Genetic polymorphisms and their correlation with dyslipidemia in Chinese patients diagnosed with diabetes mellitus

Abstract

BACKGROUND

Dyslipidemia is a common complication in patients with diabetes mellitus (DM) that increases the risk of cardiovascular disease. Genetic polymorphisms have been implicated in the development of dyslipidemia.

AIM

To investigate the association between polymorphisms of candidate genes involved in lipid metabolism and dyslipidemia in Chinese patients with DM.

METHODS

A cross-sectional study was conducted on 1098 Chinese patients with DM recruited from multiple healthcare centers. Demographic and clinical data were collected, and dyslipidemia was defined according to the National Cholesterol Education Program Adult Treatment Panel III guidelines. Genomic DNA was extracted from blood samples and genotyping for selected polymorphisms of candidate genes (APOE, LPL, CETP, and others) was performed using PCR and DNA sequencing techniques. Statistical analyses were performed using logistic regression models adjusted for potential confounding factors.

RESULTS

The study population consisted of 578 males (52.6%) and 520 females (47.4%), with a mean age of 58.4 ± 12.2 years. The prevalence of dyslipidemia was 64.8%. Significant associations were found between dyslipidemia and the APOE rs7412 T/T, APOE rs429358 C/C, LPL rs328 G/G, and CETP rs708272 G/G genotypes after adjusting for covariates. Subgroup analyses showed generally consistent associations across subgroups, although some variations in effect sizes were observed.

CONCLUSION

This study identified significant associations between genetic polymorphisms of APOE, LPL, and CETP genes and dyslipidemia in Chinese patients with DM.

Core Tip: Genetic polymorphisms of APOE, LPL, and CETP genes are significantly associated with dyslipidemia in Chinese patients with diabetes mellitus (DM). Understanding these associations can provide valuable insights into personalized management strategies for dyslipidemia in this population, emphasizing the importance of genetic factors in lipid metabolism and cardiovascular risk assessment. Subgroup analyses further support the robustness of these associations across different patient groups, highlighting the potential for targeted interventions based on genetic profiles in the management of dyslipidemia in Chinese individuals with DM.

INTRODUCTION

The prevalence of diabetes in China has rapidly increased in recent decades, driven by factors such as urbanization, dietary changes, and sedentary lifestyles[1]. According to the latest national survey, the prevalence of diabetes among adults aged 20 years or older in China was estimated to reach 12.8% in 2021, with a higher prevalence in urban areas than in rural areas[2]. The substantial burden of diabetes in China, coupled with the high risk of cardiovascular complications associated with dyslipidemia, underscores the importance of understanding the underlying genetic factors contributing to this comorbidity.

Dyslipidemia in patients with diabetes is a complex condition influenced by various genetic and environmental factors, as well as their interaction with each other. Genetic polymorphisms have been recognized as key contributors to individual variations in lipid metabolism and development of dyslipidemia[3]. Several candidate genes involved in lipid metabolism pathways have been extensively studied for their potential association with dyslipidemia in patients with DM.

One of the well-studied genes is the apolipoprotein E (APOE) gene, which plays a crucial role in the regulation of cholesterol metabolism and lipoprotein transport[4]. The APOE gene is polymorphic, with three major alleles (ε2, ε3, and ε4) that result in different isoforms of the APOE protein. Previous studies have suggested that the APOE ε4 allele is associated with higher levels of low-density lipoprotein cholesterol (LDL-C) and an increased risk of cardiovascular disease, while the APOE ε2 allele is associated with lower levels of LDL-C[5,6]. However, the association between polymorphisms of the APOE gene and dyslipidemia in patients with diabetes is inconsistent across populations, highlighting the need for further investigation.

Another important gene involved in lipid metabolism is the lipoprotein lipase (LPL) gene, which encodes an enzyme that plays a key role in the hydrolysis of triglycerides (TGs) and clearance of chylomicrons and very-low-density lipoproteins (VLDLs) from circulation[7]. Genetic variations in the LPL gene are associated with altered LPL activity and lipid profiles, potentially contributing to the development of dyslipidemia[8].

