Posttraumatic Osteomyelitis Risks Associated with NLRP3 Gene Polymorphisms in the Chinese Population
by
, ,
Yudun Qu
1,
Jiaxuan Li
1,
Wei Zhang
2,
Changliang Xia
2,
Shuanji Ou
2,
Yang Yang
2,
Nan Jiang
3,
Yunfei Ma
3,
Yong Qi
2,* and
Changpeng Xu
2,* 1
Department of Orthopaedics, Guangdong Second Provincial General Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510317, China
2
Department of Orthopaedics, Guangdong Second Provincial General Hospital, NO.466 Xingang Road, Haizhu District, Guangzhou 510317, China
3
Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou 510445, China
*
Authors to whom correspondence should be addressed.
J. Pers. Med. 2023, 13(2), 253; https://doi.org/10.3390/jpm13020253
Submission received: 24 November 2022
/
Revised: 12 January 2023
/
Accepted: 19 January 2023
/
Published: 30 January 2023
(This article belongs to the Section Clinical Medicine, Cell, and Organism Physiology)
Abstract
:The purpose of this case–control study was to examine possible links between NLRP3 gene polymorphisms and the risk of developing posttraumatic osteomyelitis (PTOM) in the Chinese population. A total of 306 patients with PTOM and 368 normal controls were genotyped for NLRP3 (rs35829419, rs10754558, rs7525979, rs4612666), ELP2 (rs1785929, rs1789547, rs1785928, rs12185396, rs681757, rs8299, rs2032206, rs559289), STAT3 (rs4796793, rs744166, rs1026916, rs2293152, rs1053004), CASP1 (rs501192, rs580253, rs556205, rs530537), NFKBIA (rs696), NFKB1 (rs4648068), CARD8 (rs204321), and CD14 (rs2569190) using the genotyping technique SNaPshot. The genotype distributions of NLRP3 gene rs10754558 (p = 0.047) and rs7525979 (p = 0.048) significantly differed between the patients and the healthy controls. Additionally, heterozygous models indicated a significant association between NLRP3 rs10754558 and the likelihood of developing PTOM (OR = 1.600, p = 0.039), as did recessive and homozygous models of NLRP3 rs7525979 (OR = 0.248, p = 0.019 and 0.239, p = 0.016, respectively). Collectively, our findings suggest that, in the Chinese population, the risk of developing PTOM was increased by the association between NLRP3 rs10754558 and rs7525979. Therefore, our findings may provide novel insights and guidance in the prevention and development of PTOM.
1. Introduction
The likelihood of developing post-traumatic osteomyelitis (PTOM), a bone marrow infection with or without soft tissue infection, after trauma or orthopedic surgery, ranges from 0.4–16.1% [1], with incidences of 1–2% for closed fractures and approximately 30% for open fractures [2], with the highest incidence being 55% [3]. The diagnosis and treatment of PTOM are challenging owing to its “wide range” or “highly diverse” characteristics. This implies that the clinical presentation of each patient is unique. Furthermore, the clinical effect and prognosis are influenced by several factors, including the location of the bone, the extent and duration of the infection, the type and virulence of the pathogen, and the treatment strategy. Patients experience substantial physical, psychological [4], and socioeconomic stress [5] owing to prolonged illness and an increased risk of recurrent infections [6]. Therefore, reducing morbidity is as crucial as increasing healing rates. Therefore, a comprehensive understanding of PTOM pathogenesis, which is related to environmental and host factors, is essential.
Genetic predisposition plays a significant role in the development of PTOM. According to Wang et al., the GG genotype of the cyclooxygenase-2 (COX-2) gene enhances vulnerability to PTOM in the Chinese population [7]. Alves De Souza et al. reported that the Brazilian population might be more likely to acquire PTOM owing to interleukin (IL) gene polymorphisms rs16944 and rs2234663 [8]. These findings indicate that single-nucleotide polymorphisms (SNPs) contribute to PTOM onset.
