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2000
Volume 25, Issue 6
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Introduction

Single nucleotide polymorphisms (SNPs) are pivotal in clinical genetics, serving to link genotypes with disease susceptibility and response to environmental factors, including pharmacogenetics. They also play a crucial role in population genetics for mapping the human genome and localizing genes. Despite their utility, challenges arise when molecular genetic studies yield insufficient or uninformative data, particularly for socially significant diseases. This study aims to address these gaps by proposing a method to predict allelic variants of SNPs.

Methods

Using quantitative PCR and analyzing body composition data from 150 patients with their voluntary informed consent, we employed IBM SPSS Statistics 29.0 for data analysis. Our prototype formula, exemplified by allelic variant ADRB2 (rs1042713) = 0.257 + 0.639 * allelic variant ADRB2 (rs1042714) - 0.314 * allelic variant ADRB3 (rs4994) + 0.191 * allelic variant PPARA (rs4253778) - 0.218 * allelic variant PPARD (rs2016520) + 0.027 * body weight + 0.00001 * body weight2, demonstrates the feasibility of predicting SNP allelic variants.

Results

This method holds promise for diverse diseases, including those of significant social impact, due to its potential to streamline and economize molecular genetic research. Its ability to stratify disease risk in the absence of complete SNP data makes it particularly compelling for clinical and laboratory geneticists.

Conclusion

However, its translation into clinical practice necessitates the establishment of a comprehensive SNP database, especially for frequently analyzed SNPs within the implementing institution.

© 2025 Bentham Science Publishers
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References

  1. CottonR.G.H. Al AqeelA.I. Al-MullaF. CarreraP. ClaustresM. EkongR. HylandV.J. MacraeF.A. MarafieM.J. PaalmanM.H. PatrinosG.P. QiM. RamesarR.S. ScottR.J. SijmonsR.H. SobridoM.J. VihinenM. members of the Human Variome Project Data Collection from Clinics, Data Collection from Laboratories and Publication, Credit and Incentives Working Groups Capturing all disease-causing mutations for clinical and research use: Toward an effortless system for the Human Variome Project.Genet. Med.2009111284384910.1097/GIM.0b013e3181c371c520010362
     [Google Scholar]
  2. Kimura-KataokaK. UekiM. TakeshitaH. FujiharaJ. IidaR. KawaiY. YasudaT. Identification of the functional alleles of the nonsynonymous single-nucleotide polymorphisms potentially implicated in systemic lupus erythematosus in the human deoxyribonuclease I gene.DNA Cell Biol.201433849250210.1089/dna.2014.236824819173
     [Google Scholar]
  3. DawsonE. AbecasisG.R. BumpsteadS. ChenY. HuntS. BeareD.M. PabialJ. DiblingT. TinsleyE. KirbyS. CarterD. PapaspyridonosM. LivingstoneS. GanskeR. LõhmussaarE. ZernantJ. TõnissonN. RemmM. MägiR. PuurandT. ViloJ. KurgA. RiceK. DeloukasP. MottR. MetspaluA. BentleyD.R. CardonL.R. DunhamI. A first-generation linkage disequilibrium map of human chromosome 22.Nature2002418689754454810.1038/nature0086412110843
     [Google Scholar]
  4. RenN. DaiS. MaS. YangF. Strategies for activity analysis of single nucleotide polymorphisms associated with human diseases.Clin. Genet.2023103439240010.1111/cge.1428236527336
     [Google Scholar]
  5. SitinjakB.D.P. MurdayaN. RachmanT.A. ZakiyahN. BarlianaM.I. The potential of single nucleotide polymorphisms (SNPs) as biomarkers and their association with the increased risk of coronary heart disease: A systematic review.Vasc. Health Risk Manag.20231928930110.2147/VHRM.S40503937179817
     [Google Scholar]
  6. GrayI.C. CampbellD.A. SpurrN.K. Single nucleotide polymorphisms as tools in human genetics.Hum. Mol. Genet.20009162403240810.1093/hmg/9.16.240311005795
     [Google Scholar]
  7. RivaA. KohaneI.S. A SNP-centric database for the investigation of the human genome.BMC Bioinformatics2004513310.1186/1471‑2105‑5‑3315046636
