WO2016049828A1 - 肥胖人群特异性生物标志组合物及其用途 - Google Patents

肥胖人群特异性生物标志组合物及其用途 Download PDF

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WO2016049828A1
WO2016049828A1 PCT/CN2014/087851 CN2014087851W WO2016049828A1 WO 2016049828 A1 WO2016049828 A1 WO 2016049828A1 CN 2014087851 W CN2014087851 W CN 2014087851W WO 2016049828 A1 WO2016049828 A1 WO 2016049828A1
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training set
biomarker
subject
biomarker composition
obesity
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PCT/CN2014/087851
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English (en)
French (fr)
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冯强
刘志鹏
陈晓敏
范艳群
郭珍玉
李光磊
王俊
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深圳华大基因科技有限公司
深圳华大基因研究院
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Priority to PCT/CN2014/087851 priority Critical patent/WO2016049828A1/zh
Priority to CN201480082311.5A priority patent/CN107076753B/zh
Publication of WO2016049828A1 publication Critical patent/WO2016049828A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/70—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving creatine or creatinine

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  • the present invention relates to plasma-specific metabolite profiles, and in particular to biomarker compositions screened by plasma-specific metabolite profiles of obese subjects.
  • the present invention also relates to the use of the biomarker composition for risk assessment, diagnosis, early diagnosis, and pathological staging of obesity, as well as risk assessment, diagnosis, early diagnosis, and pathological staging of obesity.
  • Obesity also known as obesity, is a chronic metabolic disease caused by multiple factors, closely related to heredity, environment, and lifestyle [1]. With the development of society and the improvement of living standards, the incidence of obesity has risen sharply and become a global problem. According to statistics, the prevalence of obesity in Europe and the United States is about 20%, about 65% of Americans are not overweight or obese [2], and the prevalence of obesity in children is 20% to 25%. According to the survey of nutrition and health status of Chinese residents in 2002, the prevalence of overweight and obesity among adults over 18 years old was 22.8% and 7.1%, respectively. The prevalence of overweight and obesity in urban population was 30.0% and 12.3%, respectively. The obesity rate is as high as 8.1%.
  • Obesity directly harms the health of human beings.
  • the body's adipose tissue not only stores energy, but also secretes active substances such as adipokines, chemokines and free fatty acids. Abnormal secretion of each active ingredient can cause dyslipidemia, insulin resistance, type II diabetes, hypertension and arteries.
  • Metabolic syndrome such as atherosclerosis [3], the third National Health and Nutrition Examination Survey (NHANES III) in the United States showed that between 1988 and 1994, the incidence of metabolic syndrome in overweight and obese people was 6.8% and 28.7, respectively. %[4]; At the same time, studies have shown that more than 14% of cancer patients die from obesity [5]; in the United States, about 300,000 deaths are directly related to obesity each year.
  • the World Health Organization ranks obesity as one of the top ten threats to human health and announces to the world that “obesity will be the top health problem affecting the world”.
  • the detection of obesity is mainly determined by BMI value (25 ⁇ 29.9kg/m 2 overweight; >30kg/m 2 obesity), physical examination, blood, liver and kidney function and blood lipid function test, echocardiogram, abdominal B-ultrasound, pelvic cavity B Ultra- and thyroid B-ultrasound are achieved, and the sensitivity and specificity of these methods are poor, and the false positive rate of detection results is high. Therefore, it is necessary to develop a detection method with high accuracy and specificity.
  • Metabolomics is a systematic biology discipline developed after genomics and proteomics. It can be used for the types, quantities and changes of endogenous metabolites after the influence of internal or external factors. Although a single platform cannot analyze and detect all metabolites, by analyzing the entire metabolic profile of different phenotypic organisms, exploring the correspondence between metabolites and physiological and pathological changes can provide a basis for disease diagnosis.
  • NMR nuclear magnetic resonance
  • the problem to be solved by the present invention is to provide a biomarker combination (ie, a biomarker composition) that can be used for the diagnosis of obesity and risk assessment of disease. ), as well as methods for assessing and diagnosing the risk of obesity.
  • the invention adopts an analytical method using liquid chromatography-mass spectrometry to analyze the metabolite profiles of plasma samples of the obese and control groups, and analyzes the metabolite profiles of the obese and control groups by pattern recognition to determine the specificity.
  • Liquid chromatography mass spectrometry data and related specific biomarkers provide a basis for subsequent theoretical research and clinical diagnosis.
  • a first aspect of the invention relates to a biomarker composition
  • a biomarker composition comprising at least one or more of the following biomarkers: L-Pipecolate, Creatine, L-Glutamic acid L-Glutamate, L-Phenylalanine, Urate, Glycine- ⁇ Glycyl-Valine, L-Tyrosine, L-Kynurenine, L-Aspartyl-L-phenylalanine (L-Aspartyl-L) -phenylalanine), L-Octanoylcarnitine, Glutamylphenylalanine, Gamma-Glutamylrosrosine, 17-Hydroxyprogesterone , l-palmitoylglycerophosphocholine and 1-Oleoylglycerophosphocholine, for example, one, two, three, four, five species 6, 6 species, 8 species, 9 species, 10 species, 11 species, 12 species, 13 species, 14 species or
  • the above 15 biomarkers are shown in Table 1.
  • biomarkers In one embodiment of the invention, it contains at least the following biomarkers:
  • L-methylpiperidine glycine-valine
  • L-octanoylcarnitine L-octanoylcarnitine
  • 17-hydroxyprogesterone 1-palmitoylglycerol phosphatidylcholine
  • 1 species, 2 species, 3 species, 4 species, and 5 species for example 1 species, 2 species, 3 species, 4 species, and 5 species.
  • the biomarker composition comprises the following biomarkers:
  • L-methylpiperidine sarcosine, L-glutamate, L-phenylalanine, urate, glycine-valine, L-tyrosine, L-kynurenine, L- Aspartyl-L-phenylalanine, L-octanoylcarnitine, glutamylphenylalanine, ⁇ -glutamic acid tyrosine, 17-hydroxyprogesterone, 1-palmitoylglycerol phosphatidylcholine Base and l-oleoylglycerol phosphatidylcholine.
  • the biomarker composition comprises the following biomarkers:
  • L-methylpiperidine sarcosine, urate, glycine-valine, L-tyrosine, L-kynurenine, L-aspartyl-L-phenylalanine, L- Octanoylcarnitine, glutamylphenylalanine, ⁇ -glutamic acid tyrosine, 17-hydroxyprogesterone, 1-palmitoylglycerol phosphatidylcholine and 1-oleoylglycerol phosphatidylcholine.
  • a second aspect of the invention relates to a reagent composition comprising for detecting the first party of the invention An agent for any of the biomarker compositions.
