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INTRODUCTION
Gout is a chronic inflammatory disease induced by the deposition of monosodium urate crystals (MSUc) in joints and other soft tissues as a result of chronic hyperuricemia (values above 6.8 mg/dL)1. Its prevalence ranges between 1 and 10% worldwide, being 3.6-times more prevalent in men compared to women2. As a result of an active process of MSUc nucleation, gout presents a wide range of clinical manifestations, including acute attacks of arthritis, renal failure, hypertension, and cardiovascular disease. In fact, the inflammatory response in gout is triggered by MSUc deposition, which is recognized by Toll-like receptors types 2 and 4 (TLR2/TLR4), and CD14, as an adaptor molecule3. The NLR family pyrin domain-containing 3 (NLRP3) can be activated by the purinergic receptor P2X ligand-gated ion channel 7 (P2X7R) that senses the ATP concentration and induces downstream events that include the release of pro-inflammatory cytokines such as interleukins 1β (IL-1β) and 18 (IL-18)4-6. The ending of the inflammatory response is regulated by the proliferator–activated receptor gamma (PPARγ) that acts as an anti-inflammatory response7.
Although the mechanism of gout pathogenesis is still an enigma, it is known that gout is a multifactorial syndrome where genetics is a key factor8. Recently, it has been proposed that single nucleotide polymorphisms (SNPs) may be related to gout susceptibility, suggesting that some SNPs may be involved in over activation and regulation of the NLRP3 inflammasome. To date, only a few reports have focused on SNPs as precursors of gout9. In addition, these studies have reported preliminary data exclusively from the European or Asian population, and no data regarding Latin American population are currently available on this topic. This latter population is of high interest because of its rich genetic background due to its diverse ancestries. Hence, studying this population could increase the knowledge on the role of SNPs in the pathogenesis of gout, allowing for the analysis of differences and analogies between heterogeneous populations. Therefore, we investigated the possible association between the SNPs within NLRP3 inflammasome genes and gout susceptibility in Mexican population.
METHODS
Study population
The study included a cohort of Mexican patients hospitalized or attending the outpatient Rheumatology clinic of the National Rehabilitation Institute Luis Guillermo Ibarra Ibarra (INRLGII) and General Hospital of Mexico Eduardo Liceaga. Inclusion criteria for gout patients were having a diagnosis of gout according to the American College of Rheumatology (ACR) and the European League Against Rheumatism classification criteria (EULAR)10. Exclusion criteria included the presence of concomitant chronic or autoimmune rheumatic disease, history of cancer, and ongoing immunosuppressive treatment. Healthy subjects, matched by sex, were included as a control group. These subjects were recruited from an internal database of our institution in which inclusion criteria were the absence of any type of chronic, autoimmune, or dysmetabolic disease, and with normal uric acid values (below 6.8 mg/dL). Body mass index (BMI) and serum biochemical parameters were obtained from all the participants. The Research Ethics Committee from the INRLGII approved the protocol (number INR16/16). The study was conducted according to the Declaration of Helsinki11. All the patients and participants signed the written informed consent which includes the approval for publication.
Biochemical parameters determination
Glucose, total cholesterol, and triglycerides concentrations were analyzed in blood plasma by an enzymatic–colorimetric method using the commercial Kit Uric Acid FS TBHBA, Kit Triglycerides FS, Kit Cholesterol FS, and Kit Glucose GOD FS (Diagnostic Systems, Germany). Quantification was made by spectrophotometry in a Beckman Coulter DTX 880 Multimode Detector.
