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Chronic osteomyelitis risk is associated with NLRP3 gene rs10754558 polymorphism in a Chinese Han Population

BMC Medical Genomics volume 17, Article number: 38 (2024) Cite this article

Abstract

Background

Single nucleotide polymorphisms (SNPs) in the nucleotide-binding domain leucine-rich repeat protein-3 (NLRP3) gene are reported to be linked to many inflammatory disorders. However, uncertainty persists over the associations between these SNPs and susceptibilities to chronic osteomyelitis (COM). This study aimed to investigate potential relationships between NLRP3 gene SNPs and the risks of developing COM in a Chinese Han cohort.

Methods

The four tag SNPs of the NLRP3 gene were genotyped in a total of 428 COM patients and 368 healthy controlsusing the SNapShot technique. The genotype distribution, mutant allele frequency, and the four genetic models (dominant, recessive, homozygous, and heterozygous) of the four SNPs were compared between the two groups.

Results

A significant association was found between rs10754558 polymorphism and the probability of COM occurence by the heterozygous model (P = 0.037, odds ratio [OR] = 1.541, 95% confidence interval [CI] = 1.025–2.319), indicating that rs10754558 may be associated with a higher risk of developing COM.In addition, possible relationship was found between rs7525979 polymorphism and the risk of COM development by the outcomes of homozygous (P = 0.073, OR = 0.453, 95% CI = 0.187–1.097) and recessive (P = 0.093, OR = 0.478, 95% CI = 0.198–1.151) models, though no statistical differences were obtained.

Conclusions

Outcomes of the present study showed, for the first time, that rs10754558 polymorphism of the NLRP3 gene may increase the risk of COM development in this Chinese Han population, with genotype CG as a risk factor. Nonetheless, this conclusion requires verification from further studies with a larger sample size.

Peer Review reports

Background

Chronic osteomyelitis (COM) is a frequent and challenging complication of trauma, affecting up to one third of patients during recovery from severe limb injury or open fracture [1, 2]. In addition, hematogenous seeding and soft tissue infections, especially in patients with diabetes and pressure ulcers, can also lead to osteomyelitis (OM) [3]. Staphylococcus aureus remains to be the primary pathogen of COM [4, 5]. Owing to insufficient blood supply to the infected bone, patients with COM often require both surgical debridement and antibiotic treatment [6, 7]. COM has been shown to substantially increase the long-term mortality risk [8]. Approximately 30% of the acute OM cases progress into chronic phase [9], causing anincreased risk of mortality, perpetuating disability, and worsened quality of life [8, 10, 11]. Previous epidemiological studies have reported that COM elevates the risk of coronary heart disease [12], stroke [13], diabetes mellitus [14], renal disease [15], and even depression [10]. To comprehensively tackle COM-related issues, substantial efforts should be devoted to investigate the pathophysiology of the condition as well as to conduct clinical studies. The pathogenesis of OM is related to both environmental factors and genetic factors, and contemporary evidence suggests that genetic susceptibility may also play a crucial role in this process [16, 17]. With the rapid development and application of sequencing and genetic association analysis, genetic variants that may cause OM have been extensively explored.

Numerous genetic studies examining nucleotide-binding domain leucine-rich repeat protein-3 (NLRP3) polymorphisms have demonstrated the involvement of this gene in a range of inflammatory conditions, including Parkinson’s and Alzheimer’s diseases [18], type 2 diabetes, atherosclerosis [19], inflammatory bowel disease [20], gout, and recurrent fever [21]. Genetic differences in the NLRP3 have been hypothesized to have a substantial role in determining the severity of inflammatory responses, thereby predisposing susceptibility to disease [22]. However, the association between NLRP3 and COM, as well as the underlying mechanisms remain unclear. Previous studies had reported that several single nucleotide polymorphisms (SNPs), such as TaqI (rs731236) and FokI (rs2228570) in the vitamin D receptor (VDR) gene [23], rs689466 in the cyclooxygenase-2 (COX-2) gene [24], the Alu insertion/deletion (rs4646972) in the tissue plasminogen activator (tPA) gene [25], and rs1799750 in the matrix metalloprotease 1 (MMP-1) gene [26], may be associated with the risk of developing COM. In the HaploReg v4.2-Broad Institute (HaploReg v4.2 (broadinstitute.org)), we found that rs10754558, rs7525979, rs35829419, and rs4612666 were all involved in inflammatory responses and all played key roles in the mouse OM model (Supplementary Table S1).

To better understand potential role of genetic factor in the pathogenesis of COM, this study examined potential links between NLRP3 SNPs and susceptibilities to COM in a Chinese Han population.