The NOD-like receptor thermal protein domain-associated protein 3, also called NLRP3, or cryopyrin, is present on chromosome 1q44 and contains nine exons [9]. Granulocytes, monocytes, dendritic cells, T cells, B cells, epithelial cells, and osteoblasts are critical cell types that express cytoplasmic proteins [10]. Upon detecting tissue damage, NLRP3 inflammasomes result in interleukin 1b (IL-1β), caspase 1 (Casp1)-mediated proteolysis, and interleukin-18 (IL-18) processing and in secretion, activation, and caspase 1 (Casp1)-mediated proteolysis [11]. In addition to neurological illnesses such as Parkinson’s disease and Alzheimer’s disease, NLRP3 signaling may play a role in various chronic and metabolic diseases, including type 2 diabetes, gout, inflammatory bowel disease, obesity, atherosclerosis, and periodic fever [12]. This is believed to be the root cause of degenerative disorders. However, studies on the relationship between PTOM risk and NLRP3 SNPs, particularly in the Chinese population, are limited. Therefore, the objective of the current study was to evaluate any possible associations between PTOM susceptibility in the Chinese population and 25 SNPs associated with inflammation. Our findings may provide novel insights and guidance in the prevention and development of PTOM.
2. Materials and Methods
2.1. Patients and Controls
A total of 627 patients with chronic OM, 306 of whom had PTOM—which is defined as orthopedic surgery or trauma-related bone infection, accompanied by or without soft tissue infection—were enrolled between August 2016 to October 2019 [13] (Figure 1). One of the following confirmation criteria was used to determine the diagnosis of PTOM [14]: (1) a histopathological examination showing infection, (2) a clean sinus or fistula that connects to the implant or bone immediately, and (3) two or more cultures displaying the same pathogen. The patient group contained 251 men and 55 women with a median age was 44 years with an interquartile range (IQR) between 31.00–54.00. A total of 368 healthy controls, including 268 males and 100 females with a median age of 46 years and IQR between 37.00–52.00, were enrolled. The patient and control groups were matched in terms of age and sex after the adjustment (p > 0.05). The research methodology was approved by the hospital’s medical ethics committee, and each participant or their legal guardian completed an informed consent form on their behalf (approval letter No. NFEC-2019-087).
2.2. Determination of NLRP3 Polymorphisms
Approximately 2 mL of peripheral blood from each patient was collected into ethylenediaminetetraacetic acid (EDTA) tubes. Genomic DNA was extracted from leukocytes using the salting-out method [15] and stored at −80 °C until further use. The Multiplex SNaPshot method [16] is a primer extension-based method for genotyping known SNP positions through the manuscript-automated DNA analyzer invented by Applied Biosystems, which was used for genotyping 25 tag SNPs in NLRP3, ELP2, STAT3, CASP1, NFKBIA, NFKB1, CARD8, and CD14 (Applied Biosystems, Foster City, CA, USA). Table S1 lists the forward (F), reverse (R), and extension primers used in the PCR and extension procedures [17]. The detailed protocols have been previously described [14].
2.3. Outcome Parameters
The outcome criteria included the genotype distribution of 25 SNPs in PTOM patients and normal controls, mutation allele frequencies, and four genetic models (homozygous, heterozygous, dominant, and recessive). Differences were also observed in the levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the blood of individuals with abnormal genotypes.
2.4. Statistical Analysis
Statistical Product and Service Solutions (SPSS) program Version 13.0 was used for statistical analysis (SPSS Inc., Chicago, IL, USA). The Kolmogorov–Smirnov test was used to determine the normality of the data distribution. Continuous variables were reported as mean, standard deviation (SD), or median with IQR based on the data distribution. For comparisons between two distinct groups or between two or more groups, normally distributed data were compared using Student’s t-test or one-way analysis of variance (ANOVA). Otherwise, the Kruskal–Wallis H test or Mann–Whitney U test was used.
