Abstract
Kashin-Beck disease (KBD) is an endemic osteochondropathy. A specific gene called SRY-box transcription factor 6 (SOX6) is important for forming cartilage. This study aims to explore the potential correlation between SOX6 single nucleotide polymorphisms (SNPs) and KBD risk for the first time. In the case–control study, 735 unrelated Chinese Han individuals were enrolled. The four mutation sites of the SOX6 gene (rs4539287 G/A, rs3203295 C/A, rs7928675 C/A, and rs10832681 A/G) were screened and genotyped on the Agena MassARRAY platform. The correlation between SOX6 SNPs and KBD risk was explored based on logistic regression analysis. The interaction between SNP and SNP was analyzed based on the multi-factor dimensionality reduction (MDR) method. Overall analysis revealed a remarkable correlation between rs7928675 and rs10832681 and the reduction of KBD risk (p < 0.05). Subgroup analyses further indicated that these two SNPs have a significant protective effect on KBD risk among participants aged ≤65 years, males, and non-smokers (p < 0.05). MDR displayed a marked interaction between rs3203295 and rs10832681. Our study revealed that SOX6 rs7928675 and rs10832681 are markedly correlated with a reduced risk of KBD in the Chinese Han population, providing a new direction for the prevention, diagnosis, and treatment of KBD.
Keywords: Kashin-Beck disease, risk, SOX6, SNPs, case–control study
1. Introduction
Kashin-Beck disease (KBD) is an endemic, chronic, degenerative osteoarthropathy that is common in children and adolescents, characterized by joint pain, swelling, and stiffness, and it can lead to chondrocyte necrosis, joint deformities, and joint functional damage in severe cases [1,2]. KBD is quite common worldwide and mainly distributed in the low-selenium (Se) zone of China, which runs from northeast to southwest [3,4]. According to China Health and Family Planning Statistical Yearbook 2016, KBD may pose a threat to over 1.16 million individuals residing in 337 counties across 13 provinces of China [5]. Previous studies have suggested that KBD is caused by a combination of genetic and environmental factors [6]. Related studies further revealed a certain correlation between some susceptibility gene variants and the KBD risk, which may be closely related to the occurrence and development of KBD [6–8]. Zhang et al. found a significant correlation between the ABI3BP variants (CNV452, rs9850273, and rs7613610) and KBD risk based on a genome-wide copy number variation study and association analysis involving 2,743 Chinese Han adults [9]. Wu et al. conducted an association analysis of genetic association, messenger ribonucleic acid (mRNA), and protein expression of the ATG4C gene in KBD patients. The study revealed that ATG4C was a novel autophagy-related susceptibility gene of KBD, and its genetic variants (rs11208030, rs4409690, rs12097658, and rs6587988) were significantly associated with KBD risk [10]. Yu et al. evaluated the correlation between selenoprotein gene polymorphism and KBD risk as of January 2021 through meta-analysis. The study found that only DIO2 (rs225014), SEPS1 (−105G > A), and Sep15 (rs5859) gene polymorphisms were significantly associated with KBD risk, while GPX1 (rs1050450, rs1800668, rs3811699), DIO2 (rs225014, rs1352815, rs1388382), TrxR2 (rs1139793, rs5746841), glutathione peroxidase 4 (GPX4) (rs713041, rs4807542), and selenoprotein P (SEPP1) (rs757925191g/a) did not show statistical significance with KBD risk [11]. As of now, the pathogenesis of KBD remains elusive and the genetic risk of KBD explained by reported susceptibility genes is relatively limited. Therefore, continuing to develop more genetic variation sites related to KBD will be crucial for the prevention, diagnosis, and precise treatment of KBD.
SRY-box transcription factor 6 (SOX6), located on chromosome 11, encodes a protein that is a transcriptional activator required for proper development of the central nervous system, cartilage formation, and maintenance of heart and skeletal muscle cells [12,13]. It was reported that SOX6 is necessary for effective cartilage formation, as its inactivation can affect the differentiation of chondrocytes and neuronal cells, resulting in mild bone defects and bone-related disorders like Tolchin-Le Caignec syndrome, osteoporosis, and osteochondroma [14,15]. Recent studies have revealed that SOX6 variants can cause neurological syndromes related to hyperactivity disorder, cranial osteoporosis, and osteochondroma [16]. Meanwhile, SOX6 is a multi-effector gene in osteoporosis, and there is a latent interaction between its multiple genetic mutations and the risk of osteoporosis [17,18]. However, the occurrence of KBD disease is closely related to chondrocyte destruction, but there are currently no reports on the potential relationship between SOX6 polymorphism and KBD risk.
