CN113866284B - Intestinal microbial metabolism markers for heart failure diagnosis and application thereof - Google Patents
Intestinal microbial metabolism markers for heart failure diagnosis and application thereof Download PDFInfo
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Abstract
The application provides a group of intestinal microbial metabolic markers for heart failure diagnosis and application thereof. In particular, the application provides a biomarker panel comprising acetyl-l-carnitine or a combination of acetyl-l-carnitine with one or more biomarkers selected from the group consisting of: the biomarker provided by the application can be used for evaluating heart failure diagnosis and prognosis evaluation of a subject to be tested and predicting risk of heart failure related diseases. The application also provides a kit comprising the biomarker set and application of the kit in heart failure diagnosis.
Description
Technical Field
The application relates to the field of biological medicine, in particular to a group of intestinal microbial metabolism markers for heart failure diagnosis and application thereof.
Background
Currently, detection of biomarkers for cardiovascular and cerebrovascular diseases is mainly focused on macromolecules such as troponin, CK-MB, myoglobin and cardiac fatty acid binding proteins, markers of myocardial damage; markers for risk factors for coronary artery disease, such as TC, LDL-C, HDL-C, lpa, TG, CRP, LP-PLA2, and the like; thromboembolic biomarkers D-dimer, biomarkers B-type natriuretic peptide (BNP) or N-terminal B-type pro-natriuretic peptide (NT-proBNP) for diagnosis, efficacy evaluation and prognosis evaluation of heart failure. Most of the existing detection methods for macromolecules adopt a biochemical method, an immunological method and the like, the specificity and the accuracy of the method are low, and the higher requirements of clinic cannot be completely met. Some biomarkers, such as NT-proBNP and BNP, can also have some effect on the results due to the effects of demographic characteristics, sample preservation conditions (markers are active and susceptible to degradation), and drugs.
Metabolomics is another new branch of research in genomics, transcriptomics, proteomics that arises after genomics, and reflects the direct relationship of genes, proteins, and metabolic activity itself by measuring changes in the concentration of cellular, tissue, and body metabolites. Because metabonomics changes are the ultimate reflection of the body's effects on genes, diseases, environments, drugs, etc., and their endogenous metabolites are the key or end-point reactions of the body's series of life events, metabonomics can help people to better understand the various complex interactions and their nature in the body, and can be used for diagnosis of human diseases. The high performance liquid chromatography-mass spectrometry (LC-MS/MS) is one of the most widely applied technical platforms in metabonomics research, and has the characteristics of high sensitivity, high flux, wide linear range and the like.
The human intestinal microbiota is a complex community and the intestinal microbiota plays an important role in immune and defenses, digestion and metabolism, inflammation and cell proliferation. Choline, betaine and carnitine, which are main nutrients from red meat, eggs, dairy products and salted fish, participate in biological activities such as energy metabolism in human bodies. Fermentation of these nutrients by the intestinal microorganisms results in the release of Trimethylamine (TMA) after ingestion, and the conversion of flavin-containing monooxygenase 3 (FMO 3) to trimethylamine oxide (TMAO) by the host liver enzymes. There is increasing evidence that: TMAO, one of the small molecules of intestinal microbial metabolism, is involved in cholesterol metabolism, promotes platelet high aggregation, increases thrombus formation, and promotes vascular inflammatory reaction to cause arterial plaque formation. However, there are no metabolic markers currently associated with the diagnosis of cardiovascular disease (particularly heart failure).
Thus, there is a strong need in the art to find more useful LC-MS/MS detection platforms, and to detect higher levels of stability, specificity, sensitivity and accuracy of one or more intestinal microbial metabolic markers on the "gut mandrel" associated with the diagnosis of cardiovascular disease (particularly heart failure).
Disclosure of Invention
The application aims to provide a method for detecting one or more intestinal microbial metabolic markers on an intestinal mandrel, which is related to diagnosis of cardiovascular diseases (particularly heart failure) and has higher stability, specificity, sensitivity and accuracy through more use of an LC-MS/MS detection platform.
In a first aspect of the application there is provided a set of biomarkers comprising acetyl-l-carnitine or a combination of acetyl-l-carnitine with one or more biomarkers selected from the group consisting of: gamma-butylbetaine, l-carnitine, trimethylamine oxide.
In another preferred embodiment, the biomarker panel is used for (a) diagnosing heart failure; (b) performing a prognostic assessment of heart failure; and/or (c) predicting risk for heart failure-related diseases, or for preparing a kit or reagent for (a) diagnosing heart failure; (b) performing a prognostic assessment of heart failure; and/or (c) risk prediction for heart failure related diseases.
