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A Systems Biology Strategy Reveals Biological Pathways and Plasma Biomarker Candidates for Potentially Toxic Statin-Induced Changes in Muscle
Open accessAbstract
BACKGROUND: Aggressive lipid lowering with high doses of statins increases the risk of statin-induced myopathy. However, the cellular mechanisms leading to muscle damage are not known and sensitive biomarkers are needed to identify patients at risk of developing statin-induced serious side effects. METHODOLOGY: We performed bioinformatics analysis of whole genome expression profiling of muscle specimens and UPLC/MS based lipidomics analyses of plasma samples obtained in an earlier randomized trial from patients either on high dose simvastatin (80 mg), atorvastatin (40 mg), or placebo. PRINCIPAL FINDINGS: High dose simvastatin treatment resulted in 111 differentially expressed genes (1.5-fold change and p-value<0.05), while expression of only one and five genes was altered in the placebo and atorvastatin groups, respectively. The Gene Set Enrichment Analysis identified several affected pathways (23 gene lists with False Discovery Rate q-value<0.1) in muscle following high dose simvastatin, including eicosanoid synthesis and Phospholipase C pathways. Using lipidomic analysis we identified previously uncharacterized drug-specific changes in the plasma lipid profile despite similar statin-induced changes in plasma LDL-cholesterol. We also found that the plasma lipidomic changes following simvastatin treatment correlate with the muscle expression of the arachidonate 5-lipoxygenase-activating protein. CONCLUSIONS: High dose simvastatin affects multiple metabolic and signaling pathways in skeletal muscle, including the pro-inflammatory pathways. Thus, our results demonstrate that clinically used high statin dosages may lead to unexpected metabolic effects in non-hepatic tissues. The lipidomic profiles may serve as highly sensitive biomarkers of statin-induced metabolic alterations in muscle and may thus allow us to identify patients who should be treated with a lower dose to prevent a possible toxicity.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Prevention of Coronary Heart Disease with Pravastatin in Men with Hypercholesterolemia
Shepherd, J., Cobbe, S. M., Ford, I., Isles, C. G., Lorimer, A. R., Macfarlane, P. W., McKillop, J. H., & Packard, C. J. (1995) · New England Journal of Medicine · doi:10.1056/nejm199511163332001 · Cited by 7,449
Sacks, F. M., Pfeffer, M. A., Moye, L. A., Rouleau, J. L., Rutherford, J. D., Cole, T. G., Brown, L., Warnica, J. W., Arnold, J. M. O., Wun, C.-C., et al. (1996) · New England Journal of Medicine · doi:10.1056/nejm199610033351401 · Cited by 7,439
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905
Bruckert, E., Hayem, G., Dejager, S., Yau, C., & Bégaud, B. (2005) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-005-5686-z · Cited by 1,226
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Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B. S., van Ommen, B., Pujos-Guillot, E., Verheij, E., Wishart, D., & Wopereis, S. (2009) · Metabolomics · doi:10.1007/s11306-009-0168-0 · Cited by 565
Orešič, M., Simell, S., Sysi-Aho, M., Näntö-Salonen, K., Seppänen-Laakso, T., Parikka, V., Katajamaa, M., Hekkala, A., Mattila, I., Keskinen, P., et al. (2008) · The Journal of Experimental Medicine · doi:10.1084/jem.20081800 · Cited by 449
Self-citationNygren, H., Seppänen-Laakso, T., Castillo, S., Hyötyläinen, T., & Orešič, M. (2011) · Methods in Molecular Biology · doi:10.1007/978-1-61737-985-7_15 · Cited by 154
Self-citationMuntean, D. M., Thompson, P. D., Catapano, A. L., Stasiolek, M., Fabis, J., Muntner, P., Serban, M.-C., & Banach, M. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2016.09.001 · Cited by 82
Leskinen, T., Rinnankoski-Tuikka, R., Rintala, M., Seppänen-Laakso, T., Pöllänen, E., Alen, M., Sipilä, S., Kaprio, J., Kovanen, V., Rahkila, P., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012609 · Cited by 75
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Metadata
- Authors
- Laaksonen, R., Katajamaa, M., Päivä, H., Sysi-Aho, M., Saarinen, L., Junni, P., Lütjohann, D., Smet, J., Van Coster, R., Seppänen-Laakso, T., Lehtimäki, T., Soini, J., & Orešič, M.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2006-12-20
- Volume
- 1
- Issue
- 1
- Pages
- e97
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2102452049
- Cited by
- 223
- References
- 6
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- gold
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