KR20190045841A - Method for diagnosing Behcet's disease by using urine metabolomics - Google Patents

Method for diagnosing Behcet's disease by using urine metabolomics Download PDF

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KR20190045841A
KR20190045841A KR1020180123343A KR20180123343A KR20190045841A KR 20190045841 A KR20190045841 A KR 20190045841A KR 1020180123343 A KR1020180123343 A KR 1020180123343A KR 20180123343 A KR20180123343 A KR 20180123343A KR 20190045841 A KR20190045841 A KR 20190045841A
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김경헌
차훈석
안중경
김정연
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고려대학교 산학협력단
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Abstract

The present invention relates to a method for diagnosing a Behcet′s disease using urine metabolome analysis, and a Behcet′s disease diagnostic kit, which rapidly and accurately diagnose a patient with a Behcet′s disease. The Behcet′s disease diagnostic kit of the present invention comprises a quantitative device for at least one urine metabolome selected from the group consisting of guanine, 3-hydroxypyridine, hypoxanthine, L-citrulline, isothreonate, pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, and mannose.

Description

소변 대사체 분석을 이용한 베체트병의 진단방법{Method for diagnosing Behcet's disease by using urine metabolomics}[0001] The present invention relates to a method for diagnosing Behcet's disease using urine metabolism,

본 발명은 소변 대사체 분석을 통해 베체트병을 진단하는 방법에 관한 것이다.The present invention relates to a method for diagnosing Behcet's disease through urine metabolism analysis.

베체트병은 전신적인 혈관염으로, 구강이나 성기 등의 피부점막 부위의 궤양, 관절염부터 혈관 및 중추 신경계 등의 중요 장기 침범 등의 증상을 특징으로 하는 원인미상의 질병이다. 즉, 혈액이 흐르는 어디든 발생할 수 있는 전신성 혈관염으로, 다양한 임상 증상 및 중증도의 증상을 보인다. 베체트병은 한국, 중국, 일본, 터키 등 지중해 연안부터 극동 아시아에 이르는 지역에 그 발병 빈도가 매우 높다. 베체트병은 다양한 임상 양상을 보이며, 일부에서는 심각한 합병증과 장애를 유발할 수 있으므로, 베체트병을 조기에 진단하는 것은 매우 중요하다. 베체트병의 발병률이 높은 것으로 알려진 터키를 기준으로, 매년 환자 1인당 일반적인 베체트병의 치료에 약 3,300 달러의 경비가 소요되며, 신경학적 증상을 보이는 베체트병 환자는 약 5,000 달러의 경비가 소요되는 것으로 보고되었다. 특히 20대에서 40대에서 가장 높은 질병의 활성도를 보이기 때문에, 젊은 연령에서 심각한 합병증의 발생으로 인한 경제적, 사회적 손실이 매우 클 수 있다. 또한, 베체트병 환자의 약 42%는 연중 120일 가량을 일을 하지 못하는 것으로 추산된다. 우리나라에서도 베체트병은 매우 흔한 질환이기에, 베체트병을 인한 직간접적인 의료비로 인해 지출해야 하는 경비가 매우 클 것으로 예상된다. Behcet's disease is systemic vasculitis, a disease of unknown etiology characterized by symptoms such as ulcers in the mucous membranes of the mouth or genital area, arthritis, and important organs such as blood vessels and central nervous system. In other words, systemic vasculitis, which can occur anywhere blood flows, manifests symptoms of various clinical symptoms and severity. Behcet's disease is very common in areas ranging from the Mediterranean to the Far East, including Korea, China, Japan and Turkey. Early diagnosis of Behcet's disease is very important because of the various clinical manifestations of Behcet's disease and in some cases, serious complications and disability. According to Turkey, which is known to have a high incidence of Behçet's disease, about 3,300 dollars is spent for the treatment of generalized Behçet's disease per patient per year, and about 5,000 dollars for Behçet's disease with neurological symptoms . In particular, since the disease activity is the highest in the 20s to 40s, the economic and social losses due to the occurrence of serious complications in younger age can be very large. In addition, it is estimated that approximately 42% of patients with Behcet's disease do not work around 120 days a year. In Korea, Behcet 's disease is a very common disease, so it is expected that the expenses to be paid due to direct or indirect medical expenses due to Behcet' s disease are very high.

따라서 베체트병을 신속, 정확하게 감별 진단할 수 있는 생체표지자의 발굴은 의학뿐만 아니라, 사회경제적으로 매우 중요할 것으로 생각된다. Therefore, excavation of biomarkers that can differentiate rapidly and accurately from Behcet 's disease is thought to be important not only in medicine but also socioeconomically.

현재 베체트병 환자와 건강한 사람을 구분할 수 있는 객관적인 진단용 생체표지자가 없으므로, 베체트병의 진단은 주로 임상적인 증상에 의존한다. 그러나 베체트병은 다양한 임상 증상을 보이기에, 임상적 증상에 기반한 진단은 낮은 민감도 및 특이성을 보인다. 또한, 발병 후 베체트병의 확진까지 오랜 시간이 걸리는 문제점이 있다. 이를 극복하기 위해서, 객관적인 진단적 생체표지자를 발명하는 것은 매우 중요하다. 그러므로 베체트병을 진단할 수 있는 객관적인 진단적 생체표지자를 발굴할 수 있으면, 베체트병을 조기에 진단함으로써, 확진에 걸리는 시간을 줄이고, 질병 수준에 적절한 맞춤형 치료를 가능하게 하여, 증상 악화 및 고가의 불필요한 치료를 피할 수 있다. 또한, 질환 관련 예후에 정확한 정보를 제공하여 더 좋은 치료 성적을 거둘 수 있다. Because there is currently no objective diagnostic biomarker that can differentiate patients with Behcet's disease from healthy individuals, the diagnosis of Behcet's disease depends largely on clinical symptoms. However, because Behcet 's disease has various clinical symptoms, diagnosis based on clinical symptoms has low sensitivity and specificity. Also, there is a problem that it takes a long time until the diagnosis of Behcet's disease after the onset. To overcome this, it is very important to invent an objective diagnostic biomarker. Therefore, if an objective diagnostic biomarker capable of diagnosing Behcet's disease can be identified, early diagnosis of Behcet's disease will reduce the time required for confirmation, tailor-made treatment appropriate for the disease level, Unnecessary treatment can be avoided. In addition, accurate information on disease-related prognosis can provide better treatment results.

최근 다양한 질환에서 생체표지자 발굴을 위해서 대사체학적 접근 방법은 많은 각광을 받고 있다. 현재까지 문헌 보고에 따르면 베체트병을 진단하기 위한 생체표지자를 찾기 위해 단백질체학을 이용한 시도가 있었으나, 단백질 생체표지자는 대사물질 생체표지자 보다 검출의 신속성 및 편리성이 떨어져 실제 진단에 이용되기는 어렵다는 문제점이 있다. 또한, 베체트병을 진단하기 위해 혈액에서 대사체학적 접근 방법을 이용하여 생체표지자를 발굴한 바 있다. 소변에서 대사체학적 접근법에 의해 발굴된 생체표지자를 동시에 이용한다면, 더욱 적절한 진단을 할 수 있을 것으로 예상된다. 또한, 소변은 비침습적 방법을 통해 보다 쉽고 용이하게 채취할 수 있으므로, 베체트병의 진단을 위해 적절한 검체로 이용할 수 있을 것으로 예상된다.Recently, metabolic approaches have been attracting much attention in order to detect biomarkers in various diseases. To date, the literature reports have attempted to use biomarkers to detect biomarkers for the diagnosis of Behcet's disease, but protein biomarkers are less likely to be used for actual diagnosis because of their rapidity and convenience of detection than the metabolite biomarkers have. In order to diagnose Behcet's disease, a biomarker approach has been used to identify biomarkers in the blood. It is expected that more appropriate diagnosis will be possible if the biomarkers discovered by metabolic approach in urine are used simultaneously. It is also expected that urine can be used as a suitable sample for the diagnosis of Behcet's disease because it can be easily and easily collected through a noninvasive method.

베체트병은 유전적, 환경적 요인을 포함한 다양한 원인에 의해 면역계가 활성화되어 다양한 임상을 보이는 매우 복합적인 질환이므로, 대사체학적 접근은 베체트병에서 생체표지자를 발굴하는 데 유용하고 적합한 방법이라 생각되며 연구자들이 아는 한 베체트병의 감별 진단을 위해 소변을 이용하여 대사체학적 접근법을 시도한 연구는 없었다. 베체트병 진단과 관련된 선행기술은 다음과 같다.Because the disease is a very complex disease with immune system activation due to various causes including genetic and environmental factors, the metabolic approach seems to be a useful and appropriate method for detecting biomarkers in Behcet's disease As far as researchers know, there have been no studies that have attempted a metabolic approach using urine for the differential diagnosis of Behcet's disease. The prior art related to diagnosis of Behcet's disease is as follows.

베체트병에서 생체표지자 발굴을 위해 현재까지 보고된 기술들은 주로 단백질체학적 접근이었지만, 그 결과가 뚜렷하지 못하거나 실제 베체트병 진단에 사용되기는 어려웠다[비특허문헌 1]. Techniques reported to date for the detection of biomarkers in Behcet's disease have been largely a proteomic approach, but the results have not been evident or have been difficult to be used in the diagnosis of Behcet's disease [Non-Patent Document 1].

비특허문헌 2는 베체트병에 의한 관절염과 다른 혈청음성 염증관절염의 활막액의 대사체 차이를 통해 감별 진단하고자 하는 연구로, 베체트병 환자와 일반인을 구분하여 진단하는 것이 아니라 관절염 환자 들 중 베체트병 관절염을 진단해 내기 위한 것이었다. 또한, 비특허문헌 3은 혈청양성 류마티스관절염과 베체트병을 포함한 다른 염증관절염의 활막액의 대사체 비교를 통해 혈청양성 류마티스관절염의 진단을 위한 생체표지자를 발굴하고자 하였으며, 베체트병 진단이 아닌 류마티스 관절염 환자 들 중 베체트병 관절염을 진단해 내기 위한 것이었다.Non-Patent Document 2 is a study to differentiate the metabolic difference of synovial fluid between arthritis caused by Behcet's disease and other seronegative inflammatory arthritis. It is not a diagnosis of Behcet's disease and general people, To diagnose arthritis. In addition, non-patent document 3 attempts to identify a biomarker for the diagnosis of serum-positive rheumatoid arthritis by comparing the metabolites of synovial fluid of other inflammatory arthritis including serum-positive rheumatoid arthritis and Behcet's disease. To diagnose Behcet's disease arthritis among the patients.

