KR102231928B1 - Biomarker composition for predicting the prognosis of diabetes after bariatric surgery - Google Patents

Biomarker composition for predicting the prognosis of diabetes after bariatric surgery Download PDF

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KR102231928B1
KR102231928B1 KR1020190115286A KR20190115286A KR102231928B1 KR 102231928 B1 KR102231928 B1 KR 102231928B1 KR 1020190115286 A KR1020190115286 A KR 1020190115286A KR 20190115286 A KR20190115286 A KR 20190115286A KR 102231928 B1 KR102231928 B1 KR 102231928B1
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권영근
장미
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Abstract

The present invention provides a biomarker which can predict the prognosis of diabetes after bariatric surgery, using the concentration of a specific amino acid in the patient′s blood, and predicts the prognosis before surgery in advance, thereby enabling individual patient-customized diet and exercise prescription for post-surgery patient management.

Description

비만대사수술 이후 당뇨병 예후 예측용 바이오마커 조성물{Biomarker composition for predicting the prognosis of diabetes after bariatric surgery}Biomarker composition for predicting the prognosis of diabetes after bariatric surgery}

본 발명은 비만대사수술 이후 당뇨병의 예후를 예측할 수 있는 바이오마커 및 이를 이용한 예측 모델 개발 등에 관한 것이다.The present invention relates to a biomarker capable of predicting the prognosis of diabetes after obesity metabolic surgery and to the development of a predictive model using the same.

비만은 표준량보다 많은 양의 지방이 체내에 축적되어 건강을 위협하는 질병이며, 이러한 지방의 과도한 축적에 우리 몸이 적절하게 대처할 수 없는 한계에 도달하여 결국 생명을 위협하는 수 많은 질환을 일으킬 수 있는 상태이거나 이미 관련 질환이 발생한 경우를 고도비만 또는 병적비만으로 정의할 수 있다. Obesity is a disease that threatens health by accumulating a greater amount of fat than the standard amount in the body, and it reaches the limit that our body cannot adequately cope with this excessive accumulation of fat, which can eventually lead to a number of life-threatening diseases. A condition or a related disease can be defined as severe obesity or morbid obesity.

한편, 미국의 경우 비만 환자의 10%가 당뇨병을 가진 반면 당뇨병 환자의 80%가 비만하다. 2012년 대한당뇨병학회에서 발간한 자료에 의하면 국내의 경우는 체질량지수 23.0-24.9 kg/m2 를 과체중, 25 kg/m2 이상을 비만으로 정의하였을 때, 제2형 당뇨병 환자의 약 50%가 비만이며 약 25%가 과체중에 해당한다. 비만대사수술은 외과적으로 위장관의 구조 변화를 일으켜 장기적이고 충분한 체중 감소를 유도하고 이를 통해 비만과 관련된 당뇨 등의 질환을 치료 또는 개선하여 의료비를 절감하며 환자의 삶의 질을 향상시키고자 하는 수술이다. 우리나라의 경우 2003년에 처음으로 비만대사수술이 시행되었으며, 2019년 비만대사수술이 국민건강보험 적용 대상에 포함된 이후로 수술시행이 가파르게 증가하는 추세이다.Meanwhile, in the United States, 10% of obese patients have diabetes, while 80% of diabetic patients are obese. According to data published by the Korean Diabetes Association in 2012, in the case of Korea, when the body mass index of 23.0-24.9 kg/m 2 was defined as overweight and 25 kg/m 2 or more was defined as obesity, about 50% of type 2 diabetic patients It is obese, and about 25% is overweight. Obesity metabolic surgery is an operation to reduce medical expenses and improve the quality of life of patients by inducing long-term and sufficient weight loss by causing structural changes in the gastrointestinal tract surgically, thereby treating or improving diseases such as diabetes related to obesity. to be. In Korea, the first obesity metabolic surgery was performed in 2003, and since the obesity metabolic surgery was included in the National Health Insurance coverage in 2019, the number of operations has been increasing rapidly.

1995년에 미국의 외과의사 Pories에 의해 비만대사수술 중 하나인 루와이 위우회술(Roux-en-Y gastric bypass)을 시행받은 고도비만 환자 중에서 제2형 당뇨병 환자 146명 중 121명 (82.9%) 그리고 152명의 내당능장애 환자 중 150 (98.7%)명 이 정상 혈당과 정상 당화혈색소를 유지함이 밝혀진 이후, 체중조절을 주목적으로 한 비만수술(bariatric surgery)의 개념이 대사수술(metabolic surgery)이라는 개념으로 진화하였다. 또한, 정상 체중인 제2형 당뇨병 환자도 수술적 치료로 체중감량은 물론 고혈당 조절이 가능하다는 사실이 입증되면서 비만대사수술이 당뇨병 치료의 옵션으로 부각되고 있다.Among the highly obese patients who underwent Roux-en-Y gastric bypass, one of the obesity metabolic surgery, by surgeon Pories in the United States in 1995, 121 of 146 patients with type 2 diabetes (82.9%) and After it was found that 150 (98.7%) of 152 patients with impaired glucose tolerance maintained normal blood sugar and normal glycated hemoglobin, the concept of bariatric surgery with the main purpose of weight control evolved into the concept of metabolic surgery. I did. In addition, as it has been proven that patients with normal weight type 2 diabetes can lose weight as well as control hyperglycemia through surgical treatment, obesity metabolic surgery is emerging as an option for diabetes treatment.

비만대사수술이 당뇨병 증상을 개선시키는 것에 효과적인 것은 틀림없으나, 수술 이후에 호전의 정도가 기대에 미치지 못할 수도 있다. 이에, 본 발명자들은 비만대사수술 이후에 당뇨병 예후를 보다 정확하게 예측하기 위한 지표에 관하여 예의 연구하여 본 발명을 완성하였다.Obesity metabolic surgery must be effective in improving diabetes symptoms, but the degree of improvement after surgery may not meet expectations. Accordingly, the present inventors have completed the present invention by studying the indicators for more accurately predicting the diabetes prognosis after obesity metabolic surgery.

Pories　WJ, Dohm LG, Mansfield CJ. Beyond the BMI: the search for better guidelines for bariatric surgery.　Obesity.　2010;18:865-71. doi: 10.1038/oby.2010.8.　Pories　WJ, Dohm LG, Mansfield CJ. Beyond the BMI: the search for better guidelines for bariatric surgery. Obesity. 2010;18:865-71. doi: 10.1038/oby.2010.8.

본 발명이 이루고자 하는 기술적 과제는 환자의 혈중 아미노산 프로파일을 비만대사수술 이후에 당뇨병 예후를 예측할 수 있는 바이오마커로 제공하고, 상기 바이오마커를 이용하여 당뇨병 예후를 예측하는 방법을 제공하는 것이다.The technical problem to be achieved by the present invention is to provide a patient's blood amino acid profile as a biomarker capable of predicting a diabetes prognosis after obesity metabolic surgery, and to provide a method for predicting a diabetes prognosis using the biomarker.

그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당해 기술분야의 통상의 기술자에게 명확하게 이해될 수 있을 것이다.However, the technical problem to be achieved by the present invention is not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

상기 과제를 해결하기 위하여, 혈중 1) 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan: tryp), 및 티로신(tyrosine: tyr)의 농도; 2) 루이신, 페닐알라닌, 및 트립토판의 농도; 3) 거대중성아미노산(LNAA)의 총 농도; 4) 분지사슬아미노산(BCAA)의 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 6) 티로신 농도; 7) 루이신 농도; 8) 루이신 및 페닐알라닌의 농도; 9) 키뉴레닌(kynurenine: kyn)과 트립토판의 비; 10) 5-하이드록시트립토판(5-hydroxytryptophan: 5-HTP) 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 12) 트립토판 농도; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산(3-Hydroxyanthranilic acid: 3-HAA) 농도; 15) 잔투렌산(xanthurenic acid: XA) 농도; 16) 키뉴레닌 농도; 17) 안트라닐산(anthranilic acid: AA)과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌(3-hydroxykynurenine: 3-HA)과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상을 당뇨병 예후 예측을 위한 바이오마커로 제공한다. In order to solve the above problem, the concentration of 1) leucine, phenylalanine, tryptophan, and tyrosine (tyros) in blood; 2) the concentration of leucine, phenylalanine, and tryptophan; 3) total concentration of macroneutral amino acids (LNAA); 4) total concentration of branched chain amino acids (BCAA); 5) ratio of phenylalanine and total LNAA; 6) tyrosine concentration; 7) leucine concentration; 8) concentration of leucine and phenylalanine; 9) ratio of kynurenine (kyn) and tryptophan; 10) 5-hydroxytryptophan (5-HTP) concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 12) tryptophan concentration; 13) phenylalanine concentration; 14) 3-Hydroxyanthranilic acid (3-HAA) concentration; 15) xanthurenic acid (XA) concentration; 16) kynurenine concentration; 17) ratio of anthranilic acid (AA) and kynurenine; 18) the ratio of 3-hydroxykynurenine (3-HA) and kynurenine; And 19) a ratio of L-DOPA and tyrosine; at least one selected from the group consisting of is provided as a biomarker for predicting diabetes prognosis.

본 발명의 일 구현예로서, 상기 바이오마커는 비만대사수술 또는 위 절제수술 전에 그 수술 이후에 예상되는 당뇨병 증상이나 그 정도를 예측하기 위한 것일 수 있다.As an embodiment of the present invention, the biomarker may be for predicting diabetes symptoms or their degree expected after the surgery before obesity metabolic surgery or gastrectomy.

보다 구체적으로, 본 발명은 혈중 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan), 및 티로신(tyrosine)의 농도; 및 루이신, 페닐알라닌, 및 트립토판의 농도;로 이루어진 군으로부터 선택되는 하나 이상을 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), 및 DI (Disposition index)로 이루어진 군으로부터 선택되는 하나 이상의 지표 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. More specifically, the present invention provides a concentration of leucine, phenylalanine, tryptophan, and tyrosine in blood; And concentrations of leucine, phenylalanine, and tryptophan; as a biomarker composition for predicting diabetes prognosis comprising at least one selected from the group consisting of, the biomarker composition is HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), and DI (Disposition index). .

또한, 본 발명은 혈중 거대중성아미노산(large neutral amino acids: LNAA)의 총 농도를 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, In addition, the present invention is a biomarker composition for predicting diabetes prognosis comprising the total concentration of large neutral amino acids (LNAA) in blood,

상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), 및 DI (Disposition index)로 이루어진 군으로부터 선택되는 하나 이상의 지표 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. The biomarker composition is for predicting changes in one or more indicators selected from the group consisting of QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), and DI (Disposition index) expected after obesity metabolic surgery or gastrectomy. It provides a phosphorus biomarker composition.

