CN107076753B - Specific biomarker composition for obese people and application thereof - Google Patents
Specific biomarker composition for obese people and application thereof Download PDFInfo
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- CN107076753B CN107076753B CN201480082311.5A CN201480082311A CN107076753B CN 107076753 B CN107076753 B CN 107076753B CN 201480082311 A CN201480082311 A CN 201480082311A CN 107076753 B CN107076753 B CN 107076753B
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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
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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
- G01N33/70—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving creatine or creatinine
Abstract
The invention discloses a plasma specific metabolite spectrum, in particular a biomarker composition obtained by screening the plasma specific metabolite spectrum of obese people. The invention also discloses application of the biomarker composition in risk assessment, diagnosis, early diagnosis and pathological staging of obesity, and a risk assessment, diagnosis, early diagnosis and pathological staging method of obesity. The biomarker composition of the present invention may be used for early diagnosis of obesity.
Description
Technical field
The present invention relates to plasma specific metabolite profiles, more particularly to by obese subjects plasma specific metabolite profile
Screen obtained biological marker composition.Risk the invention further relates to the biological marker composition for obesity is commented
Estimate, diagnose, early diagnosing and the assessment of the risk of the purposes of pathological staging and obesity, diagnosis, early diagnosis and
Pathological staging method.
Background technique
Obesity, also known as obesity are a kind of by multifactor caused, closely related with heredity, environment, life style etc.
Chronic metabolic diseases [1].With the development and improvement of living standard of society, the disease incidence of obesity steeply rises and becomes complete
The problem of ball.According to statistics, obesity is not super there are about 65% American in the illness rate of the country such as America and Europe 20% or so
It is again exactly fat [2], wherein children obesity illness rate is up to 20%~25%.China's state's resident's nutrition in 2002 and health status tune
Look into the results show that the overweight and fat illness rate of 18 years old or more adult is respectively 22.8% and 7.1%, Urban population is overweight and
Fat illness rate is respectively 30.0% and 12.3%, and children obesity rate is up to 8.1%.
Obesity directly endangers the health of the mankind.The adipose tissue of human body can not only store energy, while can also divide
Adipocyte Factor, chemotactic factor (CF) and free fatty acid isoreactivity substance are secreted, the abnormal secretion of each active constituent can cause blood lipid different
Often, the metabolic syndromes [3] such as insulin resistance, II type glycosuria disease, hypertension and atherosclerosis, the U.S. third time whole people are strong
Between health and nutrition survey (NHANES III) show 1988~1994, metabolic syndrome is in overweight and obese people masses hairs
Sick rate is respectively 6.8% and 28.7% [4]；Some researches show that the death of 14% or more cancer patient is related to obesity simultaneously
[5]；In the U.S., there are about the death of 300,000 people is directly related with obesity every year.Therefore, obesity is classified as shadow by the World Health Organization
One of the ten big threats of human health are rung, and announce " obesity will be as the primary health problem for influencing the whole world " to the whole world.
Currently, the detection of obesity mainly passes through BMI value measurement (25~29.9 kg/m2It is overweight；>30kg/m2It is fat), physique detection,
Blood hepatic and renal function and blood fat function inspection, ultrasound electrocardiogram, abdominal B-scan ultrasonography, Visual quality, thyroid gland B ultrasound etc. are realized, and these sides
Method sensibility and specificity is poor, and testing result false positive rate is higher, therefore, it is necessary to develop, a kind of accuracy is high, high specificity
Detection method.
Metabolism group is the door system biology subject to grow up after genomics and proteomics, can be used
In research organism inside or external factor influence after its endogenous metabolism species, quantity and changing rule.Although single
A platform is unable to analysis detection and goes out all metabolins, but is analyzed by the entire metabolism spectrum to different phenotype organisms,
The corresponding relationship between metabolin and physiological and pathological variation is sought, foundation can be provided for medical diagnosis on disease.Metabolism group initial stage grinds
Study carefully using NMR as main analytical tools [6-7], with the appearance of efficient quickly HPLC/MS technology, is applied to metabolism group
It is more and more to learn the report that aspect is studied, such as: Plumb [8] screens drug metabolic markers in mouse urine with LC-MS；
Lafaye etc. [9] is with HPLC-MS analysis heavy metal in the intracorporal toxic reaction of mouse.Currently, fat based on metabolism group screening
Though the research of disease marker has relevant report [10-12], the relationship and marker and obesity between these markers it
Between the inherent mechanism of relationship be still not clear, therefore, screening and obesity-related metabolic markers, especially multiple metabolic indicators
Object is used in combination, and is of great significance [13-14] to metabolism group research, clinical diagnosis and the treatment of obesity.