  • the agent for detecting the above biomarker is, for example, a ligand which can bind to a biomarker, such as an antibody; optionally, the reagent for detection may also carry a detectable label.
  • the reagent composition is a combination of all detection reagents.
  • a third aspect of the invention relates to the use of the biomarker composition of any one of the first aspects of the invention and/or the reagent composition of any of the second aspects for the preparation of a kit for use in the treatment of obesity Disease risk assessment, diagnosis, early diagnosis or pathological staging.
  • the kit further comprises training set data for the biomarker composition content of any of the first aspects of the invention of the obese subject and the normal subject.
  • the training set data is as shown in Table 2-1 and Table 2-2.
  • the invention also relates to a method for risk assessment, diagnosis, early diagnosis or pathological staging of obesity, the method comprising determining any one of the first aspects of the invention in a sample of a subject (eg plasma, whole blood) The step of the content of each biomarker in the biomarker composition of the item.
  • a sample of a subject eg plasma, whole blood
  • the method for determining the content of each biomarker in the biomarker composition of any one of the first aspects of the invention in a sample of a subject is liquid chromatography mass spectrometry The method of joint use.
  • the method further comprises establishing a biomarker combination of any one of the first aspects of the invention of the obese subject and the normal subject (control) sample (eg, plasma, whole blood) The steps of the training set of the content.
  • control sample eg, plasma, whole blood
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data of the training set is as shown in Table 2-1 and Table 2-2.
  • the method further comprises taking a sample of the subject (eg, The content of each biomarker in the biomarker composition of any one of the first aspects of the invention, such as plasma, whole blood, is compared to the training set data of the biomarker composition of the obese subject and the normal subject. step.
  • a sample of the subject eg, The content of each biomarker in the biomarker composition of any one of the first aspects of the invention, such as plasma, whole blood
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data of the training set is as shown in Table 2-1 and Table 2-2.
  • comparing refers to comparing using a receiver operating characteristic curve.
  • the result of the comparison step is determined by the fact that if the subject is assumed to be a non-obese patient, the probability of a non-obese patient who is diagnosed by ROC is less than 0.5 or the probability of obesity is greater than 0.5, indicating that the original hypothetical subject There is a high probability of obesity, a high risk, or a diagnosis of obesity.
  • the method comprises the steps of:
  • the probability of non-obese patients who are diagnosed by ROC is less than 0.5 or the probability of obesity is greater than 0.5, indicating that the original hypothetical subject has a high probability of obesity. Patients at higher risk or diagnosed with obesity.
  • the invention also relates to the biomarker composition of any of the first aspects of the invention for use in a fertilizer Risk assessment, diagnosis, early diagnosis or pathological staging of obesity.
  • the method for determining the content of each biomarker in the biomarker composition of any one of the first aspects of the invention in a sample of a subject is liquid chromatography mass spectrometry The method of joint use.
  • the method further comprises the step of establishing a training set of the biomarker composition content of any one of the first aspects of the invention of the obese subject and the normal subject.
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data of the training set is as shown in Table 2-1 and Table 2-2.
  • a subject sample eg, plasma, whole blood
  • the content of each biomarker in the biomarker composition of any one of the first aspects of the invention and the obese subject The step of comparing the training set data of the biomarker composition of the normal subject.
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data of the training set is as shown in Table 2-1 and Table 2-2.
  • the method of comparison refers to a comparison using a method of a receiver operating characteristic curve.
  • the result of the comparison step is determined by the method, if the subject is assumed to be a non-obese patient, the probability of non-obese patients who are diagnosed by ROC is less than 0.5 or the probability of obesity is greater than 0.5. , indicating that the originally assumed subject has a high probability of obesity, a high risk, or is diagnosed as an obese patient.
  • the amount of each biomarker in the biomarker composition, as well as the biomarker content data in the training set is obtained by the following steps:
  • Sample collection and treatment collect plasma samples from clinical patients or model animals; the samples are subjected to liquid-liquid extraction through organic solvents, including but not limited to ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, and Methyl chloride, acetonitrile, etc.; or protein precipitation, protein precipitation methods include the addition of organic solvents (such as methanol, ethanol, acetone, acetonitrile, isopropanol), various acid-base precipitation, heating precipitation, filtration / ultrafiltration, solid phase Extraction, centrifugation, etc.
  • organic solvents including but not limited to ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, and Methyl chloride, acetonitrile, etc.
  • protein precipitation, protein precipitation methods include the addition of organic solvents (such as methanol, ethanol, acetone, acetonit
  • sample is dried or not dried and then reused with various organic solvents (eg methanol, acetonitrile, isopropanol, chloroform, etc., preferably methanol, acetonitrile) or water (alone or Dissolve in combination, salt-free or salt-free; sample is not derivatized or derivatized with reagents such as trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide, etc. .
  • organic solvents eg methanol, acetonitrile, isopropanol, chloroform, etc., preferably methanol, acetonitrile
  • water alone or Dissolve in combination, salt-free or salt-free
  • reagents such as trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide, etc.
  • the treatment in the step (1) comprises the sample being subjected to liquid-liquid extraction through an organic solvent; or by protein precipitation; the sample is dried or not dried, and the organic solvent or water alone or in combination is used.
  • the dissolution is carried out, the water is salt-free or salt-containing, and the salt includes sodium chloride, phosphate, carbonate, etc.; the sample is not derivatized or derivatized with a reagent.
  • the organic solvent in the step (1) is subjected to liquid-liquid extraction, and the organic solvent includes, but not limited to, ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, dichloromethane, acetonitrile. .
  • the step (1) protein precipitation includes, but is not limited to, adding an organic solvent, various acid-base salt precipitation, heat precipitation, filtration/ultrafiltration, solid phase extraction, centrifugation, alone or in combination.
  • the treatment is carried out in that the organic solvent comprises methanol, ethanol, acetone, acetonitrile, isopropanol.
  • step (1) preferably comprises treatment using a protein precipitation method, preferably using ethanol for protein precipitation.
  • the step (1) sample is dried or not Drying is carried out by dissolving in an organic solvent or methanol, and the organic solvent includes methanol, acetonitrile, isopropanol, chloroform, preferably methanol or acetonitrile.
  • the step (1) sample is subjected to a derivatization treatment using a reagent comprising trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide .
  • the metabolite spectrum in step (2) is processed to obtain raw data, which is preferably data such as peak height or peak area of each peak and mass and retention time.
  • step (2) peak detection and peak matching are performed on the raw data, and the peak detection and peak matching are preferably performed using XCMS software.
  • the types of mass spectrometry are roughly classified into ion traps, quadrupoles, electrostatic field orbital ion traps, and time-of-flight mass spectrometers.