The cholesteryl ester transfer protein (CETP) gene is another candidate gene of interest in the context of dyslipidemia and cardiovascular risk. CETP is a plasma protein that facilitates the transfer of cholesteryl esters from high-density lipoprotein cholesterol (HDL-C) to VLDL and low-density lipoprotein (LDL) particles, thereby playing a crucial role in the regulation of HDL-C levels[9]. Genetic variations in the CETP gene have been associated with altered CETP activity and HDL-C levels, potentially influencing the risk of cardiovascular disease[10]. Several studies have investigated the association between polymorphisms of LPL or CETP genes and dyslipidemia in patients with diabetes, with varying results across different populations[11,12].

It is important to note that the association between genetic polymorphisms and dyslipidemia may be influenced by various factors, including ethnicity, environmental exposure, and gene-gene interactions. Ethnic differences in genetic background and lifestyle factors may contribute to variability in observed associations across different populations[13]. Environmental factors such as diet, physical activity, and medication use can also modulate the effects of genetic polymorphisms on lipid metabolism and development of dyslipidemia[14]. Furthermore, gene-gene interactions and epistatic effects may play a role in the complex regulation of lipid metabolism and manifestation of dyslipidemia in patients with diabetes[15].

Consequently, elucidating the genetic factors contributing to dyslipidemia in patients with diabetes is crucial for improving our understanding of the underlying molecular mechanisms and pathways involved in the development of dyslipidemia. These data can then facilitate personalized risk stratification of patients with diabetes based on their genetic profiles, allowing for more targeted and efficient management strategies, as well as guiding the development of personalized treatment approaches, such as pharmacogenomics-based therapies, based on an individual's genetic profile[16].

This study aims to investigate the association between genetic polymorphisms and dyslipidemia in Chinese patients with diabetes mellitus (DM), a population with a high burden of diabetes and cardiovascular diseases. Understanding the genetic factors that contribute to dyslipidemia in this population may provide valuable insights for personalized risk assessment, targeted therapeutic interventions, and improved management of cardiovascular complications in individuals with diabetes.

MATERIALS AND METHODS

Study population

This cross-sectional study included 1098 Chinese patients with DM recruited from multiple healthcare centers across different regions of China between 2018 and 2019. The study sites included tertiary hospitals, specialized diabetes clinics, and community health centers to ensure a diverse and representative sample of the Chinese population with diabetes.

Patients were eligible for inclusion if they were diagnosed with type 1 or type 2 diabetes according to the criteria established by the American Diabetes Association[9]. The diagnostic criteria for DM include one or more of the following: (1) Fasting plasma glucose level ≥ 7.0 mmol/L (126 mg/dL); (2) 2-h plasma glucose level ≥ 11.1 mmol/L (200 mg/dL) during an oral glucose tolerance test; (3) hemoglobin A1c level ≥ 6.5%; or (4) random plasma glucose level ≥ 11.1 mmol/L (200 mg/dL) in the presence of classic symptoms of hyperglycemia.

Patients were excluded from the study if they had any of the following conditions: (1) Severe liver or kidney disease; (2) acute or chronic inflammatory disorders; (3) malignancies; (4) pregnancy or breastfeeding; or (5) any other medical conditions that could potentially interfere with the interpretation of the study results.

Demographic and clinical data, including age, sex, body mass index (BMI), duration of diabetes, lipid profiles (total cholesterol, LDL-C, HDL-C, and TG), blood pressure, medication history (including lipid-lowering agents, antidiabetic medications, and other relevant medications), and comorbidities (such as hypertension and cardiovascular disease) were collected from electronic medical records or through in-person interviews and physical examination.

