Screening method of risk factors related to ossification progress of ankylosing spondylitis

Abstract

The invention provides a method for screening risk factors related to the ossification progress of ankylosing spondylitis, the method associates the ossification progress of AS patients with folic acid metabolism defects for the first time, establishes a group trajectory analysis model through a GBTM method, determines an optimal linear model of the ossification progress of ankylosing spondylitis, and provides a method for screening risk factors related to the ossification progress of ankylosing spondylitis. And then carrying out single-factor logistic regression analysis and multi-factor logistic regression analysis to finally obtain the independent risk factors of the rapid progress of spinal ossification of the ankylosing spondylitis patient, namely baselines Hb and ALP.

Description

Technical Field

[0001] The present invention relates to the technical field of medical screening, and in particular to a method for screening risk factors associated with the progression of ankylosing spondylitis ossification. Background Art

[0002] Ankylosing spondylitis (AS) is a chronic, progressive rheumatic disease that affects the axial skeleton, causing characteristic back pain, structural and functional damage, and leading to a decline in patients' physical function and quality of life, as well as permanent limitation of spinal mobility. Structural damage to the spine in AS, including bone destruction and abnormal bone formation in the spine, is one of its most significant clinical manifestations. It is significantly correlated with disease activity and spinal mobility in AS patients. Currently, the Modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) is used clinically as the gold standard for evaluating AS spinal structural damage. At the same time, the Outcome Measures in Rheumatology Clinical Trials (OMERACT) and the Assessment in Ankylosing Spondylitis International Society (ASAS) both recommend mSASSS as the preferred scoring method for imaging progression of AS. Understanding the risk factors associated with the progression of AS spinal ossification and then establishing a prediction model to distinguish people with different progression rates can enable clinicians to conduct targeted interventions on patients earlier and achieve individualized precision treatment. However, screening out independent risk factors associated with the progression of AS ossification is a difficult problem that needs to be solved urgently. Summary of the invention

[0003] In order to overcome the above technical defects, the object of the present invention is to provide a method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification, which comprises:

[0004] Step S1: Collect baseline data and clinical information of several patients with ankylosing spondylitis who meet the inclusion and exclusion criteria; baseline data include demographic characteristics, medical history, clinical manifestations, medication, and the evaluation results of the disease activity of the patients in the past week using the ankylosing spondylitis spinal activity rating scale; clinical information includes imaging data, baseline test indicators, and folate metabolism genotype test results; imaging data include patients' previous sacroiliac joint X-ray or CT data, as well as full spine X-ray data, and sacroiliac joint grading and mSASSS scoring; baseline test indicators include blood routine indicators, erythrocyte sedimentation rate, blood biochemical indicators, blood lipid indicators, bone metabolism indicators and vitamins, and folate metabolism genotype is obtained by nucleic acid mass spectrometry;

[0005] Step S2: Establish a group trajectory analysis model to divide patients with different ossification progression rates into multiple subgroups: take patient age as the independent variable, take the mSASSS score of ankylosing spondylitis patients as the dependent variable, draw a line graph with the mSASSS score of 0 when the patient age is 0 as the starting time point, use the lcmm package in R 4.3.2 software to build a GBTM model, and use GBTM to build 5 linear models. Different linear models divide patients into different numbers of subgroups. The optimal number of subgroups is comprehensively judged by the Bayesian information criterion, BIC difference and average posterior probability, so as to determine the optimal linear model for the progression of ankylosing spondylitis ossification;

[0006] Step S3: Based on the optimal linear model, univariate logistic regression analysis was used to compare and analyze the differences in the baseline data and clinical information of patients in different subgroups between patients with different ossification progression rates, and to obtain factors associated with rapid ossification progression of AS;

[0007] Step S4: After univariate logistic regression analysis, factors with P < 0.1 were selected for multivariate logistic regression analysis, and the backward stepwise method was used to screen out independent risk factors associated with the progression of ankylosing spondylitis ossification with P < 0.05 as the standard.