  • the mass deviations of these four types of analyzers are 0.2 amu, 0.4 amu, 3 ppm, and 5 ppm, respectively.
  • the experimental results obtained by the present invention are analyzed by ion trap, so it is applicable to all mass spectrometers using ion traps and quadrupoles as mass analyzers, including Thermo Fisher's LTQ Orbitrap Velos, Fusion, Elite, etc., Waters' TQS, TQD, etc. , AB Sciex 5500, 4500, 6500, etc., Agilent's 6100, 6490, etc., Bruker's amaZon speed ETD and so on.
  • the peak intensity of the mass spectrum is used to indicate the content of the biomarker.
  • the training set and test set have the meanings well known in the art.
  • the training set refers to a data set comprising the content of each biomarker in an obese subject and a normal subject test sample comprising a certain number of samples.
  • the test set is a data set used to test the performance of the training set.
  • a training set of biomarkers for obese subjects and normal subjects is constructed, and based on this, the biomarker content values of the samples to be tested are evaluated.
  • the data of the training set is as shown in Table 2-1 and Table 2-2.
  • the subject may be a human or a model animal.
  • the mass-to-charge ratio unit is amu, and amu refers to the atomic mass unit, also known as Dalton (Daton, Da, D), which is a unit for measuring the mass of an atom or a molecule, which is defined as carbon. 1/12 of 12 atomic mass.
  • one or more of the biomarkers may be selected for risk assessment, diagnosis or pathological staging of obesity, etc., preferably, at least ten of them are selected, namely, sarcosine, L-Valley Lysine, L-phenylalanine, urate, L-tyrosine, L-kynurenine, L-aspartyl-L-phenylalanine, glutamylphenylalanine, ⁇ -glutamic acid tyrosine and l-oleoylglycerol phosphatidylcholine were evaluated, or these 15 biomarkers (L-methyl piperidine, sarcosine, L-glutamate, L) were simultaneously selected.
  • -phenylalanine, urate, glycine-valine, L-tyrosine, L-kynurenine, L-aspartyl-L-phenylalanine, L-octanoylcarnitine, Glutamic phenylalanine, ⁇ -glutamic acid tyrosine, 17-hydroxyprogesterone, l-palmitoyl phosphatidylcholine and 1-oleoylglycerol phosphatidylcholine are evaluated to obtain the desired Sensitivity and specificity.
  • the normal content range (absolute value) of each biomarker in the sample can be derived using sample detection and calculation methods well known in the art.
  • the absolute value of the detected biomarker content can be compared with the normal content value, optionally It can also be combined with statistical methods to derive the risk assessment, diagnosis and pathological staging of obesity.
  • In vivo small molecules are the basis of life activities. The changes of disease state and body function will inevitably cause changes in the metabolism of endogenous small molecules in the body. Studies have shown that the plasma metabolite profiles of obese and control groups are obvious. difference.
  • the invention compares and analyzes the metabolite profiles of the obese group and the control group, and obtains a plurality of related biomarkers, and combines high-quality metabolite data of obese people and normal population biomarkers as a training set, which can accurately Risk assessment, early diagnosis, and pathological staging of obesity.
  • This method is currently used with blood Compared with methods such as liver and kidney function and blood lipid function test, it has the characteristics of convenience and quickness, high sensitivity and good specificity.
  • biomarkers are endogenous compounds that are present in the human body.
  • the metabolite profile of the subject's blood is analyzed by the method of the invention, and the mass value in the metabolite profile indicates the presence of the corresponding biomarker and the corresponding position in the metabolite profile.
  • the biomarkers of the obese population exhibit a range of content values in their metabolite profiles.
  • FIG. 1 PLS-DA score graph.
  • the prismatic shape (white) represents the control group and the triangle (black) represents the obese group.
  • FIG. 1 Principal component analysis load map.
  • a triangle (black) represents a variable with a VIP value greater than one.
  • Figure 4 Volcano-plot diagram. Above the horizontal dashed line are differential metabolites, where the two sides of the two vertical dashed lines (black sphere) are metabolites with a fold-change greater than 1.2 and a Q-value less than 0.05, and a material between the two vertical dashed lines (gray The sphere type is a metabolite with a fold-change of less than 0.8 and a Q-value of less than 0.05.
  • FIG. 1 S-plot diagram.
  • a triangle (black) is a variable with a VIP greater than one.
  • Figure 6 Principal component analysis score map.
  • the prismatic shape (white) represents the control group and the triangle (black) represents the obese group.
  • Figure 9.15 Random combination selection plot of potential markers.
  • the left side of the vertical line mark is the 10 markers that need to be detected at least.
  • Plasma samples of obesity and normal subjects of the present invention were obtained from Shanghai Ruijin Hospital.
  • ESI ion source positive ion mode acquisition data, scan quality m / z 50 ⁇ 1000.
  • the ion source parameter ESI sheath gas is 10, auxiliary gas is 5, capillary temperature is 350 ° C, and cone hole voltage is 4.5 KV.
  • Peak detection and peak matching were performed on the raw data using XCMS software (eg available from http://metlin.scripps.edu/xcms/), and PMS-DA (partial least squares-discriminant analysis) was used to measure obese metabolites using RLS-DA (partial least squares-discriminant analysis).
  • Spectral (Fig. 1a) and control metabolite profiles (Fig. 1b) were used for pattern recognition analysis of differential variables to establish a PLS-DA mathematical model.
  • the plasma metabolite profiles of obese populations were established by comparing the blood metabolite profiles of the obese and control groups (Fig. 1). The results showed that the metabolite profiles of the obese and control groups were significantly different.
  • ESI ion source positive ion mode acquisition data, scan quality m / z 50 ⁇ 1000.
  • the ion source parameter ESI sheath gas is 10, auxiliary gas is 5, capillary temperature is 350 ° C, and cone hole voltage is 4.5 KV.
  • the original data was pre-processed by XCMS software to obtain two-dimensional matrix data, and the statistical difference of peaks of wilcox-test metabolites was analyzed. Partial least squares-discriminant analysis (PLS-DA) was used. Pattern analysis of differential variables in the obese metabolite profile (Fig. 1a) and control metabolite profiles (Fig. 1b), combined with VIP, Volcano-plot and S-plot plots to screen potential biomarkers Volunteer.
  • PLS-DA Partial least squares-discriminant analysis
  • the PLS-DA method was used to distinguish between the obese group and the control group (Fig. 2), and further filtered by VIP values (Principal Component Analysis Loading-plot) (Fig. 3), Volcano-plot (Fig. 4), and S-plot (Fig. 5). Potential markers. As can be seen from Fig. 3 and Fig. 4, there were significant differential metabolites in the obese group and the control group. As shown in Figure 5, each point in the S-plot diagram represents a variable, and the S-plot diagram indicates the dependence of the variable on the model. Variables with framed triangle markers are variables with a VIP greater than 1, which have large deviations and have good correlation with the model, see Figures 2 and 5.
