Health homeostasis model, elastic homeostasis model, and usage method therefor

Abstract

A method for constructing a model for assessing the phenotypic state of a receiver and a phenotypic state model, a method for constructing a model for assessing the elastic homeostasis of a receiver and an elastic homeostasis model, a method for assessing the health state of a receiver, a method for assessing the elastic homeostasis of a receiver, a method for assessing the risk of a receiver suffering from a metabolic disease, a method for assessing whether a therapy for treating a metabolic disease of a receiver is effective, a method and system for assessing whether a metabolic health intervention solution of a receiver is effective, and a kit. The method for constructing a model for assessing the phenotypic state of a receiver comprises: selecting a plurality of markers that are involved in or affect a total of four categories of metabolism, that is, glycometabolism, lipid metabolism, amino acid metabolism and metabolic stress; selecting one to several categories as coordinate axes for establishing a model; collecting samples of n healthy receivers and samples of m receivers who are in an impaired-health or sub-healthy condition; measuring the levels of markers related to the selected coordinate axes in each sample; training a machine learning model by using the levels of the markers; and performing screening to obtain a target marker, and calculating a weight coefficient of the target marker, so as to establish the model for assessing the phenotypic state of the receiver.

Description

Healthy steady-state model and elastic steady-state model and their use Technical Field

[0001] The present application relates to the field of biology, and more particularly to a method for constructing a phenotypic state model of a subject, and a method for constructing a subject's elastic homeostasis model. The present application also relates to a method for assessing a subject's health status using the phenotypic state model and the elastic homeostasis model. Background Art

[0002] As early as 1948, the World Health Organization defined health as a state of complete physical, mental, and social functioning, not merely the absence of disease or infirmity. However, defining and even quantifying "health" remains an international challenge, leading to the current definition of "health" based on diagnostic indicators and standards for clinical diseases. However, there is currently a lack of systematic methods for assessing and quantifying the health phenotype of pre-disease populations. Quantifying and assessing the health phenotype of pre-disease populations has important practical significance and value for early disease prevention and intervention.

[0003] In recent years, some European scholars have proposed defining health as the ability of organs to adapt to challenges. They believe that the key characteristic of metabolic health is the body's ability to maintain homeostatic balance in response to stress, namely homeostatic resilience. This refers to the ability to regain homeostatic balance under a wide range of environmental conditions (such as stress, hunger, or eating). Measuring and quantifying homeostatic resilience may become an essential component of quantifying health. However, international research and methods for assessing and quantifying homeostatic capacity are relatively limited, and they are mainly focused on Western populations. Our previous research and that of others have found significant differences between Chinese and Western populations in terms of genetic background and nutritional metabolism.

[0004] Therefore, establishing a quantitative and evaluation method for the health phenotypic status suitable for the genetic and metabolic phenotypic characteristics of the Chinese population, as well as establishing a quantitative and evaluation method for the homeostasis index suitable for the genetic and metabolic phenotypic characteristics of the Chinese population, is of great significance to the health assessment of the Chinese population and the early prevention and intervention of diseases.

[0005] Summary of the Invention

[0006] In response to the above problems, this application provides two models for health assessment, which can be used separately or together. The two models are: 1) phenotypic state model, which is used to assess the health status of an individual; 2) elastic homeostasis model, which is used to assess the resilience of an individual in response to stress, that is, the ability to maintain the current health status. The method for establishing a model provided in this application and the model constructed by this method are particularly suitable for assessing the health status of the Chinese population. Therefore, they have great application potential in health assessment, disease risk prediction and the establishment of individualized intervention programs.

[0007] Therefore, in a first aspect, the present application provides a method for constructing a model for assessing the phenotypic state of a subject, the method comprising:

[0008] (I) selecting multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress;

[0009] Selecting one to several (e.g., two, three, four) categories from the four categories as coordinate axes for establishing the model (e.g., a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and / or a metabolic pressure coordinate axis);

[0010] (II) collecting samples from n healthy subjects and m healthy impaired or sub-healthy subjects, and measuring the level of a marker associated with the selected coordinate axis in each sample;

[0011] (III) using the levels of the markers to train a machine learning model, screen out target markers, and calculate weight coefficients of the target markers, thereby establishing an assessment model for the phenotypic status of the subject;

[0012] wherein n and m are each independently a positive integer, and n+m≥5 (e.g., n and m are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 50, 70, 90, 110, 130, 150).

[0013] In certain embodiments, in step (I), the selected markers can be selected from a combination of any several (e.g., 3-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, 24-27, 27-30, 30-40, 40-50, 50-100) markers listed in the following Appendix 1.

[0014] In certain embodiments, in step (II), the level of the marker can be directly measured or further calculated based on the measurement results.

[0015] In certain embodiments, in step (II), the method for determining the level of the marker includes: biochemical analysis, physiological analysis, immunological methods (such as enzyme-linked immunosorbent assay (ELISA), colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immune lateral flow analysis), molecular biology detection methods (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection methods (such as light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis methods, chromatography, mass spectrometry, electrophoresis, hybridization (such as Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy.

[0016] In certain embodiments, prior to step (III), the levels of all markers are normalized from 0 to 1 using the formula (x-min) / (max-min), wherein x is the level of a marker in a particular subject, and min and max are the minimum and maximum values ​​of the marker level in all subjects (n healthy subjects and m healthy-impaired or sub-healthy subjects), respectively.

[0017] In certain embodiments, in step (III), the machine learning model is a cross-validation-elastic net model.

[0018] In certain embodiments, in step (III), a cross-validation-elastic net model is used to independently model markers based on different coordinate axes, and the area under the receiver operating characteristic curve (ROC) (Area under curve, AUC) is used as the evaluation criterion. The target marker is screened on each coordinate axis by the optimal AUC and the weight coefficient of the marker is obtained, which is used as an evaluation model for the subject's phenotypic status.

[0019] In certain embodiments, 90%-70% of the subjects serve as a training set, and 10%-30% of the subjects serve as a validation set.

[0020] In certain embodiments, in step (III), the cross-validation is selected from five-fold cross-validation, ten-fold cross-validation, and leave-one-out cross-validation.

[0021] In certain specific embodiments, in step (III), a five-fold cross-validation-elastic network model is used to perform independent modeling based on markers on different coordinate axes, with 80% of the subjects used as a training set and 20% of the subjects used as a validation set.

[0022] In certain specific embodiments, in step (III), a ten-fold cross-validation-elastic network model is used to perform independent modeling based on markers of different coordinate axes, with 90% of the subjects used as a training set and 10% of the subjects used as a validation set.

[0023] In certain embodiments, when four metabolic categories are selected as coordinate axes for establishing the model, steps (I) to (III) of the above method are implemented by the following steps:

[0024] (a) Select multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress;

[0025] Furthermore, the above four metabolic categories were selected as the coordinate axes for building the model;

[0026] (b) Collect samples from n healthy subjects and m healthy or subhealthy subjects, and measure the levels of markers associated with the four coordinate axes in each sample;

[0027] (c) All marker levels were normalized from 0 to 1 using the formula (x-min) / (max-min), where x is the level of a marker in a particular subject, and min and max are the minimum and maximum values ​​of the marker level in all subjects (n healthy subjects and m healthy impaired or subhealthy subjects), respectively.

[0028] (d) A cross-validation-elastic net model is used to build models based on the markers of the four selected coordinate axes. The area under the receiver operating characteristic curve (AUC) is used as the evaluation criterion. The target marker is screened on each coordinate axis using the optimal AUC (e.g., the AUC closest to 1) and the marker weight coefficient is obtained. This is used as the evaluation model for the subject's phenotypic status;

[0029] wherein n and m are each independently a positive integer, and n+m≥5 (e.g., n and m are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 50, 70, 90, 110, 130, 150).

[0030] In certain embodiments, when any one, two or three metabolic categories are selected as the coordinate axes for establishing the model, steps (I) to (III) of the above method are achieved by the following steps:

[0031] (a) Select multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress;

[0032] Furthermore, any one, two or three of the above metabolic categories were selected as the coordinate axes for building the model;

[0033] (b) collecting samples from n healthy subjects and m healthy impaired or sub-healthy subjects, and measuring the levels of markers associated with the selected coordinate axes in each sample;

[0034] (c) All marker levels were normalized from 0 to 1 using the formula (x-min) / (max-min), where x is the level of a marker in a particular subject, and min and max are the minimum and maximum values ​​of the marker level in all subjects (n healthy subjects and m healthy impaired or subhealthy subjects), respectively.

[0035] (d) Using a cross-validation-elastic net model to build models based on the markers of the selected coordinate axes, the area under the receiver operating characteristic curve (ROC) (AUC) is used as the evaluation criterion. The target marker is screened on each coordinate axis using the optimal AUC (e.g., the AUC closest to 1) and the weight coefficient of the marker is obtained. This is used as the evaluation model for the subject's phenotypic status;

[0036] wherein n and m are each independently a positive integer, and n+m≥5 (e.g., n and m are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 50, 70, 90, 110, 130, 150).

[0037] In certain embodiments, the method further comprises, after step (III), performing 70% simplification, 80% simplification, 90% simplification, or no simplification on the selected target marker according to the weight coefficient corresponding to the target marker obtained in step (III) to obtain a 70% simplified model, an 80% simplified model, a 90% simplified model, or a complete model, respectively.

[0038] In certain embodiments, the 70% simplified model comprises any one, two, three, or four coordinate axes. In certain embodiments, the 70% simplified model comprises a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and / or a metabolic pressure coordinate axis. In certain embodiments, the 70% simplified model selects one, two, three, or four of the carbohydrate metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis, and the metabolic pressure coordinate axis as the coordinate axes for establishing the model.

[0039] In certain embodiments, the target markers selected under the sugar metabolism coordinate axis of the 70% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose and Pyruvate.

[0040] In certain embodiments, the target markers selected under the amino acid metabolism coordinate axis of the 70% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine and Glutamate.

[0041] In certain embodiments, the target markers selected under the lipid metabolism coordinate axis of the 70% simplified model include: SysBP, TG, Leptin, Carnitine C3 and Deoxycholic acid.

[0042] In certain embodiments, the target markers selected under the metabolic stress axis of the 70% simplified model include: IL8, Glucose, ICAM-1, SysBP and Dopamine.

[0043] In certain embodiments, the 80% simplified model comprises any one, two, three, or four coordinate axes. In certain embodiments, the 80% simplified model comprises a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and / or a metabolic pressure coordinate axis. In certain embodiments, one, two, three, or four of the carbohydrate metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis, and the metabolic pressure coordinate axis are selected as the coordinate axes for establishing the model.

[0044] In certain embodiments, the target markers selected under the sugar metabolism coordinate axis of the 80% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid and C-pept.

[0045] In certain embodiments, the target markers selected under the amino acid metabolism coordinate axis of the 80% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine and Taurine.

[0046] In certain embodiments, the target markers selected under the lipid metabolism coordinate axis of the 80% simplified model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2 and Succinate.

[0047] In certain embodiments, the target markers selected under the metabolic pressure coordinate axis of the 80% simplified model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine and IL18.

[0048] In certain embodiments, the 90% simplified model comprises any one, two, three, or four coordinate axes. In certain embodiments, the 90% simplified model comprises a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and / or a metabolic pressure coordinate axis. In certain embodiments, the 90% simplified model selects one, two, three, or four of the carbohydrate metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis, and the metabolic pressure coordinate axis as the coordinate axes for establishing the model.

[0049] In certain embodiments, the target markers selected under the sugar metabolism coordinate axis of the 90% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate and 3-phosphoglycerate.

[0050] In certain embodiments, the target markers selected under the amino acid metabolism coordinate axis of the 90% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine and Ornithine.

[0051] In certain embodiments, the target markers selected under the lipid metabolism coordinate axis of the 90% simplified model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate and tChol.

[0052] In certain embodiments, the target markers selected under the metabolic pressure coordinate axis of the 90% simplified model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b and FT4.

[0053] In certain embodiments, the complete model comprises any 1, 2, 3 or 4 axes. In certain embodiments, the complete model comprises a carbohydrate metabolism axis, a lipid metabolism axis, an amino acid metabolism axis and / or a metabolic pressure axis.

[0054] In certain embodiments, the target markers selected under the glucose metabolism coordinate axis of the complete model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI and Insulin.

[0055] In certain embodiments, the target markers selected under the amino acid metabolism coordinate axis of the complete model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine and Phenylalanine hydroxylase activity index.

[0056] In certain embodiments, the target markers selected under the lipid metabolism coordinate axis of the complete model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12 and Carnitine C6.

[0057] In certain embodiments, the target markers selected under the metabolic stress axis of the complete model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, adiponectin, Vanillomandelic acid and tChol.

[0058] In certain embodiments, in step (III), the target markers and their corresponding weight coefficients are as follows:

[0059] In certain embodiments, the healthy subject has one or more characteristics selected from the following (e.g., 1-5, 5-10, 10-15, 15-20). In certain embodiments, the healthy subject has all of the following characteristics (1) to (20), all of which are measured in a fasting state.

