System for osteoarthritis etiological diagnosis, joint replacement prognosis and joint health prediction

Abstract

The invention discloses a system for osteoarthritis pathogenesis diagnosis, joint replacement prognosis and joint health prediction. The system comprises an osteoarthritis pathogenesis diagnosis unit and a joint replacement operation prognosis and prediction unit, the osteoarthritis pathogenesis diagnosis unit is used for detecting an osteoarthritis characteristic expression profile of a replacement articular cartilage tissue of a to-be-detected osteoarthritis patient, and classifying the osteoarthritis characteristic expression profile into one of four clusters through a predictproba method; the joint replacement prognosis and prediction unit is used for detecting a microorganism composition structure of a replacement articular cartilage tissue of a to-be-detected osteoarthritis patient on the basis of an analysis result of the osteoarthritis pathogenesis diagnosis unit, analyzing cartilage microorganism DNA deposition and supplementing and correcting a clustering analysis result of the osteoarthritis pathogenesis diagnosis unit; and performing prognosis evaluation on the replacement joint based on the supplementary correction result, and predicting the progress of other joints or knee osteoarthritis after unicondylar replacement of the patient. The method is helpful for judging the cause of osteoarthritis and the potential risk of joint replacement.

Description

Technical Field

[0001] The present invention belongs to the technical field of diagnosis and treatment of osteoarthritis, and particularly relates to a system for diagnosing the etiology of osteoarthritis, predicting the prognosis of joint replacement, and predicting joint health. Background Art

[0002] By 2019, approximately 528 million people globally suffered from osteoarthritis, a 113% increase since 1990. Approximately 73% of osteoarthritis patients are over 55 years old, and 60% are female. The knee joint is the most commonly affected joint, with 365 million patients, followed by the hand and hip joints. When there is severe joint degeneration, intractable pain, limited joint function, joint deformity or osteophyte hyperplasia, and conservative treatment is ineffective, joint replacement surgery is required to relieve pain, improve function, and enhance the quality of life. Statistical data shows that from 2011 to 2019, the volume of artificial hip and knee joint replacement surgeries in China showed a rapid growth trend. The total knee arthroplasty (TKA) increased from 54,000 cases in 2012 to 374,000 cases in 2019, with a compound annual growth rate of 32%. With the progress of population aging, the number of joint replacement surgeries continues to increase, and correspondingly, the number of joint revision surgeries also increases.

[0003] Joint replacement surgeries are mainly divided into two types: TKA and unicompartmental knee arthroplasty (UKA). Postoperative complications are the main reasons for the short service life of prostheses and the high revision rate, including infection, pain, and aseptic loosening of prostheses. 1) Once periprosthetic infection occurs after joint replacement, the treatment is extremely challenging. In some cases, only empirical cultures are performed for several possible pathogens. This is because in actual operation, the most likely pathogens may be selected for culture based on the patient's medical history, clinical manifestations, and other laboratory test results. It should be noted that there are cases of negative cultures when dealing with bacterial cultures of prosthetic infections. A negative culture does not mean there is no infection, but may be due to various factors that make it difficult for bacteria to be detected by conventional culture methods. For example, the use of antibiotics; the formation of bacterial biofilms; and the inability to detect some slow-growing bacteria in a timely manner. 2) Pain management after joint replacement is an important issue. Some patients may experience unexplained pain after surgery, which affects joint function and quality of life. Generally, the use time of the tourniquet is reduced after surgery, or cryotherapy is adopted, that is, an ice pack is applied to the surgical site to relieve pain. 3) Repeated friction between prosthetic components generates wear particles, which can induce an inflammatory response, activate macrophages to release pro-inflammatory cytokines, promote osteoclast activation, lead to bone resorption and prosthetic loosening. Osteoporotic patients have lower bone density and fragile trabecular bone structures, making it difficult to effectively support the prosthesis. In addition, secondary infections after surgery further damage bone tissue and accelerate prosthetic loosening.