Summary of the invention
For biomarker in existing obesity diagnostic method sensibility and specificity it is poor the disadvantages of, the present invention wanted
It solves the problems, such as to be to provide biomarker combinations (the i.e. biological marker group that can be used in obesity diagnosis and risk assessment
Close object) and obesity risk assessment and diagnosis method.
The present invention uses analysis method associated with liquid chromatography mass, analyzes the blood plasma of obesity group and control group group
The metabolite profile of sample, and the metabolite profile that obesity group and control group group are compared in analysis is carried out with pattern-recognition, it determines
Specific liquid chromatography mass data and relative specific biomarker, for subsequent theoretical research and clinical diagnosis provide according to
According to.
First aspect present invention is related to biological marker composition, at least contains one of following biomarker or number
Kind: L- methyl piperidine (L-Pipecolate), sarcosine (Creatine), Pidolidone salt (L-Glutamate), L- phenylpropyl alcohol ammonia
Acid (L-Phenylalanine), lithate (Urate), glycine-valine (Glycyl-Valine), l-tyrosine (L-
Tyrosine), L- kynurenin (L-Kynurenine), L- aspartyl-L-phenylalanine (L-Aspartyl-L-
Phenylalanine), L- octanoylcarnitine (L-Octanoylcarnitine), glutamy phenylalanine
(Glutamylphenylalanine), gamma-glutamic acid tyrosine (Gamma-Glutamyltyrosine), 17- hydroxy progesterone
Ketone (17-Hydroxyprogesterone), l- palmitoyl glycerol phosphatidyl choline (1-
) and l- oleoyl glycerolphosphocholine (1- Palmitoylglycerophosphocholine
Oleoylglycerophosphocholine), such as contain a kind therein, 2 kinds, 3 kinds, 4 kinds, 5 kinds, 6 kinds, 7 kinds, 8 kinds, 9
Kind, 10 kinds, 11 kinds, 12 kinds, 13 kinds, 14 kinds or 15 kinds.
In embodiments of the invention, above-mentioned 15 kinds of biomarkers are as shown in table 1.
In one embodiment of the invention, at least contain following biomarker:
Sarcosine, Pidolidone salt, L-phenylalanine, lithate, l-tyrosine, L- kynurenin, L- aspartyl-L-
Phenylalanine, glutamy phenylalanine, gamma-glutamic acid tyrosine and l- oleoyl glycerolphosphocholine；
Optionally, also contain L- methyl piperidine, glycine-valine, L- octanoylcarnitine, 17- hydroxyl progesterone and l-
One of palmitoyl glycerol phosphatidyl choline is several, such as a kind, 2 kinds, 3 kinds, 4 kinds, 5 kinds.
In one embodiment of the invention, the biological marker composition contains following biomarker:
L- methyl piperidine, sarcosine, Pidolidone salt, L-phenylalanine, lithate, glycine-valine, L- junket ammonia
Acid, L- kynurenin, L- aspartyl-L-phenylalanine, L- octanoylcarnitine, glutamy phenylalanine, gamma-glutamic acid junket ammonia
Acid, 17- hydroxyl progesterone, l- palmitoyl glycerol phosphatidyl choline and l- oleoyl glycerolphosphocholine.
In one embodiment of the invention, the biological marker composition contains following biomarker:
L- methyl piperidine, sarcosine, lithate, glycine-valine, l-tyrosine, L- kynurenin, L- winter ammonia
Acyl-L-phenylalanine, L- octanoylcarnitine, glutamy phenylalanine, gamma-glutamic acid tyrosine, 17- hydroxyl progesterone, l- palm
Acyl glycerolphosphocholine and l- oleoyl glycerolphosphocholine.
Second aspect of the present invention is related to reagent composition, contains the biology for detecting any one of first aspect present invention
The reagent of marking composition.
In the present invention, the reagent for detecting above-mentioned biomarker is, for example, that can match in conjunction with biomarker
Body, such as antibody；Optionally, the reagent for detection can also have detectable label.The reagent composition is
The combination of all detection reagents.