Dyslipidemia was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III[17]. Patients were considered to have dyslipidemia if they met one or more of the following criteria: (1) Total cholesterol ≥ 6.2 mmol/L (240 mg/dL); (2) LDL-C ≥ 4.1 mmol/L (160 mg/dL); (3) HDL-C < 1.0 mmol/L (40 mg/dL) for men or < 1.3 mmol/L (50 mg/dL) for women; or (4) TG ≥ 2.3 mmol/L (200 mg/dL). Patients who were currently receiving lipid-lowering medications were also considered to have dyslipidemia.

Genotyping

Venous blood samples (approximately 5-10 mL) were collected from all participants after overnight fasting, and genomic DNA was extracted using standard kits such as the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) or similar commercial kits. The quality and quantity of the extracted DNA were assessed using spectrophotometric methods (e.g., NanoDrop) and agarose gel electrophoresis, respectively.

Candidate genes involved in lipid metabolism, including APOE, LPL, and CETP genes, were selected based on their well-established roles in lipid metabolism and previous evidence of their association with dyslipidemia. The selection of specific polymorphisms within these genes was guided by prior association studies, functional significance, and minor allele frequencies in the Chinese population.

Genotyping of selected gene polymorphisms was performed using PCR and DNA sequencing. Primers specific to the regions of interest were designed using bioinformatics tools (e.g., Primer 3) and synthesized by commercial vendors.

For PCR amplification, reaction mixtures were prepared using the following components: a genomic DNA template, forward and reverse primers, DNA polymerase, deoxyribonucleotide triphosphates (dNTPs), and appropriate buffers. The cycling conditions for PCR were optimized based on specific target regions and primer sequences, typically involving an initial denaturation step, followed by multiple cycles of denaturation, annealing, and extension steps, followed by a final extension step.

After PCR amplification, the amplified products were purified using suitable methods (e.g., spin column purification or gel extraction) to remove unincorporated primers, dNTPs, and other contaminants. The purified PCR products were then subjected to DNA sequencing using Sanger or next-generation sequencing (NGS) techniques, depending on the scale and requirements of the study.

NGS techniques, such as targeted resequencing or amplicon sequencing, can be employed for more comprehensive genotyping of multiple genetic regions simultaneously.

The obtained DNA sequences were analyzed using the CLC Genomics Workbench software (version 20.0, QIAGEN, Aarhus, Denmark) to identify specific genotypes for each individual and the polymorphism of interest. Quality control measures, including assessment of sequence quality scores (Phred score > 30), coverage depth (> 50 ×), and alignment to the human reference genome (GRCh38), were implemented to ensure the accuracy and reliability of the genotype calls.

Statistical analysis

Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Continuous variables are presented as mean ± SD or median (interquartile range). The genotype and allele frequencies of the studied genetic polymorphisms were calculated and assessed for conformity employing the Hardy-Weinberg equilibrium (HWE) using appropriate statistical tests (χ² test or exact tests). Association between genetic polymorphisms and dyslipidemia was assessed using logistic regression models. Binary logistic regression was employed when dyslipidemia was treated as a binary outcome (presence or absence), whereas multinomial logistic regression was used if dyslipidemia was categorized into multiple levels or subtypes based on specific lipid abnormalities (e.g., high LDL-C, low HDL-C, and high TG). In the logistic regression models, the presence or absence (or levels) of dyslipidemia was treated as the dependent variable, and genetic polymorphisms were included as independent variables. Odds ratios (ORs) and their corresponding 95% confidence intervals (95%CIs) were calculated to estimate the risk of dyslipidemia associated with specific genotypes or alleles, using either codominant, dominant, or recessive genetic models, as appropriate. Potential confounding factors, such as age, sex, BMI, duration of diabetes, medication use, and other relevant clinical variables, were adjusted for in the multivariate analysis to account for their potential influence on the observed associations. Subgroup analyses or stratified analyses were conducted, if necessary, to explore potential effect modifications or interactions between genetic polymorphisms and other covariates on the risk of dyslipidemia. Statistical significance was defined as a P value < 0.05.

