Introduction

Statin, a cholesterol-lowering drug, is commonly used to prevent cardiovascular disease because it is highly effective at lowering cholesterol levels. However, statin efficacy has an inter-individual variability on lipid profiles which can be partially caused by genetic variation. As indicated by a genome-wide association study (GWAS) on Europeans and South Asians, the rs646776 polymorphism has a strong association with plasma lipoproteins, hence the increased risk of coronary artery disease (CAD)1. In brief, rs646776 is a single nucleotide polymorphism (SNP) on chromosome 1 (., 1p13.3) which resides in the intergenic region of three distinct genes: , , and 2. Minor allele carriers of the SNP are associated with decreased cardiovascular disease risk since it has been shown to lower circulating cholesterol levels, particularly low-density lipoprotein cholesterol (LDL-C), among statin users3. Similarly, another GWAS found that rs646776 was associated with significantly larger LDL-C reductions of up to 0.47%4.

Evidence from animal and studies showed that , out of the three candidates in the gene cluster, was the main regulator of lipoprotein metabolism, especially of LDL-C. However, there was no unidirectional evidence for the effect of expression on plasma lipid profiles5. In fact, a large meta-analysis from a GWAS on Europeans has associated rs646776, particularly owing to its direct involvement in lipid metabolism, with an enhanced statin-related LDL-C response from interacting with apoB at the Golgi apparatus to stimulate LDL-C uptake6. Therefore, we speculate that individuals who inherit these genetic variants have improved lipid profiles, most likely in terms of reduced LDL-C or increased high-density lipoprotein cholesterol (HDL-C) levels, and hence a lower risk of CAD.

Although statins are well-known for their ability to reduce cardiovascular morbidity, an individual's response to a given drug can be influenced by several factors. Besides genetics, the demographic profile of the subject (., age, body mass index, gender) and clinical factors determine the inter-individual variability of statin efficacy7. This study aimed to determine the association between the rs646776 polymorphism and statin-related lipid profile changes in a subset of the Malaysian population. Stratification according to the subject’s gender allowed us to evaluate the gene–gender effects on statin-affected lipid profiles. To our knowledge, this study provides the first frequency data for the SNP of Southeast Asian origin.

Methods

Statistical analysis

The data analysis was carried out using the SPSS software version 26.0 (IBM, United States). The data were reported as frequency (%) and mean ± standard error of the mean (SEM) for categorical and continuous data, respectively. The normality of the continuous data was checked using a test of normality, the Kolmogorov–Smirnov test. For the categorical data, the Chi-square test was used in a contingency table to obtain the odds ratio (OR); the test required at least 80% of the cells to have an expected count greater than 5. Student’s t-test was used to assess differences between two continuous data; in the case of non-parametric data, the Mann–Whitney test was used instead. Repeated measures ANOVA was used to compare means across one or more variables that are based on related subjects; in the case of non-parametric data, the Friedman test was used. When conducting multiple statistical tests, the Bonferroni correction was used to adjust the probability () values to avoid a Type I error. The observed genotype frequency was checked for the Hardy–Weinberg equilibrium (HWE) using https://gene-calc.pl/hardy-weinberg-page. Since the homozygous mutant GG genotypes are rare, a genetic dominant model (AA vs. GA + GG genotypes) was applied to assess the SNP association. Multiple binary logistic regression was used to determine which independent factors were associated with the likelihood of achieving the LDL target of < 2.6 mmol/L. Throughout the analysis, a -value of 0.05 or lower was considered statistically significant.