[0008] Furthermore, step S1 further includes: for count data, frequency, rate or composition ratio is used for description, and chi-square test or Fisher's exact test is used for statistical analysis; for measurement data, Shapiro-Wilk test is used for normality test, and mean ± standard deviation is used for description if it conforms to normal distribution, and median is used if it does not conform to normal distribution; for two groups of data that conform to normal distribution and have equal variance, independent sample t test is used for analysis, and non-parametric test is used if it does not conform to normal distribution.

[0009] Furthermore, in step S1, the inclusion and exclusion criteria are as follows: (1) Inclusion criteria: 1) Signed informed consent; 2) Patients aged ≥18 years when signing the informed consent; 3) Diagnosed with ankylosing spondylitis according to the New York criteria for ankylosing spondylitis; 4) At least one full spine X-ray result in the medical record; (2) Exclusion criteria: 1) History of inflammatory arthritis caused by other causes other than ankylosing spondylitis, or any inflammatory arthritis occurring before the age of 17; 2) Participants in other clinical trials; 3) Patients with a history of or concurrent malignant tumors; 4) Patients with a history of or concurrent immunodeficiency diseases, diabetes, gout or other diseases that may affect metabolism; 5) Patients taking anticoagulants, hormones, etc. for a long time that may affect drug metabolism; 6) Patients with incomplete medical records; 7) Patients whose blood samples lack plasma or blood sediment; 8) Patients refuse to participate in this study.

[0010] Further, in step S1, the folate metabolism genotype includes rs1801133C677>T, rs1801131A1298>C and rs1801394A66>G.

[0011] Further, in step S2, the optimal linear model is a trajectory model that divides the patients into two subgroups, the two subgroups being a slowly progressing group and a rapidly progressing group.

[0012] Further, in step S3, the results of univariate logistic regression analysis were as follows: factors associated with rapid progression of AS ossification, expressed as odds ratios with 95% confidence intervals, included baseline mSASSS, folate metabolism gene C677>T for CT or TT type, baseline Hb (hemoglobin), CRP, GGT, and ALP (alkaline phosphatase).

[0013] Furthermore, in step S4, factors with P < 0.1 in the univariate logistic regression analysis and risk factors identified in previous clinical practice and research were selected for correction. The results showed that the only risk factor associated with ossification progression in AS patients was baseline mSASSS. Then the baseline mSASSS was removed from the model, and a multivariate logistic regression analysis was performed based on the folate metabolism gene C677>T being CT or TT type, baseline Hb, CRP, GGT and ALP. Finally, the independent risk factors for rapid progression of spinal ossification in patients with ankylosing spondylitis were found to be baseline Hb and ALP.

[0014] Compared with the prior art, the above technical solution has the following beneficial effects:

[0015] This application is the first to link the ossification progression of AS patients with folate metabolism defects. Through the GBTM method, the mSASSS scores of spinal ossification progression of 84 AS patients were used as the dependent variable, and age was used as the independent variable. Linear trajectory models of two to five groups were established. The results showed that the two groups of trajectory models had the smallest BIC and the AvePP closest to 1, which best reflected the heterogeneous clustering of ossification progression of AS patients in this data set. Then, the two groups (slow progression group and fast progression group) distinguished by GBTM were used as classification criteria to explore possible risk factors affecting the speed of ossification progression in AS patients, and the general information of AS patients, baseline blood test indicators, and polymorphism data of folate metabolism genes MTHFR C677>T, A1298>C, and MTRR A66>G were analyzed. The results of descriptive analysis showed that compared with the slow progression group, AS patients in the rapid progression group had the following characteristics: higher baseline mSASSS, C677>T was more likely to be CT type or TT type (both CT and TT mutations of C677>T will lead to a decrease in MTHFR enzyme activity, thereby causing a disorder in folate-homocysteine-methionine metabolism, which is helpful in clinically identifying rapidly progressive AS patients based on baseline test indicators and general information), and higher baseline Hb, CRP, GGT, and ALP. There were no significant differences between the two groups in terms of gender, age, peripheral joint involvement, and the proportion of extra-articular manifestations. Through univariate logistic regression analysis and multivariate logistic regression analysis, it was finally found that the independent risk factors for rapid progression of spinal ossification in patients with ankylosing spondylitis were baseline Hb and ALP. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flowchart for inclusion and exclusion of study subjects;

[0017] Figure 2 This is a line graph of age-mSASSS score of AS patients;

[0018] Figure 3 It is a linear trajectory model that divides patients into 2 groups, where Class 1 represents the slowly progressing group and Class 2 represents the rapidly progressing group. DETAILED DESCRIPTION

[0019] The advantages of the present invention are further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art should understand that the following specific description is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0020] This embodiment provides a method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification, which includes steps S1 to S4:

[0021] Step S1: Collect baseline data and clinical information of several patients with ankylosing spondylitis who meet the inclusion and exclusion criteria.