비특허문헌 2와 3과 같이, 활막액 대사체를 사용하는 경우에는 활막액 시료의 채취에 특수한 침습적 접근이 필요하여 일반 검진에서 진단용으로 채취가 어렵고 특정 질병의 subgroup 구분에서만 유용하게 이용될 수 있어 일반적 진단에 사용되기 어려운 문제가 있다.As in non-patent documents 2 and 3, when a synovial fluid metabolite is used, it is necessary to take a special invasive approach to collecting a synovial fluid sample. Therefore, it is difficult to collect it for general diagnosis and diagnosis, There is a problem that is difficult to be used for general diagnosis.

비특허문헌 4는 베체트병 환자 및 건강한 사람의 혈액 검체 내의 대사체 차이를 이용하여 베체트병을 진단하고자 하였다. 혈액 시료의 경우에는 침습적 접근이 필요하여 채취에 허가된 전문 인력을 필요로 하며, 따라서 피시험자가 직접 샘플을 채취하여 자가 진단을 할 수 없는 문제가 있다. Non-Patent Document 4 attempts to diagnose Behcet's disease by using metabolic differences in blood samples of patients with Behcet's disease and healthy persons. In the case of blood samples, an invasive approach is required, requiring specialized personnel who are allowed to collect samples, and thus there is a problem that the subject can not take a self-diagnosis by taking samples directly.

Seido et al. Proteomic surveillance of autoantigens in patients with Behcet's disease by a proteomic approach. (2010) Microbiol Immunol 54:354-361.Seido et al. Proteomic surveillance of autoantigens in patients with Behcet's disease by a proteomic approach. (2010) Microbiol Immunol 54: 354-361. Ahn J et al. A comparative metabolomic evaluation of Behcet's disease with arthritis and seronegative arthritis using synovial fluid. (2015) PLOS ONE 10:e0135856 Ahn J et al. A comparative metabolomic evaluation of Behcet's disease with arthritis and seronegative arthritis using synovial fluid. (2015) PLOS ONE 10: e0135856 Kim s et al. Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis. (2014) PLOS ONE 9:e97501Kim s et al. Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis. (2014) PLOS ONE 9: e97501 Ahn J et al. Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography with time-of-flight mass spectrometry. (2018) Joint Bone Spine 95:337-343Ahn J et al. Potential metabolomic biomarkers for a reliable diagnosis of Behcet's disease using gas chromatography with time-of-flight mass spectrometry. (2018) Joint Bone Spine 95: 337-343

본 발명자들은 베체트병 환자를 특이적으로 감별 진단하기 위해 대사체학적 접근을 통하여 신속하고 정확하고 편리한 진단을 위한 비침습적 방법으로 채취가 가능한 소변 샘플을 통해 베체트병을 진단 할 수 있는 생체표지자를 발굴하였다. GC/TOF MS를 이용하여 베체트병 환자와 일반인의 소변 내 대사체 분석을 통해 110개의 대사체를 검출하였다. 이 중 urea를 제외한 109개의 대사물질에 대해서 직교부분최소제곱회귀법(OPLS-DA)과 variable importance for projection (VIP) 값, Receiver operating characteristic (ROC) curve의 area under the 3curve (AUC)의 값, fold change, p-value 등을 산출하여 10개의 강력한 베체트병 환자의 소변 내 대사물질 생체표지자(guanine, 3-hydroxypyridine, hypoxanthine, L-citrulline, isothreonate, pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, mannose) 를 제시하였다. 또한 10 개의 생체표지자를 동시에 이용한 강력한 베체트병 진단 panel을 만들었으며, 이를 외부 검체(validation set)를 이용하여 임상적 타당성을 검증하였다.The present inventors discovered a biomarker capable of diagnosing Behcet's disease through a urine sample which can be collected by a non-invasive method for rapid, accurate and convenient diagnosis through a metabolic approach in order to specifically differentiate a disease of Behcet's disease Respectively. Using the GC / TOF MS, 110 metabolites were detected in urine metabolites of patients with Behcet 's disease and the general population. For the 109 metabolites except urea, the values of OPLS-DA, variable importance for projection (VIP), area under the 3Curve (AUC) of the receiver operating characteristic (ROC) change, p -value such as metabolism in the urine of 10 patients with Behcet's disease strong material biomarkers to produce a (guanine, 3-hydroxypyridine, hypoxanthine , L -citrulline, isothreonate, pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose , mannose) were presented. In addition, a strong BD test panel using 10 biomarkers at the same time was constructed and its clinical validity was verified using an external sample (validation set).

따라서, 본 발명은 소변 대사체 분석을 통해 베체트병을 진단하기 위한 키트를 제공하는데 그 목적이 있다. Accordingly, it is an object of the present invention to provide a kit for diagnosing Behcet's disease through urine metabolism analysis.

또한, 본 발명은 베체트병을 진단하기 위한 대사체 차별성을 분석하는 방법을 제공하는데 목적이 있다. It is another object of the present invention to provide a method for analyzing metabolic differentiation for diagnosing Behcet's disease.

본 발명은 구아닌(guanine), 3-하이드록시피리딘(3-hydroxypyridine), 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 피롤-2-카르복실레이트(pyrrole-2-carboxylate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상의 소변 대사체에 대한 정량 장치를 포함하는 베체트병 진단 키트를 제공한다.The invention guanine (guanine), 3- hydroxymethyl pyridine (3-hydroxypyridine), hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline), iso-threo carbonate (isothreonate), pyrrole-2-carboxylate a quantitative device for at least one urine metabolite selected from the group consisting of pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, and mannose. The present invention also provides a diagnostic kit for a Behcet's disease.

또한, 본 발명은 정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성을 검출하는 방법으로,In addition, the present invention is a method for detecting metabolite differentiation between urine obtained from a normal control and Behcet's disease,

(1) GC/TOF MS(gas chromatography/time-of-flight mass spectrometry)를 이용한 대사체 분석 단계; (1) metabolism analysis step using GC / TOF MS (gas chromatography / time-of-flight mass spectrometry);

(2) GC/TOF MS에서 동정된 대사체에 대해 부분최소자승판별분석(PLS-DA)를 이용하여 대사체 프로파일의 차이를 확인하는 단계;(2) identifying differences in metabolite profiles using partial least squares discriminant analysis (PLS-DA) for the metabolites identified in GC / TOF MS;

(3) PLS-DA에서 도출된 대사체의 VIP(Variable Importance for Projection) 값이 1.0 이상인 값을 대사체 바이오마커 후보물질로 선정하고, PLS-DA의 로딩 값을 통해 대사체 바이오마커 후보물질의 증감 확인하는 단계;(3) The value of Variable Importance for Projection (VIP) of the metabolite derived from PLS-DA was selected as a candidate for metabolite biomarker, and the value of PLS-DA as a metabolite biomarker candidate Ascertaining and decreasing;

(4) ROC 곡선(Receiver Operating Characteristic curve)을 이용하여 대사체 바이오마커를 검증하는 단계(4) Verifying the metabolite biomarker using the ROC curve (Receiver Operating Characteristic curve)

를 순차적으로 적용하여, 소변으로부터 대사체 바이오마커를 분석하는 것을 포함하는 정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성 분석 방법을 제공한다.To analyze the metabolite differentiation between urine obtained from normal control and Behcet's disease, including analysis of metabolic biomarker from urine.

본 발명을 통하여 대사체학을 소변 분석에 이용해 베체트병을 특이적으로 진단할 수 있는 생체표지자를 최초로 규명하였다. 이는 아직까지 완전히 밝혀져 있지 않은 베체트병의 발병 기전을 밝히는 연구의 기반이 될 수 있다. 또한, 다양한 임상 증상에 최적화된 치료제 개발에 응용될 수 있다. 베체트병의 진단을 용이하게 하는 생체표지자의 발견은 베체트병 환자를 신속하고 정확하게 진단하고, 임상적 진단에 걸리는 긴 시간을 크게 줄여서 맞춤형 치료를 빠르게 제공하여 일상생활로 복귀를 빠르게 하는 등의 사회 경제적 파급 효과도 상당할 것으로 기대된다. 특히, 기존의 혈액 샘플을 이용하여 베체트병을 진단하는 경우와 달리 피시험자가 직접 소변 샘플을 비침습적으로 채취하여 자가진단이 가능하다. 또한, 혈액 샘플과 소변 샘플 모두를 이용하여 진단한다면 특이성을 보다 높일 수 있다.Through the present invention, a biomarker capable of specifically diagnosing Behcet's disease by using metabolomics in urine analysis was first identified. This may be the basis for a study that reveals the pathogenesis of Behcet's disease, which has yet to be fully understood. In addition, it can be applied to the development of therapeutic agents optimized for various clinical symptoms. The discovery of a biomarker that facilitates the diagnosis of Behcet's disease can be used to quickly and accurately diagnose Behcet's disease patients, greatly reduce the length of time required for clinical diagnosis, and thus provide customized treatment to speed up return to daily life, The ripple effect is also expected to be significant. In particular, unlike the case of diagnosing Behcet's disease using an existing blood sample, the subject can non-invasively take a urine sample directly for self-diagnosis. In addition, if you are diagnosed with both blood and urine samples, you can increase your specificity.