The chi-square test was used to determine whether the genotype distribution of healthy controls supported the Hardy–Weinberg equilibrium (HWE). The genotype distribution and frequency of mutant alleles in patients and healthy controls were compared using chi-square or Fisher’s exact test. To examine the potential relationship between genetic polymorphisms and the risk of developing PTOM according to four genetic models (dominant, recessive, homozygous, and heterozygous models), binomial logistic regression analysis with sex, age, and genotype distribution as covariates was used (CI). All reported p-values were two-sided, and statistical significance was set at p < 0.05.
3. Results
3.1. Clinical Characteristics of Patients with PTOM
Road traffic accidents were the most frequent cause of injury (46%), followed by blunt injuries (31%) and injuries from falls (12%). The tibia (51%), femur (30%), and calcaneus (7%) comprised the top three bones with open fractures, constituting two-thirds of all fractures. Staphylococcus aureus accounted for 32% of all pathogen detections, whereas the total proportion of positive intraoperative specimen cultures was 62%. The distribution of infection sites in the 306 patients with traumatic osteomyelitis is shown in Figure 1.
3.2. Results of HWE Tests
In healthy controls, all 25 genotyped SNPs exhibited HWE balance. p > 0.05 indicates the genetic balance of the population; otherwise, it will increase the possibility of sampling bias (Table S2).
3.3. Relationship between 25 SNPs and PTOM Susceptibility
3.3.1. NLRP3 rs10754558 and rs7525979
The genotype distribution of NLRP3 rs10754558 significantly differed between patients and healthy controls (p = 0.047). A further comparison revealed a significant association between rs10754558 and the risk of developing PTOM in the heterozygous (OR = 1.600, 95% CI: 1.021–2.507, p = 0.039) model, implying that the population with the CG genotype has a higher risk of developing PTOM. A positive association between NLRP3 rs7525979 and PTOM susceptibility was identified in the recessive (OR = 0.248, 95% CI: 0.071–0.872, p = 0.019) and homozygous (OR = 0.239, 95% CI: 0.068–0.844, p = 0.016) models, indicating that the TT gene type appears to place the population at a higher risk of developing PTOM (Table 1).
3.3.2. Serological IL-6 and TNF-α Levels in Various NLRP3 rs10754558 and rs7525979 Patient Genotypes
The various genotypes of NLRP3 rs10754558 and rs7525979 did not significantly vary in median serological IL-6 levels (p = 0.217 and p = 0.879, respectively; Table 2). Notably, PTOM patients with the rs10754558 CG genotype had median IL-6 levels higher than those with CC (p = 0.445), GG (p = 0.146), and GG + CC (p = 0.213; Figure 2) genotypes. Patients with genotypes CC (p = 0.053), GG (p = 0.065), and GG + CC (p = 0.014) had higher median TNF-α levels (Figure 2) compared with those of the CG genotype. Patients with the TT genotype of rs7525979 had higher IL-6 levels than those with the CT, CC, or CT + CC genotypes (p = 0.906, p = 0.953, and p = 0.996, respectively); however, the difference was not statistically significant (Figure 3). The median TNF-α levels of PTOM patients with the TT genotype of rs7525979 were higher than those of patients with the CT (p = 0.663), CC (p = 0.461), and CT + CC (p = 0.507) genotypes (Figure 3). Summarizing the research on the susceptibility of NLRP3 highlighted its crucial role in inflammatory diseases (Table 3).
3.3.3. Lack of Association between the Other 21 SNPs with PTOM Susceptibility
Using this case–control study as evidence, we could not find any association between the other 21 SNPs and PTOM susceptibility in the Chinese population (p > 0.05; Table S3).