  • the potential markers are screened, and the variables with the VIP value greater than 1 are extracted in the PLS-DA model, and the Volcano-plot map and the S-plot map are further selected according to the load map.
  • the correlation variables, as well as the combination of P values less than 0.05, Q value less than 0.05, 146 different markers were obtained, of which 15 potential biomarkers were identified by mass spectrometry, as shown in Table 1. .
  • PCA is a non-supervised pattern recognition method that can visually describe differences between samples in a multidimensional space.
  • PCA analysis was performed on 188 obese and control samples using the obtained 146 differential markers.
  • the two groups were substantially separated in the first principal component direction, indicating that the obese group and the control group were separated.
  • the plasma metabolic profiles There is a clear distinction between the plasma metabolic profiles, and these markers are well differentiated between obese and control groups.
  • Obese group and control were performed on 15 potential markers that have been validated using a random forest model [15] (RandomForest) and a receiver operating characteristic curve (ROC, also called receiver operating characteristic curve) [16].
  • Group discrimination The peak area data of the metabolite profiles of 141 obese and control groups were selected by ROC modeling (see references [15] and [16]) as training sets (Table 2-1 and Table 2-2), and 81 were selected.
  • One test sample (including 55 obesity samples and 26 normal control samples) was used as a test set.
  • the random forest model was used to calculate the typing ability of the 15 potential biomarkers for the obese and normal groups.
  • the results of the typing ability (from high to low) are shown in Table 3.
  • the markers in the table should be at least the front.
  • the 10 markers were tested ( Figure 9) so that the AUC value was around 0.90 while maintaining high sensitivity and specificity.

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Abstract

本发明公开了血浆特异性代谢物谱，特别是由肥胖人群血浆特异性代谢物谱筛选得到的生物标志组合物。本发明还公开了所述生物标志物组合物用于肥胖症的患病风险评估、诊断、早期诊断以及病理分期的用途，以及肥胖症的患病风险评估、诊断、早期诊断以及病理分期方法。本发明的生物标志组合物可用于早期诊断肥胖症。

Description

肥胖人群特异性生物标志组合物及其用途 技术领域
本发明涉及血浆特异性代谢物谱，特别是涉及由肥胖受试者血浆特异性代谢物谱筛选得到的生物标志组合物。本发明还涉及所述生物标志组合物用于肥胖症的患病风险评估、诊断、早期诊断以及病理分期的用途，以及肥胖症的患病风险评估、诊断、早期诊断以及病理分期方法。
背景技术
肥胖，又称肥胖症，是一种由多因素引起的，与遗传、环境、生活方式等密切相关的慢性代谢疾病[1]。随着社会的发展和生活水平的提高，肥胖症的发病率急剧上升并成为全球性的问题。据统计，肥胖症在欧美等国家的患病率在20％左右，约有65％的美国人不是超重就是肥胖[2]，其中儿童肥胖患病率达20％～25％。我国2002年国居民营养与健康状况调查结果显示，18岁以上成年人的超重和肥胖患病率分别为22.8％和7.1％，城市人群超重和肥胖患病率分别为30.0％和12.3％，儿童肥胖率高达8.1％。
肥胖症直接危害人类的身体健康。人体的脂肪组织不仅能储存能量，同时也能分泌脂肪因子、趋化因子和游离脂肪酸等活性物质，各活性成分的不正常分泌可引起血脂异常、胰岛素抵抗、II型糖尿症、高血压和动脉粥样硬化等代谢综合征[3]，美国第三次全民健康和营养调查(NHANES III)表明，1988年～1994年间，代谢综合征在超重和肥胖人群众的发病率分别为6.8％和28.7％[4]；同时有研究表明，14％以上的癌症患者的死亡与肥胖相关[5]；在美国，每年约有30万人的死亡与肥胖直接相关。因此，世界卫生组织将肥胖症列为影响人类健康的十大威胁之一，并向全世界宣布“肥胖症将成为影响全球的首要健康问题”。目前，肥胖症的检测主要通过BMI值测定(25～29.9kg/m2超重；＞30kg/m2肥胖)、体格检测、血肝肾功能和血脂功能检查、 超声心电图、腹部B超、盆腔B超、甲状腺B超等实现，而这些方法敏感性和特异性差，检测结果假阳性率较高，因此，有必要开发一种准确度高、特异性强的检测方法。
