WO2019009446A1 - Method for distinguishing between multiple sclerosis and neuromyelitis optica spectrum disorder - Google Patents

Abstract

The present invention relates to a composition for distinguishing between multiple sclerosis and neuromyelitis optica spectrum disorder, and a method for distinguishing between multiple sclerosis and neuromyelitis optica spectrum disorder using the composition. Specifically, the method of the present invention may clearly distinguish between multiple sclerosis and neuromyelitis optica spectrum disorder which have similar clinical features by measuring the concentrations of citric acid and lactic acid which are specific metabolites in cerebrospinal fluid and comparing said concentrations with the concentrations of citric acid and lactic acid in normal subjects, and therefore may be usefully used to diagnose multiple sclerosis and neuromyelitis optica spectrum disorder.

Description

How to distinguish between multiple sclerosis and optic neuritis

The present invention relates to a composition for distinguishing MS from multiple sclerosis and optic neuritis, and to a method for distinguishing multiple sclerosis and optic neuritis from optic neuropathy.

Multiple sclerosis (MS) is an inflammatory dehydration autoimmune disease that affects the central nervous system, and it is not frequent in Asian and African populations, but it occurs most frequently in European people, especially in 30s, In European white people, it affects about 200 people per 100,000 people.

Multiple sclerosis is accompanied by visual disturbances, myasthenia gravis and accompanying gait disorders, skin sensory disturbances with pain or numbness, language and hearing impairment, and cognitive impairment. These disorders are gradually aggravated as mitigation and recurrence repeat many. These neurological disorders result from damage of a few seconds surrounding the axons of nerve cells due to inflammatory responses in the central nervous system. The lesions are scattered in the white matter region of the brain and spinal cord. In the lesion, inflammatory reaction accompanied by inflammatory cell infiltration around the vein and necrosis of the nerve fibers are observed. In addition, And the like. The causes and pathologic progress of multiple sclerosis have not been fully elucidated yet, but they are known to be autoimmune reactions involving both cellular and humoral immune systems.

The neuromyelitis optica spectrum disorder (NMOSD) is an inflammatory dehydration autoimmune disease that affects the central nervous system and optic nerve. It occurs more frequently in Asia than in Europe or the Americas. The prevalence of women is about 9 times higher than that of men, It occurs in 0.5 to 4.4 persons per 100,000 worldwide.

Optic neuritis is characterized by bilateral acute optic neuritis and transverse myelitis occurring simultaneously or at intervals of several weeks. Symptoms such as rapid visual impairment, weakness of strength, gait disturbance, lower limb movement disorder, sensory depression, appear. In the lesion, dehydration and necrosis were observed in the white matter and gray matter of the spinal cord, neutrophils and eosinophils were present in the necrosis area, the thickening and vitreousization of the small blood vessels were observed, and immunoglobulin and complement immunocomplexes were observed around the blood vessels And around a few seconds.

Multiple sclerosis and optic neuropathic sclerosis are both inflammatory and dehydrating diseases of the central nervous system and have a characteristic clinical course with recurrence and mitigation. Clinical features such as repeated invasion of brain, optic nerve or spinal cord are very similar, It is difficult to distinguish between the two diseases. However, it is important to make an accurate diagnosis early because the treatment of some MS is exacerbating the optic neuropathy category disease.

The development of new methods for distinguishing between MS and optic neuropathic diseases has been demanded and research is under way to find biomarkers to distinguish them. Recently, metabolites scyllo-inositol and acetate, which differ in the concentration of the two groups of patients using ¹ H-NMR spectroscopy, can be used as biomarkers to distinguish between the two diseases from sera obtained from NMOSD or MS patients (Moussallieh FM et al. , Mult. Scler. , 2014 Apr; 20 (5): 558-65).

Accordingly, the present inventors tried to develop a method for distinguishing MS from multiple sclerosis. DISCLOSURE OF THE INVENTION The present inventors have conducted a ¹ H NMR spectroscopic analysis of multiple sclerosis patients, optic nerve spinal cord disease patients, and normal cerebrospinal fluid , And citric acid and lactic acid as biomarkers were selected to complete the present invention.

It is an object of the present invention to provide a method for distinguishing between patients suspected of having multiple sclerosis and optic neuritis by measuring and comparing the content of a specific metabolite.

