Effect of AMPK Subunit Alpha 2 Polymorphisms on Postherpetic Pain Susceptibility in Southwestern Han Chinese

Introduction

Herpes zoster is a viral skin disease that often causes severe pain.¹ Postherpetic neuralgia (PHN) is defined as persistent or recurrent neuropathic pain (NeP) for more than three months after clinical healing of the rash.² PHN incidence in patients with herpes zoster is approximately 5 to 30% and approximately 30–50% in patients with a course of more than one year.³ The daily life and mental state of patients are affected in severe cases.⁴ PHN is a typical form of NeP.⁵ NeP is clinically characterized by hyperalgesia, allodynia, and dysesthesia.⁶ Studies on NeP pathogenesis^7–10 and treatment^11,12 have made a lot of breakthrough progress. However, few studies have described the pathogenesis of PHN. Therefore, more studies on PHN pathogenesis and intervention measures are needed urgently.

Adenosine 5’-monophosphate (AMP)-activated protein kinase (AMPK) is an important protein that regulates cell energy homeostasis, as well as a hotspot in translational medicine research.¹³ In mammalian cells, AMPK may be involved in the regulation of cell energy balance.¹⁴ Our previous study found that abnormal energy metabolism in rat spinal cord astrocytes is closely related to NeP, and that AMPK plays an important role in abnormal cellular capacity metabolism.¹⁴ In our recent study, we also found that dexmedetomidine could alleviate NeP by activating AMPK in a mouse model of chronic constriction injury.¹⁵ AMPK consists of three subunits - α, β, and γ, the first of which is the functional unit.¹⁶ AMPKα has two subtypes, α1 and α2, encoded by the PRKAA1 and PRKAA2 genes, respectively.^17–19 Studies have shown that drug transporter gene polymorphisms can affect the activity of their protein products and lead to abnormal drug metabolism.^20,21 Therefore, AMPK gene polymorphisms may also affect AMPK activity and lead to disease. The current literature on PRKAA2 and diseases mainly includes diabetes-related studies.^22,23 Other studies on AMPK polymorphisms have mainly focused on cancer and polycystic ovary syndrome,^24–26 and no studies related to PHN have been reported. Thus, the purpose of this study was to investigate the relationship between PRKAA2 and PHN in the southwestern Chinese Han population.

Materials and Methods

Patients

This study included 132 PHN patients and 118 healthy subjects from the Chongqing Hospital of Traditional Chinese Medicine. The inclusion criteria were: PHN (persistent or recurrent pain for more than three months after clinical healing of the rash)² and pain with a numerical rating scale ≥ 4. Patients with hepatic, renal, neuromuscular, or nerve system diseases were excluded from the study.

Table 1 PCR Primer Sequences of rs10789038 and rs2796498 Polymorphisms

The clinical characteristics of PHN patients and healthy subjects are collected, including sex, age, body mass index (BMI), systolic blood pressure, diastolic blood pressure, triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting blood glucose. The research proposal was approved by the Ethics Committee of Chongqing Hospital of Traditional Chinese Medicine (2021-ky-67), and the study was conducted according to the Declaration of Helsinki principles. All subjects signed written consent forms. All subjects included in the experiment were of Han Chinese ethnicity.

DNA Extraction and Genotyping

Genomic DNA was isolated from peripheral venous blood using an SQ blood DNA kit (Cat. No. D5032-01, Omega, Norcross, Georgia, USA) according to the manufacturer’s protocol and stored at 4 °C (long-term storage at −80 °C). The rs10789038 and rs2796498 polymorphisms of PRKAA2 were used for genotyping. The primers used for the polymerase chain reaction (PCR) are listed in Table 1. The PCR reaction system consisted of 25 μL with less than 20 ng of DNA template, 0.4 μmol·L⁻¹ of primer, and 12.5 μL PCR Mix [2X T5 Super PCR Mix (Basic), Cat. No. TSE008, Tsingke Biotechnology, Chongqing, China)]. The PCR reaction procedure was set according to the reference conditions of the kit, and the experiment was carried out after optimizing our preliminary experiment. The PCR products were sequenced by Tsingke Biotechnology Co. Ltd (Chongqing, China). The correlation between the rs10789038 and rs2796498 polymorphisms of PRKAA2 and PHN was analyzed according to the gene sequencing results.

