Journal of APPLIED BIOMEDICINE
ISSN 1214-0287 (on-line)
ISSN 1214-021X (printed)
Volume 10 (2012), No 1, p 19-28
DOI 10.2478/v10136-012-0001-3
Variant within CELSR2/PSRC1/SORT1, but not within CILP2/PBX4, PCSK9 and APOB genes, has a potential to influence statin treatment efficacy
Jaroslav Alois Hubacek, Vera Adamkova, Vera Lanska, Dana Dlouha, Jitka Rynekrova, Lukas Zlatohlavek, Martina Prusikova, Richard Ceska, Michal Vrablik
Address: Jaroslav Alois Hubacek, IKEM-CEM-LMG, Videnska 1958/9, 140 21 Prague 4, Czech Republic
jahb@ikem.cz
Received 29th July 2011.
Revised 20th September 2011.
Published online 25th October 2011.
Full text article (pdf)
Summary
Key words
Introduction
Patients and methods
Results
Discussion
Acknowledgement
References
SUMMARY
Statins have become a cornerstone of cardiovascular prevention. However, their lipid lowering efficacy and, thus also, impact on event risk reduction, differ substantially between individuals. The major part of this inter-individual difference can be explained by genetic factors. Using the GWA approach, candidate genes that may modify the response to statin treatment have been detected. Variants rs646776 (CELSR2/PSRC1/SORT1), rs16996148 (CILP2/PBX4), rs11206510 (PCSK9) and rs693 (APOB) were analysed in 370 (146 males) dyslipidemic patients treated with statins (46.6% simvastatin, 41.5% atorvastatin, 11.9% lovastatin, 10 or 20 mg/day) and 470 normolipidemic controls (188 males). Lipid levels were available prior to and after 8-12 weeks of therapy. There was a significant decrease both in the total (7.36±1.28 to 5.43±1.01 mmol/l) and LDL-cholesterol (4.72±1.35 to 3.19±0.98 mmol/l) after treatment. The genotype frequencies of the three SNPs differed between patients and controls (rs646776, rs16996148, rs693). The carriers of the minor rs599838 genotype had a significantly lower response to statin treatment compared to common homozygotes (LDL-cholesterol, delta -20.3% vs. delta -32.0%). No other significant associations with lipid changes were detected. Together with variations of other, multiple gene loci the variant at CELSR2/PSRC1/SORT1 gene cluster may be useful for individualization of statin treatment leading to better outcomes of the treatment.
KEY WORDS
dyslipidemia; statins; gene variants; pharmacogenetics; treatment efficacy; CELSR2/PSRC1/SORT1; CILP2/PBX4; PCSK9; APOB
Abbreviations:
CELSR2/PSRC1/SORT1, cadherin EGF lag seven-pass G-type receptor 2/ proline/serine-rich
coiled-coil protein 1/ sortilin1;
CILP2/PBX4, cartilage intermediate layer protein 2/
pre-B-cell leukemia transcription factor 4;
PCSK9, proprotein convertase subtilisin/kexin-type 9;
GWA, genome wide association.
INTRODUCTION
Inhibitors of hydroxy-methylglutaryl coenzyme A
reductase (statins) have become a cornerstone of
cardiovascular prevention over the past two decades.
Through the reduction of plasma atherogenic
lipoprotein levels together with a number of other
pleiotropic effects, statins reduce the risk of cardio- as
well as cerebrovascular events in a broad spectrum of
population groups (Sadowitz et al. 2010).
However, the lipid lowering efficacy of statins
varies widely among individuals - thus the use of the
same statin in different patients produces
LDL-cholesterol lowering between 8 and 55%,
triglyceride (TG) reduction of 7 to 30% and
HDL-cholesterol rising from 0 to 10% (Sever et al.
