Metabolic composition for diagnosing rheumatoid arthritis and application thereof

Abstract

The invention discloses a metabolic composition for diagnosing rheumatoid arthritis and application of the metabolic composition, and belongs to the technical field of biomedicine. The metabolic composition is prepared from imidazole-4-acetic acid, ergothioneine, N-acetyl-L-methionine, 2-ketone-3-deoxy-D-gluconic acid, 1-methyl nicotinamide and dehydroepiandrosterone sulfate, and the metabolic composition is prepared from the following raw materials in parts by weight: 20-30% of L-methionine, 20-30% of L-methionine The characteristic combination formed by the six metabolites shows obvious difference in RA patients, RA and non-RA groups can be effectively distinguished, and the overall diagnosis accuracy of RA is obviously improved. The discovery provides powerful supplement for early and accurate recognition of RA, and is particularly suitable for complex cases with negative traditional indexes but highly suspected RA in clinic.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and particularly relates to a metabolite composition for diagnosing rheumatoid arthritis and application thereof. BACKGROUND

[0002] Rheumatoid arthritis (RA) is a common systemic autoimmune disease characterized by chronic inflammation of the synovial membrane. The pathological features include excessive proliferation of synovial tissue, massive inflammatory cell infiltration, neovascularization, and progressive destruction of cartilage and bone tissue. RA has a high rate of disability, and is more common in middle-aged women, with an onset age often between 30-50 years. The global prevalence rate is about 0.5%-1%, and the disease burden is increasing year by year, which has become one of the chronic diseases that seriously affect the quality of life of the population.

[0003] RA has insidious onset, and early symptoms are mainly repeated swelling and pain of small joints in the hands and morning stiffness. The clinical symptoms are complex and diverse, and there are significant differences between individuals, making early identification challenging. As the disease progresses, patients may have mild joint function impairment within 1 year, and within 2 years, imaging changes such as joint space narrowing, cartilage destruction, bone erosion, and cystic changes can be seen, and eventually may develop into joint deformity and irreversible dysfunction. Therefore, early diagnosis and timely treatment are the key to preventing joint damage, improving prognosis, and improving quality of life.

[0004] The current clinical diagnosis of RA mainly depends on the classification criteria jointly published by the American College of Rheumatology / European League Against Rheumatism (ACR / EULAR) in 2010, which comprehensively considers clinical manifestations, imaging examinations, and serological indicators, especially rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibodies (anti-CCP). However, these traditional serological indicators have certain limitations, and negative results may occur in some patients, especially in the early stage of RA or so-called "seronegative RA (SNRA)", RF and anti-CCP negative are not uncommon in clinical practice. According to the ACR / EULAR classification criteria, when RF and anti-CCP are both negative, only when more than 10 joints are involved can the diagnosis of RA be met, which makes it easy for some early patients to be misdiagnosed as other rheumatic diseases. In addition, RF and anti-CCP are both antibody-based indicators, which are easily affected by individual immune status, disease stage, and detection platform differences, and have limited diagnostic sensitivity and specificity. Therefore, it is urgent to find new, stable and reliable biomarkers, especially alternative or supplementary indicators that can early identify SNRA patients, to improve the overall diagnostic efficiency and accuracy of RA and promote the clinical translation application of precision medicine in the early screening and typing of RA.

[0005] In the field of biomarker discovery, multi-omics integration strategies based on blood-derived samples (such as serum, plasma, and peripheral blood mononuclear cells) are increasingly valued. Genomics, transcriptomics, and proteomics have been widely used to identify molecular features associated with diseases. At the same time, as a multidisciplinary research field, metabolomics is committed to the comprehensive analysis of small molecule metabolites in biological systems, and has gradually become a research hotspot because it can directly reflect the biochemical changes of the body in physiological or pathological states. SUMMARY

[0006] One of the purposes of the present application is to provide a metabolite composition for diagnosing rheumatoid arthritis, which comprises imidazole-4-acetic acid, ergothioneine, N-acetyl-L-methionine, 2-keto-3-deoxy-D-gluconate, 1-methylnicotinamide, and dehydroepiandrosterone sulfate.

[0007] The second purpose of the present application is to provide a product for diagnosing rheumatoid arthritis, which contains reagents for detecting the concentration of the above metabolite composition.

[0008] In one embodiment of the present application, the product is a kit.

[0009] The third object of the present application is to provide the use of the reagent for detecting the concentration of the above-mentioned metabolite composition in the preparation of a diagnostic product for rheumatoid arthritis.