[0004] Long-term follow-up found that the revision rate of UKA is higher than that of TKA. Since UKA mainly targets single-compartment lesions, the remaining compartments after surgery still need treatment to control the progression of osteoarthritis in this joint. Osteoarthritis is an age-related degenerative joint disease that is not limited to a single joint and may affect multiple joints throughout the body, such as the knee joint, hip joint, hand joints, and spine. Joint replacement surgery is an effective means of treating end-stage osteoarthritis, not the endpoint of treatment. Moreover, osteoarthritis may affect multiple joints simultaneously, which is more common in elderly patients. Therefore, other uninvolved or mildly involved joints after joint replacement still need to be managed through methods such as medications, rehabilitation, or lifestyle adjustments. There is an urgent need to develop a system based on the articular cartilage tissue obtained during joint replacement surgery to directly and accurately identify potential pathogenic factors, effectively prevent complications after joint replacement, and manage the health of the patient's other joints. Summary of the Invention

[0005] To solve the above technical problems, the object of the present invention is to provide a system for diagnosing the etiology of osteoarthritis, predicting the prognosis of replaced joints, and predicting joint health, where the prognosis refers to evaluating postoperative complications and rehabilitation of the replaced joint. The prediction refers to predicting the progression of knee osteoarthritis after unicompartmental knee arthroplasty (osteoarthritis of the remaining compartments after unicompartmental knee arthroplasty) or osteoarthritis of other joints.

[0006] To achieve the above object, a system for diagnosing the etiology of osteoarthritis, predicting the prognosis of replaced joints, and predicting joint health according to the present invention includes an osteoarthritis etiology diagnosis unit and a prognosis and prediction unit for joint replacement surgery.

[0007] The osteoarthritis etiology diagnosis unit is used to detect the osteoarthritis characteristic expression profile of the articular cartilage tissue of a patient with osteoarthritis to be tested and classify it into one of four clustering clusters through the predict_proba method.

[0008] The osteoarthritis characteristic expression profile includes the following 14 genes: ACAN, COMP, COL2A1, COL1A1, MMP13, ADAMTS4, CCR2, MCP1, COX2, NTRK1, CGRP, IL1-ra, TRPV4, PIEZO1.

[0009] The four clustering clusters are Cluster_0, Cluster_1, Cluster_2, and Cluster_3, respectively. Among them, in Cluster_0, COL1A1, IL1-ra, and TRPV4 are highly expressed, while NTRK1 and CGPR are lowly expressed. In Cluster_1, ADAMTS4 is highly expressed and IL1-ra is lowly expressed. In Cluster_2, CCR2, MCP1, NTRK1, and CGPR are highly expressed, while TRPV4 and PIEZO1 are lowly expressed. In Cluster_3, CCR2, MCP1, NTRK1, and CGPR are highly expressed. The osteoarthritis characteristic expression profile for detecting the replaced joint cartilage tissue of the patient with osteoarthritis to be tested is specifically as follows: Quantitative analysis of the mRNA expression of the replaced joint cartilage tissue is performed using qPCR technology. The qPCR detection uses glyceraldehyde 3-phosphate dehydrogenase as the internal reference gene, and the average expression level of this entire population is used as the control. The results are presented as the mean value. A value greater than 1 is higher than the average level, and a value less than 1 is lower than the average level.

[0010] Based on the analysis results of the osteoarthritis etiology diagnosis unit, the prognosis and prediction unit for joint replacement detects the microbial composition structure of the replaced joint cartilage tissue of the patient with osteoarthritis to be tested, analyzes the deposition of microbial DNA in cartilage, supplements and corrects the clustering analysis results of the osteoarthritis etiology diagnosis unit, and based on the supplemented and corrected results, conducts prognosis assessment of the replaced joint and predicts the progression of knee osteoarthritis after replacement of other joints or unicompartmental of the patient.

[0011] The microorganisms in the replaced joint cartilage tissue include, but are not limited to, Afipia, Anoxybacillus_A, Brevibacillus_D, Brevibacillus, Comamonas, Cutibacterium, Diaphorobacter, Pseudomonas, Ralstonia, Sphingomonas, Stenotrophomonas, Streptococcus, Xanthomonas.