Another object of the present invention is to provide a kit for the differential diagnosis of multiple sclerosis and optic nerve spinal disease diseases using a preparation or apparatus for measuring the content of a specific metabolite.

In order to accomplish the above object, the present invention provides a method for diagnosing MSCT comprising: 1) measuring at least one concentration selected from the group consisting of citric acid and lactic acid from a sample obtained from a patient suspected of having multiple sclerosis or optic neuritis; And 2) comparing the at least one concentration selected from the group consisting of citric acid and lactic acid of step 1) with the concentration of the normal individual. Analysis method.

In addition, the present invention provides a kit for the differential diagnosis of multiple sclerosis and optic neuropathy category diseases comprising a preparation for quantitative determination of citric acid or lactic acid or a quantitative analysis device.

The method of the present invention can clearly distinguish diseases of multiple sclerosis and optic neuritis, which have similar clinical features, by comparing the concentration of citric acid and lactic acid, which are specific metabolites in cerebrospinal fluid, And may be useful for diagnosing multiple sclerosis and optic neuritis.

FIG. 1 is a graph showing the results of analysis of metabolites quantified from cerebrospinal fluid of normal, MS, or NMOSD patient group by PCA method.

FIG. 2A is a graph showing the OPLS-DA model of the normal group, the MS group, and the NMOSD group by analyzing the metabolite quantified from cerebrospinal fluid of the normal group, the MS group, or the NMOSD group by the orthogonal partial least squares discriminant method.

FIG. 2B is a graph showing the OPLS-DA model of the normal group and the MS patient group by analyzing the metabolite quantified from the cerebrospinal fluid of the normal group, the MS group, or the NMOSD group by the least squares discriminant method.

FIG. 2C is a graph showing the OPLS-DA model of the normal group and the NMOSD patient group by analyzing the metabolite quantified from cerebrospinal fluid of the normal group, MS or NMOSD patient group by the orthogonal partial least squares discriminant method.

FIG. 3A is a graph showing changes in expression of metabolites quantified from cerebrospinal fluid of normal, MS, or NMOSD patients.

FIG. 3B is a graph comparing changes in the expression of metabolites quantified from cerebrospinal fluid of normal, MS, or NMOSD patients.

FIG. 4A is a graph showing the trend of the level of metabolites metabolized from cerebrospinal fluid of normal, MS or NMOSD patient groups in MS or NMOSD patients as an S-plot of the OPLS-DA model.

FIG. 4B is a graph showing the tendency of the metabolite quantified from cerebrospinal fluid of the normal group, MS or NMOSD patient group to change in the level of MS or NMOSD patients as box salt.

FIG. 4C is a graph showing the tendency of the metabolite quantified from cerebrospinal fluid of normal, MS or NMOSD patient group to change in the levels of MS or NMOSD patients as box titration. FIG.

FIG. 4D is a graph showing the tendency that the metabolite quantified from cerebrospinal fluid of the normal group, MS, or NMOSD patient group changes in the level of MS or NMOSD patients as box salt.

FIG. 5A is a graph showing the AUC values of biomarkers usable in combination among the metabolites quantified from normal cerebrospinal fluid of normal, MS, or NMOSD patients.

FIG. 5B is a graph showing the AUC values of biomarkers usable in combination among the metabolites quantified from normal cerebrospinal fluid of normal, MS, or NMOSD patients.

Hereinafter, the present invention will be described in detail.

The present invention relates to a method for the diagnosis of multiple sclerosis or optic neuritis, comprising the steps of: 1) measuring at least one concentration selected from the group consisting of citric acid and lactic acid from a sample obtained from a patient suspected of having multiple sclerosis or optic neuritis; And 2) comparing the at least one concentration selected from the group consisting of citric acid and lactic acid of step 1) with the concentration of the normal individual. Analysis method.

The method for analyzing metabolites according to the present invention provides a step of measuring at least one concentration selected from the group consisting of citric acid and lactic acid from a sample obtained from a patient suspected of having multiple sclerosis or optic neuritis.

The sample may be any sample that can change the concentration level of citric acid and lactic acid due to multiple sclerosis or optic neuritis. Specifically, the sample may be urine, blood, serum, plasma or cerebrospinal fluid. In one embodiment of the present invention, the sample may be a cerebrospinal fluid.