[0010] In one embodiment of the present application, the diagnostic product for rheumatoid arthritis further comprises standard samples of imidazole-4-acetic acid, ergothioneine, N-acetyl-L-methionine, 2-keto-3-deoxy-D-gluconate, 1-methylnicotinamide and dehydroepiandrosterone sulfate.

[0011] In one embodiment of the present application, the method for using the diagnostic product for rheumatoid arthritis comprises the following steps:

[0012] (1) sample pretreatment;

[0013] (2) ultra-high performance liquid chromatography detection;

[0014] (3) mass spectrometry detection.

[0015] In one embodiment of the present application, the specific operation of step (1) is as follows:

[0016] (1) mixing the plasma sample;

[0017] (2) adding 280-320 μL of precipitant to 80-120 μL of the plasma sample and mixing;

[0018] (3) centrifuging to obtain the supernatant.

[0019] In one embodiment of the present application, after step (3) mass spectrometry detection, step (4) analysis and processing of the detection results by using a regression model is further included.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] In our previous studies, based on metabolomics screening and multi-stage validation, we identified a group of diagnostic biomarkers for RA with potential clinical application value, including: imidazole-4-acetic acid (HMDB No.: HMDB0002024), ergothioneine (HMDB No.: HMDB0003045), N-acetyl-L-methionine (HMDB No.: HMDB0011745), 2-keto-3-deoxy-D-gluconic acid (HMDB No.: HMDB0001353), 1-methylnicotinamide (HMDB No.: HMDB0000699), and dehydroepiandrosterone sulfate. Sulfate (HMDB No.: HMDB0001032). This characteristic combination of six metabolites exhibits significant differential diagnostics in RA patients, effectively distinguishing RA from non-RA populations and significantly improving the overall diagnostic accuracy of RA. Furthermore, the diagnostic efficacy of this biomarker combination is independent of traditional serological markers, demonstrating good identification ability in RA patients who are negative for both RF and anti-CCP, showing its potential application in serologically negative RA patients. This finding provides a strong supplement to the early and accurate identification of RA, especially applicable to complex cases in clinical practice where traditional markers are negative but RA is highly suspected. We established a detection method based on a targeted mass spectrometry platform, optimized the sample pretreatment process and mass spectrometry parameters, and achieved high-throughput, reproducible quantitative detection of this metabolite combination in serum samples. Validation analysis in a multi-center cohort showed that this combination exhibits high diagnostic performance in different populations and clinical contexts, demonstrating good cross-platform stability and translational potential. In summary, this combination of metabolites can not only serve as a potential serological biomarker for RA, compensating for the shortcomings of existing diagnostic methods, but also provide strong support for building a more accurate and comprehensive RA diagnostic system. Attached Figure Description

[0022] Figure 1 This shows the expression of each metabolite in the discovery queue in Example 1.

[0023] Figure 2 The ROC curves of the metabolite combinatorial model in Example 1 are shown in the RA and HC classifications.

[0024] Figure 3ROC curve performance of the metabolite panel in classifying RA vs. OA for Example 1.

[0025] Figure 4A Typical mass spectrum of metabolite imidazole-4-acetic acid detected in Example 1.

[0026] Figure 4B Typical mass spectrum of metabolite 1-methylnicotinamide detected in Example 1.

[0027] Figure 4C Typical mass spectrum of metabolite N-acetyl-L-methionine detected in Example 1.

[0028] Figure 4D Typical mass spectrum of metabolite dehydroepiandrosterone sulfate detected in Example 1.

[0029] Figure 4E Typical mass spectrum of metabolite ergothioneine detected in Example 1.

[0030] Figure 4F Typical mass spectrum of metabolite 2-keto-3-deoxy-D-gluconate detected in Example 1. DETAILED DESCRIPTION

[0031] Example 1

[0032] Detection method

[0033] I. Preparation of standard solutions

[0034] Imidazole-4-acetic acid standard (CAS: 645-65-8), 1-methylnicotinamide standard (CAS: 6456-44-6), dehydroepiandrosterone sulfate standard (CAS: 1099-87-2), N-acetyl-L-methionine standard (CAS: 65-82-7), ergothioneine standard (CAS: 497-30-3), 2-keto-3-deoxy-D-gluconate (22256-90-2) were accurately weighed into 10 mL volumetric flasks and dissolved in pure methanol to prepare 10 times standard stock solutions.

