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Acta Diabetologica

, Volume 54, Issue 11, pp 1031–1038 | Cite as

Association between lipoprotein(a) level and type 2 diabetes: no evidence for a causal role of lipoprotein(a) and insulin

  • Nikolaus Buchmann
  • Markus Scholz
  • Christina M. Lill
  • Ralph Burkhardt
  • Rahel Eckardt
  • Kristina Norman
  • Markus Loeffler
  • Lars Bertram
  • Joachim Thiery
  • Elisabeth Steinhagen-Thiessen
  • Ilja Demuth
Original Article
  • 334 Downloads

Abstract

Aims

Inverse relationships have been described between the largely genetically determined levels of serum/plasma lipoprotein(a) [Lp(a)], type 2 diabetes (T2D) and fasting insulin. Here, we aimed to evaluate the nature of these relationships with respect to causality.

Methods

We tested whether we could replicate the recent negative findings on causality between Lp(a) and T2D by employing the Mendelian randomization (MR) approach using cross-sectional data from three independent cohorts, Berlin Aging Study II (BASE-II; n = 2012), LIFE-Adult (n = 3281) and LIFE-Heart (n = 2816). Next, we explored another frequently discussed hypothesis in this context: Increasing insulin levels during the course of T2D disease development inhibits hepatic Lp(a) synthesis and thereby might explain the inverse Lp(a)–T2D association. We used two fasting insulin-associated variants, rs780094 and rs10195252, as instrumental variables in MR analysis of n = 4937 individuals from BASE-II and LIFE-Adult. We further investigated causality of the association between fasting insulin and Lp(a) by combined MR analysis of 12 additional SNPs in LIFE-Adult.

Results

While an Lp(a)–T2D association was observed in the combined analysis (meta-effect of OR [95% CI] = 0.91 [0.87–0.96] per quintile, p = 1.3x10-4), we found no evidence of causality in the Lp(a)–T2D association (p = 0.29, fixed effect model) when using the variant rs10455872 as the instrumental variable in the MR analyses. Likewise, no evidence of a causal effect of insulin on Lp(a) levels was found.

Conclusions

While these results await confirmation in larger cohorts, the nature of the inverse Lp(a)–T2D association remains to be elucidated.

Keywords

Insulin Lipoprotein(a) Mendelian randomization Type 2 diabetes 

Notes

Acknowledgements

We thank Dr. Tian Liu for her help with processing and managing the BASE-II GWAS data.

Funding

The BASE-II study was supported by the German Federal Ministry of Education and Research (BMBF Grant Numbers 16SV5536K, 16SV5538, 16SV5837 and 16SV5537). This work is supported by LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative.

Statement of data availability

Due to concerns for participant privacy, data are available only upon request. External scientists may apply to the Steering Committee of BASE-II for data access. Please contact Katrin Schaar, scientific coordinator, at schaar@mpib-berlin.mpg.de.

Author contributions

N.B. researched data and wrote the manuscript. M.S. researched data, designed/performed MR analyses and wrote the manuscript. C.L., R.B., K.N. and L.B. reviewed/edited the manuscript. R.E., M.L. and J.T. critically reviewed the manuscript. E.S.T. contributed to discussion and critically reviewed the manuscript. I.D. conceived the project, contributed to the analyses and wrote the manuscript. I.D. and M.S. are the guarantors of this work and, as such, had full access to all BASE-II data (I.D.) and LIFE-Adult/LIFE-Heart data (M.S.) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Compliance with ethical standards

Conflict of interest

Dr. Steinhagen-Thiessen reports grants and personal fees from Sanofi, personal fees from MSD, Fresenius, personal fees from Amgen and Chiesi, outside the submitted work. Dr. Demuth reports grants from Sanofi, personal fees from uniQure biopharma B.V., outside the submitted work. Dr. Scholz received funding from Merck Serono in the framework of a pharmacokinetic modelling project not related to the present work. All other authors had nothing to disclose.

Human and animal rights

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the 1975 Declaration of Helsinki, as revised in 2008 (5).

Informed consent

Informed consent was obtained from all patients for being included in the study.