The determination of the concentration in the method according to the invention can be carried out by any method known to be capable of measuring the concentration of metabolites, specifically citric acid and lactic acid, contained in the sample. Specifically, the concentration measurement can be performed by a group consisting of nuclear magnetic resonance spectroscopy, chromatography, UV spectroscopy, IR spectroscopy, fluorescence spectroscopy, enzyme-linked immunosorbent assay (ELISA) And may be measured by any one or more devices selected.

In addition, the method for analyzing metabolites according to the present invention provides a step of comparing at least one concentration selected from the group consisting of citric acid and lactic acid to the concentration of normal individuals.

If the concentration of citric acid is decreased in comparison with the normal individuals in the above step, it can be judged as multiple sclerosis. Specifically, when the concentration of citric acid is lower than that of the normal individuals in the above step and the concentration of lactic acid is not significantly different from that of the normal individuals, it can be judged as multiple sclerosis. On the other hand, if the concentration of lactic acid in the above step is higher than that of the normal individuals, it can be judged to be an optic neuropathy category disease. Specifically, if the concentration of lactic acid is higher than that of the normal individuals and the concentration of citric acid is not significantly different from that of the normal individuals, the disease can be classified as optic neuritis.

Specifically, in one embodiment of the present invention, the present inventors obtained cerebrospinal fluid from patients with normal human, multiple sclerosis or optic neuritis, and analyzed the concentration of the metabolites contained in the obtained cerebrospinal fluid to select 32 metabolites See Table 2).

Of the 32 selected metabolites, eight metabolites with significantly different expression in patients with multiple sclerosis or optic neuritis were further selected. Especially, the concentration of citric acid and lactic acid was significantly higher than that of normal patients with multiple sclerosis and optic nerve (Table 3, Figs. 3a and 3b). The results were also the same for the S-plot and box titration of the OPLS-DA model (see Figures 4a, 4b, 4c and 4d).

Thus, the method according to the present invention can be useful for distinguishing patients suspected of having multiple sclerosis or optic neuritis by measuring the concentrations of citric acid and lactic acid.

In addition, the present invention provides a kit for the differential diagnosis of multiple sclerosis and optic neuropathy category diseases comprising a preparation for quantitative determination of citric acid or lactic acid or a quantitative analysis device.

The kit can be used to distinguish between multiple sclerosis and optic neuropathic cirrhosis diseases on the same principle as the metabolic analysis method for providing information necessary for distinguishing diagnosis of multiple sclerosis and optic nerve spinal cord diseases as described above.

Specifically, the metabolite analysis method is a method for measuring at least one concentration selected from the group consisting of citric acid and lactic acid from a sample obtained from a patient suspected of having multiple sclerosis or optic neuritis, and comparing the measured concentration to the concentration of normal individuals , It is possible to distinguish between multiple sclerosis and optic neuritis. In the above method, the sample may be urine, blood, serum, plasma or cerebrospinal fluid.

The preparation for quantitative determination of citric acid or lactic acid contained in the kit may include any substance known to be capable of measuring the concentration of citric acid or lactic acid. Meanwhile, the quantitative analysis apparatus may be any one selected from the group consisting of nuclear magnetic resonance spectroscopy, chromatography, ultraviolet spectroscopy, infrared spectroscopy, fluorescence spectroscopy, ELISA and mass spectrometry.

When the kit according to the present invention is used to distinguish between multiple sclerosis and optic neuritis, it can be judged to be multiple sclerosis when the concentration of citric acid is reduced compared to a normal individual. Specifically, when the concentration of citric acid is lower than that of the normal individuals in the above step and the concentration of lactic acid is not significantly different from that of the normal individuals, it can be judged as multiple sclerosis. On the other hand, when the kit according to the present invention is used to distinguish between multiple sclerosis and optic neuritis, it can be judged to be a optic neuritis case if the concentration of lactic acid is increased compared to a normal individual. Specifically, if the concentration of lactic acid is higher than that of normal individuals and the concentration of citric acid is not significantly different from that of normal individuals, it can be judged to be a cystic disease of the optic nerve.

Specifically, in one embodiment of the present invention, 32 metabolites were screened (see Table 2) by analyzing the metabolites contained in cerebrospinal fluid isolated from normal subjects, MS patients or patients with optic nerve spinal cord disease, Of the selected metabolites, eight metabolites with significantly different expression in patients with multiple sclerosis or optic neuritis were selected. Particularly, it was confirmed that concentrations of citric acid and lactic acid among the selected metabolites were different in patients with multiple sclerosis and optic neuritis (Table 3, FIGS. 3A and 3B).