[0022] (I) Research subjects and inclusion and exclusion criteria

[0023] 1. Research subjects

[0024] This study was a retrospective, observational study, and the subjects were AS patients who visited the Department of Rheumatology and Immunology and the Department of Clinical Genetics of the Second Affiliated Hospital of Naval Medical University from September 2022 to January 2024. This study followed the relevant provisions and principles of the Declaration of Helsinki, and all patients or their legal guardians signed informed consent.

[0025] 2. Inclusion and Exclusion Criteria

[0026] (1) Inclusion criteria

[0027] 1) Sign the informed consent;

[0028] 2) Patients aged ≥18 years when signing the informed consent form;

[0029] 3) diagnosed with AS according to the New York criteria for ankylosing spondylitis;

[0030] 4) The medical records contain at least one full spine X-ray result.

[0031] (2) Exclusion criteria

[0032] 1) A history of inflammatory arthritis caused by causes other than AS (including but not limited to rheumatoid arthritis, mixed connective tissue disease, systemic lupus erythematosus, dermatomyositis, etc.), or any inflammatory arthritis occurring before the age of 17;

[0033] 2) Participants in other clinical trials;

[0034] 3) Patients with a history of or concurrent malignant tumors, such as breast cancer, gastric cancer, etc.;

[0035] 4) Patients with previous or concurrent immunodeficiency disease, diabetes, gout or other diseases that may affect metabolism;

[0036] 5) Those who take anticoagulants, hormones, etc. for a long time, which may affect drug metabolism;

[0037] 6) Those with incomplete medical records;

[0038] 7) Blood samples lacking plasma or blood sediment;

[0039] 8) The patient refused to participate in this study.

[0040] (II) Data Collection

[0041] 1. Collection of outpatient questionnaires

[0042] Baseline data collection is completed when patients are admitted to the hospital or seen as an outpatient, including:

[0043] 1) Demographic characteristics: gender, age, age of onset, height, weight, etc.;

[0044] 2) Medical history: including history of smoking, drinking, hypertension, coronary heart disease, diabetes, etc.;

[0045] 3) Clinical manifestations: including morning stiffness duration, joint pain, and presence or absence of extra-articular manifestations;

[0046] 4) Medication status: including previous medication history and current medication status;

[0047] 5) Scale assessment: Ankylosing Spondylitis Disease Activity Score (ASDAS) was used to assess the patient's disease activity in the past week;

[0048] 2. Medical record system collection

[0049] The patient's imaging data and test indicators are collected through the medical record system, including:

[0050] 1) Imaging data: The patient's previous sacroiliac joint X-ray or CT data, as well as the whole spine X-ray data were collected. Two physicians independently graded the sacroiliac joints and scored the mSASSS. The specific standards are shown in Appendix 2 and 3. If there is any disagreement, the senior physician in the department will make the judgment.

[0051] 2) Test indicators: record the baseline test indicators in the patient's medical record, including routine blood test indicators: white blood cells (WBC), hemoglobin (Hb), neutrophil percentage (N%), platelets (PLT), high-sensitivity C-reactive protein (CRP), etc.; erythrocyte sedimentation rate: erythrocyte sedimentation rate (ESR); blood biochemical indicators: alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT), uric acid (UA), creatinine (Cr), alkaline phosphatase (ALP), etc.; blood lipid indicators: high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), total cholesterol (TCHO), triglycerides (TG), HCY, etc.; bone metabolism indicators: osteocalcin, calcitonin, β-collagen degradation products, etc.; vitamins: 25-hydroxyvitamin D (Vit D).