In this research, we conducted a case–control study on 352 KBD patients and 383 normal individuals in the Chinese Han population, and for the first time evaluated the correlation between SOX6 single nucleotide polymorphisms (SNPs) (rs4539287, rs3203295, rs7928675, and rs10832681) and the risk of KBD, providing a novel biomarker for the prevention, diagnosis, and treatment of KBD in the future.
2. Materials and methods
2.1. Study participants
This case–control study enrolled 352 KBD patients and 383 normal individuals from the People’s Hospital of Changwu County. Among them, all patients were diagnosed with KBD through clinical and radiological examination of the skeletal system, and patients with primary osteoarthritis, rheumatoid arthritis, and a family history of joint diseases were excluded. The control group was randomly selected and underwent radiation testing, with no KBD, no osteoarthritis, and no family history of cancer. Prior to the experiment, all subjects were aware of this research purpose and process.
Ethics approval and consent to participate: The study was approved by the ethics committees of the People’s Hospital of Changwu County (ethics committee registration number: 2022-A03).
2.2. DNA extraction and genotyping of SNPs
Screen and genotype four SNPs of the SOX6 gene as candidate mutation sites, including rs4539287, rs3203295, rs7928675, and rs10832681. The screening process involves three key steps: (1) obtaining the polymorphism data of the SOX6 gene from the 1,000 genome project database, (2) using Haploview v 4.2 to screen SNPs, the filtering threshold is Hardy–Weinberg equilibrium (HWE) >0.01 and minor allele frequency (MAF) >0.05, and (3) combining primer design and random selection to further screen SNPs.
Under fasting conditions, we collected fresh venous blood (5 mL) from all subjects and used the DNA extraction kits (GoldMag Biotechnology) to extract genomic DNA. Then, the Nanodrop 2000 spectrophotometer (Thermo, USA) was used to determine DNA concentration and purity. Afterward, four candidate SNPs were genotyped on the Agena MassARRAY platform (Agena Bioscience, USA) and the data were processed using Agena Typer v 4.0 software, with the primer sequence information displayed in Table S1.
2.3. Statistical analysis
Using G*Power v 3.1.9.7 to calculate sample size through independent samples t-test. Based on SPSS v 25.0 (SPSS Inc., Chicago, IL, USA), the basic characteristics of the case group and control group were analyzed using independent samples t-test or chi-squared test. In the control group, the chi-square test analyzed the genotype frequency distribution of candidate SNPs, with p > 0.05 indicating that the genotype frequency conformed to HWE.
In overall and subgroup analyses, combined with genetic models (co-dominant, dominant, recessive, and additive models), logistic regression analysis was used to calculate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to further explore the correlation between candidate SNPs and KBD risk. Among them, we adjusted the ORs and 95% CIs through confounding factors. Subsequently, these positive results were verified through false positive report probability (FPRP) analysis. Among them, the FPRP value was less than 0.2 indicating that the correlation between SOX6 SNPs and KBD risk was worth noting. We also explored the interaction between SNP and SNP based on the multi-factor dimensionality reduction (MDR) method. Figure 1 displays the detailed research process.
Figure 1.
Flowchart of this study.
3. Results
3.1. Information about sample characteristics
This research involved 735 subjects, including 352 KBD patients (183 males and 169 females), with an average age of 65.32 ± 8.49 years; 383 healthy individuals (201 males and 182 females), with an average age of 64.19 ± 7.64 years. Among KBD patients, 195 patients (55.4%) experienced disease progression for more than 38 years, while 157 patients (44.6%) experienced disease progression for 38 years or less. Statistical analysis showed that there was no marked difference in age (p = 0.060), gender (p = 0.894), BMI (p = 0.692), and smoking (p = 0.974) between the case group and the control group, except for drinking (p = 0.024). More detailed information regarding the sample features is shown in Table 1.
Table 1.