Materials and methods

Study design and patient enrollment

This investigation was designed as a case-control study and conducted in a Chinese Han population. Patients undergoing treatment for COM between 2016 and 2019 at the Nanfang Hospital, Southern Medical University, a tertiary healthcare facility in Southern China, were screened for enrollment. COM was defined by symptoms persisting for at least 10 weeks and/or radiological appearances suggestive signs of bone infection. In addition, at least one of the following operative findings was required to be present for inclusion: (i) two or more positive culture sterile site specimens with an indistinguishable organism; (ii) histology suggestive of COM (a mean of > 5 neutrophils per high power field, averaged over at least 5 fields); or (iii) sinus abscess or purulence, present during surgery. These criteria align with established methods of confirming the diagnosis of COM. Healthy controls were individuals without abnormalities or a history of any disorders, as determined by thorough examinations in the physical examination center. Informed consent forms were completed by all the participants or their guardians, and the medical ethics committee of the Southern Medical University Nanfang Hospital approved the study (approval no. NFEC-2019-087).

SNP selection and genotyping

Data regarding NLRP3 gene expression in osteoblasts were obtained through gene sequencing (BioProject number: PRJNA1001643). The model included a normal osteogenic differentiation group (tumor necrosis factor [TNF-α] = 0 ng/mL) and an inflammasome group (TNF-α = 5 ng/mL). The expression of the NLRP3 gene was then compared between the samples belonging to these two groups. We analyzed four selected SNPs (rs35829419, rs10754558, rs7525979, and rs4612666) in the NLRP3 inflammasome gene. Previous studies on inflammasome gene polymorphisms related to infection were referred. The expressions of these four SNPs in tissues were verified in the Genotype-Tissue Expression (GTEx) database. The GTEx project is an ongoing effort to build a comprehensive public resource to study tissue-specific gene expression and regulation. It provides open access to information, including gene expression, quantitative trait loci data, and histology images.

Peripheral blood samples (2 mL) were collected from each participant in tubes containing ethylenediaminetetraacetic acid. Polymerase chain reaction (PCR) was performed in a total reaction volume of 15 µL, which contained 1.0 µL of template DNA, 1.5 µL of 10× PCR buffer, 1.5 µL of 25 mmol/L MgCl2, 0.3 µL of 10 mmol/L dNTP mix, 0.15 µL of 10 µmol/mL primers, and 0.3 µL of 5 µ/µL Taq DNA polymerase (Fermentas, Waltham, MA, USA). Table 1 lists the forward, reverse, and extension primers used [23]. Reactions were performed using a PCR machine (model no. EDC-810, Dongsheng Co., Ltd., Beijing, China) with the following settings: initial denaturation at 94 °C for 3 min, followed by 35 cycles of denaturation at 94 °C for 15 s, annealing at 55 °C for 15 s, extension at 72 °C for 30 s, and a final extension step at 72 °C for 3 min. PCR products were purified in the following mixture: 3.0 µL of PCR products, 0.2 µL of 20 µ/µL Exonuclease I (ExoI; Fermentas), 0.8 µL of 1 µ/µL FastAP thermosensitive alkaline phosphatase (Fermentas), 0.7 µL of ExoI buffer, and 2.3 µL of ddH2O. Reactions were conducted at 37 °C for 15 min and 80 °C for 15 min.

Table 1 Forward, reverse, and extension primers of the 4 tag SNPs for PCR
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Using the Multiplex SNaPshot technique, the four SNPs of the NLRP3 gene were genotyped. SNaPshot extension reactions were performed following the instructions of the ABI SNaPshot Multiplex PCR Kit (Applied Biosystems, Waltham, MA, USA) with slight revisions. The total reaction volume (6.0 µL) included 2.0 µL of purified PCR products, 1.0 µL of SNaPshot Multiplex Ready Reaction Mix, 0.2 µL of 10 µmol/mL pooled extension primer, and 2.8 µL of ddH2O. Extension reactions were performed as follows: pre-denaturation at 96 °C for 1 min, followed by 30 cycles of denaturation at 96 °C for 10 s, annealing at 52 °C for 5 s, and extension at 60 °C for 30 s. The product of this reaction (1.0 µL) was mixed with 9.0 µL of formamide and denatured at 95 °C for 3 min. The fluorescently labeled fragments were separated by capillary electrophoresis on an ABI PRISM 3730 XL Genetic Analyzer (Applied Biosystems). The results of the SNaPshot genotyping method are shown in Fig. 1.