Statistics

The Hardy–Weinberg equilibrium of rs10789038 and rs2796498 was determined using the Nielsen method.²⁷ Genotypes and allele frequencies were compared between PHN patients and healthy subjects using the Chi-square test. Clinical data from PHN patients and healthy subjects were compared using the independent sample t-test. The correlation between each single nucleotide polymorphism (SNP) and PHN susceptibility was analyzed via binary logistic regression adjusted for age, sex, and BMI. The dominant and recessive models, as well as the homozygote and heterozygote comparisons were also assessed using binary logistic regression. Linkage disequilibrium and haplotypes were assessed as previously described.²⁸ SPSS 26.0 (Chicago, Illinois, USA) was used to perform statistical analysis. Statistical significance was set at P < 0.05.

Results

Subject Characteristics

The clinical data of the PHN patients and healthy subjects are shown in Table 2. Overall, the PHN patients were older than the healthy subjects (P < 0.05); however, the other clinical data between the PHN patients and healthy subjects were not significantly different (all P >0.05).

Table 2 The Demographic and Clinical Characteristics of PHN Patients and Healthy Subjects

Association Study

The frequencies of the two SNPs are shown in Table 3. The rs10789038 and rs2796498 SNPs in PHN patients and healthy subjects conformed to the Hardy–Weinberg equilibrium (P > 0.05). This indicates that the population under investigation reached genetic equilibrium, showing that the data included in this study are credible. The genotype and allele frequencies of PHN patients and healthy subjects showed significant differences in rs10789038 (P < 0.05), but not in rs2796498 (P > 0.05).

Table 3 Comparisons of Allelic Frequencies of PRKAA2 rs10789038 and rs2796498 Polymorphisms in PHN Patients and Healthy Subjects

Table 4 shows the different analysis models of the effect of PRKAA2 gene polymorphisms on PHN. All statistical analyses were adjusted for sex, age, and BMI. Among all PRKAA2 rs10789038 polymorphism genotypes, GG correlated negatively with AA; thus, subjects with the AA genotype might be at a higher risk of experiencing PHN [GG vs AA: ^aOR (95% CI) = 0.153 (0.032–0.731); ^aP = 0.019]. In the dominant model, the GG genotype would significantly decrease the risk of developing PHN, compared with the GG+AG genotype [AA+AG vs GG: ^aOR (95% CI) = 0.190 (0.039–0.927), ^aP = 0.040]. Compared with the AA genotype, AG+GG significantly decreased the risk of developing PHN in the recessive models [AA vs AG+GG: ^aOR (95% CI) = 0.547 (0.314–0.951), ^aP = 0.033]. Compared with the AA genotype of rs10789038, GG significantly decreased the risk of developing PHN [AA vs GG: ^aOR (95% CI) = 0.172 (0.035–0.842), ^aP = 0.030]. However, compared with the homozygous AA genotype of rs10789038, AG showed no statistical difference in PHN incidence [AA vs AG: ^aOR (95% CI) = 0.633 (0.354–1.133), ^aP = 0.124]. However, no significant correlation was seen between each genotype and the occurrence of PHN in all model analyses of the PRKAA2 rs2796498 gene polymorphism (P > 0.05).

Table 4 The Association Between PRKAA2 Genetic Polymorphisms and the Risk of PHN (Adjusted for Age, Gender and BMI)

Haplotype Analysis

The results showed no linkage disequilibrium between PRKAA2 rs10789038 and rs2796498. The D’ value was 0.783, and the r² value was 0.243.

Haplotype analysis of PHN patients and healthy controls showed four haplotypes in the two SNPs, with significant differences between them (P < 0.05, Table 5). The GG haplotype was found only in healthy subjects, suggesting that it may reduce the risk of PHN (P < 0.001). There was no significant difference in the other three haplotypes between PHN patients and healthy subjects (P > 0.05).

Table 5 Haplotype Analysis of PRKAA2 rs10789038 and rs2796498 with the Risk of PHN

Discussion

This is the first study to investigate the relationship between AMPKα2 gene polymorphisms and PHN in the southwestern Chinese Han population.