     [Google Scholar]
  8. FrazerK.A. MurrayS.S. SchorkN.J. TopolE.J. Human genetic variation and its contribution to complex traits.Nat. Rev. Genet.200910424125110.1038/nrg255419293820
     [Google Scholar]
  9. LaingR.E. HessP. ShenY. WangJ. HuS.X. The role and impact of SNPs in pharmacogenomics and personalized medicine.Curr. Drug Metab.201112546048610.2174/13892001179549526821453271
     [Google Scholar]
  10. O’DonnellC.J. NabelE.G. Genomics of cardiovascular disease.N. Engl. J. Med.2011365222098210910.1056/NEJMra110523922129254
     [Google Scholar]
  11. DellesC. PadmanabhanS. Genetics and hypertension: Is it time to change my practice?Can. J. Cardiol.201228329630410.1016/j.cjca.2012.02.00422482397
     [Google Scholar]
  12. ZellerT. BlankenbergS. DiemertP. Genomewide association studies in cardiovascular disease--an update 2011.Clin. Chem.20125819210310.1373/clinchem.2011.17043122125304
     [Google Scholar]
  13. PadyukovL. Genetics of rheumatoid arthritis.Semin. Immunopathol.2022441476210.1007/s00281‑022‑00912‑035088123
     [Google Scholar]
  14. A ArmstrongR. Risk factors for Alzheimer’s disease.Folia Neuropathol.20195728710510.5114/fn.2019.8592931556570
     [Google Scholar]
  15. SawyerS. MitchellG. McKinleyJ. Chenevix-TrenchG. BeesleyJ. ChenX.Q. BowtellD. TrainerA.H. HarrisM. LindemanG.J. JamesP.A. A role for common genomic variants in the assessment of familial breast cancer.J. Clin. Oncol.201230354330433610.1200/JCO.2012.41.746923109704
     [Google Scholar]
  16. RehmanK. IqbalZ. ZhiqinD. AyubH. SabaN. KhanM.A. YujieL. DuanL. Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer.Cancer Cell Int.202323124710.1186/s12935‑023‑03084‑537858151
     [Google Scholar]
  17. NasykhovaY.A. BarbitoffY.A. SerebryakovaE.A. KatserovD.S. GlotovA.S. Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes.World J. Diabetes201910737639510.4239/wjd.v10.i7.37631363385
     [Google Scholar]
  18. ZukO. HechterE. SunyaevS.R. LanderE.S. The mystery of missing heritability: Genetic interactions create phantom heritability.Proc. Natl. Acad. Sci. USA201210941193119810.1073/pnas.111967510922223662
     [Google Scholar]
  19. SternB. McGarrityT. BakerM. Incorporating colorectal cancer genetic risk assessment into gastroenterology practice.Curr. Treat. Options Gastroenterol.201917470271510.1007/s11938‑019‑00267‑w31741210
     [Google Scholar]
  20. MayhewA.J. MeyreD. Assessing the heritability of complex traits in humans: Methodological challenges and opportunities.Curr. Genomics201718433234010.2174/138920291866617030716145029081689
     [Google Scholar]
  21. ZampattiS. FabrizioC. RagazzoM. CampoliG. CaputoV. StrafellaC. PellicanoC. CascellaR. SpallettaG. PetrosiniL. CaltagironeC. TermineA. GiardinaE. Precision medicine into clinical practice: A web-based tool enables real-time pharmacogenetic assessment of tailored treatments in psychiatric disorders.J. Pers. Med.202111985110.3390/jpm1109085134575628
     [Google Scholar]
  22. Flores-DorantesM.T. Díaz-LópezY.E. Gutiérrez-AguilarR. Environment and gene association with obesity and their impact on neurodegenerative and neurodevelopmental diseases.Front. Neurosci.20201486310.3389/fnins.2020.0086332982666
     [Google Scholar]
  23. AngelopoulouE. KorosC. HatzimanolisA. StefanisL. ScarmeasN. PapageorgiouS.G. Exploring the genetic landscape of mild behavioral impairment as an early marker of cognitive decline: An updated review focusing on alzheimer’s disease.Int. J. Mol. Sci.2024255264510.3390/ijms2505264538473892
     [Google Scholar]
  24. ZhangJ. YangJ. WenC. A new SNP genotyping technology by target SNP-Seq.Methods Mol. Biol.2023263836537110.1007/978‑1‑0716‑3024‑2_2636781656
     [Google Scholar]
  25. LamyP. GroveJ. WiufC. A review of software for microarray genotyping.Hum. Genomics20115430430910.1186/1479‑7364‑5‑4‑30421712191
     [Google Scholar]
  26. YangZ. SweedlerJ.V. Application of capillary electrophoresis for the early diagnosis of cancer.Anal. Bioanal. Chem.2014406174013403110.1007/s00216‑014‑7722‑y24668067