4. Discussion
In recent years, it has become more evident that genetic predisposition is also implicated in the etiology of PTOM, with SNPs playing a significant role. We hypothesized that there might be a connection between SNPs in NLRP3 and PTOM development in light of the significance of NLRP3 in the inflammatory response and the well-established evidence that these SNPs are linked to the emergence of several other inflammatory disorders. Concordantly, the results of this study suggest that NLRP3 rs10754558 and rs7525979 polymorphisms may be associated with increased susceptibility to PTOM in the Chinese population. Individuals with the CG genotype of rs10754558 and the TT genotype of rs7525979 may be at a high risk of PTOM. Furthermore, patients with the rs10754558 and rs7525979 genotypes at a higher risk of developing PTOM also had higher serum levels of IL-6 or TNF-α. The other 21 SNPs were not associated with susceptibility to PTOM in the Chinese population.
To reveal the relationship between NLRP3 and inflammatory diseases, we summarized the research on the correlation between NLRP3 and inflammatory diseases in the past decade (Table 3). PTOM development is influenced by external and host variables, as has already been established. Extrinsic variables, including the type and severity of the trauma, the position of the bone, the state of the soft tissues, the type and pathogenicity of the pathogen, and an early treatment plan, directly impact PTOM development. Intrinsic as well as environmental variables are crucial for PTOM development. Most earlier investigations examined the possible impact of host characteristics on lifestyle (such as obesity, smoking, and drinking), immunological state (such as immunocompromised status or immunodeficiency), and systemic and partial consequences (such as diabetes, anemia, cancer, venous stasis, and persistent lymphedema) [3,13,18,19,20].
NLRP3 rs10754558 was correlated with an increased risk of developing PTOM, and the CG genotype was identified as a risk factor for PTOM for the first time. In a case–control study, Zhou et al. reported that the G allele of the NLRP3 rs10754558 polymorphism was related to the onset of coronary artery disease in the Chinese population [21]. The GG and CG genotypes of NLRP3 rs10754558 were strongly related to ulcerative colitis [22]. Moreover, CC genotype holders at the NLRP3 rs10754558 locus may have a more severe systemic inflammatory response [23]. Addobbati et al. found that NLRP3 gene rs10754558 is vital in the development and onset of rheumatoid arthritis [24]. This is the first study to report that it plays a role in the development of rheumatoid arthritis. Although the genotype distribution differs across studies, it is likely affected by sample size, ethnicity, and the cause of the infection. In the current study, the median blood IL-6 levels of PTOM patients with the CG genotype were higher than those with other genotypes. This suggests that rs10754558 is implicated in PTOM via the control of IL-6 levels.
The TT genotype might be a risk factor for the association of the NLRP3 variant rs7525979 with PTOM susceptibility. Based on the experimental findings of this study, there is a strong link between them, even if prior studies have not offered pertinent data. The genotype distribution, mutant allele frequency, and heterozygous model p-values between the patient and normal groups were all <0.05, suggesting that the variant allele T may be a risk factor for PTOM, and the TT genotype may be a high-risk population for PTOM despite the lack of significantly different IL-6 levels. This may be attributed to the different periods in which serological indicators were measured in the selected patients. To obtain more comprehensive findings, investigations with larger sample sizes are required. The median IL-6 levels of patients with rs7525979 genotype TT were higher than those of patients with other genotypes, suggesting a similar mechanism to that of rs10754558.
The two SNPs (NLRP3 rs10754558 and rs7525979) substantially linked to PTOM development in this Chinese population were all identified in promoter regions, resulting in altered expression levels of their respective proteins. This may play a role in the genesis of PTOM. These two SNPs may affect the effectiveness of PTOM therapy, in addition to being implicated in PTOM pathophysiology. Future research is required to ascertain whether SNPs also affect the risk of infection recurrence given that its recurrence rate after bone infection is reportedly higher than 20–30% [25,26]. One approach is to utilize these SNPs to predict the likelihood of PTOM in certain individuals, as the current investigation found that SNPs in two NLRP3 genes may be implicated in PTOM development. However, before this application can be achieved, at least two necessary steps must be undertaken. First, the sample size must be increased to obtain conclusion information regarding the association between such SNPs and the likelihood of PTOM development. Second, cohort studies must be conducted to comprehensively evaluate the predictive power of such SNPs for PTOM.