[0012] The basis for supplementing and correcting the results of cluster analysis of the etiological diagnosis unit of osteoarthritis based on microbial DNA deposition is as follows: Cluster_1 and Cluster_2 have high microbial DNA deposition, Cluster_0 has high contents of Xanthomonas, Bacillus_A, Pseudomonas_E, Streptococcus, and Klebsiella, while having low contents of Sphingomonas, Anoxybacillus_A, and Acinetobacter; Cluster_1 has high contents of Acinetobacter and Comamonas, while having a low content of Streptococcus; Cluster_2 has high contents of Sphingomonas, Anoxybacillus_A, Ralstonia, Afipia, Bradyrhizobium, and Streptococcus; Cluster_3 has a high content of Stenotrophomonas while having low contents of Klebsiella, Bradyrhizobium, Cupriavidus, Cutibacterium, and Comamonas.

[0013] Detect the microbial composition structure of the replaced joint cartilage tissue of the patient to be tested for osteoarthritis. Specifically: Use 2bRAD-M to sequence and analyze the osteoarthritis cartilage tissue, compare the obtained short sequences with the species-specific tag database, analyze the species composition and relative abundance of the microbial community, and determine the high and low contents according to the size of the relative abundance.

[0014] The prognostic assessment of the replaced joint is specifically as follows: Based on the etiological diagnosis unit of osteoarthritis, combined with the microbial DNA deposition in the replaced joint cartilage, conduct a prognosis on the replaced joint and carry out personalized prevention or treatment, including infection prevention, targeted treatment of infection events, management of postoperative pain, prevention of joint prosthesis loosening, or promotion of prosthesis bone integration, etc.

[0015] The prediction of the progression of osteoarthritis in other joints of the patient or in the knee after unicompartmental replacement is specifically as follows: Based on the etiological diagnosis unit of osteoarthritis, combined with the microbial DNA deposition in the replaced joint cartilage, predict the development of osteoarthritis in the patient and carry out personalized prevention or treatment.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) By detecting the gene expression of cartilage tissue and the deposition of microbial DNA in the replacement joint, it helps to judge the etiology of osteoarthritis and the potential risks of joint replacement; 2) Judge the possibility of infection after total knee replacement or unicompartmental knee replacement, and take targeted prevention measures, such as selecting antibiotics, dosage and treatment duration; 3) Judge the situation of prosthesis osseointegration, and adjust the joint flora structure and metabolism through means such as in-situ injection of the joint, improving intestinal nutrition, and formulating a rehabilitation plan to promote prosthesis osseointegration and prevent aseptic loosening of the prosthesis; 4) Identify the causes of the onset of osteoarthritis and determine personalized treatment plans for knee osteoarthritis after unicompartmental knee replacement or osteoarthritis of other joints of the patient; 5) Used to investigate the root cause of pain after joint replacement and relieve pain after joint replacement through anti-inflammatory and other means. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the expression pattern of osteoarthritis marker genes in the replacement joints of 4 types of osteoarthritis patients. Among them, A) is the clustering of 121 osteoarthritis patients according to the expression of osteoarthritis marker genes in the cartilage of the replacement joint; B) is the gene expression profile of these four expression patterns.

[0018] Figure 2 It is the demographic and clinical information of osteoarthritis patients.

[0019] Figure 3 It is the heterogeneity of the microbial composition structure of the cartilage tissue of osteoarthritis patients. Among them, A) is the microbial abundance of the four clustering clusters; B) shows that the Alpha diversity of the four clustering clusters is similar; C) reflects the existence of beta diversity in the four clustering clusters (PERMANOVA P VALUE = 0.044); D) The heat map reflects the microbial genus distribution of each clustering cluster.

[0020] Figure 4 It is the Spearman correlation analysis between the microbial abundance at the genus level in the joint cartilage of osteoarthritis patients and the gene expression profile of osteoarthritis marker genes. DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention will be further described below in conjunction with the drawings, tables and specific embodiments.

[0022] Example 1

[0023] (1) Strictly follow aseptic operation and collect the cartilage tissue of the replacement joints of 121 primary osteoarthritis patients. The inclusion criteria are patients with stage III or IV osteoarthritis who need joint replacement surgery.