Supplementary material

592_2017_1036_MOESM1_ESM.pptx (112 kb)
Supplementary material 1 (PPTX 112 kb)
592_2017_1036_MOESM2_ESM.xlsx (25 kb)
Supplementary material 2 (XLSX 24 kb)
592_2017_1036_MOESM3_ESM.docx (18 kb)
Supplementary material 3 (DOCX 18 kb)

References

  1. 1.
    Berg K (1963) A new serum type system in man—the Lp system. Acta Pathol Microbiol Scand 59:369–382CrossRefPubMedGoogle Scholar
  2. 2.
    Danesh J, Collins R, Peto R (2000) Lipoprotein(a) and coronary heart disease. Meta-analysis of prospective studies. Circulation 102:1082–1085CrossRefPubMedGoogle Scholar
  3. 3.
    Nordestgaard BG, Chapman MJ, Ray K et al (2010) Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J 31:2844–2853CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kronenberg F, Utermann G (2013) Lipoprotein(a): resurrected by genetics. J Intern Med 273:6–30CrossRefPubMedGoogle Scholar
  5. 5.
    Mora S, Kamstrup PR, Rifai N, Nordestgaard BG, Buring JE, Ridker PM (2010) Lipoprotein(a) and risk of type 2 diabetes. Clin Chem 56:1252–1260CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Ye Z, Haycock PC, Gurdasani D et al (2014) The association between circulating lipoprotein(a) and type 2 diabetes: is it causal? Diabetes 63:332–342CrossRefPubMedGoogle Scholar
  7. 7.
    Kamstrup PR, Nordestgaard BG (2013) Lipoprotein(a) concentrations, isoform size, and risk of type 2 diabetes: a Mendelian randomisation study. Lancet Diabetes Endocrinol 1:220–227CrossRefPubMedGoogle Scholar
  8. 8.
    Liu C, Xu MX, He YM, Zhao X, Du XJ, Yang XJ (2017) Lipoprotein(a) is not significantly associated with type 2 diabetes mellitus: cross-sectional study of 1604 cases and 7983 controls. Acta Diabetol 54:443–453CrossRefPubMedGoogle Scholar
  9. 9.
    Neele DM, de Wit EC, Princen HM (1999) Insulin suppresses apolipoprotein(a) synthesis by primary cultures of cynomolgus monkey hepatocytes. Diabetologia 42:41–44CrossRefPubMedGoogle Scholar
  10. 10.
    Ding L, Song A, Dai M et al (2015) Serum lipoprotein(a) concentrations are inversely associated with T2D, prediabetes, and insulin resistance in a middle-aged and elderly Chinese population. J Lipid Res 56:920–926CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Heller FR, Jamart J, Honore P et al (1993) Serum lipoprotein(a) in patients with diabetes mellitus. Diabetes Care 16:819–823CrossRefPubMedGoogle Scholar
  12. 12.
    Gerstorf D, Bertram L, Lindenberger U et al (2016) Editorial. Gerontology 62:311–315CrossRefPubMedGoogle Scholar
  13. 13.
    Bertram L, Bockenhoff A, Demuth I et al (2014) Cohort profile: the Berlin aging study II (BASE-II). Int J Epidemiol 43:703–712CrossRefPubMedGoogle Scholar
  14. 14.
    Schroder J, Ansaloni S, Schilling M et al (2014) MicroRNA-138 is a potential regulator of memory performance in humans. Front Hum Neurosci 8:501CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Loeffler M, Engel C, Ahnert P et al (2015) The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15:691CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Beutner F, Teupser D, Gielen S et al (2011) Rationale and design of the Leipzig (LIFE) Heart Study: phenotyping and cardiovascular characteristics of patients with coronary artery disease. PLoS ONE 6:e29070CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Nikpay M, Goel A, Won HH et al (2015) A comprehensive 1000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 47:1121–1130CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Burkhardt R, Kirsten H, Beutner F et al (2015) Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood. PLoS Genet 11:e1005510CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    World Health Organization (2003) Screening for type 2 diabetes: report of a World Health Organization and International Diabetes Federation meeting. World Health Organization, GenevaGoogle Scholar
  20. 20.
    Ryden L, Grant PJ, Anker SD (2013) ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the task force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J 34:3035–3087CrossRefPubMedGoogle Scholar
  21. 21.
    Nelson C, Startz R (1988) The distribution of the instrumental variables estimator and its t-ratio when the instrument is a poor one. National Bureau of Economic Research, CambridgeCrossRefGoogle Scholar
  22. 22.
    Qi Q, Workalemahu T, Zhang C, Hu FB, Qi L (2012) Genetic variants, plasma lipoprotein(a) levels, and risk of cardiovascular morbidity and mortality among two prospective cohorts of type 2 diabetes. Eur Heart J 33:325–334CrossRefPubMedGoogle Scholar
  23. 23.
    Erqou S, Kaptoge S, Perry PL, Emerging Risk Factors Collaboration et al (2009) Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA 302:412–423CrossRefPubMedGoogle Scholar
  24. 24.
    Nelson CR, Startz R (1990) The distribution of the instrumental variables estimator at its t-ratio when the instrument is a poor one. J Bus 63:125–140CrossRefGoogle Scholar
  25. 25.
    Efron B (1981) Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika 68:589–599CrossRefGoogle Scholar
  26. 26.
    Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163CrossRefPubMedGoogle Scholar
  27. 27.
    Burgess S, Dudbridge F, Thompson SG (2016) Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med 35:1880–1906CrossRefPubMedGoogle Scholar
  28. 28.
    Dupuis J, Langenberg C, Prokopenko I (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42:105–116CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Scott RA, Lagou V, Welch RP (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44:991–1005CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–525CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Murase T, Okubo M, Amemiya-Kudo M, Ebara T, Mori Y (2008) Impact of elevated serum lipoprotein(a) concentrations on the risk of coronary heart disease in patients with type 2 diabetes mellitus. Metabolism 57:791–795CrossRefPubMedGoogle Scholar
  33. 33.
    Andersson DK, Lundblad E, Svardsudd K (1993) A model for early diagnosis of type 2 diabetes mellitus in primary health care. Diabet Med J Br Diabet Assoc 10:167–173CrossRefGoogle Scholar
  34. 34.
    Cobbaert C, Mulder P, Lindemans J, Kesteloot H (1997) Serum LP(a) levels in African aboriginal Pygmies and Bantus, compared with Caucasian and Asian population samples. J Clin Epidemiol 50:1045–1053CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l. 2017