SNPs selection and bioinformatic analysis
A detailed search to identify publications related to NLRP3 inflammasome polymorphism and gout susceptibility was performed in the electronic databases PubMed and ENBASE. Finally, eight SNPs from seven different genes were chosen (Table 1). The SNPs with a minor allele frequency (MAF) above 1% for the Mexican population were selected; the identification was made using data from the 1000 Genomes Project that provides the description of common human genetic variation12 (Table 1). Genetic variation data for all the SNPs were obtained from the HapMap project (http://www.hapmap.ncbi.nlm.nih.gov/) and the public National Center for Biotechnology Information dbSNP database (http://www.ncbi.nlm.nih.gov/snp). The SNPs should not be in linkage disequilibrium. Since the Mexican−Mestizo population is admixed, the ancestry was analyzed using 11 ancestry informative markers (AIMs) differentiating mainly the Amerindian, African, and European ancestries (Table 1) as previously reported13,14.
Table 1. Single-nucleotide polymorphisms within TLPR3 inflammasome genes and ancestry informative markers
| Gene | dbSNP ID | Chromosome Location | SNP Type | Polymorphism |
|---|---|---|---|---|
| Inflammasome | ||||
| TLR4 | rs2149356 | 9:120474199 | Intron variant | G/T, Transversion Substitution |
| CD14 | rs2569190 | 5:140012916 | 5 prime UTR variant | A/G, Transition Substitution |
| NLRP3 | rs3806268 | 1:247587477 | Synonymous variant | A/G, Transition Substitution |
| NLRP3 | rs10754558 | 1:247612036 | 3 prime UTR variant | C/G, Transversion Substitution |
| CARD8 | rs2043211 | 19:48737706 | Stop gained | A/T, Transversion Substitution |
| IL-1β | rs1143623 | 2:113595829 | Upstream gene variant | C/G, Transversion Substitution |
| P2RX7 | rs3751142 | 12:121622419 | Synonymous variant | T/G, Transversion Substitution |
| PPARGC1B | rs45520937 | 5:149212430 | Missense variant | A/G, Transition Substitution |
| AIMs | ||||
| – | rs2695 | 9:82884577 | Intergenic variant | C/T, Transition Substitution |
| POGZ | rs4528122 | 1:151441314 | Intron variant | C/T, Transition Substitution |
| FMN2 | rs986690 | 1:240288119 | Intron Variant | A/G, Transition Substitution |
| AQR | rs2862 | 15:34853352 | 3 prime UTR variant | C/T, Transition Substitution |
| SAP30L | rs3340 | 5:154452307 | Intron Variant | C/T, Transition Substitution |
| CKM | rs4884 | 19: 45306777 | Synonymous variant | A/G, Transition Substitution |
| – | rs722098 | 21: 15313279 | Intergenic variant | A/G, Transition Substitution |
| CA10 | rs203096 | 17:51934409 | Intron variant | G/T, Transversion Substitution |
| CCL17 | rs223830 | 16: 57418059 | 3 prime UTR variant | C/T, Transition Substitution |
| DRD2 | rs1800498 | 11: 113420866 | Intron variant | A/G, Transition Substitution |
| PRKCE | rs281478 | 2: 46173272 | Intron variant | C/G, Transversion Substitution |
SNP: single nucleotide polymorphism; dbSNP: the NCBI database of genetic variation; AIMs: ancestry informative markers.
Samples
A peripheral blood sample was collected from participants by specialized health staff. Genomic DNA (gDNA) was obtained using the commercial kit QIAmp DNA® Blood Mini Kit (QIAGEN, Hilden, Germany); gDNA concentration and purity were analyzed using the NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific). A Biobank of gDNA (50 ng/mL) and blood sera were kept at −80°C for further analysis.
Genotyping assay
SNPs were genotyped using pre-designed Taqman probes (NLRP3, C_26646014_10 and C_26052028_10; TLR4, C_11270899_20; CD14, C_16043997_10; CARD8, C_11708080_1_; IL-1β, C_1839941_10; and PPARGC1B, C_86507005_10). To perform the genotyping assay, 2.5 ng/µL of gDNA were mixed with 2.5 μL of TaqMan® Universal PCR Master Mix (Applied BioSystems) on 96-well plates and amplified using the StepOnePlus Real-Time PCR Systems (Applied Biosystems) according to the manufacturer protocol. Conditions were as follows: an initial denaturation step at 94°C for 10 min, the amplification was performed by 40 cycles (95°C during 15 s), and the final elongation cycle at 60°C for 1 min. gDNA of gout patients and healthy subjects was genotyped by duplicate on the same plate. The allelic discrimination was analyzed by the Step One software V2.3. No discordant genotypes were observed in the duplicate samples.
