[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

The joint effect of PPARG upstream genetic variation in association with long-term persistent obesity: Tehran cardio-metabolic genetic study (TCGS)

  • Original Article
  • Published:
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity Aims and scope Submit manuscript

Abstract

Purpose

This study is the first study that aims to assess the association between SNPs located at the PPARG gene with long term persistent obesity. In this cohort association study, all adult individuals who had at least three consecutive phases of BMI (at least nine years) in Tehran genetic Cardio-metabolic Study (TCGS) were included.

Methods

Individuals who always had 30 ≤ BMI < 35 and individuals who always had 20 < BMI ≤ 25 were assigned to the long-term persistent obese group and persistent normal weight group, respectively. Other individuals were excluded from the study. We used four gamete rules to make SNP sets from correlated nearby SNPs and kernel machine regression to analyze the association between SNP sets and persistent obesity or normal weight.

Results

The normal group consisted of 1547 individuals with the mean age of 40 years, and the obese group consisted of 1676 individuals with mean age of 48 years. Two groups had a significant difference between all measured clinical characteristics at entry time. The kernel machine result shows that nine correlated SNPs located upstream of PPARG have a significant joint effect on persistence obesity.

Conclusion

This is the first study on the association between PPARG variants with persistent obesity. Three of the nine associated markers were reported in previous GWAS studies to be associated with related diseases. For the studied markers in the PPARG gene, the Iranian allele frequency was near the American and European populations.

Level III

Case–control analytic study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

In this study, the information of individuals who had participated in TLGS and TCGS were used.

Code availability

We used Haploview, R ( SKAT package), and Plink 2 software.

References

  1. Anjum I, Fayyaz M, Wajid A et al (2018) Does obesity increase the risk of dementia: a literature review. Cureus. https://doi.org/10.7759/cureus.2660

    Article  PubMed  PubMed Central  Google Scholar 

  2. Borecki IB, Higgins M, Schreiner PJ et al (1998) Evidence for multiple determinants of the body mass index: the national heart, lung, and blood institute family heart study. Obes Res 6:107–114. https://doi.org/10.1002/j.1550-8528.1998.tb00323.x

    Article  CAS  PubMed  Google Scholar 

  3. Loos RJF (2012) Genetic determinants of common obesity and their value in prediction. Best Pract Res Clin Endocrinol Metab 26:211–226. https://doi.org/10.1016/j.beem.2011.11.003

    Article  CAS  PubMed  Google Scholar 

  4. Levesque RJR (2018) Obesity and overweight. In: Levesque RJR (ed) Encyclopedia of adolescence. Springer, Cham. https://doi.org/10.1007/978-3-319-33228-4_447

  5. Stienstra R, Duval C, Müller M, Kersten S (2007) PPARs, obesity, and inflammation. PPAR Res. https://doi.org/10.1155/2007/95974

    Article  PubMed  PubMed Central  Google Scholar 

  6. Platt C, Coward RJ (2017) Peroxisome proliferator activating receptor-γ and the podocyte. Nephrol Dial Transplant 32:423–433. https://doi.org/10.1093/ndt/gfw320

    Article  CAS  PubMed  Google Scholar 

  7. Tyagi S, Gupta P, Saini A et al (2011) The peroxisome proliferator-activated receptor: a family of nuclear receptors role in various diseases. J Adv Pharm Technol Res 2:236–240. https://doi.org/10.4103/2231-4040.90879

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Aoun P, Simpkins JW, Agarwal N (2003) Role of PPAR-γ ligands in neuroprotection against glutamate-induced cytotoxicity in retinal ganglion cells. Investig Ophthalmol Vis Sci 44:2999–3004. https://doi.org/10.1167/iovs.02-1060

    Article  Google Scholar 

  9. Hihi AK, Michalik L, Wahli W (2002) PPARs: Transcriptional effectors of fatty acids and their derivatives. Cell Mol Life Sci 59:790–798. https://doi.org/10.1007/s00018-002-8467-x

    Article  CAS  PubMed  Google Scholar 

  10. Fajas L, Auboeuf D, Raspé E et al (1997) The organization, promoter analysis, and expression of the human PPARγ gene. J Biol Chem 272:18779–18789