[0035] Take the corresponding amount of standard stock solution in 10 mL volumetric flask, dilute the standard solution in turn to get a series of calibration solutions, the concentration of the gradient dilution solution is imidazole-4-acetic acid: 100 ng / mL, 50 ng / mL, 20 ng / mL, 10 ng / mL, 5 ng / mL, 2 ng / mL; 1-methylnicotinamide: 500 ng / mL, 250 ng / mL, 100 ng / mL, 50 ng / mL, 25 ng / mL, 10 ng / mL; dehydroepiandrosterone sulfate: 5000 ng / mL, 2500 ng / mL, 1000 ng / mL, 500 ng / mL, 250 ng / mL, 100 ng / mL; N-acetyl-L-methionine: 200 ng / mL, 100 ng / mL, 40 ng / mL, 20 ng / mL, 10 ng / mL, 4 ng / mL; ergothioneine, 2-keto-3-deoxy-D-gluconic acid: 40 μg / mL, 20 μg / mL, 10 μg / mL, 5 μg / mL, 2.5 μg / mL, 1.25 μg / mL.

[0036] Precisely take the appropriate amount of each standard in 10 mL volumetric flask, the concentration is 250 ng / mL, 500 ng / mL, 2500 ng / mL, 100 ng / mL.

[0037] II. Sample pretreatment

[0038] Take the whole blood sample of the subject, centrifuge at 3000 rpm for 5 min, and take the supernatant to obtain the plasma sample for cold storage at-20℃.

[0039] 1. Take the plasma sample stored at-20℃ in an ice water bath, thaw, and vortex for 30 s;

[0040] 2. Take 100 μL of the sample in an EP tube, add 300 μL of the precipitant (containing internal standard), and vortex for 5 min;

[0041] 3. Centrifuge at 4℃, 12000 rpm (centrifugal force 13800 (×g), radius 8.6 cm) for 5 min to obtain the supernatant;

[0042] 4. Take 100 μL of the supernatant for machine detection.

[0043] III. Sample detection

[0044] 1. The detection conditions of imidazole-4-acetic acid, 1-methylnicotinamide, dehydroepiandrosterone sulfate, and N-acetyl-L-methionine are as follows:

[0045] (1) Liquid chromatography conditions

[0046] The liquid phase system is a TQS ultra-high performance liquid chromatograph.

[0047] The chromatographic column is a Waters ACQUITY UPLC BEH Amide (100*2.1mm, 1.7um, Waters) liquid chromatographic column.

[0048] The mobile phase A phase is a 25 mM sodium acetate-25 mM aqueous ammonia-water solution.

[0049] The mobile phase B phase is acetonitrile.

[0050] The column oven temperature is 35 DEG C, the sample tray is set to 4 DEG C, the injection volume is 1 mu L, and the gradient elution program is as shown in the following table 1:

[0051] Table 1 Gradient elution program of mobile phase Time / min Phase A / % Phase B / % Flow rate / (mL / min) 1.00 2 98 0.4 4.00 95 5 0.4 4.5 95 5 0.4 4.51 2 98 0.4 5.00 2 98 0.4

[0052] (2) Mass spectrometry conditions

[0053] The Waters TQS triple quadrupole mass spectrometer equipped with an ESI ion source is used for mass spectrometry analysis in a multiple reaction monitoring (MRM) mode. The ion source parameters are as follows: Capillary voltage (+3800 / -3000 V), Desolvation temperature: 500 DEG C, Desolvation: 1000 L / Hr, cone: 150 L / Hr, source temperature: 150 DEG C, nebulizer pressure: 7 Bar.

[0054] 2. The detection conditions of ergothioneine and 2-keto-3-deoxy-D-gluconic acid are as follows:

[0055] (1) Liquid chromatography conditions

[0056] The liquid phase system is a Shimadzu ultra-high performance liquid chromatograph.

[0057] The chromatographic column is a Finnignt Kinetex C18 (2.6 µm, 2.1*100 mm) liquid chromatographic column.

[0058] The mobile phase A phase is a 0.1 mM aqueous ammonium fluoride solution.

[0059] The mobile phase B phase is methanol.

[0060] The column oven temperature is 40 DEG C, the sample tray is set to 10 DEG C, the injection volume is 5 mu L, and the gradient elution program is as shown in the following table 2:

[0061] Table 2 Gradient elution program of mobile phase Time / min Phase A / % Phase B / % Flow rate / (mL / min) 1.00 0 100 0.4 2.00 92 8 0.4 4.00 92 8 0.4 4.10 0 100 0.4 5.00 0 100 0.4

[0062] (2) Mass spectrometry conditions

[0063] The present application uses Shimadzu-8050CL triple quadrupole mass spectrometer equipped with ESI ion source to perform mass spectrometry analysis in multiple reaction monitoring (MRM) mode. The ion source parameters are as follows: Capillary voltage: +4000 / -3000 V, interface temperature: 400℃, atomization gas flow: 3 L / min, heating gas flow: 15 L / min, DL temperature: 250℃, heating block temperature: 500℃, dry gas flow: 5 L / min.