[0060] (1) Age <35 years old;

[0061] (2) BMI < 24 kg / m 2 ;

[0062] (3) Waist circumference: <90 cm (male), <80 cm (female);

[0063] (4)Glucose[3.9~5.6mmol / L];

[0064] (5) Insulin [2.6-24.9 IU / L];

[0065] (6) DiaBP [60-85 mmHg];

[0066] (7) SysBP [90-130 mmHg];

[0067] (8)TG<1.7mmol / L;

[0068] (9) HDL>1.03mmol / L (male);>1.3mmol / L (female);

[0069] (10) LDL < 3.4 mmol / L;

[0070] (11) tChol < 5.2 mmol / L;

[0071] (12) ASAT [15-40 U / L] (male); [13-35 U / L] (female);

[0072] (13) ALAT [9-50 U / L] (male); [7-40 U / L] (female);

[0073] (14) GGTP [8-61 U / L] (male); [5-36 U / L] (female);

[0074] (15) FT3 [3.1-6.8 pmol / L];

[0075] (16) FT4 [12-22 pmol / L];

[0076] (17)TSH [0.27-4.20 IU / L];

[0077] (18) Creatinine [50-104 μmol / L] (male); [45-84 μmol / L] (female);

[0078] (19) eGFR>90ml / min per 1.73m 2 ;

[0079] (20) CRP [0-5 mg / L];

[0080] In certain embodiments, the subjects in the health-damaged or sub-healthy reference group do not meet at least one of the characteristics of (1) to (20) above. In certain embodiments, the subjects in the health-damaged or sub-healthy reference group do not meet any of 1-5, 5-10, 10-15, or 15-20 of the characteristics of (1) to (20) above.

[0081] In certain embodiments, the health-damaged or sub-healthy subjects also have the following characteristics: age 50 to 70 years old; BMI ≥ 24 kg / m2 .

[0082] In certain embodiments, the sample is whole blood, serum, plasma, red blood cells, urine, feces, or a body fluid obtained from a subject.

[0083] In certain embodiments, the subject is a mammal, eg, a mouse, a pig, a rabbit, a monkey, or a human.

[0084] In a second aspect, the present application provides a phenotypic state model, wherein the model is constructed by the method described in the first aspect.

[0085] In certain embodiments, after obtaining the weight coefficient corresponding to the target marker by the method described in the first aspect, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all target markers and their corresponding weight coefficients.

[0086] In certain embodiments, the phenotypic state score is: the level of target marker 1 × the weight coefficient of target marker 1 + the level of target marker 2 × the weight coefficient of target marker 2 + the level of target marker 3 × the weight coefficient of target marker 3 + ... + the level of target marker x × the weight coefficient of target marker x; wherein x is the number of all target markers.

[0087] In certain embodiments, the level of the target marker is normalized from 0 to 1 using the formula (x-min) / (max-min), where x is the level of a target marker in a subject, and min and max are the minimum and maximum values ​​of the target marker in all n healthy subjects and m healthy-impaired or sub-healthy subjects, respectively.

[0088] In certain embodiments, the models are used to assess the metabolic status of a subject (eg, a subject not clinically diagnosed with a metabolic disease).

[0089] In certain embodiments, a greater value of a subject's phenotypic state score indicates a worse phenotypic health state (eg, metabolic state) of the subject.

[0090] Individual phenotypic status scores for any one axis

[0091] In certain embodiments, when only one metabolic category is selected as the coordinate axis for establishing the model, the phenotypic status score of the subject on the coordinate axis is obtained by accumulating the product of the levels of all target markers after 0-1 normalization processing and their corresponding weight coefficients on the coordinate axis.

[0092] In certain embodiments, the formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker of a certain subject, and min and max are the minimum and maximum values ​​of the target marker level in all n healthy subjects and m healthy-impaired or sub-healthy subjects, respectively.

[0093] Comprehensive phenotypic status score for any 2, 3, or 4 axes

[0094] In certain embodiments, when 2, 3, or 4 metabolic categories are selected as coordinate axes for establishing the model, the phenotypic status score of the subject on each coordinate axis is obtained by accumulating the product of the levels of all target markers after 0-1 normalization under each coordinate axis and their corresponding weight coefficients; then the scores of each coordinate axis are independently normalized on a 0-1 basis, and the average of the 0-1 normalized scores obtained on all coordinate axes is the phenotypic status score.

[0095] In certain embodiments, the formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker in a subject, and min and max are the minimum and maximum values ​​of the target marker level in all n healthy subjects and m healthy, impaired or sub-healthy subjects, respectively. In certain embodiments, the formula for 0-1 normalization of the score of each coordinate axis is (y-min) / (max-min), wherein y is the score of a coordinate axis, and min and max are the minimum and maximum values ​​of the score in that coordinate axis, respectively.

[0096] In certain embodiments, when the model comprises a glucose metabolism axis, the model is used to predict a subject's health status in glucose metabolism (eg, to predict a subject's risk of developing a metabolic disease).

[0097] In certain embodiments, the target markers of the carbohydrate metabolism coordinate axis and the corresponding weight coefficients are selected from the following: Glycerol-3-phosphate, -10.469; HbA1c, 10.324; Glucose, 9.915; Pyruvate, -8.575; 4-Methyl-2-oxovaleric acid, -5.738; C-pept, 5.568; Glyceraldehyde-3-phosphate, -4.492; 3-phosphoglycerate, -3.861; Valine, 3.236; GLP1, 3.099; Isoleucine, -2.396; L-Lactate, 2.333; GIP, 2.136; Glucagon, -1.346; HIRI, 1.176; Insulin, 0.021, or any combination thereof.

[0098] In certain embodiments, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the target markers after 0-1 normalization and their corresponding weight coefficients.

[0099] In certain embodiments, the phenotypic status score is: Glycerol-3-phosphate level×(−10.469)+HbA1c level×10.324+Glucose level×9.915+…+Insulin level×0.021.

[0100] In certain embodiments, if the subject's phenotypic state score is less than -3.309, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to -3.309 or less than 1.085, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 1.085 or less than 3.917, the subject's health state is at a minimally impaired level; if the subject's phenotypic state score is greater than or equal to 3.917, the subject's health state is at an impaired level.

[0101] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk for a metabolic disease (eg, diabetes).

[0102] In certain embodiments, when the model comprises a lipid metabolism axis, the model is used to predict a subject's health status in lipid metabolism (eg, to predict a subject's risk of developing a metabolic disease).

[0103] In certain embodiments, the target markers of the lipid metabolism coordinate axis and the corresponding weight coefficients are selected from the following: SysBP, 2.757; TG, 2.627; Leptin, 2.298; Carnitine C3, 1.98; Deoxycholic acid, -1.914; Carnitine, 1.739; Isocitrate, 1.723; Carnitine C14:2, -1.619; Succinate, 1.359; tChol, 1.35; LDL, 1.13; DiaBP, 1.126; Carnitine C10:1, -1.062; Carnitine C16OH, -0.809; L-Malate, -0.412; Carnitine C16:1OH, -0.362; Carnitine C12, -0.114; Carnitine C6, 0.056, or any combination thereof.

[0104] In certain embodiments, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the target markers after 0-1 normalization and their corresponding weight coefficients.

[0105] In certain embodiments, the phenotypic status score is: the level of SysBP×2.757+the level of TG×2.627+the level of Leptin×2.298+…+the level of Carnitine C6×0.056.

[0106] In certain embodiments, if the subject's phenotypic state score is less than -1.076, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to -1.076 or less than 0.336, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 0.336 or less than 1.451, the subject's health state is at a minimally impaired level; if the subject's phenotypic state score is greater than or equal to 1.451, the subject's health state is at an impaired level.

[0107] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk of developing a metabolic disease (eg, hyperlipidemia).

[0108] In certain embodiments, when the model comprises an amino acid metabolism axis, the model is used to predict a subject's health status in amino acid metabolism (eg, to predict a subject's risk of developing a metabolic disease).

[0109] In certain embodiments, the target markers of the amino acid metabolism coordinate axis and the corresponding weight coefficients are selected from: Threonine, -4.164; Vitamin-B5-Pantothenic-acid, 4.095; Cystine, 4.061; Glutamate, 3.147; Beta-alanine, -2.722; Taurine, 2.156; Asparagine, -2.14; Ornithine, 1.769; Aspartate, 1.4; Tyrosine, 0.892; Lysine, 0.618; Phenylalanine hydroxylase activity index, 0.318, or any combination thereof.

[0110] In certain embodiments, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the target markers after 0-1 normalization and their corresponding weight coefficients.

[0111] In certain embodiments, the phenotypic status score is: Threonine level × (-4.164) + Vitamin-B5-Pantothenic-acid level × 4.095 + Cystine level × 4.061 + ... + Phenylalanine hydroxylase activity index level × 0.318.

[0112] In certain embodiments, if the subject's phenotypic state score is less than -1.596, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to -1.596 or less than 0.313, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 0.313 or less than 1.938, the subject's health state is at a minimally impaired level; if the subject's phenotypic state score is greater than or equal to 1.938, the subject's health state is at an impaired level.

[0113] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk for a metabolic disease.

[0114] In certain embodiments, when the model comprises a metabolic stress axis, the model is used to predict the health status of a subject under metabolic stress (eg, to predict the risk of a subject developing a metabolic disease).

[0115] In certain embodiments, the target markers of the metabolic stress axis and their corresponding weight coefficients are selected from the group consisting of: IL8, 6.755; Glucose, 4.739; ICAM-1, 4.522; SysBP, 3.818; Dopamine, 3.707; LDL, 3.457; E-selectin, 2.7; 5-Hydroxy-L-tryptophan, -2.548; GGTP, 2.291; IL6, 2.165; N6-acetyl lysine, 2.149; IL18, 2.148; Cortisol, -1.978; TSH, 1.776; Normetanephrine, 1.602; IL1b, 1.566; FT4, -1.366; GOLA, 1.326; Tyrosine, 1.127; DiaBP, 1.089; CEA, -1.048; Phenylalanine, -0.789; Adiponectin, -0.486; Vanillomandelic acid, 0.401; tChol, 0.22, or any combination thereof.

[0116] In certain embodiments, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the target markers after 0-1 normalization and their corresponding weight coefficients.

[0117] In certain embodiments, the phenotypic state is scored as: level of IL8×6.755+level of Glucose×4.739+level of ICAM-1×4.522+…+level of tChol×0.22.

[0118] In certain embodiments, if the subject's phenotypic state score is less than -2.662, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to -2.662 or less than 0.389, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 0.389 or less than 3.079, the subject's health state is at a minimally impaired level; if the subject's phenotypic state score is greater than or equal to 3.079, the subject's health state is at an impaired level.

[0119] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk for a metabolic disease.

[0120] In certain embodiments, when the model comprises a carbohydrate metabolism axis, a lipid metabolism axis, an amino acid metabolism axis, and a metabolic stress axis, the model is used to predict the health status of a subject (e.g., for predicting the risk of a subject suffering from a metabolic disease).

[0121] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis, and metabolic pressure coordinate axis and their corresponding weight coefficients are as described above.

[0122] In certain embodiments, the phenotypic status score of the subject on each coordinate axis is obtained by accumulating the product of the levels of all target markers after 0-1 normalization on each coordinate axis and their corresponding weight coefficients; then the scores on each coordinate axis are independently normalized on a 0-1 basis, and the average of the 0-1 normalized scores obtained on all coordinate axes is the phenotypic status score.

[0123] In certain embodiments, the formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker of a certain subject, and min and max are the minimum and maximum values ​​of the target marker level in all n healthy subjects and m healthy-impaired or sub-healthy subjects, respectively.

[0124] In certain embodiments, the formula for 0-1 normalization of the scores of each coordinate axis is (y-min) / (max-min), where y is the score of a certain coordinate axis, and min and max are the minimum and maximum values ​​of the score in the coordinate axis, respectively.

[0125] In certain embodiments, if the subject's phenotypic state score is less than 0.226, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to 0.226 or less than 0.401, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 0.401 or less than 0.558, the subject's health state is at a minimally impaired level; if the subject's phenotypic state score is greater than or equal to 0.558, the subject's health state is at an impaired level.

[0126] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk for a metabolic disease.