Sample characteristics information
| Characteristics | Case n = 352 | Control n = 383 | p | |
|---|---|---|---|---|
| Age (years) | Mean ± SD | 65.32 ± 8.49 | 64.19 ± 7.64 | 0.060 a |
| >65 | 166 (47.2%) | 149 (38.9%) | ||
| ≤65 | 186 (52.8%) | 234 (61.1%) | ||
| Gender | Male | 183 (52%) | 201 (52.5%) | 0.894 b |
| Female | 169 (48%) | 182 (47.5%) | ||
| BMI | ≥24 | 133 (37.8%) | 173 (45.2%) | 0.692 a |
| <24 | 219 (62.2%) | 210 (54.8%) | ||
| Smoking | Yes | 120 (34.1%) | 131 (34.2%) | 0.974 b |
| No | 232 (65.9%) | 252 (65.8%) | ||
| Drinking | Yes | 44 (12.5%) | 71 (18.5%) | 0.024b |
| No | 308 (87.5%) | 312 (81.5%) | ||
| Course of disease (years) | >38 | 195 (55.4%) | — | − |
| ≤38 | 157 (44.6%) | − | ||
| Number of affected joints | >5 | 220 (62.5%) | − | − |
| ≤5 | 132 (37.5%) | − | ||
| Grade | 2 vs 1 | 167 (47.4%) | 69 (19.6%) | − |
| 3 vs 1 | 116 (33%) | 69 (19.6%) | ||
| 3 vs 2 | 116 (33%) | 167 (47.4%) | ||
| Hypertension | Non-hypertension | 222 (63.1%) | − | − |
| Hypertension | 130 (36.9%) | − | ||
| Diabetes | Non-diabetes | 332 (94.3%) | − | − |
| Diabetes | 20 (5.7%) | − | ||
Notes: aIndependent samples t-test; bChi-squared test; p < 0.05 indicates statistical significance. Abbreviation: SD, standard deviation. Bold indicates no significant difference between cases and controls.
3.2. Information about SOX6 SNPs
The basic information of four candidate SNPs (rs4539287 G/A, rs3203295 C/A, rs7928675 C/A, rs10832681 A/G) is shown in Table 2. These SNPs are classified as untranslated regions (UTR) variants. The chi-square test demonstrated that these SNPs all comply with HWE (p > 0.05). The allele model analysis revealed that the minimum allele C of rs7928675 (OR = 0.78, 95% CI 0.64–0.97, p = 0.022) and the minimum allele A of rs10832681 (OR = 0.79, 95% CI 0.64–0.97, p = 0.028) could markedly reduce the KBD risk. In addition, the genotype frequency and allele frequency of the four candidate SNPs in the case group and control group are shown in Figure 2a–h and Table S2.
Table 2.
Basic information of four candidate SNPs of SOX6
| Gene | SNP ID | Function annotation | Chr: position | Alleles (A/B) | MAF | HWE (p value) | OR (95% CI) | p a | |
|---|---|---|---|---|---|---|---|---|---|
| Cases | Controls | ||||||||
| SOX6 | rs4539287 | 3′-UTR variant, 2KB upstream variant | 11: 15,971,511 | G/A | 0.304 | 0.304 | 0.718 | 1.00 (0.80–1.25) | 0.993 |
| SOX6 | rs3203295 | 5′-UTR variant, intron variant 2KB upstream variant | 11: 16,740,086 | C/A | 0.419 | 0.371 | 0.188 | 1.22 (0.99–1.51) | 0.058 |
| SOX6 | rs7928675 | 5′-UTR variant, intron variant, 2KB upstream variant | 11: 16,740,259 | C/A | 0.396 | 0.456 | 1.000 | 0.78 (0.64–0.97) | 0.022* |
| SOX6 | rs10832681 | 3′-UTR variant | 11: 16,778,008 | A/G | 0.358 | 0.414 | 0.058 | 0.79 (0.64–0.97) | 0.028* |
Notes: aChi-squared test; Bold and *p < 0.05 indicates statistical significance.
Abbreviations: SNP, single nucleotide polymorphisms; alleles (A/B), minor/major allele; MAF, minor allele frequency; HWE, Hardy–Weinberg equilibrium; OR, odds ratio; 95% CI, 95% confidence interval.
Figure 2.
Genotype frequency and allele frequency of SOX6 SNPs in KBD case and healthy control: (a) genotype frequency of rs4539287 between the two groups, (b) genotype frequency of rs3203295 between the two groups, (c) genotype frequency of rs7928675 between the two groups, (d) genotype frequency of rs10832681 between the two groups, (e) allele frequency of rs4539287 between the two groups, (f) allele frequency of rs3203295 between the two groups, (g) allele frequency of rs7928675 between the two groups, and (h) allele frequency of rs10832681 between the two groups.