2003). Also the time to reach maximum efficacy
differs significantly between individuals (Hachem and
Mooradian 2006). On the other hand, the individual
response does not significantly change over time and
it is most likely to have a genetic background. Gene
variants impact both the pharmacokinetics (e.g. genes
encoding for statin metabolism or transmembrane
transport proteins) and pharmacodynamics (e.g.
hydroxy-methylglutaryl coenzyme A reductase and
cholesterol-7alpha hydroxylase genes, as key
enzymes in cholesterol homeostasis) of statins.
It is evident that the heterogeneity of statin effects
on plasma lipid and lipoprotein levels results in the
heterogeneous impact of treatment on event rates.
Thus, it seems obvious that the genetic determination
of the greater efficacy of particular statin type
translates into the improved prognosis of a patient
compared with another statin. Understanding the
genetic determination of statin treatment efficacy
would enable improved targeting of treatment and
individualization of expensive therapy. Moreover,
selecting the most effective statin type for an
individual based on his/her genetic equipment would
lead to a reduction in the doses necessary to achieve
target levels of atherogenic lipoproteins. This should
produce another benefit of such an approach - a
reduction in the incidence and severity of side effects.
However, nowadays this approach cannot be used in
daily clinical practice as the data on the genetic
background of statin action in the body is limited.
Therefore, identification of the gene variants
responsible for the observed heterogeneity of statin
effects represents a promising strategy to shift current
limits of this therapy in terms of cardiovascular risk
reduction. It is evident that the genetic determination
of statin treatment efficacy is under polygenic control
with significant influences of environmental (most
importantly dietary) factors (Mangravite and Krauss
2007, Maggo et al. 2011). Thus, the most feasible
approach to studying the pharmacogenetics of statin
therapy is the testing of the multiple variants in
selected genes with plausible roles in statin
processing within the body. Recently published
results of genome wide association studies
(Kathiresan et al. 2008, Sandhu et al. 2008) have
revealed several gene regions that significantly
influence plasma cholesterol levels. It is possible that
these genes also have the potential to modulate the
final impact of statin treatment on lipoprotein levels
in the plasma. These new genes, or newly detected
variants within the well known and characterized
genes, include CELSR2/PSRC1/SORT1 (rs646776),
CILP2/PBX4 (rs16996148), APOB (rs693) and
PCSK9 (rs11206510).
To evaluate the putative role of gene variants
within these newly identified gene regions in the
modification of individual treatment response to
statins, we conducted a retrospective study in a cohort
of lipid clinic patients treated with statins.
PATIENTS AND METHODS
Patient selection
Patients with primary dyslipidaemia indicated to
statin treatment were retrospectively selected from
databases of Lipid Clinics of the 3rd Department of
Internal Medicine of the 1st Faculty of Medicine,
Charles University and the Institute for Clinical and
Experimental Medicine, in Prague. Three hundred
and seventy patients were included, the average age
was 59.3±12.7 years (146 males, aged 56.3±12.6
years and 224 females, aged 61.4±12.3 years), 23.0%
were diabetics and 48.9% had hypertension. All
patients received standardized lifestyle advice at their
first visit to the clinics and were instructed to
maintain a low-cholesterol diet according to the
standardized education provided by an experienced
dietitian. Table 1 shows the baseline characteristics of
the study group. We compared the pre-treatment lipid
levels with the first values obtained after initiation of
statin treatment, usually after 12 weeks (range 10 to
13 weeks) of therapy. Patients taking simvastatin
(46.6%), atorvastatin (41.5%) and lovastatin (11.9%)
in doses of 10 (~90% of individuals) or 20 mg/day
were enrolled in the study. We did not include
subjects on combination lipid-lowering therapy (e.g.
statin-fibrate, statin-ezetimibe) and those who
experienced weight loss of more than 5% between
visits suggesting a substantial impact of lifestyle
changes. Also, individuals fulfilling the clinically and
laboratory criteria of familial hypercholesterolemia
were not included in the study.