Statistical analysis
The STATA version 14.0 (Texas USA) and the SPSS version 25.0 (Chicago, IL) were used for statistical analysis. Descriptive analysis was used for the demographic and clinical characteristics between groups (cases and controls). Significant differences were assessed by Student’s t-test. Parametric data were reported as mean ± standard deviation (SD), ranges or percentages as appropriate. The Hardy-Weinberg (HW) equilibrium calculation was performed using Chi-square test with one degree of freedom. Allelic and genotype frequencies were estimated for all polymorphisms and groups were compared using Fisher’s exact test. To analyze the AIMs genotypes, we inferred the ancestry percentage of the three main parental populations (European, Amerindian, and African) for each subject using the STRUCTURE v2.3.4 software (Pritchard Lab, Stanford University, USA) and the following parameters: a burning period of 150 thousand, 700 thousand Markov chain Monte Carlo repeats, an admixture model with an alpha initial value of one and using an individual alpha for each population, independent allele frequencies among populations, and a constant lambda.
Multiple conditional logistic regression model analyses were carried out to estimate the association of each SNP with gout; results were estimated by the odd’s ratio (OR) and 95% confidence interval (CI). The analysis was adjusted by sex, age, BMI. and ancestry, taking into account dominant, codominants, recessive, and additive inheritance models. The association between SNPs and the metabolic values was determined by linear regression analyses. p < 0.05 was used for declaring significant association.
RESULTS
Characteristics of the study population
We included 504 subjects; 42 of them were excluded (17 did not give their consent to participate in the study; 12 had incomplete clinical information, and in 13 samples were insufficient or contaminated). Finally, a total of 463 subjects were included in the study, 220 gout patients and 243 healthy subjects. The study population came from 20 different states of Mexico, 71.1% from the central area, and 28.9% from the rest of the country. The study included individuals with generations born in the same state. Table 2 describes the demographic and clinical data of gout patients and healthy subjects. Most of the participants were male (98% and 95% in the gout group and healthy controls, respectively). The gout group was older than the control group (p < 0.0001). Patients with gout had higher BMI compared with healthy individuals (p < 0.0001). Similarly, uric acid values (p < 0.0001), total cholesterol (p < 0.0001), and triglycerides (p < 0.0001) were higher in gout patients with respect to healthy controls. Serum fasting glucose levels were not different between groups (p > 0.0525).
Table 2. Clinical and biochemical characteristics of the study population (n = 463)
| Variable | Control (%) 243 (52.48) | Gout (%) 220 (47.52) | p-value |
|---|---|---|---|
| Male (%)/Female (%) | 199 (95)/11 (5) | 208 (98)/4 (2) | |
| Age (years) | 44.51 ± 8.38 | 52.37 ± 12.49 | < 0.0001* |
| Weight (kg) | 76.52 ± 10.36 | 79.47 ± 14.93 | 0.0097* |
| Height (m) | 1.69 ± 0.07 | 1.67 ± 0.07 | < 0.0001* |
| BMI (kg/m2) | 26.63 ± 2.70 | 28.77 ± 4.28 | < 0.0001* |
| Uric Acid (mg/dL) | 5.43 ± 1.00 | 6.77 ± 2.01 | < 0.0001* |
| Fasting Glucose (mg/dL) | 100.0 ± 3392 | 98.19 ± 18.52 | 0.0525 |
| Triglycerides (mg/dL) | 137.0 ± 51.35 | 198.7 ± 120.5 | < 0.0001* |
| Total Cholesterol (mg/dL) | 159.8 ± 37.33 | 171.3 ± 38.19 | < 0.0001* |
Data are means ± SD; p values were assessed by Student’s t test (α = 0.05) and *p values were calculated by the Mann–Whitney U test (α = 0.05). BMI: body mass index.