또한, 본 발명은 혈중 분지사슬아미노산(Branched Chain Amino Acid: BCAA)의 총 농도; 및 페닐알라닌(phenylalanine)과 총 거대중성아미노산(large neutral amino acids: LNAA)의 비;로 이루어진 군으로부터 선택되는 하나 이상을 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 QUICKI (Quantitative insulin sensitivity check index) 및 DI (Disposition index)로 이루어진 군으로부터 선택되는 하나 이상의 지표 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. In addition, the present invention is the total concentration of the branched chain amino acid (Branched Chain Amino Acid: BCAA) in the blood; And a ratio of phenylalanine and large neutral amino acids (LNAA); as a biomarker composition for predicting diabetes prognosis, the biomarker composition comprises at least one selected from the group consisting of obesity metabolic surgery or It provides a biomarker composition for predicting changes in one or more indicators selected from the group consisting of QUICKI (Quantitative insulin sensitivity check index) and DI (Disposition index) expected after gastrectomy.

또한, 본 발명은 혈중 티로신(tyrosine)의 농도를 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 QUICKI (Quantitative insulin sensitivity check index)의 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. In addition, the present invention is a biomarker composition for predicting the prognosis of diabetes containing the concentration of tyrosine in blood, the biomarker composition predicts changes in QUICKI (Quantitative insulin sensitivity check index) expected after obesity metabolic surgery or gastrectomy It provides a biomarker composition for that.

또한, 본 발명은 혈중 루이신(leucine) 농도; 루이신 및 페닐알라닌(phenylalanine) 농도; 키뉴레닌(kynurenine)과 트립토판(trypophan)의 비(ratio); 5-하이드록시트립토판(5-hydroxytryptophan) 농도; 및 5-하이드록시트립토판과 트립토판의 비;로 이루어진 군으로부터 선택되는 하나 이상을 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 IGI (Insulinogenic index) 및 DI (Disposition index)로 이루어진 군으로부터 선택되는 하나 이상의 지표 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. In addition, the present invention is a blood leucine (leucine) concentration; Leucine and phenylalanine concentration; Ratio of kynurenine and trypophan; 5-hydroxytryptophan concentration; And a ratio of 5-hydroxytryptophan and tryptophan; a biomarker composition for predicting diabetes prognosis comprising at least one selected from the group consisting of, wherein the biomarker composition is an IGI (Insulinogenic index) expected after obesity metabolic surgery or gastrectomy. And it provides a biomarker composition for predicting one or more indicator changes selected from the group consisting of DI (Disposition index).

또한, 본 발명은 혈중 트립토판(tryptophan) 농도; 페닐알라닌(phenylalanine) 농도; 3-히드록시안트라닐산(3-Hydroxyanthranilic acid) 농도; 및 잔투렌산(xanthurenic acid) 농도;로 이루어진 군으로부터 선택되는 하나 이상을 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 IGI (Insulinogenic index)의 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. In addition, the present invention is tryptophan (tryptophan) concentration in the blood; Phenylalanine concentration; 3-Hydroxyanthranilic acid concentration; And xanthurenic acid (xanthurenic acid) concentration; As a biomarker composition for predicting diabetes prognosis comprising at least one selected from the group consisting of, the biomarker composition is a change in IGI (Insulinogenic index) expected after obesity metabolic surgery or gastric resection It provides a biomarker composition for predicting.

또한, 본 발명은 혈중 키뉴레닌(kynurenine) 농도; 안트라닐산(anthranilic acid)과 키뉴레닌의 비(ratio); 3-하이드록시키뉴레닌(3-hydroxykynurenine)과 키뉴레닌의 비; 및 L-DOPA과 티로신(tyrosine)의 비;로 이루어진 군으로부터 선택되는 하나 이상을 포함하는 당뇨병 예후 예측용 바이오마커 조성물로서, 상기 바이오마커 조성물은 비만대사수술 또는 위 절제술 이후 예상되는 DI (Disposition index)의 변화를 예측하기 위한 것인 바이오마커 조성물을 제공한다. In addition, the present invention is a blood kynurenine (kynurenine) concentration; Ratio of anthranilic acid and kynurenine; The ratio of 3-hydroxykynurenine and kynurenine; And a ratio of L-DOPA and tyrosine (tyrosine); As a biomarker composition for predicting diabetes prognosis comprising at least one selected from the group consisting of, wherein the biomarker composition is DI (Disposition index) expected after obesity metabolic surgery or gastric resection ) It provides a biomarker composition for predicting the change.

또한, 본 발명은 상기 바이오마커를 이용하여 비만대사수술 전에 그 수술 후 당뇨병 예후 예측을 위한 정보제공방법을 제공한다.In addition, the present invention provides a method of providing information for predicting the prognosis of diabetes after the operation before obesity metabolic surgery using the biomarker.

구체적으로, 본 발명은 하기의 단계를 포함하는 당뇨병 예후 예측을 위한 정보제공방법을 제공한다:Specifically, the present invention provides a method of providing information for predicting a diabetes prognosis comprising the following steps:

(가) 환자의 혈액 샘플을 수득하는 단계;(A) obtaining a blood sample from the patient;

(나) 상기 샘플의 아미노산 함량을 측정하여 아미노산 프로파일을 제작하는 단계;(B) measuring the amino acid content of the sample to produce an amino acid profile;

(다) 상기 프로파일로부터 혈중 1) 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan: tryp), 및 티로신(tyrosine: tyr)의 농도; 2) 루이신, 페닐알라닌, 및 트립토판의 농도; 3) 거대중성아미노산(LNAA)의 총 농도; 4) 분지사슬아미노산(BCAA)의 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 6) 티로신 농도; 7) 루이신 농도; 8) 루이신 및 페닐알라닌의 농도; 9) 키뉴레닌(kynurenine: kyn)과 트립토판의 비; 10) 5-하이드록시트립토판(5-hydroxytryptophan: 5-HTP) 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 12) 트립토판 농도; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산(3-Hydroxyanthranilic acid: 3-HAA) 농도; 15) 잔투렌산(xanthurenic acid: XA) 농도; 16) 키뉴레닌 농도; 17) 안트라닐산(anthranilic acid: AA)과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌(3-hydroxykynurenine: 3-HA)과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상의 바이오마커를 산출하는 단계; 및(C) concentrations of 1) leucine, phenylalanine, tryptophan, and tyrosine (tyros) in blood from the above profile; 2) the concentration of leucine, phenylalanine, and tryptophan; 3) total concentration of macroneutral amino acids (LNAA); 4) total concentration of branched chain amino acids (BCAA); 5) ratio of phenylalanine and total LNAA; 6) tyrosine concentration; 7) leucine concentration; 8) concentration of leucine and phenylalanine; 9) ratio of kynurenine (kyn) and tryptophan; 10) 5-hydroxytryptophan (5-HTP) concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 12) tryptophan concentration; 13) phenylalanine concentration; 14) 3-Hydroxyanthranilic acid (3-HAA) concentration; 15) xanthurenic acid (XA) concentration; 16) kynurenine concentration; 17) ratio of anthranilic acid (AA) and kynurenine; 18) the ratio of 3-hydroxykynurenine (3-HA) and kynurenine; And 19) a ratio of L-DOPA and tyrosine; calculating one or more biomarkers selected from the group consisting of; And

(라) 상기 (다) 단계에서 산출된 바이오마커(아미노산의 농도 또는 비)를 이용하여 HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), 및 DI (Disposition index)로 이루어진 군으로부터 하나 이상의 지표 변화를 예측하는 단계.(D) HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), using the biomarker (amino acid concentration or ratio) calculated in step (c) above. And predicting at least one indicator change from the group consisting of DI (Disposition index).

또한, 본 발명은 (a) 비만대사수술을 앞둔 환자의 혈청 샘플에 포함된 아미노산을 정량분석하여 아미노산 프로파일을 제작하는 단계; (b) 1) 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan: tryp), 및 티로신(tyrosine: tyr)의 농도(A); 2) 루이신, 페닐알라닌, 및 트립토판의 농도(B); 3) 거대중성아미노산(LNAA)의 총 농도; 4) 분지사슬아미노산(BCAA)의 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 6) 티로신 농도; 7) 루이신 농도; 8) 루이신 및 페닐알라닌의 농도(C); 9) 키뉴레닌(kynurenine: kyn)과 트립토판의 비; 10) 5-하이드록시트립토판(5-hydroxytryptophan: 5-HTP) 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 12) 트립토판 농도; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산(3-Hydroxyanthranilic acid: 3-HAA) 농도; 15) 잔투렌산(xanthurenic acid: XA) 농도; 16) 키뉴레닌 농도; 17) 안트라닐산(anthranilic acid: AA)과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌(3-hydroxykynurenine: 3-HA)과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상의 바이오마커를 산출하는 단계; 및 (c) 상기 (b) 단계에서 산출된 바이오마커를 이용하여 HOMA-IR, QUICKI, IGI, 및 DI로 이루어진 군으로부터 선택되는 하나 이상의 지표의 변화를 예측하되, 산출되는 바이오마커와 예측하고자 하는 지표에 따라 하기 수학식 1 내지 34를 이용하는 것을 특징으로 하는 당뇨병 관련 지표 변화 예측 단계;를 포함하는 비만대사수술 전에 수술 후 당뇨병 예후를 예측하는 시스템을 제공한다. In addition, the present invention comprises the steps of: (a) quantitatively analyzing amino acids contained in a serum sample of a patient before obesity metabolic surgery to produce an amino acid profile; (b) 1) concentration of leucine, phenylalanine, tryptophan (tryp), and tyrosine (tyros) (A); 2) the concentration of leucine, phenylalanine, and tryptophan (B); 3) total concentration of macroneutral amino acids (LNAA); 4) total concentration of branched chain amino acids (BCAA); 5) ratio of phenylalanine and total LNAA; 6) tyrosine concentration; 7) leucine concentration; 8) the concentration of leucine and phenylalanine (C); 9) ratio of kynurenine (kyn) and tryptophan; 10) 5-hydroxytryptophan (5-HTP) concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 12) tryptophan concentration; 13) phenylalanine concentration; 14) 3-Hydroxyanthranilic acid (3-HAA) concentration; 15) xanthurenic acid (XA) concentration; 16) kynurenine concentration; 17) ratio of anthranilic acid (AA) and kynurenine; 18) the ratio of 3-hydroxykynurenine (3-HA) and kynurenine; And 19) a ratio of L-DOPA and tyrosine; calculating one or more biomarkers selected from the group consisting of; And (c) predicting the change of one or more indicators selected from the group consisting of HOMA-IR, QUICKI, IGI, and DI using the biomarker calculated in step (b), but the calculated biomarker and the desired It provides a system for predicting a diabetes prognosis after surgery before obesity metabolic surgery, including; diabetes-related index change prediction step, characterized in using the following equations 1 to 34 according to the index.