代谢组学是继基因组学和蛋白质组学之后发展起来的一门系统生物学学科，可用于研究生物体在内在或者外在因素影响后其内源性代谢物种类、数量及变化规律。尽管单个平台不能分析检测出所有的代谢物，但通过对不同表型有机体的整个代谢谱进行分析，探寻代谢物与生理病理变化之间的对应关系，能为疾病诊断提供依据。代谢组学初期的研究以NMR为主要分析工具[6-7]，随着高效快速的HPLC/MS技术的出现，将其应用于代谢组学方面研究的报道越来越多，如：Plumb等[8]用LC-MS筛选老鼠尿液中药物代谢标志物；Lafaye等[9]用HPLC-MS分析重金属在老鼠体内的毒性反应。目前，基于代谢组学筛选肥胖症标志物的研究虽已有相关报道[10-12]，但这些标志物之间的关系，及标志物与肥胖症之间关系的内在机制尚不明确，因此，筛选与肥胖症相关的代谢标志物，特别是多个代谢标志物的联合使用，对肥胖症的代谢组学研究、临床诊断和治疗具有重大意义[13-14]。
发明内容
针对现有肥胖症诊断方法中生物标志物的敏感性和特异性差等缺点，本发明所要解决的问题是提供能够用于肥胖症诊断和患病风险评估的生物标志物组合(即生物标志组合物)，以及肥胖症的患病风险评估和诊断的方法。
本发明采用液相色谱质谱联用的分析方法，分析肥胖症群体和对照组群体的血浆样本的代谢物谱，并用模式识别进行分析比较肥胖症群体和对照组群体的代谢物谱，确定特异性液相色谱质谱数据以及相关特异性生物标志物，为后续理论研究和临床诊断提供依据。
本发明第一方面涉及生物标志组合物，其至少含有以下生物标志物中的一种或数种：L-甲基哌啶(L-Pipecolate)、肌氨酸(Creatine)、L-谷氨酸盐(L-Glutamate)、L-苯丙氨酸(L-Phenylalanine)、尿酸盐(Urate)、甘氨酸-缬 氨酸(Glycyl-Valine)、L-酪氨酸(L-Tyrosine)、L-犬尿氨酸(L-Kynurenine)、L-门冬氨酰-L-苯丙氨酸(L-Aspartyl-L-phenylalanine)、L-辛酰肉碱(L-Octanoylcarnitine)、谷氨酰苯丙氨酸(Glutamylphenylalanine)、γ-谷氨酸酪氨酸(Gamma-Glutamyltyrosine)、17-羟基黄体酮(17-Hydroxyprogesterone)、l-棕榈酰甘油磷脂酰胆碱(1-Palmitoylglycerophosphocholine)和l-油酰甘油磷脂酰胆碱(1-Oleoylglycerophosphocholine)，例如含有其中的1种、2种、3种、4种、5种、6种、7种、8种、9种、10种、11种、12种、13种、14种或15种。
在本发明的实施方案中，上述15种生物标志物如表1所示。
在本发明的一个实施方案中，其至少含有以下生物标志物：
肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸和l-油酰甘油磷脂酰胆碱；
任选地，还含有L-甲基哌啶、甘氨酸-缬氨酸、L-辛酰肉碱、17-羟基黄体酮以及l-棕榈酰甘油磷脂酰胆碱中的一种或数种，例如1种、2种、3种、4种、5种。
在本发明的一个实施方案中，所述生物标志组合物含有以下生物标志物：
L-甲基哌啶、肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、甘氨酸-缬氨酸、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、L-辛酰肉碱、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸、17-羟基黄体酮、l-棕榈酰甘油磷脂酰胆碱和l-油酰甘油磷脂酰胆碱。
在本发明的一个实施方案中，所述生物标志组合物含有以下生物标志物：
L-甲基哌啶、肌氨酸、尿酸盐、甘氨酸-缬氨酸、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、L-辛酰肉碱、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸、17-羟基黄体酮、l-棕榈酰甘油磷脂酰胆碱和l-油酰甘油磷脂酰胆碱。
本发明第二方面涉及试剂组合物，其含有用于检测本发明第一方 面任一项的生物标志组合物的试剂。
在本发明中，用于检测上述生物标志物的试剂例如为可以与生物标志物结合的配体，例如抗体；任选地，所述用于检测的试剂还可以带有可检测的标记。所述试剂组合物为所有检测试剂的组合。
本发明第三方面涉及本发明第一方面任一项的生物标志组合物和/或第二方面任一项的试剂组合物用于制备试剂盒的用途，所述试剂盒用于肥胖症的患病风险评估、诊断、早期诊断或病理分期。
在本发明的实施方案中，所述试剂盒还包括肥胖症受试者和正常受试者的本发明第一方面任一项的生物标志组合物含量的训练集数据。
在本发明的一个实施方案中，其中所述的训练集数据如表2-1和表2-2所示。
本发明还涉及一种用于肥胖症的患病风险评估、诊断、早期诊断或病理分期的方法，所述方法包括测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的步骤。
在本发明的一个实施方案中，其中测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
在本发明的一个实施方案中，所述方法还包括建立肥胖症受试者和正常受试者(对照组)样本(例如血浆、全血)的本发明第一方面任一项的生物标志组合物含量的训练集的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2-1和表2-2所示。
在本发明的一个实施方案中，所述方法还包括将受试者样本(例 如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与肥胖症受试者和正常受试者的生物标志组合物的训练集数据进行比较的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2-1和表2-2所示。
在本发明的一个实施方案中，其中所述进行比较是指采用受试者工作特征曲线进行比较。
在本发明的一个实施方案中，
其中比较步骤的结果判定方法为，若假定受试者为非肥胖症患者，进行ROC诊断得到的非肥胖症患者的概率小于0.5或者患肥胖症的概率大于0.5，则表明原假定的受试者患肥胖症的概率大、风险较高或者诊断为肥胖症患者。
在本发明的具体实施方案中，所述方法包括以下步骤：
1)利用液相色谱质谱联用的方法测定受试者血浆中本发明第一方面任一项的生物标志组合物中各生物标志物的含量；
2)利用液相色谱质谱联用的方法测定肥胖症受试者和正常受试者血浆中的本发明第一方面任一项的生物标志组合物的含量，并利用随机森林模型建立生物标志组合物含量的训练集(例如表2-1和表2-2所示)；
3)采用ROC曲线，将受试者血浆中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与肥胖症受试者和正常受试者的生物标志组合物的训练集数据进行比较；
4)若假定受试者为非肥胖症患者，进行ROC诊断得到的非肥胖症患者的概率小于0.5或者患肥胖症的概率大于0.5，则表明原假定的受试者患肥胖症的概率大、风险较高或者诊断为肥胖症患者。
本发明还涉及本发明第一方面任一项的生物标志组合物，用于肥 胖症的患病风险评估、诊断、早期诊断或病理分期。
在本发明的一个实施方案中，其中测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
在本发明的一个实施方案中，还包括建立肥胖症受试者和正常受试者的本发明第一方面任一项的生物标志组合物含量的训练集的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2-1和表2-2所示。