Regarding the ancestry of the overall population, after an accurate analysis, we found a median of 35% for both, European and Amerindian ancestry, and 30% for African. No statistically significant differences in terms of percentage of genomic content in European, Amerindian, and African were found between gout patients and healthy controls (p = 0.44, p = 0.58, and p = 0.48, respectively) (Supplementary table).
Allele and genotype distributions of NLRP3 inflammasome SNPs
The genotype distributions were consistent with those expected from the HW equilibrium analysis between the study groups in almost all the SNPs (p > 0.05), except for the rs2149365 (TLR4) in the gout group (p = 0.006) (Table 3). The MAF of the 8 SNPs in the control group was similar as the reported in 1000 Genomes Project (Table 3).
Table 3. HW Equilibrium and MAF of 8 SNPs of NLPR3 Inflammasome in controls and diverse populations
| Gene | SNP | HWE Control (n = 243) | HWE Gout (n = 220) | Minor Allele | MAF Control | MAF (1000 Genomes) | ||
|---|---|---|---|---|---|---|---|---|
| Mexican-Americans | Europeans | Africans | ||||||
| TLR4 | rs2149356 | 0.534 | 0.006 | T | 0.29 | 0.32 | 0.31 | 0.82 |
| CD14 | rs2569190 | 0.480 | 0.246 | G | 0.49 | 0.45 | 0.51 | 0.70 |
| NLRP3 | rs3806268 | 0.683 | 0.683 | A | 0.42 | 0.46 | 0.55 | 0.01 |
| NLRP3 | rs10754558 | 0.110 | 0.652 | G | 0.16 | 0.20 | 0.46 | 0.25 |
| CARD8 | rs2043211 | 0.615 | 0.405 | T | 0.25 | 0.27 | 0.33 | 0.18 |
| IL-1β | rs1143623 | 0.108 | 0.223 | C | 0.45 | 0.54 | 0.71 | 0.92 |
| P2RX7 | rs3751142 | 0.179 | 0.622 | T | 0.41 | 0.31 | 0.08 | 0.16 |
| PPARGC1B | rs45520937 | 0.087 | 0.226 | A | 0.28 | 0.28 | 0.04 | 0.00 |
HWE: Hardy-Weinberg equilibrium; MAF: minor allele frequency.
Association analysis of NLRP3 inflammasome genes
The association analysis was performed adjusting for age, sex, BMI, and ancestry under a logistic regression model. When analyzing the dominant model, only the missense SNP rs45520937 of PPARGC1B was associated with risk of developing gout (OR = 2.30; 95% CI: 1.09-4.86; p = 0.030), while neither codominant, recessive, nor additive models showed a significant association (Tables 4 and 5).
Table 4. Association of 8 SNPs of NLPR3 inflammasome genes with gout disease
| z | SNP | MAF | OR (95% CI) | p-value | |
|---|---|---|---|---|---|
| Controls (n = 243) | Gout (n = 220) | ||||
| TLR4 | rs2149356 | 28.7 | 31.8 | 0.68 (0.33-1.40) | 0.295 |
| CD14 | rs2569190 | 48.7 | 45.9 | 0.53 (0.25-1.13) | 0.101 |
| 0.41 (0.16-1.05) | 0.064& | ||||
| NLRP3 | rs3806268 | 41.9 | 39.0 | 0.70 (0.33-1.47) | 0.346 |
| NLRP3 | rs10754558 | 16.3 | 15.6 | 0.56 (0.26-1.22) | 0.147 |
| CARD8 | rs2043211 | 24.7 | 24.1 | 1.35 (0.67-2.71) | 0.407 |
| IL-1β | rs1143623 | 44.7 | 37.0 | 1.20 (0.57-2.48) | 0.632 |
| P2RX7 | rs3751142 | 41.0 | 39.3 | 1.12 (0.48-2.61) | 0.802 |
| PPARGC1B | rs45520937 | 28.3 | 34.9 | 2.30 (1.09-4.86) | 0.030 |
Associations with gout were adjusted for age, sex, BMI and ancestry. p-values were calculated By logistic regression, using dominant model and &additive model. MAF: minor allele frequency; OR: odds ratio; CI: confidence interval.