[0052] 3. Folate metabolism genotype detection

[0053] The folate metabolism genotypes (rs1801133C677>T, rs1801131A1298>C, rs1801394A66>G) were obtained by nucleic acid mass spectrometry.

[0054] For count data, frequency, rate or composition ratio were used for description, and chi-square test or Fisher's exact test was used for statistical analysis; for measurement data, Shapiro-Wilk test was used for normality test, and those that met normal distribution were expressed as mean ± standard deviation. The data were described, and the median (quartile) M (P0.25, P0.75) was used for those that did not meet the requirements. For two groups of data that met the normal distribution and had equal variance, the independent sample t test was used for analysis, and non-parametric tests were used for those that did not meet the requirements. The statistical results were considered statistically significant when P < 0.05.

[0055] In this study, blood samples and clinical information of 125 AS patients were collected. After exclusion according to the exclusion criteria, 84 patients were finally included in the study. The specific screening process and grouping are as follows Figure 1 Some of the baseline characteristics of the patients are shown in Table 1.

[0056] Table 1 Baseline characteristics of the study population

[0057] Project Name, Unit Numeric Number of patients, n 84 Age, years old 38.67±10.73 Gender (male, female), n (%) 73(86.90)，11(13.10) Age at onset, years 28.31±9.35 HLA-B27 positive, n (%) 69(82.14) Smoking history (yes, no), n (%) 5(5.95)，79(94.05) C677<T,n(%) - CC 29(34.5) CT 40(47.6) TT 15(17.9) A1298<C,n(%) - AA 60(71.4) AC 20(23.8) CC 4(4.8) A66<G,n(%) - AA 40(47.6) AG 38(45.2) GG 6(7.2) Peripheral joint involvement, n (%) 49(58.3) Extra-articular manifestations, n (%) 21(25)

[0058] Step S2: Establish a group trajectory analysis model to determine the optimal linear model for the progression of ankylosing spondylitis ossification.

[0059] Patient age was used as the independent variable (x), mSASSS score of ankylosing spondylitis patients was used as the dependent variable (y), and mSASSS score of 0 when patient age was 0 was used as the starting time point. Graphpad Prism 8 software was used to draw a line graph, and the LCMM package in R 4.3.2 software was used to construct the group trajectory model (Group-Based Trajectory Modeling, GBTM) model, and repeated fitting was performed to determine the optimal number of subgroups. The group trajectory model, also known as the latent class growth model, is an analysis method for longitudinal data. The principle is to assume that there is heterogeneity in the population, that is, different individuals may follow different development trajectories or patterns, thereby forming different subgroups. The main purpose is to explore how many different subgroups are contained in the population and determine the development trajectory of each subgroup. Five linear models were constructed using GBTM, and different linear models divided patients into different numbers of subgroups. This embodiment fits a linear model that divides the study population into one to five subgroups, and names them A, B, C, D, and E trajectory models, respectively. Through the Bayesian information criterion (BIC), BIC difference (ΔBIC) and average posterior probability (AvePP) and other indicators, and taking into account professional knowledge, actual grouping and model simplicity, the best grouping of AS ossification progression was determined, and finally the best number of subgroups was comprehensively judged to determine the optimal linear model of AS ossification progression.

[0060] Specifically, the mSASSS score times of the 84 patients included in this example are as follows: 54 patients with 1 mSASSS score, 23 patients with 2 mSASSS scores, and 7 patients with ≥ 3 mSASSS scores. Given that the construction of the group trajectory model requires information from at least 2 time points, this study sets the mSASSS score to 0 when the patient's age is 0 as the starting time point. With the patient's age as the independent variable (x) and the patient's mSASSS score as the dependent variable, a line graph is drawn, see Figure 2 , it can be seen that there is great heterogeneity in the progression rate of spinal ossification in different patients. Using GBTM, a total of 5 linear models were constructed, and their statistical evaluation indicators are shown in Table 2. Comprehensively comparing various indicators, the "B model", that is, the trajectory model that divides patients into 2 subgroups, has an Avepp value of >0.7 for each individual, the smallest BIC value, and the best fitting effect. Therefore, in this study, the B model was selected as the optimal model, and the 2 subgroups divided by this model were named the slow progression group (class 1) and the rapid progression group (class 2) ( Figure 3 ).