Third aspect present invention is related to the biological marker composition and/or second aspect of any one of first aspect present invention
The reagent composition of any one is used to prepare the purposes of kit, and the kit is assessed for the risk of obesity, examined
Disconnected, early diagnosis or pathological staging.
In embodiments of the invention, the kit further includes the present invention of obese subjects and normal subjects
The training set data of the biological marker composition levels of any one of first aspect.
In one embodiment of the invention, wherein the training set data is as shown in table 2-1 and table 2-2.
The invention further relates to a kind of sides for the risk assessment of obesity, diagnosis, early diagnosis or pathological staging
Method, the method includes the biological markers of any one of first aspect present invention in measurement subject's sample (such as blood plasma, whole blood)
In composition the step of the content of each biomarker.
In one embodiment of the invention, wherein the present invention the in measurement subject's sample (such as blood plasma, whole blood)
On the one hand the method for the content of each biomarker is side associated with liquid chromatography mass in the biological marker composition of any one
Method.
In one embodiment of the invention, the method also includes establishing obese subjects and normal subjects
The training set of the biological marker composition levels of any one of the first aspect present invention of (control group) sample (such as blood plasma, whole blood)
The step of.
In one embodiment of the invention, wherein the training set be using multivariate statistics disaggregated model (such as with
Machine forest model) establish training set.
In one embodiment of the invention, wherein the data of the training set are as shown in table 2-1 and table 2-2.
In one embodiment of the invention, the method also includes will be in subject's sample (such as blood plasma, whole blood)
The content of each biomarker and obese subjects and normally in the biological marker composition of any one of first aspect present invention
The step of training set data of the biological marker composition of subject is compared.
In one embodiment of the invention, wherein the training set be using multivariate statistics disaggregated model (such as with
Machine forest model) establish training set.
In one embodiment of the invention, wherein the data of the training set are as shown in table 2-1 and table 2-2.
In one embodiment of the invention, wherein it is described be compared refer to using Receiver operating curve into
Row compares.
In one embodiment of the invention,
Wherein the result judgement method of comparison step is, if hypothesis subject is non-obese disease patient, carries out ROC and diagnoses
To non-obese disease patient probability is less than 0.5 or the probability of suffering from obesity is greater than 0.5, then show that the subject of former hypothesis suffers from
The probability of obesity is big, risk is higher or is diagnosed as obesity patient.
In specific embodiments of the present invention, it the described method comprises the following steps:
1) life of any one of first aspect present invention in subject's blood plasma is measured using method associated with liquid chromatography mass
The content of each biomarker in object marking composition；
2) this hair associated with liquid chromatography mass in method measurement obese subjects and normal subjects' blood plasma is utilized
The content of the biological marker composition of any one of bright first aspect, and establish biological marker composition using Random Forest model and contain
The training set (such as shown in table 2-1 and table 2-2) of amount；
3) ROC curve is used, it will be each in the biological marker composition of any one of first aspect present invention in subject's blood plasma
The content of biomarker and the training set data of the biological marker composition of obese subjects and normal subjects compare
Compared with；
If 4) assume, subject is non-obese disease patient, and the probability for carrying out the non-obese disease patient that ROC is diagnosed is less than
0.5 or the probability of suffering from obesity be greater than 0.5, then show that the probability that the subject of former hypothesis suffers from obesity is big, risk is higher or
Person is diagnosed as obesity patient.
The invention further relates to the biological marker compositions of any one of first aspect present invention, the risk for obesity
Assessment, diagnosis, early diagnosis or pathological staging.
In one embodiment of the invention, wherein the present invention the in measurement subject's sample (such as blood plasma, whole blood)
On the one hand the method for the content of each biomarker is side associated with liquid chromatography mass in the biological marker composition of any one
Method.
It in one embodiment of the invention, further include the present invention for establishing obese subjects and normal subjects
On the one hand the step of training set of the biological marker composition levels of any one.
In one embodiment of the invention, wherein the training set be using multivariate statistics disaggregated model (such as with
Machine forest model) establish training set.
In one embodiment of the invention, wherein the data of the training set are as shown in table 2-1 and table 2-2.
It in one embodiment of the invention, further include by the present invention the in subject's sample (such as blood plasma, whole blood)
Any one of on the one hand the content of each biomarker and obese subjects and normal subjects in biological marker composition
The step of training set data of biological marker composition is compared.