Table 5. Association of PPARGC1B rs45520937 SNP with gout disease
| Model | Genotype | Control | Gout | OR (95% CI) | p-value |
|---|---|---|---|---|---|
| Codominant | G/G | 39 (53.4%) |
38 (40.9%) |
1 | 0.074 |
| A/G | 27 (37%) |
43 (46.2%) |
2.14 (0.97-4.71) |
||
| A/A | 7 (9.6%) |
12 (12.9%) |
2.99 (0.88-10.12) |
||
| Dominant | G/G | 39 (53.4%) |
38 (40.9%) |
1 | 0.030 |
| A/G-A/A | 34 (46.6%) |
55 (59.1%) |
2.30 (1.09-4.86) |
||
| Recessive | G/G-A/G | 66 (90.4%) |
81 (87.1%) |
1 | 0.210 |
| A/A | 7 (9.6%) |
12 (12.9%) |
2.03 (0.65-6.37) |
Associations with gout were adjusted for age, sex, BMI, and ancestry. p-values were calculated by logistic regression. MAF: minor allele frequency; OR: odds ratio; CI: confidence interval.
A lower risk for gout was found in the adaptor molecule CD14 rs2569190 polymorphism when it was analyzed under an additive model, adjusted for age, BMI, and ancestry; nevertheless, the p-value showed only a tendency to the significance (OR = 0.41; 95% CI: 0.16-1.05; p = 0.064) (Table 4). The remaining SNPs did not present differences in the genotype and allele frequencies between gout patients and healthy controls in any of the inheritance models (Table 4).
Association of SNPs with biochemical values in the control group
To analyze whether SNPs were associated with metabolic values in the control group, a linear regression analysis was performed. Interestingly, under a dominant model adjusted for age, sex, BMI, and ancestry, the IL-1β rs1143623 SNP was associated with lower glucose levels (B [SE] = −7.32 [2.47]; p = 0.004), while the rs3751142 SNP of P2RX7 was associated with higher triglycerides levels (B [SE] = 25.03 [11.31], p = 0.041) (Table 6). Furthermore, under an additive model adjusted for age, sex, BMI, and ancestry, CD14 rs2569190 polymorphism was associated with lower glucose levels, although the p-value only showed a tendency to the significance (B [SE] = −2.99 [1.59], p = 0.064; Table 6). No significant associations were found in the rest of the SNPs analyzed.