Fig. 1
figure 1

SNaPshot genotyping. The rs10754558, rs7525979, rs35829419, and rs4612666 single nucleotide polymorphisms of the nucleotide-binding domain and leucine-rich repeat protein-3 gene were analyzed using the SNaPshot technique (Supplementary Table S2)

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Comparative analysis of NLRP3 gene polymorphisms and clinical indicators

Genotype distribution, mutant allele frequency, and the four genetic models (dominant, recessive, homozygous, and heterozygous) of the four NLRP3 SNPs were compared between the COM patients and healthy controls. Age, sex ratio, culture-positive rate, polymicrobial infection rate, and preoperative serum levels of two inflammatory biomarkers, interleukin (IL)-6 and TNF-α, were compared between the four genotypes of NLRP3 gene polymorphisms among the patient group. Serum levels of IL-6 and TNF-α were detected using electrochemiluminescence immunoassay (Roche cobas e601, Basel, Switzerland). The upper limit of normal values, provided by the Medical Clinical Laboratory of Nanfang Hospital, were 7.0 and 8.1 pg/mL for IL-6 and TNF-α, respectively.

Statistical analysis

The Kolmogorov–Smirnov test assessed data normality. Normally distributed continuous variables are reported as the mean ± standard deviation, and a one-way analysis of variance or the student’s t-test was used to compare between the two or among over two groups. Continuous variables with a non-normal distribution were reported as the median withinterquartile range (IQR), and the Kruskal–Wallis or Mann–Whitney U tests were used for group comparisons. Dichotomous variables with percentage-based data were compared using the Chi-square or the Fisher’s exact test.

Analysis of the linkage disequilibrium at different loci of NLRP3 genotypes was performed. The genotype distributions of the healthy controls were examined using the Chi-square test to determine whether Hardy–Weinberg equilibrium (HWE) were sustained. The Chi-square test or Fisher’s exact test was used to examine the genotype distributions and mutant allele frequencies between the two groups. Sex, age, and genotype distribution were covariates. Binary logistic regression analysis was performed to examine the potential relationships between the four gene polymorphisms and the likelihood of developing COM using four genetic models with matching odds ratios (ORs) and 95% confidence intervals (CI). Statistical analyses were performed using R software (Version 4.2.3, LD-heatmap package) and SPSS version 25.0 (IBM Corp., Armonk, NY, USA). Statistical significance was established as a P-value < 0.05.

Results

Clinical characteristics of the participants

This study included 368 healthy controls (268 men and 100 women) and 428 COM patients (337 men and 91 women). There was no statistically significant difference in the sex ratio between the two groups (3.70 vs. 2.68, χ2 = 3.792, P = 0.051). The median ages of the two groups were not significantly different (patient group: 47 years, IQR, 33.00–59.00; control group: 46 years, IQR, 37.00–52.00; Z = 0.114, P = 0.662). Figure 2 shows clinical characteristics of COM of this Chinese cohort. The most prevalent type of COM was post-traumatic OM (PTOM)(58.35%), which often occurred after open injury (61.96%). The most typical infection location was the tibia (38.31%), followed by the foot (21.03%) and the femur (11.68%). Of all the intraoperative specimen cultures, 27.46% were tested positive, with monomicrobial infection accounting for 71.67%. The most commonly detected pathogen was Staphylococcus aureus (34.17%).

Fig. 2
figure 2

Distribution map of infection sites of the COM patients. The number of patients involved is given at each site, along with the percentage of the total number of 428 patients

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Expression of NLRP3 in different states and linkage disequilibrium analysis

The expression of the NLRP3 gene under inflammatory microenvironment was significantly higher than that under normal microenvironment (P = 0.015) (Fig. 3). However, according to the GTEx database, the expression of NLRP3 gene variants (rs10754558, rs7525979, rs35829419, and rs4612666) in the whole-blood and the musculoskeletal tissues did not show significant differences. The r2 values of the NLRP3 gene SNPs were calculated using R software (Version 4.2.3). The results of the linkage disequilibrium analysis were shown in the r2 hot graph in Fig. 4. Deep-blue blocks represent high linkage disequilibrium (r2 = 0.8–1), and light-blue blocks represent low linkage disequilibrium (r2 = 0–0.2). There was no linkage disequilibrium among the three SNPs of NLRP3 (rs10754558, rs7525979, and rs35829419).

Fig. 3
figure 3

Expression of the NLRP3 gene in osteoblasts. NLRP3 gene expression was analyzed in osteoblasts cultured under normal conditions (control) and those cultured in the presence of TNF-α (inflammatory microenvironment). NLRP3, nucleotide-binding domain and leucine-rich repeat protein-3; TNF-α, tumor necrosis factor-α, *P = 0.015

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Fig. 4
figure 4

Linkage disequilibrium analysis. The r2 hot graph for the single nucleotide polymorphisms of the nucleotide-binding domain and leucine-rich repeat protein-3 gene: rs10754558, rs7525979, and rs35829419

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Frequency of the four NLRP3 gene SNPs in COM patients and healthy controls

In the healthy controls, all the four genotyped NLRP3 gene SNPs were in HWE: P(HWE) for rs35829419 = 0.979, P(HWE) for rs10754558 = 0.377, P(HWE) for rs7525979 = 0.386, and P(HWE) for rs4612666 = 0.607. As shown in Table 2, the heterozygous model of the rs10754558 SNP and susceptibility to COM were significantly associated (P = 0.037, OR = 1.541, 95% CI = 1.025–2.319), indicating that individuals with the CG genotype may be at a higher risk in developing COMin this Chinese Han population.