또한, 본 발명은 연산장치에에 결합되어 (a) 비만대사수술을 앞둔 환자의 혈청 샘플에 포함된 아미노산을 정량분석하여 아미노산 프로파일을 제작하는 단계; (b) 1) 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan: tryp), 및 티로신(tyrosine: tyr)의 농도(A); 2) 루이신, 페닐알라닌, 및 트립토판의 농도(B); 3) 거대중성아미노산(LNAA)의 총 농도; 4) 분지사슬아미노산(BCAA)의 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 6) 티로신 농도; 7) 루이신 농도; 8) 루이신 및 페닐알라닌의 농도(C); 9) 키뉴레닌(kynurenine: kyn)과 트립토판의 비; 10) 5-하이드록시트립토판(5-hydroxytryptophan: 5-HTP) 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 12) 트립토판 농도; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산(3-Hydroxyanthranilic acid: 3-HAA) 농도; 15) 잔투렌산(xanthurenic acid: XA) 농도; 16) 키뉴레닌 농도; 17) 안트라닐산(anthranilic acid: AA)과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌(3-hydroxykynurenine: 3-HA)과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상의 바이오마커를 산출하는 단계; 및 (c) 상기 (b) 단계에서 산출된 바이오마커를 이용하여 HOMA-IR, QUICKI, IGI, 및 DI로 이루어진 군으로부터 선택되는 하나 이상의 지표의 변화를 예측하되, 산출되는 바이오마커와 예측하고자 하는 지표에 따라 하기 수학식 1 내지 34를 이용하는 것을 특징으로 하는 당뇨병 관련 지표 변화 예측 단계;를 실행시킬 수 있는 매체에 저장된 컴퓨터 프로그램을 제공한다. In addition, the present invention is coupled to a computing device (a) producing an amino acid profile by quantitative analysis of amino acids contained in a serum sample of a patient before obesity metabolic surgery; (b) 1) concentration of leucine, phenylalanine, tryptophan (tryp), and tyrosine (tyros) (A); 2) the concentration of leucine, phenylalanine, and tryptophan (B); 3) total concentration of macroneutral amino acids (LNAA); 4) total concentration of branched chain amino acids (BCAA); 5) ratio of phenylalanine and total LNAA; 6) tyrosine concentration; 7) leucine concentration; 8) the concentration of leucine and phenylalanine (C); 9) ratio of kynurenine (kyn) and tryptophan; 10) 5-hydroxytryptophan (5-HTP) concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 12) tryptophan concentration; 13) phenylalanine concentration; 14) 3-Hydroxyanthranilic acid (3-HAA) concentration; 15) xanthurenic acid (XA) concentration; 16) kynurenine concentration; 17) ratio of anthranilic acid (AA) and kynurenine; 18) the ratio of 3-hydroxykynurenine (3-HA) and kynurenine; And 19) a ratio of L-DOPA and tyrosine; calculating one or more biomarkers selected from the group consisting of; And (c) predicting the change of one or more indicators selected from the group consisting of HOMA-IR, QUICKI, IGI, and DI using the biomarker calculated in step (b), but the calculated biomarker and the desired A computer program stored in a medium capable of executing the diabetes-related index change prediction step, characterized in that the following Equations 1 to 34 are used according to the index, is provided.

[수학식 1][Equation 1]

HOMA-IR 변화(%) = -61.71 + (0.32 * A)HOMA-IR change (%) = -61.71 + (0.32 * A)

[수학식 2][Equation 2]

QUICKI 변화(%) = -24.83 + (0.11 * A)QUICKI change (%) = -24.83 + (0.11 * A)

[수학식 3][Equation 3]

Insulinogenic index 변화(%) = -350.3 + (1.01 * A)Insulinogenic index change (%) = -350.3 + (1.01 * A)

[수학식 4][Equation 4]

Disposition index 변화(%) = -236.5 + (0.78 * A)Disposition index change (%) = -236.5 + (0.78 * A)

[수학식 5][Equation 5]

HOMA-IR 변화(%) = -57.33+ (0.39 * B)HOMA-IR change (%) = -57.33+ (0.39 * B)

[수학식 6][Equation 6]

QUICKI 변화(%) = -23.26+ (0.12 * B)QUICKI change (%) = -23.26+ (0.12 * B)

[수학식 7][Equation 7]

Insulinogenic index 변화(%) = -378.7+ (1.35* B)Insulinogenic index change (%) = -378.7+ (1.35* B)

[수학식 8][Equation 8]

Disposition index 변화(%) = -250.7+ (1.02* B)Disposition index change (%) = -250.7+ (1.02* B)

[수학식 9][Equation 9]

QUICKI 변화(%) = -29.09+ (0.06 * LNAA total)QUICKI change (%) = -29.09+ (0.06 * LNAA total)

[수학식 10][Equation 10]

Insulinogenic index 변화(%) = -320.9+ (0.52 * LNAA total)Insulinogenic index change (%) = -320.9+ (0.52 * LNAA total)

[수학식 11][Equation 11]

Disposition index 변화(%) = -257.9+ (0.47 * LNAA total)Disposition index change (%) = -257.9+ (0.47 * LNAA total)

[수학식 12][Equation 12]

QUICKI 변화(%) = -13.43 + (0. 06 * BCAA total)QUICKI change (%) = -13.43 + (0. 06 * BCAA total)

[수학식 13][Equation 13]

Disposition index 변화(%) = -155.6+ (0. 51 * BCAA total)Disposition index change (%) = -155.6+ (0. 51 * BCAA total)

[수학식 14][Equation 14]

QUICKI 변화(%) = 86.31+ (-523.5* phenylalanine/LNAA)QUICKI change (%) = 86.31+ (-523.5* phenylalanine/LNAA)

[수학식 15][Equation 15]

Disposition index 변화(%) = 564.5+ (-3728* phenylalanine/LNAA)Disposition index change (%) = 564.5+ (-3728* phenylalanine/LNAA)

[수학식 16][Equation 16]

QUICKI 변화(%) = -2.58+ (0.22 * tyrosine)QUICKI change (%) = -2.58+ (0.22 * tyrosine)

[수학식 17][Equation 17]

Insulinogenic index 변화(%) = -241.9 + (2.13* leucine)Insulinogenic index change (%) = -241.9 + (2.13* leucine)

[수학식 18][Equation 18]

Disposition index 변화(%) = -166.7+ (1.77* leucine)Disposition index change (%) = -166.7+ (1.77* leucine)

[수학식 19][Equation 19]

Insulinogenic index 변화(%) = -316.1+ (1.59 * C)Insulinogenic index change (%) = -316.1+ (1.59 * C)

[수학식 20][Equation 20]

Disposition index 변화(%) = -206.8+ (1.22* C)Disposition index change (%) = -206.8+ (1.22* C)

[수학식 21][Equation 21]

Insulinogenic index 변화(%) = 151.5+ (-2601* Kyn/Tryp)Insulinogenic index change (%) = 151.5+ (-2601* Kyn/Tryp)

[수학식 22][Equation 22]

Disposition index 변화(%) = 190.9+ (-2816* Kyn/Tryp)Disposition index change (%) = 190.9+ (-2816* Kyn/Tryp)

[수학식 23][Equation 23]

Insulinogenic index 변화(%) = 149.2+ (-60.06* 5-HTP)Insulinogenic index change (%) = 149.2+ (-60.06* 5-HTP)

[수학식 24][Equation 24]

Disposition index 변화(%) = 168.2+ (-54.8* 5-HTP)Disposition index change (%) = 168.2+ (-54.8* 5-HTP)

[수학식 25][Equation 25]

Insulinogenic index 변화(%) = 119.1+ (-3534* 5-HTP/Tryp)Insulinogenic index change (%) = 119.1+ (-3534* 5-HTP/Tryp)

[수학식 26][Equation 26]

Disposition index 변화(%) = 140.3+ (-3217* 5-HTP/Tryp)Disposition index change (%) = 140.3+ (-3217* 5-HTP/Tryp)

[수학식 27][Equation 27]

Insulinogenic index 변화(%) = -212.8+ (2.89* tryptophan)Insulinogenic index change (%) = -212.8+ (2.89* tryptophan)

[수학식 28][Equation 28]

Insulinogenic index 변화(%) = -322.9+ (3.90* phenylalanine)Insulinogenic index change (%) = -322.9+ (3.90* phenylalanine)

[수학식 29][Equation 29]

Insulinogenic index 변화(%) = -89.68+ (1306* 3-HAA)Insulinogenic index change (%) = -89.68+ (1306* 3-HAA)

[수학식 30][Equation 30]

Insulinogenic index 변화(%) = -26.6+ (1690* XA)Insulinogenic index change (%) = -26.6+ (1690* XA)

[수학식 31][Equation 31]

Disposition index 변화(%) = 222.2+ (-43.3* Kyn)Disposition index change (%) = 222.2+ (-43.3* Kyn)

[수학식 32][Equation 32]

Disposition index 변화(%) = -75.5+ (4349* AA/Kyn)Disposition index change (%) = -75.5+ (4349* AA/Kyn)

[수학식 33][Equation 33]

Disposition index 변화(%) = -94.4+ (3558* 3-HA/Kyn)Disposition index change (%) = -94.4+ (3558* 3-HA/Kyn)

[수학식 34][Equation 34]

Disposition index 변화(%) = 131.6+ (-198321* L-DOPA/Tyr) Disposition index change (%) = 131.6+ (-198321* L-DOPA/Tyr)

본 발명은 환자의 혈중 특정 아미노산의 농도를 이용한 비만대사수술 이후에 당뇨병 예후를 예측할 수 있는 바이오마커를 제공하여, 수술 전에 예후를 미리 예측함으로서 수술 후 환자 관리에 있어 개별 환자 맞춤형 식이 및 운동 처방을 가능하게 한다.The present invention provides a biomarker that can predict the diabetes prognosis after obesity metabolic surgery using a specific amino acid concentration in the patient's blood, and predicts the prognosis before surgery, thereby providing individual patient-specific diet and exercise regimen in postoperative patient management. Make it possible.

도 1a 내지 도 1d는 아미노산 프로파일을 이용한 회귀모델 중 가장 신뢰도가 높은 leucine + phenylalanine + tryptophan + tyrosine 농도와 당뇨병 관련 지표들 (HOMA-IR, QUICKI, Insulinogenic index, Dispositon index)의 비만대사수술 전후 변화량 사이의 회귀식을 나타낸 그래프이다.1A to 1D are the most reliable leucine + phenylalanine + tryptophan + tyrosine concentrations among the regression models using amino acid profiles and the amount of changes before and after obesity metabolic surgery in diabetes-related indicators (HOMA-IR, QUICKI, Insulinogenic index, Dispositon index). It is a graph showing the regression equation of.

본 발명자들은 비만대사수술 이후에 당뇨병의 예후를 수술 전에 정확하게 예측하기 위한 지표에 관하여 예의 연구하여 본 발명을 완성하였다.The present inventors have completed the present invention by studying an index for accurately predicting the prognosis of diabetes before surgery after obesity metabolic surgery.

당뇨병이란 혈당 수치가 높게 오랜 기간 지속되는 대사성 질환으로서 췌장에서 충분한 인슐린을 만들어내지 못하는 제1형 당뇨병과 세포가 인슐린 저항성에 의해 인슐린에 반응하지 못하는 제2형 당뇨병으로 구분된다. Diabetes is a metabolic disease that has high blood sugar levels for a long period of time, and is classified into type 1 diabetes, in which the pancreas does not produce enough insulin, and type 2 diabetes, in which cells cannot respond to insulin due to insulin resistance.