[0024] (2) The related gene expressions of articular cartilage samples were detected (Table 1), including genes related to the composition and function of the extracellular matrix, genes related to extracellular matrix degradation, genes related to inflammation, genes related to pain, genes related to cartilage protection, and genes related to mechanosensation. Quantitative analysis of mRNA expression in the replaced articular cartilage tissue was performed using qPCR technology. Specifically, the cartilage samples were ground into powder under liquid nitrogen conditions, and 50 - 100 mg of the samples were added to 1 ml of Trizol UP and 0.2 ml of RNA Extraction Agent for RNA extraction. The mixture was repeatedly pipetted and mixed at room temperature, and after 5 min, the lysate was aspirated. The total RNA was gradually extracted using a column-type total RNA extraction kit, and the concentration and purity of the extracted RNA were tested using a spectrophotometer (Thermo Scientific, USA). Reverse transcription was performed using HiScript III RT SuperMix for qPCR (+gDNAwiper) and qPCR was performed using Taq Pro Universal SYBR qPCR Master Mix. The reaction system was constructed according to the reagent instructions, and the amplification conditions were as follows: a. Removal of genomic DNA: 42°C for 2 min; b. Reverse transcription: 37°C for 15 min; 85°C for 5 s; c. Pre-denaturation: 95°C for 30 s; d. Denaturation: 95°C for 30 s; e. Annealing / extension: 60°C for 10 s; e. Repeat steps c and d, and the number of cycles was 40. qPCR was performed using a Bio-Rad CFX96 Connect real-time system (Bio-Rad, USA). The primer information is shown in Table 1. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal reference gene. Each sample was repeated 2 times. The 2 -ΔΔCt -method was used for relative quantitative analysis.

[0025] (3) According to the gene expression of osteoarthritis-related genes in cartilage tissue, a Gaussian mixture model (GMM) clustering algorithm was used for clustering analysis ( Figure 1 ), and the patients were divided into different clustering clusters Cluster_0, Cluster_1, Cluster_2, and Cluster_3.

[0026] Specifically, taking the population average expression level as a control, the gene expression profiles of 121 samples and the gene expression characteristics of each clustering cluster ( Figure 1) to speculate on the causes of osteoarthritis in patients in different clusters. Among them, COL1A1, IL1-ra, and TRPV4 were highly expressed in Cluster_0, while NTRK1 and CGPR were lowly expressed. Cluster_0 was composed of individuals with cartilage fibrosis. It may be because after cartilage injury, a large amount of type I collagen (COL1A1) was produced during self-repair instead of type II collagen (COL2A1) originally present in hyaline cartilage, resulting in tissue fibrosis, low inflammation, and pain, which was secondary osteoarthritis caused by cartilage injury. In Cluster_1, ADAMTS4 was highly expressed and IL1-ra was lowly expressed. Cluster_1 was composed of individuals with strong degradation of cartilage tissue while type II collagen and aggrecan were well-preserved, with low inflammation and pain, which may be age-related joint degeneration. In Cluster_2, CCR2, MCP1, NTRK1, and CGPR were highly expressed, while TRPV4 and PIEZO1 were lowly expressed. TRPV4 and PIEZO1 respond to mechanical stimuli, and their expression and activity are related to maintaining the mechanical balance of tissues. The expression levels of TRPV4 and PIEZO1 in Cluster_2 were the lowest among the 4 Clusters, indicating that the change in the line of force led to mechanical imbalance, while CCR2, MCP1, NTRK1, and CGPR were relatively high, indicating moderate inflammation and pain. In Cluster_3, CCR2, MCP1, NTRK1, and CGPR were highly expressed, while TRPV4 and PIEZO1 were lowly expressed. The extremely high expression of CCR2, MCP1, NTRK1, and CGPR in Cluster_3 indicated that the recruitment of inflammatory cells led to increased cartilage degradation and enhanced pain signals. The high and low expression levels are shown in Table 2.

[0027] It should be noted that there were no significant differences in demographics and clinical information among osteoarthritis patients in different clusters ( Figure 2 ), which verified the concealment of the leading factors causing the deterioration of osteoarthritis from the opposite side, and the necessity of exploring the causes and risk factors of osteoarthritis through this system of the present invention.