[0127] In a third aspect, the present application provides a method for constructing a model for assessing elastic homeostasis of a subject, the method comprising:

[0128] (I) selecting multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress;

[0129] Select one to several (e.g., two, three, four) categories from the four categories as coordinate axes for establishing the model (e.g., a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and / or a metabolic stress coordinate axis), and select x target markers from the markers under each coordinate axis; wherein x is any positive integer (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60);

[0130] (II) selecting Y healthy subjects as reference subjects, collecting biological samples from each reference subject, and measuring the level of the target marker in each biological sample at T time points before and after eating; wherein Y is an integer greater than or equal to 2; and T is an integer greater than or equal to 3;

[0131] (III) Calculate the difference in the curve area of ​​the target marker level between each two reference subjects at each two adjacent time points, obtain T-1 Δs for each two reference subjects, calculate the sum of T-1 Δs to obtain the overall difference in the curve area of ​​the target marker level between each two reference subjects, and obtain the total difference in the curve area of ​​the target marker level between all reference subjects. ΔS;

[0132] (IV) collecting a biological sample from the test subject, measuring the level of the target marker in the biological sample at the time point selected in step (II), calculating the difference Δh between the target marker level curves of the test subject and each reference subject at each two adjacent time points, obtaining a total of T-1 Δh for the test subject and each reference subject, calculating the sum of the T-1 Δh to obtain the overall difference ΔH between the target marker level curves of the test subject and each reference subject, obtaining a total of Y ΔH to construct a model for evaluating the elastic homeostasis of the subject;

[0133] (V) By comparison The sizes of Y ΔS and Y ΔH are used to evaluate or judge the elastic homeostasis of the subject to be tested.

[0134] In certain embodiments, a greater amount by which ΔH is greater than ΔS indicates a worse elastic homeostasis of the subject being tested.

[0135] In certain embodiments, the level of the target marker is determined for each biological sample at 0 h before and T-1 time points after eating.

[0136] In certain embodiments, when T is 6, the level of the target marker is determined for each biological sample at 0 h before eating, and 0.5 h, 1 h, 2 h, 3 h, and 4 h after eating.

[0137] In certain embodiments, t represents any selected time point, and t+1 represents the next time point adjacent to the time point, wherein t≤T-1.

[0138] For example, when the selected time points are 0h before eating, 0.5h, 1h, 2h, 3h, and 4ht after eating, and t is 0.5h, then t+1 is 1h. For example, when the selected time points are 0h before eating, 0.5h, 1h, 2h, 3h, and 4ht after eating, and t is 1h, then t+1 is 2h.

[0139] In certain embodiments, Y is selected from the group consisting of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60.

[0140] In certain embodiments, T is selected from the group consisting of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60.

[0141] In certain embodiments, the level of the marker can be directly measured or further calculated based on the measurement results.

[0142] In certain embodiments, the method for determining the level of the marker includes: biochemical analysis, physiological analysis, immunological methods (such as enzyme-linked immunosorbent assay (ELISA), colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immune lateral flow analysis), molecular biology detection methods (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection methods (such as light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis methods, chromatography, mass spectrometry, electrophoresis, hybridization (such as Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy.

[0143] In certain embodiments, in step (III), the calculation step of the overall change curve area difference ΔS between each two reference subjects (eg, subject a and subject b) is as follows:

[0144] i. Through the formula Calculate separately and The value of

[0145] ii. Calculate the difference in the curve area of ​​the target marker between the two reference subjects at each two adjacent time points ΔS (t,t+1) ;like That is, there is no intersection between the change curves at time points t and t+1, so Where Δt is the time interval between the tth and t+1th time points (for example, if t and t+1 are 0.5h and 1h respectively, then Δt = 0.5); if That is, there is an intersection between the change curves at time points t and t+1, then Where Δt is the time interval between the tth and t+1th time points (for example, if t and t+1 are 0.5h and 1h respectively, then Δt=0.5),

[0146] iii. According to the formula The area difference ΔS between the change curves of the target marker in two reference subjects (eg, subject a and subject b) is obtained.

[0147] In certain embodiments, wherein a t and b t are the measured values ​​of the target markers in reference subject a and reference subject b at the t-th time point, a1 and b1 are the measured values ​​of the target markers in reference subject a and reference subject b at 0 h before eating, respectively.

[0148] In certain embodiments, in step (IV), the calculation steps of the overall change curve area difference ΔH between the test subject (e.g., subject d) and each reference subject (e.g., subject a) are as follows:

[0149] i. Through the formula Calculate separately and The value of

[0150] ii. Calculate the difference in the change curve area ΔH between the test subject and the reference subject at each two adjacent time points. (t,t+1) ;like That is, there is no intersection between the change curves at time points t and t+1, so Where Δt is the time interval between the two time points t and t+1. For example, if t and t+1 are 0.5h and 1h respectively, then Δt = 0.5; if That is, there is an intersection between the change curves at time points t and t+1, then in

[0151] iii. According to the formula The area difference between the change curves of the target marker in the tested subject (eg, subject d) and the reference subject is obtained.

[0152] In certain embodiments, a t and d tare the measured values ​​of the target markers in the reference subject a and the tested subject d at the t-th time point, respectively; a1 and d1 are the measured values ​​of the target markers in subjects a and d 0 h before eating, respectively.

[0153] In certain embodiments, when two reference subjects (e.g., reference subject a, reference subject b) are selected, step (V) of the method is achieved by:

[0154] (i) calculating the difference in the change curve area ΔS(a, b) between the reference subject a and the reference subject b on the target marker, and calculating the difference in the change curve area ΔH(a, d) between the test subject d and the reference subject a, and the difference in the change curve area ΔH(b, d) between the test subject d and the reference subject b;

[0155] (ii) the elasticity homeostasis score of the test subject on the target marker is m / n, where m is the number of test subjects whose ΔH is greater than ΔS on the target marker, and n is the number of reference subjects; wherein a larger elasticity homeostasis score of a subject indicates a worse elasticity homeostasis of the subject, i.e., the subject's current health state is more likely to decline into a worse health state;

[0156] For example, if ΔH(a, d) is greater than ΔS(a, b) and ΔH(b, b) is not greater than ΔS(a, b), then m is 1, n is 2, and the elastic steady-state fraction of the target marker for the test subject d is 1 / 2;

[0157] Optionally, the elastic steady-state score of the subject to be tested on one or several coordinate axes is calculated, and the elastic steady-state score of the subject to be tested on one or several coordinate axes is the weighted average of the elastic steady-state scores of all target markers selected on one or several coordinate axes (for example, the glucose metabolism coordinate axis includes target markers E and F, then the elastic steady-state score of the subject on the glucose metabolism coordinate axis is (β E × elastic steady-state fraction E +β F × elastic steady-state fraction F ) / 2, β is the weight coefficient).

[0158] In certain embodiments, an approach is employed in which all molecules are given equal weight, i.e., β X and β Y Both are 1.

[0159] In certain embodiments, when 3 or more reference subjects are selected, step (V) of the method is achieved by the following method:

[0160] (i) obtained by step (III) ΔS, construct the reference distribution of ΔS of the target marker, calculate the mean μ and standard deviation σ of all ΔS among the reference subjects, and calculate the normal distribution N(μ, σ 2 ) (Preferably, the quantile value is selected from the 0.95 quantile z of the normal distribution 0.95 , 0.90 quantile z 0.90 or the z-quantile of 0.85 0.85 ); wherein, for a target marker whose ΔS between reference subjects is non-normally distributed, the ΔS is logarithmically transformed based on a constant e before constructing a reference distribution of the ΔS of the target marker;

[0161] (ii) the elasticity homeostasis score of the target marker of the test subject is m / n, where m is the number of ΔH greater than the quantile value, and n is the number of reference subjects; wherein a larger elasticity homeostasis score of a subject indicates a worse elasticity homeostasis of the subject, i.e., the subject's current health state is more likely to decline into a worse health state;

[0162] For example, three reference subjects a, b, and c are selected. If the ΔH(d, a) between the test subject d and the reference subject a is greater than the quantile value, and the ΔH(d, b) between the test subject d and the reference subject b is greater than the quantile value, and the ΔH(c, d) between the test subject d and the reference subject c is not greater than the quantile value, then the number of ΔHs greater than the quantile value is 2; then m is 2, n is 3, and the elastic steady-state fraction of the target marker E of the test subject d is 2 / 3;

[0163] Optionally, the elastic steady-state score of the subject to be tested on one or several coordinate axes is calculated, and the elastic steady-state score of the subject to be tested on one or several coordinate axes is the weighted average of the elastic steady-state scores of all target markers selected on one or several coordinate axes (for example, the glucose metabolism coordinate axis includes target markers E and F, then the elastic steady-state score of the subject on the glucose metabolism coordinate axis is (β E × elastic steady-state fraction E +β F × elastic steady-state fraction F ) / 2, β is the weight coefficient).

[0164] In certain embodiments, an approach is employed in which all molecules are given equal weight, i.e., β X and β Y Both are 1.

[0165] In a fourth aspect, the present application provides an elastic steady-state model, which is constructed by the method described in the third aspect.

[0166] In certain embodiments, when the elastic homeostasis model includes a glucose metabolism coordinate axis, the elastic homeostasis model is used to assess and predict the risk of a subject entering a worse health state (e.g., health state from excellent level to standard level, health state from standard level to slightly damaged level, health state from slightly damaged level to damaged level).

[0167] In certain embodiments, the target marker selected by the sugar metabolism coordinate axis is selected from the group consisting of: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, or any combination thereof.

[0168] In certain embodiments, the target markers selected for the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol.

[0169] In certain embodiments, the target markers selected by the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate and C-pept.

[0170] In certain embodiments, the target markers selected for the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine.

[0171] In certain embodiments, when the elastic homeostasis model includes a lipid metabolism coordinate axis, the elastic homeostasis model is used to predict the risk of a subject entering a worse health state (e.g., health state from excellent level to standard level, health state from standard level to slightly damaged level, health state from slightly damaged level to damaged level).

[0172] In certain embodiments, the target marker selected by the lipid metabolism coordinate axis is selected from: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, or any combination thereof.

[0173] In certain embodiments, the target markers selected by the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate and 3-Hydroxybutanoic acid.

[0174] In certain embodiments, the target markers selected by the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1 and Carnitine C18.

[0175] In certain embodiments, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH and Deoxycholic acid.

[0176] In certain embodiments, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA and Carnitine C3.

[0177] In certain embodiments, the target markers selected by the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH and Carnitine C12.

[0178] In certain embodiments, when the elastic homeostasis model comprises an amino acid metabolism coordinate axis, the elastic homeostasis model is used to predict the risk of a subject entering a worse health state (e.g., health state from excellent level to standard level, health state from standard level to slightly damaged level, health state from slightly damaged level to damaged level).

[0179] In certain embodiments, the target marker selected by the amino acid metabolism coordinate axis is selected from: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, or any combination thereof.

[0180] In certain embodiments, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine and Lysine.

[0181] In certain embodiments, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine.

[0182] In certain embodiments, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine and Phenylalanine.

[0183] In certain embodiments, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine.

[0184] In certain embodiments, when the elastic homeostasis model includes a metabolic stress axis, the elastic homeostasis model is used to predict the risk of a subject entering a worse health state (e.g., health state from excellent level to standard level, health state from standard level to slightly damaged level, health state from slightly damaged level to damaged level).

[0185] In certain embodiments, the target marker selected by the metabolic stress axis is selected from the group consisting of: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof.

[0186] In certain embodiments, the target markers selected for the metabolic stress axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine and Metanephrine.

[0187] In certain embodiments, the target markers selected for the metabolic stress axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline and VCAM-1.

[0188] In certain embodiments, the target markers selected for the metabolic stress axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine and Dopamine.

[0189] In certain embodiments, the target markers selected for the metabolic stress coordinate axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, and P-selectin.

[0190] In certain embodiments, when the model comprises a carbohydrate metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis, and a metabolic stress coordinate axis, the model is used to predict the risk of a subject entering a worse health state (e.g., health state from an excellent level to a standard level, health state from a standard level to a minimally damaged level, health state from a minimally damaged level to an impaired level).

[0191] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis are selected from the group consisting of: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine,GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof. ,

[0192] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol.

[0193] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis, and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, and Glyceraldehyde-3-phosphate.

[0194] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate and L-Malate.

[0195] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6 and Carnitine C14:2.

[0196] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH and Carnitine C14.

[0197] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insuin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1 and Carnitine C16:1.

[0198] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine.

[0199] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creati Nine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH and 3-Methylhistidine.

[0200] In certain embodiments, the target markers selected for the carbohydrate metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic stress coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Pr oline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine,GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1 and P-selectin. ,

[0201] In a fifth aspect, the present application provides a method for evaluating the health status of a subject, wherein the method comprises: using the phenotypic state model described in the second aspect to obtain the health status of the subject.

[0202] In certain embodiments, the health status is selected from the group consisting of excellent, satisfactory, minimally impaired, and impaired.

[0203] In certain embodiments, the method further includes: based on obtaining the health status of the subject, using the elastic homeostasis model described in the fourth aspect to predict the risk of the subject entering a worse health state (for example, the health status changes from an excellent level to a standard level, the health status changes from a standard level to a slightly damaged level, and the health status changes from a slightly damaged level to an injury level).

[0204] In a sixth aspect, the present application provides a method for evaluating elastic homeostasis of a subject, wherein the method comprises: using the elastic homeostasis model described in the fourth aspect to evaluate the elastic homeostasis of the subject; wherein, the greater the elastic homeostasis score of the subject, the worse the elastic homeostasis of the subject.