RESULTS

Demographic and clinical characteristics

Demographic and clinical characteristics of the study population are summarized in Table 1. The study included 1098 Chinese patients with DM, comprising 578 men (52.6%) and 520 women (47.4%). The mean age of the study participants was 58.4 ± 12.2 years. Among the participants, 842 (76.7%) had type 2 diabetes, while 256 (23.3%) had type 1 diabetes.

Table 1.

Demographic and clinical characteristics of the study population

Characteristic	Value
Age (yr), mean ± SD	58.4 ± 12.2
Sex, n (%)
Male	578 (52.6)
Female	520 (47.4)
Type of diabetes, n (%)
Type 1	256 (23.3)
Type 2	842 (76.7)
Duration of diabetes (yr), median (IQR)	7.0 (3.0-12.0)
BMI (kg/m²), mean ± SD	25.8 ± 4.1
Lipid profile, mean ± SD
Total cholesterol (mmol/L)	5.2 ± 1.3
LDL-C (mmol/L)	3.1 ± 1.0
HDL-C (mmol/L)	1.2 ± 0.4
Triglycerides (mmol/L)	2.0 ± 1.5
Dyslipidemia, n (%)	712 (64.8)
Hypertension, n (%)	528 (48.1)
Cardiovascular disease, n (%)	196 (17.9)

IQR: Interquartile range; BMI: Body mass index; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol.

Distribution of specific lipid abnormalities is shown in Table 2. The prevalence of dyslipidemia among the study participants was 64.8% (712 of 1098 patients).

Table 2.

Distribution of lipid abnormalities among patients with dyslipidemia

Lipid abnormality	n (%)
High total cholesterol (≥ 6.2 mmol/L)	284 (39.9)
High LDL-C (≥ 4.1 mmol/L)	312 (43.8)
Low HDL-C (< 1.0 mmol/L for men, < 1.3 mmol/L for women)	368 (51.7)
High triglycerides (≥ 2.3 mmol/L)	420 (59.0)

Patients may have had more than one lipid abnormality. LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol.

Analysis of polymorphisms of candidate genes

Genotyping was performed for three candidate genes: APOE, LPL, and CETP. The genotypes and allele frequencies of the studied polymorphisms are shown in Table 3. All genotype distributions were in HWE (P > 0.05) for the studied polymorphisms.

Table 3.

Genotype and allele frequencies of polymorphisms of candidate genes

Gene	Polymorphism	Genotype frequency	Allele frequency
APOE	rs7412	C/C: 0.892	C: 0.943
C/T: 0.101	T: 0.057
T/T: 0.007
APOE	rs429358	T/T: 0.652	T: 0.806
T/C: 0.308	C: 0.194
C/C: 0.040
LPL	rs328	C/C: 0.628	C: 0.789
C/G: 0.322	G: 0.211
G/G: 0.050
CETP	rs708272	A/A: 0.402	A: 0.636
A/G: 0.468	G: 0.364
G/G: 0.130

Logistic regression between genetic polymorphisms and dyslipidemia

Associations between genetic polymorphisms and dyslipidemia were analyzed using logistic regression models adjusted for age, sex, BMI, duration of diabetes, and other relevant covariates (Table 4).

Table 4.

Association between genetic polymorphisms and dyslipidemia

Gene	Polymorphism	Genetic model	Genotype	OR (95%CI)	P value
APOE	rs7412	Codominant	C/C, C/T, T/T	1.00 (reference), 1.28 (0.92-1.79), 2.81 (1.12-7.04)	0.036
APOE	rs7412	Dominant	C/C, C/T + T/T	1.00 (reference), 1.41 (1.03-1.93)	0.031
APOE	rs429358	Codominant	T/T, T/C, C/C	1.00 (reference), 1.19 (0.95-1.49), 2.14 (1.38-3.32)	0.001
APOE	rs429358	Dominant	T/T, T/C + C/C	1.00 (reference), 1.36 (1.11-1.67)	0.003
LPL	rs328	Codominant	C/C, C/G, G/G	1.00 (reference), 1.23 (0.99-1.53), 2.05 (1.26-3.33)	0.007
LPL	rs328	Dominant	C/C, C/G + G/G	1.00 (reference), 1.34 (1.10-1.64)	0.004
CETP	rs708272	Codominant	A/A, A/G, G/G	1.00 (reference), 0.88 (0.70-1.11), 0.62 (0.43-0.89)	0.011
CETP	rs708272	Dominant	A/A, A/G + G/G	1.00 (reference), 0.82 (0.66-1.01)	0.064
CETP	rs708272	Recessive	A/A + A/G, G/G	1.00 (reference), 0.64 (0.46-0.89)	0.008