Thus, the kit of the present invention comprising a formulation or device capable of measuring the concentration of citric acid and lactic acid can be usefully used to distinguish between patients suspected of having multiple sclerosis or optic neuritis.

Hereinafter, the present invention will be described in detail by the following examples.

However, the following examples are illustrative of the present invention, and the contents of the present invention are not limited thereto.

Example 1: Obtaining cerebrospinal fluid

First, there were 17 normal patients at the National Cancer Center, 50 MS patients diagnosed with multiple sclerosis (MS) based on the McDonald diagnostic criteria released in 2010, and the International Panel for NMO diagnosis (IPND) CSF was obtained from 57 NMOSD patients diagnosed with optic neuritis (NMOSD). Normal cerebrospinal fluid was obtained by lumbar puncture to exclude meningitis. The cerebrospinal fluid thus obtained was stored at -80 DEG C until use. In addition, demographic data and clinical data of the patient such as sex, age, sampling date, and EDSS (Expanded Disability Status Scale) score were collected from the normal person, the MS patient, or the NMOSD patient together with the information on the disease state, Respectively. All experiments were performed according to the Institutional Review Board of the National Cancer Center (NCC2014-0416).

Demographic and clinical data from healthy, MS and NMOSD patients

	Normal	MS Patient	NMOSD patients
Total number of people	17	50	57
Number of women and men	Women: 13 men: 4 people	Women: 33 men: 17 people	Women: 51 men: 6 people
Mean, standard deviation and range of age at onset	None	Average: 30.20 Standard deviation: 8.06 Range: 14-48	Average: 31.49 Standard deviation: 13.51 Range: 6-64
Mean, standard deviation and range of sample age	Average: 33.35 Standard deviation: 8.36 Range: 22-49	Average: 36.10 Standard deviation: 11.56 Range: 14-50	Average: 35.64 Standard deviation: 11.50 Range: 10-65
Median and range of EDSS scores	None	Median: 2.0 Range: 0-8.0	Median: 3.5 Range: 0-9.0
Disease state	None	Relapse: 20 remission: 30	Relapse: 36 remission: 21

Example 2 Comparison of Metabolites Present in Patients with Multiple Sclerosis and Optic Spinal Cord Category

<2-1> Quantitative metabolite profiling of normal, MS and NMOSD patient samples by 1H NMR spectral analysis

¹ H NMR spectral analysis of cerebrospinal fluid of normal, MS and NMOSD patients obtained in Example 1 was carried out as follows.

First, 400 μl of CSF samples obtained from normal, MS and NMOSD patients were mixed with 95 μl of NMR buffer containing 580 mM sodium phosphate buffer. Include propanoic acid (TSP), and 10% D ₂ O - NMR final sample is trimethylsilyl in 100 mM of sodium phosphate buffer (pH 7.0), 2 mM. To identify the metabolites, a 1-dimensional ¹ H-NOESY NMR pulse sequence (noesygppr1d) was obtained with a Bruker ASCEND Ⅲ 600 spectrometer equipped with a low temperature probe at 298 K. The NOESY pulse sequence was generated to saturate to suppress the residue signal. The ¹ H-NMR spectrum of each sample showed a spectrum width of 12019.2 Hz, a spectrum size of 65,536 points, a pulse width (90) of 13.0 μs, a relaxation delay (RD) of 5 s, and a mixing time of 10 ms It consists of 256 scans with parameters.

The spectra were processed with Bruker Topspin 3.1 (Bruker GmbH, Germany) and Chenomx NMR suite 7.7 (Chenomx Inc., Canada) for quantitative metabolite profiling of cerebrospinal fluid samples from healthy, MS and NMOSD patients, ¹ H- ¹³ C HSQC and 2D ¹ H-TOCSY spectra. Each free induction decay (FID) was filled up to 64,000 points with zero and the line broadening (LB) was transformed to 0.3 Hz. NMR spectra were manually adjusted stepwise by Bruker Topspin 3.1 and baseline corrected using the Chenomx NMR suite 7.7 and based on TSP at 0 ppm. That is, the baseline model was constructed on each spectrum using a multi-point baseline correction algorithm. Metabolites were identified and quantified by internal standard (TSP) comparisons using databases stored in the Chenomx NMR suite 7.7. Metabolic analysis was performed using MetaboAnalyst (v3.0), a web server-based program, and SPSS version 23 (IBM) was used for statistical analysis.