Table 6. Association of 8 SNPs with biochemical values in the control group
| Gene | SNP | Uric acid | Glucose | Triglycerides | Cholesterol | ||||
|---|---|---|---|---|---|---|---|---|---|
| B (SE) | p-value | B (SE) | p-value | B (SE) | p-value | B (SE) | p-value | ||
| TLR4 | rs2149356 | 0.08 (0.27) |
0.541 | −0.71 (2.54) |
0.781 | −10.95 (10.44) |
0.456 | −2.12 (7.20) |
0.631 |
| CD14 | rs2569190 | −0.21 (0.31) |
0.549 | −2.35 (2.79) |
0.367 | 10.06 (12.01) |
0.459 | 8.65 (7.95) |
0.300 |
| −0.22 (0.18) |
0.295& | −2.99 (1.59) |
0.064& | 8.98 (6.97) |
0.337& | 1.55 (4.68) |
0.751& | ||
| NLRP3 | rs3806268 | −0.16 (0.28) |
0.887 | −4.78 (2.52) |
0.087 | −10.10 (10.83) |
0.398 | −1.21 (7.30) |
0.846 |
| NLRP3 | rs10754558 | −0.06 (0.29) |
0.485 | −0.11 (2.71) |
0.950 | 12.83 (11.37) |
0.344 | −2.13 (7.64) |
0.760 |
| CARD8 | rs2043211 | −0.02 (0.27) |
0.737 | 2.86 (2.52) |
0.253 | −4.58 (10.70) |
0.638 | 6.07 (7.14) |
0.360 |
| IL-1β | rs1143623 | −0.27 (0.29) |
0.333 | −7.32 (2.47) |
0.004 | 2.05 (11.32) |
0.883 | −9.53 (7.40) |
0.149 |
| P2RX7 | rs3751142 | 0.53 (0.29) |
0.173 | −0.40 (2.78) |
0.884 | 25.03 (11.31) |
0.041 | 1.36 (7.78) |
0.789 |
| PPARGC1B | rs45520937 | 0.31 (0.26) |
0.117 | −3.03 (2.47) |
0.252 | 0.51 (10.50) |
0.746 | 8.60 (6.94) |
0.153 |
Models were adjusted for sex, age, BMI, and ancestry (n = 243), and &p-values for recessive models were adjusted for age, BMI, and ancestry (n = 243).
DISCUSSION
To date, there is still a lack of evidence regarding the genetic variations in the pathogenesis of chronic inflammatory diseases, including gout15, especially in Latin America. To the best of our knowledge, this is the first study demonstrating a potential association of the SNPs within the NLRP3 inflammasome genes with the risk for developing gout in a Mexican cohort. As previously described, NLRP3 inflammasome activation plays a key role in gout since it triggers swelling and inflammation mediated by MSUc recognition, mainly in dendritic and phagocytic cells.
In our study population, we analyzed 8 SNPs of seven different genes of the NLRP3 inflammasome signaling pathway, which were previously associated with gout disease in other populations16-25. Since those studies were centered exclusively on the European and Asian populations, we focused our study on a Mexican cohort taking into account their biological admixture population.
Our cohort study shows that uric acid, cholesterol, and triglycerides values were statistically significantly higher in the gout group compared to the control group. However, these parameters are in the normal range values for the overall population. Our analysis of SNP rs2149356 of TLR4 gene located at intron four did not show an association with the risk of gout. This result is in line with Torres et al.26, who studied this polymorphism in a Spanish cohort, reporting no association with a risk for developing gout. Contrary, Qing et al.27 reported a solid association between the TT genotype of the SNP rs2149356 with the risk of gout in a Chinese population (OR = 1.96; p = 7.9 x 10-5). Moreover, they demonstrated an over-expression of TLR4 and IL-b induced probably by this SNP. Finally, Rashed et al.22 showed that T allele is associated with gout risk in European population (OR = 1.12, p = 0.012), but not in a Polynesian cohort, in which they contrarily reported a decreased risk of gout (OR = 0.80, p = 0.011).
In our analysis, SNP rs2569190 of the CD14 gene, under a dominant model adjusted for age, sex, BMI, and ancestry, did not show significant differences in the genotype and allele frequencies, but when the analysis was adjusted for age, BMI, and ancestry under an additive model, there was a tendency to a significantly lower risk for gout (p = 0.064, Table 4). Interestingly, it may suggest that SNP rs2569190 plays a protective role for gout in the Mexican population, contrary to what has been reported for the European and New Zealand Polynesian populations16. Further, we found no association between the NLRP3 rs3806268 and rs10754558 SNPs and the risk of developing gout, in line with previous results of Meng et al.17 for the rs3806268 SNP. However, Deng et al.19 contrast these results since they reported a positive association of the rs3806268 SNP with the risk of gout when compared with the AA allele (OR = 1.83, p > 0.03) in a Chinese population. With respect to the NLRP3 rs10754558 SNP, the previous studies conducted by Mckinney et al., Deng et al., and Wang et al.16,19,23, in line with our results, did not find a significant association with risk of gout in the European, Polynesian, and Chinese populations, respectively. In contrast, Zhang et al.24 found that the allelic frequency G of this SNP was significantly increased in gout patients compared with the group control in Chinese population (OR = 1.30, p = 0.003).