We hypothesized that PRKAA2 gene polymorphisms may also cause changes in AMPK function, leading to unidentified changes in the body. A literature search revealed many studies on PRKAA2 gene polymorphisms. A large part of the research has focused on the relationship between PRKAA2 and diabetes mellitus and its related complications. For example, Li et al²² found that the rs10789038 and rs2796498 gene polymorphisms were related to the incidence of type 2 diabetes, and rs2796498 was associated with the occurrence of diabetic nephropathy. Keshavarz et al²⁹ reported that PRKAA2 rs1418442 was slightly associated with the incidence of type 2 diabetes. Shen et al³⁰ reported that the PRKAA2 rs2746342 polymorphism was significantly related to the occurrence of type 2 diabetes, while the rs2143754 polymorphism participates in fasting plasma glucose modulation. Horikoshi et al²³ found a PRKAA2 gene polymorphism associated with insulin resistance and type 2 diabetes. In addition, studies have reported the association between PRKAA2 gene polymorphisms and other diseases. Hoffman et al²⁵ demonstrated PRKAA2 gene polymorphism involvement in non-Hodgkin lymphoma pathogenesis and progression. However, variants in PRKAA2 genes were not associated with polycystic ovary syndrome.²⁴ Campa et al²⁶ found no statistically significant correlation between breast cancer risk and SNPs in PRKAA2 genes in the European Prospective Investigation on Cancer.

In the present study, the correlation between the PRKAA2 SNPs (rs10789038 and rs2796498) and PHN was investigated in the southwestern Chinese Han population. PHN patients were older than the healthy subjects. Although the healthy group was statistically significantly younger, the difference in age was only 1.8 years. Due to the small SD and large sample size, the mean was significantly different in the statistical test, but not large enough to assume that it influenced the makeup of the groups. Therefore, we believe that the difference was casual. In healthy subjects, the variation frequency of rs10789038 A>G in our study was 37.29%, which was similar to the 36.92% reported by Li et al.²² However, Li et al²² reported that the rs2796498 A>G gene variant frequency was 64.02%, which is different from the 84.75% that healthy subjects showed in this study. Gene-environment or gene-region interactions may be the reason for this difference. Our subjects were from southwest China (Chongqing City and surrounding provinces, including Guizhou, Sichuan, and Yunnan), while the participants of Li et al²² were from north China (Zhengzhou, Henan Province). In addition, the subjects in our study were from a specific ethnicity (Chinese Han population), while Li et al had a more diverse population. Therefore, the different genetic makeup (phenotype) may also affect studies. Li et al²² found that rs2796498 was closely related to diabetic nephropathy. However, we found no significant correlation between rs2796498 and PHN occurrence, which may be related to different gene frequencies. On the other hand, rs10789038 correlated significantly with PHN occurrence (P < 0.001). These results also indicate that the PRKAA2 rs10789038 GG genotype may have reduced PHN incidence, whereas rs2796498 did not show this correlation (adjusted for sex, age, and BMI).

Haplotype analysis enables the combined detection of interacting susceptibility alleles on complex traits and the effects of linkage disequilibrium variants with specific haplotypes.^31,32 This approach is more effective than analyzing SNPs alone. In the present study, haplotype analysis of PHN patients and healthy controls showed four haplotypes in the two SNPs, and significant differences were found between the four haplotypes (P < 0.05). The GG haploid type was found only in healthy subjects, suggesting that it may reduce the risk of PHN (P < 0.001).

There are several strengths of this study. This study was the first to investigate the relationship between PRKAA2 and PHN in the Chinese Han population. In addition, we show the first evidence that the rs10789038 polymorphism is significantly associated with susceptibility to PHN.

However, this study also has some limitations. The sample size was relatively small, and we selected only two SNPs to conduct genotype analysis, which cannot represent all PRKAA2 gene polymorphisms. Second, the participants in this study were of southwestern Chinese Han ethnicity, a genetically homogenous group; therefore, this study may provide basis for further studies that evaluate the hypothesis tested here in other populations in China or in other ethnicities. Further research with larger sample sizes, more ethnic populations, and more polymorphic loci is required.

Conclusion

The present results indicate that PRKAA2 rs10789038 had a significant effect on PHN susceptibility, while rs2796498 did not affect southwestern Han Chinese patients. However, multi-locus, multi-ethnic, and large-sample studies are needed to evaluate the role of PRKAA2 polymorphisms in PHN.