In another preferred embodiment, the biomarker panel further comprises a biomarker selected from the group consisting of: choline chloride, crotonobetaine, betaine, trimethyllysine, or combinations thereof.
In another preferred embodiment, the heart failure-related disorder is selected from the group consisting of: hypertension, coronary heart disease, cardiomyopathy, diabetes, obesity, metabolic syndrome, or a combination thereof.
In another preferred embodiment, the biomarker or set of biomarkers is derived from a blood, plasma, or serum sample.
In another preferred embodiment, an increase in the level (e.g., content) of each biomarker in the set of biomarkers, as compared to a reference value, is indicative of heart failure in the subject.
In another preferred embodiment, each biomarker is identified by mass spectrometry, preferably by chromatographic mass spectrometry, such as liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, the set is used to evaluate the diagnosis of heart failure in a subject.
In a second aspect the application provides a combination of reagents for the diagnosis of heart failure, the combination of reagents comprising reagents for detecting individual biomarkers in a collection according to the first aspect of the application.
In another preferred embodiment, the reagent comprises a substance for mass spectrometry for detecting each biomarker in the collection according to the first aspect of the application.
In a third aspect the application provides a kit comprising a collection according to the first aspect of the application and/or a combination of reagents according to the second aspect of the application.
In another preferred embodiment, each biomarker in the collection according to the first aspect of the application is used as a standard.
In another preferred embodiment, the kit further comprises a description describing a reference data set derived from the level of each biomarker in the collection according to the first aspect of the present application for heart failure patients and/or healthy controls.
In a fourth aspect, the application provides the use of a biomarker panel for preparing a kit for (a) diagnosing heart failure; (b) performing a prognostic assessment of heart failure; and/or (c) risk prediction for heart failure related diseases, wherein the biomarker set comprises one or more biomarkers selected from the group consisting of: acetyl l-carnitine, gamma-butylbetaine, l-carnitine and trimethylamine oxide.
In another preferred embodiment, the biomarker comprises acetyl-l-carnitine or a combination of acetyl-l-carnitine with one or more biomarkers selected from the group consisting of: gamma-butylbetaine, l-carnitine, trimethylamine oxide.
In another preferred embodiment, the diagnosis comprises the steps of:
(1) Providing a sample derived from the subject to be tested, and detecting the level (e.g., the amount) of each biomarker in the collection in the sample;
(2) Comparing the measured content of step (1) with a reference data set or a reference value (e.g., a reference value of a healthy control);
preferably, the reference data set includes levels (e.g., amounts) of each biomarker in the set as derived from heart failure patients and healthy controls.
In another preferred embodiment, the sample is selected from the group consisting of: blood, plasma, and serum.
In another preferred embodiment, the level (e.g., content) measured in step (1) is compared to a reference data set or reference value.
In another preferred embodiment, an increase in the level (e.g., content) of each biomarker in the set of biomarkers when compared to a reference value indicates that the subject has heart failure.
In another preferred embodiment, the level (e.g., content) of each biomarker is detected by mass spectrometry, preferably by chromatographic mass spectrometry, such as liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, the method further comprises the step of treating the sample prior to step (1).
In a fifth aspect, the application provides (a) a method for diagnosing heart failure; (b) performing a prognostic assessment of heart failure; and/or (c) a method for risk prediction of heart failure related diseases, comprising the steps of:
(1) Providing a sample derived from a subject to be tested, and detecting the level (e.g., amount) of each biomarker in a collection of samples, said collection comprising one or more biomarkers selected from the group consisting of: acetyl l-carnitine, gamma-butylbetaine, l-carnitine and trimethylamine oxide;
(2) Comparing the level (e.g., content) measured in step (1) with a reference data set or a reference value (e.g., a reference value for a healthy control);
preferably, the reference data set includes levels (e.g., amounts) of each biomarker in the set as derived from heart failure patients and healthy controls.
In a sixth aspect, the present application provides a method for establishing heart failure diagnosis, prognosis evaluation and heart failure related disease risk prediction, said method comprising: a step of identifying a differentially expressed substance in the blood sample between the patient and the healthy control,
wherein the differentially expressed species comprises a biomarker in one or more biomarker sets, wherein the biomarker sets comprise one or more biomarkers selected from the group consisting of: acetyl l-carnitine, gamma-butylbetaine, l-carnitine and trimethylamine oxide.
It is understood that within the scope of the present application, the above-described technical features of the present application and technical features specifically described below (e.g., in the examples) may be combined with each other to constitute new or preferred technical solutions. And are limited to a space, and are not described in detail herein.