[0205] In a seventh aspect, the present application provides a method for assessing a subject's risk of developing a metabolic disease, wherein the method comprises: using the phenotypic state model described in the second aspect to obtain the subject's health status, and / or using the elastic homeostasis model described in the fourth aspect to obtain the subject's elastic homeostasis.

[0206] In certain embodiments, the health status is selected from the group consisting of excellent, satisfactory, minimally impaired, and impaired.

[0207] In certain embodiments, if the subject's health status is at the minimally impaired level or the damaged level, the subject's metabolic health status is close to that of a subject with a metabolic disease.

[0208] In certain embodiments, if the subject's health status is at a minimally impaired level or an impaired level, the subject is at risk of developing a metabolic disease, or already has a metabolic disease.

[0209] In certain embodiments, if a subject has poorer elastic homeostasis, the subject has a higher risk of developing a metabolic disease.

[0210] In certain embodiments, the metabolic disease is selected from the group consisting of diabetes, hypertension, coronary artery disease, hyperlipidemia and hyperuricemia.

[0211] In an eighth aspect, the present application provides a method for evaluating whether a therapy for treating a metabolic disease in a subject is effective, the method comprising: measuring the health status and / or elastic homeostasis of the subject before and after undergoing metabolic disease therapy through the phenotypic state model described in the second aspect and / or the elastic homeostasis model described in the fourth aspect.

[0212] In certain embodiments, a metabolic disease therapy is assessed to be effective if the subject's health and / or elastic homeostasis after receiving the metabolic disease therapy is better than the subject's health and / or elastic homeostasis before receiving the metabolic disease therapy.

[0213] In certain embodiments, the subject obtains a phenotypic state score a after undergoing metabolic disease therapy and a phenotypic state score b before undergoing metabolic disease therapy through the phenotypic state model described in the second aspect. If the phenotypic state score a is lower than the phenotypic state score b, the therapy is evaluated to be effective.

[0214] In certain embodiments, the subject obtains an elasticity steady-state score a after undergoing metabolic disease therapy and an elasticity steady-state score b before undergoing metabolic disease therapy through the elasticity steady-state model described in the fourth aspect. If the elasticity steady-state score a is lower than the elasticity steady-state score b, the therapy is evaluated to be effective.

[0215] In the ninth aspect, the present application provides a method for evaluating whether an intervention program to improve a subject's metabolic health is effective, the method comprising: measuring the health status and / or elastic homeostasis of the subject before and after the intervention through the phenotypic state model described in the second aspect and / or the elastic homeostasis model described in the fourth aspect.

[0216] In certain embodiments, an intervention is assessed to be effective if the subject's health and / or elastic homeostasis after the intervention is better than the subject's health and / or elastic homeostasis before the intervention.

[0217] In certain embodiments, the subject obtains a phenotypic state score a after undergoing metabolic health intervention and a phenotypic state score b before undergoing metabolic health intervention through the phenotypic state model described in the second aspect. If the phenotypic state score a is lower than the phenotypic state score b, the metabolic health intervention program is evaluated to be effective.

[0218] In certain embodiments, the subject obtains the elastic steady-state score a after the metabolic health intervention and the elastic steady-state score b before the metabolic health intervention through the elastic steady-state model described in the fourth aspect. If the elastic steady-state score a is lower than the elastic steady-state score b, the metabolic health intervention plan is evaluated to be effective.

[0219] In a tenth aspect, the present application provides a system for executing the methods described in aspects 5 to 9, the system comprising:

[0220] (1) a storage component for storing data, wherein the storage component has instructions for determining an assessment of a subject stored therein;

[0221] (2) a computer processor for processing data, wherein the computer processor is coupled to the storage component and configured to execute instructions stored in the storage component to receive the subject data and analyze the subject data according to one or more coordinate axes;

[0222] (3) A display component for displaying evaluation information of the subject.

[0223] In certain embodiments, the storage component further comprises instructions for performing cross-validation, receiver operating characteristic (ROC) analysis, and / or linear discriminant analysis.

[0224] In certain embodiments, the cross-validation is selected from five-fold cross-validation, ten-fold cross-validation, and leave-one-out cross-validation.

[0225] In certain embodiments, the system is a diagnostic system for performing a computer-implemented method for assessing the phenotypic state of a subject;

[0226] Wherein, the computer executes the phenotypic state model described in the second aspect.

[0227] In certain embodiments, the computer performs the steps comprising:

[0228] a) receiving input subject data, wherein the data comprises the levels of markers in a biological sample of the subject obtained by the method according to the first aspect;

[0229] b) analyzing the level of each marker of the subject based on the method described in the first aspect, and calculating the phenotypic status score of the subject;

[0230] Optionally, the subject's phenotypic status score is compared to a reference score to display diagnostic information about the subject (eg, the subject's health status).

[0231] In certain embodiments, the system is a diagnostic system for performing a computer-implemented method for assessing elastic homeostasis in a subject;

[0232] Wherein, the computer executes the bullet-type steady-state model described in the fourth aspect.

[0233] In certain embodiments, the computer performs the steps comprising:

[0234] a) receiving input subject data, wherein the data comprises the levels of markers in a biological sample of a subject obtained by the method according to the third aspect;

[0235] b) analyzing the level of each marker of the subject based on the method described in the third aspect, and calculating the elastic homeostasis score of the subject;

[0236] Optionally, the subject's elastic homeostasis score is compared to a reference score to display diagnostic information for the subject (eg, the subject's elastic homeostasis).

[0237] In the eleventh aspect, the present application provides a kit comprising reagents for determining the level of markers in a biological sample, wherein the markers are selected from one or more of the following four metabolic categories: (a) markers involved in or affecting carbohydrate metabolism, (b) markers involved in or affecting lipid metabolism, (c) markers involved in or affecting amino acid metabolism, and (d) markers involved in or affecting metabolic stress.

[0238] In certain embodiments, the kits are used to assess the health status (eg, metabolic status) of a subject.

[0239] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0240] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose and Pyruvate;

[0241] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine and Glutamate;

[0242] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3 and Deoxycholic acid;

[0243] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP and Dopamine.

[0244] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0245] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid and C-pept;

[0246] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine and Taurine;

[0247] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2 and Succinate;

[0248] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine and IL18.

[0249] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0250] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate and 3-phosphoglycerate;

[0251] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine and Ornithine;

[0252] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate and tChol;

[0253] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b and FT4.

[0254] In certain embodiments, the marker involved in or affecting glucose metabolism is selected from the group consisting of: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI, Insulin, or any combination thereof.

[0255] In certain embodiments, the markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI and Insulin.

[0256] In certain embodiments, the marker involved in or affecting lipid metabolism is selected from the group consisting of: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12, Carnitine C6, or any combination thereof.

[0257] In certain embodiments, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12 and Carnitine C6.

[0258] In certain embodiments, the marker involved in or affecting amino acid metabolism is selected from the group consisting of Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine, Phenylalanine hydroxylase activity index, or any combination thereof.

[0259] In certain embodiments, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine and Phenylalanine hydroxylase activity index.

[0260] In certain embodiments, the marker involved in or affecting metabolic stress is selected from the group consisting of IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid, tChol, or any combination thereof.

[0261] In certain embodiments, markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid and tChol.

[0262] In certain embodiments, the kit is used to assess elastic homeostasis (eg, metabolic homeostasis) in a subject.

[0263] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0264] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol;

[0265] (2) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate and C-pept;

[0266] (3) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine.

[0267] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0268] (1) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine and Lysine;

[0269] (2) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine;

[0270] (3) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine and Phenylalanine;

[0271] (4) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine and Methionine.

[0272] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0273] (1) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate and 3-Hydroxybutanoic acid;

[0274] (2) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1 and Carnitine C18;

[0275] (3) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH and Deoxycholic acid;

[0276] (4) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA and Carnitine C3;

[0277] (5) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH and Carnitine C12.

[0278] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0279] (1) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine and Metanephrine;

[0280] (2) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline and VCAM-1;

[0281] (3) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine and Dopamine;

[0282] (4) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1 and P-selectin.

[0283] In certain embodiments, the marker involved in or affecting glucose metabolism is selected from:

[0284] Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, or any combination thereof.

[0285] In certain embodiments, the marker involved in or affecting lipid metabolism is selected from the group consisting of: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, or any combination thereof.

[0286] In certain embodiments, the marker involved in or affecting amino acid metabolism is selected from the group consisting of: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, or any combination thereof.

[0287] In certain embodiments, the marker involved in or affecting metabolic stress is selected from the group consisting of: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof.

[0288] In certain embodiments, the kit is used to assess a subject's risk of developing a metabolic disease.

[0289] In certain embodiments, the metabolic disease is selected from the group consisting of diabetes, hypertension, coronary artery disease, hyperlipidemia and hyperuricemia.

[0290] In certain embodiments, the reagent measures the level of the marker in the biological sample by the following methods: biochemical analysis, physiological analysis, immunological methods (e.g., enzyme-linked immunosorbent assay (ELISA), colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immunolateral flow analysis), molecular biology detection methods (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection methods (e.g., light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis methods, chromatography, mass spectrometry, electrophoresis, hybridization (e.g., Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy.

[0291] In certain embodiments, the sample is whole blood, serum, plasma, urine, feces, or a body fluid obtained from a subject.

[0292] In certain embodiments, the sample is whole blood obtained from a subject.

[0293] In certain embodiments, the subject is a mammal, eg, a mouse, a pig, a rabbit, a monkey, or a human.

[0294] In a twelfth aspect, the present application provides a use of a reagent for determining a marker level in a biological sample in the preparation of a kit, wherein the marker is selected from one or more of the following four metabolic categories: (a) a marker involved in or affecting glucose metabolism, (b) a marker involved in or affecting lipid metabolism, (c) a marker involved in or affecting amino acid metabolism, and (d) a marker involved in or affecting metabolic stress;

[0295] In certain embodiments, the kit is used to assess the health status (eg, metabolic status) of a subject.

[0296] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0297] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose and Pyruvate;

[0298] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine and Glutamate;

[0299] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3 and Deoxycholic acid;

[0300] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP and Dopamine.

[0301] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0302] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid and C-pept;

[0303] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine and Taurine;

[0304] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2 and Succinate;

[0305] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine and IL18.

[0306] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0307] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate and 3-phosphoglycerate;

[0308] (2) The markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine and Ornithine;

[0309] (3) The markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate and tChol;

[0310] (4) The markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b and FT4.

[0311] In certain embodiments, the marker involved in or affecting glucose metabolism is selected from the group consisting of: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI, Insulin, or any combination thereof.

[0312] In certain embodiments, the markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI and Insulin.

[0313] In certain embodiments, the marker involved in or affecting lipid metabolism is selected from the group consisting of: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12, Carnitine C6, or any combination thereof.

[0314] In certain embodiments, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12 and Carnitine C6.

[0315] In certain embodiments, the marker involved in or affecting amino acid metabolism is selected from the group consisting of Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine, Phenylalanine hydroxylase activity index, or any combination thereof.

[0316] In certain embodiments, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine and Phenylalanine hydroxylase activity index.

[0317] In certain embodiments, the marker involved in or affecting metabolic stress is selected from the group consisting of IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid, tChol, or any combination thereof.

[0318] In certain embodiments, markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid and tChol.

[0319] In certain embodiments, the kit is used to assess elastic homeostasis (eg, metabolic homeostasis) in a subject.

[0320] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0321] (1) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol;

[0322] (2) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate and C-pept;

[0323] (3) The markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine.

[0324] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0325] (1) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine and Lysine;

[0326] (2) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine;

[0327] (3) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine and Phenylalanine;

[0328] (4) The markers involved in or affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine and Methionine.

[0329] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0330] (1) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate and 3-Hydroxybutanoic acid;

[0331] (2) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1 and Carnitine C18;

[0332] (3) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH and Deoxycholic acid;

[0333] (4) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA and Carnitine C3;

[0334] (5) The markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH and Carnitine C12.

[0335] In certain embodiments, the kit has one or more features selected from the group consisting of:

[0336] (1) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine and Metanephrine;

[0337] (2) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline and VCAM-1;

[0338] (3) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine and Dopamine;

[0339] (4) The markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1 and P-selectin.

[0340] In certain embodiments, the marker involved in or affecting glucose metabolism is selected from the group consisting of: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, or any combination thereof.

[0341] In certain embodiments, the marker involved in or affecting lipid metabolism is selected from the group consisting of: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, or any combination thereof.

[0342] In certain embodiments, the marker involved in or affecting amino acid metabolism is selected from the group consisting of: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, or any combination thereof.

[0343] In certain embodiments, the marker involved in or affecting metabolic stress is selected from the group consisting of: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof.

[0344] In certain embodiments, the kit is used to assess a subject's risk of developing a metabolic disease.

[0345] In certain embodiments, the metabolic disease is selected from the group consisting of diabetes, hypertension, coronary artery disease, hyperlipidemia and hyperuricemia.