     [Google Scholar]
  27. WongY.F. ChungT.K.H. WangV.W. SmithD.I. DNA microchips to identify molecular signatures in cervical cancers.Methods Mol. Biol.20073858710110.1007/978‑1‑59745‑426‑1_718365706
     [Google Scholar]
  28. NasedkinaT.V. FedorovaO.E. GlotovA.S. ChupovaN.V. SamochatovaE.V. MaiorovaO.A. ZemlyakovaV.V. RoudnevaA.E. ChudinovA.V. YurasovR.A. KozhekbaevaJ.M. BarskyV.E. KrynetskiyE.Y. KrynetskaiaN.F. ChengC. RibeiroR.C. EvansW.E. RoumyantsevA.G. ZasedatelevA.S. Rapid genotyping of common deficient thiopurine S-methyltransferase alleles using the DNA-microchip technique.Eur. J. Hum. Genet.200614999199810.1038/sj.ejhg.520164716724002
     [Google Scholar]
  29. LewisC.M. VassosE. Polygenic risk scores: From research tools to clinical instruments.Genome Med.20201214410.1186/s13073‑020‑00742‑532423490
     [Google Scholar]
  30. GaoC. PolleyE.C. HartS.N. HuangH. HuC. GnanaolivuR. LilyquistJ. BoddickerN.J. NaJ. AmbrosoneC.B. AuerP.L. BernsteinL. BurnsideE.S. EliassenA.H. GaudetM.M. HaimanC. HunterD.J. JacobsE.J. JohnE.M. LindströmS. MaH. NeuhausenS.L. NewcombP.A. O’BrienK.M. OlsonJ.E. OngI.M. PatelA.V. PalmerJ.R. SandlerD.P. TamimiR. TaylorJ.A. TerasL.R. Trentham-DietzA. VachonC.M. WeinbergC.R. YaoS. WeitzelJ.N. GoldgarD.E. DomchekS.M. NathansonK.L. CouchF.J. KraftP. Risk of breast cancer among carriers of pathogenic variants in breast cancer predisposition genes varies by polygenic risk score.J. Clin. Oncol.202139232564257310.1200/JCO.20.0199234101481
     [Google Scholar]
  31. MaY. ZhouX. Genetic prediction of complex traits with polygenic scores: A statistical review.Trends Genet.20213711995101110.1016/j.tig.2021.06.00434243982
     [Google Scholar]
  32. LeeA. MavaddatN. WilcoxA.N. CunninghamA.P. CarverT. HartleyS. Babb de VilliersC. IzquierdoA. SimardJ. SchmidtM.K. WalterF.M. ChatterjeeN. Garcia-ClosasM. TischkowitzM. PharoahP. EastonD.F. AntoniouA.C. BOADICEA: A comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.Genet. Med.20192181708171810.1038/s41436‑018‑0406‑930643217
     [Google Scholar]
  33. KheraA.V. ChaffinM. WadeK.H. ZahidS. BrancaleJ. XiaR. DistefanoM. Senol-CosarO. HaasM.E. BickA. AragamK.G. LanderE.S. SmithG.D. Mason-SuaresH. FornageM. LeboM. TimpsonN.J. KaplanL.M. KathiresanS. Polygenic prediction of weight and obesity trajectories from birth to adulthood.Cell20191773587596.e910.1016/j.cell.2019.03.02831002795
     [Google Scholar]
  34. DamavandiN. SoleymaniniyaA. Bahrami ZadeganS. Samiee ArefM.H. ZeinaliS. Development of a genetic risk score for obesity predisposition evaluation.Mol. Genet. Genomics202229761495150310.1007/s00438‑022‑01923‑035947209
     [Google Scholar]
  35. ViljakainenH. SorlíJ.V. DahlströmE. AgrawalN. PortolésO. CorellaD. Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children.Sci. Rep.20231311526510.1038/s41598‑023‑42430‑537709841
     [Google Scholar]
  36. BriggsS.E.W. LawP. EastJ.E. WordsworthS. DunlopM. HoulstonR. Hippisley-CoxJ. TomlinsonI. Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: Population based cohort study.BMJ2022379e07170710.1136/bmj‑2022‑07170736351667
     [Google Scholar]
  37. Whirl-CarrilloM. McDonaghE.M. HebertJ.M. GongL. SangkuhlK. ThornC.F. AltmanR.B. KleinT.E. Pharmacogenomics knowledge for personalized medicine.Clin. Pharmacol. Ther.201292441441710.1038/clpt.2012.9622992668
     [Google Scholar]
  38. ZhangH. WuJ. YuL. Association of Gln27Glu and Arg16Gly polymorphisms in Beta2-adrenergic receptor gene with obesity susceptibility: A meta-analysis.PLoS One201496e10048910.1371/journal.pone.010048924960039
     [Google Scholar]
  39. de Souza e SilvaS. LeiteN. Furtado-AlleL. de SouzaR.L.R. CorazzaP.R.P. TradiottoM.C. MilanoG.E. da SilvaL.R. PizziJ. LopesM.F.A. LopesW.A. TureckL.V. ADRB2 gene influences responsiveness to physical exercise programs: A longitudinal study applied to overweight or obese Brazilian children and adolescents.Gene202282014629610.1016/j.gene.2022.146296