3.3. Association between SOX6 SNPs and KBD risk (overall analysis)
Based on logistic regression analysis, this study conducted an overall correlation analysis between SOX6 SNPs and KBD risk. The results revealed that (Figure 3) rs7928675 and rs10832681 were markedly correlated with a reduction in KBD risk. Specifically, rs7928675 markedly reduced the risk of KBD under homozygous (OR = 0.66, 95% CI 0.43–1.00, p = 0.049), heterozygote (OR = 0.63, 95% CI 0.45–0.87, p = 0.006), additive (OR = 0.78, 95% CI 0.64–0.96, p = 0.020), and dominant (OR = 0.64, 95% CI 0.47–0.87, p = 0.004) models. Similarly, rs10832681 also markedly reduced the risk of KBD under homozygous (OR = 0.57, 95% CI 0.35–0.93, p = 0.023), additive (OR = 0.75, 95% CI 0.60–0.94, p = 0.013), and dominant (OR = 0.71, 95% CI 0.52–0.96, p = 0.027) models. However, there was no significant correlation between rs4539287 and rs3203295 and KBD risk.
Figure 3.
Association between four candidate SNPs of SOX6 and KBD risk (overall analysis). *p < 0.05 indicates statistical significance.
3.4. Association between SOX6 SNPs and KBD risk (subgroup analysis)
In order to thoroughly explore the correlation between SOX6 SNPs and KBD risk, this study performed subgroup analyses of age, gender, BMI, and smoking for all participants, as well as subgroup analyses of the course of disease, number of affected joints, grade (Ⅰ–Ⅲ), hypertensive complications, and diabetes complications for participants for KBD patients. The significant results of the subgroup association analysis between SOX6 SNPs and KBD risk are shown in Figure 4.
Figure 4.
Association between four candidate SNPs of SOX6 and KBD risk (subgroup analysis). Only significant results were shown. p < 0.05 indicates statistical significance.
Age-stratified analysis revealed that there was no correlation between SOX6 SNPs and KBD risk in participants >65 years old (Table S3). However, for those aged 65 years or younger, rs7928675 under the heterozygote (OR = 0.57, 95% CI 0.37–0.89, p = 0.013) and dominant (OR = 0.60, 95% CI 0.40–0.91, p = 0.015) models, and rs10832681 under the heterozygote (OR = 0.62, 95% CI 0.40–0.95, p = 0.029), additive (OR = 0.72, 95% CI 0.53–0.99, p = 0.040), and dominant (OR = 0.62, 95% CI 0.41–0.93, p = 0.021) models could markedly reduce KBD risk.
Gender-stratified analysis showed that there was no marked correlation between SOX6 SNPs and the risk of KBD among female participants (Table S4). However, for male participants, rs7928675 under the heterozygous (OR = 0.55, 95% CI 0.34–0.88, p = 0.012), additive (OR = 0.73, 95% CI 0.55–0.97, p = 0.030), dominant (OR = 0.56, 95% CI 0.36–0.86, p = 0.008), and rs10832681 under the homozygous (OR = 0.47, 95% CI 0.23–0.97, p = 0.042) and additive (OR = 0.71, 95% CI 0.52–0.98, p = 0.039) models could markedly decrease KBD risk. In addition, rs3203295 showed a significant increase in KBD risk in male participants under the homozygous (OR = 1.96, 95% CI 1.07–3.57, p = 0.029), additive (OR = 1.35, 95% CI 1.01–1.80, p = 0.041), and received (OR = 1.80, 95% CI 1.04–3.12, p = 0.037) models.
BMI-stratified analysis showed that rs10832681 (Table S5) was related to a decreased KBD risk in participants with a BMI of 24 or higher under the heterozygote (OR = 0.50, 95% CI 0.29–0.85, p = 0.010) and dominant (OR = 0.55, 95% CI 0.33–0.91, p = 0.020) models. In those with BMI below 24, except for rs4539287, there was a certain correlation between the other three SOX6 SNPs and KBD risk. Of these three, rs3203295 could markedly increase the risk of KBD under the homozygous (OR = 2.16, 95% CI 1.24–3.74, p = 0.006), additive (OR = 1.42, 95% CI 1.09–1.86, p = 0.009), and recessive (OR = 1.96, 95% CI 1.19–3.23, p = 0.008) models. While rs7928675 under the heterozygote (OR = 0.53, 95% CI 0.34–0.83, p = 0.005) and dominant (OR = 0.57, 95% CI 0.38–0.86, p = 0.008) models, and rs10832681 under the homozygous (OR = 0.49, 95% CI 0.25–0.94, p = 0.032) and recessive (OR = 0.53, 95% CI 0.29–0.96, p = 0.038) models could markedly reduce the risk of KBD.