Primer sequences	Product size	Interpretation of the genotypes
FO: 5'-TGGTGAAAAGGACACCTTCC-3'	Outer; 500 bp	Heterozygous; 500 bp, 420 bp and 129 bp
FI (G allele): 5'-TATTTGGGAGCAGTGTCATGGACGTG-3'	G allele; 420 bp	Homozygous recessive; 500 bp and 420 bp
RI (A allele): 5'-GGCTGATAAGCCTGTCCCTCTGACT -3'	A allele; 129 bp	Wild type; 500 bp and 129 bp
RO: 5'-CTTTTCTGCCGTGGATTCTC- 3'

Characteristics	n = 122
Age (years + SEM)	53.62 ± 6.67
Gender, n (%)
Female	71 (58.2)
Male	51 (41.8)
Race, n (%)
Malay	117 (95.9)
Chinese	3 (2.5)
Indian	1 (0.8)
Others	1 (0.8)
Genotype of rs646776, n (%)
aPresent study
AA	102 (83.6)
GA	20 (16.3)
GG	0
Reference population
AA	461 (91.4)
GA	40 (7.9)
GG	3 (0.5)
Type and dosage of statin, n (%)
Atorvastatin
10 mg/day	33 (27.0)
20 mg/day	32 (26.2)
30 mg /day	4 (3.3)
40 mg /day	8 (6.6)
Simvastatin
10 mg /day	4 (3.3)
20 mg /day	23 (18.9)
40 mg /day	3 (2.5)
Pravastatin
10 mg /day	2 (1.6)
20 mg /day	7 (5.7)
Lovastatin
20 mg /day	6 (4.9)
Comorbidities, n (%)
Hypertension	52 (42.6)
Diabetic + hypertension	44 (36.0)
Diabetic	12 (9.8)
Others	8 (6.6)
None	6 (4.9)
Concomitant drugs, n (%)
Anti-diabetic drugs class
Biguanides only	1 (0.8)
Two> anti-diuretic drugs or more	11 (9.0)
Anti-hypertensive drugs class
Calcium channel blockers only	17 (13.9)
Angiotensin-converting enzyme (ACE) inhibitors only	5 (4.1)
Alpha-blockers only	1 (0.8)
Beta blockers only	1 (0.8)
Two > anti-hypertensive drugs or more	28 (23.0)
Anti-diabetic + Anti-hypertensive	44 (36.1)
Others	8 (6.6)
None	6 (4.9)
Baseline level (mmol/L ± SD)
TC (normal range <5.2 mmol/L)	5.67 ± 1.56
HDL-C (normal range >1.0 in males and >1.3 in women)	1.33 ± 0.63
LDL-C (normal range <2.6 mmol/L)	3.61 ± 1.32
TG (normal range <1.7 mmol/L)	1.60 ± 0.89

Lipid profile (mmol/L)	AA (n = 102)	GA (n = 20)	P-value
TC (mean ± SD)
Baseline level	5.66 ± 1.64	5.69 ± 1.03	0.936
0-6 months treatment	4.61 ± 1.75	5.25 ± 0.91	0.111
7-12 months treatment	5.02 ± 1.58	5.11 ± 1.55	0.816
aBaseline vs 0-6 months	<.001	0.451
aBaseline vs 7-12 months	0.004	0.344
HDL-C (mean ± SD)
Baseline level	1.32 ± 0.66	1.43 ± 0.37	0.450
0-6 months treatment	1.17 ± 0.41	1.42 ± 0.34	0.013
7-12 months treatment	1.27 ± 0.35	1.45 ± 0.38	0.043
aBaseline vs 0-6 months	0.158	1.000
aBaseline vs 7-12 months	1.000	1.000
LDL-C (mean ± SD)
Baseline level	3.68 ± 1.33	3.28 ± 1.21	0.205
0-6 months treatment	2.73 ± 1.27	3.11 ± 0.85	0.197
7-12 months treatment	3.04 ± 1.14	3.21 ± 1.02	0.539
aBaseline vs 0-6 months	<.001	1.000
aBaseline vs 7-12 months	<.001	1.000
TG (mean ± SD)
Baseline level	1.58 ± 0.89	1.64 ± 0.91	0.783
0-6 months treatment	1.48 ± 1.08	1.59 ± 0.62	0.663
7-12 months treatment	1.60 ± 1.09	1.38 ± 0.69	0.378
aBaseline vs 0-6 months	0.905	1.000
aBaseline vs 7-12 months	1.000	1.000