NLRP3 rs35829419 and rs4612666 were not statistically different from PTOM in genotype distribution, mutant allele frequency, the homozygous/heterozygous model, or IL-6 and TNF-α levels, indicating that rs35829419 and rs4612666 may not be associated with the occurrence and development of PTOM. In the remaining seven genes, some studies have found that STAT3 (rs4796793, rs744166, rs1026916, rs2293152, rs1053004) is involved in the regulation of inflammatory osteolysis in bone infection [27], and the level of CASP-1 (rs501192, rs580253, rs556205, rs530537) in peripheral blood mononuclear cells of freshly isolated multifocal osteomyelitis patients in active and remission stages is significantly higher than that of healthy controls [28], suggesting that STAT3 and CASP-1 may be the risk genes for PTOM. In our research, we did not find that ELP2 (rs1785929, rs178989547, rs1785928, rs12185396, rs681757, rs8299, rs2032206, rs559289), NFKBIA (rs696), NFKB1 (rs4648068), CARD8 (rs204321), or CD14 (rs2569190) were associated with the risk of PTOM in Chinese populations. We also did not find evidence that ELP2, NFKBIA, NFKB1, CARD8, or CD14 were associated with PTOM in previous studies.
This research also had several limitations. First, the sample size was small because this was a genome-wide association study (GWAS). It is challenging to include suitable study participants in a limited time, and future studies will extend to a wider population with a control group of other ethnic origins, such as whites, blacks, and other minorities. Second, the relationship between the different NLRP3 rs10754558 and rs7525979 genotypes remains unclear because we could not discover any serological proof of IL-1β and IL-18. Third, the exact processes of NLRP3 rs35829419 and rs4612666 in the pathogenesis of PTOM must be investigated further, as this study is only a preliminary report. Finally, the detailed mechanisms of the two SNPs in PTOM pathogenesis should be further investigated. Currently, the incidence of chronic osteomyelitis, especially traumatic osteomyelitis, is gradually increasing, and the efficacy of current treatment methods remains unclear. Therefore, the risk of postoperative infection remains high. Therefore, our findings may provide novel insights and guidance in the prevention and development of PTOM.
In conclusion, the GG genotype for rs10754558 and the TT genotype for rs7525979 of the NLRP3 were associated with elevated odds of developing PTOM in this Chinese cohort.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jpm13020253/s1. Table S1: PCR primers and extension primers of the 25 SNPs. Table S2: HWE test outcomes of the 25 gene polymorphisms for healthy controls. Table S3: Comparisons of genotype distribution, allele frequency, and genetic models of the remaining 21 SNPs between PTOM patients and healthy controls.
Author Contributions
Y.Q. (Yudun Qu), N.J. and C.X. (Changpeng Xu) designed the study. Y.Q. (Yudun Qu), N.J. and C.X. (Changpeng Xu) managed and analyzed the data. Y.Q. (Yudun Qu) and Y.M. drafted the initial study protocol. Y.Q. (Yudun Qu) wrote the first draft of the manuscript. J.L., W.Z., C.X. (Changliang Xia), Y.M., S.O., Y.Y. and Y.Q. (Yong Qi) contributed to the data interpretation and preparation of the report. All writers contributed ideas to the paper and gave their consent for submission. The corresponding authors attest that all authors in the text meet the criteria for authorship and that no other authors who meet the criteria have been left out. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (No. 81972083), the Science and Technology Planning Project of Guangzhou (No. 202201020303, 202102080052, 202102010057, 201804010226), and the Science Foundation of Guangdong Second Provincial General Hospital (No. 3D-A2020004, 3D-A2020002, YQ2019-009, C2020019).