OR: Odds ratio; 95%CI: 95% confidence interval.

For the APOE gene, the T/T genotype of the rs7412 polymorphism and the C/C genotype of the rs429358 polymorphism were associated with increased odds of dyslipidemia compared to the respective reference genotypes after adjusting for covariates. Specifically, carriers of the APOE rs7412 T/T genotype had an OR of 2.81 (95%CI: 1.12-7.04, P = 0.036) for dyslipidemia, while carriers of the APOE rs429358 C/C genotype had an OR of 2.14 (95%CI: 1.38-3.32, P = 0.001) for dyslipidemia.

The LPL rs328 polymorphism also showed a significant association with dyslipidemia. Individuals carrying the G/G genotype had an OR of 2.05 (95%CI: 1.26-3.33, P = 0.007) for dyslipidemia compared to the reference C/C genotype after adjusting for covariates.

For the CETP rs708272 polymorphism, the G/G genotype was associated with a decreased odds of dyslipidemia compared to the reference A/A genotype (OR = 0.62, 95%CI: 0.43-0.89, P = 0.011). The dominant model showed a borderline significance (P = 0.064), while the recessive model revealed a significant protective effect of the G/G genotype (OR = 0.64, 95%CI: 0.46-0.89, P = 0.008).

Subgroup analyses

Subgroup analyses were performed to investigate potential effect modifications according to age, sex, BMI, and type of diabetes. The associations between genetic polymorphisms and dyslipidemia were generally consistent across the subgroups, although some variations in effect sizes were observed (Table 5).

Table 5.

Subgroup analyses of association between genetic polymorphisms and dyslipidemia

Subgroup	APOE rs7412, OR (CI)	APOE rs429358, OR (CI)	LPL rs328, OR (CI)	CETP rs708272, OR (CI)
Age
< 60 yr	2.62 (0.91-7.54)	2.08 (1.25-3.46)	1.92 (1.06-3.48)	0.69 (0.44-1.08)
≥ 60 yr	3.11 (0.89-10.86)	2.24 (1.19-4.21)	2.27 (1.12-4.60)	0.51 (0.27-0.96)
Sex
Male	3.18 (1.03-9.81)	2.31 (1.33-4.03)	2.14 (1.14-4.03)	0.58 (0.34-0.99)
Female	2.41 (0.64-9.08)	1.96 (1.07-3.59)	1.94 (0.94-4.01)	0.68 (0.39-1.19)
BMI
< 25 kg/m²	2.95 (0.86-10.12)	2.06 (1.15-3.69)	2.18 (1.11-4.28)	0.60 (0.34-1.06)
≥ 25 kg/m²	2.68 (0.78-9.21	2.23 (1.27-3.92)	1.93 (1.00-3.73)	0.65 (0.38-1.11)
Diabetes type
Type 1	2.73 (0.61-12.21)	2.39 (1.09-5.24)	1.81 (0.72-4.55)	0.71 (0.32-1.57)
Type 2	2.88 (1.00-8.28)	2.04 (1.24-3.36)	2.18 (1.27-3.75)	0.59 (0.38-0.92)

OR: Odds ratio; 95%CI: 95% confidence interval; BMI: Body mass index.

DISCUSSION

In this study, we investigated the association between dyslipidemia and polymorphisms of candidate genes involved in lipid metabolism in Chinese patients with DM. Our findings revealed significant associations between specific genotypes of the APOE, LPL, and CETP genes and risk of dyslipidemia in this population.