도 1은 OPLS-DA를 이용하여 베체트병 환자와 건강한 대조군의 소변 내 대사체 프로파일링 차이 비교한 것으로,
A는 OPLS-DA로 만들어진 다변량 통계 모델의 score plot으로, 베체트병 환자와 건강한 대조군의 대사체가 확연한 차이를 보임을 나타내며,
B는 OPLS-DA로 만들어진 다변량 통계 모델의 loading plot으로 만들어진 모델(표 2)에 각 대사물질의 abundance가 어떻게 기여하는지 보여주고,
C는 OPLS-DA 모델의 permutation 결과로, OPLS-DA 모델이 오버피팅 되지 않았으며, 분석에 사용된 샘플에 국한되지 않고 외부 샘플 분석에도 이용될 수 있음을 나타낸다[BD: 베체트병 환자; HC: 건강한 대조군].
도 2는 베체트병에서 유의미하게 증가한 3개 대사물질(A)과 유의미하게 감소한 7개 대사물질(B)의 수준 비교 그래프이다.
도 3은 베체트병 환자 내 steroid, colchicine, azathioprine 투여 그룹과 비투여 그룹 간의 대사체적 차이를 PLS-DA로 나타낸 결과이다[각각의 약물투여 그룹과 비투여 그룹 간의 차이가 Q 2 값이 매우 낮아 재현성이 없고, 통계학적으로 그룹 간의 대사체적 차이가 없음을 보임].
도 4는 베체트병에서 유의미하게 증가한 3개 대사물질 구아닌, 피롤-2-카르복실레이트, 3-하이드록시피리딘과 유의미하게 감소한 7개 대사물질(하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose), 만노오스(mannose))을 이용해 베체트병을 진단하는 대사체적 생체표지자 panel을 OPLS-DA를 통해 분석한 결과이다[t[1] 하나의 축을 이용하였을 때, R 2 Y 값이 0.650로 적절하게 구분됨을 보였으며, Q 2 값이 0.600로 모델이 재현성이 있음을 확인함].
도 5는 소변 검체를 이용한 베체트병 진단을 위한 대사체적 진단 panel의 ROC(receiver operating characteristic curve) 결과이다[10개의 대사체 조합을 이용한 생체표지자 panel이 베체트병의 진단에 있어 sensitivity 96.7%, specificity 93.3%, AUC 0.974의 결과를 보임].
도 6은 소변 검체를 이용한 베체트병 진단을 위한 대사체적 진단 panel의 외부 검체 검증 결과이다[주성분 분석에서 14개의 베체트병 환자 및 11개의 건강한 대조군의 소변 샘플 중 11개의 베체트병 환자 및 11개의 건강한 대조군을 정확하게 예측할 수 있음을 보임]. Figure 1 compares the profiling differences in urine between patients with Behçet's disease and healthy control using OPLS-DA,
A is a score plot of a multivariate statistical model of OPLS-DA, indicating that the metabolites of patients with Behcet's disease and healthy control differ significantly,
B shows how the abundance of each metabolite contributes to the model created by the loading plot of the multivariate statistical model of OPLS-DA (Table 2)
C is the permutation result of the OPLS-DA model, indicating that the OPLS-DA model is not overfitting and can be used for external sample analysis as well as for the sample used for analysis [BD: Behcet's disease patients; HC: healthy control].
FIG. 2 is a graph comparing the levels of three metabolites (A) significantly increased in Behcet's disease and seven metabolites (B) significantly decreased in Behcet's disease.
3 is Behcet's disease in steroid, colchicine, azathioprine is a result showing the metabolic volume difference between the administration group and the non-administration group as PLS-DA [reproducibility of the difference between the respective drug administration group and the non-administered group so low that the Q 2 value And there is no statistically significant difference between the groups.
4 is a three metabolites significantly increased in the BD guanine, pyrrole-2-carboxylate, 3-hydroxy-pyridin-seven metabolites (Santin high width (hypoxanthine significantly decreased and), L - tulrin (L City metabolic biomarkers for the diagnosis of Behcet's disease using cortulline, isothreonate, galactonate, gluconic acid lactone, sedoheptulose, mannose) The result of analyzing the marker panel through the OPLS-DA [t [1] shows that the R 2 Y value is appropriately classified as 0.650 when one axis is used, and the Q 2 value is 0.600 and the model is reproducible Confirmed].
FIG. 5 is a receiver operating characteristic curve (ROC) result of a metabolic diagnostic panel for diagnosis of Behcet's disease using urine specimens. The biomarker panel using 10 metabolism combinations showed sensitivity of 96.7%, specificity of 93.3% %, AUC 0.974].
Figure 6 shows the results of external test of the metabolic panel for the diagnosis of Behcet's disease using urine samples. [In the principal component analysis, 11 of Behcet's disease patients and 11 healthy control patients among 14 patients with Behcet's disease and 11 healthy control groups, Can be accurately predicted].

이하, 본 발명의 구성을 구체적으로 설명한다.Hereinafter, the configuration of the present invention will be described in detail.

본 발명은 구아닌(guanine), 3-하이드록시피리딘(3-hydroxypyridine), 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 피롤-2-카르복실레이트(pyrrole-2-carboxylate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상의 혈액 대사체에 대한 정량 장치를 포함하는 베체트병 진단 키트에 관한 것이다.The invention guanine (guanine), 3- hydroxymethyl pyridine (3-hydroxypyridine), hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline), iso-threo carbonate (isothreonate), pyrrole-2-carboxylate a quantitative device for one or more blood metabolites selected from the group consisting of pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, and mannose. The present invention relates to a diagnostic kit for a Behcet's disease.

본 발명자들은 베체트병의 바이오마커를 찾기 위해 환자들의 소변으로부터 샘플을 채취하여 메탄올로 추출하고 GC/TOF MS를 이용하여 베체트병 환자들과 정상인들의 대사체 프로파일 차이를 비교 분석하고, 이 차이를 이용하여 베체트병 환자들을 진단할 수 있는 바이오마커 발굴 연구를 수행하였다. The present inventors used a GC / TOF MS to compare the metabolite profiles of patients with Behcet's disease to those of normal subjects, and then, using this difference We conducted a biomarker discovery study to diagnose patients with Behcet 's disease.

그 결과, 총 110개의 대사체를 동정하였고, 이 중 유기산류가 가장 많이 검출되었으며, 그 다음으로 아미노산류, 당류, 지방산류, 아민류, 인산류 등의 순서로 검출되었다. As a result, a total of 110 metabolites were identified. Among them, organic acids were detected most frequently, followed by amino acids, sugars, fatty acids, amines, and phosphoric acids.

Urea를 제외한 109개의 대사 물질을 통계분석에 이용하였으며, 30명의 베체트병 환자와 30명의 건강한 대조군의 소변을 비교하였을 때, 직교부분최소제곱회귀법(OPLS-DA)을 통해 베체트병 환자들과 건강한 대조군의 소변 내 대사체가 분명한 차이를 보임을 확인하였으며, VIP 값이 1.0 이상, fold change 1.5 이상, AUC 0.800 이상, p-value 0.01 미만의 10개의 대사물질을 신규 생체표지자 후보 물질로 선정하였다. 또한, 베체트병의 특이적 대사체 프로파일과 후보 생체표지자가 베체트병 치료를 위해 투여한 약물에 의한 영향이 아니라는 것을 확인하기 위해 베체트병에서 투여한 약물에 따라 그룹을 나누어 PLS-DA 분석을 시행하였다, 그 결과 베체트병에서 투여한 약물에 따른 대사체적 차이가 없음을 확인하였다. A total of 109 metabolites, excluding Urea, were used for statistical analysis. The urine samples from 30 patients with Behcet's disease and 30 healthy controls were compared with those of Behcet's disease patients and healthy control subjects using the orthogonal least squares regression (OPLS-DA) And 10 metabolites with a VIP value of 1.0 or more, fold change of 1.5 or more, AUC of 0.800 or more, and p-value of less than 0.01 were selected as candidates for new biomarkers. In addition, in order to confirm that the specific metabolite profile of the disease and the effect of the candidate biomarker on the drug used for the treatment of Behcet's disease, the group was divided into PLS-DA analysis according to the drugs administered in Behcet's disease As a result, it was confirmed that there was no difference in metabolism according to the drug administered in the disease.

또한, 보다 쉽고 강력한 진단을 위하여, 후보 생체표지자로 선정된 10개의 대사물질로 구성된 베체트병을 감별하는 대사체적 생체표지자 panel을 생성하였다. 10개 대사체의 생체표지자 panel이 베체트병의 진단적 목적의 이용 가능성을 확인하기 위해 ROC curve를 이용하여 검증하였으며, sensitivity가 96.7%, specificity가 93.3%, AUC 값 0.974로 베체트병을 진단하는 데 매우 우수한 결과를 보였다. 또한, 이 모델의 적정성을 확인하기 위해 다시 외부에서 받은 14개의 베체트병 환자와 11개의 건강한 대조군의 소변을 이용하여 10개 대사체 생체표지자 panel의 외부 샘플 진단 가능 여부를 살펴보았다. 그 결과 우리가 발견한 10개의 대사물질을 이용한 생체표지자 panel이 베체트병 진단에 적절함을 검증할 수 있었다.In addition, for easier and more robust diagnosis, a metabolic biomarker panel was constructed to discriminate between BDs composed of 10 metabolites selected as candidate biomarkers. A biomarker panel of 10 metabolites was verified using the ROC curve to confirm the diagnostic utility of Behcet's disease. The sensitivity, specificity, and AUC of the test were 96.7%, 93.3%, and 0.974, respectively, Showed very good results. In order to confirm the adequacy of this model, we also examined the possibility of external sampling of 10 metabolic biopsy panels using urine from 14 patients with Behcet's disease and 11 healthy controls. As a result, we could confirm that the biomarker panel using the 10 metabolites we found is appropriate for the diagnosis of Behcet 's disease.

보다 바람직하게는 fold change가 2.0 이상인 만노오스(mannose), 시툴린(L-citrulline), 하이폭산틴(hypoxanthine), 글루콘산 락톤(gluconic acid lactone), 구아닌(guanine) 및 3-하이드록시피리딘(3-hydroxypyrydine)으로 구성되는 군으로부터 선택된 하나 이상의 대사물질을 사용하여 베체트병을 진단할 수 있다. L -citrulline, hypoxanthine, gluconic acid lactone, guanine and 3-hydroxypyridine (hereinafter referred to as " 3-hydroxypyridine " -hydroxypyrydine) can be used to diagnose Behcet's disease.

