Statins are highly effective cholesterol lowering agents. However, there is a very large inter-subject variation in the response to statin therapy, and the mechanisms responsible for such variability remain poorly understood. This observation holds when outcome is defined as a cardiovascular event or measured as a biomarker such as LDL-C or CRP. Moreover, single biomarkers provide limited prediction for clinical response to treatment. This study was designed to exploit the large differences in response to simvastatin treatment in the CAP trial and enable the development of metabolite signatures that predict individual response to treatment. Our primary biomarker of response to simvastatin treatment was change in LDL-C. The study subjects were selected to provide data at both extremes of the range of differences seen with treatment. In addition, we used CRP as a secondary measure of treatment response. Both LDL-C and CRP have been shown to be predictive of cardiovascular outcome in large statin trials, however, each biomarker has limited predictive power for individual cardiovascular outcome. Because this work was initiated as a pilot study, the experimental design was to accentuate the differences between the extremes. Additional studies are underway to investigate the compete spectrum of response from patients in the CAP trial. The CAP trial was selected for the initial and the larger study so the metabolomic data collected in these studies and the interpretation of that data could be incorporated with the pharmacogenomic data being collected as part of the original trial design.
Metabolomics applies powerful analytical tools that enable mapping of biochemical pathways modified by disease or drug treatment. In addition, metabolomic “signatures” present in patients who do and do not respond to drug therapy, i.e., signatures that reflect the drug response phenotype, could lead to mechanistic hypotheses that provide insights into the underlying basis for individual variation in drug response. This study defines global effects of simvastatin on metabolite concentrations and distribution within multiple lipid classes.
The largest treatment effects induced by simvastatin were decreased total lipid class changes (Fig. ). The changes in some lipid classes have been studied previously (Simon et al. 2006
; Ozerova et al. 2001
), but no detailed map has been established that provides all of the concentration and composition of fatty acids within each lipid class. Because our samples were selected from individuals who were among the highest or lowest responders for changes in LDL concentration, it is not surprising that there were large changes in CE and FC in the good responders group while there were limited changes in the poor responders.
Other changes that were not expected included decreases in phospholipids PC and PE which have key roles in lipoprotein membrane structure and function. The concentration of PC, in particular, was closely correlated with the concentration of CE (R2
= 0.479, data not shown). We found the relationship between PC and CE concentrations held for both good and poor responders and pre- to post-treatment. We would expect this result if the changes in PC and PE concentration were in response to therapy-induced changes in cholesterol concentration. While simvastatin has been postulated to directly decrease the synthesis of phospholipids (Yanagita et al. 1994
), the close relationship between CE and PC indicates potential a secondary effect of cholesterol synthesis on phospholipids rather than a direct effect of simvastatin.
Precursor/product ratios were used to estimate changes in the production of specific metabolites. Good responders had a significant increase in the ratio of 20:4n6/20:3n6. This ratio was used as a measure of delta-5 desaturase activity. Simvastatin treatment has been shown to increase the activity of delta-6 and delta-5 desaturases via SREBP resulting in increased formation of arachidonic acid from linoleic acid (18:2n6) (Rise et al. 2007
). In this study, only good responders had a statistically significant increase in delta-5 desaturase product/substrate ratios (20:4n6/20:3n6), yet both the good and the poor responders had significant increases in the mole percentage of arachidonic acid within multiple lipid classes (Figs. , ). In addition, the good responders had decreased mole percentages of 18:2n6 across multiple lipid classes, again emphasizing the increased activation of the metabolic pathway. Previous research has shown the decreased linoleic acid and increased arachidonic acid could be the result of increased delta-5 and delta-6 desaturase activities in response to simvastatin treatment (Jula et al. 2005
; Rise et al. 2007
; Rise et al. 2001
). However, the previous published research did not differentiate either between lipid classes or between good and poor responders. Good responders appeared to have had a much stronger increase in the use of linoleic acid for the production of arachidonic acid than the poor responders, and this effect was detected in both PC and TG (Fig. ).
Fig. 6 Metabolic pathway changes with simvastatin treatment in subjects segregated by response to LDL-C. Ratios of precursor/products in TG and PC lipid metabolites were used to estimate changes in the metabolic pathways for biosynthesis of n6 fatty acids pre- (more ...)
Interestingly, PC18:3n3 and TG18:3n3 were also decreased in good responders with statin treatment. The same hepatic enzymes that modify n6 fatty acids will also desaturate and elongate 18:3n3–22:5n3 and 22:6n3. In the good responders, we did find a significant increase in the percentage of PC22:6n3, but not TG22:6n3, perhaps reflecting the lower concentration and percentage of this metabolite in TG. This study demonstrates that the changes in desaturation and elongation of PUFAs were more pronounced in good responders, but were also present in the poor responders (Figs. , ).
Differences in the composition of CE, specifically CE palmitic (16:0) and arachidonic acid and the ratio of 16:0/20:4n6, have been associated with atherosclerotic potential and risk of coronary disease (Liu et al. 1995
; Messner et al. 1998
; Sundstrom et al. 2001
; Ma et al. 1997
; Lee et al. 2004
). Studies have indicated a relationship between CE composition and insulin resistance or metabolic syndrome, known risk factors for CVD (Moilanen et al. 1986
; Klein-Platat et al. 2005
). In this study, the ratio of CE16:0/20:4n6 was similar in both good and poor responders pre-treatment (1.57 ± 0.42 and 1.52 ± 0.28, respectively), and changed significantly post-treatment, but the ratio decreased more in the good responder group (good 1.22 ± 0.27 (P
< 0.0001) and poor 1.38 ± 0.29 (P
= 0.0066) responders).