[0061] Table 2 Evaluation indicators of different trajectory models of spinal ossification progression in AS patients

[0062]

[0063] *BIC: Bayesian Information Criterion. The closer the value is to 0, the better the model fits. ΔBIC: BIC difference between complex and simple models. The larger the ΔBIC, the more the complex model is accepted. AvePP: Average posterior probability. The closer the value is to 1, the better the model fits. >0.7 is the acceptable standard. AE: Linear model established by dividing all patients into 1-5 groups.

[0064] Step S3: Univariate logistic regression analysis (SPSS26.0.0 was used for univariate logistic regression analysis).

[0065] Based on the optimal linear model, univariate logistic regression analysis was used to compare the differences in baseline data and clinical information of patients in different subgroups among patients with different ossification progression rates, and to obtain factors associated with rapid ossification progression in AS.

[0066] The results of univariate logistic regression analysis were expressed as odds ratios with 95% confidence intervals. Factors associated with rapid progression of AS ossification included baseline mSASSS, folate metabolism gene C677>T for CT or TT type, baseline Hb, CRP, GGT, and ALP.

[0067] Tables 3 and 4 show the baseline characteristics of the study population. There were 36 patients in the rapid progression group, including 34 male patients, with an age range of 24-72 years and an average age of 41.17±11.85 years; there were 48 patients in the slow progression group, including 39 male patients, with an age range of 20-71 years and an average age of 36.79±11.06 years. There was no significant difference in age and gender distribution between the two groups (P>0.05). The baseline mSASSS score in the rapid progression group was significantly higher than that in the slow progression group (P<0.0001), and the proportion of patients with folate metabolism gene C677>T CT and TT type was higher than that in the slow progression group, and there was a statistically significant difference in the composition ratio between the two groups (P<0.05).

[0068] Comparison of baseline blood test indicators between the two groups showed significant differences in some baseline indicators between the rapid progression group and the slow progression group, including Hb (P<0.05), CRP (P<0.05), GGT (P<0.05), and ALP (P<0.01).

[0069] Table 3 Baseline characteristics of AS patients (general information)

[0070]

[0071] *P<0.05, ****P<0.0001

[0072] Table 4 Baseline characteristics of AS patients (blood test indicators)

[0073]

[0074] WBC: white blood cell count; N%: neutrophil percentage; Hb: hemoglobin; PLT: platelet; ESR: erythrocyte sedimentation rate; CRP: high-sensitivity C-reactive protein; ALT: alanine aminotransferase; AST: aspartate aminotransferase (ALT); GGT: gamma-glutamyl transferase; ALP: alkaline phosphatase; UA: uric acid; Cr: creatinine; TCHO: total cholesterol; TG: triglyceride; HLDC: high-density lipoprotein; LDLC: low-density lipoprotein; Vit D: vitamin D. *P<0.05, **P<0.01.

[0075] The medication conditions of the two groups of patients are shown in Table 5. There was no statistical difference in the use of biological agents and nonsteroidal anti-inflammatory drugs between the two groups of patients (P>0.05).

[0076] Table 5 Baseline characteristics of AS patients (medication status)

[0077] Medication status Rapid Progress Group Slowly progressing group P-value Biologics, n 36 48 0.3527 Adalimumab, n (%) 10(27.78) 12(25) - Secukinumab, n (%) 6(16.67) 9(18.75) - Etanercept / Etanercept, n (%) 15(41.67) 19(39.58) - Not used, n (%) 5(13.89) 8(16.67) - Nonsteroidal anti-inflammatory drugs, n 36 48 0.1581 Use, n (%) 8(22.22) 18(37.50) - Not used, n (%) 28(77.78) 30(62.5) -

[0078] See Table 6. From the results of univariate LR, expressed by odds ratio (OR) and 95% confidence interval (CI), the factors associated with rapid progression of AS ossification included: baseline mSASSS (OR: 1.392, 95% CI: 1.143-1.696, P<0.01), folate metabolism gene C677>T as CT or TT type (OR: 3.505, 95% CI: 1.287-9.546, P<0.05), Hb (OR: 1.045, 95% CI: 1.008-1.083, P<0.05), GGT (OR: 1.031, 95% CI: 1.001-1.061, P<0.05), and ALP (OR: 1.032, 95% CI: 1.011-1.053, P<0.01).