본 명세서에서 용어 "진단"은 특정 질병 또는 질환에 대한 한 객체의 감수성(susceptibility)을 판정하는 것, 한 객체가 특정 질병 또는 질환을 현재 가지고 있는지 여부를 판정하는 것(예컨대, 베체트병 의 동정), 특정 질병 또는 질환에 걸린 한 객체의 예후(prognosis)를 판정하는 것, 또는 테라메트릭스(therametrics)(예컨대, 치료 효능에 대한 정보를 제공하기 위하여 객체의 상태를 모니터링 하는 것)을 포함한다.As used herein, the term " diagnosis " includes determining the susceptibility of an object to a particular disease or disorder, determining whether an object currently has a particular disease or disorder (e.g., identifying a Behcet's disease) , Determining the prognosis of an object that has suffered a particular disease or disorder, or therametrics (e.g., monitoring the status of an object to provide information about treatment efficacy).

본 발명의 진단 키트에 포함된 정량 장치는 크로마토그래피/질량분석기일 수 있다. The quantification device included in the diagnostic kit of the present invention may be a chromatography / mass spectrometer.

본 발명에서 이용되는 크로마토그래피는 가스 크로마토그래피(Gas Chromatography), 액체-고체 크로마토그래피(Liquid-Solid Chromatography, LSC), 종이 크로마토그래피(Paper Chromatography, PC), 박층 크로마토그래피(Thin-Layer Chromatography, TLC), 기체-고체 크로마토그래피(Gas-Solid Chromatography, GSC), 액체-액체 크로마토그래피(Liquid-Liquid Chromatography, LLC), 포말 크로마토그래피(Foam Chromatography, FC), 유화 크로마토그래피(Emulsion Chromatography, EC), 기체-액체 크로마토그래피(Gas-Liquid Chromatography, GLC), 이온 크로마토그래피(Ion Chromatography, IC), 겔 여과 크로마토그래피(Gel Filtration Chromatograhy, GFC) 또는 겔 투과 크로마토그래피(Gel Permeation Chromatography, GPC)를 포함하나, 이에 제한되지 않고 당업계에서 통상적으로 사용되는 모든 정량용 크로마토그래피를 사용할 수 있다. 바람직하게는, 본 발명에서 이용되는 크로마토그래피는 가스 크로마토그래피이다. 더불어 본 발명에서 이용되는 질량분석기는 MALDI-TOF MS 또는 TOF MS이고, 보다 바람직하게는 TOF MS이다.Chromatography used in the present invention can be carried out by gas chromatography, liquid-solid chromatography (LSC), paper chromatography (PC), thin-layer chromatography (TLC) (GSC), Liquid-Liquid Chromatography (LLC), Foam Chromatography (FC), Emulsion Chromatography (EC), and the like. But not limited to, gas-liquid chromatography (GLC), ion chromatography (IC), gel filtration chromatography (GFC), or Gel Permeation Chromatography , But it is not limited thereto and any quantitative chromatography commonly used in the art can be used. Preferably, the chromatography used in the present invention is gas chromatography. In addition, the mass analyzer used in the present invention is MALDI-TOF MS or TOF MS, more preferably TOF MS.

본 발명의 소변 대사체는 가스 크로마토그래피에서 각 성분들이 분리되며, Q-TOF MS를 거쳐 얻어진 정보를 이용하여 정확한 분자량 정보뿐만 아니라 구조 정보(elemental composition)를 통해 구성 성분을 확인한다.The urine metabolites of the present invention are separated from each other by gas chromatography, and the constituent components are identified through the elemental composition as well as accurate molecular weight information using the information obtained through Q-TOF MS.

본 발명의 바람직한 구현예에 따르면, 구아닌, 피롤-2-카르복실레이트 및 3-하이드록시피리딘으로 이루어진 군에서 선택된 하나 이상의 농도가 증가되는 경우, 베체트병을 나타내고 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상의 농도가 감소되는 경우, 베체트병을 나타낸다. According to a preferred embodiment of the present invention, when the concentration of at least one selected from the group consisting of guanine, pyrrole-2-carboxylate and 3-hydroxypyridine is increased, it indicates a disease of Behcet's disease and hypoxanthine, L- At least one selected from the group consisting of L- citrulline, isothreonate, galactonate, gluconic acid lactone, sedoheptulose, and mannose. When the concentration is decreased, it represents a disease of Behcet's disease.

본 명세서에서, 용어 "소변 대사체 농도의 증가"는 건강한 정상인에 비해 베체트병 환자의 소변 대사체 농도가 측정 가능할 정도로 유의하게 증가된 것을 의미하며, 바람직하게는 70% 이상 증가된 것을 의미하고, 보다 바람직하게는 30% 이상 증가된 것을 의미한다. In the present specification, the term " increase in urine metabolite concentration " means that the urinary metabolite concentration of the patients with Behçet's disease is significantly increased as compared with a healthy normal person, preferably 70% More preferably by 30% or more.

본 명세서에서, 용어 "소변 대사체 농도의 감소"는 건강한 정상인에 비해 베체트병 환자의 소변 대사체 농도가 측정 가능할 정도로 유의하게 감소된 것을 의미하며, 바람직하게는 40% 이상 감소된 것을 의미하고, 보다 바람직하게는 20% 이상 감소된 것을 의미한다. As used herein, the term " reduction in urine metabolite concentration " means that the urinary metabolite concentration in the patients with Behcet's disease is significantly reduced to a measurable level, preferably 40% or more, More preferably by 20% or more.

본 발명에 따르면, 구아닌, 피롤-2-카르복실레이트 및 3-하이드록시피리딘으로 이루어진 군에서 선택된 하나 이상은 건강한 정상인에 비해 베체트병 환자에서 유의하게 증가된 농도를 나타내고, 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상은 건강한 정상인에 비해 베체트병 환자에서 유의하게 감소된 농도를 나타낸다(표 3).According to the present invention, at least one selected from the group consisting of guanine, pyrrole-2-carboxylate and 3-hydroxypyridine exhibits a significantly increased concentration in patients with Behcet's disease as compared to healthy normal subjects, hypoxanthine, L -citrulline, isothreonate, galactonate, gluconic acid lactone, sedoheptulose, and mannose in the group consisting of L- cysteine, L- citrulline, isothreonate, galactonate, gluconic acid lactone, One or more selected patients showed a significantly reduced concentration in patients with Behcet's disease compared to healthy controls (Table 3).

본 발명은 또한 정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성을 검출하는 방법으로,The present invention also provides a method for detecting metabolite differentiation between urine obtained from a normal control and Behcet's disease,

(1) GC/TOF MS(gas chromatography/time-of-flight mass spectrometry)를 이용한 대사체 분석 단계; (1) metabolism analysis step using GC / TOF MS (gas chromatography / time-of-flight mass spectrometry);

(2) GC/TOF MS에서 동정된 대사체에 대해 부분최소자승판별분석(PLS-DA)를 이용하여 대사체 프로파일의 차이를 확인하는 단계;(2) identifying differences in metabolite profiles using partial least squares discriminant analysis (PLS-DA) for the metabolites identified in GC / TOF MS;

(3) PLS-DA에서 도출된 대사체의 VIP(Variable Importance for Projection) 값이 1.0 이상인 값을 대사체 바이오마커 후보물질로 선정하고, PLS-DA의 로딩 값을 통해 대사체 바이오마커 후보물질의 증감 확인하는 단계; 및(3) The value of Variable Importance for Projection (VIP) of the metabolite derived from PLS-DA was selected as a candidate for metabolite biomarker, and the value of PLS-DA as a metabolite biomarker candidate Ascertaining and decreasing; And

(4) ROC 곡선(Receiver Operating Characteristic curve)을 이용하여 대사체 바이오마커를 검증하는 단계;(4) verifying the metabolite biomarker using an ROC curve (Receiver Operating Characteristic curve);

를 순차적으로 적용하여, 소변으로부터 대사체 바이오마커를 분석하는 것을 포함하는 정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성 분석 방법에 관한 것이다.To a method for analyzing metabolite differentiation between urine obtained from a normal control group and Behcet's disease, which comprises analyzing metabolite biomarkers from urine.

본 발명의 두 생체시료군 간의 대사체 차별성 분석 방법은 베체트병과 정상군에서 얻은 소변 시료군 간의 대사체 차별성을 분석하는 방법을 예로 들어 구체적으로 설명한다.The metabolic differentiation analysis method between the two biological sample groups of the present invention will be described in detail as an example of a method of analyzing the metabolic differentiation between the diseased sample group obtained from the diseased disease group and the diseased disease group.

우선, 정산인과 베체트병 환자에서 채취한 소변 샘플을 100% 메탄올로 추출한 후 GC/TOF MS 분석에 사용할 수 있도록 공지 기술을 이용하여 유도체화 과정을 거친다. First, urine samples collected from patients with measles and Behcet's disease are extracted with 100% methanol and subjected to derivatization using known techniques so that they can be used for GC / TOF MS analysis.

상기 GC/TOF MS를 이용한 소변의 대사체 분석 방법은 소변 추출물을 GC/TOF MS 기기로 분석하고, 분석 결과를 통계처리 가능한 수치로 변환한 다음, 변환된 수치를 이용하여 통계학적으로 두 생체시료군의 차별성을 검증하는 것을 포함한다.The method of analyzing urine metabolism using the GC / TOF MS was performed by analyzing the urine extract with a GC / TOF MS instrument, converting the analysis result into a statistically processable value, And verifying the differentiation of the group.

GC/TOF MS 분석 결과를 통계처리 가능한 수치로 변환하는 것은 총 분석시간을 단위시간 간격으로 나누어 단위시간 동안 나타난 크로마토그램 피크의 면적 또는 높이 중 가장 큰 수치를 단위시간 동안의 대표값으로 정하는 것일 수 있다.Converting the GC / TOF MS analysis results to a statistically processable value can be done by dividing the total analysis time by the unit time interval and setting the largest value of the area or height of the chromatogram peak during the unit time as the representative value for the unit time have.