Controls selection
As a control group, a subset of 470 individuals (188
males and 282 females) selected from the Czech
post-MONICA (MONItoring of CArdiovacular
disease) study (2559 individuals, 1191 males, average
age 49 years) was used (Thunsdall-Pedoe et al. 2003).
The selection criteria were i) no history of
cardiovascular disease, ii) no lipid-lowering treatment
and iii) plasma lipid values below 5.0 mmol/l for total
cholesterol, below 2.0 mmol/l for plasma TG and over
0.75 mmol/l (for males) or 0.8 mmol/l (for females)
for HDL-cholesterol (Table 1).
All participants of the study were of Caucasian
ethnicity from the Central European Czech
population. Written informed consent was obtained
from all the study participants and the local ethics
committee approved the design of the study according
to the Declaration of Helsinki of 1975.
Genotype analysis
Three millilitres of whole blood collected into EDTA
tubes for DNA isolation were stored at -20 °C.
The DNA was isolated using the standard salting
out method (Miller et al. 1988) and individual
variants of four gene loci (rs646776 -
CELSR2/PSRC1/SORT1, rs16996148 - CILP2/PBX4,
rs11206510 - PCSK9, rs693 - APOB) were
genotyped using the polymerase chain reaction (PCR)
and restriction analysis. A PCR device DYAD (MJ
Research, Waltham, USA) was used to perform the
PCR reaction in a total volume of 25 l. DNA was
amplified under the following conditions: initial
denaturation of 96 °C for 3 min, followed by 35
cycles of 95 °C for 15 sec, appropriate annealing
temperature for 30 sec and 72 °C for 30 sec. The last
amplification step was extended for 3 min at 72 °C. A
10 microl of PCR product was digested in a total volume
of 25 microl with an appropriate restriction enzyme at
37 °C overnight in the buffer provided by the
manufacturer. For more details regarding PCR
conditions, oligonucleotides and restriction enzymes
used, see Table 2. Restriction fragments were
separated on 10% PAA gel using the MADGE
technique (Day and Humphries 1994).
Analysis of plasma lipids
The lipoprotein parameters in fasting plasma samples
were assessed using autoanalysers and conventional
enzymatic methods with reagents from Boehringer
Mannheim Diagnostics (Mannheim, Germany) and
Hoffmann-La Roche (Basel, Switzerland) in CDC
(Atlanta, USA) accredited local laboratories.
Statistical analyses
The Hardy-Weinberg test (http://www.tufts.
edu/~mcourt01/Documents/Court%20lab%20-%20HW%20calculator.xls) was applied to confirm the
independent segregation of the alleles. The
Chi-square test, ANOVA and ANCOVA for
adjustments were used for statistical analysis. All
tests were two tailed and a significance level 2 alpha=0.05
was considered to be significant. Differences in lipid
decreases were expressed and analysed in per cent of
the decrease. The changes of plasma lipids were
compared between subjects with different genotypes
for individual polymorphisms.
RESULTS
Basic characteristics
As expected, there was a significant decrease both in
the total (7.36±1.285.43±1.01 mmol/l) and
LDL-cholesterol (4.72±1.353.19±0.98 mmol/l)
after treatment (Table 1). The cholesterol decrease
was independent (ANCOVA; both for total
cholesterol and for LDL-cholesterol) of the type of
statin and the dose, most likely because of the
relatively low number of patients included, and as the
majority of the patients were treated with the lowest
dose of statins.
The call rates for individual variants vary between
94.1% for the rs11206510 within the PCSK9 gene in
the patients and 98.5% for the rs16996148 within the
CILP2/PBX4 cluster in the controls.
In the entire population, the allelic frequencies of
individual polymorphisms were comparable with the
so far published frequencies obtained in other
Caucasian populations. The Hardy-Weinberg test
confirmed the independent segregation of individual
alleles with two exceptions (rs16996148 in controls
and rs693 in patients). These differences could easily
be explained as the groups had not been selected as
representative general population samples. No gender
differences in genotype frequencies were observed
either in the patients or in the controls (data not
shown).