[0346] In certain embodiments, the reagent measures the level of the marker in the biological sample by the following methods: biochemical analysis, physiological analysis, immunological methods (e.g., enzyme-linked immunosorbent assay (ELISA), colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immunolateral flow analysis), molecular biology detection methods (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection methods (e.g., light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis methods, chromatography, mass spectrometry, electrophoresis, hybridization (e.g., Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy.

[0347] In certain embodiments, the sample is whole blood, serum, plasma, urine, feces, or a body fluid obtained from a subject.

[0348] In certain embodiments, the sample is whole blood obtained from a subject.

[0349] In certain embodiments, the subject is a mammal, eg, a mouse, a pig, a rabbit, a monkey, or a human.

[0350] In certain embodiments, the annotations or HMDB IDs of the markers involved in this application are shown in the following Appendix 1.

[0351] Schedule 1

[0352] Definition of terms

[0353] As used herein, the term "metabolic syndrome" encompasses factors and indicators that increase metabolic risk (e.g., obesity, dyslipidemia, elevated blood pressure, and elevated blood glucose). Metabolic syndrome has multiple diagnostic criteria, and the criteria used herein are those from the Adult Treatment Panel III (ATP III) developed by the National Cholesterol Education Program (NCEP). Metabolic syndrome is diagnosed when at least three of the following five criteria are met: 1) abdominal obesity: waist circumference >90 cm (male), waist circumference >80 cm (female); 2) triglyceride ≥1.7 mmol / L; 3) high-density lipoprotein (HDL): <1.03 mmol / L (male), <1.3 mmol / L (female); 4) Diabetic blood pressure (Diabetes Blood Pressure) ≥130 mmHg, or Systolic blood pressure (Systolic blood pressure) ≥85 mmHg; and 5) fasting blood glucose ≥5.6 mmol / L.

[0354] As used herein, the term "metabolic disease" includes factors and indicators that can indicate the risk of developing metabolic-related diseases (e.g., obesity, dyslipidemia, elevated blood pressure and elevated blood sugar), and also includes metabolic-related diseases such as diabetes, hypertension, coronary artery atherosclerotic heart disease, hyperlipidemia, hyperuricemia, etc.

[0355] As used herein, the term "a subject at risk of developing a metabolic disease" includes subjects with the following conditions: (1) the subject has abnormal levels of metabolism-related markers in their bodies (e.g., obesity, dyslipidemia, elevated blood pressure, elevated blood sugar); (2) the subject has a risk of developing a metabolic-related disease (e.g., diabetes, hypertension, coronary artery disease, hyperlipidemia, hyperuricemia).

[0356] As used herein, the term "biomarker" (also referred to herein as a marker) includes: physical examination measurements obtained using instruments or other measurement methods, such as height, weight, blood pressure, and body fat percentage; detectable and / or quantifiable compounds, nucleic acids, nucleic acid fragments, polynucleotides or oligonucleotides; and related indices calculated using the above-mentioned markers, such as body mass index (BMI) and Matsuda index.

[0357] As used herein, the terms "subject," "individual," and "patient" are used interchangeably herein and refer to any mammalian subject, particularly humans, for whom evaluation, diagnosis, and treatment are desired. Other subjects may include cattle, dogs, cats, guinea pigs, rabbits, rats, mice, horses, and the like. In some cases, the methods of the present invention are used in experimental animals, veterinary applications, and the development of animal disease models (including but not limited to rodents, including mice, rats, and hamsters; and primates).

[0358] As used herein, the term "biological sample" refers to a sample of tissue, cells, or fluid isolated from a subject, including, but not limited to, samples of blood, plasma, serum, blood cells (e.g., peripheral blood mononuclear cells (PBMC), band cells, neutrophils, monocytes, or T cells), fecal matter, urine, bone marrow, bile, spinal fluid, lymph fluid, skin samples, external secretions of the skin, respiratory, intestinal, and genitourinary tracts, tears, saliva, milk, organs, biopsies, and in vitro cell culture components, including, but not limited to, conditioned medium produced by cells and tissues grown in culture, such as recombinant cells and cellular components.

[0359] As used herein, the term "axis" refers to the category of markers for establishing a phenotypic state model and / or an elastic homeostasis model, and different categories of markers can participate in or affect metabolism in different aspects. When the model of the present application includes more than one axis, the subject's phenotypic state score can be presented in a two-dimensional / three-dimensional manner, for example, A in Figure 3 of the present application. In certain embodiments, the model of the present application includes 1, 2, 3 or 4 axes. In certain embodiments, the axis of the present application is selected from a carbohydrate metabolism axis, a lipid metabolism axis, an amino acid metabolism axis, and a metabolic pressure axis.

[0360] As used herein, the term "difference in the area of ​​the curve of changes in the level of the target marker at two adjacent time points" means that by measuring the level of the target marker at two adjacent time points and drawing a curve based on the two measured level values, the area under the curve at these two adjacent time points is the "difference in the area of ​​the curve of changes in the level of the target marker at two adjacent time points."

[0361] As used herein, the term "the overall change curve area difference of the target marker level between two reference subjects" refers to the overall change curve area difference of the target marker level between two reference subjects obtained by calculating the sum of the change curve area differences of the target marker level at every two adjacent time points between the two reference subjects.

[0362] As used herein, the term "weighted average" refers to multiplying each value by the corresponding weight coefficient, then summing the values ​​to obtain an overall value, and then dividing by the total number of units. In certain embodiments, the "weighted average of the elastic steady-state scores of all target markers" refers to multiplying the elastic steady-state score of each target marker by its respective weight coefficient, then summing the values ​​to obtain an overall value, and then dividing by the total number of target markers. In certain embodiments, the weight coefficients of the elastic steady-state scores of all target markers are the same or different. In certain embodiments, the weight coefficients of the elastic steady-state scores of all target markers are 1.

[0363] Advantageous Effects of the Invention

[0364] This application establishes a phenotypic state model for the Chinese population that can assess the health status of subjects, and by prioritizing the target markers in the model, a simplified model and a complete model are obtained. The model is used to assess the health status of the subject, and the subject's current health status category (e.g., excellent, standard, minimally damaged, damaged) and health score can be obtained.

[0365] In addition, this application also establishes an elastic homeostasis model for the Chinese population, through which the stability of the subject in this health status category can be obtained (the more stable, the better the health homeostasis of the subject, and the more unstable, the easier it is for the subject's health status to enter a worse health status category), thereby further evaluating the subject's health status or evaluating the subject's risk of metabolic diseases.

[0366] Therefore, the model establishment method provided in this application and the model constructed by this method are particularly suitable for evaluating the health status of the Chinese population, and can also predict the risk of subjects developing certain diseases (for example, metabolic diseases). They have great application potential in future health assessments, disease risk predictions, and the establishment of individualized intervention programs.

[0367] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, but it will be understood by those skilled in the art that the following drawings and examples are intended only to illustrate the present invention and are not intended to limit the scope of the invention. Various objects and advantages of the present invention will become apparent to those skilled in the art based on the following detailed description of the accompanying drawings and preferred embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0368] Figure 1 shows the standard mixed drink intervention experimental process and the participating population.

[0369] Figure 2 shows an overview of the health status graph.

[0370] Figure 3A shows the phenotypic status scores of the subjects on the four metabolic axes; Figure 3B shows the distribution of phenotypic status scores of different groups, where green dots represent subjects in the healthy reference group and red dots represent subjects in the healthy-injury reference group.

[0371] FIG4 shows a schematic diagram of the health classification of phenotypic status scores.

[0372] Figure 5 shows the calculation process of the elastic homeostasis score of a single marker based on a healthy reference population.

[0373] This application proposes a new method for quantifying the elastic homeostasis of a subject, comprising the following steps:

[0374] Step A. For a given subject, six measurements of a single marker x at six time points form a response curve (Figure 5A), which reflects the fluctuation of marker x after a standard meal tolerance test. The area difference ΔS between the response curves of two subjects represents the difference in elastic homeostasis between the two subjects.

[0375] Step B. We assume that the predefined optimal healthy reference group has the best elastic homeostasis and calculate the difference in response curve area ΔS between n optimal healthy reference subjects. ΔS, referred to as reference area differences ( Figure 5B ), represent the magnitude of differences between subjects with the best elastic homeostasis;

[0376] Step C. Construct a reference distribution by using the reference area difference (Figure 5C) and calculate the 0.95 quantile z of the reference distribution 0.95 , when the area ΔS between the response curves of two subjects is greater than z 0.95 When , it is considered that the response curves of the two subjects have significant differences, that is, the elastic steady states have significant differences;

[0377] Step D. Calculating the difference in the response curve area between the subject k to be evaluated and the n reference subjects ( FIG. 5D ), which characterizes the difference in elastic homeostasis between the subject k to be evaluated and each reference subject;

[0378] Step E. Compare the n area differences of the subject k to be evaluated calculated in the previous step with the 0.95 quantile z of the reference distribution 0.95 For comparison (Figure 5E), the area difference is greater than z 0.95 This means that the subject k has a significant difference in elastic homeostasis from the reference subject;

[0379] Step F. The elasticity score of marker x in subject k is greater than z 0.95 The frequency of the area difference is characterized by m / n (Figure 5F), where m is greater than z 0.95 The frequency of the area difference is greater than z0.95 A greater frequency of area differences indicates that there are significant differences in elastic homeostasis between subject k and more reference subjects.

[0380] Figure 6 shows the Spearman partial correlation between the elastic homeostasis score and the major bacterial phyla of the gut microbiota (corrected for age, sex, and BMI).

[0381] Figure 7 shows the correlation between the elastic steady-state score (ordinate) and physical activity (abscissa MET hours / week) on different metabolic axes; Spearman represents the Spearman correlation coefficient, and p < 0.05 is considered to be statistically significant.

[0382] Figure 8 shows the correlation between the elastic steady-state score (ordinate) on different metabolic axes and the length of sleep at night; Spearman represents the Spearman correlation coefficient, and p<0.05 is considered to be statistically significant. DETAILED DESCRIPTION

[0383] The invention will now be described with reference to the following examples which are intended to illustrate the invention but not to limit it.

[0384] Unless otherwise indicated, the experiments and procedures described in the examples were performed essentially according to conventional methods well known in the art and described in various references. For example, conventional techniques of immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics, and recombinant DNA used in the present invention can be found in Sambrook, Fritsch, and Maniatis, MOLECULAR CLONING: A LABORATORY MANUAL, 2nd ed. (1989); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY (FM Ausubel et al., eds., (1987)); METHODS IN ENZYMOLOGY series (Academic Press): PCR 2: A PRACTICAL APPROACH (MJ MacPherson, BD Hames, and GR Taylor, eds. (1995)); and ANIMAL CELL CULTURE. CULTURE) (RI Freshney, ed. (1987)).

[0385] In addition, if specific conditions are not specified in the examples, the experiments were performed under conventional conditions or the conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments is not specified, they are all conventional products that can be obtained commercially. It is understood that the examples describe the present invention by way of example and are not intended to limit the scope of the present invention. All publications and other references mentioned herein are incorporated herein by reference in their entirety.

[0386] The Chinese and English translations of the units involved in the following embodiments are as follows:

[0387] Example 1. Research methods

[0388] 1.1 Experimental design

[0389] This study was an exploratory, open-label, acute intervention study. It was approved by the Ethics Committee of the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and the Medical Ethics Committee of Longhua Hospital, Shanghai University of Traditional Chinese Medicine (approval numbers: ER-SIBS-251908 and 2019LCSY089, respectively). The protocol was registered with ClinicalTrials.gov (NCT04173728) under the name "Study of metabolic homeostasis in Chinese."