32 metabolites identified in cerebrospinal fluid of normal, MS and NMOSD patients

Metabolite	Normal		MS Patient		NMOSD patients
	Average	Standard deviation (±)	Average	Standard deviation (±)	Average	Standard deviation (±)
2-hydroxybutyrate < RTI ID = 0.0 >	0.0420	0.0086	0.0544	0.0204	0.0718	0.0373
2-hydroxyisovalerate < / RTI >	0.0044	0.0006	0.0044	0.0001	0.0053	0.0022
3-hydroxybutyrate (3-hydroxybutyrate)	0.0057	0.0010	0.0061	0.0013	0.0062	0.0011
3-hydroxyisobutyrate (3-hydroxyisobutyrate)	0.0072	0.0016	0.0074	0.0016	0.0076	0.0019
3-hydroxyisovalerate < / RTI >	0.0038	0.0004	0.0035	0.0014	0.0046	0.0025
Acetate	0.2993	0.0033	0.2626	0.0565	0.2563	0.0396
Acetoacetate (acetoacetate)	0.0033	0.0006	0.0046	0.0048	0.0051	0.0023
Acetone	0.1112	0.0364	0.1868	0.0864	0.2135	0.0627
Alanine	0.0408	0.0051	0.0381	0.0110	0.0545	0.0409
Choline	0.0021	0.0003	0.0025	0.0013	0.0028	0.0015
Citrate	0.4352	0.0532	0.3451	0.0969	0.4698	0.1218
Creatine	0.0541	0.0060	0.0513	0.0126	0.0531	0.0121
Creatinine	0.0539	0.0027	0.0510	0.0092	0.0535	0.0108
Dimethyl sulfone	0.0072	0.0012	0.0095	0.0144	0.0074	0.0019
Ethanol (ethanol)	0.0127	0.0054	0.0199	0.0303	0.0202	0.0227
Fructose	0.0565	0.0102	0.0533	0.0161	0.0517	0.0156
Formate	0.0449	0.0061	0.0530	0.0101	0.0576	0.0197
Glucose	4.5681	0.2540	4.0432	0.6704	4.1501	0.9873
Glutamine	0.3291	0.0226	0.3203	0.0555	0.3434	0.0616
Hisitidine	0.0081	0.0010	0.0071	0.0014	0.0077	0.0024
Isobutyrate	0.0086	0.0019	0.0088	0.0019	0.0091	0.0023
Isoleucine	0.0079	0.0013	0.0085	0.0030	0.0112	0.0056
Lactate	1.8832	0.17415	1.8521	0.3670	2.4513	0.9397
Leucine	0.0132	0.0020	0.0133	0.0041	0.0161	0.0065
Methanol	0.0730	0.0185	0.0641	0.0213	0.0631	0.0413
Myo-inositol	0.1651	0.0166	0.1548	0.0346	0.1569	0.0272
Phenylalanine	0.0109	0.0012	0.0107	0.0023	0.0125	0.0035
Propylene glycol	0.0073	0.0104	0.0049	0.0028	0.0065	0.0076
Pyroglutamate (pyroglutamate)	0.0332	0.0030	0.0454	0.0265	0.0443	0.0135
Pyruvate	0.0085	0.0013	0.0093	0.0046	0.0103	0.0055
Tyrosine	0.0107	0.0013	0.0109	0.0023	0.0123	0.0032
Valine	0.0183	0.0033	0.0184	0.0060	0.0237	0.0120

As a result, as shown in Table 2, a total of 32 metabolites were identified through ¹ H NMR spectrum analysis of normal cerebrospinal fluid (MS) and NMOSD patients (Table 2).

<2-2> PCA analysis

In order to investigate the differentiation of basic groups, data of each group obtained in Example <2-1> were analyzed by principal component analysis (PCA) method. The sample was considered extreme when it was located outside the 95% confidence ellipsoidal area of the model.

As a result, as shown in FIG. 1, PCA analysis showed that 8 samples (3 MS patients and 5 NMOSD patients) were located outside the boundary of the 95% confidence ellipsoidal region, and these samples were excluded from the additional statistical analysis One).