Institutional Review Board Statement
Approval letter No. NFEC-2019-087.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available because of the respect and protection of the privacy of the patients but are available from the corresponding author upon reasonable request.
Acknowledgments
The authors thank all participants and staff who made this study possible.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Distribution map of single infection sites in 306 patients with traumatic osteomyelitis.
Figure 2.
Serological IL-6 and TNF-α levels among different rs10754558 genotypes in PTOM patients.
Figure 3.
Serological IL-6 and TNF-α levels among different rs7525979 genotypes in PTOM patients.
Table 1.
Comparisons of genotype distribution, allele frequency, and genetic models of rs35829419, rs10754558, rs7525979, and rs4612666 between PTOM patients and healthy controls.
Table 1.
Comparisons of genotype distribution, allele frequency, and genetic models of rs35829419, rs10754558, rs7525979, and rs4612666 between PTOM patients and healthy controls.
| SNP | Item | Allele or Genotype | Patients | Controls | p-Values | OR (95% CI) |
|---|---|---|---|---|---|---|
| rs35829419 | Genotype (n, %) | AA | 0 (0.0) | 0 (0.0) | NA | NA |
| AC | 2 (0.65) | 1 (0.27) | ||||
| CC | 304 (99.35) | 367 (99.73) | ||||
| Allele frequency | A vs. C | 2/610 | 1/735 | 0.873 | 2.410 (0.218, 26.640) | |
| Dominant model | AA + AC vs. CC | 2/304 | 1/367 | 0.873 | 2.414 (0.218, 26.756) | |
| Recessive model | AA vs. AC + CC | 0/306 | 0/368 | NA | NA | |
| Homozygous model | AA vs. CC | 0/304 | 0/367 | NA | NA | |
| Heterozygous model | AC vs. CC | 2/304 | 1/367 | 0.873 | 2.414 (0.218, 26.756) | |
| rs10754558 | Genotype (n, %) | CC | 97 (31.70) | 135 (36.68) | 0.047 | NA |
| CG | 169 (55.23) | 169 (45.92) | ||||
| GG | 40 (13.07) | 64 (17.39) | ||||
| Allele frequency | C vs. G | 363/249 | 439/297 | 0.901 | 0.986 (0.793, 1.227) | |
| Dominant model | CC + CG vs. GG | 266/40 | 304/64 | 0.122 | 1.400 (0.913, 2.148) | |
| Recessive model | CC vs. CG + GG | 97/209 | 135/233 | 0.175 | 0.801 (0.581, 1.104) | |
| Homozygous model | CC vs.GG | 97/40 | 135/64 | 0.564 | 1.150 (0.716, 1.846) | |
| Heterozygous model | CG vs.GG | 169/40 | 169/64 | 0.039 | 1.600 (1.021, 2.507) | |
| rs7525979 | Genotype (n, %) | TT | 3 (0.98) | 14 (3.84) | 0.048 | NA |
| CT | 80 (26.14) | 102 (27.95) | ||||
| CC | 223 (72.88) | 249 (68.22) | ||||
| Allele frequency | T vs. C | 86/526 | 130/600 | 0.062 | 0.755 (0.561, 1.015) | |
| Dominant model | TT + CT vs. CC | 83/223 | 116/249 | 0.188 | 0.799 (0.572, 1.117) | |
| Recessive model | TT vs. CT + CC | 3/303 | 14/351 | 0.019 | 0.248 (0.071, 0.872) | |
| Homozygous model | TT vs. CC | 3/223 | 14/249 | 0.016 | 0.239 (0.068, 0.844) | |
| Heterozygous model | CT vs. CC | 80/223 | 102/249 | 0.450 | 0.876 (0.621, 1.235) | |
| rs4612666 | Genotype (n, %) | CC | 73 (23.86) | 98 (26.63) | 0.265 | NA |
| CT | 168 (54.90) | 179 (48.64) | ||||
| TT | 65 (21.24) | 91 (24.73) | ||||
| Allele frequency | C vs. T | 314/298 | 375/361 | 0.896 | 1.014 (0.819, 1.257) | |
| Dominant model | CC + CT vs. TT | 241/65 | 277/91 | 0.285 | 1.218 (0.848, 1.750) | |
| Recessive model | CC vs. CT + TT | 73/233 | 98/270 | 0.401 | 0.863 (0.608, 1.225) | |
| Homozygous model | CC vs. TT | 73/65 | 98/91 | 0.852 | 1.043 (0.672, 1.618) | |
| Heterozygous model | CT vs. TT | 168/65 | 179/91 | 0.160 | 1.314 (0.897, 1.925) |
PTOM: posttraumatic osteomyelitis; OR: odds ratio; CI: confidence interval.