Table 2 Comparisons of genotype distribution, allele frequency, and genetic models of rs10754558, rs7525979, rs35829419 and rs4612666 between the COM patients and healthy controls
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Although no significant association was identified between rs7525979 and the risk of developing COM, results of the recessive (P = 0.093) and homozygous (P = 0.073) models suggested that this SNP site may be linked to a decreased risk of COM development, with genotype TT as a possibly protective factor. However, this results need to be further tested. No significant correlations were found between rs35829419 or rs4612666 and the risks of COM development among these participants (Table 2).

IL-6 and TNF-α levels among different genotypes of rs10754558 and rs7525979 in the COM patients

Serum levels of IL-6 and TNF-α in the COM patients showed no significant differences among different genotypes of rs10754558 (P = 0.213 and 0.662, respectively; Table 3). Additionally, multiple comparisons showed that no significant differences were found regarding serological IL-6 or TNF-α levels between CG and GG (P = 0.180), CG and CC (P = 0.158), or CG and CC + GG (P = 0.072) genotype groups (Fig. 5). Moreover, serum levels of IL-6 and TNF-α did not differ significantlyamong different genotypes of rs7525979 (P = 0.900 and 0.843, respectively; Table 3) (Fig. 6).

Table 3 Comparisons of clinical features among different genotypes of the NLRP3 gene polymorphisms among the included COM patients
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Fig. 5
figure 5

Cytokine levels in patients with chronic osteomyelitis. Serum levels of a IL-6 and b TNF-α were compared among different genotypes of the single nucleotide polymorphism rs10754558. IL-6, interleukin-6; TNF-α, tumor necrosis factor-α

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Fig. 6
figure 6

Cytokine levels in patients with chronic osteomyelitis. Serum levels of a IL-6 and b TNF-α were compared among different genotypes of the single nucleotide polymorphism rs7525979. IL-6, interleukin-6; TNF-α, tumor necrosis factor-α

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Discussion

DOur study results showed that, in this ChineseHan cohort, NLRP3 SNP rs1074558 may increase the risk of COM development, with CG genotype in a higher risk to develop COM. Despite the current study being unable to obtain statistical evidence supporting this association between the COM patients and healthy controls, the results of the recessive and homozygous models suggested that rs7525979 polymorphism may hinder COM development. Nonetheless, this conclusion requires verification from further studies with a larger sample size.

COM, which often develops following trauma and orthopaedic surgery, still poses great challenges tophysicians. The frequency of infection in the long bones after open fractures varies between 4% and 64%, with infection recurrence rates ranged between 20% and 30% [27]. Among the 428 COM patientsincluded in our study, PTOMaccounted for 58.35%, with infection following open injury occupying 61.96% and the positive rate of culture as27.46%. The physical and psychological well-being of patients is severely affected by the protracted course of the disease and inflammatory bone disintegration. Huang et al. [28] found that COM significantly increased the risk of death in the older population. Along with the aforementioned comorbidities, patients with COM also suffer fromhigher rates of deep vein thrombosis [15], erectile dysfunction [29], dementia [30], and end-stage renal disease [31], as well as mental health disorders, including depression [10]. In addition, while rare, there is a risk of malignant change in patients with COM [32].Consequently, COM is accompanied by an enormous healthcare and economic burdens [33].

Previous studies have found that IL-1β secretion and mRNA expression levels of key inflammasome components, namely apoptosis-associated speck-like protein containing a CARD and Caspase-1, are abnormally increased in patients with chronic recurrent multifocal osteomyelitis [34, 35]. The release of IL-1β can lead to autocrine stimulation and production of other cytokines, such as IL-6. In the present study, we did not detect the serological level of IL-1β. Thus, the potential influences of such SNPs on serological levels of IL-1β cannot be assessed. Instead, we evaluated the impact of genotype by comparing readily available serum inflammatory markers, IL-6 and TNF-α.