[0028] (4) There are no blood vessels and lymphatic vessels in cartilage, so cartilage is considered an aseptic tissue. Research shows that the microbial community structure of osteoarthritic cartilage tissue is different from that of healthy people, suggesting that microorganisms are transferred from the intestine or other organs to the joint through the blood. The distribution of the body's microorganisms can reflect an individual's living habits and has important value in predicting the occurrence of other diseases. Assuming that the living habits of osteoarthritis patients, such as diet and history of pathogen infection, are the internal causes of the development of osteoarthritis and are also the basis for the treatment of infections after joint replacement, prosthesis bone integration, and knee osteoarthritis after unicompartmental knee arthroplasty. Therefore, further microbial genomics sequencing was used to identify the quantity and species characteristics of microbial DNA deposition in the cartilage tissue of the replaced joint, and to explore the causes and develop personalized prevention and treatment strategies.

[0029] The 2bRAD-M was used to sequence and analyze osteoarthritis cartilage tissue, specifically: total DNA was extracted from arthroplasty cartilage tissue; subsequently, type IIB restriction endonucleases were used to digest the DNA to generate equally long digested tags; then these digested tags were recovered and amplified by PCR to increase the sample volume; after that, the amplified digested tags were constructed into a sequencing library and sequenced on a high-throughput sequencing platform; finally, the short sequences obtained from the sequencing were aligned with a species-specific tag database to analyze the species composition and relative abundance of the microbial community.

[0030] It was found that Cluster_1 and Cluster_2 cartilage tissues had more microbial DNA deposition, while Cluster_0 and Cluster_4 had less ( Figure 3 ). There were differences in the microbial composition structures among the clusters, manifested as: at the genus level, the contents of Xanthomonas, Bacillus_A, Pseudomonas_E, Streptococcus, and Klebsiella in Cluster_0 were high, while the contents of Sphingomonas, Anoxybacillus_A, and Acinetobacter were low; the contents of Acinetobacter and Comamonas in Cluster_1 were high, while the content of Streptococcus was low; the contents of Sphingomonas, Anoxybacillus_A, Ralstonia, Afipia, Bradyrhizobium, and Streptococcus in Cluster_2 were high; the content of Stenotrophomonas in Cluster_3 was high while the contents of Klebsiella, Bradyrhizobium, Cupriavidus, Cutibacterium, and Comamonas were low ( Figure 3 ). The differences in the microbial contents and composition structures among the clusters verified the accuracy of cluster analysis through the osteoarthritis characteristic expression profiles. The high and low expression contents are shown in Table 3.

[0031] (5) Further tracing the etiology, co-expression correlation analysis was performed on the microbial genera identified by the microbiome sequencing and the gene expression of cartilage tissue ( Figure 4 ):

[0032] It is found that the content of the pain-related gene NTRK1 is positively correlated with Ralstonia, Afipia, Sphingomonas, Anoxybacillus_A, Brevibacillus_D, and Brevibacillus, and negatively correlated with Comamonas, Stenotrophomonas, Cutibacterium, and Pseudomonas; CGRP is negatively correlated with Pseudomonas, Stenotrophomonas, Xanthomonas, and Diaphorobacter, and positively correlated with Sphingomonas. Therefore, although the cartilage of Cluster_1 has a high bacterial content but a low pain sensation, reducing the content of Sphingomonas, Anoxybacillus_A, Ralstonia, and Afipia in the body of Cluster_2 patients may effectively reduce the pain sensation.

[0033] The content of COL1A1 related to cartilage fibrosis is positively correlated with Pseudomonas and Stenotrophomonas, and negatively correlated with Ralstonia, Sphingomonas, and Afipia. This situation also verifies that pain is the body's self-protection. When the content of pain-related bacteria changes in the opposite direction, the pain sensation decreases, and Cluster_0 osteoarthritis individuals tend to exercise, resulting in increased wear and cartilage fibrosis.