제2형 당뇨병에서 생체 내 인슐린 저항성을 측정하는 방법으로는 DeFronzo 등에 의해 개발된 글루코스 클램프 기법과 Shen 등에 의해 개발된 인슐린 억제 검사가 있으나 임상에 적용하기 어려운 문제가 있어 공복시 혈중 인슐린 농도와 혈당을 통해 얻을 수 있는 인슐린 감수성 및 저항성 대리 지표를 이용한다. Methods of measuring insulin resistance in vivo in type 2 diabetes include the glucose clamp technique developed by DeFronzo et al. and the insulin suppression test developed by Shen et al. Use the surrogate indicators of insulin sensitivity and resistance that can be obtained.

HOMA-IR (homeostatic model assessment for insulin resistance)은 'HOMA-IR = [공복 혈중 인슐린(uU/mL)] × [공복 혈당(mmol/L)] / 22.5'의 식으로 얻어지는 인슐린 저항성의 대리 지표이고, QUICKI (Quantitative insulin sensitivity check index)은 ‘QUICKI = 1 / [log (공복 혈중 인슐린, uU/mL) + log (공복 혈당, mg/dL)]'의 공식으로 얻어진 인슐린 저항성 대리 지표이다.HOMA-IR (homeostatic model assessment for insulin resistance) is a surrogate indicator of insulin resistance obtained by the formula'HOMA-IR = [fasting blood insulin (uU/mL)] × [fasting blood sugar (mmol/L)] / 22.5' , QUICKI (Quantitative insulin sensitivity check index) is an insulin resistance surrogate index obtained by the formula of'QUICKI = 1 / [log (fasting blood insulin, uU/mL) + log (fasting blood glucose, mg/dL)]'.

또한, DI (Disposition index)는 베타세포 기능 지수(AIR)과 인슐린 감수성 지수 (Whole Body Insulin Sensitivity Index: WBISI )의 곱으로 얻어지는 인슐린 감수성을 고려한 인슐린 분비능 지표이고, IGI (Insulinogenic index)는 포도당 부하 30분 후 0분과 비교한 혈당의 증가폭에 대한 인슐린 증가폭의 비 [△Ins(30'-0')/△Glu(30'-0')]로 얻어지는 인슐린 분비능 지표이다.In addition, DI (Disposition index) is an insulin secretion ability index that considers insulin sensitivity obtained by multiplying beta cell function index (AIR) and insulin sensitivity index (WBISI), and IGI (Insulinogenic index) is glucose load 30 It is an index of insulin secretion ability obtained as the ratio of the increase in insulin to the increase in blood sugar compared to 0 minutes after minute [△Ins(30'-0')/△Glu(30'-0')].

본 발명자들은 구체적인 실시예를 통하여, 비만대사수술을 앞둔 환자의 혈액 샘플을 채취하고 혈청에 아미노산 함량을 분석하여 각 샘플의 아미노산 프로파일을 제작하고, leucine, BCAA total, tryptophan(Tryp), phenylalanine, tyrosine(Tyr), LNAA total, leucine + phenylalanine, leucine + phenylalanine + tryptophan, leucine + phenylalanine + tryptophan + tyrosine, phenylalanine to LNAA ratio, kynurenine(Kyn), anthranilic acid(AA), 3-Hydroxyanthranilic acid (3-HAA), xanthurenic acid(XA), AA to Kyn ratio, 3-hydroxykynurenine(3-HA) to Kyn ratio, Kyn to Tryp ratio, 5-hydroxytryptophan(5-HTP), 5-HTP to Tryp ratio, 또는 L-DOPA to Tyr ratio을 독립변수로 하여 각각의 지표에 의해 수술 후 HOMA-IR, QUICKI, IGI, 및 DI 변화를 예측하기 위한 회귀모델을 제작하였다. 이어서, 상기 회귀모델에 의한 예측값을 수술 3개월 후 실측값과 비교하여 검정한 결과, 제작된 회귀모델은 비만대사수술 이후 환자의 당뇨병 관련 지표에 관한여 상당히 정확한 예측값을 제공할 수 있음을 확인하였다. 한편, 제작된 회귀모델 중에서도 leucine + phenylalanine + tryptophan + tyrosine 농도를 독립변수한 예측값이 가장 신뢰도가 높았다. Through specific examples, the present inventors collected blood samples from patients before obesity metabolic surgery and analyzed amino acid content in serum to produce amino acid profiles of each sample, and leucine, BCAA total, tryptophan (Tryp), phenylalanine, tyrosine (Tyr), LNAA total, leucine + phenylalanine, leucine + phenylalanine + tryptophan, leucine + phenylalanine + tryptophan + tyrosine, phenylalanine to LNAA ratio, kynurenine (Kyn), anthranilic acid (AA), 3-Hydroxyanthranilic acid (3-HAA) , xanthurenic acid(XA), AA to Kyn ratio, 3-hydroxykynurenine(3-HA) to Kyn ratio, Kyn to Tryp ratio, 5-hydroxytryptophan(5-HTP), 5-HTP to Tryp ratio, or L-DOPA to Using the Tyr ratio as an independent variable, a regression model was constructed to predict changes in HOMA-IR, QUICKI, IGI, and DI after surgery by each index. Subsequently, as a result of comparing the predicted value by the regression model with the measured value 3 months after surgery, it was confirmed that the produced regression model can provide a fairly accurate predictive value regarding the diabetes related index of the patient after obesity metabolic surgery. . On the other hand, among the produced regression models, the predicted value of leucine + phenylalanine + tryptophan + tyrosine concentration as an independent variable was the most reliable.

이에, 본 발명은 비만대사수술을 앞둔 환자의 혈장에 존재하는 특정 아미노산의 함량비 또는 그 농도를 수술 후 당뇨병 예후를 예측할 수 있는 정보를 제공할 수 있는 바이오마커로서 제공할 수 있으며, 상기 바이오마커를 이용하여 수술 전에 비만대사수술에 의한 당뇨병 개선의 효과를 예측할 수 있다. Accordingly, the present invention can provide the content ratio or concentration of a specific amino acid present in the plasma of a patient before obesity metabolic surgery as a biomarker capable of providing information for predicting the prognosis of diabetes after surgery, and the biomarker Can be used to predict the effect of diabetes improvement by obesity metabolic surgery before surgery.

바이오마커란 일반적으로 단백질이나 리보핵산 등을 이용하여 몸 안에 변화를 알아낼 수 있는 지표를 의미하는 것으로서, 본 발명은 비만대사수술을 앞둔 환자의 혈중 각 아미노산을 당뇨병 예후를 예측할 수 있는 지표, 즉 바이오마커로서 제공한다. The biomarker generally refers to an index that can detect changes in the body using protein or ribonucleic acid. In the present invention, each amino acid in the blood of a patient who is undergoing obesity metabolic surgery is an index that can predict the diabetes prognosis, that is, biomarker. Serve as a marker.

본 발명에서 분지사슬아미노산(Branched Chain Amino Acid: BCAA)은 루이신(Leucine), 이소루이신(Isoleucine), 및 발린(Valine)을 포함하는 것으로서, 비만동물에서 섭취하는 음식에 따라 그 함량변화가 급격하게 변하며 지방조직의 대사에 따라 BCAA의 함량이 변함이 알려져 있으나, 총 BCAA의 농도를 당뇨병 예후를 예측하기 위한 지표로 이용할 수 있음에 대해서는 알려진 바가 없다. In the present invention, the branched chain amino acid (BCAA) contains leucine, isoleucine, and valine, and its content varies depending on the food consumed in obese animals. It is known that the content of BCAA changes rapidly and according to the metabolism of adipose tissue, but there is no known that the concentration of total BCAA can be used as an index for predicting the prognosis of diabetes.

또한, 본 발명에서 거대중성아미노산(large neutral amino acids: LNAA)는 총 분지사슬아미노산과 트립토판(Tryptophan), 페닐알라닌(Phenylalanine), 및 티로신(Tyrosine)을 포함하는 것으로서, Trp/LNAA가 신경전달물질의 분비와 관계가 있음에 대해서 알려져 있으나, 총 LNAA의 농도 또는 phenylalanine/LNAA를 이용하여 당뇨병 예후를 예측할 수 있음에 대해서는 알려진 바가 없다.In addition, in the present invention, large neutral amino acids (LNAA) include total branched chain amino acids and tryptophan, phenylalanine, and tyrosine, and Trp/LNAA is a neurotransmitter. Although it is known that it is related to secretion, it is not known that the prognosis of diabetes can be predicted using the total LNAA concentration or phenylalanine/LNAA.

한편, 비만대사수술은 조절형 위 밴드 수술, 위소매절제술, 및 루와이 위 우회술이 있으며, 위소매절제술은 위를 부분 절제하여 그 용량을 줄이는 수술이다. 이에, 본 발명의 당뇨병 예후 예측용 바이오마커는 위종양이나 위궤양 등을 이유로 위 절제술을 앞둔 환자의 수술 후 당뇨병 예후를 예측하는데도 이용될 수 있다. On the other hand, obesity metabolic surgery includes adjustable gastric band surgery, gastric sleeve resection, and Ruwai gastric bypass surgery, and gastric sleeve resection is a surgery to reduce the dose by partially resecting the stomach. Accordingly, the biomarker for predicting the diabetes prognosis of the present invention may be used to predict the diabetes prognosis after surgery of a patient who has undergone gastrectomy due to gastric tumors or gastric ulcers.

따라서, 본 발명은 하기 단계를 포함하는 당뇨병 예후 예측 방법을 제공한다:Accordingly, the present invention provides a method for predicting diabetes prognosis comprising the following steps:

(1) 환자의 혈액 샘플을 수득하는 단계; (2) 상기 샘플의 아미노산 함량을 측정하여 아미노산 프로파일을 제작하는 단계; 및 (3) 상기 프로파일로부터 혈중 1) 루이신, 페닐알라닌, 트립토판, 및 티로신의 농도; 2) 루이신, 페닐알라닌, 및 트립토판의 농도; 3) LNAA의 총 농도; 4) BCAA 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 6) 티로신 농도; 7) 루이신 농도; 8) 루이신 및 페닐알라닌의 농도; 9) 키뉴레닌과 트립토판의 비; 10) 5-하이드록시트립토판 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 12) 트립토판 농도; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산 농도; 15) 잔투렌산 농도; 16) 키뉴레닌 농도; 17) 안트라닐산과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상의 바이오마커를 산출하는 단계.(1) obtaining a blood sample from the patient; (2) measuring the amino acid content of the sample to produce an amino acid profile; And (3) concentrations of 1) leucine, phenylalanine, tryptophan, and tyrosine in blood from the above profile; 2) the concentration of leucine, phenylalanine, and tryptophan; 3) total concentration of LNAA; 4) BCAA total concentration; 5) ratio of phenylalanine and total LNAA; 6) tyrosine concentration; 7) leucine concentration; 8) concentration of leucine and phenylalanine; 9) ratio of kynurenine and tryptophan; 10) 5-hydroxytryptophan concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 12) tryptophan concentration; 13) phenylalanine concentration; 14) 3-hydroxyanthranylic acid concentration; 15) xanthurenic acid concentration; 16) kynurenine concentration; 17) ratio of anthranilic acid and kynurenine; 18) the ratio of 3-hydroxykynurenine and kynurenine; And 19) a ratio of L-DOPA and tyrosine; calculating one or more biomarkers selected from the group consisting of.