Studies have shown that genetic variables, with SNP as a key component, may play a role in the etiology of infection. According to several clinical studies, the VDR [23], tPA [25], COX-2 [24], and MMP-1 [26] genes, and the most frequently reported members of the IL family, have been implicated in the associations between SNPs and COM risk across various ethnicities. Previous investigations reported that IL-1 (rs1800587) [36, 37], IL-4 (rs2243250, rs2243248) [37], and IL-6 (rs1800795) [37] gene polymorphisms are positively correlated with the development of COM and raise the risk of developing OM. And we found that these SNPs pathways are associated with inflammation. As a crucial component of the inflammatory system, the NLRP3 inflammasome may also be linked to the incidence and progression of COM [38]. More importantly, our team investigated a possible association between NLRP3 gene polymorphisms and the risks of developing PTOM in a Chinese population. The results suggested that NLRP3 SNPs, rs10754558 (P = 0.047) and rs7525979 (P = 0.048), were significantly different between patients and healthy controls. However, this study only discussed the susceptibility to PTOM and NLRP3 gene expressions, and could not clarify whether this gene susceptibility was related to COM. At the same time, we summarized the SNPs that are associated with COM, as well as the mechanistic pathways involved and the action (cis or trans) and location of SNPs (Table 4). Therefore, we conducted a more detailed study [39].

Table 4 Previously reported SNP sites associating with OM development
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The rs10754558 polymorphism of the NLRP3 gene may be associated with an increased risk of COM in this Chinese Han population; to the best of our knowledge, no study has reported on this association to date. Although we did not find a difference in the frequency of the mutant alleles, C and G, of rs10754558 in the patient group, the heterozygous model of rs10754558 showed a statistical association of this polymorphism with COM, indicating that individuals with the CG genotype of this polymorphism are in an increased risk of COM development. Other genotypes did not significantly associated with COM. In addition, although there was no statisticalcorrelation between the results of the recessive and homozygous models of rs7525979 (P = 0.093 and 0.073, respectively), the frequency of the TT genotype was higher in the COM patients than that in healthy controls. Further research is required to verify whether patients with TT genotype of rs7525979 are in a lower risk to develop thisdisease. Several possible reasons may help explain why only the rs10754558 variants showed significant results while the others failed. The first reason may rest with the specificity of rs10754558 itself. Aside from the present findings, we also found that this SNP is associated with the development of several disorders, such as chronic kidney disease [40], acne vulgaris [41] and autoimmune diseases [42]. Second, the limited sample size of our study may have masked the correlations between the other SNPs and COM susceptibilities. Therefore, we will explore whether and how these genetic variants play roles in epigenetics in the future. Moreover, no significant differences were found among different genotypes of rs10754558 or rs7525978 SNPs, neither regarding clinical characteristics, nor regarding serological levels of IL-6 or TNF-α. These results suggest that further studies are required to investigate the potential influences of NLRP3 polymorphisms on clinical features and serum levels of the inflammatory biomarkers as well. Nonetheless, outcomes of the present study also need to be confirmed by a larger cohort.

Our study had several limitations. Firstly, despite a larger sample size of the current report compared to the majority of the previously published studies, it remains inadequate for an SNP analysis. Additionally, a broader pool of participants should be applied in future research since the pathogenic process for the development of COM is multifactorial. Secondly, a few participants could not be genotyped using the SNapShot method. Owing to the limited volume of blood collection from each participant, genotyping could not be repeated using alternative methods, resulting in a reduced number of samples with sequencing outcomes. However, this did not affect the final results. Thirdly, we did not detect serum level of IL-1β, thus, IL-1β levels among patients with different genotypes could not be compared, and correlations between the NLRP3 gene polymorphisms and serum IL-1β levels remain unclear. Fourthly, the environmental variables may play a role in the pathophysiology of COM in addition to the genetic factors. The majority of the patients in this study were transferred from neighboring hospitals; thus, it was difficult to determine their precise medical history prior to bone infection (e.g., the severity of the original injury and treatment approach). As a result, we were unable to further investigate how these external factors affected the development of COM. Finally, we found that NLRP3 gene polymorphisms are associated with an increased risk of COM; however, the function of the genetic variants has not been analyzed. Therefore, further functional validations of NLRP3 gene polymorphisms are needed.

Conclusions

This study confirmed that NLRP3 gene rs10754558 polymorphism is significantly associated with the prevalence of COM in a Chinese Han population. Nonetheless, this conclusion requires validation by future studies with a larger sample size. Based on our findings, detecting overexpression of the NLRP3 mutant gene may be a promising strategy to identify risk group for COM development. Future studies exploring this association that include a larger population of COM patients are essential, especially if genetic screening is to be employed as a prediction tool for the risk of disease development. Further, determining whether pharmacological inhibition of the NLRP3 pathway should be incorporated into the treatment regimen for COM may be beneficial.

Data availability

The datasets generated and analyzed during the current study are not publicly available to respect and protect the privacy of the patients; however, they are available from the corresponding author upon reasonable request.