    Article  CAS  Google Scholar 

  11. Ren D, Collingwood TN, Rebar EJ et al (2002) PPARγ knockdown by engineered transcription factors: exogenous PPARγ2 but not PPARγ1 reactivates adipogenesis. Genes Dev 16:27–32. https://doi.org/10.1101/gad.953802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lou S, Ren L, Xiao J et al (2012) Expression profiling based graph-clustering approach to determine renal carcinoma related pathway in response to kidney cancer. Eur Rev Med Pharmacol Sci 16:775–780

    CAS  PubMed  Google Scholar 

  13. Sarzynski MA, Jacobson P, Rankinen T et al (2011) Associations of markers in 11 obesity candidate genes with maximal weight loss and weight regain in the SOS bariatric surgery cases. Int J Obes. https://doi.org/10.1038/ijo.2010.166

    Article  Google Scholar 

  14. Liou TH, Chen HH, Wang W et al (2011) ESR1, FTO, and UCP2 genes interact with bariatric surgery affecting weight loss and glycemic control in severely obese patients. Obes Surg. https://doi.org/10.1007/s11695-011-0457-3

    Article  PubMed  Google Scholar 

  15. Wilbe M, Kozyrev SV, Farias FHG et al (2015) Multiple changes of gene expression and function reveal genomic and phenotypic complexity in SLE-like disease. PLoS Genet. https://doi.org/10.1371/journal.pgen.1005248

    Article  PubMed  PubMed Central  Google Scholar 

  16. Javanrouh N, Daneshpour MS, Soltanian AR, Tapak L (2018) Kernel machine SNP set analysis provides new insight into the association between obesity and polymorphisms located on the chromosomal 16q.12.2 region: Tehran lipid and glucose study. Gene 658:146–151. https://doi.org/10.1016/j.gene.2018.03.006

    Article  CAS  PubMed  Google Scholar 

  17. Javanrouh N, Soltanian AR, Tapak L et al (2019) A novel association of rs13334070 in the RPGRIP1L gene with adiposity factors discovered by joint linkage and linkage disequilibrium analysis in Iranian pedigrees: Tehran Cardiometabolic Genetic Study (TCGS). Genet Epidemiol 43:342–351. https://doi.org/10.1002/gepi.22179

    Article  PubMed  Google Scholar 

  18. Ionita-Laza I, Lee S, Makarov V et al (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. https://doi.org/10.1016/j.ajhg.2013.04.015

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lee S, Wu MC, Lin X (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics. https://doi.org/10.1093/biostatistics/kxs014

    Article  PubMed  PubMed Central  Google Scholar 

  20. Azizi F, Ghanbarian A, Momenan AA et al (2009) Prevention of non-communicable disease in a population in nutrition transition: Tehran lipid and glucose study phase II. Trials 10:5. https://doi.org/10.1186/1745-6215-10-5

    Article  PubMed  PubMed Central  Google Scholar 

  21. Daneshpour MS, Fallah M-S, Sedaghati-Khayat B et al (2017) Rationale and design of a genetic study on cardiometabolic risk factors: protocol for the Tehran cardiometabolic genetic study (TCGS). JMIR Res Protoc 6:e28. https://doi.org/10.2196/resprot.6050

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265. https://doi.org/10.1093/bioinformatics/bth457

    Article  CAS  Google Scholar 

  23. Wang N, Akey JM, Zhang K et al (2002) Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation. Am J Hum Genet 71:1227–1234. https://doi.org/10.1086/344398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. https://doi.org/10.1086/519795

    Article  PubMed  PubMed Central  Google Scholar 

  25. Purcell S, Chang C (2015) PLINK 1.9. https://www.cog-genomics.org/plink2. Accessed 10 Oct (2009)

  26. Walford GA, Gustafsson S, Rybin D et al (2016) Genome-wide association study of the modified stumvoll insulin sensitivity index identifies BCL2 and FAM19A2 as novel insulin sensitivity loci. Diabetes 65:3200–3211. https://doi.org/10.2337/db16-0199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wessel J, Chu AY, Willems SM et al (2015) Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 6:1–16

    Article  Google Scholar 

  28. Okuda H, Okamoto K, Abe M et al (2020) Genome-wide association study identifies new loci for albuminuria in the Japanese population. Clin Exp Nephrol 49:1458. https://doi.org/10.1007/s10157-020-01884-x

