Skip to main content
SpringerLink
Log in

The Gene Score for Predicting Hypertriglyceridemia: New Insights from a Czech Case–Control Study

  • Original Research Article
  • Published:
Molecular Diagnosis & Therapy Aims and scope Submit manuscript

Abstract

Background

Plasma triglyceride (TG) values are significant predictors of cardiovascular and total mortality. The plasma levels of TGs have an important genetic background. We analyzed whether 32 single nucleotide polymorphisms (SNPs) identified in genome-wide association studies are discriminators of hypertriglyceridemia (HTG) in the Czech population.

Objectives

The objective of this study was to replicate and test the original findings in an independent study and to re-analyze the gene score leading to HTG.

Methods

In total, we analyzed 32 SNPs in 209 patients with plasma TG levels over 10 mmol/L (HTG group) and compared them in a case–control design with 524 treatment-naïve controls (normotriglyceridemic [NTG] group) with plasma TG values below 1.8 mmol/L.

Results

Sixteen SNPs were significantly associated with an increased risk of HTG development, with odds ratios (ORs) (95% confidence interval [CI]) varying from 1.40 (1.01–1.95) to 4.69 (3.29–6.68) (rs964184 within the APOA5 gene). Both unweighted (sum of the risk alleles) and weighted gene scores (WGS) (log of the achieved ORs per individual genotype) were calculated, and both gene scores were significantly different between groups. The mean score of the risk alleles was significantly increased in the HTG group compared to the NTG group (18.5 ± 2.5 vs. 15.7 ± 2.3, respectively; P < 0.00001). Subjects with a WGS over 9 were significantly more common in the HTG group (44.5%) than in the NTG group, in which such a high score was observed in only 4.7% of subjects (OR 16.3, 95% CI 10.0–36.7; P < 0.0000001).

Conclusions

An increased number of risk genetic variants, calculated both in a weighted or unweighted manner, significantly discriminates between the subjects with HTG and controls. Population-specific sets of SNPs included into the gene score seem to yield better discrimination power.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Hokanson JE, Austin MA. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: a meta-analysis of population-based prospective studies. J Cardiovasc Risk. 1996;3:213–9.

    Article  CAS  PubMed  Google Scholar 

  2. Cullen P. Evidence that triglycerides are an independent coronary heart disease risk factor. Am J Cardiol. 2000;86:943–9.

    Article  CAS  PubMed  Google Scholar 

  3. Liu J, Zeng FF, Liu ZM, Zhang CX, Ling WH, Chen YM. Effects of blood triglycerides on cardiovascular and all-cause mortality: a systematic review and meta-analysis of 61 prospective studies. Lipids Health Dis. 2013;12:159.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pikhart H, Hubáček JA, Peasey A, Kubínová R, Bobák M. Association between fasting plasma triglycerides, all-cause and cardiovascular mortality in Czech population. Results from the HAPIEE study. Physiol Res. 2015;64(Suppl 3):S355–61.

    CAS  PubMed  Google Scholar 

  5. Budoff M. Triglycerides and triglyceride-rich lipoproteins in the causal pathway of cardiovascular disease. Am J Cardiol. 2016;118:138–45.

    Article  CAS  PubMed  Google Scholar 

  6. Dron JS, Hegele RA. Genetics of triglycerides and the risk of atherosclerosis. Curr Atheroscler Rep. 2017;19:31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Scherer J, Singh VP, Pitchumoni CS, Yadav D. Issues in hypertriglyceridemic pancreatitis: an update. J Clin Gastroenterol. 2014;48:195–203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Musambil M, Al-Rubeaan K, Al-Qasim S, Al Naqeb D, Al-Soghayer A. Primary hypertriglyceridemia: a look back on the clinical classification and genetics of the disease. Curr Diabetes Rev. 2019. https://doi.org/10.2174/1573399815666190502164131 (Epub 2019 May 2).

    Article  Google Scholar 

  9. Brahm AJ, Hegele RA. Chylomicronaemia—current diagnosis and future therapies. Nat Rev Endocrinol. 2015;11:352–62.

    Article  CAS  PubMed  Google Scholar 

  10. Triglyceride Coronary Disease Genetics Consortium and Emerging Risk Factors Collaboration, Sarwar N, Sandhu MS, Ricketts SL, Butterworth AS, Di Angelantonio E, et al. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies. Lancet. 2010;375:1634–9.

    Article  CAS  PubMed Central  Google Scholar 

  11. Vitali C, Khetarpal SA, Rader DJ. HDL cholesterol metabolism and the risk of CHD: new insights from human genetics. Curr Cardiol Rep. 2017;19:132.