[0079] Table 6 Univariate and multivariate LR results of risk factors related to AS ossification progression

[0080]

[0081]

[0082] a: Multivariate LR results after removing the baseline mSASSS. *P<0.05, **P<0.01

[0083] Step S4: Multivariate logistic regression analysis.

[0084] After univariate logistic regression analysis, factors with P < 0.1 were selected for multivariate logistic regression analysis, and the backward stepwise method was used to screen out independent risk factors associated with the progression of ankylosing spondylitis ossification with P < 0.05 as the standard. To avoid the influence of collinearity or interaction between multiple variables on the results, variables with small missing values ​​were selected first.

[0085] Specifically, factors with P < 0.1 in the univariate logistic regression analysis and risk factors identified in previous clinical practice and research were selected for correction. The results showed that the only risk factor associated with ossification progression in AS patients was baseline mSASSS. Then the baseline mSASSS was removed from the model, and a multivariate logistic regression analysis was performed based on the folate metabolism gene C677>T for CT or TT type, baseline Hb, CRP, GGT and ALP. Finally, the independent risk factors for rapid progression of spinal ossification in patients with ankylosing spondylitis were found to be baseline Hb and ALP.

[0086] See Table 6. In the multivariate LR analysis, factors with P < 0.1 in the univariate analysis and risk factors identified in previous clinical practice and research (such as gender, age at diagnosis, HLA-B27, peripheral joint involvement, extra-articular manifestations, baseline ESR, CRP, etc.) were selected for correction. The results showed that the only risk factor associated with the progression of ossification in AS patients was baseline mSASSS (OR: 1.388, 95% CI: 1.141-1.691, P < 0.01). Removing baseline mSASSS from the model showed that Hb (OR: 1.057, 95% CI: 1.014-1.103, P < 0.01) and ALP (OR: 1.037, 95% CI: 1.015-1.060, P < 0.01) were independent risk factors for rapid progression of spinal ossification in AS patients.

[0087] In summary, this embodiment uses the GBTM method to establish linear trajectory models of two to five groups, taking the mSASSS score of spinal ossification progression of 84 AS patients as the dependent variable and age as the independent variable. The results show that the two groups of trajectory models have the smallest BIC and the AvePP closest to 1, which best reflects the heterogeneous clustering of ossification progression of AS patients in this data set. Then, the two groups distinguished by GBTM were used as classification criteria to explore the possible risk factors affecting the speed of ossification progression in AS patients, and the general information of AS patients, baseline blood test indicators, and polymorphism data of folate metabolism genes MTHFR C677>T, A1298>C, and MTRR A66>G were analyzed. Among these 84 AS patients, there were 36 people in the rapid progression group, accounting for 42.86% of the total, and 48 people in the slow progression group, accounting for 57.14% of the total. The results of descriptive analysis showed that compared with the slow progression group, AS patients in the rapid progression group had the following characteristics: higher baseline mSASSS, C677>T was more likely to be CT type or TT type, and higher baseline Hb, CRP, GGT, and ALP. There were no significant differences between the two groups in terms of gender, age, peripheral joint involvement, and the proportion of extra-articular manifestations. Through univariate and multivariate analysis, the baseline Hb and ALP levels of AS patients were independent risk factors for rapid ossification progression.

