Rare Variants of Genes in Metabolic Pathways of Abdominal Obesity Formation in Men With Coronary Atherosclerosis: A Cross-Sectional Study



Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

BACKGROUND: Using common and rare molecular genetic markers, genetic risk scores for ischemic heart disease are being developed, and their associations with the severity of atherosclerosis, levels of low-density lipoprotein cholesterol, triglycerides, and non-HDL cholesterol are being studied.

AIM: To analyze nucleotide sequence variants in genes associated with metabolic processes, depending on the presence of unstable atherosclerotic plaques in the coronary arteries of patients with and without abdominal obesity.

METHODS: The study included 42 men aged 42–69 years (mean age 55.74 ± 5.85 years) with coronary atherosclerosis, either with or without abdominal obesity (22 patients without obesity and 17 with obesity). Whole-exome sequencing was performed using standard kits. Normality testing for variable distribution was performed using the Shapiro–Wilk test. Data for categorical variables are presented as absolute and relative values—n (%), for continuous variables—as Me (25; 75), where Me is the median, 25th and 75th percentiles (1st and 3rd quartiles).

RESULTS: Patients with coronary atherosclerosis and abdominal obesity were found to have rare variants with a minor allele frequency ≤0.001 (dbGaP) in the ADIPOQ (rs76533408) and APLNR (rs199589565, rs150922621) genes. Patients with coronary atherosclerosis, abdominal obesity, and unstable atherosclerotic plaques were found to have a rare variant rs190996557 in the CCL2 gene. A number of genetic variants associated with an increased risk of metabolic disorders and cardiovascular disease were identified in patients with coronary atherosclerosis and overweight. The rs4994 variant (G = 0.07) in the ADRB3 gene, associated with decreased hormone-sensitive lipase expression and an increased risk of developing obesity and ischemic heart disease, was identified. The rs3745368 variant of the RETN gene (A = 0.04) was associated with a predisposition to type 2 diabetes mellitus, hypertension, and insulin resistance. The rs696217 variant of the GHRL gene (T = 0.08) was associated with the risk of developing obesity.

CONCLUSION: Rare genetic variants in the ADIPOQ (rs76533408), APLNR (rs199589565, rs150922621) and CCL2 (rs190996557) genes were identified in patients with coronary atherosclerosis and abdominal obesity.

About the authors

Evgeniia V. Garbuzova

Research Institute of Internal and Preventive Medicine, Branch of the Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Author for correspondence.
Email: stryukova.j@mail.ru
ORCID iD: 0000-0001-5316-4664
SPIN-code: 9177-6439

MD, Cand. Sci. (Medicine), research associate, Genetic and Environmental Determinants of the Human Life Cycle

Russian Federation, Novosibirsk

Sergey E. Semaev

Research Institute of Internal and Preventive Medicine, Branch of the Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Email: semaev@bionet.nsc.ru
ORCID iD: 0000-0003-3999-8501
SPIN-code: 9641-2299

junior research associate, lab. of molecular genetic research of therapeutic human diseases

Russian Federation, Novosibirsk

Elena V. Shakhtschneider

Research Institute of Internal and Preventive Medicine, Branch of the Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Email: shakhtshneyderev@bionet.nsc.ru
ORCID iD: 0000-0001-6108-1025
SPIN-code: 9453-9067

MD, Dr. Sci. (Medicine), leader research associate, lab. of molecular genetic research of therapeutic human diseases

Russian Federation, Novosibirsk

Dinara E. Ivanoshchuk

Research Institute of Internal and Preventive Medicine, Branch of the Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Email: dinara2084@mail.ru
ORCID iD: 0000-0002-0403-545X
SPIN-code: 8294-6980

research associate, lab. of molecular genetic research of therapeutic human diseases

Russian Federation, Novosibirsk

Olga V. Timoshchenko

Research Institute of Internal and Preventive Medicine, Branch of the Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Email: lentis@yandex.ru
ORCID iD: 0000-0002-6584-2060
SPIN-code: 2202-3800

MD, Cand. Sci. (Medicine), research associate, lab. of Etiopathogenesis and Clinic of therapeutic human diseases