Table 2.
Serological levels of WBC, CRP, PCT IL-6, TNF-α, and SAA among different genotypes of rs35829419, rs10754558, rs7525979, and rs4612666 in the patient group.
Table 2.
Serological levels of WBC, CRP, PCT IL-6, TNF-α, and SAA among different genotypes of rs35829419, rs10754558, rs7525979, and rs4612666 in the patient group.
| Items | rs35829419 | rs10754558 | rs7525979 | rs4612666 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AA | AC | CC | p Value | CC | CG | GG | p Value | TT | CT | CC | p Value | CC | CT | TT | p Value | |
| WBC (×10/L)9 Median (IQR) | NA | 6.065 (5.460, 6.065) | 6.980 (5.720, 8.320) | 0.365 | 7.03 (5.72, 8.61) | 6.96 (5.63, 8.25) | 6.82 (5.62, 8.30) | 0.917 | 6.35 (5.88, 6.35) | 7.44 (5.75, 8.23) | 6.81 (5.66, 8.37) | 0.711 | 6.79 (5.52, 8.82) | 6.99 (5.86, 8.34) | 6.86 (5.68, 8.00) | 0.624 |
| CRP (mg/L) Median (IQR) | NA | 5.840 (2.100, 5.840) | 4.345 (1.485, 11.920) | 0.870 | 5.61 (1.73, 15.24) | 4.09 (1.40, 9.83) | 3.25 (1.42, 8.10) | 0.282 | 8.70 (7.10, 8.70) | 5.30 (1.67, 15.24) | 4.05 (1.42, 9.98) | 0.160 | 3.00 (0.98, 8.61) | 4.58 (1.99, 12.47) | 4.03 (1.55, 16.42) | 0.129 |
| ESR (mm/h) Median (IQR) | NA | 7.000 (1.000, 7.000) | 15.000 (7.000, 36.000) | 0.184 | 20.00 (7.75, 42.50) | 14.00 (7.00, 32.00) | 13.00 (4.25, 28.25) | 0.399 | 36.00 (5.00, 36.00) | 14.00 (6.00, 43.00) | 16.00 (7.00, 32.00) | 0.698 | 13.00 (5.00, 22.75) | 16.00 (7.00, 39.50) | 15.00 (7.75, 44.00) | 0.257 |
| PCT (ng/mL) Median (IQR) | NA | NA | 0.037 (0.021, 0.060) | NA | 0.03 (0.02, 0.05) | 0.04 (0.03, 0.07) | 0.03 (0.02, 0.04) | 0.258 | 0.03 (0.03, 0.03) | 0.04 (0.03, 0.05) | 0.03 (0.02, 0.06) | 0.488 | 0.03 (0.02, 0.06) | 0.04 (0.02, 0.07) | 0.03 (0.02, 0.05) | 0.585 |
| IL-6 (pg/mL) Median (IQR) | NA | 7.660 (6.010, 7.660) | 5.270 (2.895, 10.930)) | 0.518 | 6.83 (2.81, 14.49) | 4.67 (2.75, 9.65) | 5.86 (3.18, 8.51) | 0.217 | 12.16 (1.54, 12.16) | 5.71 (2.50, 11.76) | 5.05 (3.00, 9.89) | 0.879 | 4.72 (2.98, 8.32) | 5.61 (2.94, 11.19) | 5.60 (2.39, 14.41) | 0.455 |
| TNF-α (pg/mL) Median (IQR) | NA | 7.015 (6.150, 7.015) | 8.320 (6.570, 10.800) | 0.394 | 8.55 (6.43, 10.80) | 8.31 (6.50, 11.05) | 8.06 (6.93, 9.77) | 0.855 | 8.02 (7.23, 8.02) | 9.08 (6.78, 11.33) | 8.08 (6.49, 10.73) | 0.679 | 7.71 (6.71, 10.10) | 8.88 (6.74, 11.50) | 7.38 (6.29, 10.70) | 0.526 |