본 발명의 일 구현예에 따르면, GC/TOF MS 분석 결과 총 110개의 대사체를 동정하였고, 이 중 유기산류가 가장 많이 검출 되었으며, 그 다음으로 아미노산류, 당류, 지방산류, 아민류, 인산류 등의 순서로 검출 되었다.According to one embodiment of the present invention, a total of 110 metabolites were identified by GC / TOF MS analysis, and organic acids were detected the most, followed by amino acids, saccharides, fatty acids, amines, .

상기 GC/TOF MS 분석 결과 나온 대사체의 강도를 총 동정된 대사체의 강도 합으로 나누어 각 대사체를 표준화하고, PLS-DA 분석을 실시한다. Each metabolite is standardized and the PLS-DA analysis is performed by dividing the intensity of the metabolite from the GC / TOF MS analysis by the sum of the intensities of the metabolites.

대사체의 PLS-DA 로딩 값과 VIP 값으로 구성된 V-plot를 작성하고, VIP 값이 1.0 이상인 값을 대사체 바이오마커 후보물질로 선정하고, PLS-DA의 로딩 값의 증감을 확인하며, 이때 로딩 값이 양수인 것은 대사체의 증가 경향을, 로딩 값이 음수인 것은 대사체의 감소 경향을 나타내는 것이다.A V-plot composed of the PLS-DA loading value and the VIP value of the metabolite is prepared. A value with a VIP value of 1.0 or more is selected as a candidate for the metabolism biomarker, and the increase / decrease of the loading value of PLS-DA is confirmed. When the loading value is positive, the increasing tendency of the metabolism and when the loading value is negative is the decreasing tendency of metabolism.

GC/TOF MS에서 분석된 혈액의 대사체의 강도를 이용하여 대사체의 증감을 확인할 수 있다.The increase / decrease of the metabolism can be confirmed by using the intensity of the metabolism of blood analyzed by GC / TOF MS.

ROC 곡선을 통해 상기 대사체 바이오마커를 검증한다.The metabolite biomarker is verified through the ROC curve.

본 발명의 일 구현예에 따르면, 베체트병을 진단하기 위한 바이오마커로, 구아닌(guanine), 3-하이드록시피리딘(3-hydroxypyridine), 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 피롤-2-카르복실레이트(pyrrole-2-carboxylate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상을 사용할 수 있다. According to one embodiment of the present invention, a biomarker for the diagnosis of Behcet's disease, guanine (guanine), 3- hydroxymethyl pyridine (3-hydroxypyridine), hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline ), Isothreonate, pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose and mannose mannose) may be used.

본 발명의 정상군과 베체트병에서 얻은 소변 시료군 간의 대사체 차별성 분석 방법을 통해 보다 일관성 있고 신뢰도 높은 정확한 베체트병을 진단할 수 있고, 이를 치료제 개발에 적용할 수 있다. The metabolic differentiation analysis method between the normal group of the present invention and the urine sample group obtained from the disease of Betezchez disease can diagnose a more accurate and reliable Betezche disease, and can be applied to the development of a therapeutic agent.

이하, 본 발명에 따르는 실시예를 통하여 본 발명을 보다 상세히 설명하나, 본 발명의 범위가 하기 제시된 실시예에 의해 제한되는 것은 아니다. Hereinafter, the present invention will be described in more detail with reference to the following examples. However, the scope of the present invention is not limited by the following examples.

[[ 실시예Example ]]

실시예Example 1:  One: GCGC // TOFTOF MS를MS 이용한  Used 대사체Metabolism 동정  Sympathy

베체트병 환자 및 건강한 대조군 각각의 소변 10μl에 순수 메탄올 990μl을 섞고 강하게 볼텍싱 한 후에 원심분리하여 대사체를 추출하였다.950 μl of pure methanol was mixed with 10 μl of urine of each of the patients with Behcet's disease and healthy controls, and vigorously vortexed, followed by centrifugation to extract the metabolites.

GC/TOF MS 분석을 위한 유도체화 과정은 다음과 같다. The derivatization process for GC / TOF MS analysis is as follows.

추출한 검체를 스피드 백으로 건조시킨 후에 5μl의 40%(w/v) 농도의 O-methylhydroxylamine hydrochloride/pyridine을 넣고 30℃ 200 rpm에서 90분간 반응을 시켰다. 그리고 45 μl의 N-methyl-N-(trimethylsilyl)trifluoroacetamide를 넣고 37℃ 200 rpm에서 30분간 반응을 실시하였다. After extracting the specimen, 5 μl of 40% (w / v) O- methylhydroxylamine hydrochloride / pyridine was added to the sample and the reaction was carried out at 30 ° C and 200 rpm for 90 minutes. Then, 45 μl of N- methyl- N - (trimethylsilyl) trifluoroacetamide was added and reaction was carried out at 37 ° C. and 200 rpm for 30 minutes.

GC/TOF MS 분석을 위한 기기 조건은 다음과 같다. The equipment conditions for GC / TOF MS analysis are as follows.

분석할 때 사용한 컬럼은 RTX-5Sil MS capillary column (30 m length, 0.25 mm film thickness, 및 25 mm inner diameter)이며, GC 컬럼 온도 조건은 먼저 50℃에서 5분간 유지시킨 후 330℃까지 승온시킨 다음 1분간 유지하였다. 1μl의 샘플을 비분할법(splitless)으로 주입(injection)하였다. Transfer line 온도와 Ion source 온도는 각각 280℃, 250℃로 유지시켰다. GC/TOF MS 결과를 보유하고 있는 라이브러리에서 찾아 110개의 대사체를 동정하였으며, 기기 검출 영역 이상으로 고농도인 urea가 통계 분석에 악영향을 미칠 수 있으므로 109개의 대사체를 분석에 사용하였다(표 1). The column used was RTX-5Sil MS capillary column (30 m length, 0.25 mm film thickness, and 25 mm inner diameter). The GC column temperature condition was maintained at 50 ° C for 5 minutes, then heated to 330 ° C And maintained for 1 minute. 1 [mu] l of the sample was injected in a splitless manner. Transfer line temperature and ion source temperature were maintained at 280 ℃ and 250 ℃, respectively. A total of 110 metabolites were identified in the library containing GC / TOF MS results, and 109 high metabolites were used for analysis (Table 1), because high concentrations of urea above the detection region of the instrument may adversely affect statistical analysis .

하기 표 1은 베체트병 환자와 건강한 대조군의 소변 검체를 이용하여 대사체 분석 결과 확인된 109개 대사체를 나타낸 것으로, 각각의 대사체군별로 분류하였을 때, 유기산 23.6%, 아미노산 21.8%, 당 21.8%, 지방산 12.7%, 아민 11.8%, 인 1.8%, 기타 6.4%로 나타났다.Table 1 below shows the metabolites of 109 metabolites identified by the metabolite analysis using the urine samples of patients with Behcet's disease and healthy controls. When classified by metabolic group, organic acid 23.6%, amino acid 21.8%, and sugar 21.8% , Fatty acids 12.7%, amines 11.8%, phosphorus 1.8% and others 6.4%.

IdentifiedIdentified metabolitesmetabolites AminesAmines 3-hydroxypyridine3-hydroxypyridine 5'-deoxy-5'-methylthioadenosine5'-deoxy-5'-methylthioadenosine AdenosineAdenosine benzamide비메amide guanineguanine hypoxanthinehypoxanthine inosineinosine O-phosphorylethanolamine O- phosphorylethanolamine putrescineputrescine spermidinespermidine thyminethymine xanthinexanthine AminoAmino acidsacids 5-aminovalerate5-aminovalerate alaninefield asparagineasparagine asparagine dehydratedasparagine dehydrated glutamateglutamate glycineglycine isoleucineisoleucine isothreonateisothreonate L-citrulline L- citulline L-cysteine L- cysteine L-homoserine L- homoserine lysinelysine methionine메티오 로 N-methylalanine N- methylalanine ornithineornithine oxoprolineoksoproline phenylalanine피 phenylalanine prolineproline serineserine threoninethreonine tryptophantryptophan tyrosinetyrosine valinevaline β-alanine β- alanine FattyFatty acidsacids 1-monopalmitin1-monopalmitin arachidic acidarachidic acid arachidonic acidarachidonic acid capric acidcapric acid heptadecanoic acidheptadecanoic acid lauric acidlauric acid lignoceric acidlignoceric acid myristic acidmyristic acid octadecanoloctadecanol oleic acidoleic acid palmitic acidpalmitic acid pelargonic acidpelargonic acid pentadecanoic acidpentadecanoic acid stearic acidstearic acid OrganicOrganic acidsacids 2-hydroxyvalerate2-hydroxyvalerate 2-ketoadipate2-ketoadipate 3-hydroxypropionate3-hydroxypropionate 3-phenyllactate3-phenyllactate adipateadipate aminomalonateaminomalonate aspartateaspartate citramalatecitramalate citratecitrate fumaratefumarate galactonategalactonate galacturonategalacturonate gluconategluconate gluconic acid lactonegluconic acid lactone glycerateglycerate glycolateglycolate hexonatehexonate isocitrateisocitrate lactatelactate malatemalate malonatemalonate N-carbamoylaspartate N- carbamoylaspartate oxalateoksalate pyrrole-2-carboxylatepyrrole-2-carboxylate succinatesuccinate α-ketoglutarate α- ketoglutarate SugarsSugars andand sugarsugar alcoholsalcohols 1,5-anhydroglucitol1,5-anhydroglucitol arabitolarabitol cellobiosecellobiose fructosefructose galactinolgalactinol galactose가카성 glucoseglucose glycerolglycerol lactuloselactulose lyxoselyxose maltotriosemaltotriose mannitolmannitol mannosemannose melezitosemelissose melibiosemelibiose myo-inositolmyo-inositol palatinitolpalatinitol phytolphytol riboseribose sedoheptulose시edoheptulose sedoheptulose anhydroussedoheptulose anhydrous threitolthreitol threosethreose xylosexylose PhosphatesPhosphates phosphatephosphate sedoheptulose-7-phopsphatesedoheptulose-7-phopsphate OthersOthers indole-3-lactateindole-3-lactate nicotinamidenicotinamide salicylaldehydesalicylaldehyde taurinetaurine uraciluracil uric aciduric acid xanthurenic acidxanthurenic acid

실시예Example 2:  2: OPLSOPLS -- DA를DA 이용한 베체트병 환자와 건강한 대조군의 소변 내  Of patients with Behcet's disease and healthy controls 대사체Metabolism 프로파일 차이 Profile difference

실시예 1로부터 나온 대사체의 강도(intensity)를 총 동정된 대사체의 강도 합으로 나누어 각 대사체를 표준화하였다. 그 후 SIMCA-P+ (ver. 14.1)를 이용하여 OPLS-DA 분석을 실시하였다.Each metabolite was standardized by dividing the intensity of the metabolite from Example 1 by the sum of the intensities of the metabolites. OPLS-DA analysis was then performed using SIMCA-P + (ver. 14.1).