Abbreviations

NLRP3:

NOD-like receptor thermal protein domain associated protein 3

COM:

Chronic osteomyelitis

OM:

Osteomyelitis

OR:

Odds ratio

CI:

Confidence interval

IQR:

Interquartile range

IL:

Interleukin

SNPs:

Single nucleotide polymorphisms

TNF-α:

Tumor necrosis factor-α

HWE:

Hardy-Weinberg equilibrium

PTOM:

Post-traumatic osteomyelitis

VDR:

Vitamin D receptor

tPA:

Tissue plasminogen activator

COX-2:

Cyclooxygenase-2

MMP-1:

Matrix metalloproteinase-1

References

  1. Hofmann SR, Kapplusch F, Girschick HJ, Morbach H, Pablik J, Ferguson PJ, Hedrich CM. Chronic recurrent multifocal osteomyelitis (CRMO): Presentation, Pathogenesis, and treatment. Curr Osteoporos Rep. 2017;15(6):542–54.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dudareva M, Hotchen AJ, Ferguson J, Hodgson S, Scarborough M, Atkins BL, McNally MA. The microbiology of chronic osteomyelitis: changes over ten years. J Infect. 2019;79(3):189–98.

    Article  PubMed  Google Scholar 

  3. Meroni G, Tsikopoulos A, Tsikopoulos K, Allemanno F, Martino PA, Soares Filipe JF. A journey into animal models of human osteomyelitis: a review. Microorganisms 2022, 10(6).

  4. Granata V, Possetti V, Parente R, Bottazzi B, Inforzato A, Sobacchi C. The osteoblast secretome in Staphylococcus aureus osteomyelitis. Front Immunol. 2022;13:1048505.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Masters EA, Ricciardi BF, Bentley KLM, Moriarty TF, Schwarz EM, Muthukrishnan G. Skeletal infections: microbial pathogenesis, immunity and clinical management. Nat Rev Microbiol. 2022;20(7):385–400.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Cunha BA. Osteomyelitis in elderly patients. Clin Infect Diseases: Official Publication Infect Dis Soc Am. 2002;35(3):287–93.

    Article  Google Scholar 

  7. Mader JT, Shirtliff ME, Bergquist S, Calhoun JH. Bone and joint infections in the elderly: practical treatment guidelines. Drugs Aging. 2000;16(1):67–80.

    Article  PubMed  CAS  Google Scholar 

  8. Valour F, Bouaziz A, Karsenty J, Ader F, Lustig S, Laurent F, Chidiac C, Ferry T. Determinants of methicillin-susceptible Staphylococcus aureus native bone and joint infection treatment failure: a retrospective cohort study. BMC Infect Dis. 2014;14:443.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lew DP, Waldvogel FA. Osteomyelitis. Lancet (London England). 2004;364(9431):369–79.

    Article  PubMed  CAS  Google Scholar 

  10. Tseng CH, Huang WS, Muo CH, Chang YJ, Kao CH. Increased depression risk among patients with chronic osteomyelitis. J Psychosom Res. 2014;77(6):535–40.

    Article  PubMed  Google Scholar 

  11. Patel R. Periprosthetic Joint infection. N Engl J Med. 2023;388(3):251–62.

    Article  PubMed  Google Scholar 

  12. Hsiao LC, Muo CH, Chen YC, Chou CY, Tseng CH, Chang KC. Increased risk of coronary heart disease in patients with chronic osteomyelitis: a population-based study in a cohort of 23 million. Heart. 2014;100(18):1450–4.

    Article  PubMed  Google Scholar 

  13. Tseng CH, Chen JH, Muo CH, Chang YJ, Sung FC, Hsu CY. Increased risk of ischaemic stroke amongst patients with chronic osteomyelitis: a population-based cohort study in Taiwan. Eur J Neurol. 2015;22(4):633–9.

    Article  PubMed  Google Scholar 

  14. Lin SY, Lin CL, Tseng CH, Wang IK, Wang SM, Huang CC, Chang YJ, Kao CH. The association between chronic osteomyelitis and increased risk of diabetes mellitus: a population-based cohort study. Eur J Clin Microbiol Infect Diseases: Official Publication Eur Soc Clin Microbiol. 2014;33(9):1647–52.

    Article  Google Scholar 

  15. Lin TY, Chen YG, Huang WY, Lin CL, Peng CL, Sung FC, Kao CH. Association between chronic osteomyelitis and deep-vein thrombosis. Analysis of a nationwide population-based registry. Thromb Haemost. 2014;112(3):573–9.

    Article  PubMed  CAS  Google Scholar 

  16. Paludo E, Ibelli AMG, Peixoto JO, Tavernari FC, Lima-Rosa CAV, Pandolfi JRC, Ledur MC. The involvement of RUNX2 and SPARC genes in the bacterial chondronecrosis with osteomyelitis in broilers. Animal: An International Journal of Animal Bioscience. 2017;11(6):1063–70.