     [Google Scholar]
  40. DaghestaniM. OmairM. DaghestaniM. Abdel-RazeqS. KayaN. WarsyA. Influence of b2 adrenergic receptor polymorphism (rs1042713 and rs1042714) on anthropometric, hormonal and lipid profiles in polycystic ovarian syndrome.J. Med. Biochem.2021401748510.5937/jomb0‑2618333584143
     [Google Scholar]
  41. ShakhanovaA. AukenovN. NurtazinaA. MassabayevaM. BabenkoD. AdiyevaM. ShaimardonovN. Association of polymorphism genes LPL, ADRB2, AGT and AGTR1 with risk of hyperinsulinism and insulin resistance in the Kazakh population.Biomed. Rep.20201353510.3892/br.2020.134232843963
     [Google Scholar]
  42. XuS. LiuW. GongL. LiX. ChuW. HanM. ShiS. ZhouD. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents.Open Life Sci.20231812022064610.1515/biol‑2022‑064637554965
     [Google Scholar]
  43. de LuisD.A. IzaolaO. PrimoD. GómezJ.J.L. Role of beta-2 adrenergic receptor polymorphism (rs1042714) on body weight and glucose metabolism response to a meal-replacement hypocaloric diet.Nutrition202311611217010.1016/j.nut.2023.11217037572548
     [Google Scholar]
  44. BojarczukA. EgorovaE.S. Dzitkowska-ZabielskaM. AhmetovI.I. Genetics of exercise and diet-induced fat loss efficiency: A systematic review.J. Sports Sci. Med.202423123625710.52082/jssm.2024.23638455434
     [Google Scholar]
  45. da FonsecaA.C.P. AssisI.S.S. SalumK.C.R. PalhinhaL. AbreuG.M. ZembrzuskiV.M. Campos JuniorM. Nogueira-NetoJ.F. CambraiaA. Souza JuniorM.L.F. Maya-MonteiroC.M. CabelloP.H. BozzaP.T. CarneiroJ.R.I. Genetic variants in DBC1, SIRT1, UCP2 and ADRB2 as potential biomarkers for severe obesity and metabolic complications.Front. Genet.202415136341710.3389/fgene.2024.136341738841722
     [Google Scholar]
  46. IshidaY. MatsushitaM. YoneshiroT. SaitoM. FuseS. HamaokaT. KuroiwaM. TanakaR. KurosawaY. NishimuraT. MotoiM. MaedaT. NakayamaK. Genetic evidence for involvement of β2-adrenergic receptor in brown adipose tissue thermogenesis in humans.Int. J. Obes.202448811101117Epub ahead of print10.1038/s41366‑024‑01522‑638632325
     [Google Scholar]
  47. JiT. FangB. WuF. LiuY. ChengL. LiY. WangR. ZhuL. Diet change improves obesity and lipid deposition in high-fat diet-induced mice.Nutrients20231523497810.3390/nu1523497838068835
     [Google Scholar]
  48. BorsoiF.T. Neri-NumaI.A. de OliveiraW.Q. de AraújoF.F. PastoreG.M. Dietary polyphenols and their relationship to the modulation of non-communicable chronic diseases and epigenetic mechanisms: A mini-review.Food Chem.: Mol. Sci.2023610015510.1016/j.fochms.2022.10015536582744
     [Google Scholar]
  49. YılmazR. AteşÖ. GülA. KasapT. ÖzerS. EnsariE. Association between trp64arg polymorphism of the β3 adrenoreceptor gene and female sex in obese turkish children and adolescents.Pediatr. Gastroenterol. Hepatol. Nutr.201922546046910.5223/pghn.2019.22.5.46031555571
     [Google Scholar]
  50. Velazquez-RomanJ. Angulo-ZamudioU.A. León-SicairosN. Medina-SerranoJ. DeLira-BustillosN. Villamil-RamírezH. Canizales-QuinterosS. Macías-KaufferL. Campos-RomeroA. Alcántar-FernándezJ. Canizalez-RomanA. Association of FTO, ABCA1, ADRB3, and PPARG variants with obesity, type 2 diabetes, and metabolic syndrome in a Northwest Mexican adult population.J. Diabetes Complications2021351110802510.1016/j.jdiacomp.2021.10802534420811
     [Google Scholar]
  51. ZafarU. KhaliqS. AliZ. LoneK. Adrenergic receptor beta-3 rs4994 (T>C) and liver X receptor alpha rs12221497 (G>A) polymorphism in Pakistanis with metabolic syndrome.Chin. J. Physiol.201962519620210.4103/CJP.CJP_45_1931670283
     [Google Scholar]
  52. SzendreiB. González-LamuñoD. AmigoT. WangG. PitsiladisY. BenitoP.J. Gomez-CandelaC. CalderónF.J. CupeiroR. PRONAF Study Group Influence of ADRB2 Gln27Glu and ADRB3 Trp64Arg polymorphisms on body weight and body composition changes after a controlled weight-loss intervention.Appl. Physiol. Nutr. Metab.201641330731410.1139/apnm‑2015‑042526888112
     [Google Scholar]