Genotype differences between the groups analysed
The genotype frequencies were significantly different
between the patients and controls for three out of the
four analysed variants (Table 3). The largest
difference was observed for the rs16996148 variant
within the CILP2/PBX4 gene cluster. We did not
detect (chi-square) any homozygous carriers of the
less common T allele among the dyslipidaemic
patients. Furthermore, the frequencies of the
CELSR2/PSRC1/SORT1 (rs599838) and APOB
(rs693) genotypes also differed significantly between
the analysed groups and, thus, confirm the important role of these SNPs in the determination of plasma
lipid levels.
Table 1. Basic characteristics of the analysed patients treated with statins and healthy controls. Individual values are given
for the patients before and after statin treatment.
| Character |
Patients |
Controls | | Number |
|
370 |
|
470 | | Age |
|
59.3±12.7 |
|
42.5±10.2 | | % of males |
|
35.4 |
|
40 | |
Before |
|
After |
| | Total cholesterol |
7.36±1.29 |
|
5.43±1.00* |
4.35±0.42* | | LDL-cholesterol |
4.72±1.35 |
|
3.18±0.98* |
n.a. | | HDL-cholesterol |
1.53±0.48 |
|
1.49±0.40* |
1.38±0.35* | | Triglycerides |
2.16±1.22 |
|
1.66±0.92* |
1.03±0.37* | | Smoking prevalence |
|
25.7% |
|
24.9% | | Diabetes prevalence |
|
23.0% |
|
1.5% | | Hypertension |
|
48.9% |
|
16.8% |
* Statistically significant vs values obtained before treatment.
Table 2. Primer sequences, restriction enzymes and size of the restriction fragments used for detection of polymorphisms
of interest.
| Polymorphism |
Primer sequence |
Annealing
temperature |
PCR
product |
Enzyme |
Size
(bp) |
Allele | | CILP2/... |
5' tgg ctc ttg tcc act ggc cac
atc ccc |
70 °C |
135 bp |
Hin1II |
137 |
G | | rs16996148 |
5' ttc tcc cat gcc tcc agg ccc
cca ag |
|
|
|
82+54 |
T | | APOB |
5'aga gga aac caa ggc cac agt
tgc |
57.5 °C |
163 bp |
XhoI |
136 |
C | | rs693 |
5' tac att cgg tct cgt gta tct tct |
|
|
|
110+26 |
T | | CELSR2/... |
5' atc cag cta ttt ggg agc agt
gtc ctg g |
66 °C |
137 bp |
Hin1II |
139 |
A | | rs646776 |
5'aag gtc tgg tct ctg gaa aac
aga ag |
|
|
|
107+32 |
G | | PCSK9 |
5' tcc agc att gcc agc ttc tct
gtc tc |
68.9 °C |
130 bp |
Hin6I |
130 |
T | | rs11206510 |
5' agc caa aga cgg cca cca
cag aca gc |
|
|
|
104+26 |
C |
Table 3. Genotype distributions within the groups analysed. Differences between the groups were calculated by chi-square.
Frequencies of variants marked by * are statistically significant.