Drawings
Fig. 1 is a standard regression curve of acetyl l-carnitine standard solution obtained in example 1, wherein the compound name is acetyl l-carnitine, m/z is 204.00>85.10, the standard curve formula is f (x) =0.562436 x+0.00401188, the correlation coefficient (R) = 0.9975895, and the fitting degree (R2) = 0.9951848.
Fig. 2 is a standard regression curve of betaine standard solution obtained in example 1, wherein the compound name is betaine, m/z is 118.10>85.10, the standard curve formula is f (x) =0.430087 x+0.212109, the correlation coefficient (R) = 0.9967028, and the fitting degree (R ζ2) = 0.9934164.
Fig. 3 is a standard regression curve of the choline chloride standard solution obtained in example 1, wherein the compound name is choline chloride, m/z is 104.15>85.10, the standard curve formula is f (x) =0.392948 x+0.0579829, the correlation coefficient (R) = 0.9951687, and the fitting degree (r≡2) = 0.9903608.
Fig. 4 is a standard regression curve of the crotonobetaine standard solution obtained in example 1, wherein the compound name is crotonobetaine, m/z is 144.00>85.10, the standard curve formula is f (x) =0.515572 x+0.00747575, the correlation coefficient (R) = 0.9963506, and the fitting degree (r≡2) = 0.9927145.
Fig. 5 is a standard regression curve of the gamma-butylbetaine standard solution obtained in example 1, wherein the compound name is gamma-butylbetaine, m/z is 146.00>87.05, the standard curve formula is f (x) =0.392311 x+0.00581357, the correlation coefficient (R) = 0.9979180, and the fitting degree (R2) = 0.9958404.
Fig. 6 is a standard regression curve of the standard solution of l-carnitine obtained in example 1, wherein the compound name is l-carnitine, m/z is 162.10>60.10, the standard curve formula is f (x) =0.290605 x+0.109496, the correlation coefficient (R) = 0.9987178, and the fitting degree (R2) = 0.9974372.
Fig. 7 is a standard regression curve of trimethyllysine standard solution obtained in example 1, wherein the compound name is trimethyllysine, m/z is 189.20>84.10, standard curve formula is f (x) =0.449088 x-0.00433157, correlation coefficient (R) = 0.9992299, fitting degree (R ζ2) = 0.9984604.
Fig. 8 is a standard regression curve of the trimethylamine oxide standard solution obtained in example 1, wherein the compound name is trimethylamine oxide, m/z is 76.15>58.15, the standard curve formula is f (x) =0.3055433 x+0.0665364, the correlation coefficient (R) = 0.9981962, and the fitting degree (r≡2) = 0.9963956.
Fig. 9 is a graph of a curve model of a subject's working curve (ROC) for joint detection of indicators.
Detailed Description
The present inventors have studied extensively and intensively, and have unexpectedly found a novel biomarker for heart failure for the first time. Specifically, the application discovers a biomarker set, wherein the biomarker set comprises one or more biomarkers of heart failure, can be used for evaluating diagnosis, prognosis evaluation and related disease risk prediction of heart failure of a subject to be tested, has the advantages of high sensitivity and high specificity, and has important application value. On this basis, the inventors completed the present application.
Terminology
The terms used in the present application have meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present application, some definitions and related terms are explained as follows:
as used herein, the terms "comprising," "including," and "containing" are used interchangeably, and include not only closed-form definitions, but also semi-closed-form and open-form definitions. In other words, the term includes "consisting of … …", "consisting essentially of … …".
As used herein, the term "liquid-mass combination" is simply referred to as "liquid-mass combination" and "high performance liquid-mass combination" are used interchangeably.
Description will be made taking acetyl l-carnitine, l-carnitine and trimethylamine oxide as examples.
As used herein, the term "acetyl-l-carnitine" is abbreviated as ALC, i.e. "acetyl-l-carnitine" is used interchangeably with "ALC".
As used herein, the term "l-carnitine" is abbreviated as carnitine, i.e. "l-carnitine" is used interchangeably with carnitine.
As used herein, the term "trimethylamine oxide" is abbreviated to TMAO, i.e., "trimethylamine oxide" is used interchangeably with "TMAO".
As used herein, the term "ultra-high performance liquid chromatography" is abbreviated as UPLC, i.e. "ultra-high performance liquid chromatography" is used interchangeably with "UPLC".
As used herein, "mass spectrometry" (MS) refers to analytical techniques for identifying compounds by their mass MS techniques generally include (1) ionizing compounds to form charged compounds; and (2) detecting the molecular weight of the charged compound and calculating the mass to charge ratio (m/z) the compound may be ionized by any suitable means and a "mass spectrometer" is typically included with the ionizer and the ion detector.