Our results showed that carriers of the APOE rs7412 T/T genotype (corresponding to the ε2 allele) and the APOE rs429358 C/C genotype (corresponding to the ε4 allele) had increased odds of dyslipidemia compared to the respective reference genotypes. The ε4 allele is thought to contribute to dyslipidemia through its effect on the metabolism and clearance of LDL particles. Individuals carrying the ε4 allele have been shown to exhibit higher LDL-C levels and an increased risk of coronary heart disease[18]. Interestingly, our study also found an association between the APOE ε2 allele (rs7412 T/T genotype) and increased odds of dyslipidemia. While the ε2 allele has generally been associated with lower LDL-C levels and a decreased risk of cardiovascular disease in non-diabetic populations[19], its effects in patients with diabetes may be different. Several studies have reported an increased risk of dyslipidemia, particularly elevated TG levels, in patients with diabetes carrying the ε2 allele[20]. Although the underlying mechanisms are not fully understood, they may involve alterations in lipoprotein metabolism and insulin resistance in the context of diabetes[21]. Moreover, our study found that carriers of the LPL rs328 G/G genotype had increased odds of dyslipidemia compared to carriers of the reference C/C genotype. The CETP rs708272 G/G genotype was associated with decreased odds of dyslipidemia compared with the reference A/A genotype. These findings are consistent with previous reports suggesting that the G allele of the LPL rs328 polymorphism is associated with reduced LPL activity and increased levels of TGs and VLDL particles[22], and that the G allele of the CETP rs708272 polymorphism is associated with lower CETP activity and higher HDL-C levels[10].

The observed associations between genetic polymorphisms and dyslipidemia in our study are biologically plausible and consistent with the known functions of the APOE, LPL, and CETP genes in lipid metabolism. However, it is important to note that the mechanisms underlying these associations may be complex and influenced by various factors, including gene-gene and gene-environment interactions, as well as the specific metabolic context of diabetes.

Our subgroup analyses revealed that the associations between genetic polymorphisms and dyslipidemia were generally consistent across subgroups stratified by age, sex, BMI, and type of diabetes, although some variations in the effect sizes were observed. These findings suggest that the influence of genetic factors on dyslipidemia may be present across different subpopulations of patients with diabetes; however, the magnitude of this effect could be modulated by other factors.

However, this study has some limitations. First, this was a cross-sectional study, which precludes establishment of a causal relationship between genetic polymorphisms and dyslipidemia. Second, although we focused on three candidate genes (APOE, LPL, and CETP) based on previous literature and their established roles in lipid metabolism, there are likely other genetic factors and pathways involved in the development of dyslipidemia that were not explored in our study. Third, our study did not account for potential gene-environment interactions that could modulate the effects of genetic polymorphisms on dyslipidemia. Fourth, our study has focused only on the Chinese population, which may limit the generalizability of our findings to other ethnic groups.

Despite these limitations, our study included a relatively large sample size of Chinese patients with diabetes, comprehensive genotyping of multiple candidate genes, and adjustments for potential confounding factors, which may contribute to the growing body of evidence supporting the role of genetic factors in the development of dyslipidemia in patients with DM. Understanding the genetic determinants of dyslipidemia in this population has several important implications.

CONCLUSION

Overall, this study provides valuable insights on the genetic basis of dyslipidemia in Chinese patients with DM and highlights the potential of leveraging this information to improve risk assessment, disease management, and therapeutic interventions in this high-risk population. Identifying individuals at higher genetic risk for dyslipidemia could facilitate personalized risk assessment and targeted screening strategies. From a therapeutic standpoint, knowledge of the genetic factors that influence lipid metabolism could pave the way for the development of personalized or precision medicine approaches. Pharmacogenomic studies are warranted to explore the potential of tailoring lipid-lowering therapies based on an individual's genetic profile to maximize treatment efficacy and minimize adverse effects.

Data sharing statement

No data available.