Independent variables	P-value	Odds ratio Exp (B)	95% CI
SORT1/CELSR2/PSRC1 (rs646776)	0.709	1.219	0.431 - 3.449
Age	0.235	1.037	0.977 - 1.102
aGender	0.135	0.560	0.262 - 1.199
Type of Statin
Atorvastatin, mg/day
10	0.725	0.000	0.000
20	0.934	1.045	0.365 - 2.998
30	0.112	6.900	0.637 - 74.690
40	0.695	1.380	0.275 - 6.921
Simvastatin, mg/day
10	0.827	0.767	0.071 - 8.299
S20	0.731	0.812	0.247 - 2.670
S40	0.913	1.150	0.093 - 14.188
Pravastatin, mg/day
10	0.999	0.000	0.000
P20	0.522	1.725	0.324 - 9.172
Lovastatin, mg/day
20	0.106	4.600	0.721 - 29.332
Baseline lipid (mmol/L)
Total cholesterol	0.524	0.925	0.729 - 1.175
High-density lipoprotein	0.519	0.744	0.303 - 1.826
Low-density lipoprotein	0.688	0.941	0.699 - 1.267
Triglycerides	0.389	0.820	0.522 - 1.289

Results

Discussion

Statins, a lipid-lowering drug, have been used in the primary and secondary prevention of coronary heart disease (CHD). However, considerable heterogeneity in terms of their effects on lipid profiles in a subset of patients may be influenced by interactions between genetics, patient demographics, and other clinical factors8. Our study findings demonstrated a significant association between the rs646776 polymorphism and the lipid profiles of statin users with hyperlipidemia. These results are consistent with previous findings concerning the Chinese population9. In particular, the minor allele G carriers (GA genotype) of the SNP had higher HDL-C levels after 0–6 months (= 0.013) and 7–12 months (= 0.043) of statin treatment compared to the wild-type genotypes (Table 3). We believe that the difference in HDL-C levels between the GA genotypes is attributable to overexpression of the PSRC1 protein, which suppresses foam cell formation by upregulation of cholesterol transportation-related proteins in macrophages and the liver10

Interestingly, females with the variant allele carriers of rs646776 showed higher HDL-C levels (< 0.05) whereas males did not, suggesting a protective effect of the SNP against CHD and gene–gender interaction with the outcome of statin treatment. This supports our previous report that gender factors have a role in both statin efficacy and its muscular side effects11, 12. Apart from increased HDL-C levels in the blood, rs646776 variants were associated with lower TC (=0.007) and LDL-C (=0.006) levels3. However, we were unable to relate the association of the rs646776 variants with statin-related TC and LDL-C lowering effects in the present study. Instead, the wild-type AA genotypes had significantly lower TC (< 0.01) and LDL-C (0.001) levels following statin treatment, although this was not observed in the GA genotypes (Table 3). The effect of the minor allele G on TC and LDL-C could be best described by quantile-dependent expressivity, which implies that rs646776 has an exclusive effect on statin efficacy13. The effect size of the SNP was determined by cholesterol levels, with smaller genetic effect sizes found at lower cholesterol levels (., after statin treatment) or resulting in the opposite effect of the SNP on the percent of LDL-C change, particularly when cholesterol is decreased pharmacologically13. This explained the discrepancies in the SNP associations.