  53. XieC. HuaW. ZhaoY. RuiJ. FengJ. ChenY. LiuY. LiuJ. YangX. XuX. The ADRB3 rs4994 polymorphism increases risk of childhood and adolescent overweight/obesity for East Asia’s population: An evidence-based meta-analysis.Adipocyte202091778610.1080/21623945.2020.172254932008426
     [Google Scholar]
  54. SakamotoY. OnikiK. KumagaeN. MoritaK. OtakeK. OgataY. SaruwatariJ. Beta-3-adrenergic receptor rs4994 polymorphism is a potential biomarker for the development of nonalcoholic fatty liver disease in overweight/obese individuals.Dis. Markers2019201911310.1155/2019/406532731929840
     [Google Scholar]
  55. AlroujiM. AlhumaydhiF.A. AlsayariA. SharafS.E. ShafiS. AnwarS. ShahwanM. AtiyaA. ShamsiA. Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations.PLoS One20231812e029318510.1371/journal.pone.029318538117829
     [Google Scholar]
  56. WangL.F. WangX.N. HuangC.C. HuL. XiaoY.F. GuanX.H. QianY.S. DengK.Y. XinH.B. Inhibition of NAMPT aggravates high fat diet-induced hepatic steatosis in mice through regulating Sirt1/AMPKα/SREBP1 signaling pathway.Lipids Health Dis.20171618210.1186/s12944‑017‑0464‑z28449683
     [Google Scholar]
  57. ShenS. ShenM. KuangL. YangK. WuS. LiuX. WangY. WangY. SIRT1/SREBPs-mediated regulation of lipid metabolism.Pharmacol. Res.202419910703710.1016/j.phrs.2023.10703738070792
     [Google Scholar]
  58. MihanfarA. AkbarzadehM. Ghazizadeh DarbandS. SadighparvarS. MajidiniaM. SIRT1: A promising therapeutic target in type 2 diabetes mellitus.Arch. Physiol. Biochem.20241301132810.1080/13813455.2021.195697634379994
     [Google Scholar]
  59. CostantinoS. MengozziA. VelagapudiS. MohammedS.A. GoricaE. AkhmedovA. MongelliA. PuglieseN.R. MasiS. VirdisA. HülsmeierA. MatterC.M. HornemannT. MelinaG. RuschitzkaF. LuscherT.F. PaneniF. Treatment with recombinant Sirt1 rewires the cardiac lipidome and rescues diabetes-related metabolic cardiomyopathy.Cardiovasc. Diabetol.202322131210.1186/s12933‑023‑02057‑237957697
     [Google Scholar]
  60. RaksheP.S. DuttaB.J. ChibS. MauryaN. SinghS. Unveiling the interplay of AMPK/SIRT1/PGC-1α axis in brain health: Promising targets against aging and NDDs.Ageing Res. Rev.20249610225510.1016/j.arr.2024.10225538490497
     [Google Scholar]
  61. LeeG.H. LeeH.Y. LimY.J. KimJ.H. JungS.J. JungE.S. ChaeS.W. LeeJ. LimJ. RashidM.M.U. MinK.H. ChaeH.J. Angelica gigas extract inhibits acetylation of eNOS via IRE1α sulfonation/RIDD-SIRT1-mediated posttranslational modification in vascular dysfunction.Aging20231523136081362710.18632/aging.20534338095615
     [Google Scholar]
  62. Salazar-GarcíaS. Ibáñez-SalazarA. Lares-VillaseñorE. Gaytan PachecoN. Uresti-RiveraE. Portales-PérezD.P. De la Cruz-MossoU. Vargas-MoralesJ.M. Analysis of SIRT1 genetic variants in young Mexican individuals: Relationships with overweight and obesity.Front. Genet.202415127820110.3389/fgene.2024.127820138645486
     [Google Scholar]
  63. TaghizadehN. MohammadiS. yousefiZ. GolpourP. TaheriA. MalekiM.H. NourbakhshM. NourbakhshM. AzarM.R. Assessment of global histone acetylation in pediatric and adolescent obesity: Correlations with SIRT1 expression and metabolic-inflammatory profiles.PLoS One20231810e029321710.1371/journal.pone.029321737862340
     [Google Scholar]
  64. Méndez-MancillaA. Turiján-EspinozaE. Vega-CárdenasM. Hernández-HernándezG.E. Uresti-RiveraE.E. Vargas-MoralesJ.M. Portales-PérezD.P. miR-21, miR-221, miR-29 and miR-34 are distinguishable molecular features of a metabolically unhealthy phenotype in young adults.PLoS One2024194e030042010.1371/journal.pone.030042038662716
     [Google Scholar]
  65. DolgikhО.V. ZaitsevaN.V. NikonoshinaN.A. Features neurohumoral and immune profile in children with functional disorders of the autonomic nervous system associated with sirtuin gene SIRT1 (rs7069102) polymorphism.Bull. Exp. Biol. Med.2022172558358610.1007/s10517‑022‑05439‑w35352247
     [Google Scholar]
  66. SalimS. KartawidjajaputraF. SuwantoA. SIRT1 as a potential biomarker for obesity.J. Nutr. Sci. Vitaminol.202066Suppl.S324S32810.3177/jnsv.66.S32433612619