在本发明的一个实施方案中，还包括将受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与肥胖症受试者和正常受试者的生物标志组合物的训练集数据进行比较的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2-1和表2-2所示。
在本发明的一个实施方案中，其中所述进行比较的方法是指采用受试者工作特征曲线的方法进行比较。
在本发明的一个实施方案中，其中比较步骤的结果判定方法为，若假定受试者为非肥胖症患者，进行ROC诊断得到的非肥胖症患者的概率小于0.5或者患肥胖症的概率大于0.5，则表明原假定的受试者患肥胖症的概率大、风险较高或者诊断为肥胖症患者。
在本发明的实施方案中，所述生物标志组合物中各生物标志物的含量，以及所述训练集中各生物标志物含量数据的获得，是通过以下步骤：
(1)样本的收集与处理：收集临床病人或者模型动物的血浆样本；样本经过有机溶剂进行液液萃取，有机溶剂包括但不限于乙酸乙酯、氯仿、乙醚、正丁醇、石油醚、二氯甲烷、乙腈等；或者经过蛋白沉淀，蛋白沉淀方法包括加入有机溶剂(例如甲醇、乙醇、丙酮、乙腈、异丙醇)、各类酸碱盐沉淀、加热沉淀、过滤/超滤、固相萃取，离心等方法单独或者综合的方式进行处理；样本进行干燥或者不进行干燥再利用各种有机溶剂(例如甲醇，乙腈，异丙醇，氯仿等，优选为甲醇、乙腈)或者水(单独或者组合，不含盐或者含盐)溶解；样本不进行衍生化或者利用试剂(例如三甲基硅烷，氯甲酸乙酯，N-甲基三甲基硅基三氟乙酰胺等)进行衍生化处理。
(2)液相色谱质谱分析测定(HPLC-MS)：采用基于液相色谱和质谱的方法得到血浆中的代谢物谱，代谢物谱经过处理得到各个峰的峰高或者峰面积(peak intensity)以及质荷比和保留时间(retention time)等数据，其中的峰面积即代表生物标志物的含量。
在本发明的一个具体实施方式中，步骤(1)中的处理包括样本经过有机溶剂进行液液萃取；或者经过蛋白沉淀；样本进行干燥或者不进行干燥，再利用单独或者组合的有机溶剂或者水进行溶解，所述水不含盐或者含盐，盐包括氯化钠，磷酸盐，碳酸盐等；样本不进行衍生化或者利用试剂进行衍生化处理。
在本发明的一个具体实施方式中，步骤(1)有机溶剂进行液液萃取中，所述有机溶剂包括但不限于乙酸乙酯、氯仿、乙醚、正丁醇、石油醚、二氯甲烷、乙腈。
在本发明的一个具体实施方式中，步骤(1)蛋白沉淀中，包括但不限于加入有机溶剂、各类酸碱盐沉淀、加热沉淀、过滤/超滤、固相萃取、离心方法单独或者组合的方式进行处理，其中所述有机溶剂包括甲醇、乙醇、丙酮、乙腈、异丙醇。
在本发明的一个具体实施方式中，步骤(1)中优选地包括使用蛋白沉淀方法进行处理，优选地使用乙醇进行蛋白沉淀。
在本发明的一个具体实施方式中，步骤(1)样本进行干燥或者不 进行干燥，再利用有机溶剂或者水溶解中，所述有机溶剂包括甲醇、乙腈、异丙醇、氯仿，优选为甲醇、乙腈。
在本发明的一个具体实施方式中，步骤(1)样本利用试剂进行衍生化处理中，所述试剂包括三甲基硅烷，氯甲酸乙酯，N-甲基三甲基硅基三氟乙酰胺。
在本发明的一个具体实施方式中，步骤(2)中代谢物谱经过处理得到原始数据，所述原始数据优选地是各个峰的峰高或者峰面积以及质量数和保留时间等数据。
在本发明的一个具体实施方式中，步骤(2)中，对原始数据进行峰检测和峰匹配，优选地采用XCMS软件进行所述峰检测和峰匹配。
质谱分析类型大致分为离子阱、四级杆、静电场轨道离子阱、飞行时间质谱四类，这四类分析器的的质量偏差分别为0.2amu、0.4amu、3ppm、5ppm。本发明得到的实验结果是离子阱分析的，所以适用于所有以离子阱和四级杆为质量分析器的质谱仪器，包括Thermo Fisher的LTQ Orbitrap Velos、Fusion、Elite等，Waters的TQS、TQD等，AB Sciex的5500、4500、6500等，Agilent的6100、6490等，Bruker的amaZon speed ETD等。
在本发明的实施方案中，用质谱的峰面积(peak intensity)表示生物标志物的含量。
在本发明中，随机森林模型和ROC曲线的使用方法为本领域所公知(参见参考文献[15]和[16])，本领域技术人员可以根据具体情况进行参数设置和调整。
在本发明中，所述训练集和测试集具有本领域公知的含义。在本发明的实施方案中，所述训练集是指包含一定样本数的肥胖受试者和正常受试者待测样本中的各生物标志物的含量的数据集合。所述测试集是用来测试训练集性能的数据集合。
在本发明中，构建了肥胖症受试者和正常受试者的生物标志物的训练集，并以此为基准，对待测样本的生物标志物含量值进行评估。
在本发明的实施方案中，所述训练集的数据如表2-1和表2-2所示。
在本发明中，所述受试者可以为人或者模型动物。
在本发明中，质荷比的单位为amu，amu是指原子质量单位，也称为道尔顿(Dalton，Da，D)，是用来衡量原子或分子质量的单位，它被定义为碳12原子质量的1/12。
在本发明中，可以选用生物标志物中的一种或多种进行肥胖症的患病风险评估、诊断或病理分期等，优选地，至少选取其中的十种，即肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸和l-油酰甘油磷脂酰胆碱进行评估，或者同时选用这15种生物标志物(L-甲基哌啶、肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、甘氨酸-缬氨酸、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、L-辛酰肉碱、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸、17-羟基黄体酮、l-棕榈酰甘油磷脂酰胆碱和l-油酰甘油磷脂酰胆碱)进行评估，以获得理想的灵敏度和特异性。
本领域技术人员知晓，当进一步扩大样本量时，利用本领域公知的样本检测和计算方法，可以得出每种生物标志物在样本中的正常含量值区间(绝对数值)。这样当采用除质谱以外的其它方法对生物标志物的含量进行检测时(例如利用抗体和ELISA方法等)，可以将检测得到的生物标志物含量的绝对值与正常含量值进行比较，任选地，还可以结合统计学方法，以得出肥胖症的患病风险评、诊断以及病理分期等。
机体内源性小分子是生命活动的基础，疾病的状态与机体功能的变化必然会引起内源性小分子在体内代谢的变化，研究表明，肥胖组和对照组的血浆代谢物谱存在明显的差异。本发明通过对肥胖组和对照组代谢物谱的比较和分析，得到多种相关的生物标志物，结合高质量的肥胖人群和正常人群生物标志物的代谢物谱数据作为训练集，能够准确地对肥胖症进行患病风险评估、早期诊断和病理分期。该方法与目前常用血 肝肾功能和血脂功能检查等方法相比，具有方便快捷的特点，且灵敏度高，特异性好。
不希望受任何理论的限制，发明人指出这些生物标志物是存在于人体中的内源性化合物。通过本发明所述的方法对受试者血液的代谢物谱进行分析，代谢物谱中的质量数值指示相应生物标志物的存在及在代谢物谱中的对应位置。同时，肥胖群体的所述生物标志物在其代谢物谱中表现出一定的含量范围值。
附图说明
图1.肥胖组(a)和对照组(b)质谱总离子流图。
图2.PLS-DA得分图。棱形(白色)代表对照组，三角形(黑色)代表肥胖组。
图3.主成分分析荷载图。三角形(黑色)代表VIP值大于1的变量。
图4.Volcano-plot图。水平虚线以上部分是差异代谢物，其中两条竖直虚线两侧的物质(黑色球型)是fold-change大于1.2且Q-value小于0.05的代谢物，两条竖直虚线间的物质(灰色球型)是fold-change小于0.8且Q-value小于0.05的代谢物。