In one embodiment of the invention, wherein the training set be using multivariate statistics disaggregated model (such as with
Machine forest model) establish training set.
In one embodiment of the invention, wherein the data of the training set are as shown in table 2-1 and table 2-2.
In one embodiment of the invention, wherein the method being compared refers to using Receiver Operating Characteristics
The method of curve is compared.
In one embodiment of the invention, wherein the result judgement method of comparison step is, if assuming, subject is
Non-obese disease patient carries out the probability of non-obese disease patient that ROC is diagnosed less than 0.5 or the probability of suffering from obesity is greater than
0.5, then show that the probability that the subject of former hypothesis suffers from obesity is big, risk is higher or is diagnosed as obesity patient.
In embodiments of the invention, the content of each biomarker and described in the biological marker composition
The acquisition of each biomarker content data in training set, through the following steps that:
(1) collection and processing of sample: the plasma sample of clinical patient or animal pattern is collected；Sample is by organic molten
Agent carry out liquid-liquid extraction, organic solvent include but is not limited to ethyl acetate, chloroform, ether, n-butanol, petroleum ether, methylene chloride,
Acetonitrile etc.；Or pass through albumen precipitation, albumen precipitation method include be added organic solvent (such as methanol, ethyl alcohol, acetone, acetonitrile,
Isopropanol), all kinds of acid-alkali salts precipitating, thermal precipitation, filtering/ultrafiltration, Solid Phase Extraction, the independent or comprehensive side of the methods of centrifugation
Formula is handled；Sample be dried or without it is dry recycle various organic solvents (such as methanol, acetonitrile, isopropanol,
Chloroform etc., preferably methanol, acetonitrile) perhaps water (not saliferous or saliferous alone or in combination) dissolution；Sample is without spreading out
Biochemistry is carried out using reagent (such as trimethyl silane, ethyl chloroformate, N- methyl trimethoxy base silicon substrate trifluoroacetamide etc.)
Derivatization treatment.
(2) LC Mass measurement (HPLC-MS): blood plasma is obtained using based on liquid chromatogram and mass spectrographic method
In metabolite profile, metabolite profile obtains the peak height or peak area (peak intensity) and matter at each peak by processing
The data such as lotus ratio and retention time (retention time), peak area therein are the content for representing biomarker.
In the specific embodiment of the present invention, the processing in step (1) includes that sample is carried out by organic solvent
Liquid-liquid extraction；Or pass through albumen precipitation；Sample is dried or without drying, recycles alone or in combination organic
Perhaps water is dissolved the water not saliferous or saliferous to solvent, and salt includes sodium chloride, phosphate, carbonate etc.；Sample not into
Row derivatization performs the derivatization processing using reagent.
In the specific embodiment of the present invention, step (1) organic solvent is carried out in liquid-liquid extraction, described organic molten
Agent includes but is not limited to ethyl acetate, chloroform, ether, n-butanol, petroleum ether, methylene chloride, acetonitrile.
In the specific embodiment of the present invention, in step (1) albumen precipitation, including but not limited to it is added organic molten
Agent, all kinds of acid-alkali salts precipitating, thermal precipitation, filtering/ultrafiltration, Solid Phase Extraction, the mode of centrifugal method alone or in combination carry out
Processing, wherein the organic solvent includes methanol, ethyl alcohol, acetone, acetonitrile, isopropanol.
In the specific embodiment of the present invention, preferably includes in step (1) and carried out using albumen precipitation method
Processing, it is preferred to use ethyl alcohol carries out albumen precipitation.
In the specific embodiment of the present invention, step (1) sample is dried or without drying, recycles
In organic solvent or water dissolution, the organic solvent includes methanol, acetonitrile, isopropanol, chloroform, preferably methanol, acetonitrile.
In the specific embodiment of the present invention, step (1) sample is performed the derivatization in processing using reagent, described
Reagent includes trimethyl silane, ethyl chloroformate, N- methyl trimethoxy base silicon substrate trifluoroacetamide.
In the specific embodiment of the present invention, metabolite profile obtains initial data by processing in step (2), institute
State peak height or peak area and the data such as mass number and retention time that initial data is preferably each peak.
In the specific embodiment of the present invention, in step (2), blob detection and peak match are carried out to initial data,
The blob detection and peak match are preferably carried out using XCMS software.