| CILP2/PBX4 |
|
|
|
|
| | rs16996148 * |
GG |
GT |
TT | |
N |
% |
N |
% |
N |
% | | Patients |
322 |
89.4 |
38 |
10.6 |
0 |
0.0 | | Controls |
368 |
79.5 |
81 |
17.5 |
14 |
3.0 | | | Apolipoprotein B |
|
|
|
|
| | rs693 * |
CC |
CT |
TT | |
N |
% |
N |
% |
N |
% | | Patients |
72 |
20.2 |
204 |
57.3 |
80 |
22.5 | | Controls |
126 |
27.9 |
238 |
52.8 |
87 |
19.3 | | | CELSR2/PSRC1/SORT1 |
|
|
|
|
| | rs646776 * |
AA |
AG |
GG | |
N |
% |
N |
% |
N |
% | | Patients |
242 |
67.8 |
102 |
28.5 |
13 |
3.6 | | Controls |
256 |
57.5 |
176 |
39.6 |
23 |
5.2 | | | PCSK9 |
|
|
|
|
| | rs11206510 |
TT |
TC |
CC | |
N |
% |
N |
% |
N |
% | | Patients |
236 |
67.8 |
95 |
27.3 |
17 |
4.9 | | Controls |
295 |
65.1 |
147 |
32.5 |
11 |
2.4 |
Associations between the SNPs and statin treatment
efficacy
The carriers of the minor rs599838 genotype within
the CELSR2/PSRC1/SORT1 cluster had a signi-ficantly lower response to statin treatment compared
to common homozygotes (LDL-cholesterol,
delta -20.3% vs. delta -32.0 %, significant both for unadjusted
and adjusted for sex and age values, ANOVA) with
heterozygotes having a decrease similar to the
common homozygotes (delta -28.9%).
To turn to the other variants analysed, we did not
find a significant association between the genetic
polymorphism and changes of plasma lipid levels
induced by statin therapy (decrease of total or LDL-
cholesterol, triglycerides; increase of HDL-
cholesterol) (Table 4).
Table 4. Changes of the lipid parameters according to individual genotypes. Percentages of the decrease of plasma cholesterol
in different fractions and plasma TG levels were calculated from the baseline values (before treatment) for each patient.
Significant difference was observed for the rs646776 variant and is indicated by asterisks.
| CILP2/PBX4 |
|
|
| | rs16996148 |
GG |
GT |
| | T-C |
25.5±11.9 |
25.1±10.9 |
| | LDL-C |
29.3±17.0 |
31.6±15.5 |
| | HDL-C |
0.3±18.8 |
-1.8±20.2 |
| | TG |
17.5±29.1 |
16.4±27.5 |
| |
|
|
| | Apolipoprotein B |
|
|
| | rs693 |
CC |
CT |
TT | | T-C |
23.5±12.4 |
26.4±11.6 |
24.5±10.8 | | LDL-C |
29.4±16.3 |
31.2±17.8 |
30.3±15.6 | | HDL-C |
-3.3±20.7 |
0.9±19.5 |
0.4±16.7 | | TG |
16.3±27.9 |
16.1±32.6 |
21.4±26.6 | |
|
|
| | CELSR2/PSRC1/SORT1 |
|
|
| | rs646776 |
AA |
AG |
GG | | T-C |
25.8±11.3 |
25.1±12.3 |
23.0±13.4 | | LDL-C * |
32.0±16.5 |
28.9±17.7 |
20.3±20.1 | | HDL-C |
0.4±19.2 |
0.0±20.3 |
-3.0±16.1 | | TG |
17.2±31.7 |
18.5±25.9 |
22.6±25.2 | |
|
|
| | PCSK9 |
|
|
| | rs11206510 |
TT |
TC |
CC | | T-C |
24.9±11.6 |
24.2±10.9 |
24.7±11.9 | | LDL-C |
29.1±17.4 |
29.5±16.8 |
24.9±16.1 | | HDL-C |
0.9±17.6 |
-1.4±19.8 |
3.9±20.8 | | TG |
18.9±26.5 |
13.9±30.1 |
25.5±24.3 |
DISCUSSION
In adult dyslipidaemic patients of Slavonic Caucasian
descent we have detected a significant effect of the
SNP within the CELSR2/PSRC1/SORT1 gene cluster
on statin treatment efficacy. The presence of the less
frequent genotype was associated with an
approximately 30% reduction of the LDL-lowering
efficacy of statins. No significant effect of the
variants within genes/gene clusters for CILP2/PBX4,
PCSK9 and APOB on statin mediated lipid decrease
was observed. In subgroups divided according to
genotypes of these SNPs, not even trends were
detectable.