Russian Federation, Novosibirsk

References

  1. Bilitou A, Were J, Farrer A, et al. Prevalence and Patient Outcomes of Adult Primary Hypercholesterolemia and Dyslipidemia in the UK: Longitudinal Retrospective Study Using a Primary Care Dataset from 2009 to 2019. Clinicoecon Outcomes Res. 2022;14:189–203. doi: 10.2147/CEOR.S347085 EDN: PTXODS
  2. Dwivedi AK, Dubey P, Cistola DP, Reddy SY. Association Between Obesity and Cardiovascular Outcomes: Updated Evidence from Meta-analysis Studies. Curr Cardiol Rep. 2020;22(4):25. doi: 10.1007/s11886-020-1273-y EDN: QRKDMJ
  3. He Z, Luo J, Lv M, et al. Characteristics and evaluation of atherosclerotic plaques: an overview of state-of-the-art techniques. Front Neurol. 2023;14:1159288. doi: 10.3389/fneur.2023.1159288 EDN: QUQRJZ
  4. Seo YB, Kang SG, Song SW. Relationship between metabolically healthy obesity and coronary artery calcification. Obes Res Clin Pract. 2024;18(1):28–34. doi: 10.1016/j.orcp.2024.01.006 EDN: SUSYRQ
  5. Ershova AI, Meshkov AN, Kutsenko VA, et al. Validation of genetic risk scores for coronary artery disease, developed on European population samples, in Russian population. Cardiovascular Therapy and Prevention. 2023;22(12):3856. doi: 10.15829/1728-8800-2023-3856 EDN: BMUPKW
  6. Semaev S, Shakhtshneider E. Genetic Risk Score for Coronary Heart Disease: Review. J Pers Med. 2020;10(4):239. doi: 10.3390/jpm10040239 EDN: RRZOZH
  7. Waksman R, Seruys PW, Schaar J. Handbook of the vulnerable plaque. 2nd ed. London: CRC Press; 2006. 424 p. doi: 10.3109/9781439804537
  8. Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–424. doi: 10.1038/gim.2015.30 EDN: UTYXCB
  9. Stenson PD, Ball EV, Mort M, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003;21(6):577–581. doi: 10.1002/humu.10212
  10. Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–D1067. doi: 10.1093/nar/gkx1153
  11. Fokkema IF, Taschner PE, Schaafsma GC, et al. LOVD v.2.0: the next generation in gene variant databases. Hum Mutat. 2011;32(5):557–563. doi: 10.1002/humu.21438
  12. Bairqdar A, Shakhtshneider E, Ivanoshchuk D, et al. Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity. J Pers Med. 2023;13(10):1500. doi: 10.3390/jpm13101500 EDN: NHGZFW
  13. Jiao ZT, Luo Q. Molecular Mechanisms and Health Benefits of Ghrelin: A Narrative Review. Nutrients. 2022;14(19):4191. doi: 10.3390/nu14194191 EDN: OEADPM
  14. El Eid L, Reynolds CA, Tomas A, Ben Jones. Biased agonism and polymorphic variation at the GLP-1 receptor: Implications for the development of personalised therapeutics. Pharmacol Res. 2022;184:106411. doi: 10.1016/j.phrs.2022.106411 EDN: JNGDHC
  15. Dorsey-Trevino EG, Kaur V, Mercader JM, et al. Association of GLP1R Polymorphisms With the Incretin Response. J Clin Endocrinol Metab. 2022;107(9):2580–2588. doi: 10.1210/clinem/dgac374 EDN: BXXPBE
  16. Wei J, Liu F, Lu Z, et al. Differential m6A, m6Am, and m1A Demethylation Mediated by FTO in the Cell Nucleus and Cytoplasm. Mol Cell. 2018;71(6):973–985.e5. doi: 10.1016/j.molcel.2018.08.011
  17. Chen J, Tan B, Karteris E, et al. Secretion of adiponectin by human placenta: differential modulation of adiponectin and its receptors by cytokines. Diabetologia. 2006;49(6):1292–1302. doi: 10.1007/s00125-006-0194-77 EDN: UDGBNN
  18. Siitonen N, Pulkkinen L, Lindström J, et al. Association of ADIPOQ gene variants with body weight, type 2 diabetes and serum adiponectin concentrations: the Finnish Diabetes Prevention Study. BMC Med Genet. 2011;12:5. doi: 10.1186/1471-2350-12-5 EDN: OAPFWN
  19. Palit SP, Patel R, Jadeja SD, et al. A genetic analysis identifies a haplotype at adiponectin locus: Association with obesity and type 2 diabetes. Sci Rep. 2020;10(1):2904. doi: 10.1038/s41598-020-59845-z
  20. Wu J, Liu Z, Meng K, Zhang L. Association of adiponectin gene (ADIPOQ) rs2241766 polymorphism with obesity in adults: a meta-analysis. PLoS One. 2014;9(4):e95270. doi: 10.1371/journal.pone.0095270 EDN: AFBOKT
  21. Abbas A, Hoidy W. Association of ADIPOQ (rs 2241766) Gene Polymorphism with Type 2 Diabetes Mellitus Patients A Case-Control Study. Biomedicine and Chemical Sciences. 2022;1(2):88–92. doi: 10.48112/bcs.v1i2.124 EDN: QXWQXR
  22. Reyes Leon-Cachon RB, Salinas-Santander MA, Alejandra Aguilar-Tamez D, et al. ADIPOQ-rs2241766 polymorphism is associated with changes in cholesterol levels of Mexican adolescents. J Appl Biomed. 2022;20(4):146–153. doi: 10.32725/jab.2022.017 EDN: CILHRR