이어서, 상기 (3) 단계에서 산출된 지표를 본 발명의 회귀모델(하기 표 2 참조)에 적용하여 비만대사수술 후 환자의 당뇨병 예후에 관한 정보를 제공할 수 있다.Subsequently, the index calculated in step (3) may be applied to the regression model of the present invention (see Table 2 below) to provide information on the patient's diabetes prognosis after obesity metabolic surgery.

한편, 본 발명은 상기 아미노산 프로파일 제작 단계, 지표(특정 아미노산 농도 또는 아미노산간의 비) 산출 단계, 및 산출된 지표를 독립변수로 하는 회귀모델을 이용하여 당뇨병 예후를 예측하는 단계를 수행하는 컴퓨터프로그램을 제공할 수 있으며, 상기 컴퓨터프로그램은 연산장치에 저장되어 제공될 수 있다.Meanwhile, the present invention provides a computer program for performing the step of producing an amino acid profile, calculating an index (a specific amino acid concentration or ratio between amino acids), and predicting a diabetes prognosis using a regression model using the calculated index as an independent variable. It may be provided, and the computer program may be provided by being stored in an operation device.

이상으로 본 발명 내용의 특정한 부분을 상세히 기술하였는바, 당업계의 통상의 지식을 가진 자에게 있어서, 이러한 구체적 기술은 단지 바람직한 실시 양태일 뿐이며, 이에 의해 본 발명의 범위가 제한되는 것이 아닌 점은 명백할 것이다. 따라서 본 발명의 실질적인 범위는 첨부된 청구항들과 그것들의 등가물에 의하여 정의된다고 할 것이다.As described above, a specific part of the present invention has been described in detail, and for those of ordinary skill in the art, it is obvious that this specific technique is only a preferred embodiment, and the scope of the present invention is not limited thereby. something to do. Therefore, it will be said that the practical scope of the present invention is defined by the appended claims and their equivalents.

[실시예][Example]

실시예 1. 아미노산 프로파일 제작Example 1. Amino acid profile construction

비만대사수술을 앞둔 환자의 혈액 샘플을 멸균시험관(plain tube)에 수득하고 30분간 실온에서 응고(clotting)시킨 후, 원심분리 과정을 거쳐 혈청(serum)만 수득하였다. 상기 수득한 혈청에서 cold CAN을 사용하여, 단백질 침전 및 추출과정을 거친 후, 액체 크로마토그래피 질량분석(LC-MS)을 수행하여, 상기 샘플의 아미노산을 각각 정량하였다. 정량분석의 경우 40mg/ml BSA in PBS의 artificial serum matrix를 사용하였으며, 내부정량 표준물질은 13C tryptophan을 사용하였다. 정량분석 후, 아미노산의 절대값(absolute amount)과 비율(ratio)을 이용하여 아미노산 프로파일을 제작하였다. Blood samples from patients before obesity metabolic surgery were obtained in a sterile test tube, clotting at room temperature for 30 minutes, and then centrifugation to obtain only serum. The obtained serum was subjected to protein precipitation and extraction using cold CAN, and then liquid chromatography mass spectrometry (LC-MS) was performed, and amino acids in the sample were each quantified. For quantitative analysis, an artificial serum matrix of 40mg/ml BSA in PBS was used, and 13C tryptophan was used as an internal quantification standard. After quantitative analysis, an amino acid profile was produced using the absolute amount and ratio of amino acids.

실시예 2. 회귀모델 구축Example 2. Construction of a regression model

이어서, 상기 실시예 1에서 제작한 아미노산 프로파일에서 Leucine level; Isoleucine level; Valine level; Tryptophan level; Phenylalanine level; Tyrosine level; total BCAAs level; Leucine to total BCAA ratio; Isoleucine to total BCAA ratio; Valine to total BCAA ratio; total LNAAs level; Leucine to total LNAA ratio; Isoleucine to total LNAA ratio; Valine to total LNAA ratio; tryptophan to total LNAA ratio; tyrosine to total LNAA ratio; phenylalanine to total LNAA ratio; Kyn; AA; 3-HK; 3-HAA; KA; XA; AA/Kyn; 3-HK/Kyn; Kyn/Tryp; 5-HTP; serotonine; 5-HIAA; L-DOPA; 5-hydroxytryptophan to tryptophan ratio; tyrosine to phenylalanine ratio; 또는 L-DOPA to tyrosine ratio 을 변수로 하여 회귀모델을 제작하고, 각 지표에 따라 비만대사수술 이후 당뇨병 예후를 예측하였다. 당뇨병 예후는 HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), DI (Disposition index)의 변화를 통해 예측할 수 있다. 각 지표의 변화는 (수술 후 index - 수술 전 index)/(수술 후 index) *100으로 정의한다. Subsequently, Leucine level in the amino acid profile prepared in Example 1; Isoleucine level; Valine level; Tryptophan level; Phenylalanine level; Tyrosine level; total BCAAs level; Leucine to total BCAA ratio; Isoleucine to total BCAA ratio; Valine to total BCAA ratio; total LNAAs level; Leucine to total LNAA ratio; Isoleucine to total LNAA ratio; Valine to total LNAA ratio; tryptophan to total LNAA ratio; tyrosine to total LNAA ratio; phenylalanine to total LNAA ratio; Kyn; AA; 3-HK; 3-HAA; KA; XA; AA/Kyn; 3-HK/Kyn; Kyn/Tryp; 5-HTP; serotonine; 5-HIAA; L-DOPA; 5-hydroxytryptophan to tryptophan ratio; tyrosine to phenylalanine ratio; Alternatively, a regression model was constructed using the L-DOPA to tyrosine ratio as a variable, and the prognosis of diabetes after obesity metabolic surgery was predicted according to each index. Diabetes prognosis can be predicted through changes in HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), and DI (Disposition index). The change of each index is defined as (postoperative index-preoperative index)/(postoperative index) *100.

실시예 3. 회귀모델 검정Example 3. Regression model test

비만대사수술 이후 각 환자를 3개월 동안 추적하면서 당뇨병 관련 4가지 지표들(HOMA-IR, QUICKI, Insulinogenic index, Disposition index)의 변화를 관찰하였고, 상기 실시예 2에서 제작한 회귀모델에 따라 예측된 값과 실제 변화량을 비교하여 검정하였다(표 1 참조). After obesity metabolic surgery, each patient was followed for 3 months, and changes in four diabetes related indicators (HOMA-IR, QUICKI, Insulinogenic index, Disposition index) were observed, and predicted according to the regression model prepared in Example 2 above. It was tested by comparing the value and the actual amount of change (see Table 1).