图5.S-plot图。三角形(黑色)是VIP大于1的变量。
图6.主成分分析得分图。棱形(白色)代表对照组，三角形(黑色)代表肥胖组。
图7.随机森林模型(Randomforest模型)的ROC图。Training ROC是基于训练集，AUC＝1；Test ROC是基于测试集，AUC＝0.9042。
图8.随机去掉训练集中的148.06和166.08质荷比的ROC测试集图，AUC＝0.8790。
图9.15个潜在标记物的随机组合挑选图。竖直线标记处左侧是至少需要检测的10个标记物。
具体实施方式
下面将结合实施例对本发明的实施方案进行详细描述，但是本领 域技术人员将会理解，下列实施例仅用于说明本发明，而不应视为限定本发明的范围。实施例中未注明具体条件者，按照常规条件或制造商建议的条件进行。所用试剂或仪器未注明生产厂商者，均为可以通过市购获得的常规产品。
本发明的肥胖症和正常受试者的血浆样本来自上海瑞金医院。
实施例1
1.1样本收集：收集志愿者的晨血，立即置于-80℃低温冰箱中储存。肥胖组共收集84份血浆样本，对照组共收集104份血浆样本。
1.2样本的处理：冰冻的样本置于室温下解冻，取血浆样本500μL至于2.0mL离心管中，加入甲醇1000μL稀释，10000rpm离心5min，备用。
1.3液相色谱质谱联用分析
仪器设备
HPLC-MS-LTQ Orbitrap Discovery(Thermo，Germany)
色谱条件
色谱柱：C18柱(150mm×2.1mm，5μm)；流动相A：0.1％甲酸水溶液，流动相B：0.1％甲酸乙腈溶液；梯度洗脱程序：0～3min，5％B，3～36min，5％～80％B，36～40min，80％～100％B，40～45min，100％B，45～50min，100％～5％B，50～60min，5％B；流速：0.2mL/min；进样体积20μL。
质谱条件
ESI离子源，正离子模式采集数据，扫描质量m/z50～1000。离子源参数ESI：鞘气为10，辅气为5，毛细管温度为350℃，锥孔电压4.5KV。
1.4数据处理
采用XCMS软件(例如得自http：//metlin.scripps.edu/xcms/)对原始数据进行峰检测和峰匹配，采用R软件利用PLS-DA(partial least squares-discriminant analysis)对肥胖组代谢物谱(图1a)和对照组代谢物谱(图1b)进行差异性变量进行模式识别分析，建立PLS-DA数学模型。
1.5比较和确定特征性代谢物谱
通过比较肥胖组与对照组的血液代谢物谱图，建立肥胖群体血浆代谢物谱(图1)，结果表明，肥胖组和对照组的代谢物谱图是有明显差异的。
实施例2
2.1样本收集：收集志愿者的晨血，立即置于-80℃低温冰箱中储存。肥胖组共收集84份血浆样本，对照组共收集104份血浆样本。
2.2样本的处理：冰冻的样本置于室温下解冻，取血浆样本500μL至于2.0mL离心管中，加入甲醇1000μL稀释，10000rpm离心5min，备用。
2.3液相色谱质谱联用分析
仪器设备
HPLC-MS-LTQ Orbitrap Discovery(Thermo，Germany)
色谱条件
色谱柱：C18柱(150mm×2.1mm，5μm)；流动相A：0.1％甲酸水溶液，流动相B：0.1％甲酸乙腈溶液；梯度洗脱程序：0～3min，5％B，3～36min，5％～80％B，36～40min，80％～100％B，40～45min，100％B，45～50min，100％～5％B，50～60min，5％B；流速：0.2mL/min；进样体积20μL。
质谱条件
ESI离子源，正离子模式采集数据，扫描质量m/z50～1000。离子源参数ESI：鞘气为10，辅气为5，毛细管温度为350℃，锥孔电压4.5KV。
2.4数据处理
采用XCMS软件对原始数据进行相关前处理，得到二维矩阵数据，wilcox-test统计代谢物峰的显著性差异；采用正交偏最小二乘法判别分析(PLS-DA，partial least squares-discriminant analysis)对肥胖组代谢物谱(图1a)和对照组代谢物谱(图1b)进行差异性变量进行模式识别分析，结合VIP、Volcano-plot图和S-plot图筛选出潜在的生物标 志物。
2.5代谢谱分析和潜在的生物标志物
2.5.1正交偏最小二乘法判别分析(PLS-DA)
采用PLS-DA方法来区分肥胖组和对照组(图2)，进一步通过VIP值(主成分分析Loading-plot)(图3)、Volcano-plot(图4)和S-plot(图5)筛选潜在标志物。从图3、图4可知，肥胖组和对照组存在明显的差异性代谢物。如图5所示，S-plot图中每个点代表一个变量，S-plot图表明变量与模型的相关性。带框三角形标记的变量为VIP大于1的变量，它们具有较大的偏差并且与模型有良好的相关性，见图2、5。
2.5.2潜在生物标记物
根据模式识别模型PLS-DA的VIP值筛选潜在标志物，在PLS-DA模型中提取VIP值大于1的变量，并进一步根据荷载图，Volcano-plot图和S-plot图进一步选择具有较大偏差和相关性的变量，以及结合P值小于0.05，Q值小于0.05的变量，得到差异性的标记物146个，其中经过质谱二级鉴定的有15个潜在的生物标记物，如表1所示。
表1 潜在的生物标记物
Figure PCTCN2014087851-appb-000001
Figure PCTCN2014087851-appb-000002
Figure PCTCN2014087851-appb-000003
2.5.3主成分分析(PCA)
PCA是一种无师监督模式识别方法，可以直观地在多维空间上描述样本间的差异。使用得到的146个差异的标记物对188个肥胖组和对照组样本进行PCA分析，从图6可知，在PCA模型中，两组在第一主成分方向上基本分开，表明肥胖组和对照组的血浆代谢谱存在明显的区别，这些标记物能很好地区分肥胖组和对照组。
2.5.4受试者诊断曲线(ROC)
使用随机森林模型[15](RandomForest)和受试者诊断曲线(reveiver operating characteristic curve，ROC，也叫受试者工作特征曲线)[16]对已经验证的15个潜在标记物进行肥胖组和对照组的判别。通过选取141个肥胖组与对照组代谢物谱的峰面积数据采用ROC建模(参见参考文献[15]和[16])作为训练集(表2-1和表2-2)，另外选取81个测试样本(含肥胖症样本55个，正常对照样本26个)作为测试集，测试结果为AUC＝0.9042，FN(假阴性)＝0.290，FP(假阳性)＝0.076(图7)，具有较高的准确度和特异性，具有良好的开发为诊断方法的前景，从而为是否出现肥胖的早期诊断提供依据。
Figure PCTCN2014087851-appb-000005
Figure PCTCN2014087851-appb-000006
Figure PCTCN2014087851-appb-000007
Figure PCTCN2014087851-appb-000008
Figure PCTCN2014087851-appb-000009
Figure PCTCN2014087851-appb-000010
Figure PCTCN2014087851-appb-000011
Figure PCTCN2014087851-appb-000012
Figure PCTCN2014087851-appb-000013