[0390] 1.2 Subjects

[0391] (1) Inclusion criteria:

[0392] Volunteers with metabolic syndrome:

[0393] Age 30-70

[0394] ●Meet at least three of the following criteria [(NCEP-ATPIII), Asian American Standards]

[0395] 1) Abdominal obesity: waist circumference > 90cm (male), waist circumference > 80cm (female)

[0396] 2) Triglyceride TG ≥ 1.7 mmol / L

[0397] 3) HDL: <1.03mmol / L (male), <1.3mmol / L (female)

[0398] 4) Systolic blood pressure ≥130 mmHg, or ≥85 mmHg

[0399] 5) Blood glucose ≥ 5.6 mmol / L

[0400] Note: The above values ​​are all measured in fasting state

[0401] Volunteers without metabolic syndrome:

[0402] (1) Failure to meet at least three of the five criteria for metabolic syndrome;

[0403] (2) Meet the following three requirements:

[0404] Age 20-70

[0405] Body mass index (BMI) ≥18.5 kg / m 2

[0406] Glucose <5.6 mmol / L

[0407] Note: The above values ​​are all measured in fasting state

[0408] (2) Exclusion criteria:

[0409] ● Diabetic patients or those taking insulin or hypoglycemic drugs;

[0410] ●Pregnant or breastfeeding;

[0411] Are trying to change their weight within 3 months before the study

[0412] ● Regular use of antibiotics, antimicrobials, and other salicylic acid drugs for 3 days or more within 3 months before the study;

[0413] Alcoholics (women consuming >40 g of alcohol per day, equivalent to 250 ml of rice wine, 400 ml of beer, or 80 ml (2 liang) of baijiu; men consuming >80 g of alcohol per day);

[0414] ● Use of lipid-lowering drugs (fibrates, bile acids, statins, etc.) within 3 months before the study;

[0415] Severe liver or kidney disease (alanine aminotransferase and serum creatinine levels exceed 1.5 times the normal range set by the hospital);

[0416] Gastrointestinal diseases that affect food digestion and absorption (severe gastrointestinal inflammation, active gastrointestinal ulcers, acute cholecystitis / cholecystectomy, etc.), severe diarrhea or constipation within 3 months before the study (watery stools 3 or more times per day for 3 or more days; severe constipation refers to 2 or fewer bowel movements per week accompanied by difficulty in defecation);

[0417] ● Gastrointestinal resection surgery (except appendicitis and hernia surgery);

[0418] Serious cardiovascular and cerebrovascular diseases (such as heart failure, myocardial infarction, cerebral infarction, acute myocarditis, severe arrhythmia, those undergoing interventional therapy, etc.) or stage III hypertension;

[0419] ● Those with cancer or who have received radiation and chemotherapy within 5 years;

[0420] Hyperthyroidism or hypothyroidism;

[0421] ● Infectious diseases such as hepatitis B, active pulmonary tuberculosis, and AIDS;

[0422] Anyone with mental illness, epilepsy, or taking anti-epileptic treatment or antidepressant drugs;

[0423] Anemia (hemoglobin <120 g / L in adult men and <110 g / L in nonpregnant adult women);

[0424] ● Participated in other clinical studies within the previous 3 months;

[0425] ●Lack of cognitive ability.

[0426] Collection location: Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.

[0427] 1.3 Sample size

[0428] Based on international experience in acute intervention studies, the number of volunteers for each phenotype is approximately 20. This study planned to recruit a total of 120 volunteers, who were divided into six groups matched by age, BMI, and gender. A total of 111 volunteers actually completed the study, of whom 107 had stool samples collected. The group distribution is shown in Table 1.

[0429] Table 1 Population grouping

[0430] 1.4 Intervention measures

[0431] All volunteers were given a standard mixed drink (including 75 grams of carbohydrates, 60 grams of fat, and 20 grams of protein; total energy is approximately 3900 kJ) in the morning when fasting (fasting for more than 10 hours), and blood was collected at a total of 6 time points: fasting (t = 0) and postprandial (t = 0.5, 1, 2, 3, 4 hours). The specific flow chart is shown in Figure 1.

[0432] 1.5 Randomization and blinding

[0433] This study is open-label and unblinded, with all participants receiving the same intervention. All participants will be assigned a project number by the investigator to anonymize their individual samples and biomarker information. The investigator who assigns the number will not participate in subsequent sampling, testing, or data analysis.

[0434] 1.6 Anthropometry and body composition testing

[0435] On the day of the experiment, volunteers were asked to undergo physical measurements in a fasting state, including height, weight, waist-hip circumference, blood pressure, and heart rate.

[0436] Body composition testing was conducted within one week before / after the experiment using dual-energy X-ray absorptiometry (DXA, model: QDR4500W). The main measurement items included: muscle, fat distribution and proportion, and bone density of the whole body and various parts of the body.

[0437] 1.7 Sample collection and storage

[0438] Blood samples were collected from volunteers in the fasting state and 0.5, 1, 2, 3, and 4 hours after a standard meal. At each time point, two tubes were collected: one containing potassium dipotassium ethylenediaminetetraacetic acid (K2EDTA) for plasma and one containing a coagulant for serum. All samples were centrifuged at 2000 g for 15 minutes at 4°C. Plasma and serum were aliquoted and stored at -80°C until further use. The red blood cell layer and the white blood cell layer were aspirated using a rubber-tipped pipette for subsequent erythrocyte membrane glycosylated hemoglobin (HbA1c) analysis and DNA extraction. Blood samples for glucagon-like peptide-1 (GLP-1), glucagon, and activated ghrelin were supplemented with a DPP-4 inhibitor, aprotinin, and a serine protease inhibitor, respectively, after collection.

[0439] 1.8 Laboratory testing

[0440] The levels of serum glucose, insulin, triglyceride (TG), total cholesterol (tChol), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), C-reactive protein (CRP), gamma-glutamyl transpeptidase (GGT), alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), free fatty acid (FFA), free thyroxine (FT4), free triiodothyronine (FT3), thyroid stimulating hormone (TSH), and creatinine were measured using a fully automatic biochemical analyzer (Hitachi 7080).

[0441] Erythrocyte membrane HbA1c was measured by turbidimetry (Roche Cobas C311).

[0442] Enzyme-linked immunosorbent assay (ELISA) (kit purchased from R&D Company) was used to detect serum adiponectin, E-selectin, P-selectin, human serum amyloid A protein (SSA1), intercellular cell adhesion molecule (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1).

[0443] The levels of C-peptide, activated ghrelin, gastric inhibitory peptide (GIP), GLP-1, glucagon, leptin, gamma interferon, interleukin 1B (IL1B), IL6, IL8, IL18, IL10, tumor necrosis factor-alpha (TNF-α) and vascular endothelial growth factor A (VEGFA) were detected by a multiplex detection kit (MILLIPLEX assay kit, Millipore Sigma).

[0444] The coefficient of variation (CV) of the quality control (QC) data of all markers tested was less than 10%. Markers with a missing rate > 10% were removed, and missing values ​​were filled with 1 / 10 of the minimum detection value.

[0445] 1.9 Targeted metabolomics testing

[0446] Sample preparation process: 1) Take out the plasma sample from the -80℃ refrigerator, dissolve it on ice, and take out 100 μL; 2) Add 400 μL of 100% methanol to each sample; 3) Add 10 μL of 0.1mM / L standard diluent to each sample, shake to mix, and observe whether there is a white precipitate; 4) Shake thoroughly at 4℃ for 10 minutes; 5) Centrifuge at 20,000g for 15 minutes at 4℃ to observe whether the precipitation is complete (lower layer protein); 6) Take 400 μL of supernatant and place it in a new Eppendorf tube; 8) Freeze-dry the sample; 9) Add 100 μL of 50% methanol reconstitution solution to each tube of freeze-dried sample for reconstitution; 10) After shaking evenly, take 25 μL and add it to the inner liner tube for sample loading, and the remaining sample is stored at -80℃ for later use.

[0447] Targeted metabolomics detection was performed using an ultra-high performance liquid chromatography (UPHLC, Shimadzu Nexera X2 LC-30AD) system and a hybrid triple quadrupole / linear ion trap mass spectrometer (SCIEX 5500 QTRAP). Liquid chromatography was performed using a Waters ACQUITY UPLC BEH Amide column ( 1.7 μm, 2.1 mm x 100 mm) with an elution rate of 300 μL / min and an elution time of 20 minutes. Mobile phase A consisted of a 5:95 volume ratio of acetonitrile:water (containing 20 mM ammonium acetate and adjusted to pH 9.0 with ammonium hydroxide); mobile phase B consisted of acetonitrile. The elution gradient is shown in Table 2. Mass spectrometer operating parameters were: curtain gas, 35.0; collision gas, medium; temperature, 500°C; ion source gas 1, 40; ion source gas 2, 50; ion spray voltage, 5500 in positive mode, and -4500 in negative mode. A quality control sample was inserted every 10 samples. Quantification was performed using MultiQuant™ 3.0 software (Sciex, Foster City, CA) based on the peak area ratio of the analytes to the corresponding stable isotope-labeled internal standards (IS).

[0448] Metabolites with missing data exceeding 10% and QC sample coefficient of variation (CV) greater than 20% were removed, resulting in a total of 110 valid metabolites. Valid metabolite data were corrected for batches based on QC samples using the statTarget package (R 4.0.2), and missing values ​​were multiply imputed using the mice package (R).

[0449] Table 2. UHPLC gradient elution program

[0450] 1.10 Calculation of Correlation Index

[0451] Based on existing literature and detection marker data, the corresponding organ function index or marker-related index was calculated. The detailed calculation method is shown in Table 3 below.

[0452] Table 3 Index calculation summary

[0453] 1.11 Statistical Methods

[0454] Conventional methods: student's t-test, Wilcoxon test, analysis of variance, Spearman correlation analysis, mixed linear model analysis, general linear regression model, principal components analysis (PCA), elastic net regression model analysis, etc.

[0455] Example 2. Baseline characteristics of the population

[0456] Using the markers and detection methods described in Example 1, 111 volunteers were tested. These tests included a physical examination (height, weight, waist circumference, hip circumference, and blood pressure), a standardized meal tolerance test (blood samples were collected on an empty stomach and at 0, 0.5, 1, 2, 3, and 4 hours after a standard meal, and urine samples were collected on an empty stomach and 4 hours after a meal), and DXA body composition analysis. A total of 6 × 111 blood samples and 107 stool samples were collected.

[0457] A variance analysis was performed on the main screening markers of volunteers in each group (Table 4). It was found that the differences in other markers except heart rate among the six groups were statistically significant, that is, P < 0.05 after FDR (False Discovery Rate) correction.

[0458] According to the marker levels in Table 6, a healthy reference group (n=11) and a healthy-injured reference group (n=15) were screened from 111 volunteers. The baseline characteristics of the two reference groups are shown in Table 5.

[0459] Table 4. Baseline characteristics of volunteers

[0460] Table 4. Baseline characteristics of volunteers

[0461] Table 5. Baseline characteristics of the reference group

[0462] Note: Continuous data are presented as mean ± standard deviation (normal distribution) or median (lower quartile - upper quartile) (skewed distribution) and were analyzed using the Student's t-test or Wilcoxon rank-sum test. For digit count variables, the chi-square test was used for male and female data. Differences were considered statistically significant when p < 0.05 after FDR correction and are indicated in bold.

[0463] Table 6

[0464] Example 3. Marker Axis

[0465] To assess the subjects' different metabolic-related phenotypic states and elastic homeostasis, the markers included in the model were divided into four metabolic axes: glucose metabolism, lipid metabolism, amino acid metabolism, and metabolic stress, based on their physiological characteristics. Separate models were then established to assess "phenotypic state" and "elastic homeostasis." The first three axes included markers related to three metabolic pathways, while the fourth axis primarily encompassed markers related to risk factors, injury markers, inflammation, and oxidative stress. Detailed information on the marker axes is provided in Table 7.

[0466] Table 7. Axis information of markers

[0467] Example 4. Health Status Graph

[0468] The "Health Status Map" health assessment system includes two parts: "Phenotypic Status" assessment and "Elastic Homeostasis" assessment (these two parts can be assessed separately or together).

[0469] Among them, the "phenotypic status score (V score for short)" is an evaluation of the individual's current health phenotypic status, and the "elastic homeostasis score (U score for short)" is an evaluation of the stability of the individual's health phenotypic status. Under this evaluation system, a complete description of an individual's health includes two parts, namely the current health status phenotype (describing the position of the process from health to disease) and the degree of stability at this position (describing the sensitivity to beneficial interventions / harmful exposures). In this theoretical model, the state of a person from health to disease is a continuous state. Under different states, different individuals have different abilities to maintain stability (i.e., homeostatic elasticity). Individuals with good homeostatic elasticity have strong stability and weak susceptibility, and can better maintain their current health status under harmful environmental exposure; while individuals with poor homeostatic elasticity have poor stability and strong susceptibility, and are more likely to develop in the direction of disease under harmful environmental exposure, but at the same time can produce stronger intervention effects when receiving health interventions, which can serve as a window period for intervention.

[0470] Figure 2 is a schematic diagram of the concept of the “Health State Spectrum,” which assesses an individual’s “phenotypic state” and “elastic homeostasis” along different metabolic axes.

[0471] Example 5. Establishment of phenotypic state model and statistical analysis

[0472] When establishing the “phenotype status” model, 11 volunteers under 35 years old with BMI < 24 kg / m were selected from 111 volunteers. 2 The volunteers whose main clinically relevant markers were within the healthy range (specific markers are shown in Table 6) were selected as the healthy reference group, and 15 volunteers aged 50-70 years with BMI ≥ 24 kg / m 2Volunteers with ≥50% of the total metabolic risk served as the healthy injury reference group. Ten-fold cross-elastic network models were used to independently model markers of different metabolic axes (Table 7). Each time, 90% of the volunteers served as the training set and 10% of the volunteers served as the validation set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used as the evaluation criterion. Finally, the corresponding markers were screened on each metabolic axis and the optimal equation model was established to calculate the scores of each volunteer on different coordinate axes. The larger the phenotypic status score of each individual on the four axes, the worse the individual's health phenotypic status on that metabolic axis. Before modeling, the detection values ​​of all markers were normalized from 0 to 1 (the formula is (x-min) / (max-min), where x is the detection value of each individual on a specific marker, and min and max are the maximum and minimum values ​​of the specific marker in 26 individuals (i.e., 11 healthy reference groups and 15 healthy-impaired reference groups). Subsequently, post-hoc analysis of the "phenotypic status" score results of each axis was performed to examine the differences in health phenotypic status among different age groups, BMI groups, and metabolic syndrome groups.