  23. Pogozheva AV, Sorokina EY Association of rs266729 and rs16861194 polymorphisms of the ADIPOQ gene with the risk of obesity in residents of the Moscow region. Almanac of Clinical Medicine. 2021;49(5):315–322. doi: 10.18786/2072-0505-2021-49-038 EDN: FZUATX
  24. Al-Nbaheen MS. Effect of Genetic Variations in the ADIPOQ Gene on Susceptibility to Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes. 2022;15:2753–2761. doi: 10.2147/DMSO.S377057 EDN: FZSRIO
  25. Yoshikawa M, Asaba K, Nakayama T. The APLNR gene polymorphism rs7119375 is associated with an increased risk of development of essential hypertension in the Chinese population: A meta-analysis. Medicine. 2020;99(50):e22418. doi: 10.1097/MD.0000000000022418 EDN: FXBLYM
  26. Ding Q, Xing J, Bai F, et al. C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke. Oxid Med Cell Longev. 2023;2023:5199810. doi: 10.1155/2023/5199810 EDN: PWDHMG
  27. Yadegari M, Zare-Feyzabadi R, Zakariaeiseraji M, et al. Interaction between the genetic variant of rs696217-ghrelin and food intake and obesity and dyslipidemia. Ann Hum Genet. 2022;86(1):14–23. doi: 10.1111/ahg.12443 EDN: MOPEOK
  28. Buraczynska M, Golacki J, Zaluska W. Leu72Met Polymorphism in Ghrelin Gene: A Potential Risk Factor for Hypertension in Type 2 Diabetes Patients. Diabetes Metab Syndr Obes. 2023;16:557–564. doi: 10.2147/DMSO.S393373 EDN: DOQIBY
  29. Ma X, Huang J, Lu D, et al. Genetic Variability of the Glucose-Dependent Insulinotropic Peptide Gene Is Involved in the Premature Coronary Artery Disease in a Chinese Population with Type 2 Diabetes. J Diabetes Res. 2018;2018:6820294. doi: 10.1155/2018/6820294
  30. Xu T, Liu M, Liu Q, et al. Associations of TCF7L2 rs11196218 (A/G) and GLP-1R rs761386 (C/T) Gene Polymorphisms with Obesity in Chinese Population. Diabetes Metab Syndr Obes. 2021;14:2465–2472. doi: 10.2147/DMSO.S310069 EDN: LHWYQW
  31. Wu Y, Ma Y. CCL2-CCR2 signaling axis in obesity and metabolic diseases. J Cell Physiol. 2024;239(4):e31192. doi: 10.1002/jcp.31192 EDN: IUKTLR
  32. Claussnitzer M, Dankel SN, Kim KH, et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N Engl J Med. 2015;373(10):895–907. doi: 10.1056/NEJMoa1502214
  33. Al-Barqaawi MA, Al-Kashwan TA, Mahdi AG, et al. The impact of omentin-1 gene polymorphisms (rs2274907 and rs2274908) on serum lipid concentrations and coronary artery disease in a sample of Iraqi individuals (A pilot study). Clin Biochem. 2022;100:29–34. doi: 10.1016/j.clinbiochem.2021.11.005 EDN: YMDSXF
  34. Onur Cura D, Yildiz S, Ataman E, et al. Relationship between plasminogen activator inhibitor-1 gene alterations and fibrosis in peritoneal dialysis patients. Ther Apher Dial. 2021;25(1):97–102. doi: 10.1111/1744-9987.13501 EDN: BUBJYH
  35. Morange PE, Saut N, Alessi MC, et al. Association of plasminogen activator inhibitor (PAI)-1 (SERPINE1) SNPs with myocardial infarction, plasma PAI-1, and metabolic parameters: the HIFMECH study. Arterioscler Thromb Vasc Biol. 2007;27(10):2250–2257. doi: 10.1161/ATVBAHA.107.149468
  36. Fan Q, Li H, Qin Y, et al. Association of SERPINE1 rs6092 with type 2 diabetes and related metabolic traits in a Chinese population. Gene. 2018;661:176–181. doi: 10.1016/j.gene.2018.04.011
  37. Kadowaki H, Yasuda K, Iwamoto K, et al. A mutation in the beta 3-adrenergic receptor gene is associated with obesity and hyperinsulinemia in Japanese subjects. Biochem Biophys Res Commun. 1995;215(2):555–560. doi: 10.1006/bbrc.1995.2500
  38. Xie C, Hua W, Zhao Y, et al. The ADRB3 rs4994 polymorphism increases risk of childhood and adolescent overweight/obesity for East Asia's population: an evidence-based meta-analysis. Adipocyte. 2020;9(1):77–86. doi: 10.1080/21623945.2020.1722549 EDN: KUGVIN
  39. Damavandi N, Soleymaniniya A, Bahrami Zadegan S, et al. Development of a genetic risk score for obesity predisposition evaluation. Mol Genet Genomics. 2022;297(6):1495–1503. doi: 10.1007/s00438-022-01923-0 EDN: SGKMSL
  40. Diniz IG, Noce RRD, Pereira AP, et al. Common BMI and diabetes-related genetic variants: A pilot study among indigenous people in the Brazilian Amazon. Genet Mol Biol. 2022;45(2):e20210153. doi: 10.1590/1678-4685-GMB-2021-0153

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

© 2026 Eco-Vector

License URL: https://eco-vector.com/for_authors.php#07