도 1에 나타낸 바와 같이, 베체트병 환자와 건강한 대조군의 소변 내 대사체 프로파일링이 명확하게 차이가 나는 것을 확인하였다. As shown in FIG. 1, it was confirmed that profiling of metabolites in the urine of patients with Behcet's disease and healthy controls was significantly different.

표 2에 각 대사체가 모델에 미치는 영향의 정도를 나타내는 지표인 loading 및 VIP values를 표시하였다. Table 2 shows loading and VIP values, which are indicators of the degree of impact of each metabolite on the model.

MetabolitesMetabolites Loading valuesLoading values VIP valuesVIP values MetabolitesMetabolites Loading valuesLoading values VIP valuesVIP values 1,5-anhydroglucitol1,5-anhydroglucitol -0.0593-0.0593 0.71130.7113 lignoceric acidlignoceric acid -0.0642-0.0642 1.05901.0590 1-monopalmitin1-monopalmitin -0.1023-0.1023 1.48341.4834 lysinelysine -0.0013-0.0013 0.81950.8195 2-hydroxypyridine2-hydroxypyridine -0.0192-0.0192 0.27000.2700 lyxoselyxose -0.0551-0.0551 0.75330.7533 2-hydroxyvalerate2-hydroxyvalerate 0.12320.1232 1.21381.2138 malatemalate -0.0845-0.0845 1.05761.0576 2-ketoadipate2-ketoadipate -0.1000-0.1000 1.32081.3208 malonatemalonate -0.0948-0.0948 0.98210.9821 3-hydroxypropionate3-hydroxypropionate -0.0196-0.0196 0.39130.3913 maltotriosemaltotriose -0.1108-0.1108 1.38781.3878 3-hydroxypyridine3-hydroxypyridine -0.1188-0.1188 1.18661.1866 mannitolmannitol -0.0519-0.0519 0.54840.5484 3-phenyllactate3-phenyllactate -0.0726-0.0726 0.76350.7635 mannosemannose 0.18830.1883 1.88231.8823 5-aminovalerate5-aminovalerate -0.0015-0.0015 0.18580.1858 melezitosemelissose -0.1391-0.1391 1.55411.5541 5'-deoxy-5'-methylthioadenosine5'-deoxy-5'-methylthioadenosine 0.06560.0656 0.67510.6751 melibiosemelibiose -0.1041-0.1041 1.06221.0622 adenosineadenosine 0.09880.0988 0.97300.9730 methionine메티오 로 -0.0280-0.0280 0.36810.3681 adipateadipate 0.06340.0634 0.65780.6578 myo-inositolmyo-inositol 0.00370.0037 0.42710.4271 alaninefield -0.0434-0.0434 0.58910.5891 myristic acidmyristic acid -0.1031-0.1031 1.39641.3964 alpha-keto glutaratealpha-keto glutarate 0.07480.0748 0.80830.8083 N-carbamoylaspartateN-carbamoylaspartate -0.0447-0.0447 0.44260.4426 aminomalonateaminomalonate 0.01290.0129 0.53100.5310 nicotinamidenicotinamide 0.13710.1371 1.36561.3656 arabitolarabitol 0.03060.0306 0.73380.7338 N-methylalanineN-methylalanine 0.11270.1127 1.13331.1333 arachidic acidarachidic acid -0.1367-0.1367 1.63011.6301 octadecanoloctadecanol -0.1541-0.1541 1.71121.7112 arachidonic acidarachidonic acid -0.0296-0.0296 0.35090.3509 oleic acidoleic acid -0.0252-0.0252 0.60950.6095 asparagineasparagine -0.0419-0.0419 0.67070.6707 O-phosphorylethanolamineO-phosphorylethanolamine 0.00820.0082 0.21120.2112 asparagine dehydratedasparagine dehydrated -0.0549-0.0549 0.65390.6539 ornithineornithine -0.0078-0.0078 0.64790.6479 aspartic acidaspartic acid -0.0002-0.0002 0.13250.1325 oxalateoksalate 0.08660.0866 0.94150.9415 benzamide비메amide -0.0668-0.0668 0.67040.6704 oxoprolineoksoproline 0.12140.1214 1.29781.2978 capric acidcapric acid 0.07420.0742 0.84570.8457 palatinitolpalatinitol 0.00740.0074 0.16720.1672 cellobiosecellobiose -0.0088-0.0088 0.32570.3257 palmitic acidpalmitic acid -0.1548-0.1548 1.83111.8311 citramalatecitramalate 0.11690.1169 1.17891.1789 pelargonic acidpelargonic acid -0.0562-0.0562 1.09901.0990 citratecitrate 0.14250.1425 1.46141.4614 pentadecanoic acidpentadecanoic acid 0.03510.0351 0.35000.3500 fructosefructose -0.0652-0.0652 0.74390.7439 phenylalanine피 phenylalanine -0.0275-0.0275 0.28350.2835 fumaratefumarate -0.0495-0.0495 0.53530.5353 phosphatephosphate -0.0275-0.0275 0.39710.3971 galactinolgalactinol 0.07530.0753 0.76050.7605 phytolphytol -0.0349-0.0349 0.41570.4157 galactonategalactonate 0.19090.1909 1.94951.9495 prolineproline -0.0106-0.0106 0.32570.3257 galactose가카성 0.10390.1039 1.12821.1282 putrescineputrescine -0.0598-0.0598 0.67290.6729 galacturonategalacturonate 0.09930.0993 1.06501.0650 pyrrole-2-carboxylatepyrrole-2-carboxylate -0.1471-0.1471 1.59411.5941 gluconategluconate 0.12260.1226 1.20991.2099 riboseribose 0.06830.0683 0.73090.7309 gluconic acid lactonegluconic acid lactone 0.12640.1264 1.27581.2758 salicylaldehydesalicylaldehyde -0.0550-0.0550 1.10261.1026 glucoseglucose -0.0077-0.0077 0.39530.3953 sedoheptulose시edoheptulose 0.16920.1692 1.74241.7424 glutamateglutamate 0.02980.0298 0.31660.3166 sedoheptulose anhydroussedoheptulose anhydrous 0.02750.0275 0.92730.9273 glycerateglycerate 0.10720.1072 1.12581.1258 sedoheptulose-7-phopsphatesedoheptulose-7-phopsphate -0.0626-0.0626 0.62280.6228 glycerolglycerol -0.0282-0.0282 0.74820.7482 serineserine -0.0072-0.0072 0.38370.3837 glycineglycine -0.0204-0.0204 0.67320.6732 spermidinespermidine -0.0049-0.0049 0.05420.0542 glycolateglycolate 0.05280.0528 0.77630.7763 stearic acidstearic acid -0.1473-0.1473 1.73481.7348 guanineguanine -0.1303-0.1303 1.30161.3016 succinatesuccinate 0.01580.0158 0.30170.3017 heptadecanoic acidheptadecanoic acid -0.1045-0.1045 1.35281.3528 taurinetaurine 0.01730.0173 0.52940.5294 hexonatehexonate 0.00930.0093 0.61130.6113 threitolthreitol 0.13150.1315 1.43461.4346 hypoxanthinehypoxanthine 0.15350.1535 1.51401.5140 threoninethreonine -0.0198-0.0198 0.77590.7759 indole-3-lactateindole-3-lactate -0.0149-0.0149 0.62590.6259 threosethreose -0.1359-0.1359 1.60011.6001 inosineinosine -0.0190-0.0190 0.49950.4995 thyminethymine 0.07910.0791 0.77970.7797 isocitrateisocitrate 0.14250.1425 1.45831.4583 tryptophantryptophan 0.04880.0488 0.93560.9356 isoleucineisoleucine -0.0178-0.0178 0.24880.2488 tyrosinetyrosine 0.06190.0619 1.06721.0672 isothreonateisothreonate 0.19310.1931 1.98921.9892 uraciluracil 0.01760.0176 0.25510.2551 lactatelactate -0.0555-0.0555 1.19981.1998 uric aciduric acid -0.0388-0.0388 0.45790.4579 lactuloselactulose -0.0436-0.0436 0.43620.4362 valinevaline -0.0324-0.0324 0.43960.4396 lauric acidlauric acid -0.0764-0.0764 1.15321.1532 xanthinexanthine 0.03610.0361 0.45110.4511 L-citrullineL-citrulline 0.19170.1917 2.01432.0143 xanthurenic acidxanthurenic acid 0.08340.0834 0.93890.9389 L-cysteineL-cysteine 0.08070.0807 1.12931.1293 xylosexylose -0.0548-0.0548 0.86910.8691 L-homoserineL-homoserine -0.0861-0.0861 0.91580.9158 β-alanineβ-domain 0.02370.0237 0.30000.3000

실시예Example 3: 베체트병 환자에 특이적인 생체표지자 대사물질들의 선별 3: Selection of biomarker metabolites specific for patients with Behcet's disease

베체트병 환자에서 특이적으로 증감한 생체표지자를 찾기 위해서, 각각의 대사물질로부터 실시예 2로부터 도출된 대사체 프로파일링의 차이에 영향을 미치는 VIP 값과 fold channge, AUC, p-value를 구하였다. VIP 값이 1.0 이상, fold change 1.5 이상, AUC 0.800 이상, p-value 0.01 미만의 기준을 각각의 대사물질에 대해 구하였고, 10개의 대사물질이 베체트병 진단에 적절함을 보였다(표 3). In order to find specifically biotransformed biomarkers in patients with Behcet's disease, the VIP value, fold channel, AUC, and p- value, which affect the difference in metabolism profiling derived from Example 2, were calculated for each metabolite . VIP values of 1.0 or more, fold change of 1.5 or more, AUC of 0.800 or more, and p- value of less than 0.01 were obtained for each metabolite, and 10 metabolites were appropriate for the diagnosis of Behcet's disease (Table 3).