Mass spectral analysis type is roughly divided into ion trap, level four bars, electrostatic field orbit ion trap, four class of flight time mass spectrum,
The mass deviation of these four types of analyzers is respectively 0.2amu, 0.4amu, 3ppm, 5ppm.The experimental result that the present invention obtains is
Ion trap analysis, so being suitable for all using ion trap and level four bars as the mass spectrometer of mass analyzer, including Thermo
LTQ Orbitrap Velos of Fisher, Fusion, Elite etc., TQS, TQD etc. of Waters, AB Sciex 5500,
4500,6100,6490 etc. of 6500 etc., Agilent, amaZon speed ETD of Bruker etc..
In embodiments of the invention, containing for biomarker is indicated with mass spectrographic peak area (peak intensity)
Amount.
In the present invention, the application method of Random Forest model and ROC curve is well known in the art (referring to bibliography
[15] and [16]), those skilled in the art can carry out parameter setting and adjustment as the case may be.
In the present invention, the training set and test set have meaning well known in the art.In embodiment of the present invention
In, the training set refers to each biological marker in the obese subjects comprising certain sample number and normal subjects' sample to be tested
The data acquisition system of the content of object.The test set is the data acquisition system for testing training set performance.
In the present invention, the training set of the biomarker of obese subjects and normal subjects is constructed, and with this
On the basis of, the biomarker content value of sample to be tested is assessed.
In embodiments of the invention, the data of the training set are as shown in table 2-1 and table 2-2.
In the present invention, the subject can be people or animal pattern.
In the present invention, the unit of mass-to-charge ratio is amu, and amu refers to atomic mass unit, also referred to as dalton (Dalton,
Da, D), it is the unit for measuring atom or molecular mass, it is defined as the 1/12 of 12 atomic mass of carbon.
One of biomarker or a variety of risks for carrying out obesity in the present invention, can be selected to assess,
Diagnosis or pathological staging etc., it is preferable that at least choose therein ten kinds, i.e. sarcosine, Pidolidone salt, L-phenylalanine, urine
Hydrochlorate, l-tyrosine, L- kynurenin, L- aspartyl-L-phenylalanine, glutamy phenylalanine, gamma-glutamic acid junket ammonia
Acid and l- oleoyl glycerolphosphocholine are assessed, or select this 15 kinds of biomarkers (L- methyl piperidine, flesh ammonia simultaneously
Acid, Pidolidone salt, L-phenylalanine, lithate, glycine-valine, l-tyrosine, L- kynurenin, L- aspartyl-
L-phenylalanine, L- octanoylcarnitine, glutamy phenylalanine, gamma-glutamic acid tyrosine, 17- hydroxyl progesterone, l- palmityl are sweet
Oily phosphatidyl choline and l- oleoyl glycerolphosphocholine) it is assessed, to obtain ideal sensitivity and specificity.
As known to those skilled in the art, when further expansion sample size, pattern detection well known in the art and meter are utilized
Calculation method, it can be deduced that the normal contents value section (absolute figure) of every kind of biomarker in the sample.In this way when use removes
It, can when other methods other than mass spectrum detect the content of biomarker (such as utilizing antibody and ELISA method etc.)
It is compared with the absolute value for the biomarker content that will test with normal contents value, optionally, can be combined with uniting
Method is counted, to show that the risk of obesity is commented, diagnosed and pathological staging etc..
Body endogenous small molecule is the basis of vital movement, and the state of disease and the variation of body function will necessarily cause
The variation that endogenous small molecule is metabolized in vivo, studies have shown that the blood plasma metabolite profile of fat group and control group exists significantly
Difference.The present invention obtains a variety of relevant biomarkers by the comparison and analysis to fat group and control group metabolite profile,
It, can be accurately right in conjunction with the obese people of high quality and the metabolite profile data of normal population biomarker as training set
Obesity carries out risk assessment, early diagnosis and pathological staging.This method and at present common blood hepatic and renal function and blood lipid function
It the methods of can check and to compare, have the characteristics that convenient and efficient, and high sensitivity, it is specific good.
It does not wish to be bound by any theory restrictions, inventor points out that these biomarkers are the endogenous being present in human body
Compound.The method analyzes the metabolite profile of subject's blood through the invention, the mass number in metabolite profile
The presence and the corresponding position in metabolite profile that value indicates corresponding biomarker.Meanwhile the biology mark of obesity group
Will object shows certain content range value in its metabolite profile.