[0088] It should be noted that the embodiments of the present invention have better practicability and do not impose any form of limitation on the present invention. Any technician familiar with the field may use the technical content disclosed above to change or modify it into an equivalent effective embodiment. However, any modification or equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification, characterized in that: include: Step S1: Collect baseline data and clinical information of several patients with ankylosing spondylitis who meet the inclusion and exclusion criteria; baseline data include demographic characteristics, medical history, clinical manifestations, medication, and the evaluation results of the disease activity of the patients in the past week using the ankylosing spondylitis spinal activity rating scale; clinical information includes imaging data, baseline test indicators, and folate metabolism genotype test results; imaging data include patients' previous sacroiliac joint X-ray or CT data, as well as full spine X-ray data, and sacroiliac joint grading and mSASSS scoring; baseline test indicators include blood routine indicators, erythrocyte sedimentation rate, blood biochemical indicators, blood lipid indicators, bone metabolism indicators and vitamins, and folate metabolism genotype is obtained by nucleic acid mass spectrometry; Step S2: Establish a group trajectory analysis model to determine the optimal linear model for the progression of ankylosing spondylitis ossification: use the patient's age as the independent variable, the mSASSS score of ankylosing spondylitis patients as the dependent variable, and draw a line graph with the mSASSS score of 0 when the patient's age is 0 as the starting time point. Use the lcmm package in R 4.3.2 software to construct a GBTM model. Use GBTM to construct 5 linear models. Different linear models divide patients into different numbers of subgroups. The optimal number of subgroups is comprehensively judged by the Bayesian information criterion, BIC difference and average posterior probability, so as to determine the optimal linear model for the progression of ankylosing spondylitis ossification. Step S3: Univariate logistic regression analysis: Based on the optimal linear model, univariate logistic regression analysis was used to compare and analyze the differences in the baseline data and clinical information of patients in different subgroups among patients with different ossification progression rates, and to obtain factors associated with rapid ossification progression in AS; Step S4: Multivariate logistic regression analysis: After the univariate logistic regression analysis, factors with P < 0.1 were selected for multivariate logistic regression analysis. The backward stepwise method was used to screen out independent risk factors associated with the progression of ankylosing spondylitis ossification with P < 0.05 as the standard.

2. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 1, characterized in that: The step S1 further includes: for counting data, using frequency, rate or composition ratio for description, using chi-square test or Fisher's exact test for statistical analysis, for measurement data, using Shapiro-Wilk test for normality test, using mean ± standard deviation for description if it conforms to normal distribution, and using median if it does not conform to normal distribution, for two groups of data that conform to normal distribution and have equal variance, using independent sample t test for analysis, and using non-parametric test if it does not conform to normal distribution.

3. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 2, characterized in that: In step S1, the inclusion and exclusion criteria are as follows: (1) Inclusion criteria: 1) Signed informed consent; 2) Patients aged ≥18 years when signing the informed consent; 3) Diagnosed with ankylosing spondylitis according to the New York criteria for ankylosing spondylitis; 4) At least one full spine X-ray result in the medical record; (2) Exclusion criteria: 1) History of inflammatory arthritis caused by other causes other than ankylosing spondylitis, or any inflammatory arthritis occurring before the age of 17; 2) Participants in other clinical trials; 3) Patients with a history of or concurrent malignant tumors; 4) Patients with a history of or concurrent immunodeficiency diseases, diabetes, gout or other diseases that may affect metabolism; 5) Patients taking anticoagulants, hormones, etc. for a long time that may affect drug metabolism; 6) Patients with incomplete medical records; 7) Patients whose blood samples lack plasma or blood sediment; 8) Patients refuse to participate in this study.

4. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 3, characterized in that: In step S1, the folate metabolism genotypes include rs1801133C677>T, rs1801131A1298>C, and rs1801394A66>G.

5. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 4, characterized in that: In step S2, the optimal linear model is a trajectory model that divides patients into two subgroups, the two subgroups being a slowly progressing group and a rapidly progressing group.

6. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 5, characterized in that: In step S3, the results of univariate logistic regression analysis were as follows: factors associated with rapid progression of AS ossification were expressed as odds ratios with 95% confidence intervals: baseline mSASSS, folate metabolism gene C677>T for CT or TT type, baseline Hb, CRP, GGT, and ALP.

7. The method for screening independent risk factors associated with the progression of ankylosing spondylitis ossification according to claim 6, characterized in that: In step S4, factors with P < 0.1 in the univariate logistic regression analysis and risk factors identified in previous clinical practice and research were selected for correction. The results showed that the only risk factor associated with ossification progression in AS patients was baseline mSASSS. Then the baseline mSASSS was removed from the model, and a multivariate logistic regression analysis was performed based on the folate metabolism gene C677>T for CT or TT type, baseline Hb, CRP, GGT and ALP. Finally, the independent risk factors for rapid progression of spinal ossification in patients with ankylosing spondylitis were found to be baseline Hb and ALP.

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