<2-3> Establishment of OPLS-DA model for group separation

In order to characterize group differences, an orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to maximize class segregation by eliminating class-separation-related variability and to construct a model to detect potential variables involved in class distinction. We identified important characteristics based on S-plots. The S-plot of the OPLS-DA model is combined with the covariance p for the correlation p (corr) loading profile. This is consistent with the combination of the effects and reliability (modeled correlations) on the model variables with respect to the contribution or size (modeled covariance) of the model variables and the model component scores in relation to the model component scores. The quality of the OPLS-DA model was estimated by R2 (goodness of fit) and Q2 (predictive power) parameters. The quality of the model was verified by a 1000-random permutation test.

As a result, as shown in FIGS. 2A to 2C, the OPLS-DA model between the normal group, the MS patient group, and the NMOSD patient group had an R2 value of 0.443 and a Q2 value of 0.234, In particular, the MS patient group overlapped very well with the normal group and the NMOSD patient group (Fig. 2a). On the other hand, the OPLS-DA model between the normal group and the MS patient group showed improvement in the separation of the group with R2 value of 0.738 and Q2 value of 0.408 (FIG. 2B). The R2 and Q2 values observed in the OPLS-DA model for the normal and MS patients were higher than those for the permutation model, indicating predictability of the model. The OPLS-DA model between normal and NMOSD patients showed a better R2 value of 0.589 and a Q2 value of 0.405 (Fig. 2c). The R2 and Q2 values observed in the OPLS-DA model for normal and NMOSD patients were higher than those for the permutation test and were predictive and compatible.

<2-4> Univariate analysis to identify metabolites that contribute to discrimination among groups

Univariate analysis was performed to identify metabolites that contribute to discrimination among groups. Standard deviations and uniformity of quantified metabolite concentrations were evaluated using SPSS. The Kruskal-Wallis test was used because all variables did not satisfy the identity of variance based on the Kolmogorov-Smirnov test-based normality and Levine's test . In multiple comparisons between groups, p-values were adjusted using the Bonferroni calibration and calculated using the Benjamini-Hochberg method, a multiple test calibration, False Discovery Rate (FDR) Respectively. A final p-value of less than 0.05 was considered significant.

Metabolites with significant differences between normal and MS patients and normal and NMOSD patients

Metabolite	Multiple comparisons (adjusted p-values)	FDR	Metabolite change		Group mean (standard deviation) (mM)
			MS Patient	NMOSD patients	Normal	MS Patient	NMOSD patients
2-hydroxybutyric acid	* CM (0.003)	≪ 0.05	△	△	0.0420 (0.0086)	0.0544 (0.0204)	0.0718 (0.0373)
	C-N (< 0.001)	&Lt; 0.05
Acetone	C-M (0.001)	&Lt; 0.05	△	△	0.1112 (0.0364)	0.1868 (0.0864)	0.2135 (0.0627)
	C-N (< 0.001)	&Lt; 0.05
Formic acid	C-M (0.003)	&Lt; 0.05	△	△	0.0449 (0.0061)	0.0530 (0.0101)	0.0576 (0.0197)
	C-N (0.001)	&Lt; 0.05
Pyroglutamic acid	C-M (0.001)	&Lt; 0.05	△	△	0.0332 (0.0030)	0.0454 (0.0265)	0.0443 (0.0135)
	C-N (< 0.001)	&Lt; 0.05
acetate	C-M (0.001)	&Lt; 0.05	▽	▽	0.2993 (0.0033)	0.2626 (0.0565)	0.2563 (0.0396)
	C-N (< 0.001)	&Lt; 0.05
glucose	C-M (0.001)	&Lt; 0.05	▽	▽	4.5681 (0.2540)	4.0432 (0.6704)	4.1501 (0.9873)
	C-N (< 0.001)	&Lt; 0.05
Citric acid	C-M (0.003)	&Lt; 0.05	▽	-	0.4352 (0.0532)	0.3451 (0.0969)	0.4698 (0.1218)
	M-N (< 0.001)	&Lt; 0.05
Lactic acid	C-N (0.002)	0.005	-	△	1.8832 (0.1715)	1.8521 (0.3670)	2.4513 (0.9397)
	M-N (< 0.001)	&Lt; 0.05

^* C represents the normal group, M represents the MS patient group, and N represents the NMOSD patient group.