The term "about" as used herein in reference to quantitative measurement refers to the indicated value plus or minus 10%.
According to the present application, the term "biomarker set" refers to one biomarker, or a combination of two and more biomarkers.
According to the application, the content of biomarker substances is indicated by a mass spectrometry signal area normalization value.
According to the application, the reference set refers to the training set.
According to the application, the training set and the validation set have the same meaning as known from the prior art. In one embodiment of the application, the training set refers to a set of biomarker levels in heart failure patients and healthy control biological samples. In one embodiment of the application, the validation set refers to a data set used to test the performance of the training set. In one embodiment of the application, the content of the biomarker may be represented as an absolute value or a relative value according to the method of determination. For example, when a mass spectrum is used to determine the level (e.g., content) of a biomarker, the intensity or area of the peak may represent the level of the biomarker, which is a relative value level; when PCR is used to determine the level of a biomarker, the copy number of the gene or the copy number of a gene fragment may represent the level of the biomarker.
In one embodiment of the application, the reference value refers to a reference value or normal value of a healthy control. It is clear to the person skilled in the art that in case of a sufficient number of samples, the range of normal values (absolute values) for each biomarker can be obtained by means of a test and calculation method. Thus, when levels of biomarkers are detected using methods other than mass spectrometry, the absolute values of these biomarker levels can be directly compared to normal values to assess the presence of heart failure diagnosis or early diagnosis of heart failure. In the present application, statistical methods may also be used.
According to the present application, the term "biomarker", also referred to as "biomarker", refers to a measurable indicator of the biological status of an individual. Such biomarkers can be any substance in an individual as long as they are related to a particular biological state (e.g., disease) of the individual being tested, e.g., nucleic acid markers (e.g., DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (markers of species/genus), functional markers (KO/OG markers), and the like. Biomarkers are measured and evaluated, often to examine normal biological processes, pathogenic processes, or therapeutic intervention pharmacological responses, and are useful in many scientific fields.
According to the application, the term "individual" refers to an animal, in particular a mammal, such as a primate, preferably a human.
According to the present application, the term "plasma" refers to the liquid component of whole blood. Depending on the separation method used, the plasma may be completely free of cellular components, and may also contain varying amounts of platelets and/or small amounts of other cellular components.
According to the present application, terms such as "a," "an," and "the" do not refer to an individual in the singular, but include the general class which may be used to describe a particular embodiment.
It is to be noted that the explanation of the terms is provided herein only for better understanding of the present application by those skilled in the art, and is not to be construed as limiting the present application.
Detection method
According to the present application, mass Spectrometry (MS) can be classified into ion trap mass spectrometry, quadrupole mass spectrometry, orbitrap mass spectrometry and time-of-flight mass spectrometry with deviations of 0.2amu, 0.4amu, 3ppm and 5ppm, respectively. In the present application, MS data is obtained using tandem quadrupole mass spectrometry.
Kit for detecting a substance in a sample
In the present application, a kit of the application comprises a collection according to the first aspect of the application and/or a combination of reagents according to the second aspect of the application.
In another preferred embodiment, each biomarker in the collection according to the first aspect of the application is used as a standard.
In another preferred embodiment, the kit further comprises a description describing a reference data set of levels (e.g., amounts) of each biomarker in the collection according to the first aspect of the application derived from heart failure patients and/or healthy controls.
ROC-AUC
The ROC-AUC is a method for evaluating the accuracy of a model, the ROC curve is a working characteristic curve (Receiver operating characteristic curve) of a subject, the false positive probability (False positive rate) is taken as a horizontal axis, the true positive (True positive rate) is taken as a vertical axis, and the ROC-AUC is a coordinate graph formed by taking the false positive probability (False positive rate) as a vertical axis, and is a comprehensive index for reflecting sensitivity and specificity continuous variables. AUC is the area under the ROC curve (Area under the curve). The closer the ROC-AUC value is between 1.0 and 0.5, the better the diagnosis effect, the lower the accuracy at 0.5-0.7, the accuracy at 0.7-0.9, and the higher the accuracy at AUC above 0.9. Auc=0.5, indicates that the diagnostic method is completely ineffective and of no diagnostic value. AUC <0.5 does not fit the real situation and rarely occurs in practice.