Detailed description of the invention
Fig. 1 fat group (a) and control group (b) mass spectrum total ion current figure.
Fig. 2 .PLS-DA shot chart.Prismatic (white) represents control group, and triangle (black) represents fat group.
Fig. 3 principal component analysis load diagram.Triangle (black) represents the variable that VIP value is greater than 1.
Fig. 4 .Volcano-plot figure.Horizontal dotted line above section is difference metabolin, the vertical dotted line two sides of two of them
Substance (black ball-type) be that fold-change is greater than metabolin of the 1.2 and Q-value less than 0.05, between two vertical dotted lines
Substance (grey ball-type) be fold-change less than 0.8 and metabolin of the Q-value less than 0.05.
Fig. 5 .S-plot figure.Triangle (black) is the variable that VIP is greater than 1.
Fig. 6 principal component analysis shot chart.Prismatic (white) represents control group, and triangle (black) represents fat group.
The ROC of Fig. 7 Random Forest model (Randomforest model) schemes.Training ROC (a) is based on training
Collection, AUC=1；Test ROC (b) is based on test set, AUC=0.9042.
Fig. 8 removes the ROC test set figure of 148.06 and 166.08 mass-to-charge ratioes in training set, AUC=0.8790 at random.
The random combine of .15 potential markers of Fig. 9 selects figure.It is at least need to detect 10 on the left of vertical line mark
A marker.
Specific embodiment
Embodiment of the present invention is described in detail below in conjunction with embodiment, but those skilled in the art will
Understand, the following example is merely to illustrate the present invention, and should not be taken as limiting the scope of the invention.It is not specified in embodiment specific
Condition person carries out according to conventional conditions or manufacturer's recommended conditions.Reagents or instruments used without specified manufacturer is
It can be with conventional products that are commercially available.
The plasma sample of obesity and normal subjects of the invention comes from Shanghai Ruijin Hospital.
Embodiment 1
1.1 sample collections: the morning blood of volunteer is collected, is immediately placed in -80 DEG C of low temperature refrigerators and stores.Fat group is collected altogether
84 parts of plasma samples, control group collect 104 parts of plasma samples altogether.
The processing of 1.2 samples: the sample of frost is placed in thaw at room temperature, and 500 μ L of plasma sample is taken to be centrifuged as 2.0mL
1000 μ L of methanol dilution is added in Guan Zhong, and 10000rpm is centrifuged 5min, spare.
1.3 liquid chromatography mass combination analysis
Instrument and equipment
HPLC-MS-LTQ Orbitrap Discovery (Thermo, Germany)
Chromatographic condition
Chromatographic column: C18 column (150mm × 2.1mm, 5 μm)；Mobile phase A: 0.1% aqueous formic acid, Mobile phase B: 0.1%
Formic acid acetonitrile solution；Gradient elution program: 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；Flow velocity: 0.2mL/min；
20 μ L of sampling volume.
Mass Spectrometry Conditions
ESI ion source, positive ion mode acquire data, scanning quality m/z 50~1000.Source parameters ESI: sheath gas
It is 10, auxiliary gas is 5, and capillary temperature is 350 DEG C, orifice potential 4.5KV.
1.4 data processing
Using XCMS software (such as derived from http://metlin.scripps.edu/xcms/) to initial data carry out peak
Detection and peak match utilize PLS-DA (partial least squares-discriminant analysis) using R software
Otherness variable is carried out to fat group metabolite profile (Fig. 1 a) and control group metabolite profile (Fig. 1 b) and carries out pattern recognition analysis, is built
Vertical PLS-DA mathematical model.
1.5 compare and determine characteristic metabolite profile
By comparing the plasma metabolism object spectrogram of fat group and control group, obesity group blood plasma metabolite profile (Fig. 1) is established,
The result shows that the metabolin spectrogram of fat group and control group has notable difference.
Embodiment 2
2.1 sample collections: the morning blood of volunteer is collected, is immediately placed in -80 DEG C of low temperature refrigerators and stores.Fat group is collected altogether
84 parts of plasma samples, control group collect 104 parts of plasma samples altogether.
The processing of 2.2 samples: the sample of frost is placed in thaw at room temperature, and 500 μ L of plasma sample is taken to be centrifuged as 2.0mL
1000 μ L of methanol dilution is added in Guan Zhong, and 10000rpm is centrifuged 5min, spare.
