We recruited 710 stroke patients (430 male patients and 280 female patients) from the People's Hospital of Wanning who were admitted to the hospital within 72 h after the onset of symptoms. All patients with focal neurological deficit symptoms persisting for more than 24 h were examined by two professional stroke neurologists and diagnosed by brain computed tomography (CT), magnetic resonance imaging (MRI), cerebrovascular angiography, and lumbar puncture (13). This study excluded patients with transient ischemic attack, lacunar infarction, familial stroke, severe heart, kidney, liver, endocrine, and bone diseases, and cancers. A total of 701 healthy participants (424 male patients and 277 female patients) were selected as the control group from the health examination center of the People's Hospital of Wanning during the same period as the cases. All control individuals had no family history of stroke, severe heart, kidney, liver, endocrine and bone diseases, cancers, and immune-related diseases. The gender and age of the control group were matched with the patients in the case group. The basic information of all subjects, including age, sex, smoking status, drinking status, and clinical test indicators, were collected from questionnaires and clinical data. This study was approved by the Ethics Committee of the People's Hospital of Wanning and conformed to the Declaration of Helsinki. All subjects were informed of the purpose, significance, and experimental process of the study before the experiment. This study was conducted after obtaining informed consent from subjects.

The four LIPC polymorphisms (rs690, rs6083, rs3829461, and rs6074) were randomly selected in this study according to previous articles (7, 14, 15). All SNPs had a minor allele frequency (MAF) > 0.05 in the Chinese Han Beijing (CHB) population from the 1000 Genomes Project (http://www.internationalgenome.org/). The functions of candidate SNPs were predicted using the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP) and HaploReg v4.1 databases (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) (16). The results showed that the four SNPs in LIPC were both synonymous and coding sequence variants. Peripheral venous blood (5 mL) from each subject was collected for DNA extraction according to the instructions of the whole blood genomic DNA extraction kit (GoldMag Co. Ltd. Xi'an, China) (17). The DNA concentration and purity were determined by spectrometry (NanoDrop 2000 spectrophotometer, Thermo Scientific, USA). In this study, the Agena Bioscience Assay Design Suite was performed (3.1) to design primers (polymerase chain reaction primers and unique base extension primer) for the four SNPs in LIPC. These primers were synthesized by Bioengineering (Shanghai Co., Ltd). The Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA) was used to genotype the four SNPs. Ultimately, the Agena Typer Software (version 4.0, Agena Bioscience, USA) was used for data analysis and processing (18).

The differences in gender and drinking and smoking status between cases and controls were assessed using the χ²-test. The differences in age and clinical indicators between the two groups were analyzed by the independent samples t-test. PLINK software (version 1.0.7) was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) by logistic regression analysis to measure the association between LIPC polymorphisms and stroke risk in the Chinese population under genetic models (allele, genotype, dominant, and recessive and additive models) adjusted by confounding factors. In order to control the influence of confounding factors on the results, we conducted the analyses stratified by age, sex, and smoking and drinking status (19). Data analysis was performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). A P-value of < 0.05 was defined as statistical significance. The G^*Power 3.1.9.7 software was used to calculate the sample size and statistical power of this study, (20). Parameter is tails = 2, effect size = 0.2, α = 0.05, power = 0.964, and allocation ratio =1. Multifactor dimensionality reduction (MDR) analysis was performed to evaluate the impact of SNP–SNP interaction on the risk of stroke in the Chinese population (21).

LIPC-related protein analysis was conducted through the STRING online database (https://cn.string-db.org/). We downloaded the GSE16561 dataset (39 cases and 24 controls) from the GEO database. First, standardize and log the original data. Second, select matching samples (24 cases and 24 controls). Finally, the “Limma” package in R was used to analyze the difference in the expression of LIPC and its related genes in the control and stroke groups. Furthermore, the biological process of LIPC-related protein–protein interaction was analyzed through the Sangerbox database (http://sangerbox.com/home.html).

In this study, a total of 710 stroke patients and 701 controls were recruited to explore the correlation between LIPC polymorphisms and the risk of stroke in the Chinese population. In the case group, there were 360 patients older than 64 years and 270 patients younger than or equal to 64 years. In the control group, 271 individuals aged greater than 64 years and 430 individuals aged less than or equal to 64 years. The average ages of cases and controls were 64.44 ± 10.738 years and 64.12 ± 5.697 years, respectively. There were 854 male patients and 557 female patients in all subjects. Detailed information on subjects, such as smoking and drinking status, is shown in Table 1. There were no significant differences in the distribution of age, gender, and drinking and smoking status between the case and control groups (p > 0.05).

The basic information, including the chromosome number, physical location, and alleles of the candidate SNPs, is presented in Table 2. All genotype distribution of four SNPs in LIPC (rs690, rs6083, rs3829461, and rs6074) met HWE (p > 0.05), indicating that subjects in the study were in a state of genetic balance, and the genotyping results were reliable. The frequency distribution of alleles T and G of rs690 was significantly different between the case and control groups. Compared with the allele G of rs690, allele T was significantly associated with an increased risk of stroke (OR = 1.19, 95% CI: 1.01–1.40, p = 0.041) (Table 2). In the additive model, rs690 was found to be associated with an increased risk of stroke (OR = 1.20, 95% CI: 1.01–1.42, p = 0.036) (Table 3). However, no statistical significance was found between the genotypes of the three SNPs (rs6083, rs3829461, and rs6074) in LIPC and the risk of stroke (p > 0.05).