We used correlation analysis to evaluate which baseline metabolites were related to pre-treatment LDL-C concentrations and which changes in metabolites were related to LDL-C response to statins. Strong correlations with CE and PC metabolites were found for both the baseline correlations and the change in concentration correlations. These changes confirm our observation that the amount of lipoprotein cholesterol at baseline influences the amount of response to simvastatin. Of interest is the observation that the baseline mole percentages of EPA (20:5n3) and DHA (22:6n3) are positively correlated with the baseline concentrations of LDL-C. This may reflect a dietary effect on cholesterol concentrations.
Simvastatin treatment may reduce inflammation independent of its effects on cholesterol biosynthesis (Sotiriou and Cheng 2000
). Changes in cytokine biomarkers of inflammation and eicosanoid products have been identified with statin treatment (Cipollone et al. 2003
). As statin treatment has anti-inflammatory effects, the rise in the proinflammatory substrate, arachidonic acid, in many lipid classes is interesting. This may reflect a decreased use of this fatty acid for synthesis of inflammatory lipids. Since the increased compositional changes in arachidonic acid were found in both the good and poor responders, if this effect is real, it is likely that the anti-inflammatory effects of simvastatin occur in both groups. CRP was measured in the CAP study, but the results were not presented in the original paper. We used CRP as a marker of inflammation in this study, hypothesizing that both groups would have differences in inflammation and that the inflammation-induced changes in lipids would be independent of LDL-C-related changes. The absolute magnitude of the change in CRP was of similar magnitude for both groups (Table ); however the relative percentage change was higher in the poor responders than the good responders (22% vs. 15%, respectively). Because these subjects were selected based their change in LDL-C with statin treatment, the change in CRP is biased towards good or poor LDL-C response.
Correlations between metabolites and CRP were used to evaluate the relationships between the metabolites and inflammation. We found a clear positive relationship between the mole percentage of 16:0 in several lipid classes and CRP, and a negative relationship between linoleic acid and CRP at baseline. Saturated fatty acids have been associated with increased inflammation and increased linoleic acid is known to decrease inflammatory markers (Ferrucci et al. 2006
). This analysis thus confirms the known relationships between lipid metabolism and inflammation in our subjects. For each of the correlation analyses, there was virtually no overlap in the metabolites correlated with CRP and those correlated with LDL-C, consistent with the finding that the change in CRP was independent of the change in LDL-C.
Finally, we sought baseline metabolites that predicted the outcome of statin treatment, either LDL-C or CRP. Metabolites whose concentration was most predictive of LDL changes were PC18:2n6 and related PC lipid families, CE18:1n7, and FA18:3n3 and the related FAn3. The PC and CE metabolites were also correlated with baseline LDL concentrations. Since baseline LDL concentrations were also predictive of response, we assume that these metabolites are indicative of the initial status of the subjects rather than being purely predictive of the change in LDL. For the metabolites whose mole percentage was predictive of LDL changes, the strongest correlations both positive and negative were found in the DGs. DGs were not predictive of baseline LDL and therefore may represent a more selective marker to predict the response to statin treatment. The DG may reflect the overall amount of lipase activity in the system and the uptake and release of lipids by peripheral tissues.
Of the metabolites whose concentration was most predictive of CRP changes, five were plasmalogens, and the others were PC and CE metabolites. Plasmalogen and PC metabolites were also most strongly predictive of CRP response when expressed in terms of lipid composition (mole percentage). Plasmalogens are ether-linked phospholipids characterized by a vinyl aldehyde at the first position of the glycerol backbone (Lessig and Fuchs 2009
), and an ester-linked fatty acid in the second position. Plasmalogens function as reservoirs for second messenger fatty acids such as arachidonic acid and DHA, and have been postulated to act as antioxidants (Engelmann et al. 1994
; Hahnel et al. 1999
; Lessig and Fuchs 2009
). Plasmalogen concentrations decrease with age and have been identified with inflammatory-related diseases such as atherosclerosis and Alzheimer’s disease (Goodenowe et al. 2007
; Farooqui et al. 2006
; Lessig and Fuchs 2009
). Baseline PE plasmalogen concentrations were positively correlated with the response to simvastatin treatment, i.e. the higher the concentration of plasmalogens at baseline, the greater reduction in CRP with simvastatin treatment. Concurrently, PC plasmalogens were negatively correlated with CRP response to treatment. Since baseline plasmalogen concentrations were not correlated with baseline CRP concentrations, we assume that we are not seeing the relationship between baseline inflammation and response to treatment, rather that plasmalogens are predictive of the ability of the system to decrease inflammation in response to statin treatment. Plasmalogens as a class, (concentrations or mole percentages), did not respond to treatment differently between good and poor responders nor did CRP. The changes in CRP and perhaps other inflammatory molecules in these subjects appear to be independent of most metabolites, but may be related to changes in both arachidonic acid and plasmalogen concentrations. From these preliminary studies, we are unable to tell whether this anti-inflammatory effect would be enough to reduce the risk of CVD in the poor responders. The results of this study emphasize the need to analyze the full range of treatment responses rather than the mean effect of treatment across all subjects. Additional studies to investigate the effects of simvastatin treatment on concentrations of the inflammatory lipids and cytokines in good and poor responders are underway.
The limitations of this study include the relatively small sample size, the likelihood of type 1 errors due to multiple testing although we have corrected to the degree possible by use of q values, use of a single statin and a single dose, and the possibility, as suggested in a recent report (Laaksonen et al. 2006
) that metabolomic profiles can differ among individual statin drugs. Hence the present findings require confirmation in larger studies employing other statins. The major changes in the lipid profiles identified in this study, such as the total lipid classes and cholesterol changes, as well as changes in inflammation related pathways, could be shared among different members of the statin family of drugs. This is currently being investigated in larger studies and where simvastatin is compared to pravastatin.