Table. 수술 전 아미노산 프로파일과 수술 후 당뇨병 관련 지표 변화와의 상관관계Table. Correlation between preoperative amino acid profile and postoperative diabetes-related indicator changes HOMA-IR change (n= 15)HOMA-IR change (n= 15) QUICKI change (n= 15)QUICKI change (n= 15) IGI change (n=12)IGI change (n=12) DI change (n=12)DI change (n=12) β(SE)β(SE) P valueP value β (SE)β (SE) P valueP value β (SE)β (SE) P valueP value β (SE)β (SE) P valueP value LNAAsLNAAs LeuLeu 4.01 (3.13)4.01 (3.13) 0.2210.221 0.19 (0.10)0.19 (0.10) 0.0890.089 2.13 (0.60)2.13 (0.60) 0.0060.006 1.77 (0.69)1.77 (0.69) 0.0280.028 IleIle 1.26 (3.36)1.26 (3.36) 0.7130.713 0.18 (0.10)0.18 (0.10) 0.1090.109 0.96 (0.86)0.96 (0.86) 0.2890.289 1.27 (0.80)1.27 (0.80) 0.1430.143 ValVal 0.60 (2.16)0.60 (2.16) 0.7850.785 0.13 (0.06)0.13 (0.06) 0.0590.059 0.85 (0.48)0.85 (0.48) 0.1090.109 0.90 (0.46)0.90 (0.46) 0.0770.077 BCAA, SumBCAA, Sum 0.67 (1.04)0.67 (1.04) 0.5280.528 0.06 (0.03)0.06 (0.03) 0.0470.047 0.50 (0.22)0.50 (0.22) 0.0520.052 0.51 (0.21)0.51 (0.21) 0.0410.041 TrypTryp 8.28 (4.42)8.28 (4.42) 0.0820.082 0.30 (0.16)0.30 (0.16) 0.0990.099 2.89 (1.01)2.89 (1.01) 0.0170.017 2.12 (1.15)2.12 (1.15) 0.0970.097 PhePhe 11.71 (4.58)11.71 (4.58) 0.0230.023 0.33 (0.20)0.33 (0.20) 0.1290.129 3.90 (1.16)3.90 (1.16) 0.0070.007 2.53 (1.46)2.53 (1.46) 0.1130.113 TyrTyr 4.60 (2.79)4.60 (2.79) 0.1220.122 0.22 (0.09)0.22 (0.09) 0.0300.030 1.56 (0.75)1.56 (0.75) 0.0670.067 1.29 (0.78)1.29 (0.78) 0.1300.130 LNAA, SumLNAA, Sum 1.08 (0.71)1.08 (0.71) 0.1520.152 0.06 (0.02)0.06 (0.02) 0.0080.008 0.52 (0.13)0.52 (0.13) 0.0030.003 0.47 (0.14)0.47 (0.14) 0.0100.010 Leu+PheLeu+Phe 0.43 (0.23)0.43 (0.23) 0.0910.091 0.14 (0.07)0.14 (0.07) 0.0790.079 1.59 (0.38)1.59 (0.38) 0.0020.002 1.22 (0.48)1.22 (0.48) 0.0320.032 Leu+Phe+TrypLeu+Phe+Tryp 0.39 (0.18)0.39 (0.18) 0.0480.048 0.12 (0.05)0.12 (0.05) 0.0430.043 1.35 (0.25)1.35 (0.25) 0.0000.000 1.02 (0.36)1.02 (0.36) 0.0180.018 Leu+Phe+Tryp+TyrLeu+Phe+Tryp+Tyr 0.32 (0.12)0.32 (0.12) 0.0190.019 0.10 (0.03)0.10 (0.03) 0.0150.015 1.00 (0.18)1.00 (0.18) 0.0000.000 0.78 (0.26)0.78 (0.26) 0.0140.014 Leu/BCAAsLeu/BCAAs 2918 (2328)2918 (2328) 0.2310.231 -53.6 (90.0)-53.6 (90.0) 0.5620.562 660 (659)660 (659) 0.3400.340 17.6 ( 678)17.6 (678) 0.9800.980 Ile/BCAAsIle/BCAAs -806 (2981)-806 (2981) 0.7910.791 61.6 (103.4)61.6 (103.4) 0.5610.561 -417 ( 807)-417 (807) 0.6170.617 220 (799)220 (799) 0.7890.789 Val/BCAAsVal/BCAAs -2488 (2424)-2488 (2424) 0.3220.322 6.8 (91.2)6.8 (91.2) 0.9410.941 -377 ( 695)-377 (695) 0.5990.599 -182 (689)-182 (689) 0.7970.797 Leu/LNAAsLeu/LNAAs 600 (3846)600 (3846) 0.8780.878 -49.6 (133)-49.6 (133) 0.7170.717 779 (933)779 (933) 0.4240.424 448 (936)448 (936) 0.6420.642 Ile/LNAAsIle/LNAAs -2548 (2912)-2548 (2912) 0.3960.396 37.7 (107)37.7 (107) 0.7300.730 -353 (799)-353 (799) 0.6680.668 243 (788)243 (788) 0.7640.764 Val/LNAAsVal/LNAAs -4199 (2163)-4199 (2163) 0.0730.073 19.8 (96.4)19.8 (96.4) 0.8400.840 -340 (735)-340 (735) 0.6530.653 189 (726)189 (726) 0.8000.800 Tryp/LNAAsTryp/LNAAs 2803 (3677)2803 (3677) 0.4590.459 -89.7 (132)-89.7 (132) 0.5090.509 4.48 (1041)4.48 (1041) 0.9970.997 -579 (1004)-579 (1004) 0.5760.576 Phe/LNAAsPhe/LNAAs 9910 (5704)9910 (5704) 0.1040.104 -523 ( 204)-523 (204) 0.0240.024 -1370 (1851)-1370 (1851) 0.4760.476 -3728 (1443)-3728 (1443) 0.0270.027 Tyr/LNAAsTyr/LNAAs 2979 (2303)2979 (2303) 0.2170.217 84.9 ( 86.1)84.9 (86.1) 0.3420.342 323 (672)323 (672) 0.6410.641 171 (664)171 (664) 0.8020.802 Kynurenine pathwayKynurenine pathway KynKyn 98.5 (63.0)98.5 (63.0) 0.1400.140 -3.27 (2.47)-3.27 (2.47) 0.2080.208 -34.3 (16.8)-34.3 (16.8) 0.0700.070 -43.3 (14.1)-43.3 (14.1) 0.0120.012 AAAA 7121 (3040)7121 (3040) 0.0340.034 237 (127)237 (127) 0.0860.086 204 (1075)204 (1075) 0.8530.853 1908 (867)1908 (867) 0.0520.052 3-HK3-HK 6605 (3459)6605 (3459) 0.0770.077 -12.8 (153)-12.8 (153) 0.9350.935 280 (1331)280 (1331) 0.8370.837 -186 (1307)-186 (1307) 0.8890.889 3-HAA3-HAA 2527 (2508)2527 (2508) 0.3310.331 76.1 (91.1)76.1 (91.1) 0.4180.418 1306 (540)1306 (540) 0.0360.036 766 (621)766 (621) 0.2460.246 KAKA 1016 (1183)1016 (1183) 0.4050.405 20.6 (43.1)20.6 (43.1) 0.6400.640 269 (310)269 (310) 0.4060.406 -104 (313)-104 (313) 0.7470.747 XAXA 3053 (3497)3053 (3497) 0.3970.397 135 (122)135 (122) 0.2880.288 1690 (735)1690 (735) 0.0440.044 631 (869)631 (869) 0.4840.484 AA/KynAA/Kyn -2352 (7843)-2352 (7843) 0.7690.769 477 (243)477 (243) 0.0720.072 2558 (1843)2558 (1843) 0.1950.195 4349 (1415)4349 (1415) 0.0120.012 3-HK/Kyn3-HK/Kyn -9409 (5937)-9409 (5937) 0.1350.135 241 (238)241 (238) 0.3290.329 3278 (1633)3278 (1633) 0.0730.073 3558 (1527)3558 (1527) 0.0420.042 KA/KynKA/Kyn 1499 (4364)1499 (4364) 0.7360.736 159 (146)159 (146) 0.2960.296 1510 (993)1510 (993) 0.1590.159 581 (1065)581 (1065) 0.5970.597 Kyn/TryptophanKyn/Tryptophan 2708 (4338)2708 (4338) 0.5420.542 -262.3 (139.4)-262.3 (139.4) 0.0820.082 -2601 (856)-2601 (856) 0.0130.013 -2816 (750)-2816 (750) 0.0040.004 Serotonin pathwaySerotonin pathway 5-Htryp5-Htryp 54.3 (98.9)54.3 (98.9) 0.5920.592 -6.23 (3.11)-6.23 (3.11) 0.0660.066 -60.0 (17.9)-60.0 (17.9) 0.0070.007 -54.8 (18.8)-54.8 (18.8) 0.0150.015 SerotoninSerotonin 2.6 (185.4)2.6 (185.4) 0.9890.989 8.88 (5.98)8.88 (5.98) 0.1620.162 9.9 (52.1)9.9 (52.1) 0.8530.853 43.6 (49.3)43.6 (49.3) 0.3980.398 5-HIAA5-HIAA 944 (1854)944 (1854) 0.6180.618 -68.9 (63.5)-68.9 (63.5) 0.2970.297 -379 (460)-379 (460) 0.4290.429 -624 (422)-624 (422) 0.1700.170 5-Htryp/Tryp5-Htryp/Tryp -378 (5966)-378 (5966) 0.9500.950 -385 (178)-385 (178) 0.0500.050 -3534 (1039)-3534 (1039) 0.0070.007 -3217 (1096)-3217 (1096) 0.0150.015 Tyrosine pathwayTyrosine pathway L-DOPAL-DOPA 9335 (7704)9335 (7704) 0.2460.246 -132 (298)-132 (298) 0.6650.665 -1630 (2192)-1630 (2192) 0.4740.474 -2489 (2063)-2489 (2063) 0.2550.255 Tyr/PheTyr/Phe 267 (321)267 (321) 0.4200.420 18.4 (10.5)18.4 (10.5) 0.1030.103 71.7 (88.5)71.7 (88.5) 0.4360.436 83.0 (85.6)83.0 (85.6) 0.3550.355 L-DOPA/TyrL-DOPA/Tyr 12628 (447267)12628 (447267) 0.9780.978 -28956 (13442)-28956 (13442) 0.0510.051 -169132 (96522)-169132 (96522) 0.1100.110 -198321 (88156)-198321 (88156) 0.0480.048 HOMA-IR=Homeostasis model assessment of insulin resistance
QUICKI=Quantitative insulin sensitivity check index
IGI=Insulinogenic index
DI=Disposition indexHOMA-IR=Homeostasis model assessment of insulin resistance
QUICKI=Quantitative insulin sensitivity check index
IGI=Insulinogenic index
DI=Disposition index

상기 표 1에 나타난 바와 같이, leucine + phenylalanine + tryptophan + tyrosine 농도에 따른 예측값이 가장 실측치와 일치하였으나, leucine, BCAA total, tryptophan, phenylalanine, tyrosine, LNAA total, leucine + phenylalanine, leucine + phenylalanine + tryptophan, phenylalanine to LNAA ratio, kynurenine(Kyn), anthranilic acid(AA), 3-HAA, xanthurenic acid(XA), AA to Kyn ratio, 3-hydroxykynurenine(3-HA) to Kyn ratio, Kyn to Tryp ratio, 5-hydroxytryptophan(5-HTP), 5-HTP to Tryp ratio, L-DOPA to Tyr ratio 도 상당히 높은 신뢰도의 예측값을 제공함을 확인할 수 있었다.As shown in Table 1, the predicted value according to the concentration of leucine + phenylalanine + tryptophan + tyrosine was the most consistent with the measured value, but leucine, BCAA total, tryptophan, phenylalanine, tyrosine, LNAA total, leucine + phenylalanine, leucine + phenylalanine + tryptophan, phenylalanine to LNAA ratio, kynurenine(Kyn), anthranilic acid(AA), 3-HAA, xanthurenic acid(XA), AA to Kyn ratio, 3-hydroxykynurenine(3-HA) to Kyn ratio, Kyn to Tryp ratio, 5- It was confirmed that hydroxytryptophan (5-HTP), 5-HTP to Tryp ratio, and L-DOPA to Tyr ratio also provided predicted values with very high reliability.

각 변수에 따라 제작된 회귀모델과 각 모델의 p-value와 결정계수(R2)는 하기 표 2에 나타내었다. Regression models produced according to each variable, p-value and coefficient of determination (R2) of each model are shown in Table 2 below.