In three out of the four analysed variants
(CILP2/PBX4, CELSR2/PSRC1/SORT1 and APOB
regions) we detected significant differences in
genotype frequencies between the groups analysed.
This confirms the role of these variants in the genetic
determination of plasma lipid levels, as detected
through GWA studies on (mostly) west European
samples, but also in the central European Slavonic
population. The highest difference was observed for
the rs16996148 (CILP2/PBX4) variant. In this case,
the minor TT homozygotes were not detected among
the patients with dyslipidaemia, which suggests that
these individuals could be protected against the
development of dyslipidaemia. The difference in the
last gene, PCSK9, remains just below the arbitrary
recognised value for statistical significance, so it is
very likely that in a study with a slightly higher
number of participants, the role of this SNP in
determination of plasma lipids would also be
confirmed.
The loci we have studied include both well known
genes with a clear link to plasma lipid values and also
newly detected loci without well established
mechanisms influencing plasma lipid regulation. The
first group is represented by the APOB gene
(apolipoprotein B is a major protein component of
LDL particles) (Benn 2009) and the PCSK9
gene(serine protease that reduces both hepatic and
extrahepatic LDL receptor levels) (Davignon et al.
2010). The second group of genes studied, with rather
unclear mechanisms affecting plasma lipid
concentrations, was represented by two gene
clusters - the variants being located within the
intergenic regions of the CELSR2 (Waterworth et al.
2010) and CILP2 (Seki et al. 2005) gene clusters. At
the time of their first description, the genes located
within these clusters had no known association with
the metabolism of plasma lipids.
However, only very recently, SORT1, a member
of the CELSR2 gene cluster (in which we have
detected a potential to influence the treatment efficacy
of statins), was described as an intracellular receptor
for the APOB. It interacts with APOB at the
apparatus of Golgi and facilitates the hepatic transport
of APOB containing lipoproteins (Kjolby et al. 2010).
Variation of the three new gene loci modulating
concentrations of plasma lipoproteins (and thus
contributing to the development of dyslipidemia) did
not significantly influence the therapeutic response to
statin treatment. Although the new gene loci have
been repeatedly shown to determine plasma lipid
levels (Kathiresan et al. 2008, Sandhu et al. 2008,
Aulchenko et al. 2009), their contribution to the
inter-individual variability of the final impact of statin
therapy on lipoprotein concentrations seems to be
negligible. This holds true not only for the individual
variants but also for their combinations.
The observed lack of association could be
explained by the fact that there is no physiological
link to the pathway(s) involved in the metabolism or
transport of statins. In general, these pathways are
supposed to be more likely to affect statin treatment
efficacy (or there is a greater chance of detecting such
an effect), as they are less prone to environmental
modifications. Variations we have studied potentially
impact pathways that involve transport proteins or
enzymes directly linked to the processing of different
lipoprotein subpopulations (mostly LDL and TG-rich
particles) and not to the metabolism or transport of
statins.
Another possibility is the small magnitude of the
modifying effect, which could not be detected due to
the relatively small sample size. However, the
observed differences did not even suggest a trend in
the difference between the genotypes. Thus, it seems
unlikely that even a substantially increased sample
size would enable the identification of possibly
modest modifying effects.
The increasing popularity of genome wide
association studies (Rosenberg et al. 2010) leading to
identification of some very interesting and powerful
genetic determinants not just in the cardiovascular
field (Welcome Trust Case Control Consortium 2007,
Musunuru and Kathiresan 2010, Wang et al. 2010),
has also its pitfalls. Surprisingly, there is so far a
substantial lack of replication studies performed or
published, despite the fact that original GWAs usually
include very high numbers of individuals, but without
detailed analyses of interethnic or even international
differences. Also, the gene-gene or gene-environment
interactions have never been analysed in these
studies. Therefore, we have to keep in mind that the
effects of SNPs detected through the GWA approach
do not need to be generally applicable. As an example
of the context dependent effect of a gene, we were not
able in our study to confirm with a sufficient degree
of certainty the association with the most powerful
genetic determinant of plasma TG levels detected so
far (Kooner et al. 2008), the MLXIPL variant (Vrablik
et al. 2008). One of the explanations is maybe the
different genetic and/or environmental background
between the west European/German and central
European/Slavonic populations. On the other hand,
the same variant was associated with plasma TG
levels in a Japanese population (Nakayama et al.