    Article  PubMed  CAS  Google Scholar 

  17. Chen X, Jiao J, He X, Zhang J, Wang H, Xu Y, Jin T. CHI3L1 regulation of inflammation and the effects on osteogenesis in a Staphylococcus aureus-induced murine model of osteomyelitis. FEBS J. 2017;284(11):1738–47.

    Article  PubMed  CAS  Google Scholar 

  18. Heneka MT, Golenbock DT, Latz E. Innate immunity in Alzheimer’s disease. Nat Immunol. 2015;16(3):229–36.

    Article  PubMed  CAS  Google Scholar 

  19. Halle A, Hornung V, Petzold GC, Stewart CR, Monks BG, Reinheckel T, Fitzgerald KA, Latz E, Moore KJ, Golenbock DT. The NALP3 inflammasome is involved in the innate immune response to amyloid-beta. Nat Immunol. 2008;9(8):857–65.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Lamkanfi M, Dixit VM. Inflammasomes and their roles in health and disease. Annu Rev Cell Dev Biol. 2012;28:137–61.

    Article  PubMed  CAS  Google Scholar 

  21. Mangan MSJ, Olhava EJ, Roush WR, Seidel HM, Glick GD, Latz E. Targeting the NLRP3 inflammasome in inflammatory diseases. Nat Rev Drug Discovery. 2018;17(9):688.

    Article  PubMed  CAS  Google Scholar 

  22. Verma D, Lerm M, Blomgran Julinder R, Eriksson P, Söderkvist P, Särndahl E. Gene polymorphisms in the NALP3 inflammasome are associated with interleukin-1 production and severe inflammation: relation to common inflammatory diseases? Arthritis Rheum. 2008;58(3):888–94.

    Article  PubMed  CAS  Google Scholar 

  23. Jiang N, Zhao XQ, Qin CH, Hu YJ, Wang L, Xie GP, Wang SN, Chen LG, Yu B. Association of vitamin D receptor gene TaqI, BsmI, FokI and ApaI polymorphisms and susceptibility to extremity chronic osteomyelitis in Chinese population. Injury. 2016;47(8):1655–60.

    Article  PubMed  Google Scholar 

  24. Wang L, Jiang N, Lin QR, Qin CH, Hu YJ, Yu B. Cyclooxygenase-2 (COX-2) polymorphism rs689466 may contribute to the increased susceptibility to post-traumatic osteomyelitis in Chinese population. Infect Dis (London England). 2017;49(11–12):817–23.

    CAS  Google Scholar 

  25. Valle-Garay E, Montes AH, Corte JR, Meana A, Fierer J, Asensi V. tPA Alu (I/D) polymorphism associates with bacterial osteomyelitis. J Infect Dis. 2013;208(2):218–23.

    Article  PubMed  CAS  Google Scholar 

  26. Montes AH, Valle-Garay E, Alvarez V, Pevida M, García Pérez E, Paz J, Meana A, Asensi V. A functional polymorphism in MMP1 could influence osteomyelitis development. J bone Mineral Research: Official J Am Soc Bone Mineral Res. 2010;25(4):912–9.

    Article  CAS  Google Scholar 

  27. Panteli M, Giannoudis PV. Chronic osteomyelitis: what the surgeon needs to know. EFORT open Reviews. 2016;1(5):128–35.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Huang CC, Tsai KT, Weng SF, Lin HJ, Huang HS, Wang JJ, Guo HR, Hsu CC. Chronic osteomyelitis increases long-term mortality risk in the elderly: a nationwide population-based cohort study. BMC Geriatr. 2016;16:72.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wang HY, Chao CH, Lin CL, Tseng CH, Kao CH. Increased subsequent risk of erectile dysfunction among middle and old age males with chronic osteomyelitis: a nationwide population-based cohort study. Int J Impot Res. 2016;28(4):143–7.

    Article  PubMed  Google Scholar 

  30. Tseng CH, Huang WS, Muo CH, Kao CH. Increased risk of dementia among chronic osteomyelitis patients. Eur J Clin Microbiol Infect Diseases: Official Publication Eur Soc Clin Microbiol. 2015;34(1):153–9.

    Article  Google Scholar 

  31. Lin SY, Lin CL, Tseng CH, Chang YJ, Wang IK, Yeh HC, Kao CH. Association between Chronic Osteomyelitis and Risk of End-Stage Renal Disease: a Nationwide Population-based Cohort Study. Medicine. 2015;94(27):e1141.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Panteli M, Puttaswamaiah R, Lowenberg DW, Giannoudis PV. Malignant transformation in chronic osteomyelitis: recognition and principles of management. J Am Acad Orthop Surg. 2014;22(9):586–94.

    Article  PubMed  Google Scholar 

  33. Kapadia BH, Berg RA, Daley JA, Fritz J, Bhave A, Mont MA. Periprosthetic joint infection. Lancet (London England). 2016;387(10016):386–94.