　 독립변수Independent variable 회귀식Regression p-valuep-value R2R2 1One leucine + phenylalanine + tryptophan + tyrosine 농도(A)leucine + phenylalanine + tryptophan + tyrosine concentration (A) 수학식 1Equation 1 HOMA-IR 변화(%) = -61.71 + (0.32 * A)HOMA-IR change (%) = -61.71 + (0.32 * A) 0.01880.0188 0.360.36 수학식 2Equation 2 QUICKI 변화(%) = -24.83 + (0.11 * A)QUICKI change (%) = -24.83 + (0.11 * A) 0.0150.015 0.380.38 수학식 3Equation 3 Insulinogenic index 변화(%) = -350.3 + (1.01 * A)Insulinogenic index change (%) = -350.3 + (1.01 * A) 0.00030.0003 0.750.75 수학식 4Equation 4 Disposition index 변화(%) = -236.5 + (0.78 * A)Disposition index change (%) = -236.5 + (0.78 * A) 0.01410.0141 0.470.47 22 leucine + phenylalanine + tryptophan 농도(B)leucine + phenylalanine + tryptophan concentration (B) 수학식 5Equation 5 HOMA-IR 변화(%) = -57.33+ (0.39 * B)HOMA-IR change (%) = -57.33+ (0.39 * B) 0.04850.0485 0.260.26 수학식 6Equation 6 QUICKI 변화(%) = -23.26+ (0.12 * B)QUICKI change (%) = -23.26+ (0.12 * B) 0.04270.0427 0.270.27 수학식 7Equation 7 Insulinogenic index 변화(%) = -378.7+ (1.35* B)Insulinogenic index change (%) = -378.7+ (1.35* B) 0.00030.0003 0.740.74 수학식 8Equation 8 Disposition index 변화(%) = -250.7+ (1.02* B)Disposition index change (%) = -250.7+ (1.02* B) 0.01770.0177 0.440.44 33 LNAA total 농도LNAA total concentration 수학식 9Equation 9 QUICKI 변화(%) = -29.09+ (0.06 * LNAA total)QUICKI change (%) = -29.09+ (0.06 * LNAA total) 0.00830.0083 0.420.42 수학식 10Equation 10 Insulinogenic index 변화(%) = -320.9+ (0.52 * LNAA total)Insulinogenic index change (%) = -320.9+ (0.52 * LNAA total) 0.00330.0033 0.590.59 수학식 11Equation 11 Disposition index 변화(%) = -257.9+ (0.47 * LNAA total)Disposition index change (%) = -257.9+ (0.47 * LNAA total) 0.00960.0096 0.500.50 44 BCAA total 농도BCAA total concentration 수학식 12Equation 12 QUICKI 변화(%) = -13.43 + (0. 06 * BCAA total)QUICKI change (%) = -13.43 + (0. 06 * BCAA total) 0.04740.0474 0.260.26 수학식 13Equation 13 Disposition index 변화(%) = -155.6+ (0. 51 * BCAA total)Disposition index change (%) = -155.6+ (0. 51 * BCAA total) 0.04140.0414 0.35370.3537 55 phenylalanine to LNAA ratiophenylalanine to LNAA ratio 수학식 14Equation 14 QUICKI 변화(%) = 86.31+ (-523.5* phenylalanine/LNAA)QUICKI change (%) = 86.31+ (-523.5* phenylalanine/LNAA) 0.02380.0238 0.330.33 수학식 15Equation 15 Disposition index 변화(%) = 564.5+ (-3728* phenylalanine/LNAA)Disposition index change (%) = 564.5+ (-3728* phenylalanine/LNAA) 0.02730.0273 0.400.40 66 tyrosine 농도tyrosine concentration 수학식 16Equation 16 QUICKI 변화(%) = -2.58+ (0.22 * tyrosine)QUICKI change (%) = -2.58+ (0.22 * tyrosine) 0.03040.0304 0.310.31 77 leucine 농도leucine concentration 수학식 17Equation 17 Insulinogenic index 변화(%) = -241.9 + (2.13* leucine)Insulinogenic index change (%) = -241.9 + (2.13* leucine) 0.0050.005 0.5510.551 수학식 18Equation 18 Disposition index 변화(%) = -166.7+ (1.77* leucine)Disposition index change (%) = -166.7+ (1.77* leucine) 0.02830.0283 0.390.39 88 leucine + phenylalanine 농도 (C)leucine + phenylalanine concentration (C) 수학식 19Equation 19 Insulinogenic index 변화(%) = -316.1+ (1.59 * C)Insulinogenic index change (%) = -316.1+ (1.59 * C) 0.00220.0022 0.620.62 수학식 20Equation 20 Disposition index 변화(%) = -206.8+ (1.22* C)Disposition index change (%) = -206.8+ (1.22* C) 0.03150.0315 0.380.38 99 kynurenine(Kyn) to Tryp ratiokynurenine(Kyn) to Tryp ratio 수학식 21Equation 21 Insulinogenic index 변화(%) = 151.5+ (-2601* Kyn/Tryp)Insulinogenic index change (%) = 151.5+ (-2601* Kyn/Tryp) 0.01250.0125 0.470.47 수학식 22Equation 22 Disposition index 변화(%) = 190.9+ (-2816* Kyn/Tryp)Disposition index change (%) = 190.9+ (-2816* Kyn/Tryp) 0.00380.0038 0.580.58 1010 5-hydroxytryptophan(5-HTP) 농도5-hydroxytryptophan (5-HTP) concentration 수학식 23Equation 23 Insulinogenic index 변화(%) = 149.2+ (-60.06* 5-HTP)Insulinogenic index change (%) = 149.2+ (-60.06* 5-HTP) 0.00730.0073 0.520.52 수학식 24Equation 24 Disposition index 변화(%) = 168.2+ (-54.8* 5-HTP)Disposition index change (%) = 168.2+ (-54.8* 5-HTP) 0.01540.0154 0.450.45 1111 5-HTP to Tryp ratio5-HTP to Tryp ratio 수학식 25Equation 25 Insulinogenic index 변화(%) = 119.1+ (-3534* 5-HTP/Tryp)Insulinogenic index change (%) = 119.1+ (-3534* 5-HTP/Tryp) 0.00680.0068 0.530.53 수학식 26Equation 26 Disposition index 변화(%) = 140.3+ (-3217* 5-HTP/Tryp)Disposition index change (%) = 140.3+ (-3217* 5-HTP/Tryp) 0.01500.0150 0.460.46 1212 tryptophan 농도tryptophan concentration 수학식 27Equation 27 Insulinogenic index 변화(%) = -212.8+ (2.89* tryptophan)Insulinogenic index change (%) = -212.8+ (2.89* tryptophan) 0.01690.0169 0.450.45 1313 phenylalanine 농도phenylalanine concentration 수학식 28Equation 28 Insulinogenic index 변화(%) = -322.9+ (3.90* phenylalanine)Insulinogenic index change (%) = -322.9+ (3.90* phenylalanine) 0.00740.0074 0.520.52 1414 3-Hydroxyanthranilicacid(3-HAA)농도3-Hydroxyanthranilic acid (3-HAA) concentration 수학식 29Equation 29 Insulinogenic index 변화(%) = -89.68+ (1306* 3-HAA)Insulinogenic index change (%) = -89.68+ (1306* 3-HAA) 0.03610.0361 0.360.36 1515 xanthurenic acid(XA) 농도xanthurenic acid (XA) concentration 수학식 30Equation 30 Insulinogenic index 변화(%) = -26.6+ (1690* XA)Insulinogenic index change (%) = -26.6+ (1690* XA) 0.04450.0445 0.340.34 1616 kynurenine(Kyn) 농도kynurenine (Kyn) concentration 수학식 31Equation 31 Disposition index 변화(%) = 222.2+ (-43.3* Kyn)Disposition index change (%) = 222.2+ (-43.3* Kyn) 0.01190.0119 0.480.48 1717 anthranilic acid (AA) to kynurenine(Kyn) ratioanthranilic acid (AA) to kynurenine(Kyn) ratio 수학식 32Equation 32 Disposition index 변화(%) = -75.5+ (4349* AA/Kyn)Disposition index change (%) = -75.5+ (4349* AA/Kyn) 0.01180.0118 0.480.48 1818 3-hydroxykynurenine(3-HA) to Kyn ratio3-hydroxykynurenine(3-HA) to Kyn ratio 수학식 33Equation 33 Disposition index 변화(%) = -94.4+ (3558* 3-HA/Kyn)Disposition index change (%) = -94.4+ (3558* 3-HA/Kyn) 0.04200.0420 0.350.35 1919 L-DOPA to Tyr ratioL-DOPA to Tyr ratio 수학식 34Equation 34 Disposition index 변화(%) = 131.6+ (-198321* L-DOPA/Tyr)Disposition index change (%) = 131.6+ (-198321* L-DOPA/Tyr) 0.04820.0482 0.330.33

이상으로 본 발명 내용의 특정한 부분을 상세히 기술하였는바, 당업계의 통상의 지식을 가진 자에게 있어서, 이러한 구체적 기술은 단지 바람직한 실시 양태일 뿐이며, 이에 의해 본 발명의 범위가 제한되는 것이 아닌 점은 명백할 것이다. 따라서 본 발명의 실질적인 범위는 첨부된 청구항들과 그것들의 등가물에 의하여 정의된다고 할 것이다.As described above, a specific part of the present invention has been described in detail, and for those of ordinary skill in the art, it is obvious that this specific technique is only a preferred embodiment, and the scope of the present invention is not limited thereby. something to do. Therefore, it will be said that the practical scope of the present invention is defined by the appended claims and their equivalents.

Claims (10)

삭제delete 삭제delete 삭제delete 삭제delete 삭제delete 삭제delete 삭제delete 하기 단계를 포함하는 비만대사수술 전에 수술 후 당뇨병 예후 예측을 위한 정보제공방법:
(가) 환자의 혈액 샘플을 수득하는 단계;
(나) 상기 샘플의 아미노산 함량을 측정하여 아미노산 프로파일을 제작하는 단계;
(다) 상기 프로파일로부터 혈중 1) 루이신(leucine), 페닐알라닌(phenylalanine), 트립토판(tryptophan: tryp), 및 티로신(tyrosine: tyr)의 농도; 2) 루이신, 페닐알라닌, 및 트립토판의 농도; 3) 거대중성아미노산(LNAA)의 총 농도; 4) 분지사슬아미노산(BCAA)의 총 농도; 5) 페닐알라닌과 총 LNAA의 비(ratio); 8) 루이신 및 페닐알라닌의 농도; 10) 5-하이드록시트립토판(5-hydroxytryptophan: 5-HTP) 농도; 11) 5-하이드록시트립토판과 트립토판의 비; 13) 페닐알라닌 농도; 14) 3-히드록시안트라닐산(3-Hydroxyanthranilic acid: 3-HAA) 농도; 15) 잔투렌산(xanthurenic acid: XA) 농도; 17) 안트라닐산(anthranilic acid: AA)과 키뉴레닌의 비; 18) 3-하이드록시키뉴레닌(3-hydroxykynurenine: 3-HA)과 키뉴레닌의 비; 및 19) L-DOPA과 티로신의 비;로 이루어진 군으로부터 선택되는 하나 이상을 산출하는 단계; 및
(라) 상기 (다) 단계에서 산출된 아미노산의 농도 또는 비(ratio)를 이용하여 HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), 및 DI (Disposition index)로 이루어진 군으로부터 하나 이상의 지표 변화를 예측하는 단계.
A method of providing information for predicting the prognosis of diabetes after surgery before obesity metabolic surgery, including the following steps:
(A) obtaining a blood sample from the patient;
(B) measuring the amino acid content of the sample to produce an amino acid profile;
(C) concentrations of 1) leucine, phenylalanine, tryptophan, and tyrosine (tyros) in blood from the above profile; 2) the concentration of leucine, phenylalanine, and tryptophan; 3) total concentration of macroneutral amino acids (LNAA); 4) total concentration of branched chain amino acids (BCAA); 5) ratio of phenylalanine and total LNAA; 8) concentration of leucine and phenylalanine; 10) 5-hydroxytryptophan (5-HTP) concentration; 11) ratio of 5-hydroxytryptophan and tryptophan; 13) phenylalanine concentration; 14) 3-Hydroxyanthranilic acid (3-HAA) concentration; 15) xanthurenic acid (XA) concentration; 17) ratio of anthranilic acid (AA) and kynurenine; 18) the ratio of 3-hydroxykynurenine (3-HA) and kynurenine; And 19) a ratio of L-DOPA and tyrosine; calculating one or more selected from the group consisting of; And
(D) HOMA-IR (homeostatic model assessment for insulin resistance), QUICKI (Quantitative insulin sensitivity check index), IGI (Insulinogenic index), and Predicting one or more indicator changes from the group consisting of DI (Disposition Index).
 제8항에 있어서,
상기 (가) 단계의 샘플은 혈청 샘플인 것을 특징으로 하는, 정보제공방법.
The method of claim 8,
The method of providing information, characterized in that the sample of step (a) is a serum sample.
 제8항에 있어서,
상기 정보제공방법의 (다) 단계에서 1) 혈중 루이신, 페닐알라닌, 트립토판, 및 티로신의 농도(A)를 산출하는 경우 (라) 단계에서 HOMA-IR, QUICKI, IGI, 및 DI 변화 예측은 각각 수학식 1 내지 4를 이용하며,
상기 정보제공방법의 (다) 단계에서 2) 혈중 루이신, 페닐알라닌, 트립토판, 및 티로신의 농도를 산출하는 경우 (라) 단계에서 HOMA-IR, QUICKI, IGI, 및 DI 변화 예측은 각각 수학식 5 내지 8를 이용하며,
상기 정보제공방법의 (다) 단계에서 3) 혈중 총 LNAA의 농도를 산출하는 경우 (라) 단계에서 QUICKI, IGI, 및 DI 변화 예측은 각각 하기 수학식 9 내지 11을 이용하며,
상기 정보제공방법의 (다) 단계에서 4) 혈중 총 BCAA의 농도를 산출하는 경우 (라) 단계에서 QUICKI 및 DI 변화 예측은 각각 하기 수학식 12 내지 13을 이용하며,
상기 정보제공방법의 (다) 단계에서 5) 혈중 페닐알라닌과 총 LNAA의 비를 산출하는 경우 (라) 단계에서 QUICKI 및 DI 변화 예측은 각각 하기 수학식 14 내지 15를 이용하며,
상기 정보제공방법의 (다) 단계에서 8) 루이신 및 페닐알라닌의 농도(B)를 산출하는 경우 (라) 단계에서 IGI 및 DI 변화 예측은 각각 하기 수학식 19 내지 20을 이용하며,
상기 정보제공방법의 (다) 단계에서 10) 5-하이드록시트립토판 농도를 산출하는 경우 (라) 단계에서 IGI 및 DI 변화 예측은 각각 하기 수학식 23 내지 24를 이용하며,
상기 정보제공방법의 (다) 단계에서 11) 5-하이드록시트립토판과 트립토판의 비를 산출하는 경우 (라) 단계에서 IGI 및 DI 변화 예측은 각각 하기 수학식 25 내지 26를 이용하며,
상기 정보제공방법의 (다) 단계에서 13) 페닐알라닌 농도를 산출하는 경우 (라) 단계에서 IGI 변화 예측은 하기 수학식 28를 이용하며,
상기 정보제공방법의 (다) 단계에서 14) 3-히드록시안트라닐산 농도를 산출하는 경우 (라) 단계에서 IGI 변화 예측은 하기 수학식 29를 이용하며,
상기 정보제공방법의 (다) 단계에서 15) 잔투렌산 농도를 산출하는 경우 (라) 단계에서 IGI 변화 예측은 하기 수학식 30를 이용하며,
상기 정보제공방법의 (다) 단계에서 17) 안트라닐산과 키뉴레닌의 비를 산출하는 경우 (라) 단계에서 DI 변화 예측은 하기 수학식 32를 이용하며,
상기 정보제공방법의 (다) 단계에서 18) 3-하이드록시키뉴레닌과 키뉴레닌의 비를 산출하는 경우 (라) 단계에서 DI 변화 예측은 하기 수학식 33을 이용하며,
상기 정보제공방법의 (다) 단계에서 19) L-DOPA과 티로신의 비를 산출하는 경우 (라) 단계에서 DI 변화 예측은 하기 수학식 34를 이용하는 것을 특징으로 하는, 비만대사수술 전에 수술 후 당뇨병 예후 예측을 위한 정보제공방법.