    Article  PubMed  Google Scholar 

  12. Schwarzova L, Hubacek JA, Vrablik M. Genetic predisposition of human plasma triglyceride concentrations. Physiol Res. 2015;64(Suppl 3):S341–54.

    CAS  PubMed  Google Scholar 

  13. Vrablík M, Češka R. Treatment of hypertriglyceridemia: a review of current options. Physiol Res. 2015;64(Suppl 3):S331–40.

    PubMed  Google Scholar 

  14. Hegele RA, Ginsberg HN, Chapman MJ, Nordestgaard BG, Kuivenhoven JA, Averna M, et al. The polygenic nature of hypertriglyceridaemia: implications for definition, diagnosis, and management. Lancet Diabetes Endocrinol. 2014;2:655–66.

    Article  CAS  PubMed  Google Scholar 

  15. Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet. 2014;384:626–35.

    Article  CAS  PubMed  Google Scholar 

  16. Talmud PJ, Cooper JA, Morris RW, Dudbridge F, Shah T, Engmann J, et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes. 2015;64:1830–40.

    Article  CAS  PubMed  Google Scholar 

  17. Morris RW, Cooper JA, Shah T, Wong A, Drenos F, Engmann J, et al. Marginal role for 53 common genetic variants in cardiovascular disease prediction. Heart. 2016;102:1640–7.

    Article  PubMed  Google Scholar 

  18. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009;41:56–65.

    Article  CAS  PubMed  Google Scholar 

  19. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loi for blood lipids. Nature. 2010;466:707–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Johansen CT, Hegele RA. Allelic and phenotypic spektrum of plasma triglycerides. Biochem Biophys Acta. 2012;1821:833–42.

    CAS  PubMed  Google Scholar 

  21. Johansen CT, Wang J, Lanktree MB, McIntyre AD, Ban MR, Martins RA, et al. An increased burden of common and rare lipid-associated risk alleles contributes to the phenotypic spectrum of hypertriglyceridemia. Arterioscler Thromb Vasc Biol. 2011;31:1916–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cífková R, Skodová Z, Bruthans J, Adámková V, Jozífová M, Galovcová M, et al. Longitudinal trends in major cardiovascular risk factors in the Czech population between 1985 and 2007/8. Czech MONICA and Czech post-MONICA. Atherosclerosis. 2010;211:676–81.

    Article  CAS  PubMed  Google Scholar 

  23. Hubacek JA, Dlouha D, Lanska V, Adamkova V. Strong gender-specific additive effects of the NYD-SP18 and FTO variants on BMI values. Physiol Res. 2015;64(Suppl 3):S419–26.

    CAS  PubMed  Google Scholar 

  24. Hubacek JA, Stanek V, Gebauerova M, Adamkova V, Lesauskaite V, Zaliaduonyte-Peksiene D, et al. Traditional risk factors of acute coronary syndrome in four different male populations—total cholesterol value does not seem to be relevant risk factor. Physiol Res. 2017;66(Suppl. 1):S121–8.

    CAS  PubMed  Google Scholar 

  25. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for DNA extraction from human nucleated cells. Nucleic Acid Res. 1988;16:1215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Dron JS, Wang J, Cao H, McIntyre AD, Iacocca MA, Menard JR, et al. Severe hypertriglyceridemia is primarily polygenic. J Clin Lipidol. 2019;13:80–8.

    Article  PubMed  Google Scholar 

  27. Dron JS, Hegele RA. The evolution of genetic-based risk scores for lipids and cardiovascular disease. Curr Opin Lipidol. 2019;30:71–81.

    Article  CAS  PubMed  Google Scholar 

  28. Pennacchio LA, Olivier M, Hubacek JA, Cohen JC, Cox DR, Fruchart JC, et al. An apolipoprotein influencing triglycerides in humans and mice revealed by comparative sequencing. Science. 2001;294:169–73.

    Article  CAS  PubMed  Google Scholar 

  29. Pennacchio LA, Olivier M, Hubacek JA, Krauss RM, Rubin EM, Cohen JC. Two independent apolipoprotein A5 haplotypes influence human plasma triglyceride levels. Hum Mol Genet. 2002;11:3031–8.

    Article  CAS  PubMed  Google Scholar 

  30. Hubácek JA, Adámková V, Vrablík M, Kadlecová M, Zicha J, Kunes J, et al. Apolipoprotein A5 in health and disease. Physiol Res. 2009;58(Suppl 2):S101–9.