Smoking-stratified analysis found that (Table S6) rs7928675 markedly reduced the risk of KBD in non-smokers under the homozygous (OR = 0.57, 95% CI 0.34–0.96, p = 0.035), heterozygote (OR = 0.59, 95% CI 0.39–0.90, p = 0.014), additive (OR = 0.73, 95% CI 0.57–0.95, p = 0.019), and dominant (OR = 0.59, 95% CI 0.40–0.87, p = 0.008) models. And rs10832681 could markedly reduce the KBD risk in smokers under the homozygous (OR = 0.40, 95% CI 0.17–0.96, p = 0.041) and additive (OR = 0.66, 95% CI 0.45–0.98, p = 0.041) models.
The course of disease stratified analysis revealed that (Table S7) only rs10832681 markedly increased the disease risk in KBD patients with the course of disease longer than 38 years.
The number of affected joints stratified analysis showed that (Table S8) the correlation between SOX6 SNPs and KBD risk was not related to the number of damaged joints in patients.
Based on the severity of joint lesions, this study classified KBD patients into Ⅰ–Ⅲ grades and investigated the correlation between SOX6 SNPs and KBD risk among different grades. The results showed that (Table S9), with grade Ⅱ as the control group, rs4539287 could markedly decrease the KBD risk in grade Ⅲ patients under the heterozygote (OR = 0.51, 95% CI 0.31–0.84, p = 0.008) and dominant (OR = 0.57, 95% CI 0.35–0.93, p = 0.024) models.
Hypertensive and diabetes complications stratified analyses showed that (Tables S10 and S11) the link between SOX6 SNPs and the KBD risk was not markedly impacted by whether or not the patient had hypertension or diabetes. Only rs7928675 had a marked effect on increasing the KBD risk in hypertensive patients under the additive (OR = 0.76, 95% CI 0.59–0.97, p = 0.025) model.
3.5. FPRP analysis
Based on the FPRP analysis, we proceeded to conduct a reliability analysis on the positive results mentioned above (Table S12). In the overall analysis and subgroup analysis of males, BMI < 24, and non-smoking, the link between rs10832681 and KBD risk under the homozygous model should not be a concern. In the overall analysis and subgroup analysis of non-smoking, the link between rs7928675 and KBD risk under the homozygous model should not be of concern. Additionally, the correlation between rs3203295 and KBD risk under homozygous and recessive models in males should not be of concern. It is noteworthy that other positive results had FPRP values less than 0.2, showing a significant correlation between SOX6 SNPs and KBD risk, which warrants further in-depth analysis.
3.6. SNP–SNP interaction analysis
This study evaluated the correlation between SNP–SNP in SOX6 polymorphism using the MDR method. The results revealed a significant interaction between rs3203295 and rs10832681 (Figure 5a and b). Meanwhile, the 4-site combination model, which includes rs4539287, rs3203295, rs7928675, and rs10832681, was the most effective model for predicting KBD risk, with a testing balanced accuracy of 0.5355 and cross-validation consistency (CVC) of 10/10 (Table 3).
Figure 5.
Dendrogram (a) and circle graph (b) of SNP–SNP interaction among SOX6 SNPs by MDR method. The numerical values on the lines represent the strength of the interaction.
Table 3.
SNP–SNP interactions in KBD risk based on MDR analysis
| Models | Training Bal. Acc | Testing Bal. Acc | OR (95% CI) | p | CVC |
|---|---|---|---|---|---|
| rs7928675 | 0.5456 | 0.5014 | 1.48 (1.08–2.02) | 0.014 | 7/10 |
| rs4539287, rs3203295 | 0.5677 | 0.5071 | 1.66 (1.23–2.25) | 0.001 | 6/10 |
| rs4539287, rs3203295, rs7928675 | 0.6002 | 0.5185 | 2.19 (1.62–2.97) | <0.0001 | 7/10 |
| rs4539287, rs3203295, rs7928675, rs10832681 | 0.6302 | 0.5355 | 2.96 (2.16–4.06) | <0.0001 | 10/10 |
Abbreviations: MDR, multifactor dimensionality reduction; Bal. Acc, balanced accuracy; CVC, cross-validation consistency.
4. Discussion
This study aims to analyze the potential correlation between four specific SOX6 SNPs (rs4539287, rs3203295, rs7928675, and rs10832681) and KBD risk among 735 participants, to screen for more genetic variations related to the occurrence and progression of KBD. Overall analysis revealed a significant correlation between rs7928675 and rs10832681 and a decrease in KBD risk. Further subgroup analyses indicated that these two SNPs also have a significant protective effect on KBD risk among participants who are aged 65 years or younger, males, and non-smokers.