2.3 liquid chromatography mass combination analysis
Instrument and equipment
HPLC-MS-LTQ Orbitrap Discovery (Thermo, Germany)
Chromatographic condition
Chromatographic column: C18 column (150mm × 2.1mm, 5 μm)；Mobile phase A: 0.1% aqueous formic acid, Mobile phase B: 0.1%
Formic acid acetonitrile solution；Gradient elution program: 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；Flow velocity: 0.2mL/min；
20 μ L of sampling volume.
Mass Spectrometry Conditions
ESI ion source, positive ion mode acquire data, scanning quality m/z 50~1000.Source parameters ESI: sheath gas
It is 10, auxiliary gas is 5, and capillary temperature is 350 DEG C, orifice potential 4.5KV.
2.4 data processing
Related pre-treatment is carried out to initial data using XCMS software, obtains dimensional matrix data, wilcox-test statistics
The significant difference at metabolin peak；Using orthogonal Partial Least Squares discriminant analysis (PLS-DA, partial least
Squares-discriminant analysis) to fat group metabolite profile (Fig. 1 a) and control group metabolite profile (Fig. 1 b) into
Row otherness variable carries out pattern recognition analysis, filters out potential biology in conjunction with VIP, Volcano-plot figure and S-plot figure
Marker.
2.5 metabolism spectrum analysis and potential biomarker
2.5.1 orthogonal Partial Least Squares discriminant analysis (PLS-DA)
Fat group and control group (Fig. 2) are distinguished using PLS-DA method, further pass through the (principal component analysis of VIP value
Loading-plot) (Fig. 3), Volcano-plot (Fig. 4) and S-plot (Fig. 5) screen potential marker.It can from Fig. 3, Fig. 4
Know, there are apparent otherness metabolins for fat group and control group.As shown in figure 5, each point represents a change in S-plot figure
Amount, the correlation of S-plot chart bright variable and model.Variable with frame triangle mark is the variable that VIP is greater than 1, they
There is good correlation with biggish deviation and with model, sees Fig. 2,5.
2.5.2 potential source biomolecule marker
Potential marker is screened according to the VIP value of pattern recognition model PLS-DA, VIP value is extracted in PLS-DA model
Variable greater than 1, and further according to load diagram, Volcano-plot figure and S-plot figure further select to have larger inclined
The variable of difference and correlation, and variable of the P value less than 0.05, Q value less than 0.05 is combined, obtain the marker 146 of otherness
It is a, wherein having 15 potential biomarkers by the identification of mass spectrum second level, as shown in table 1.
The potential biomarker of table 1
2.5.3 principal component analysis (PCA)
PCA is a kind of no teacher's supervised recognition method, and the difference between sample can be intuitively described in hyperspace.
PCA analysis is carried out to 188 fat groups and control group sample using the marker of 146 obtained difference, as can be seen from Figure 6,
In pca model, two groups substantially separated on first principal component direction, and showing the blood plasma metabolism spectrum of fat group and control group, there are bright
Aobvious difference, these markers can distinguish fat group and control group well.
2.5.4 subject diagnoses curve (ROC)
Curve (reveiver is diagnosed using Random Forest model [15] (RandomForest) and subject
Operating characteristic curve, ROC, are also Receiver operating curve) [16] to 15 had verified that
A potential marker carries out the differentiation of fat group and control group.By the peak for choosing 141 fat groups and control group metabolite profile
Area data is used as training set (Fig. 7 a, table 2-1 and table 2-2) using ROC modeling (referring to bibliography [15] and [16]), separately
It is outer to choose 81 test sample (55, sample containing obesity, 26, normal control sample) as test set (Fig. 7 b), test knot
Fruit is AUC=0.9042, FN (false negative)=0.290, FP (false positive)=0.076 (Fig. 7), accuracy with higher and
Specificity is the prospect of diagnostic method with good exploitation, to provide foundation for the early diagnosis for whether obesity occur.
The parting energy that this 15 potential biomarkers are organized for fat group and normally is calculated using Random Forest model
Power, as shown in table 3, the marker in table will at least use 10 kinds of markers of front to parting capability result (from high toward low arrangement)
It is detected (Fig. 9), such AUC value keeps higher sensitivity and specificity 0.90 or so.