The main advantages of the application include:
(a) The application discloses a group of intestinal microbial metabolic markers on an intestinal mandrel for heart failure diagnosis for the first time, which comprise one or more of acetyl L-carnitine, gamma-butyl betaine, L-carnitine and TMAO. Among them, acetyl l-carnitine is first used in heart failure diagnosis. Proved by verification, the acetyl L-carnitine, gamma-butyl betaine, L-carnitine and TMAO in the intestinal microbial metabolism markers on the intestinal mandrel provided by the application are all over 0.8 when being used for diagnosing and distinguishing heart failure patients from healthy people (in the ROC curve evaluation method, the area value AUC under the ROC curve is more than 0.5, the closer to 1, the better the diagnosis effect is, the lower the accuracy of the AUC is when the AUC is between 0.5 and 0.7, the accuracy of the AUC is when the AUC is between 0.7 and 0.9, and the higher the accuracy of the AUC is when the AUC is more than 0.9). The effect of diagnosing and distinguishing heart failure patients from healthy people is best, and when any one index of the acetyl-L-carnitine, the gamma-butyl betaine, the acetyl-L-carnitine, the L-carnitine and the trimethylamine oxide is used in combination, the AUC is closer to 1 than that of a single index, and the diagnosis effect is better; when the three indexes of acetyl L-carnitine, gamma-butyl betaine and L-carnitine are used in combination, the AUC is closer to 1 than that of the combination of a single index and two indexes, and the diagnosis and distinguishing effects are best.
(b) The detection method has the advantages of simple operation, short analysis time (only 5 minutes are needed for joint inspection), accurate detection result and high result reproducibility (CV is less than 10%).
(c) The kit provided by the application can be used for heart failure diagnosis, improves diagnosis convenience and promotes standardization of a diagnosis method.
The application will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. The experimental methods, in which specific conditions are not noted in the following examples, are generally conducted under conventional conditions or under conditions recommended by the manufacturer. Percentages and parts are by weight unless otherwise indicated.
The reagents and materials used in the examples of the present application were all commercially available products unless otherwise specified.
Example 1 detection method and specific Experimental procedure
1. Specimen source
After patient consent was obtained, 30 plasma samples of heart failure patients (left ventricular ejection fraction <35%, BNP >400ng/L, new York Heart Association identified as II to IV) were collected, and 30 healthy persons were matched in age, sex and heart failure patients, and blood sampling time was early morning fasting state.
2. Main instruments and equipment:
shimadzu LC-MS 8050; labSolutions Insight software.
3. Reagent and material:
acetyl l-carnitine, betaine, choline chloride, crotonobetaine hydrochloride, gamma-butylbetaine, l-carnitine, trimethyllysine, trimethylamine oxide standard; acetyl L-carnitine-D3, gamma-butylbetaine-D9, betaine-D11, choline chloride-D9, L-carnitine-D9, trimethylamine oxide-D9, methanol, acetonitrile, ethanol, formic acid, ammonia water and carbon-adsorbed human plasma are all commercial reagents.
4. Liquid chromatography and mass spectrometry conditions:
chromatographic conditions:
chromatographic column: ACQUITY UPLC BEH HILIC%1.7μM，2.1×100mm)；
Protection pre-column: ACQUITY UPLC BEH HILIC VanGuard pre-column @1.7μM，2.1×5mm)；
Mobile phase a:0.1% (v/v) formic acid (A) +0.025% (v/v) aqueous ammonia (B); mobile phase B:0.1% (v/v) formic acid (A) in acetonitrile; based on the total volume of mobile phase B; flow rate 0.6mL/min: column temperature 45 deg.c: sample introduction chamber temperature: 8 ℃; the sample injection amount is 1 mu L; needle washing liquid: 50% methanol water.
Mobile phase gradient method:
mass spectrometry conditions:
positive ion MRM mode detection using electrospray ion source (ESI);
atomizing air flow rate is 3L/min, and heating air flow rate is 10L/min; the dry air flow is 10L/min; interface temperature: 300 ℃; DL temperature: 300 ℃; heating block temperature: 400 ℃;
5. the experimental process comprises the following steps:
5-1 preparation of solutions
5-1-1 mixed standard curve solution preparation:
accurately weighing appropriate amounts of acetyl L-carnitine, betaine, choline chloride, crotonobetaine hydrochloride, gamma-butylbetaine, L-carnitine, trimethyllysine and trimethylamine oxide, dissolving in 50% methanol water solution, and preparing into standard stock solutions with concentration of 5 mmol/L. The stock solution of the standard substance with the concentration of 5mmol/L is diluted into the working solution of the following standard curve in turn by using 50% methanol aqueous solution.
