, et al. IDEFICS consortium. Metabolic syndrome in young children: definitions and results of the IDEFICS study. Int J Obes (Lond). 2014 Sep;38(Suppl 2):S4-14. doi: https://doi.org/10.1038/ijo.2014.130

Liao X, Li M, Zou Y, et al. An Efficient Trimming Algorithm based on Multi-Feature Fusion Scoring Model for NGS Data. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(3):728-38. doi: https://doi.org/10.1109/TCBB.2019.2897558

Sanaullah A, Zhi D, Zhang S. d-PBWT: dynamic positional Burrows-Wheeler transform. Bioinformatics. 2021;37(16):2390-7. doi: https://doi.org/10.1093/bioinformatics/btab117

Shin DM, Hwang MY, Kim BJ, et al. GEN2VCF: a converter for human genome imputation output format to VCF format. Genes Genomics. 2020;42(10):1163-8. doi: https://doi.org/10.1007/s13258-020-00982-0

Mose LE, Perou CM, Parker JS. Improved indel detection in DNA and RNA via realignment with ABRA2. Bioinformatics. 2019;35(17):2966-73. doi: https://doi.org/10.1093/bioinformatics/btz033

Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018 Jan 4;46(D1):D1062-D1067. doi: https://doi.org/10.1093/nar/gkx1153

Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434-43. doi: https://doi.org/doi.org/10.1038/s41586-020-2308-7

Liu X, Li C, Mou C, et al. dbNSFP v4: a com-prehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med. 2020;12(1):103. doi: https://doi.org/10.1186/s13073-020-00803-9

Buniello A, MacArthur JAL, et al. The NHGRI-EBI GWAS Catalog of published genome-wide asso-ciation studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019 Jan 8;47(D1):1005-12. doi: https://doi.org/10.1093/nar/gky1120

Schoch CL, Ciufo S, Domrachev M, et al. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database (Oxford). 2020;2020:baaa062. doi: https://doi.org/10.1093/database/baaa062

Agapito G, Milano M, Cannataro M. A Python Clustering Analysis Protocol of Genes Expression Data Sets. Genes (Basel). 2022;13(10):1839. doi: https://doi.org/10.3390/genes13101839

Abaturov A, Nikulina A. Associations of GHRL gene variants with the development of obesity and metabolic disorders in children. Child's health. 2023;18(4):13-9. doi: https://doi.org/10.22141/2224-0551.18.4.2023.1596

Nikulina A. Significance of the rs754635 variant of the cholecystokinin gene in the development of obesity in children. Modern Pediatrics. Ukraine. 2023;5(133):17-23. doi: https://doi.org/10.15574/SP.2023.133.17

Lagou V, Jiang L, Ulrich A, et al. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treat-ment stratification. Nat Genet. 2023;55(9):1448-61. doi: https://doi.org/10.1038/s41588-023-01462-3

Rupp AC, Tomlinson AJ, Affinati AH, et al. Sup-pression of food intake by Glp1r/Lepr-coexpressing neurons prevents obesity in mouse models. J Clin Invest. 2023;133(19):e157515. doi: https://doi.org/10.1172/JCI157515

Melchiorsen JU, Sorensen KV, Bork-Jensen J, et al. Rare Heterozygous Loss-of-Function Variants in the Human GLP-1 Receptor Are Not Associated With Cardio-metabolic Phenotypes. J Clin Endocrinol Metab. 2023;108(11):2821-33. doi: https://doi.org/10.1210/clinem/dgad290

Steinsbekk S, Belsky D, Guzey IC. Polygenic Risk, Appetite Traits, and Weight Gain in Middle Child-hood: A Longitudinal Study. JAMA Pediatr. 2016 Feb;170(2):e154472. doi: https://doi.org/10.1001/jamapediatrics.2015.447