     [Google Scholar]
  67. DemirdağF. YavuzerS. CengizM. YavuzerH. KaraZ. AyvacıA. AvcıS. YürüyenM. UzunH. AltıparmakM.R. DöventaşA. ErdinçlerD.S. The role of NF-κB, PPAR-α, and PPAR-γ in older adults with metabolic syndrome.Horm. Metab. Res.2023551073374010.1055/a‑2109‑195837308136
     [Google Scholar]
  68. XuD. ZhuangS. ChenH. JiangM. JiangP. WangQ. WangX. ChenR. TangH. TangL. IL-33 regulates adipogenesis via Wnt/β-catenin/PPAR-γ signaling pathway in preadipocytes.J. Transl. Med.202422136310.1186/s12967‑024‑05180‑038632591
     [Google Scholar]
  69. ZhaoY. TanH. ZhangX. ZhuJ. Roles of peroxisome proliferator‐activated receptors in hepatocellular carcinoma.J. Cell. Mol. Med.2024285e18042Epub ahead of print10.1111/jcmm.1804237987033
     [Google Scholar]
  70. GosseaumeC. FournierT. JéruI. VignaudM.L. MissotteI. ArchambeaudF. DebusscheX. DroumaguetC. FèveB. GrillotS. GuerciB. HieronimusS. HorsmansY. NobécourtE. PienkowskiC. PoitouC. ThissenJ.P. LascolsO. DegrelleS. TsatsarisV. VigourouxC. VatierC. Perinatal, metabolic, and reproductive features in PPARG -related lipodystrophy.Eur. J. Endocrinol.2023188327328110.1093/ejendo/lvad02336806620
     [Google Scholar]
  71. LamichaneS. Dahal LamichaneB. KwonS.M. Pivotal roles of peroxisome proliferator-activated receptors (PPARs) and their signal cascade for cellular and whole-body energy homeostasis.Int. J. Mol. Sci.201819494910.3390/ijms1904094929565812
     [Google Scholar]
  72. LiS. HeC. NieH. PangQ. WangR. ZengZ. SongY. G Allele of the rs1801282 polymorphism in PPARγ gene confers an increased risk of obesity and hypercholesterolemia, while T Allele of the rs3856806 polymorphism displays a protective role against dyslipidemia: A systematic review and meta-analysis.Front. Endocrinol.20221391908710.3389/fendo.2022.91908735846293
     [Google Scholar]
  73. SongY. LiS. HeC. PPARγ gene polymorphisms, metabolic disorders, and coronary artery disease.Front. Cardiovasc. Med.2022980892910.3389/fcvm.2022.80892935402540
     [Google Scholar]
  74. MaculewiczE. MastalerzA. Maciejewska-SkrendoA. CięszczykP. CywińskaA. BoreckaA. GarbaczA. SzarskaE. DziudaŁ. LorenzK. ŁakomyR. LepionkaT. AnyżewskaA. BiałekA. BertrandtJ. Association between peroxisome proliferator-activated receptor-alpha, -delta and -gamma gene (PPARA, PPARD, PPARG) polymorphisms and overweight parameters in physically active men.Biol. Sport202138476777610.5114/biolsport.2022.10995735440841
     [Google Scholar]
  75. MunteanC. SasaranM.O. CrisanA. BanescuC. Effects of PPARG and PPARGC1A gene polymorphisms on obesity markers.Front. Public Health20221096285210.3389/fpubh.2022.96285236466447
     [Google Scholar]
  76. WangY. LiS. LiuZ. LiX. YuY. LiuH. Identification of PPAR‐related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking.J. Cell. Mol. Med.2024288e1830410.1111/jcmm.1830438652093
     [Google Scholar]
  77. CaoD. KhanZ. LiX. SaitoS. BernsteinE.A. VictorA.R. AhmedF. HoshiA.O. VeirasL.C. ShibataT. CheM. CaiL. YamashitaM. TemelR.E. GianiJ.F. LuthringerD.J. DivakaruniA.S. Okwan-DuoduD. BernsteinK.E. Macrophage angiotensin-converting enzyme reduces atherosclerosis by increasing peroxisome proliferator-activated receptor α and fundamentally changing lipid metabolism.Cardiovasc. Res.202311991825184110.1093/cvr/cvad08237225143
     [Google Scholar]
  78. YanaiH. AdachiH. HakoshimaM. IidaS. KatsuyamaH. Metabolic-dysfunction-associated steatotic liver disease—its pathophysiology, association with atherosclerosis and cardiovascular disease, and treatments.Int. J. Mol. Sci.202324201547310.3390/ijms24201547337895151
     [Google Scholar]
  79. OnoderaT. KimD.S. YeR. WangM.Y. ChenS. FieldB.C. StraubL. SunX.N. LiC. LeeC. ParedesM. CreweC. ZhaoS. KusminskiC.M. GordilloR. SchererP.E. Protective roles of adiponectin and molecular signatures of HNF4α and PPARα as downstream targets of adiponectin in pancreatic β cells.Mol. Metab.20237810182110.1016/j.molmet.2023.10182137806486
     [Google Scholar]