Mixing standard substance solutions, wherein the concentrations of four substance standard curves of acetyl L-carnitine, crotonobetaine, gamma-butylbetaine and trimethyllysine are 0.05, 0.25, 1, 2.5, 5, 10 and 25 mu mol/L; the concentrations of the four substance standard curves of betaine, choline chloride, L-carnitine and trimethylamine oxide are 0.1, 0.5, 2, 5, 10, 20 and 50 mu mol/L; the preparation matrix of the mixed standard solution is 50% methanol water solution.
5-1-2, preparing a mixed internal standard solution:
accurately weighing proper amounts of acetyl L-carnitine-D3, gamma-butyl betaine-D9, betaine-D11, choline chloride-D9, L-carnitine-D9 and trimethylamine oxide-D9, dissolving with methanol to obtain 1mmol/L mixed internal standard storage solution, and diluting with methanol to 10 mu mol/L mixed internal standard working solution.
5-1-3 extraction solvent
The components of the extraction solvent are acetonitrile and acetic acid, and the volume ratio of the acetic acid is 2.0%.
5-2 preparation of quality control product I and quality control product II:
and (3) taking a proper amount of Seralab carbon adsorption blank human plasma, adding a small amount of mixed standard substance concentration highest point solution, and respectively preparing quality control I and quality control II.
5-3 pretreatment and sample injection analysis of plasma samples:
respectively adding 25 mu L of mixed standard working solution and 25 mu L of plasma sample to be tested into different wells of a 96-well plate, sequentially adding 20 mu L of mixed internal standard into each well, adding 400 mu L of acetonitrile acetate solution (acetic acid concentration is 2%), centrifuging for 5min at a vortex of 1min and 4000rpm, taking 120 mu L of supernatant, and respectively feeding 1 mu L of supernatant into a liquid chromatography-mass spectrometer to determine and analyze the content (unit is mu M) of each metabolite of intestinal flora metabolites in the plasma sample to be tested. The standard regression curves of the above compounds are shown in FIGS. 1-8.
As shown in figures 1-8, the standard regression curve of the above compound is very linear, R2 > 0.99, and meets the performance requirements.
5-4 results
As shown in table 1 below, the AUC area and threshold obtained from the ROC curve are shown in table 1 when the specificity of all the indices is fixed to 96.7% (i.e., only one of the healthy human samples is positive) based on the sensitivity and specificity data automatically derived by the ROC curve method. Above this threshold is defined as +, below this threshold is defined as-.
AUC and threshold for each index of Table 1
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Example 2 construction of ROC curves comparing the ability of the above intestinal flora metabolites singly or in combination to diagnose and differentiate heart failure patients from healthy populations
The ability to diagnose heart failure is determined by the expression levels of acetyl-l-carnitine, betaine, choline chloride, crotonobetaine, gamma-butylbetaine, l-carnitine, trimethyllysine, and trimethylamine oxide in 30 heart failure samples and 30 healthy population plasma samples using a subject work curve (ROC) method. The sensitivity, AUC and threshold values obtained from ROC curve are shown in table 2, based on the sensitivity and specificity data automatically derived from ROC curve when the specificity of all the indicators is fixed to 96.7% (i.e. only one of the healthy human samples is positive).
TABLE 2 ability of single differential metabolite diagnosis to differentiate heart failure patients from healthy populations
| Single differential metabolite | Specificity (specificity) | Sensitivity of | AUC | Threshold value |
| Acetyl l-carnitine | 96.7％ | 86.7％ | 0.98 | 9.23uM |
| Gamma-butyl betaine | 96.7％ | 53.3％ | 0.884 | 1.32uM |
| L-carnitine | 96.7％ | 60％ | 0.842 | 104uM |
| Trimethylamine oxide | 96.7％ | 80％ | 0.902 | 4.3uM |
| Choline chloride | 96.7％ | 26.7％ | 0.617 | 14.6uM |
| Crotonobetaine | 96.7％ | 10％ | 0.546 | 8.7uM |
| Betaine (betaine) | 96.7％ | 20％ | 0.512 | 101uM |
| Trimethyllysine | 96.7％ | 13％ | 0.272 | 2.1uM |
As can be seen from Table 2, from the intestinal flora metabolites on the "intestinal mandrel", acetyl L-carnitine, gamma-butylbetaine, L-carnitine and TMAO are more capable of being used singly for diagnosing and distinguishing heart failure patients from healthy people, and the AUC is more than 0.8. Choline chloride, crotonobetaine, betaine and trimethyllysine have weaker capacity for diagnosing and distinguishing heart failure patients from healthy people, and the AUC is below 0.7 (in the ROC curve evaluation method, the area value AUC under the ROC curve is more than 0.5, the closer to 1, the better the diagnosis effect is, the lower the accuracy of the AUC is between 0.5 and 0.7, the accuracy of the AUC is between 0.7 and 0.9, and the higher the accuracy of the AUC is above 0.9).