2009). However, the generally higher plasma TG
levels in the Czech population at large could be the
reason why the attempts to replicate the original
results in other studies have failed.
The impact of different genetic polymorphisms on
statin induced changes of lipid levels has been
analysed in several clinical trials. So far, single
nucleotide polymorphisms (SNPs) in more than
30 different genes have been examined (Mangravite
and Krauss 2007, Maggo et al. 2011) but the results
were not replicated in larger patient groups and also
the magnitude of impact on statin efficacy was small.
It needs to be mentioned that only the impact of the
apolipoprotein E gene on statin treatment efficacy has
been analysed in other studies with sufficient power,
and even these results are far from being consistent.
Other genes analysed include for example
apolipoprotein A5 (Hubacek et al. 2009), cholesterol
7 alpha hydroxylase (Kajinami et al. 2005) and
apolipoprotein E (Hubacek and Vrablik 2011). The
knowledge in this field is quickly expanding, but, so
far, it is not sufficient to be used in clinical practice.
We are beginning to unveil the genetic
determination of the efficacy of statin treatment
(Ordovas and Mooser 2002, Mangravite et al. 2010).
Generally, it is of outstanding interest to understand
the genetic background of the efficacy of a drug, as
we frequently do not have any clinical, biochemical
or anthropometrical tests to predict the effects of
pharmacotherapy. Assessing the individual efficacy
of a drug before exposure can be carried out only by
the interdisciplinary connection of human medicine
and genetic analysis - through biomedical research
(Berger 2011). Such examination would have a
potential to detect the hyper- and hypo-responders
and, moreover, identification of those at high risk of
side effects. Thus, the results of genetic analyses will
help us select the most effective and, at the same
time, safest treatment alternative for the individual
patient. The economic and health benefits of this
approach are evident. Given that statins are among
the most widely used drugs worldwide, improved
targeting of their use and identification of the most
suitable statin type using a genetic test represents a
very attractive approach. A recent meta-analysis
showed that statins reduce cardiovascular risk by
approximately 20% per each 1 mmol/l reduction of
LDL-cholesterol levels. This should translate to
40-50% risk reduction when 2-3 mmol/l
LDL-cholesterol decreased is achieved (Baigent et al,
2010). However, as highlighted recently by the so
called Residual Risk Reduction Initiative only 30%
risk reduction with statin treatment is being achieved
on average (Fruchart et al. 2008). Improving the
efficacy and safety of statin treatment by genetic
testing might be another way of shifting the current
limits of the treatment towards greater reduction in
event rates, cardiovascular morbidity and, most
importantly, also mortality.
To accomplish this ultimate goal, comprehensive
research in large populations studying the impact of
combinations of gene variants is warranted to broaden
our understanding of the determination of statin
treatment efficacy.
Our results confirm the notion that the roles of
new gene loci identified through genome wide
association studies should be replicated in more
focused, smaller study settings but with more detailed
biochemical, anthropometrical and lifestyle
information. Only such studies would allow an
assessment of their contribution to the modulation of
lipid metabolism as well as a determination of their
role in pharmacogenetics, nutrigenetics or
actigenetics. Our study has detected a potential of the
variant within the CELSR2/PSRC1/SORT1 gene
cluster, but not within CILP2/PBX4, PCSK9 and
APOB gene loci to significantly impact on statin
treatment efficacy.
ACKNOWLEDGEMENT
Supported by project No. NS 10579-3 from the IGA,
MH, Czech Republic.
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