[0064] Four, calibration curve

[0065] The calibration solution is analyzed by UHPLC-MRM-MS / MS using the method described above.

[0066] The standard calibration curve is obtained by analysis, where y represents the peak area ratio of the target compound to the internal standard, and x represents the concentration of the target compound (ng / mL). Regression analysis is performed using the least squares method, and when the weight is set to 1 / x, the calibration solution recovery (accuracy) and the correlation coefficient (R 2 ) are the best. If the recovery of a certain calibration concentration exceeds the range of 80%-120%, the concentration calibration point is excluded.

[0067] Five, method detection limit and quantification limit

[0068] The calibration solution is diluted by 2 times in sequence and then analyzed by UHPLC-MRM-MS, and the method detection limit and quantification limit are calculated by the signal-to-noise ratio. The minimum detection limit (LLOD) of the method is defined as the compound concentration corresponding to a signal-to-noise ratio of 3, and the LLOD is ng / mL. The minimum quantification limit (LLOQ) of the method is defined as the compound concentration corresponding to a signal-to-noise ratio of 10, and the LLOQ is ng / mL.

[0069] Six, method precision and accuracy

[0070] Method precision is evaluated by repeating the sample of quality control samples (QCs) and calculating the relative standard deviation (Relative Standard Deviation, RSD); accuracy is verified by the standard addition recovery experiment, and the percentage value of the ratio of the measured concentration to the standard addition concentration, i.e. the standard addition recovery rate (Recovery). At the level of ng / mL, the standard addition recovery rate is within a reasonable range (such as 80%-120%), indicating that the method has good quantitative accuracy.

[0071] Seven, detection results of target metabolites in samples

[0072] The detection result obtained by the above detection method of the present application and the following formula (1) are used to calculate the content of the sample:

[0073] (1)

[0074] wherein, C F is the final measured concentration of the sample (unit: ng / mL), which is obtained by multiplying the concentration CC (unit: ng / mL) directly measured by the instrument by the dilution factor Dil (Dilution Factor); C M is the concentration of the target metabolite in the sample (unit: ng / mL); V F is the final volume of the sample (unit: μL); V S is the volume of the sample removed (unit: μL).

[0075] Verification results

[0076] The verification study of the present application includes a total of 4 independent cohorts, covering three regions of Fuzhou, Lanzhou and Shanghai. All the rheumatoid arthritis (RA) patients included meet the RA classification criteria jointly published by the American College of Rheumatology / European League Against Rheumatism (ACR / EULAR) in 2010. The diagnosis of osteoarthritis (OA) patients is based on the OA classification criteria formulated by the American College of Rheumatology (ACR) in 1986. The healthy control group (HC) is a donor without a history of autoimmune diseases. The samples in the Fuzhou region are provided by the First Affiliated Hospital of Fujian Medical University, the samples in the Lanzhou region are provided by the First Hospital of Lanzhou University, and the samples in the Shanghai region are provided by the Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. The specific sample quantity and subject information of each cohort are shown in Table 3.

[0077] Table 3 Subject information

[0078] The present study carries out targeted metabolomics analysis through a mass spectrometry platform, and quantitatively detects and verifies the candidate metabolites. Figure 1 The expression levels of six metabolites in the RA, OA patients and HC in the discovery cohort are shown. The results show that, compared with the OA and HC groups, the six metabolites in the RA patients all show significant differences, indicating that the combination of the metabolites has potential application value in the diagnosis of RA.