Acetyl L-carnitine is firstly applied to diagnosing and distinguishing heart failure patients from healthy people, the capacity of diagnosing and distinguishing heart failure patients from healthy people is strongest in the four different indexes, the AUC is 0.98, the sensitivity is 86.7%, and the specificity is 96.7%. TMAO is used as an index for diagnosing and distinguishing heart failure patients from healthy people, and the capacity of diagnosing and distinguishing heart failure patients from healthy people is that the AUC is 0.902, the sensitivity is 80% and the specificity is 96.7%. The gamma-butylbetaine and the L-carnitine have the inferior capability of distinguishing heart failure patients from healthy people, the AUC is 0.884 and 0.842 respectively, the sensitivity is 53.3 percent, the sensitivity is 60 percent and the specificity is 96.7 percent respectively.
In addition, it was further verified whether acetyl l-carnitine has a combined detection effect with three indexes of gamma-butylbetaine, l-carnitine and trimethylamine oxide, and the results are shown in tables 3 and 4.
TABLE 3 ability of two differential metabolite combinations to diagnose and differentiate heart failure patients from healthy populations
TABLE 4 ability of three differential metabolite combinations to differentiate heart failure patients from healthy populations
| Three differential metabolite combinations | Specificity (specificity) | Sensitivity of | AUC |
| Acetyl l-carnitine, gamma-butylbetaine and l-carnitine | 96.7％ | 96.7％ | 0.991 |
As shown in Table 3, when acetyl L-carnitine and gamma-butylbetaine are used in combination, the capability of diagnosing and distinguishing heart failure patients from healthy people can be obviously improved, the AUC is 0.99, and the diagnosing and distinguishing effect is good; under the condition that the specificity is 96.7%, the sensitivity is respectively improved from 86.7% and 53.3% in single detection to 93.3% in joint detection. When acetyl L-carnitine and L-carnitine are used in combination, the capability of diagnosing and distinguishing heart failure patients from healthy people can be obviously improved, the AUC is 0.99, and the diagnosing and distinguishing effect is good; under the condition that the specificity is 96.7%, the sensitivity is respectively improved from 86.7% and 60% in single detection to 93.3% in joint detection. When acetyl L-carnitine and trimethylamine oxide are used in combination, the capability of diagnosing and distinguishing heart failure patients from healthy people can be obviously improved, and the AUC is 0.99; the sensitivity is respectively improved from 86.7% in single detection to 96.7% in joint detection, and the specificity is reduced from 96.7% to 93.3%.
As shown in Table 4, when three indexes of acetyl L-carnitine, gamma-butyl betaine and L-carnitine are used in combination, the AUC is closer to 1 than any two of the combination, the AUC is 0.991, and the diagnosis and differentiation effects are better; under the condition that the specificity is 96.7%, the sensitivity is respectively improved from 86.7%, 53.3% and 60% in single detection to 96.7% in joint detection. Wherein, the graph of the index joint inspection ROC curve model refers to FIG. 9.
Example 3 verification of the selected index in heart failure samples in myocardial infarction samples
1. Specimen source
After patient consent was obtained, 20 plasma samples of patients with myocardial infarction (troponin > 500 pg/mL) were collected, and 30 healthy persons were matched in age and sex with those with myocardial infarction, and the blood sampling time was early morning fasting state.
2. Plasma of 20 patients with myocardial infarction and 30 healthy people is subjected to pretreatment and LC-MS/MS on-line detection by using the experimental method in the example 1, and the sensitivity and the specificity of single or index combination screened in heart failure samples in myocardial infarction samples are further verified by reconstructing an ROC curve. The sensitivity and specificity data automatically derived from the ROC curve are shown in tables 5 and 6 when the specificity of all the co-examination indexes in the fixed myocardial infarction is identical to the specificity of all the co-examination indexes in heart failure (96.7% each).
TABLE 5 verification of single or two Joint differential metabolites in heart failure in differentiating patients with myocardial infarction from healthy populations
TABLE 6 verification of the combination of three different metabolites in heart failure to differentiate patients with myocardial infarction from healthy populations
As shown in Table 5, the single index of acetyl-L-carnitine screened in heart failure samples has sensitivity of 86.7% in heart failure samples and 25% in myocardial infarction samples under the condition of 96.7% of specificity, which indicates that acetyl-L-carnitine can well distinguish between heart failure samples and healthy samples, and myocardial infarction samples have no interference on acetyl-L-carnitine.