[0473] Using the ten-fold cross-elastic network model, phenotypic status scoring equations were established on the four axes. The screening markers and the coefficients of each marker in the equation are shown in Table 13.

[0474] The phenotypic state score of a single coordinate axis is calculated by multiplying the 0-1 normalized marker level of the metabolic axis by the coefficient (Table 13), and the sum of all products is the phenotypic state score of the single coordinate axis.

[0475] The comprehensive phenotypic status score is calculated as follows: The phenotypic status scores for each axis are obtained using the above method, and then the scores for the four axes are normalized to 0-1 and averaged, i.e., mean(0-1 standardized (glucose metabolism axis score) + 0-1 standardized (lipid metabolism axis score) + 0-1 standardized (amino acid metabolism axis score) + 0-1 standardized (metabolic stress axis score)). The formula for the 0-1 standardized (glucose metabolism axis score) is (y-min) / (max-min), where y is the score of the glucose metabolism axis score, and min and max are the minimum and maximum values ​​of the glucose metabolism axis score, respectively. The same applies to the other axes.

[0476] The health status of 111 individuals was assessed using the model established above. Figure 3A shows the phenotypic status scores of all 111 individuals (including 26 reference individuals) on the four metabolic axes. Variance analysis and paired Wilcoxon test were performed on individuals in different age and BMI groups on different metabolic axes (p < 0.05 after Bonferroni correction was considered to be statistically significant, and the same letters above the box plots indicated that there was no significant difference between the two groups). Figure 3B shows the distribution of phenotypic status scores of individuals in 6 groups (different ages, BMIs).

[0477] In this model, we categorize health ratings into four levels (Figure 4): excellent, standard, minimally impaired, and impaired. Excellent is defined as an individual's phenotypic status score being less than the right 95% confidence interval value of the phenotypic status score of the healthy reference group. Impaired is defined as an individual's phenotypic status score being greater than the left 95% confidence interval value of the phenotypic status score of the healthy-impaired reference group. Standard is defined as a score greater than or equal to the right 95% confidence interval value of the healthy reference group and less than the mean of the two confidence interval reference values. Minimally impaired is defined as a score greater than or equal to the mean of the two confidence interval reference values ​​and less than the left 95% confidence interval value of the healthy-impaired reference group. Reference standards for health ratings on each axis are shown in Table 8.

[0478] Table 8 Health level assessment criteria for different metabolic axis phenotype states

[0479] Note: The scores in Table 8 are calculated using all the markers in Table 7. As the markers included in each axis change, the scores for each axis will also change accordingly. Excellent scores are those less than the right 95% confidence interval value of the healthy reference group; damage scores are those greater than the left 95% confidence interval value of the healthy-damage reference group; satisfactory scores are those greater than or equal to the right 95% confidence interval value of the healthy reference group and less than the mean of the two confidence interval reference values; and minimal damage scores are those greater than or equal to the mean of the two confidence interval reference values ​​and less than the left 95% confidence interval value of the healthy-damage reference group.

[0480] Among the markers measured in the above examples, some markers that are indicative in the early stages of the disease (for example, fasting blood glucose) were also selected, and volunteers with abnormal markers were screened out. Their health status was predicted using the model established in this application. The glucose metabolism axis score for the phenotypic status scoring of individuals with impaired fasting blood glucose (fasting blood glucose ≥ 5.6 mmol / L) is shown in Table 9, and the lipid metabolism axis score for the phenotypic status scoring of individuals with dyslipidemia is shown in Table 10.

[0481] On this basis, 20 volunteers with metabolic diseases and individuals aged 20-29 with normal BMI were selected from 111 volunteers, and their health status was predicted using the model established in this application. The results are shown in Table 11.

[0482] Table 9 Prediction of ratings of glucose metabolism axis for individuals with prediabetes

[0483] Table 10 Prediction of ratings of lipid metabolism axis for individuals with dyslipidemia

[0484] Table 11 Prediction of health ratings on different axes for individuals with metabolic syndrome

[0485] 2. Optimization of Phenotypic State Model

[0486] To simplify the health status assessment model across different axes, the model was simplified to four levels, with the criteria of explaining 70%, 80%, and 90% of the residual variance of the full model score, as well as the full model. The specific markers for each of the four levels are shown in Table 13. The full model included all markers from levels 1 to 4. The 90% simplified model included markers from levels 1 to 3, the 80% simplified model included markers from levels 1 to 2, and the 70% simplified model included markers from level 1.

[0487] The 70%, 80%, and 90% simplified models were used to evaluate the health status of 111 volunteers. The experimental results showed that the evaluation results of the simplified model were highly consistent with those of the complete model (Table 12).

[0488] Table 12. Comparison of health status classification of 111 individuals by 70% model and complete model

[0489] Table 13. Markers obtained by modeling and screening of the four axes of the phenotypic state model

[0490] Example 6. Establishment and statistical analysis of elastic steady-state model

[0491] In this example, 11 healthy reference samples selected from the markers in Table 7 were used as the healthiest change curves in response to a standard meal to establish an elastic homeostasis model ( FIG. 5 ).

[0492] The calculation of the elastic steady-state score is based on the blood marker measurement values ​​obtained for each individual at 6 time points (0, 0.5, 1, 2, 3, and 4 hours) before and after meals. The measurement values ​​of each marker at the 6 time points constitute the change curve of the feature over time. The elasticity score of the individual marker is calculated based on the area difference between the change curves of the same marker in the individual and the reference sample. Finally, the elasticity scores of a certain metabolic axis related to the individual / all markers are averaged to obtain a certain metabolic axis / overall metabolic elasticity. The higher the elastic steady-state score, the more the individual's marker post-meal response change curve deviates from the reference sample, that is, the worse the elasticity (Figure 5). The individual score is calculated as the mean of the elastic steady-state scores of all markers on the axis (Table 14).

[0493] 1. Calculation of elastic steady state

[0494] The steps for calculating the metabolic score of a single marker are as follows:

[0495] (1) For marker x, the measured values ​​of marker x in individual a and individual b are denoted as a t , b t (a t , b t are the blood marker measurements of individual a and individual b at 6 time points, where t is 0, 0.5, 1, 2, 3, and 4 hours respectively. Use the formula To convert, and This is the standardized marker measurement value, and all subsequent steps apply the standardized marker measurement value.

[0496] (2) Calculate the difference in the change curve area ΔS between each two adjacent time points of individual a and individual b according to the following formula: (t,t+1) (t=1,2,3,4,5): If That is, there is no intersection between the change curves at time points t and t+1, so Where Δt is the time interval between the two time points t and t+1. For example, if t and t+1 are 0.5h and 1h respectively, then Δt = 0.5; if That is, there is an intersection between the change curves at time points t and t+1, then in

[0497] (3) According to the formula Calculation is performed to obtain the area difference between the change curves of marker x in individual a and individual b.

[0498] (4) First, according to steps (1) to (3), the area difference of the change curve of marker x between each of the 11 reference samples is calculated. The area difference between the reference samples represents the area difference between individuals with standard homeostatic elasticity.

[0499] (5) Construct a reference distribution of the area difference of marker x. Calculate the mean μ and standard deviation σ of all 55 area differences between reference samples, and calculate the normal distribution N(μ, σ 2 ) 0.95 This distribution represents the distribution of area differences between individuals with standard homeostatic elasticity. Area differences less than the 0.95th quantile of this distribution are considered insignificant, while area differences greater than the 0.95th quantile are considered significant. For markers whose area differences between reference samples are non-normally distributed, the inter-individual area differences are log-transformed using a constant e as the base before constructing a reference distribution of area differences for marker x and performing subsequent calculations.

[0500] (6) For marker x, for any individual to be tested, calculate the difference in the area of ​​the change curve between the individual and other reference samples according to steps (1) to (3), where the difference is greater than the 0.95 quantile z 0.95 The ratio of the area differences to the total number of area differences is the homeostatic elasticity score for that individual trait. A higher homeostatic elasticity score indicates lower elasticity. The homeostatic elasticity score is a value between 0 and 1, with values ​​closer to 0 indicating healthier individuals and closer to 1 indicating less healthy individuals.

[0501] In this example, two volunteers were randomly selected from 111 volunteers and their elastic homeostasis scores were measured using the above method. Among them, individual No. 20 had elastic homeostasis scores of 0.043, 0.054, 0.041, 0.065, and 0.053 on the sugar, lipid, and amino acid metabolism axes, as well as a comprehensive elastic homeostasis score. Individual No. 111 had elastic homeostasis scores of 0.266, 0.125, 0.248, 0.044, and 0.165 on the four axes, as well as a comprehensive elastic homeostasis score. This evaluation system showed that individual No. 111 had a worse elastic homeostasis than individual No. 20 in all metabolic axes and in terms of comprehensive elastic homeostasis.

[0502] 2. Optimization of the elastic steady-state model

[0503] To simplify the model across different axes, we analyzed the priority of each marker along the four axes of the homeostasis index model (sugar metabolism, lipid metabolism, amino acid metabolism, and metabolic stress). The priority of each marker was ranked from highest to lowest based on the average homeostasis elasticity score of the marker across 111 individuals. The higher the average homeostasis elasticity score, the more important the marker. All markers were then uniformly graded based on the order of their elasticity homeostasis scores, with every three markers forming a level.

[0504] The simplified model was used to evaluate the health status of 111 volunteers. The experimental results showed that the evaluation results of the simplified model were consistent with those of the complete model.

[0505] Table 14. Indicators of the four axes of the elastic steady-state model

[0506] Example 7. Elastic Steady-State Score and Intestinal Microflora

[0507] This example aims to explore the relationship between elastic steady-state score and intestinal flora.

[0508] Fecal samples from 111 volunteers in Examples 1 and 2 were collected, and bacterial DNA was extracted from the fecal samples. The DNA fragment corresponding to the ribosomal 16SRNA (16S ribosomal DNA, 16S rDNA) was amplified by PCR. The number of amplification cycles was strictly controlled during the amplification process to keep the number of cycles as low as possible while ensuring that the amplification conditions of the same batch of samples were consistent. After the amplified product was recovered, fluorescence quantification was performed (the fluorescent reagent was Quant-iT PicoGreen dsDNA Assay Kit, and the quantitative instrument was Microplate reader, model: BioTek, FLx800). After quantification, the sequencing library was prepared using Illumina's TruSeq Nano DNA LT Library Prep Kit, and qualified libraries were selected for sequencing. After sequencing data quality control, sequence denoising or operational unit clustering (Operational Taxonomic Units, OUT) clustering was performed according to the QIIME2 dada2 analysis process or the Vsearch software analysis process. The α diversity level of each sample was evaluated based on the distribution of OTUs in different samples.

[0509] Figure 6 shows the Spearman partial correlation coefficients between the elastic homeostasis score and the major bacterial phyla of the intestinal flora, with the correlation results adjusted for age, sex, and BMI. After FDR correction, except for the elastic homeostasis score on the amino acid axis, the elastic homeostasis scores on all other axes showed a significant negative correlation with the abundance of Firmicutes and a significant positive correlation with the abundance of Bacteroidetes. In addition, the lipid metabolism homeostasis elasticity score also showed a significant negative correlation with the abundance of Proteobacteria.

[0510] Previously, in a 6-month dietary intervention study based on the Chinese population, it was found that compared with a low-fat diet, a high-fat diet would lead to a deterioration in the structure of the intestinal flora, a decrease in the number of Firmicutes, and an increase in the number of Bacteroidetes (Wan et al., 2019). By analyzing the elastic steady-state score, it was also found that the elastic steady-state score was negatively correlated with the amount of Firmicutes in the sugar metabolism, lipid metabolism axis and the comprehensive score, and was positively correlated with the amount of Bacteroidetes, and was positively correlated with the ratio of Bacteroidetes to Metabacteria. This shows that on the above-mentioned metabolic axis, the worse the individual's elasticity (that is, the higher the elastic steady-state score), the worse the structure of the intestinal flora. This also shows that the elastic steady-state score of this application has a potential correlation with the deterioration of the intestinal flora structure.

[0511] Example 8. Elasticity Homeostasis Score and Physical Activity and Sleep

[0512] This example aims to explore the relationship between the subjects' elasticity steady-state score and physical activity or sleep.

[0513] 1. Physical activity data collection

[0514] Physical activity time data for the 111 volunteers in Examples 1 and 2 were collected via face-to-face questionnaires. Metabolic equivalent task hours per week (MET hours / week) was used as the criterion for assessing physical activity. The questionnaire design and MET hour calculation were based on the short form of the International Physical Activity Questionnaire (IPAQ).

[0515] 2. Sleep data collection

[0516] The sleep data of the 111 volunteers in Examples 1 and 2 were collected using a face-to-face questionnaire. Sleep time was calculated as the difference between the average time of waking up the next day and the average time of falling asleep the previous day, accurate to the nearest minute.