This study further clarified the correlation between LIPC polymorphisms and the risk of stroke in the Chinese population after stratification analysis by age, gender, and smoking and drinking status. The results of the age-stratified analysis showed that rs690 was found to be associated with an increased risk of stroke in subjects aged ≤ 64 years (T vs. G: OR = 1.30, 95% CI: 1.04–1.63, p = 0.020; additive: OR = 1.31, 95% CI: 1.01–1.70, p = 0.044). In the subgroup aged < 64 years, rs6074 was found to be associated with an increased risk of stroke (AA vs. CC: OR = 1.98, 95% CI: 1.07–3.65, p = 0.030 AA vs. AC-CC: OR = 1.84, 95% CI: 1.01–3.34, p = 0.046; additive: OR = 1.32, 95% CI: 1.02–1.71, p = 0.034) (Table 4). Gender-stratified analysis revealed that rs690 was associated with an increased risk of stroke in male patients under the allele (T vs. G: OR = 1.25, 95% CI: 1.01–1.55, p = 0.042), homozygote (TT vs. GG: OR = 1.82, 95% CI: 1.03–3.23, p = 0.040), and additive (OR = 1.26, 95% CI: 1.01–1.57, p = 0.039) models. Compared with genotypes A/C and C/C, male patients with genotype A/A of rs6074 were more susceptible to stroke (OR = 1.76, 95% CI: 1.06–2.93, p = 0.029; recessive: OR = 1.76, 95% CI: 1.07–2.89, p = 0.025) (Table 4). Nevertheless, no association between four SNPs in LIPC and the susceptibility to stroke in female patients was detected.

After stratification by drinking status, the analysis indicated that rs6074 was associated with an increased risk of stroke in drinkers under the recessive model (AA vs. AC-CC: OR = 1.64, 95% CI: 1.00–2.68 p = 0.049). However, no SNP was observed to be related to stroke risk in the non-drinking subgroup (p > 0.05) (Table 5). In the analysis stratified by smoking status, we found that rs690 was associated with an increased risk of stroke in smokers under the allele (OR = 1.29, 95% CI: 1.02–1.63, p = 0.037), dominant (OR = 1.37, 95% CI: 1.02–1.85, p = 0.038), and additive (OR = 1.28, 95% CI: 1.00–1.63, p = 0.048) models. In the non-smoking subgroup, rs6074 was found to be associated with an increased risk of stroke (A vs. C: OR = 1.33, 95% CI: 1.05–1.68, p = 0.016; AA vs. CC: OR = 2.04, 95% CI: 1.20–3.46, p = 0.008; additive: OR = 1.30, 95% CI: 1.04–1.63, p = 0.020).

The relationship between LIPC polymorphisms and 42 clinical parameters of stroke patients was also investigated in the study (Supplementary Table 1). We found a significant difference in platelet distribution width (PDW), hematocrit (HCT), and low-density lipoprotein cholesterol (LDL-C) levels between cases and controls (p < 0.05). The TT, TG, and GG genotypes of rs690 were significantly correlated with PDW (p = 0.014) and HCT (p = 0.004) levels (Figure 1). The TT, TG, and GG genotypes of rs6074 were significantly correlated with the level of LDL-C (p = 0.033) (Supplementary Table 1 and Figure 1).

Multifactor dimensionality reduction analysis was performed to further explore the impact of LIPC SNP-SNP interaction on stroke risk. Figure 2 shows that rs690, rs6083, rs3829461, and rs6074 in LIPC had antagonistic effects on stroke risk. In the MDR analysis, by comparing the parameters of cross-validation consistency (CVC), OR, 95% CI, p-value, training balanced accuracy (Bal. Acc.), and testing balanced accuracy, we found that the four-locus model consisting of four SNPs (rs690, rs6083, rs3829461, and rs6074) was the best one to assess the risk of stroke (CVC: 10/10; Testing Bal. Acc = 0.519; OR = 1.66, 95% CI: 1.32–2.07, p = 0.0001, Table 6).

STRING database analysis results showed that there were nine main proteins related to LIPC, namely, APOB, APOE, CETP, LDLR, LIPE, LIPF, MGLL, PNPLA3, PNPLA2, and LMF1 (Figure 3). Based on the Gene Expression Omnibus (GEO) database analysis, significant differences in the expression levels of LIPC, APOB, CETP, PNPLA2, and LMF1 between the control and stroke groups were observed (Supplementary Table 2 and Figure 4). Furthermore, the Sangerbox analysis results showed that the biological processes of the main protein–protein interactions related to LIPC mainly involved the metabolism of cholesterol and glycerolipid pathways (Figure 5).