△ indicates that the value is increased compared to the normal group, ▽ indicates that the value is lower than that of the normal group, and - indicates that there is no significant change.

As a result, as shown in Table 3, 2-hydroxybutyric acid, acetone, formic acid and pyroglutamic acid were up-regulated in the MS and NMOSD patients compared to the normal group and glucose and acetate were down-regulated in the MS and NMOSD patients . On the other hand, citric acid was down-regulated only in the MS patients compared to the normal group, and lactic acid was up-regulated only in the NMOSD patients (Table 3, Figs. 3a and 3b). 4A to 4D, it was confirmed that the above results were the same in the S-plot (FIGS. 4A and 4B) and the box and whisker plot of the OPLS-DA model (FIGS. 4C and 4D) .

<2-5> Biomarker selection by ROC analysis

ROC curves (AUC), and receiver operating characteristic (ROC) analyzes to assess sensitivity and specificity were used to select biomarkers that could discriminate normal, MS, or NMOSD patients. The ROC curve calculation algorithm for multivariate biomarkers was based on the PLS-DA algorithm. Important features were identified by Monte Carlo cross validation (MCCV), which iteratively performs random subsampling. Two-thirds of the samples in each MCCV were used to assess the significance of the variables, and the important variables in the top 2, 3, 5, 10, 20, and 32 were used to construct a validated classification model at one-third of the remaining samples . The 95% confidence interval (CI) of the ROC curve was calculated using 500 bootstrapping, and the number of optimal metabolites was calculated based on the AUC value .

As a result, as shown in FIGS. 5A and 5B, the AUC value was maximized when about five biomarkers were used in combination (FIG. 5A). The most frequently used metabolites in the calculations were citric acid, lactic acid, glucose, acetone and acetate. The AUC of the model comparing the NMOSD patient group with the other group (normal group and MS patient group), between the MS patient group and the NMOSD patient group, and between the MS patient group and the other group (normal group and NMOSD patient group) was 0.861 , 0.829, and 0.771 (Fig. 5B). That is, the model comparing the NMOSD patient group with the other group showed better differentiation ability than the other models. This means that the combination of citric acid, lactic acid, glucose, acetone, and acetate biomarkers can distinguish MS patients and NMOSD patients.

Claims (7)

1) measuring at least one concentration selected from the group consisting of citric acid and lactic acid from a sample obtained from a patient suspected of having multiple sclerosis or optic neuritis; And

2) comparing the concentration of at least one selected from the group consisting of citric acid and lactic acid of step 1) with the concentration of the normal individual, and analyzing the metabolites to provide information necessary for discrimination diagnosis of optic nerve spinal disease diseases Way.

The method according to claim 1, wherein the sample of step 1) is used to provide information necessary for distinguishing between urine, blood, serum, plasma or cerebrospinal fluid, multiple sclerosis and optic neuritis.

The method according to claim 1, wherein the concentration of at least one selected from the group consisting of citric acid and lactic acid in step 1) is at least one selected from the group consisting of nuclear magnetic resonance spectroscopy, chromatography, ultraviolet spectroscopy, infrared spectroscopy, fluorescence spectroscopy, enzyme-linked immunosorbent assay (ELISA) Analyzer for determining the presence or absence of at least one disease selected from the group consisting of multiple sclerosis and optic neuritis.

The method according to claim 1, wherein the disease is judged to be multiple sclerosis when the concentration of citric acid is reduced as compared to a normal individual, and a method for analyzing metabolites to provide information necessary for discrimination diagnosis of multiple sclerosis and optic neuritis.

The metabolic product analysis method according to claim 1, wherein the disease is judged to be an optic neuropathy category disease when the concentration of lactic acid is increased compared to a normal individual, wherein the metabolic product analysis method is provided to provide information necessary for distinguishing between multiple sclerosis and optic neuritis.

A kit for the differential diagnosis of multiple sclerosis and optic neuritis, comprising a preparation for quantitative determination of citric acid or lactic acid or a quantitative analysis device.

The method according to claim 6, wherein the quantitative analysis apparatus is any one selected from the group consisting of a nuclear magnetic resonance spectrometer, chromatography, ultraviolet spectroscopy, infrared spectroscopy, fluorescence spectroscopy, enzyme-linked immunosorbent assay (ELISA) Diagnostic kit for differential diagnosis of sclerosis and optic neuritis.

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