[0079] We constructed a Logistic regression model for distinguishing RA from healthy controls (HC) based on the above 6 metabolites in the discovery cohort, and evaluated its diagnostic performance by the receiver operating characteristic curve (ROC). As shown in Figure 2As shown, the model showed an AUC of 0.9336 in the discovery cohort, demonstrating an extremely high discriminative ability. Subsequently, we externally validated the model in three independent validation cohorts, with AUCs of 0.8806 (Validation Cohort 1), 0.8676 (Validation Cohort 2), and 0.9156 (Validation Cohort 3), all showing good generalization performance. Notably, in Validation Cohorts 1-3, we further selected seronegative RA patients (double negative for RF and anti-CCP) for evaluation, and the model showed an AUC of 0.8929 in this subpopulation, indicating that the metabolite combination model also has good recognition ability in seronegative RA patients, and its diagnostic performance does not depend on traditional serological indicators. The expression of the Logistic regression model established in this study is as follows:

[0080] logit(P) = 2.1242

[0081] -5.1248 x log(Ergothioneine)

[0082] -8.7023 x log(2-Keto-3-deoxy-D-gluconic acid)

[0083] -2.9354 x log(l-Methylnicotinamide)

[0084] -5.6762 x log(Dehydroepiandrosterone sulfate) +

[0085] 8.9248 x log(Imidazoleacetic acid) +

[0086] 1.3913 x log(N-Acetyl-L-methionine).

[0087] Wherein, logit(P) represents the logit of the prediction of RA, which is defined as the natural logarithm of the ratio of P to (1-P), i.e. logit(P) = ln[P / (1-P)], wherein P represents the probability of disease predicted by the model. By inputting the metabolite variables into the model, the logit value calculated can be further converted into a probability value P, which is used to evaluate the risk of RA in individuals.

[0088] In addition, we also constructed a Logistic regression model based on the same metabolite combination for distinguishing RA and OA patients to evaluate its discriminative ability between inflammatory joint diseases. As shown in Table 4, the model showed an AUC of 0.9286 in the discovery cohort, demonstrating an extremely high discriminative ability. Subsequently, we externally validated the model in three independent validation cohorts, with AUCs of 0.8786 (Validation Cohort 1), 0.8656 (Validation Cohort 2), and 0.9156 (Validation Cohort 3), all showing good generalization performance. Figure 3As shown, the model showed good discrimination performance with AUC of 0.8277 in the discovery cohort, and AUC of 0.7991, 0.7340 and 0.8181 in validation cohort 1, 2 and 3, respectively. Further, we validated the model in the sero-negative subgroup of RA and OA, and the model showed AUC of 0.8012 in this subgroup, indicating that the model not only can effectively distinguish RA from HC, but also has the potential to distinguish RA from other joint diseases, and has strong clinical practical value. The expression of the Logistic regression model established in this study is as follows:

[0089] logit(P) = -1.7221

[0090] -1.1614 x log(Ergothioneine)

[0091] -6.6821 x log(2-Keto-3-deoxy-D-gluconic acid)

[0092] -1.331 x log(1-Methylnicotinamide)

[0093] -2.0936 x log(Dehydroepiandrosterone sulfate) +

[0094] 3.1736 x log(Imidazoleacetic acid) +

[0095] 3.4635 x log(N-Acetyl-L-methionine).

[0096] Finally, the typical mass spectrum of the mass spectrometric detection of all metabolites is shown in Figure 4A -F.

[0097] The above-described embodiments are only descriptions of the preferred modes of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements of the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A metabolic composition for diagnosing rheumatoid arthritis, characterized in that, The metabolic composition includes imidazole-4-acetic acid, ergothioneine, N-acetyl-L-methionine, 2-keto-3-deoxy-D-gluconic acid, 1-methylnicotinamide, and dehydroepiandrosterone sulfate.

2. A product for diagnosing rheumatoid arthritis, characterized in that, The product contains a reagent for detecting the concentration of the metabolic composition described in claim 1.

3. The product according to claim 2, characterized in that, The product in question is a reagent kit.

4. The use of a reagent for detecting the concentration of the metabolic composition according to claim 1 in the preparation of a diagnostic product for rheumatoid arthritis.

5. The application according to claim 4, characterized in that, The rheumatoid arthritis diagnostic product also contains standards for imidazole-4-acetic acid, ergothioneine, N-acetyl-L-methionine, 2-keto-3-deoxy-D-gluconic acid, 1-methylnicotinamide, and dehydroepiandrosterone sulfate.

6. The application according to claim 5, characterized in that, The method of using the rheumatoid arthritis diagnostic product includes the following steps: (1) Sample pretreatment; (2) Ultra-high performance liquid chromatography detection; (3) Mass spectrometry detection.

7. The application according to claim 6, characterized in that, The specific operation of step (1) is as follows: (1) Mix the plasma sample thoroughly; (2) Add 280-320 μL of precipitant to 80-120 μL of plasma sample and mix well; (3) Centrifuge to obtain the supernatant.

8. The application according to claim 6 or 7, characterized in that, Step (3) after mass spectrometry detection also includes step (4) using a regression model to analyze and process the detection results.

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