Furthermore, we were unable to link rs646776, the patient’s age, gender, and other clinical factors (the statin type and baseline lipid levels) to the patient’s attainment of an LDL-C level of < 2.6 mmol/L. This LDL-C level is the target for the 10-year prevention of CHD according to the National Cholesterol Education Program, Adult Treatment Panel III14. Higher baseline lipid values in patients before statin treatment did not influence their attainment of LDL-C < 2.6 mmol/L. rs646776 was identified as one of the two promising SNPs influencing statin-related lipid profiles in previous GWAS and multi-ethnic gene association studies. The influence of the abovementioned predicting variables on statin-affected lipid profiles is evident in this analysis as the inter-individual variability of lipid profiles is not solely determined by a single genetic polymorphism6, 15. As found in another study with a larger sample size (over 400,000 patients with Type 2 diabetes mellitus), it is possible that other patient factors, primarily their age and gender, correlate with the LDL-C profile16. With regards to the effect on LDL-C levels, a randomized controlled trial suggested that the type of statin had a significant impact on the LDL-C level17,implying that these independent factors were clinically meaningful when dealing with LDL-C profiles.

Since this is a preliminary investigation into the pharmacogenetic association between rs646776 and statin-affected lipid profiles, the findings warrant replication with other statin-using Malaysian cohorts and different ethnicities from other Asian regions. Despite conflicting results regarding the extent to which SNP alters statin-affected lipid profiles4, 6, the rs646776 SNP, or its strongly linked rs599839 SNP, has been associated with the risk of CAD in White and Hispanic populations18, 19, 20, 21. The present study has limitations: the number of subjects in this study is relatively small compared to those in the abovementioned studies. Nevertheless, we demonstrated a clear association between rs646776 and statin-affected lipid profiles, particularly HDL-C and LDL-C. Furthermore, the correlation between the studied genes and lipid profile changes was limited due to the absence of a homozygous recessive GG genotype. Larger sample sizes, typically between 400 and 500 subjects, are deemed sufficient to address research questions in gene association studies and increase the likelihood of capturing the GG genotype, as indicated by the reference population in Table 2. Last but not least, other confounding factors for lipid metabolism such as dietary intake, body mass index, and smoking status should be considered to ensure the internal validity of the results22, 23.

Conclusions

The present study found a significant association between the rs646776 polymorphism and higher HDL-C levels among females taking statins. The findings warrant study replication with other Asian ethnicities.

Abbreviations

ACE: Angiotensin-converting enzyme, ANOVA: Analysis of variance, ARMS-PCR: Amplification refractory mutation system- polymerase chain reaction, BP: Base pair, DNA: Deoxyribonucleic acid, FHFamilial hypercholesterolemia, FI: Forward inner primer, FO: Forward outer primer, GWAS:Genome wide association study, HDL-C: High-density lipoprotein-cholesterol, HMG-CoA: 3-hydroxy-3-methylglutaryl coenzyme A, HUSM: Hospital Universiti Sains Malaysia, HWE: Hardy-Weinberg equilibrium, LDL-C: Low-density lipoprotein-cholesterol, MAF: Minor allele frequencies, RI: Reverse inner primer, RO: Reverse outer primer, SEM: Standard error of the mean, SNP: Single nucleotide polymorphism, SORT1/CELSR2/PSRC1: Sortilin/cadherin EGF LAG seven-pass G-type receptor 2/proline-serine-rich coiled-coil protein 1, SPSS: Statistical package for the social sciences, TC: Total cholesterol, TG: Triglycerides

Acknowledgments

None

Author’s contributions

Conceptualization, N.S.B. and Z.Z.; methodology, N.S.B., A.F.S. and Z.Z.; software, R.Z. and A.F.S; validation, R.Z. and N.S.B.; formal analysis, R.Z. and N.S.B.; investigation, R.Z. and A.F.S.; resources, N.S.B. and Z.Z.; data curation, R.Z., A.F.S. and N.S.B.; writing - original draft preparation, R.Z. and N.S.B., writing- review and editing, R.Z. and N.S.B.; visualization, R.Z.; supervision, N.S.B. and Z.Z.; project administration, N.S.B.; funding acquisition, N.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2022/SKK10/USM/03/10.

Availability of data and materials

Data and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the USM Human Research Ethics Committee (Approval number: USM/JePeM/19070437). Informed consent was obtained from all patients involved in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.