The SOX6 gene plays a crucial role as a transcription factor in the development and formation of cartilage [19]. Yang et al. conducted a fine-mapping association analysis and found that there are numerous SOX6 SNPs related to hip bone mineral density in both Chinese and Caucasian populations, emphasizing the importance of SOX6 gene variants in affecting changes in bone mineral density [20]. A cross-sectional study by Correa-Rodríguez et al. revealed that SOX6 rs7117858 can affect the fat free mass and quantitative ultra sound characteristics of young people, indicating the significance of SOX6 variants in obesity and osteoporosis-related phenotypes during early adulthood [17]. Furthermore, a case–control study by Wu et al. revealed a relationship between SOX6 mutations and adolescent idiopathic scoliosis in the Han Chinese population [21]. These studies suggested a potential connection between SOX6 gene variants and bone-related diseases, providing important theoretical support for the first in-depth exploration of the link between SOX6 SNPs and KBD risk in this study.
KBD is more common in children and adolescents, particularly those between the ages of 5–15 years [22]. Through subgroup analysis, it has been observed that rs7928675 and rs10832681 significantly reduced the risk of KBD in younger individuals (age ≤65 years), suggesting that these SNPs may be key in KBD among young people. Meanwhile, this study also found that these SNPs have a certain protective effect in males, and non-smokers, suggesting that gender may be crucial in the association between SOX6 SNPs and KBD risk and that the adverse effects of smoking on the development of KBD may interfere with the protective effect of these SNPs. It is essential to note that although these findings provide insight into the link between SOX6 SNPs and KBD risk, more research is still necessary to verify the potential connection between factors such as gender, smoking, and KBD.
Genetic mutations can occur in coding and non-coding regions of the genome. Previous research has shown that both of them can impact gene expression, potentially leading to the development of diseases [23]. Du et al. pointed out that the polymorphism of GPx4 and the decrease in its mRNA expression level may be related to the development of KBD in the Chinese population [24]. Similarly, when Sun et al. investigated the correlation between the SEPP1 variant and the risk of KBD, they also tested the expression of SEPP1 gene mRNA in patients to analyze the potential link between SEPP1 SNPs and the pathogenesis of KBD [25]. In this study, we found that rs7928675 and rs10832681 located in the non-coding region of the SOX6 were significantly associated with a reduction in KBD risk. Previous studies have reported that SOX6 plays an important role in cartilage formation and bone development, and its inactivation may affect the differentiation of chondrocytes and neuronal cells, leading to the occurrence of bone-related diseases [14,15]. Considering that both rs7928675 and rs10832681 are located in non-coding regions, it may affect the binding ability of SOX6 regulatory elements or the role of regulatory factors, which may lead to changes in the expression level of SOX6. Therefore, we speculated that the specific alleles of these two SNPs may enhance the interaction between SOX6 regulatory elements and transcription factors, thereby increasing the expression of SOX6 and reducing the risk of KBD. However, this hypothesis needs to be further validated through experimental methods such as gene expression analysis and chromatin conformation.
It is undeniable that this study has some limitations. First, the sample size was relatively small and the majority of participants are local residents of Changwu County, with only a small number from other cities or provinces, indicating an uneven geographical distribution of the participants in this study. Considering that participants from this specific region may have specific genetic polymorphisms, in future related studies, we will expand the sample size and include participants from different regions to further validate the association between SOX6 SNPs and KBD risk in the Chinese Han population. Second, due to significant differences between the case group and the control group in drinking, this study did not conduct a drinking-standardized analysis regarding the association between SOX6 SNPs and KBD risk. In subsequent studies, we will further expand the sample size to ensure that the case group matches the control group, and then proceed with the drinking-standardized analysis of the correlation between SOX6 SNPs and KBD risk to obtain reliable results related to drinking. Additionally, this study found a marked correlation between the two candidate SNPs of SOX6 and the reduction of KBD risk, which needs to be further validated through relevant experiments.
5. Conclusions
This study revealed a significant correlation between SOX6 variants (rs7928675 and rs10832681) and a reduction in the KBD risk among the Chinese Han population, providing a new direction for the prevention, diagnosis, and treatment of KBD.