    Article  PubMed  Google Scholar 

  34. Rathinam VA, Vanaja SK, Fitzgerald KA. Regulation of inflammasome signaling. Nat Immunol. 2012;13(4):333–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ferguson PJ, Laxer RM. New discoveries in CRMO: IL-1β, the neutrophil, and the microbiome implicated in disease pathogenesis in Pstpip2-deficient mice. Semin Immunopathol. 2015;37(4):407–12.

    Article  PubMed  CAS  Google Scholar 

  36. Asensi V, Alvarez V, Valle E, Meana A, Fierer J, Coto E, Carton JA, Maradona JA, Paz J, Dieguez MA, et al. IL-1 alpha (-889) promoter polymorphism is a risk factor for osteomyelitis. Am J Med Genet Part A. 2003;119a(2):132–6.

    Article  PubMed  Google Scholar 

  37. Tsezou A, Poultsides L, Kostopoulou F, Zintzaras E, Satra M, Kitsiou-Tzeli S, Malizos KN. Influence of interleukin 1alpha (IL-1alpha), IL-4, and IL-6 polymorphisms on genetic susceptibility to chronic osteomyelitis. Clin Vaccine Immunology: CVI. 2008;15(12):1888–90.

    Article  PubMed Central  CAS  Google Scholar 

  38. Xian H, Watari K, Sanchez-Lopez E, Offenberger J, Onyuru J, Sampath H, Ying W, Hoffman HM, Shadel GS, Karin M. Oxidized DNA fragments exit mitochondria via mPTP- and VDAC-dependent channels to activate NLRP3 inflammasome and interferon signaling. Immunity. 2022;55(8):1370–1385e1378.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. 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 Personalized Med 2023, 13(2).

  40. La Russa A, Lofaro D, Montesanto A, La Russa D, Zaza G, Granata S, Di Dio M, Serra R, Andreucci M, Bonofiglio R et al. Association between NLRP3 rs10754558 and CARD8 rs2043211 variants and susceptibility to chronic kidney disease. Int J Mol Sci 2023, 24(4).

  41. Shen C, Wang QZ, Shen ZY, Yuan HY, Yu WJ, Chen XD, Xu H. Genetic association between the NLRP3 gene and acne vulgaris in a Chinese population. Clin Exp Dermatol. 2019;44(2):184–9.

    Article  PubMed  CAS  Google Scholar 

  42. Wu Z, Wu S, Liang T. Association of NLRP3 rs35829419 and rs10754558 polymorphisms with risks of Autoimmune diseases: a systematic review and Meta-analysis. Front Genet. 2021;12:690860.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

The authors thank all participants and staff who made this study possible. We would like to thank Editage (www.editage.cn) for English language editing.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers: 81972083, 82172197), Guangdong Provincial Science and Technology Project (grant number: 2020A0505100039), Guangdong Basic and Applied Basic Research Foundation (grant number: 2022A1515012385), the Science and Technology Planning Project of Guangzhou (grant numbers 202201020303, 202102080052, 202102010057, 201804010226); and the Science Foundation of Guangdong Second Provincial General Hospital (grant numbers 3D-A2020004, 3D-A2020002, YQ2019-009, C2020019).

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Author notes

  1. Yu-dun Qu and Nan Jiang contributed equally to the study.

Authors and Affiliations

  1. Department of Orthopaedics, Guangdong Second Provincial General Hospital, 466 Xingang Road, Haizhu District, 510317, Guangzhou, China

    Yu-dun Qu, Jia-xuan Li, Wei Zhang, Chang-liang Xia, Shuan-ji Ou, Yang Yang, Yong Qi & Chang-peng Xu

  2. Division of Orthopaedics and Traumatology, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China

    Nan Jiang & Yun-fei Ma

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  1. Yu-dun Qu
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Contributions

YDQ and NJ contributed equally to this study. YDQ, NJ, and CPX designed the study; YDQ, NJ, and CPX managed and analyzed the data; YDQ and CPX drafted the initial study protocol; YD Q wrote the first draft of the manuscript; JXL, WZ, CLX, YFM, SJO, YY, and YQ contributed to the data interpretation and preparation of the report. All authors 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.

Corresponding authors

Correspondence to Yong Qi or Chang-peng Xu.

Ethics declarations

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Southern Medical University (Approval letter No. NFEC-2019-087). Informed consent was obtained from all participants involved in the study.

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NA.

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The authors declare no competing interests.

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Qu, Yd., Jiang, N., Li, Jx. et al. Chronic osteomyelitis risk is associated with NLRP3 gene rs10754558 polymorphism in a Chinese Han Population. BMC Med Genomics 17, 38 (2024). https://doi.org/10.1186/s12920-024-01799-6

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Keywords

BMC Medical Genomics

ISSN: 1755-8794

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