Figure PCTCN2014087851-appb-000014
Figure PCTCN2014087851-appb-000015
Figure PCTCN2014087851-appb-000016
Figure PCTCN2014087851-appb-000017
Figure PCTCN2014087851-appb-000018
利用随机森林模型计算这15个潜在的生物标记物对于肥胖组和正常组的分型能力，分型能力结果(从高往低排列)如表3所示，表中的标记物至少要采用前面的10种标记物进行检测(图9)，这样AUC值在0.90左右，同时保持较高的灵敏度和特异性。
若随机去掉训练集中15种生物标志物的质荷比为，比如148.06和166.08的生物标志物，得到ROC测试集(上述81个测试集样本)的AUC＝0.8790，FN＝0.309和FP＝0.038，可以看出AUC值下降较明显，FN值增大，FP值降低(图8)。
表3 潜在标记物的分型能力
Figure PCTCN2014087851-appb-000019
尽管本发明的具体实施方式已经得到详细的描述，本领域技术人员将会理解。根据已经公开的所有教导，可以对那些细节进行各种修改和替换，这些改变均在本发明的保护范围之内。本发明的全部范围由所附权利要求及其任何等同物给出。
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Claims (27)

  1. 生物标志组合物，其至少含有以下生物标志物中的一种或数种：L-甲基哌啶(L-Pipecolate)、肌氨酸(Creatine)、L-谷氨酸盐(L-Glutamate)、L-苯丙氨酸(L-Phenylalanine)、尿酸盐(Urate)、甘氨酸-缬氨酸(Glycyl-Valine)、L-酪氨酸(L-Tyrosine)、L-犬尿氨酸(L-Kynurenine)、L-门冬氨酰-L-苯丙氨酸(L-Aspartyl-L-phenylalanine)、L-辛酰肉碱(L-Octanoylcarnitine)、谷氨酰苯丙氨酸(Glutamylphenylalanine)、γ-谷氨酸酪氨酸(Gamma-Glutamyltyrosine)、17-羟基黄体酮(17-Hydroxyprogesterone)、l-棕榈酰甘油磷脂酰胆碱(1-Palmitoylglycerophosphocholine)和l-油酰甘油磷脂酰胆碱(1-Oleoylglycerophosphocholine)。
  2. 权利要求1的生物标志组合物，其至少含有以下生物标志物：
    肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸和l-油酰甘油磷脂酰胆碱；
    任选地，还含有L-甲基哌啶、甘氨酸-缬氨酸、L-辛酰肉碱、17-羟基黄体酮以及l-棕榈酰甘油磷脂酰胆碱中的一种或数种。
  3. 权利要求1的生物标志组合物，其含有以下生物标志物：
    L-甲基哌啶、肌氨酸、L-谷氨酸盐、L-苯丙氨酸、尿酸盐、甘氨酸-缬氨酸、L-酪氨酸、L-犬尿氨酸、L-门冬氨酰-L-苯丙氨酸、L-辛酰肉碱、谷氨酰苯丙氨酸、γ-谷氨酸酪氨酸、17-羟基黄体酮、l-棕榈酰甘油磷脂酰胆碱和l-油酰甘油磷脂酰胆碱。
  4. 试剂组合物，其包含用于检测权利要求1-3任一项的生物标志组合物的试剂。
  5. 权利要求1-3任一项的生物标志组合物和/或权利要求4的试剂组合物用于制备试剂盒的用途，所述试剂盒用于肥胖症的患病风险评估、诊断、早期诊断或病理分期。
  6. 权利要求5的用途，所述试剂盒还包括肥胖症受试者和正常受试者的权利要求1-3任一项的生物标志组合物含量的训练集数据。
  7. 权利要求6的用途，其中所述的训练集数据如表2-1和表2-2所示。
  8. 一种用于肥胖症的患病风险评估、诊断、早期诊断或病理分期的方法，所述方法包括测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的步骤。
  9. 权利要求8的方法，其中测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
  10. 权利要求8的方法，所述方法还包括建立肥胖症受试者和正常受试者样本(例如血浆、全血)的权利要求1-3任一项的生物标志组合物含量的训练集的步骤。
  11. 权利要求10的方法，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  12. 权利要求11的方法，其中所述训练集的数据如表2-1和表2-2所示。
  13. 权利要求7-12任一项的方法，所述方法还包括将受试者样本 (例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量与训练集数据进行比较的步骤，所述训练集是指肥胖症受试者和正常受试者样本的权利要求1-3任一项的生物标志组合物含量的训练集。
  14. 权利要求13的方法，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  15. 权利要求14的方法，其中所述训练集的数据如表2-1和表2-2所示。
  16. 权利要求13-15任一项的方法，其中所述进行比较是指采用受试者工作特征曲线进行比较。
  17. 权利要求16的方法，其中比较步骤的结果判定方法为，若假定受试者为非肥胖症患者，进行ROC诊断得到的非肥胖症患者的概率小于0.5或者患肥胖症的概率大于0.5，则表明原假定的受试者患肥胖症的概率大、风险较高或者诊断为肥胖症患者。
  18. 权利要求1-3任一项的生物标志组合物，用于肥胖症的患病风险评估、诊断、早期诊断或病理分期。
  19. 权利要求18的生物标志组合物，其中测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
  20. 权利要求18的生物标志组合物，还包括建立肥胖症受试者和正常受试者的权利要求1-3任一项的生物标志组合物含量的训练集的步骤。
  21. 权利要求20的生物标志组合物，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  22. 权利要求21的生物标志组合物，其中所述训练集的数据如表2-1和表2-2所示。
  23. 权利要求18-22任一项的生物标志组合物，还包括将受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量与训练集数据进行比较的步骤，所述训练集是指肥胖症受试者和正常受试者样本的权利要求1-3任一项的生物标志组合物含量的训练集。
  24. 权利要求23的生物标志组合物，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  25. 权利要求24的生物标志组合物，其中所述训练集的数据如表2-1和表2-2所示。
  26. 权利要求23-25任一项的生物标志组合物，其中所述进行比较是指采用受试者工作特征曲线进行比较。
  27. 权利要求26的生物标志组合物，其中比较步骤的结果判定方法为，若假定受试者为非肥胖症患者，进行ROC诊断得到的非肥胖症患者的概率小于0.5或者患肥胖症的概率大于0.5，则表明原假定的受试者患肥胖症的概率大、风险较高或者诊断为肥胖症患者。
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