| SAA (mg/L) Median (IQR) | NA | 12.750 (6.800, 12.750) | 11.400 (6.400, 35.900) | 0.880 | 12.70 (6.70, 51.55) | 10.90 (6.20, 33.18) | 10.90 (6.85, 24.30) | 0.818 | 35.90 (12.70, 35.90) | 12.65 (7.38, 56.70) | 10.95 (6.20, 32.70) | 0.296 | 8.85 (5.88, 23.75) | 12.10 (6.70, 36.10) | 13.05 (6.95, 67.28) | 0.174 |
Table 3.
Diseases associated with NLRP3 gene susceptibility.
| Number | PubMed ID | SNP | Related Diseases |
|---|---|---|---|
| 1 | 33848452 30658261 | Q705K | Cystic fibrosis Multiple sclerosis |
| 2 | 31978095 28116820 | rs4612666 | Periodontitis Ankylosing spondylitis |
| 3 | 33533549 | rs3806265 | Myasthenia gravis |
| 4 | 29888470 26689701 | rs10754558 | Acne vulgaris Ischemic stroke |
| 5 | 30131971 | rs7525979 | Parkinson’s disease |
| 6 | 23588528 | rs7512998 | Primary gouty arthritis |
| 7 | 29850521 | rs3806265, rs10754557 | Psoriasis vulgaris |
| 8 | 21621776 | rs35829419 | Abdominal aortic aneurysms |
| 9 | 22112657 | rs2027432, rs12048215 | MOD syndrome |
| The present study | NA | Rs10754558, rs7525979 | PTOM |
SNP: single-nucleotide polymorphisms.
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Qu, Y.; Li, J.; Zhang, W.; Xia, C.; Ou, S.; Yang, Y.; Jiang, N.; Ma, Y.; Qi, Y.; Xu, C. Posttraumatic Osteomyelitis Risks Associated with NLRP3 Gene Polymorphisms in the Chinese Population. J. Pers. Med. 2023, 13, 253. https://doi.org/10.3390/jpm13020253
AMA Style
Qu Y, Li J, Zhang W, Xia C, Ou S, Yang Y, Jiang N, Ma Y, Qi Y, Xu C. Posttraumatic Osteomyelitis Risks Associated with NLRP3 Gene Polymorphisms in the Chinese Population. Journal of Personalized Medicine. 2023; 13(2):253. https://doi.org/10.3390/jpm13020253
Chicago/Turabian StyleQu, Yudun, Jiaxuan Li, Wei Zhang, Changliang Xia, Shuanji Ou, Yang Yang, Nan Jiang, Yunfei Ma, Yong Qi, and Changpeng Xu. 2023. "Posttraumatic Osteomyelitis Risks Associated with NLRP3 Gene Polymorphisms in the Chinese Population" Journal of Personalized Medicine 13, no. 2: 253. https://doi.org/10.3390/jpm13020253
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