Authors and Affiliations

  • Nikolaus Buchmann
    • 1
  • Markus Scholz
    • 2
    • 3
  • Christina M. Lill
    • 4
    • 5
  • Ralph Burkhardt
    • 3
    • 6
  • Rahel Eckardt
    • 1
  • Kristina Norman
    • 1
    • 7
  • Markus Loeffler
    • 2
    • 3
  • Lars Bertram
    • 5
    • 8
  • Joachim Thiery
    • 3
    • 6
  • Elisabeth Steinhagen-Thiessen
    • 1
  • Ilja Demuth
    • 1
    • 9
  1. 1.Lipid Clinic at the Interdisciplinary Metabolism CenterCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
  2. 2.Institute for Medical Informatics, Statistics and Epidemiology (IMISE)University of LeipzigLeipzigGermany
  3. 3.LIFE Leipzig Research Center of Civilization DiseasesUniversity of LeipzigLeipzigGermany
  4. 4.Genetic and Molecular Epidemiology Group, Institute of NeurogeneticsUniversity of LübeckLübeckGermany
  5. 5.Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Integrative and Experimental GenomicsUniversity of LübeckLübeckGermany
  6. 6.Institute of Laboratory Medicine, Clinical Chemistry and Molecular DiagnosticsUniversity HospitalLeipzigGermany
  7. 7.Research Group on GeriatricsCharité – Universitätsmedizin BerlinBerlinGermany
  8. 8.School of Public Health, Faculty of MedicineImperial CollegeLondonUK
  9. 9.Institute of Medical and Human GeneticsCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany

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