[0034] ACAN and COL2A1 related to normal cartilage matrix are negatively correlated with Streptococcus, an opportunistic pathogen present on the skin surface, upper respiratory tract, digestive tract, and female genital tract. The Chinese Osteoporosis Cohort Study shows that Streptococcus sanguinis and Streptococcus gordonii are positively correlated with bone resorption markers and negatively correlated with 25-OH-D3 and bone formation markers. Therefore, it is speculated that although the microbial DNA deposition in the joint cartilage of Cluster_1 patients undergoing joint replacement is relatively high, they may have a more satisfactory bone integration outcome after surgery. Reducing the content of Streptococcus in the body of Cluster_0 and Cluster_2 after osteoarthritis replacement can protect the remaining cartilage tissue and promote prosthesis bone integration.

[0035] Studies have shown that Stenotrophomonas maltophilia can activate macrophages, promote their migration, and trigger an inflammatory response, thereby exacerbating infection and disease progression. The migration and aggregation of macrophages and other immune cells lead to the infiltration of inflammatory cells at the infection site, further exacerbating the inflammatory response, promoting cartilage degradation, and enhancing pain signals. Therefore, reducing the content of Stenotrophomonas in Cluster_3 patients may reduce inflammation and alleviate the progression of knee osteoarthritis or other joint osteoarthritis after unicompartmental knee arthroplasty.

[0036] Bacteria can form biofilms, which gives them stronger viability and drug resistance in the host, thus increasing the complexity of infection and the difficulty of treatment. Stenotrophomonas maltophilia, Acinetobacter baumannii, and Klebsiella pneumoniae are multidrug-resistant bacteria; Pseudomonas aeruginosa, Sphingomonas spp., Ralstonia spp., and Afipia spp. are partially drug-resistant bacteria; Xanthomonas spp., Streptococcus spp., Comamonas spp., and Anoxybacillus spp. are relatively sensitive bacteria. Among them, although multidrug-resistant bacteria are resistant to a variety of antibiotics, the preferred treatments for Stenotrophomonas maltophilia infection are sulfamethoxazole / trimethoprim (TMP / SMX), quinolones, tigecycline, ceftazidime / avibactam, or colistin; Acinetobacter baumannii is still sensitive to penicillins and cephalosporins; and Klebsiella pneumoniae is still sensitive to certain carbapenems and aminoglycosides. Therefore, the focuses of infection prevention for Cluster_0, Cluster_1, and Cluster_3 patients are different.

[0037] The present invention has obtained a system (Table 4) for inferring the etiology of osteoarthritis, predicting the prognosis of joint replacement, alleviating pain and inflammation after joint replacement, preventing prosthetic infection after replacement, promoting prosthetic bone integration, and alleviating or treating knee osteoarthritis after unicompartmental knee arthroplasty and osteoarthritis of other joints other than the replaced joint by detecting the gene expression pattern and microbial DNA deposition characteristics of the cartilage of the osteoarthritis replacement joint.

[0038] Table 1. Selection basis and primer sequences of osteoarthritis marker genes

[0039]

[0040] Table 2. Expression of osteoarthritis marker genes in clustering clusters

[0041]

[0042]

[0043] Note: In qPCR detection, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene, and the average expression level of the whole population was used as the control. The results are presented as mean (standard error). Values greater than 1 indicate higher than the average level, and values less than 1 indicate lower than the average level.

[0044] Table 3. Relative abundances of characteristic bacteria in clustering clusters

[0045]

[0046] Note: The results of 2bRad-M sequencing are presented as relative abundances.

[0047] Table 4. Etiological analysis of osteoarthritis, prognosis of joint replacement surgery, and other joint health management

[0048]

[0049] a Targeted prevention and treatment: In addition to the conventional measures for osteoarthritis (maintaining a reasonable diet, controlling body weight to reduce joint pressure; paying attention to joint warmth, avoiding bad postures such as squatting or kneeling for a long time; promptly and correctly handling joint injuries, and regularly checking joint health), based on the cause of joint replacement, the patient's own situation is judged, and personalized measures are taken to prevent osteoarthritis in other joints of the patient or slow down the progression of osteoarthritis in other joints of the patient.