Further, when the screened acetyl L-carnitine and any one of gamma-butylbetaine, L-carnitine and trimethylamine oxide are subjected to joint detection (the specificity is the same), the sensitivity respectively reaches 93.3%, 93.3% and 96.7% in heart failure samples, and only 25%, 30% and 40% in myocardial infarction samples. The method shows that the samples for diagnosing heart failure and the healthy samples can be well distinguished during joint inspection of any one index of acetyl L-carnitine, gamma-butyl betaine, L-carnitine and trimethylamine oxide, and the myocardial infarction samples have no interference on the combination of the two indexes.
Further, as shown in table 6, when the three indexes of acetyl l-carnitine, gamma-butylbetaine and l-carnitine are combined (the specificity is the same), the sensitivity is 96.7% in heart failure samples, and only 25% in myocardial infarction samples. The method shows that the three indexes of acetyl L-carnitine and gamma-butyl betaine can be well distinguished and diagnosed into heart failure samples and healthy samples during joint inspection, and myocardial infarction samples have no interference on the combination of the three indexes.
Example 4: preparation of detection kit
Based on the metabolic marker provided by the application, a detection kit is prepared, and the kit comprises the following components: quality control product, isotope internal standard extracting solution, extracting solvent, mobile phase additive A and mobile phase additive B. Preferably, the detection kit further comprises a reference substance, a 96-well reaction plate, a 96-well filter plate, instructions and the like. The kit can be used for detecting the contents of four intestinal flora metabolites, namely acetyl-L-carnitine, gamma-butyl betaine, L-carnitine and TMAO.
Specifically, in the kit, the reference substance and/or the quality control substance contains acetyl L-carnitine, gamma-butyl betaine, L-carnitine and TMAO, the isotope internal standard extracting solution contains acetyl L-carnitine-D3, gamma-butyl betaine-D9, L-carnitine-D9 and TMAO-D9, the concentration is 5 mu M, the extracting solvent comprises component (i) acetonitrile and component (ii) acetic acid, the volume ratio (v/v) is 2.0%, the mobile phase additive A is formic acid, and the mobile phase additive B is ammonia water. The kit is stored at 2-8 ℃.
Of course, when designing the detection kit, it is not necessary to completely contain the above-mentioned 4 metabolic markers, and only a few of them may be used in combination. These standards may be packaged individually or as a mixture. The kit is designed based on the metabolic marker provided by the application, and can be used for diagnosing and distinguishing heart failure patients from healthy people.
In conclusion, the application effectively overcomes the defects in the prior art and has high industrial utilization value.
All documents mentioned in this disclosure are incorporated by reference in this disclosure as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
Claims (6)
1. A biomarker panel for diagnosing heart failure, said panel comprising acetyl-l-carnitine and choline chloride in a blood sample.
2. The biomarker panel of claim 1, further comprising one or more biomarkers selected from the group consisting of: gamma-butylbetaine, l-carnitine, trimethylamine oxide.
3. The biomarker panel of claim 1, further comprising one or more biomarkers selected from the group consisting of: crotonobetaine, betaine, trimethyllysine.
4. A kit for diagnosing heart failure, comprising the biomarker panel of claim 1.
5. Use of a biomarker panel comprising acetyl-l-carnitine and choline chloride in a blood sample in the preparation of a kit for diagnosing heart failure.
6. The use of claim 5, wherein the set of biomarkers further comprises one or more biomarkers selected from the group consisting of: gamma-butylbetaine, l-carnitine, trimethylamine oxide.
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| CN102458481A (en) * | 2009-05-28 | 2012-05-16 | 克里夫兰诊所基金会 | Trimethylamine-containing compounds for diagnosis and prediction of disease |
| CN102539546A (en) * | 2010-12-09 | 2012-07-04 | 北京国立柏林医学科技发展有限公司 | Methods for detecting content of free carnitine or content of total carnitine in body fluid |
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| CN109239212A (en) * | 2018-09-13 | 2019-01-18 | 苏州帕诺米克生物医药科技有限公司 | A method of measurement small molecule biomarker |
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| CN102458481A (en) * | 2009-05-28 | 2012-05-16 | 克里夫兰诊所基金会 | Trimethylamine-containing compounds for diagnosis and prediction of disease |
| CN102539546A (en) * | 2010-12-09 | 2012-07-04 | 北京国立柏林医学科技发展有限公司 | Methods for detecting content of free carnitine or content of total carnitine in body fluid |
| WO2013075065A1 (en) * | 2011-11-18 | 2013-05-23 | Memorial Sloan-Kettering Cancer Center | 2-hydroxyglutarate as a biomarker for chronic hypoxia |
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