    Article  CAS  Google Scholar 

  29. Flannick J, Mercader JM, Fuchsberger C et al (2019) Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570:71–76. https://doi.org/10.1038/s41586-019-1231-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xue A, Wu Y, Zhu Z et al (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:1–14. https://doi.org/10.1038/s41467-018-04951-w

    Article  CAS  Google Scholar 

  31. Aprile M, Ambrosio MR, D’Esposito V et al (2014) PPARG in human adipogenesis: differential contribution of canonical transcripts and dominant negative isoforms. PPAR Res. https://doi.org/10.1155/2014/537865

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bandera Merchan B, Tinahones FJ, Macías-González M (2016) Commonalities in the association between PPARG and vitamin D related with obesity and carcinogenesis. PPAR Res. https://doi.org/10.1155/2016/2308249

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gyamfi A, Brown C, Antwi-Baffour S (2019) Selected candidate genes and obesity among Ghanaian adults: a case-control study at the Korle-Bu Teaching Hospital (Dietherapy Unit) Accra (P15–014-19). Curr Dev Nutr 3:nzz037-nzz115. https://doi.org/10.1093/cdn/nzz037.p15-014-19

    Article  PubMed Central  Google Scholar 

  34. Black MH, Wu J, Takayanagi M et al (2015) Variation in PPARG is associated with longitudinal change in insulin resistance in Mexican Americans at risk for type 2 diabetes. J Clin Endocrinol Metab 100:1187–1195. https://doi.org/10.1210/jc.2014-3246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hishida A, Wakai K, Naito M et al (2013) Polymorphisms in PPAR genes (PPARD, PPARG, and PPARGC1A) and the risk of chronic kidney disease in Japanese: cross-sectional data from the J-MICC study. PPAR Res. https://doi.org/10.1155/2013/980471

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhang M, Yuan H, Li C, Li C (2017) The impact of peroxisome proliferator-activated receptor gamma and its interaction with abdominal obesity on diabetic nephropathy in Chinese Han. Nephron 135:224–230. https://doi.org/10.1159/000450656

    Article  CAS  PubMed  Google Scholar 

  37. Webster RJ, Warrington NM, Beilby JP et al (2010) The longitudinal association of common susceptibility variants for type 2 diabetes and obesity with fasting glucose level and BMI. BMC Med Genet 11:140. https://doi.org/10.1186/1471-2350-11-140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Qian Y, Li P, Zhang J et al (2016) Association between peroxisome proliferator-activated receptor-alpha, delta, and gamma polymorphisms and risk of coronary heart disease: a case-control study and meta-analysis. Med (United States). https://doi.org/10.1097/MD.0000000000004299

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yang L, Tian RG, Chang PY et al (2015) Association of SNPs in the PPARγ gene and hypertension in a Mongolian population. Genet Mol Res 14:19295–19308. https://doi.org/10.4238/2015.December.29.39

    Article  CAS  PubMed  Google Scholar 

  40. Hosseini-Esfahani F, Bahadoran Z, Moslehi N et al (2018) Metabolic syndrome: findings from 20 years of the Tehran lipid and glucose study. Int J Endocrinol Metab. https://doi.org/10.5812/ijem.84771

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to the patients participating in the Tehran lipid and glucose and Tehran Genetic Cardiometabolic studies. Also special thanks to the DeCODE genetic company for doing the genetic screening.

Funding

In this study, we used a data set that was funded by the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences (Tehran, Iran).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maryam S. Daneshpour.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures were following the ethical standards of the ethics committee on human subject research at Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences with the code of 13960449.

Informed consent

In this study, the information of individuals who had participated in TLGS and TCGS were used. All subjects signed a consent form at each visit.

Consent to participate

All subjects signed a consent form at each visit.

Consent for publication

The publication of this study has been approved by all co-authors, as well as by the responsible authorities at the institute.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javanrouh Givi, N., Najd Hassan Bonab, L., Barzin, M. et al. The joint effect of PPARG upstream genetic variation in association with long-term persistent obesity: Tehran cardio-metabolic genetic study (TCGS). Eat Weight Disord 26, 2325–2332 (2021). https://doi.org/10.1007/s40519-020-01063-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40519-020-01063-7

Keywords