To date, only one SNP has been analyzed within the CARD8 gene (rs2043211 SNP), which previously showed an association with gout risk in Chinese population18. Contrary, in Korean population this association was not found20, similar to our findings. About IL-1β polymorphism, our data showed no difference in the allelic and genotype frequencies, in contrast to Mckinney et al.16 who found a nominal allelic association for IL-1β rs1143623 in the combined European and Polynesian populations for the G allele (OR = 1.10, p = 0.020).
In our population, rs3751142 SNP of P2RX7 was not associated with gout risk, as was also reported by Lee et al.20 in Korean population; nevertheless, they described that the C/A genotype had a trend toward a higher risk of gout when it was combined with the T/T CARD8 rs2043211 genotype. The P2RX7 gene is particularly polymorphic, and therefore, it is necessary to study other SNPs in our population21.
The NLRP3 inflammasome is regulated by PPAR-γ, which is a key regulator controlling both metabolism and inflammatory response28. Our analysis demonstrated that in the Mexican population, the presence of the rs45520937 SNP (PPARGC1B) was associated with the risk of developing gout (A/G-A/A genotypes). This result is in agreement with Chang et al.25 who reported that the rs45520937 SNP causes Arg265Gln (p.R265Q) substitution in the exon 5 of PPARGC1B and demonstrated that rs45520937 augments the NLRP3 inflammasome response, and the upregulation of IL-1 production. We are aware that it is necessary to assess the functional impact of this SNP in our population study and taking into consideration the results of Chang et al.25, we hypothesize that overexpression of NLPR3 and the inflammatory cytokine IL-1β is involved in the pathogenesis of gout in our population. The differences between the associations of SNPs in other populations and our results could be explained by the different ancestry of the populations, and even more, by the variables taken for adjusting in the statistical analyses, which are different in all the reports.
This study also showed an association between the inflammasome SNPs with metabolic values in the control group. Under the dominant model, IL-1β rs1143623 SNP was associated with lower glucose levels and P2RX7 rs3751142 SNP was associated with higher triglycerides levels; however, there is not enough evidence for this relationship. Delgado-Lista et al.29 described that IL-1β SNP rs1143623 affects triglycerides levels, but there are no data on the influence over cholesterol levels. Our data suggest that the inflammasome SNPs could have a potential genetic influence on these metabolic parameters.
We are aware that our study has some limitations. First, the sample size is limited to definitely support strong conclusions. However, we consider our results as a first step to open new research pathways on this topic, which has not been previously explored in our country. The expansion of the study is in progress, involving more patients and centers. This will allow us to elucidate better the role of SNPs in the genesis of gout, including its functional impact. Second, the cohort did not include all the regions of Mexico; however, we believe that the geographic cohort included may provide a global outlook to reach more solid results in a near future. Third, a hyperuricemia control group was not included. Fourth, the controls were only matched for sex, since age matching was difficult due to technical reasons. However, we will consider these aspects for the ongoing study, even the aim to increase the number of patients with at least four controls each. Finally, additional functional studies are necessary to further validate if PPARGC1B rs45520937 SNP has implications on the protein structure and function. Additional studies including more extensive geographic cohorts are needed to elucidate the solid role of NLRP3 inflammasome genes in gout. Despite these limitations, we believe that our study is relevant because we explored 8 of the most common SNPs reported so far in the literature, which allowed us to contrast our results with those in other populations. This may open new research frontiers to determine the potential role of these SNPs, even in the newly established states of gout (i.e., tophaceous gout, erosive gout, first gout flare, and recurrent gout flares)30, and also in asymptomatic hyperuricemia status, which is being frequently reported in recent years31. In conclusion, our findings suggest that rs45520937 SNP of PPARGC1B was associated with gout susceptibility.