    PubMed  Google Scholar 

  31. Hubacek JA. Apolipoprotein A5 fifteen years anniversary: lessons from genetic epidemiology. Gene. 2016;592:193–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Vrablik M, Hubacek JA, Dlouha D, Catny M, Adamkova V, Ceska R. Strong association between APOA5 gene polymorphisms and hypertriglyceridemic episodes. Folia Biol (Praha) (in press).

  33. Kooner JS, Chambers JC, Aguilar-Salinas CA, Hinds DA, Hyde CL, Warnes GR, et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat Genet. 2008;40:149–51.

    Article  CAS  PubMed  Google Scholar 

  34. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vrablik M, Ceska R, Adamkova V, Peasey A, Pikhart H, Kubinova R, et al. MLXIPL variant in individuals with low and high triglyceridemia in white population in Central Europe. Hum Genet. 2008;124:553–5.

    Article  CAS  PubMed  Google Scholar 

  36. Rašlová K, Dobiášová M, Hubáček JA, Bencová D, Siváková D, Danková Z, et al. Association of metabolic and genetic factors with cholesterol esterification rate in HDL plasma and atherogenic index of plasma in a 40 years old Slovak population. Physiol Res. 2011;60:785–95.

    PubMed  Google Scholar 

  37. Hubacek JA, Adamkova V, Lanska V, Dlouha D. Polygenic hypercholesterolemia: examples of GWAS results and their replication in the Czech-Slavonic population. Physiol Res. 2017;66(Suppl. 1):S101–11.

    CAS  PubMed  Google Scholar 

  38. Mavaddat N, Pharoah PD, Michailidou K, Tyrer J, Brook MN, Bolla MK, et al. Prediction of breast cancer risk based on profiling with common genetic variants. Natl Cancer Inst. 2015;107:djv036.

    Article  CAS  Google Scholar 

  39. Humphries SE, Yiannakouris N, Talmud PJ. Cardiovascular disease risk prediction using genetic information (gene scores): is it really informative? Curr Opin Lipidol. 2008;19:128–32.

    Article  CAS  PubMed  Google Scholar 

  40. Smith JA, Ware EB, Middha P, Beacher L, Kardia SL. Current applications of genetic risk scores to cardiovascular outcomes and subclinical phenotypes. Curr Epidemiol Rep. 2015;2:180–90.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Apellaniz-Ruiz M, Gallego C, Ruiz-Pinto S, Carracedo A, Rodríguez-Antona C. Human genetics: international projects and personalised medicine. Drug Metab Pers Ther. 2016;31:3–8.

    PubMed  Google Scholar 

  42. Visvikis-Siest S, Aldasoro Arguinano AA, Stathopoulou M, Xie T, Petrelis A, Weryha G, et al. 8th santorini conference: systems medicine and personalized health and therapy, Santorini, Greece, 3–5 October 2016. Drug Metab Pers Ther. 2017;32:119–27.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM-DEM-LAR), Videnska 1958/9, 140 21, Prague 4, Czech Republic

    Jaroslav A. Hubacek & Dana Dlouha

  2. Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic

    Vera Adamkova

  3. 3rd Department of Internal Medicine, 1st Faculty of Medicine, Charles University, Prague, Czech Republic

    Lucie Schwarzova, Richard Ceska, Martin Satny & Michal Vrablik

  4. Statistical Unit, Institute for Clinical and Experimental Medicine, Prague, Czech Republic

    Vera Lanska

Authors
  1. Jaroslav A. Hubacek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dana Dlouha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vera Adamkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lucie Schwarzova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vera Lanska
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Richard Ceska
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Martin Satny
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michal Vrablik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaroslav A. Hubacek.

Ethics declarations

Conflict of Interest

All authors (J.A.H., D.D., V.A., L.S., V.L., R.C., M.S., M.V.) declare no conflict of interest related to this study.

Funding

This study was supported by the Ministry of Health of the Czech Republic, Grant number 15-28876A. All rights reserved. The sponsor played no role in the study design organization; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Ethical Standards and Informed Consent

All performed analyses were in accordance with the ethical standards of the institutional and national research committees and with the 1964 Declaration of Helsinki and its later amendments. Informed consent was obtained from all individual participants enrolled in the study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 50 kb)

Supplementary material 2 (DOC 34 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hubacek, J.A., Dlouha, D., Adamkova, V. et al. The Gene Score for Predicting Hypertriglyceridemia: New Insights from a Czech Case–Control Study. Mol Diagn Ther 23, 555–562 (2019). https://doi.org/10.1007/s40291-019-00412-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40291-019-00412-2

Search

Navigation