If the mass-to-charge ratio for removing 15 kinds of biomarkers in training set at random is, such as 148.06 and 166.08 biology
Marker obtains AUC=0.8790, FN=0.309 and the FP=0.038 of ROC test set (above-mentioned 81 test set samples),
It can be seen that AUC value decline is more apparent, FN value increases, and FP value reduces (Fig. 8).
The parting ability of the potential marker of table 3
Although a specific embodiment of the invention has obtained detailed description, it will be understood to those of skill in the art that.Root
According to all introductions having disclosed, those details can be carry out various modifications and be replaced, these change in guarantor of the invention
Within the scope of shield.Full scope of the invention is given by the appended claims and any equivalents thereof.
Bibliography:
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Claims (20)
1. a kind of risk for obesity is assessed or the biological marker composition of diagnosis, at least contain following biology mark
Will object:
Sarcosine, Pidolidone salt, L-phenylalanine, lithate, l-tyrosine, L- kynurenin, L- aspartyl-L- phenylpropyl alcohol
Propylhomoserin, glutamy phenylalanine, gamma-glutamic acid tyrosine and l- oleoyl glycerolphosphocholine.
2. biological marker composition according to claim 1, also contain selected from L- methyl piperidine, glycine-valine,
One of L- octanoylcarnitine, 17- hydroxyl progesterone and l- palmitoyl glycerol phosphatidyl choline are several.
3. biological marker composition according to claim 1, contains following biomarker:
L- methyl piperidine, sarcosine, Pidolidone salt, L-phenylalanine, lithate, glycine-valine, l-tyrosine, L-
Kynurenin, L- aspartyl-L-phenylalanine, L-octanoylcarnitine, glutamy phenylalanine, gamma-glutamic acid tyrosine, 17-
Hydroxyl progesterone, l- palmitoyl glycerol phosphatidyl choline and l- oleoyl glycerolphosphocholine.
4. reagent composition, it includes the reagents of the biological marker composition for detecting any one of claim 1-3.
5. the biological marker composition of any one of claim 1-3 and/or the reagent composition of claim 4 are used to prepare reagent
The purposes of box, risk assessment or diagnosis of the kit for obesity.
6. purposes according to claim 5, wherein the kit further includes obese subjects and normal subjects
The training set data of the content of the biological marker composition of any one of claim 1-3.
7. purposes according to claim 6, wherein the training set data is as shown in table 2-1 and table 2-2.
8. any one of -3 biological marker composition according to claim 1, wherein pass through method associated with liquid chromatography mass
The content of each biomarker in biological marker composition to measure any one of claim 1-3 in subject's sample.
9. biological marker composition according to claim 8, wherein subject's sample is blood plasma or whole blood.
10. biological marker composition according to claim 8, further includes establishing obese subjects and normal subjects
The step of training set of the biological marker composition levels of any one of claim 1-3.
11. biological marker composition described in any one of claim 10, wherein the training set is built using multivariate statistics disaggregated model
Vertical training set.
12. biological marker composition according to claim 11, wherein the multivariate statistics disaggregated model is random forest
Model.
13. biological marker composition according to claim 11, wherein the data of the training set such as table 2-1 and table 2-2
It is shown.
14. biological marker composition according to claim 10 further includes that claim 1-3 in subject's sample is any
The step of content of each biomarker is compared with training set data in the biological marker composition of item, the training set is
Refer to the training of the biological marker composition levels of any one of claim 1-3 of obese subjects and normal subjects' sample
Collection.
15. biological marker composition according to claim 14, wherein subject's sample is blood plasma or whole blood.
16. the biological marker composition of claim 14, wherein the training set is established using multivariate statistics disaggregated model
Training set.
17. biological marker composition according to claim 16, wherein the multivariate statistics disaggregated model is random forest
Model.
18. the biological marker composition of claim 16, wherein the data of the training set are as shown in table 2-1 and table 2-2.
19. biological marker composition described in claim 14, wherein described be compared refers to using Receiver Operating Characteristics
Curve is compared.
20. the biological marker composition of claim 19, wherein the result judgement method of comparison step is that subject is if assuming
Non-obese disease patient carries out the probability of non-obese disease patient that ROC is diagnosed less than 0.5 or the probability of suffering from obesity is greater than
0.5, then show that the probability that the subject of former hypothesis suffers from obesity is big, risk is higher or is diagnosed as obesity patient.
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