Abbreviations
- CIs
confidence intervals
- CVC
cross-validation consistency
- FFM
fat free mass
- FPRP
false positive report probability
- GPx4
glutathione peroxidase 4
- HWE
Hardy–Weinberg equilibrium
- KBD
Kashin-Beck disease
- MAF
minor allele frequency
- MDR
multi-factor dimensionality reduction
- ORs
odds ratios
- QUS
quantitative ultra sound
- Se
selenium
- SEPP1
selenoprotein P
- SNPs
single nucleotide polymorphisms
- SOX6
SRY-box transcription factor 6
- TOLCAS
Tolchin-Le Caignec syndrome
Supplementary Material
Acknowledgement
The authors appreciate the support of all participants for this study.
Footnotes
Funding information: This work is supported by the Science and Technology Research and Development Plan of Xianyang City (grant number 2019k02-118).
Author contributions: Y.F.J. and N.H. contributed to the conception and design of the work. A.W.H. and K.Z. contributed to drafting the work and revising it critically for important intellectual content. Z.F.Z. and S.L.W. contributed equally to analysis and interpretation of data for the work. N.H. and Y.F.J wrote the first draft of the manuscript and all authors contributed to the review, approval of the final manuscript, and agreed to be accountable for all aspects of the work.
Conflict of interest: The author claimed that there are no conflicts of interest.
Data availability statement: The raw data for this study can be obtained from the corresponding authors according to reasonable requirements.
References
- [1].Shao Q, Yang H. Severe deformity in long-term Kaschin–Beck disease. Rheumatology. 2022;61(4):1732. 10.1093/rheumatology/keab486. [DOI] [PubMed]
- [2].Cao J, Li S, Shi Z, Yue Y, Sun J, Chen J, et al. Articular cartilage metabolism in patients with Kashin–Beck disease: an endemic osteoarthropathy in China. Osteoarthr Cartil. 2008;16(6):680–8. 10.1016/j.joca.2007.09.002. [DOI] [PubMed]
- [3].Tan J, Zhu W, Wang W, Li R, Hou S, Wang D, et al. Selenium in soil and endemic diseases in China. Sci Total Environ. 2002;284(1–3):227–35. [DOI] [PubMed]
- [4].Lv Y, Yu T, Yang Z, Zhao W, Zhang M, Wang Q. Constraint on selenium bioavailability caused by its geochemical behavior in typical Kaschin–Beck disease areas in Aba, Sichuan Province of China. Sci Total Environ. 2014;493:737–49. 10.1016/j.scitotenv.2014.06.050. [DOI] [PubMed]
- [5].Commission NHaFP. China health and family planning statistical yearbook 2016. China: Beijing Union Medical University Press; 2016.
- [6].Shi X, Lv A, Ma J, Zhang F, Wen Y, Zhang Z, et al. Investigation of MMP-1 genetic polymorphisms and protein expression and their effects on the risk of Kashin-Beck disease in the northwest Chinese Han population. J Orthopaedic Surg Res. 2016;11(1):64. 10.1186/s13018-016-0398-6. [DOI] [PMC free article] [PubMed]
- [7].Bai Y, Shi X, Zhang F, Lv A, Wen Y, Guo X. COL9A1 gene polymorphism is associated with Kashin-Beck disease in a Northwest Chinese Han population. PLoS One. 2015;10(3):e0120365. 10.1371/journal.pone.0120365. [DOI] [PMC free article] [PubMed]
- [8].He X, Bai M, Liu M, Wang L, He Y, Rong H, et al. Genetic variants in the ITPR2 gene are associated with Kashin‐Beck disease in Tibetan. Mol Genet Genomic Med. 2019;7(7):e00715. 10.1002/mgg3.715. [DOI] [PMC free article] [PubMed]
- [9].Zhang F, Guo X, Zhang Y, Wen Y, Wang W, Wang S, et al. Genome-wide copy number variation study and gene expression analysis identify ABI3BP as a susceptibility gene for Kashin-Beck disease. Hum Genet. 2014;133(6):793–9. [DOI] [PubMed]
- [10].Wu C, Wen Y, Guo X, Yang T, Shen H, Chen X, et al. Genetic association, mRNA and protein expression analysis identify ATG4C as a susceptibility gene for Kashin–Beck disease. Osteoarthr Cartil. 2017;25(2):281–6. 10.1016/j.joca.2016.09.019. [DOI] [PubMed]