  80. Ortega-MeléndezA.I. Montero-MolinaS. Jiménez-OrtegaR.F. Ramírez-LópezE. Campos-GóngoraE. Velázquez-CruzR. Jiménez-SalasZ. Balderas-RenteríaI. PPARα polymorphisms association with total cholesterol and LDL-C levels in a Mexican population.Eur. Rev. Med. Pharmacol. Sci.20222662158216410.26355/eurrev_202203_2836335363365
     [Google Scholar]
  81. KamalanathanS. BageI.J. SelvarajanS. SahooJ. MathaiyanJ. NaikD. Peroxisome proliferator-activated receptor α and γ gene polymorphisms among South Indian patients with diabetic dyslipidaemia.Indian J. Endocrinol. Metab.202327211812610.4103/ijem.ijem_451_2237292073
     [Google Scholar]
  82. ZhongY. XuY. TanY. ZhangX. WangR. ChenD. WangZ. ZhongX. Lipidomics of the erythrocyte membrane and network pharmacology to explore the mechanism of mangiferin from Anemarrhenae rhizoma in treating type 2 diabetes mellitus rats.J. Pharm. Biomed. Anal.202323011538610.1016/j.jpba.2023.11538637044004
     [Google Scholar]
  83. GolovinaEL GrishkevichIR VaizovaOE SamoilovaIG PodchinenovaDV MatveevaMV KudlayDA Genetic aspects of type 1 glucagon peptide agonists clinical efficacy: A review.Ter. Arkh.202395327427810.26442/00403660.2023.03.202150
     [Google Scholar]
  84. LiS. ZhangY. XuW. LvZ. XuL. ZhaoZ. ZhuD. SongY. C Allele of the PPARδ+294T>C polymorphism confers a higher risk of hypercholesterolemia, but not obesity and insulin resistance: A systematic review and meta-analysis.Horm. Metab. Res.202355535536610.1055/a‑2043‑770737011890
     [Google Scholar]
  85. Reza-LópezS.A. González-GurrolaS. Morales-MoralesO.O. Moreno-GonzálezJ.G. Rivas-GómezA.M. González-RodríguezE. Moreno-BritoV. Licón-TrilloA. Leal-BerumenI. Metabolic biomarkers in adults with type 2 diabetes: The role of PPAR-γ2 and PPAR-β/δ polymorphisms.Biomolecules20231312179110.3390/biom1312179138136661
     [Google Scholar]
  86. HuangY.M. WangW. HsiehP.P. ChenH.H. Haplotype of ESR1 and PPARD genes is associated with higher anthropometric changes in han chinese obesity by adjusting dietary factors—an 18-month follow-up.Nutrients20221420442510.3390/nu1420442536297109
     [Google Scholar]
  87. Leońska-DuniecA. CieszczykP. JastrzębskiZ. JażdżewskaA. Lulińska-KuklikE. MoskaW. FicekK. NiewczasM. Maciejewska-SkrendoA. The polymorphisms of the PPARD gene modify post-training body mass and biochemical parameter changes in women.PLoS One2018138e020255710.1371/journal.pone.020255730157214
     [Google Scholar]
  88. SinhaR. KachruD. RicchettiR.R. Singh-RambiritchS. MuthukumarK.M. SingaravelV. IrudayanathanC. Reddy-SinhaC. JunaidI. SharmaG. Francis-LyonP.A. Leveraging genomic associations in precision digital care for weight loss: Cohort study.J. Med. Internet Res.2021235e2540110.2196/2540133849843
     [Google Scholar]
  89. MolletI.G. MacedoM.P. Pre-diabetes-linked mirna mir-193b-3p targets ppargc1a, disrupts metabolic gene expression profile and increases lipid accumulation in hepatocytes: Relevance for MAFLD.Int. J. Mol. Sci.2023244387510.3390/ijms2404387536835287
     [Google Scholar]
  90. ZhangR.N. ShenF. PanQ. CaoH.X. ChenG.Y. FanJ.G. PPARGC1A rs8192678 G>A polymorphism affects the severity of hepatic histological features and nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease.World J. Gastroenterol.202127253863387610.3748/wjg.v27.i25.386334321850
     [Google Scholar]
  91. AisyahR. SadewaA.H. PatriaS.Y. WahabA. The PPARGC1A is the gene responsible for thrifty metabolism related metabolic diseases: A scoping review.Genes20221310189410.3390/genes1310189436292779
     [Google Scholar]
  92. SchillemansT. TraganteV. MaitusongB. GiganteB. CresciS. LaguzziF. VikströmM. RichardsM. PilbrowA. CameronV. FocoL. DoughtyR.N. KuukasjärviP. AllayeeH. HartialaJ.A. TangW.H.W. LyytikäinenL.P. NikusK. LaurikkaJ.O. SrinivasanS. MordiI.R. TrompetS. KraaijeveldA. van SettenJ. GijsbertsC.M. Maitland-van der ZeeA.H. SaelyC.H. GongY. JohnsonJ.A. Cooper-DeHoffR.M. PepineC.J. CasuG. LeihererA. DrexelH. HorneB.D. van der LaanS.W. MarzilianoN. HazenS.L. SinisaloJ. KähönenM. LehtimäkiT. LangC.C. BurkhardtR. ScholzM. JukemaJ.W. ErikssonN. ÅkerblomA. JamesS. HeldC. HagströmE. SpertusJ.A. AlgraA. de FaireU. ÅkessonA. AsselbergsF.W. PatelR.S. LeanderK. Associations of polymorphisms in the peroxisome proliferator-activated receptor gamma coactivator-1 alpha gene with subsequent coronary heart disease: An individual-level meta-analysis.Front. Physiol.20221390987010.3389/fphys.2022.90987035812313