하기 표 3은 베체트병 진단을 위한 잠재적 생체표지자로 선정된 10개의 대사물질의 VIP, AUC, fold change, p-value 값[BD, 베체트병 환자; control, 건강한 대조군]을 나타낸 것이다.Table 3 below shows the VIP, AUC, fold change, and p -value values of 10 metabolites selected as potential biomarkers for BD diagnosis [BD, Behcet's disease; control, healthy control].

또한, 이 대사물질들의 절대적 intensity를 박스 플롯을 이용하여 그룹별로 비교하였다 (도 2).The absolute intensities of these metabolites were also compared in groups using box plots (Figure 2).

MetaboliteMetabolite VIPVIP FoldFold AUCAUC pp -- valuevalue MetabolitesMetabolites withwith higherhigher abundancesabundances inin thethe BDBD groupgroup thanthan inin thethe controlcontrol groupgroup guanineguanine 1.63 1.63 2.33 2.33 0.834 0.834 3.68E-053.68E-05 pyrrole-2-carboxylatepyrrole-2-carboxylate 1.40 1.40 1.95 1.95 0.806 0.806 1.01E-041.01E-04 3-hydroxypyridine3-hydroxypyridine 1.36 1.36 2.25 2.25 0.846 0.846 2.75E-032.75E-03 MetabolitesMetabolites withwith higherhigher abundancesabundances inin thethe controlcontrol groupgroup thanthan inin thethe BDBD groupgroup mannosemannose 2.02 2.02 3.28 3.28 0.860 0.860 1.11E-081.11E-08 L-citrullineL-citrulline 1.87 1.87 2.08 2.08 0.884 0.884 2.19E-082.19E-08 galactonategalactonate 1.79 1.79 1.78 1.78 0.856 0.856 1.54E-071.54E-07 isothreonateisothreonate 1.79 1.79 1.76 1.76 0.862 0.862 1.38E-071.38E-07 sedoheptulose시edoheptulose 1.55 1.55 1.69 1.69 0.820 0.820 9.53E-069.53E-06 hypoxanthinehypoxanthine 1.48 1.48 2.25 2.25 0.849 0.849 1.06E-041.06E-04 gluconic acid lactonegluconic acid lactone 1.24 1.24 2.07 2.07 0.818 0.818 8.33E-048.33E-04

AUC, area under the ROC curve; BD, Behcet's disease; VIP, variable importance on projection AUC, area under the ROC curve; BD, Behcet's disease; VIP, variable importance on projection

실시예Example 4:  4: PLSPLS -- DA를DA 이용한 베체트병 환자에서 증감한 대사물질에 약물 효과 존재 유무 검증 Verification of the presence or absence of drug effects on metabolites in patients with Behcet's disease

베체트병 환자에서 특이적으로 증감한 생체표지자가 약물에 의해 증감한 물질이 아님을 보이기 위해서, 각각의 약물투여 그룹 vs. 약물비투여 그룹을 PLS-DA를 이용해 비교한 결과, 분리 수준이 적절하지 않고 재현성이 없는 것으로 나타났다. 3개의 투여된 약물 그룹 steroid, colchicine, azathioprine에서 각각 재현성이 없는 결과를 보였으며, 약물에 따른 차이가 통계적으로 유의미하지 않았다. In order to show that the biomarker specifically increased or decreased in patients with Behcet's disease was not a substance changed by the drug, Comparisons of non-drug-treated groups with PLS-DA showed that the level of separation was not adequate and not reproducible. There was no reproducibility in the three drug groups steroid, colchicine, and azathioprine, respectively, and differences between the drugs were not statistically significant.

따라서, 실시예 3에서 보인 베체트병에서 증감한 대사물질이 질병 자체에 의한 변화이므로 생체표지자로 적절함을 확인하였다(도 3).Therefore, it was confirmed that the metabolite changed in Behcet's disease shown in Example 3 is a change due to the disease itself and thus is suitable as a biomarker (FIG. 3).

실시예Example 5: 소변 검체를 통한 베체트병의 진단을 위해 10개의 대사물질을 이용한 대사체적 진단  5: Metabolic diagnosis using 10 metabolites for the diagnosis of Behcet's disease through urine specimens panel의panel 생성 produce

실시예 3으로부터 선정된 베체트병에서 유의미하게 증가한 3개 대사물질(구아닌, 피롤-2-카르복실레이트, 3-하이드록시피리딘)과 유의미하게 감소한 7개 대사물질(하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose), 만노오스(mannose))의 베체트병 진단을 위한 생체표지자 10개를 동시에 사용하여 베체트병을 진단할 수 있는 대사체적 진단 panel을 OPLS-DA를 통해 생성시켰다. Seven metabolites (hypoxanthine, L (3-hydroxypyridines)) significantly decreased with three metabolites (guanine, pyrrole-2-carboxylate and 3-hydroxypyridine) significantly increased in Behcet's disease selected from Example 3 - Diagnosis of Behcet's disease of L -citulline, isothreonate, galactonate, gluconic acid lactone, sedoheptulose, mannose. A biopsy panel was developed through OPLS-DA to diagnose Behcet's disease using 10 biomarkers.

t[1] 하나의 축을 이용했을 때, R 2 X 값이 0.592, R 2 Y 값이 0.650, Q 2 값이 0.600으로 통계학적으로 유의미한 모델을 통하여 베체트병 환자와 건강한 대조군을 적절하고 재현성 있게 구분하였다(도 4).t [1] When one axis is used, the value of R 2 X is 0.592, R 2 Y value is 0.650 and Q 2 value is 0.600, which is statistically significant. The patients with Behcet's disease and the healthy controls were appropriately and reproducibly distinguished (Fig. 4).

실시예Example 6: 소변 검체를 이용한 베체트병의 진단을 위한  6: Diagnosis of Behcet's disease using urine specimens 대사체적Metabolite volume 진단  Diagnosis panel의panel ROC 및 외부 검체 검증을 통한 모델 검증 Model validation through ROC and external sample validation

실시예 5를 통해 생성된 소변 검체 내 10개의 바이오마커를 통한 베체트병 진단용 대사체적 생체표지자 panel이 진단에 적절한지 살펴보기 위하여 모델 내 각 검체의 PC1 score를 이용해서 ROC(receiver operating characteristic) 곡선을 그렸다. 그 결과, sensitivity가 96.7%, specificity가 93.3%, AUC값이 0.974으로 모델이 베체트병 진단에 매우 적합함을 보였다(도 5). In order to examine whether the biochemical markers of the metabolic biomarker panel for the diagnosis of Behcet's disease through the 10 biomarkers in the urine samples produced in Example 5 were appropriate for diagnosis, the receiver operating characteristic (ROC) curve was calculated using the PC1 score of each sample in the model I painted it. As a result, the sensitivity was 96.7%, the specificity was 93.3%, and the AUC value was 0.974, indicating that the model was well suited for diagnosis of Behcet's disease (FIG. 5).

또한, 이 panel이 외부 검체를 이용하여 베체트 질환의 진단을 예측할 수 있는지 살펴보기 위하여, 베체트병 환자의 소변검체 14개와 건강한 대조군의 소변 검체 11개, 총 25개의 검체를 panel에 집어넣어 예측에 이용하였다. 그 결과, 건강한 대조군 소변 검체 11개는 모두 건강한 대조군의 값을 (모델 내에서 양수) 가져서 건강한 대조군에 속하도록 예측하였으며, 베체트병 환자 소변 검체 14개 중 11개가 베체트병 환자의 값을 (모델 내에서 음수) 가져 베체트병 환자임을 예측하였다. 따라서 25개의 외부 검체 중 22개의 검체를 정확하게 베체트병 환자 혹은 건강한 대조군으로 예측할 수 있음을 나타내어, 10개의 대사체 생체표지자 panel이 외부 검체의 베체트병 진단에도 적절함을 나타내었다 (도 6). In order to examine whether this panel can predict the diagnosis of Behcet's disease using external specimens, 14 specimens of urine samples from Behcet's disease and 11 samples of urine samples from healthy controls, total 25 samples, Respectively. As a result, all 11 healthy control urine specimens had a healthy control value (positive in the model) and predicted to belong to a healthy control group. Eleven out of 14 patients with Behcet's disease urine samples showed values of Behcet's disease And negative predictors of Behcet 's disease. Thus, 22 of the 25 external samples can be accurately predicted as Behcet's disease patients or healthy controls, indicating that 10 metabolic biomarker panels are appropriate for the diagnosis of Behcet's disease of external samples (FIG. 6).