[수학식 1]
HOMA-IR 변화(%) = -61.71 + (0.32 * A)
[수학식 2]
QUICKI 변화(%) = -24.83 + (0.11 * A)
[수학식 3]
Insulinogenic index 변화(%) = -350.3 + (1.01 * A)
[수학식 4]
Disposition index 변화(%) = -236.5 + (0.78 * A)
[수학식 5]
HOMA-IR 변화(%) = -57.33+ (0.39 * B)
[수학식 6]
QUICKI 변화(%) = -23.26+ (0.12 * B)
[수학식 7]
Insulinogenic index 변화(%) = -378.7+ (1.35* B)
[수학식 8]
Disposition index 변화(%) = -250.7+ (1.02* B)
[수학식 9]
QUICKI 변화(%) = -29.09+ (0.06 * LNAA total)
[수학식 10]
Insulinogenic index 변화(%) = -320.9+ (0.52 * LNAA total)
[수학식 11]
Disposition index 변화(%) = -257.9+ (0.47 * LNAA total)
[수학식 12]
QUICKI 변화(%) = -13.43 + (0. 06 * BCAA total)
[수학식 13]
Disposition index 변화(%) = -155.6+ (0. 51 * BCAA total)
[수학식 14]
QUICKI 변화(%) = 86.31+ (-523.5* phenylalanine/LNAA)
[수학식 15]
Disposition index 변화(%) = 564.5+ (-3728* phenylalanine/LNAA)
[수학식 19]
Insulinogenic index 변화(%) = -316.1+ (1.59 * C)
[수학식 20]
Disposition index 변화(%) = -206.8+ (1.22* C)
[수학식 23]
Insulinogenic index 변화(%) = 149.2+ (-60.06* 5-HTP)
[수학식 24]
Disposition index 변화(%) = 168.2+ (-54.8* 5-HTP)
[수학식 25]
Insulinogenic index 변화(%) = 119.1+ (-3534* 5-HTP/Tryp)
[수학식 26]
Disposition index 변화(%) = 140.3+ (-3217* 5-HTP/Tryp)
[수학식 28]
Insulinogenic index 변화(%) = -322.9+ (3.90* phenylalanine)
[수학식 29]
Insulinogenic index 변화(%) = -89.68+ (1306* 3-HAA)
[수학식 30]
Insulinogenic index 변화(%) = -26.6+ (1690* XA)
[수학식 32]
Disposition index 변화(%) = -75.5+ (4349* AA/Kyn)
[수학식 33]
Disposition index 변화(%) = -94.4+ (3558* 3-HA/Kyn)
[수학식 34]
Disposition index 변화(%) = 131.6+ (-198321* L-DOPA/Tyr)The method of claim 8,
In the case of calculating the concentrations (A) of 1) leucine, phenylalanine, tryptophan, and tyrosine in the blood in step (c) of the information providing method, the prediction of changes in HOMA-IR, QUICKI, IGI, and DI in step (D) is respectively Using Equations 1 to 4,
In the case of calculating the concentration of leucine, phenylalanine, tryptophan, and tyrosine in the blood in step (c) of the information providing method, the prediction of changes in HOMA-IR, QUICKI, IGI, and DI in step (D) is Equation 5, respectively. Using to 8,
In the case of calculating the total concentration of LNAA in the blood in step 3) in step (c) of the information providing method, the prediction of changes in QUICKI, IGI, and DI in step (d) uses the following equations 9 to 11, respectively,
In the case of calculating the total blood BCAA concentration in step 4) in step (c) of the information providing method, the QUICKI and DI change prediction in step (D) use Equations 12 to 13 below, respectively,
In the case of calculating the ratio of phenylalanine in blood and total LNAA in step 5) in step (c) of the information providing method, QUICKI and DI change prediction in step (d) use the following equations 14 to 15, respectively,
In the case of calculating the concentration (B) of 8) leucine and phenylalanine in step (c) of the information providing method, the IGI and DI change prediction in step (d) uses the following equations 19 to 20, respectively,
In the case of calculating the concentration of 10) 5-hydroxytryptophan in step (c) of the information providing method, the IGI and DI change prediction in step (d) uses the following equations 23 to 24, respectively,
In the case of calculating the ratio of 11) 5-hydroxytryptophan and tryptophan in step (c) of the information providing method, the IGI and DI change prediction in step (d) uses the following Equations 25 to 26, respectively,
In the case of calculating the 13) phenylalanine concentration in step (c) of the information providing method, the IGI change prediction in step (d) uses Equation 28 below,
In the case of calculating the concentration of 14) 3-hydroxyanthranylic acid in step (c) of the information providing method, the IGI change prediction in step (d) uses Equation 29 below,
In the case of calculating the concentration of xanthuric acid in step 15) in step (c) of the information providing method, the IGI change prediction in step (d) uses Equation 30 below,
In the case of calculating the ratio of anthranilic acid and kynurenine in step 17) in step (c) of the information providing method, the DI change prediction in step (d) uses Equation 32 below,
In the case of calculating the ratio of 18) 3-hydroxykynurenine and kynurenine in step (c) of the information providing method, the DI change prediction in step (d) uses Equation 33 below,
In the case of calculating the ratio of L-DOPA and tyrosine in step 19) in step (c) of the information providing method, the DI change prediction in step (d) is characterized by using Equation 34 below. Information provision method for predicting prognosis.

[Equation 1]
HOMA-IR change (%) = -61.71 + (0.32 * A)
[Equation 2]
QUICKI change (%) = -24.83 + (0.11 * A)
[Equation 3]
Insulinogenic index change (%) = -350.3 + (1.01 * A)
[Equation 4]
Disposition index change (%) = -236.5 + (0.78 * A)
[Equation 5]
HOMA-IR change (%) = -57.33+ (0.39 * B)
[Equation 6]
QUICKI change (%) = -23.26+ (0.12 * B)
[Equation 7]
Insulinogenic index change (%) = -378.7+ (1.35* B)
[Equation 8]
Disposition index change (%) = -250.7+ (1.02* B)
[Equation 9]
QUICKI change (%) = -29.09+ (0.06 * LNAA total)
[Equation 10]
Insulinogenic index change (%) = -320.9+ (0.52 * LNAA total)
[Equation 11]
Disposition index change (%) = -257.9+ (0.47 * LNAA total)
[Equation 12]
QUICKI change (%) = -13.43 + (0. 06 * BCAA total)
[Equation 13]
Disposition index change (%) = -155.6+ (0. 51 * BCAA total)
[Equation 14]
QUICKI change (%) = 86.31+ (-523.5* phenylalanine/LNAA)
[Equation 15]
Disposition index change (%) = 564.5+ (-3728* phenylalanine/LNAA)
[Equation 19]
Insulinogenic index change (%) = -316.1+ (1.59 * C)
[Equation 20]
Disposition index change (%) = -206.8+ (1.22* C)
[Equation 23]
Insulinogenic index change (%) = 149.2+ (-60.06* 5-HTP)
[Equation 24]
Disposition index change (%) = 168.2+ (-54.8* 5-HTP)
[Equation 25]
Insulinogenic index change (%) = 119.1+ (-3534* 5-HTP/Tryp)
[Equation 26]
Disposition index change (%) = 140.3+ (-3217* 5-HTP/Tryp)
[Equation 28]
Insulinogenic index change (%) = -322.9+ (3.90* phenylalanine)
[Equation 29]
Insulinogenic index change (%) = -89.68+ (1306* 3-HAA)
[Equation 30]
Insulinogenic index change (%) = -26.6+ (1690* XA)
[Equation 32]
Disposition index change (%) = -75.5+ (4349* AA/Kyn)
[Equation 33]
Disposition index change (%) = -94.4+ (3558* 3-HA/Kyn)
[Equation 34]
Disposition index change (%) = 131.6+ (-198321* L-DOPA/Tyr)
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