     [Google Scholar]
  93. QianL. ZhuY. DengC. LiangZ. ChenJ. ChenY. WangX. LiuY. TianY. YangY. Peroxisome proliferator-activated receptor gamma coactivator-1 (PGC-1) family in physiological and pathophysiological process and diseases.Signal Transduct. Target. Ther.2024915010.1038/s41392‑024‑01756‑w38424050
     [Google Scholar]
  94. KhalilS. MohktarM. IbrahimF. The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases.Sensors2014146108951092810.3390/s14061089524949644
     [Google Scholar]
  95. CoëffierM. El MachkouriM. L’HuillierC. FolopeV. LelandaisH. GrigioniS. DéchelotteP. AchamrahN. Accuracy of bioimpedance equations for measuring body composition in a cohort of 2134 patients with obesity.Clin. Nutr.20224192013202410.1016/j.clnu.2022.07.03235970132
     [Google Scholar]
  96. YangD.W. MillerJ.A. XueW.Q. TangM. LeiL. ZhengY. DiaoH. WangT.M. LiaoY. WuY.X. ZhengX.H. ZhouT. LiX.Z. ZhangP.F. ChenX.Y. YuX. LiF. JiM. SunY. HeY.Q. JiaW.H. Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: A cost-effectiveness study.Front. Public Health202412137553310.3389/fpubh.2024.137553338756891
     [Google Scholar]
  97. FahedA.C. PhilippakisA.A. KheraA.V. The potential of polygenic scores to improve cost and efficiency of clinical trials.Nat. Commun.2022131292210.1038/s41467‑022‑30675‑z35614072
     [Google Scholar]
  98. RauhioA. Uusi-RasiK. NikkariS.T. KannusP. SievänenH. KunnasT. Association of the FTO and ADRB2 genes with body composition and fat distribution in obese women.Maturitas201376216517110.1016/j.maturitas.2013.07.00423911093
     [Google Scholar]
  99. ResendeC.M.M. SilvaH.A.M. CampelloC.P. FerrazL.A.A. de LimaE.L.S. BeserraM.A. MunizM.T.C. da SilvaL.M.P. Polymorphisms on rs9939609 FTO and rs17782313 MC4R genes in children and adolescent obesity: A systematic review.Nutrition202191-9211147410.1016/j.nut.2021.11147434628278
     [Google Scholar]
  100. ParkH.G. ChoiJ.H. Genetic variant rs9939609 in FTO is associated with body composition and obesity risk in Korean females.BMJ Open Diabetes Res. Care2023116e00364910.1136/bmjdrc‑2023‑00364937993268
     [Google Scholar]
  101. MehrdadM. FardaeiM. FararoueiM. EftekhariM.H. The association between FTO rs9939609 gene polymorphism and anthropometric indices in adults.J. Physiol. Anthropol.20203911410.1186/s40101‑020‑00224‑y32398148
     [Google Scholar]
  102. ScuteriA. SannaS. ChenW.M. UdaM. AlbaiG. StraitJ. NajjarS. NagarajaR. OrrúM. UsalaG. DeiM. LaiS. MaschioA. BusoneroF. MulasA. EhretG.B. FinkA.A. WederA.B. CooperR.S. GalanP. ChakravartiA. SchlessingerD. CaoA. LakattaE. AbecasisG.R. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.PLoS Genet.200737e11510.1371/journal.pgen.003011517658951
     [Google Scholar]
  103. JosephP.V. WangY. FourieN.H. HendersonW.A. A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles.PLoS One2018135e019784310.1371/journal.pone.019784329795655
     [Google Scholar]
  104. Method for preparing pectin.R.U. Patent 2247749 C2,2018
/content/journals/ctmc/10.2174/0115680266328330240828040922
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A Method for Predicting Allelic Variants of Single Nucleotide Polymorphisms
Curr. Top. Med. Chem. 25, 724 (2025); https://doi.org/10.2174/0115680266328330240828040922
/content/journals/ctmc/10.2174/0115680266328330240828040922
/content/journals/ctmc/10.2174/0115680266328330240828040922
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  • Article Type:     Research Article
Keyword(s): ADRB2; PPARA; PPARD; PPARG; PPARGC1A; Predictive model; SIRT1; SNP

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