- [11].Yu FF, Sun L, Zhou GY, Ping ZG, Guo X, Ba Y. Meta-analysis of association studies of selenoprotein gene polymorphism and Kashin-Beck disease: an updated systematic review. Biol Trace Elem Res. 2022;200:543–50. [DOI] [PubMed]
- [12].Akiyama H, Chaboissier M, Martin JF, Schedl A, de Crombrugghe B. The transcription factor Sox9 has essential roles in successive steps of the chondrocyte differentiation pathway and is required for expression of Sox5 and Sox6. Genes Dev. 2002;16(21):2813–28. 10.1101/gad.1017802. [DOI] [PMC free article] [PubMed]
- [13].Liu C, Lefebvre V. The transcription factors SOX9 and SOX5/SOX6 cooperate genome-wide through super-enhancers to drive chondrogenesis. Nucleic Acids Res. 2015;43(17):8183–203. 10.1093/nar/gkv688. [DOI] [PMC free article] [PubMed]
- [14].Smits P, Li P, Mandel J, Zhang Z, Deng JM, Behringer RR, et al. The transcription factors L-Sox5 and Sox6 are essential for cartilage formation. Dev Cell. 2001;1(2):277–90. [DOI] [PubMed]
- [15].Dy P, Smits P, Silvester A, Penzo-Méndez A, Dumitriu B, Han Y, et al. Synovial joint morphogenesis requires the chondrogenic action of Sox5 and Sox6 in growth plate and articular cartilage. Dev Biol. 2010;341(2):346–59. 10.1016/j.ydbio.2010.02.024. [DOI] [PMC free article] [PubMed]
- [16].Tolchin D, Yeager JP, Prasad P, Dorrani N, Russi AS, Martinez-Agosto JA, et al. De dovo SOX6 variants cause a neurodevelopmental syndrome associated with ADHD, craniosynostosis, and osteochondromas. Am J Hum Genet. 2020;106(6):830–45. 10.1016/j.ajhg.2020.04.015. [DOI] [PMC free article] [PubMed]
- [17].Correa-Rodríguez M, Schmidt-RioValle J, Rueda-Medina B. SOX6 rs7117858 polymorphism is associated with osteoporosis and obesity-related phenotypes. Eur J Clin Investig. 2018;48(10):e13011. 10.1111/eci.13011. [DOI] [PubMed]
- [18].Hsu T, Tantoh DM, Chou Y, Hsu S, Ho C, Lung C, et al. Association between osteoporosis and menopause in relation to SOX6 rs297325 variant in Taiwanese women. Menopause. 2020;27(8):887–92. 10.1097/gme.0000000000001544. [DOI] [PMC free article] [PubMed]
- [19].Nishimura R, Hata K, Takahata Y, Murakami T, Nakamura E, Yagi H. Regulation of cartilage development and diseases by transcription factors. J Bone Metab. 2017;24(3):147–53. 10.11005/jbm.2017.24.3.147. [DOI] [PMC free article] [PubMed]
- [20].Yang TL, Guo Y, Liu YJ, Shen H, Liu YZ, Lei SF, et al. Genetic variants in the SOX6 gene are associated with bone mineral density in both Caucasian and Chinese populations. Osteoporos Int. 2011;23(2):781–7. 10.1007/s00198-011-1626-x. [DOI] [PMC free article] [PubMed]
- [21].Wu Z, Wang Y, Dai Z, Qiu Y, Xu L, Zhu Z. Genetic variants of ABO and SOX6 are associated with adolescent idiopathic scoliosis in Chinese Han Population. Spine. 2019;44(18):E1063–7. 10.1097/brs.0000000000003062. [DOI] [PubMed]
- [22].Sun LY, Meng FG, Li Q, Zhao ZJ, He CZ, Wang SP, et al. Effects of the consumption of rice from non-KBD areas and selenium supplementation on the prevention and treatment of paediatric Kaschin–Beck disease: an epidemiological intervention trial in the Qinghai Province. Osteoarthr Cartil. 2014;22(12):2033–40. 10.1016/j.joca.2014.09.013. [DOI] [PubMed]
- [23].Shastry BS. SNPs: impact on gene function and phenotype. Methods Mol Biol. 2009;578:3–22. 10.1007/978-1-60327-411-1_1. [DOI] [PubMed]
- [24].Du XH, Dai XX, Xia SR, Zou XZ, Sun WY, Mo XY, et al. SNP and mRNA expression for glutathione peroxidase 4 in Kashin-Beck disease. Br J Nutr. 2011;107(2):164–9. 10.1017/s0007114511002704. [DOI] [PubMed]
- [25].Sun W, Wang X, Zou XZ, Song R, Du X, Hu J, et al. Selenoprotein P gene r25191g/a polymorphism and quantification of selenoprotein P mRNA level in patients with Kashin-Beck disease. Br J Nutr. 2010;104(9):1283–7. 10.1017/s0007114510002199. [DOI] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.