Claims

1. A system for diagnosing the cause of osteoarthritis, prognosis of joint replacement, and prediction of joint health, characterized in that: Includes a unit on the diagnosis of the etiology of osteoarthritis and a unit on the prognosis and prediction of joint replacement surgery; The osteoarthritis etiology diagnosis unit detects the osteoarthritis characteristic expression spectrum in the replaced joint cartilage tissue, and classifies the gene expression characteristics of the osteoarthritis patient's cartilage tissue into one of four clusters using the predict_proba method; The osteoarthritis characteristic expression profile includes the following 14 osteoarthritis-related marker gene combinations, specifically ACAN, COMP, COL2A1, COL1A1 related to extracellular matrix composition and function, MMP13, ADAMTS4 related to extracellular matrix degradation, MCP1, CCR2, COX2 related to inflammation, NTRK1, CGRP related to pain perception, IL1-ra, TRPV4, PIEZO1 related to cartilage protection and / or mechanical force perception; The four clusters are Cluster_0, Cluster_1, Cluster_2 and Cluster_3, wherein COL1A1, IL1-ra and TRPV4 are highly expressed, and NTRK1 and CGPR are lowly expressed in Cluster_0, ADAMTS4 is highly expressed, and IL1-ra is lowly expressed in Cluster_1, CCR2, MCP1, NTRK1 and CGPR are highly expressed, and TRPV4 and PIEZO1 are lowly expressed in Cluster_2, and CCR2, MCP1, NTRK1 and CGPR are highly expressed in Cluster_3; The post-joint replacement prognosis and prediction unit detects the microbial composition structure of the replaced joint cartilage tissue of the osteoarthritis patient to be tested, analyzes the cartilage microbial DNA deposition, supplements and corrects the cluster analysis of the osteoarthritis etiology diagnosis unit, and performs a prognosis assessment on the replaced joint based on the supplementary and corrected results, as well as predicts the progression of knee osteoarthritis or other joint health of the patient after unicompartmental knee replacement.

2. The system for diagnosing the cause of osteoarthritis, prognosis of joint replacement and prediction of joint health according to claim 1, characterized in that: The microorganisms for replacing articular cartilage tissue include Afipia, Anoxybacillus_A, Brevibacillus_D, Brevibacillus, Comamonas, Cutibacterium, Diaphorobacter, Pseudomonas, Ralstonia, Sphingomonas, Stenotrophomonas, Streptococcus and Xanthomonas.

3. The system for diagnosing the cause of osteoarthritis, prognosis of joint replacement and prediction of joint health according to claim 1, characterized in that: The basis for supplementing and revising the cluster analysis of osteoarthritis etiology diagnosis units based on microbial DNA deposition is as follows: Cluster_1 and Cluster_2 have high microbial DNA deposition, Cluster_0 has high contents of Xanthomonas, Bacillus_A, Pseudomonas_E, Streptococcus and Klebsiella, and low contents of Sphingomonas, Anoxybacillus_A and Acinetobacter; Cluster_1 has high contents of Acinetobacter and Comamonas, and low content of Streptococcus; Cluster_2 has high contents of Sphingomonas, Anoxybacillus_A, Ralstonia, Afipia, Bradyrhizobium and Streptococcus; Cluster_3 has high content of Stenotrophomonas and low contents of Klebsiella, Bradyrhizobium, Cupriavidus, Cutibacterium and Comamonas.

4. The system for diagnosing the cause of osteoarthritis, prognosis of joint replacement and prediction of joint health according to claim 1, characterized in that: The prognostic assessment of the replacement joint is specifically as follows: based on the osteoarthritis etiology diagnosis unit, combined with the microbial DNA deposition in the replaced joint cartilage, the prognosis of the replaced joint is carried out and personalized prevention or treatment is performed, including infection prevention, targeted treatment of infection events, postoperative pain management, prevention of joint prosthesis loosening or promotion of prosthesis bone integration.

5. The system for diagnosing the cause of osteoarthritis, prognosis of joint replacement and prediction of joint health according to claim 1, characterized in that: The prediction of the progression of knee osteoarthritis or other joint osteoarthritis after unicompartmental knee replacement is specifically as follows: based on the osteoarthritis etiology diagnosis unit, combined with the deposition of microbial DNA in the replaced joint cartilage, the development of knee osteoarthritis or other joint osteoarthritis after unicompartmental knee replacement is predicted, and personalized prevention or treatment is performed.

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