Claims (10)

구아닌(guanine), 3-하이드록시피리딘(3-hydroxypyridine), 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 피롤-2-카르복실레이트(pyrrole-2-carboxylate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상의 소변 대사체에 대한 정량 장치를 포함하는 베체트병 진단 키트.
Guanine (guanine), 3- hydroxymethyl pyridine (3-hydroxypyridine), hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline), iso-threo carbonate (isothreonate), pyrrole-2-carboxylate (pyrrole- The present invention includes a quantitative device for at least one urine metabolite selected from the group consisting of 2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, and mannose. Behcet's disease diagnostic kit.
제 1 항에 있어서,
상기 소변 대사체 중 만노오스(mannose), 시툴린(L-citrulline), 하이폭산틴(hypoxanthine), 글루콘산 락톤(gluconic acid lactone), 구아닌(guanine) 및 3-하이드록시피리딘(3-hydroxypyridine)으로 구성되는 군으로부터 선택된 하나 이상을 포함하는 베체트병 진단 키트.
The method according to claim 1,
It is also possible to use mannose, L- citrulline, hypoxanthine, gluconic acid lactone, guanine and 3-hydroxypyridine in the urine metabolite Wherein the diagnostic kit comprises at least one selected from the group consisting of:
제 1 항에 있어서,
정량 장치는 크로마토그래피/질량분석기인 베체트병 진단 키트.
The method according to claim 1,
The quantification device is a chromatography / mass spectrometer, the Behcet's disease diagnostic kit.
제 1 항에 있어서,
구아닌(guanine), 피롤-2-카르복실레이트(pyrrole-2-carboxylate) 및 3-하이드록시피리딘(3-hydroxypyridine)으로 이루어진 군에서 선택된 하나 이상의 농도가 증가되는 경우, 베체트병을 나타내는 것인 베체트병 진단 키트.
The method according to claim 1,
When the concentration of one or more selected from the group consisting of guanine, pyrrole-2-carboxylate and 3-hydroxypyridine is increased, Disease diagnosis kit.
제 1 항에 있어서,
하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상의 농도가 감소하는 경우, 베체트병을 나타내는 것인 베체트병 진단 키트.
The method according to claim 1,
Hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline), iso-threo carbonate (isothreonate), galactosyl carbonate (galactonate), gluconic acid lactone (gluconic acid lactone), agarose (sedoheptulose) and mannose three roads heptul ( mannose) exhibits a Behcet's disease when the concentration of at least one selected from the group consisting of < RTI ID = 0.0 >
정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성을 검출하는 방법으로,
(1) GC/TOF MS(gas chromatography/time-of-flight mass spectrometry)를 이용한 대사체 분석 단계;
(2) GC/TOF MS에서 동정된 대사체에 대해 부분최소자승판별분석(PLS-DA)를 이용하여 대사체 프로파일의 차이를 확인하는 단계;
(3) PLS-DA에서 도출된 대사체의 VIP(Variable Importance for Projection) 값이 1.0 이상인 값을 대사체 바이오마커 후보물질로 선정하고, PLS-DA의 로딩 값을 통해 대사체 바이오마커 후보물질의 증감 확인하는 단계; 및
(4) ROC 곡선(Receiver Operating Characteristic curve)을 이용하여 대사체 바이오마커를 검증하는 단계;
를 순차적으로 적용하여, 혈액으로부터 대사체 바이오마커를 분석하는 것을 포함하는 정상 대조군과 베체트병에서 얻은 소변 간의 대사체 차별성 분석 방법.
As a method for detecting metabolite differentiation between urine obtained from normal control and Behcet's disease,
(1) metabolism analysis step using GC / TOF MS (gas chromatography / time-of-flight mass spectrometry);
(2) identifying differences in metabolite profiles using partial least squares discriminant analysis (PLS-DA) for the metabolites identified in GC / TOF MS;
(3) The value of Variable Importance for Projection (VIP) of the metabolite derived from PLS-DA was selected as a candidate for metabolite biomarker, and the value of PLS-DA as a metabolite biomarker candidate Ascertaining and decreasing; And
(4) verifying the metabolite biomarker using an ROC curve (Receiver Operating Characteristic curve);
To analyze metabolic biomarkers from the blood, and to analyze metabolic differentiation between urine obtained from a normal control and Behcet's disease.
제 6 항에 있어서,
GC/TOF MS를 이용한 대사체 분석 방법은 소변 시료를 GC/TOF MS 기기로 분석하고, 분석 결과를 통계처리 가능한 수치로 변환한 다음, 변환된 수치를 이용하여 통계학적으로 정상 대조군과 베체트병에서 얻은 소변 간의 대사체의 차별성을 검증하는 것인 방법.
The method according to claim 6,
The metabolite analysis method using GC / TOF MS was performed by analyzing the urine sample with GC / TOF MS instrument, converting the analysis result into a statistically quantifiable value, and then using the converted value, Wherein the obtained urine metabolite is differentiated.
제 7 항에 있어서,
GC/TOF MS 분석 결과를 통계처리 가능한 수치로 변환하는 것은 총 분석시간을 단위시간 간격으로 나누어 단위시간 동안 나타난 크로마토그램 피크의 면적 또는 높이 중 가장 큰 수치를 단위시간 동안의 대표값으로 정하는 것인 방법.
8. The method of claim 7,
The conversion of the GC / TOF MS analysis results into a statistically quantifiable value is made by dividing the total analysis time by the unit time interval and setting the largest value of the area or height of the chromatogram peak during the unit time as the representative value for the unit time Way.
제 6 항에 있어서,
PLS-DA의 로딩 값이 양수인 것은 대사체의 증가 경향을, 로딩 값이 음수인 것은 대사체의 감소 경향을 나타내는 것인 방법.
The method according to claim 6,
Wherein the loading value of PLS-DA is positive indicates an increasing tendency of metabolism, and the loading value is negative indicates a decreasing tendency of metabolism.
제 6 항에 있어서,
대사체 바이오마커는 구아닌(guanine), 3-하이드록시피리딘(3-hydroxypyridine), 하이폭산틴(hypoxanthine), L-시툴린(L-citrulline), 이소트레오네이트(isothreonate), 피롤-2-카르복실레이트(pyrrole-2-carboxylate), 갈락토네이트(galactonate), 글루콘산 락톤(gluconic acid lactone), 세도헵툴로오스(sedoheptulose) 및 만노오스(mannose)로 이루어진 군에서 선택된 하나 이상인 방법.The method according to claim 6,
Metabolite biomarkers guanine (guanine), 3- hydroxymethyl pyridine (3-hydroxypyridine), hypoxanthine (hypoxanthine), L - when tulrin (L -citrulline), iso-threo carbonate (isothreonate), pyrrole-2-carboxylic Wherein the at least one compound is at least one selected from the group consisting of pyrrole-2-carboxylate, galactonate, gluconic acid lactone, sedoheptulose, and mannose.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112180007A (en) * 2020-09-16 2021-01-05 上海市皮肤病医院 Metabolics-based diagnostic marker for generalized pustular psoriasis and application thereof
WO2022125057A1 (en) * 2020-12-07 2022-06-16 Kimberly-Clark Worldwide, Inc. Methods and consumer products for detecting a metabolite
CN114636774A (en) * 2022-04-22 2022-06-17 苏州市疾病预防控制中心 Biological metabolism marker composition for predicting risk of juvenile hypertension

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117590007B (en) * 2024-01-19 2024-03-29 天津医科大学眼科医院 Application of biomarker in preparation of product for diagnosing Behcet's disease uveitis

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013525786A (en) * 2010-04-19 2013-06-20 バイオマーカー ストラテジーズ リミテッド ライアビリティ カンパニー Compositions and methods for predicting drug sensitivity, drug resistance and disease progression
KR101516086B1 (en) * 2013-10-25 2015-05-07 고려대학교 산학협력단 Method for diagnosing rheumatoid arthritis by using metabolomics
KR20150062690A (en) * 2013-11-29 2015-06-08 아주대학교산학협력단 Composition for diagnosis of behcet's disease
KR20160141140A (en) * 2015-05-28 2016-12-08 고려대학교 산학협력단 Method for diagnosing Behcet's disease with arthritis by using metabolomics

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU2001230573A1 (en) * 2000-02-03 2001-08-14 Hidetoshi Inoko Remedies for behcet's disease
KR100448488B1 (en) * 2001-09-03 2004-09-13 (주)프로테옴텍 A Diagnostic System Employing Apolipoprotein A-1 as a Marker of Behcet's Disease
NZ581742A (en) * 2007-06-08 2012-09-28 Biogen Idec Inc Biomarkers for predicting anti-tnf responsiveness or non-responsiveness
KR101502267B1 (en) * 2007-11-09 2015-03-18 페레그린 파마수티컬즈, 인크 Anti-vegf antibody compositions and methods
WO2011006982A2 (en) * 2009-07-17 2011-01-20 Rigshospitalet Inhibitors of complement activation
EP2857400A4 (en) * 2012-06-01 2015-12-09 Takeda Pharmaceutical Heterocyclic compound
US20140303901A1 (en) * 2013-04-08 2014-10-09 Ilan Sadeh Method and system for predicting a disease
KR101946884B1 (en) * 2017-04-25 2019-02-13 고려대학교 산학협력단 Method for diagnosing Behcet's disease by using metabolomics

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013525786A (en) * 2010-04-19 2013-06-20 バイオマーカー ストラテジーズ リミテッド ライアビリティ カンパニー Compositions and methods for predicting drug sensitivity, drug resistance and disease progression
KR101516086B1 (en) * 2013-10-25 2015-05-07 고려대학교 산학협력단 Method for diagnosing rheumatoid arthritis by using metabolomics
KR20150062690A (en) * 2013-11-29 2015-06-08 아주대학교산학협력단 Composition for diagnosis of behcet's disease
KR20160141140A (en) * 2015-05-28 2016-12-08 고려대학교 산학협력단 Method for diagnosing Behcet's disease with arthritis by using metabolomics

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Ahn J et al. A comparative metabolomic evaluation of Behcet's disease with arthritis and seronegative arthritis using synovial fluid. (2015) PLOS ONE 10:e0135856
Ahn J et al. Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography with time-of-flight mass spectrometry. (2018) Joint Bone Spine 95:337-343
Kim s et al. Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis. (2014) PLOS ONE 9:e97501
Seido et al. Proteomic surveillance of autoantigens in patients with Behcet's disease by a proteomic approach. (2010) Microbiol Immunol 54:354-361.

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112180007A (en) * 2020-09-16 2021-01-05 上海市皮肤病医院 Metabolics-based diagnostic marker for generalized pustular psoriasis and application thereof
CN112180007B (en) * 2020-09-16 2023-08-18 上海市皮肤病医院 Metabonomics-based diagnosis marker for generalized pustular psoriasis and application thereof
WO2022125057A1 (en) * 2020-12-07 2022-06-16 Kimberly-Clark Worldwide, Inc. Methods and consumer products for detecting a metabolite
GB2616793A (en) * 2020-12-07 2023-09-20 Kimberly Clark Co Methods and consumer products for detecting a metabolite
CN114636774A (en) * 2022-04-22 2022-06-17 苏州市疾病预防控制中心 Biological metabolism marker composition for predicting risk of juvenile hypertension
CN114636774B (en) * 2022-04-22 2024-05-17 苏州市疾病预防控制中心 Biological metabolism marker composition for predicting adolescent hypertension disease risk

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