[0517] The experimental results are shown in Figures 7 and 8: By analyzing the Spearman correlation between the elastic homeostasis score and physical activity or night sleep time on different axes, it was found that on the comprehensive and amino acid metabolism axes, the amount of exercise (MET hours / week) was negatively correlated with the metabolic homeostasis score, that is, the longer the physical activity time, the better the metabolic homeostasis ability; on the lipid metabolism axis, within a certain range (within 10 hours, the basic sleep time of the subjects in this study was within 10 hours), the longer the night sleep time, the better the elastic homeostasis ability.

[0518] Example 9. Correlation between elasticity homeostasis score and prevalence of metabolic syndrome

[0519] The logistic regression method was used to calculate the OR (odds ratio) between the elastic steady-state scores of different metabolic axes and the incidence of metabolic syndrome. The subjects with metabolic syndrome were the 6th group of subjects in Table 1 of Example 1, 10 males and 10 females. In the logistic regression model, whether or not the patient had metabolic syndrome was used as the corresponding variable (in the model, the patient was counted as 1 and the patient was not counted as 0), and a certain metabolic axis score processed by Z-score was used as the independent variable. The regression coefficient β of the elastic steady-state scores of different metabolic axes was obtained by the regression model, and the OR value = eβ (e is a natural constant). Among them, model 1 only included the elastic steady-state scores of different metabolic axes in the independent variable, and model 2 included age and BMI as covariates on the basis of model 1. The processing formula of Z-score is (XM) / SD (where X is the original elastic steady-state score of the subject on a certain metabolic axis, and M and SD are the mean and standard deviation of the elastic steady-state scores of all subjects on the axis, respectively).

[0520] Table 15. OR between elasticity homeostasis score and metabolic syndrome

[0521] Note: The above values ​​mean: OR [95% confidence interval].

[0522] As shown in Table 15, in Model 1, the ORs and their 95% confidence intervals for the glucose metabolism axis, lipid metabolism axis, metabolic stress axis, and the combined effect were all greater than 1. Therefore, higher elasticity scores on these axes were associated with a significantly increased prevalence of metabolic syndrome. In Model 2, after adjustment for age and BMI, the ORs and their 95% confidence intervals for all metabolic axes were greater than 1. Therefore, after adjustment for age and BMI, an increase in the elasticity score on each metabolic axis was associated with a significant increase in the prevalence of metabolic syndrome.

[0523] Although the specific embodiments of the present invention have been described in detail, those skilled in the art will understand that various modifications and changes can be made to the details based on all the teachings published, and these changes are all within the scope of protection of the present invention. The entire invention is given by the appended claims and any equivalents thereof.

Claims

1. A method for constructing a model for assessing the phenotypic state of a subject, the method comprising: (I) selecting multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress; Select one to several (e.g., 2, 3, 4) categories from the four categories as coordinate axes for establishing the model (preferably, the coordinate axes are selected from the glucose metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis and / or the metabolic pressure coordinate axis); (II) collecting samples from n healthy subjects and m healthy impaired or sub-healthy subjects, and determining the level of a marker associated with the selected coordinate axis in each sample; (III) using the levels of the markers to train a machine learning model, screen out target markers and calculate weight coefficients of the target markers, thereby establishing an assessment model for the phenotypic state of the subject; wherein n and m are each independently a positive integer, and n+m≥5 (for example, n and m are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 50, 70, 90, 110, 130, 150).

2. The method of claim 1, wherein The method has one or more characteristics selected from the following: (1) In step (I), the selected markers are a combination of 3-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, 24-27, 27-30, 30-40, 40-50, 50-100 markers; (2) In step (II), the level of the marker can be directly measured or further calculated from the measurement results; (3) In step (II), the methods for determining the level of the marker include: biochemical analysis, physiological analysis, immunological methods (such as enzyme-linked immunosorbent assay (ELISA), colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immune lateral flow analysis), molecular biological detection methods (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection methods (such as light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecules), analysis), electrochemical analytical methods, chromatography, mass spectrometry, electrophoresis, hybridization (e.g., Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy; (4) Prior to step (III), the levels of all markers are normalized from 0 to 1 using the formula (x-min) / (max-min), where x is the level of a marker of a subject, and min and max are the minimum and maximum values ​​of the marker level in all n healthy subjects and m healthy impaired or sub-healthy subjects, respectively; (5) In step (III), the machine learning model is a cross-validation-elastic net model; Preferably, the cross validation is selected from five-fold cross validation, ten-fold cross validation and leave-one-out cross validation; (6) In step (III), a cross-validation-elastic network model is used to independently model markers on different coordinate axes, and the area under the receiver operating characteristic curve (ROC) (AUC) is used as an evaluation criterion. The target marker is screened on each coordinate axis by the optimal AUC (e.g., the AUC closest to 1) and the weight coefficient of the marker is obtained, which is used as an evaluation model for the phenotypic status of the subject; Preferably, 90%-70% of the subjects are used as a training set, and 10%-30% of the subjects are used as a validation set; (7) The method further comprises, after step (III), simplifying the selected target marker by 70%, 80%, 90% or not simplifying the target marker according to the weight coefficient corresponding to the target marker obtained in step (III) to obtain a 70% simplified model, an 80% simplified model, a 90% simplified model or a complete model, respectively.

3. The method of claim 2, wherein: The 70% simplified model selects one, two, three or four of the sugar metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis and the metabolic pressure coordinate axis as coordinate axes for establishing the model; Preferably, the target markers selected under the sugar metabolism coordinate axis of the 70% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose and Pyruvate; Preferably, the target markers selected under the amino acid metabolism coordinate axis of the 70% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine and Glutamate; Preferably, the target markers selected under the lipid metabolism coordinate axis of the 70% simplified model include: SysBP, TG, Leptin, Carnitine C3 and Deoxycholic acid; and / or, Preferably, the target markers selected under the metabolic pressure coordinate axis of the 70% simplified model include: IL8, Glucose, ICAM-1, SysBP and Dopamine.

4. The method according to claim 2 or 3, wherein The 80% simplified model selects any one, two, three or four of the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis to establish the model; Preferably, the target markers selected under the sugar metabolism coordinate axis of the 80% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid and C-pept; Preferably, the target markers selected under the amino acid metabolism coordinate axis of the 80% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine and Taurine; Preferably, the target markers selected under the lipid metabolism coordinate axis of the 80% simplified model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2 and Succinate; and / or, Preferably, the target markers selected under the metabolic pressure coordinate axis of the 80% simplified model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine and IL18.

5. The method according to any one of claims 2 to 4, wherein: The 90% simplified model selects any one, two, three or four coordinate axes from the sugar metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis and the metabolic pressure coordinate axis; Preferably, the target markers selected under the sugar metabolism coordinate axis of the 90% simplified model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate and 3- phosphoglycerate; Preferably, the target markers selected under the amino acid metabolism coordinate axis of the 90% simplified model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine and Ornithine; Preferably, the target markers selected under the lipid metabolism coordinate axis of the 90% simplified model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate and tChol; and / or, Preferably, the target markers selected under the metabolic pressure coordinate axis of the 90% simplified model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b and FT4.

6. The method according to any one of claims 2 to 5, wherein The complete model selects any one, two, three or four of the sugar metabolism coordinate axis, the lipid metabolism coordinate axis, the amino acid metabolism coordinate axis and the metabolic pressure coordinate axis as the coordinate axes for establishing the model; Preferably, the target markers selected under the sugar metabolism coordinate axis of the complete model include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI and Insulin; Preferably, the target markers selected under the amino acid metabolism coordinate axis of the complete model include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine and Phenylalanine hydroxylase activity index; Preferably, the target markers selected under the lipid metabolism coordinate axis of the complete model include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12 and Carnitine C6; and / or, Preferably, the target markers selected under the metabolic pressure coordinate axis of the complete model include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, adiponectin, Vanillomandelic acid and tChol.

7. The method according to any one of claims 1 to 6, wherein The method has one or more characteristics selected from the following: (1) The sample is whole blood, serum, plasma, red blood cells, urine, feces, or body fluid obtained from a subject; (2) The sample is whole blood obtained from a subject; (3) The subject is a mammal; (4) The subject is a mouse, pig, rabbit, monkey or human.

8. A phenotypic state model, wherein: The model is constructed by the method described in any one of claims 1 to 7; Preferably, after obtaining the weight coefficient corresponding to the target marker by the method described in any one of claims 1 to 7, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all target markers and their corresponding weight coefficients; Preferably, the phenotypic state score is: the level of target marker 1×the weight coefficient of target marker 1+the level of target marker 2×the weight coefficient of target marker 2+the level of target marker 3×the weight coefficient of target marker 3+…+the level of target marker x×the weight coefficient of target marker x; wherein x is the number of all target markers; Preferably, the level of the target marker is subjected to 0-1 normalization processing, and the specific formula is (x-min) / (max-min), wherein x is the level of a target marker of a certain subject, and min and max are the minimum and maximum values ​​of the target marker in all n healthy subjects and m healthy impaired or sub-healthy subjects, respectively; Preferably, the model is used to assess the metabolic state of a subject (e.g., a subject not clinically diagnosed with a metabolic disease); Preferably, a larger value of the subject's phenotypic state score indicates a worse health phenotypic state (e.g., metabolic state) of the subject; Preferably, when only one metabolic category is selected as the coordinate axis for establishing the model, the phenotypic status score of the subject under the coordinate axis is obtained by accumulating the product of the levels of all target markers after 0-1 standardization processing under the coordinate axis and their corresponding weight coefficients; The formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker of a subject, and min and max are the minimum and maximum values ​​of the target marker level in all n healthy subjects and m healthy impaired or sub-healthy subjects, respectively; Preferably, when 2, 3 or 4 metabolic categories are selected as the coordinate axes for establishing the model, the phenotypic state score of the subject under each coordinate axis is obtained by accumulating the product of the level of all target markers after 0-1 standardization under each coordinate axis and the corresponding weight coefficient, and then the score of each coordinate axis is independently standardized by 0-1, and the average value of the 0-1 standardized scores obtained under all coordinate axes is the phenotypic state score; wherein, The formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker of a subject, and min and max are the minimum and maximum values ​​of the target marker level in all n healthy subjects and m healthy impaired or sub-healthy subjects, respectively; The formula for 0-1 normalization of the scores of each coordinate axis is (y-min) / (max-min), where y is the score of a certain coordinate axis, and min and max are the minimum and maximum values ​​of the score in the coordinate axis, respectively.

9. The model of claim 8, wherein: When the model includes a glucose metabolism coordinate axis, the model is used to predict the health status of the subject in glucose metabolism (e.g., used to predict the risk of the subject suffering from a metabolic disease); Preferably, the target markers of the sugar metabolism coordinate axis and the corresponding weight coefficients are selected from: Glycerol-3-phosphate, -10.469; HbA1c, 10.324; Glucose, 9.915; Pyruvate, -8.575; 4-Methyl-2-oxovaleric acid, -5.738; C-pept, 5.568; Glyceraldehyde-3-phosphate, -4.492; 3-phosphoglycerate, -3.861; Valine, 3.236; GLP1, 3.099; Isoleucine, -2.396; L-Lactate, 2.333; GIP, 2.136; Glucagon, -1.346; HIRI, 1.176; Insulin, 0.021, or any combination thereof; Preferably, the phenotypic status score of the subject is obtained by accumulating the product of the level of all the target markers after 0-1 standardization and the corresponding weight coefficient; Preferably, if the subject's phenotypic state score is less than -3.309, the subject's health state belongs to the excellent level; if the subject's phenotypic state score is greater than or equal to -3.309 or less than 1.085, the subject's health state belongs to the standard level; if the subject's phenotypic state score is greater than or equal to 1.085 or less than 3.917, the subject's health state belongs to the minimally impaired level; if the subject's phenotypic state score is greater than or equal to 3.917, the subject's health state belongs to the impaired level; Preferably, if the health status of the subject belongs to the minimally impaired level or the damaged level, the subject has a risk of developing a metabolic disease (eg, diabetes).

10. The model of claim 8, wherein: When the model includes a lipid metabolism axis, the model is used to predict the health status of the subject in lipid metabolism (e.g., to predict the risk of the subject suffering from a metabolic disease); Preferably, the target markers of the lipid metabolism coordinate axis and the corresponding weight coefficients are selected from: SysBP, 2.757; TG, 2.627; Leptin, 2.298; Carnitine C3, 1.98; Deoxycholic acid, -1.914; Carnitine, 1.739; Isocitrate, 1.723; Carnitine C14:2, -1.619; Succinate, 1.359; tChol, 1.35; LDL, 1.13; DiaBP, 1.126; Carnitine C10:1, -1.062; Carnitine C16OH, -0.809; L-Malate, -0.412; Carnitine C16:1OH, -0.362; Carnitine C12, -0.114; Carnitine C6, 0.056, or any combination thereof; Preferably, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the above target markers after 0-1 standardization and their corresponding weight coefficients; Preferably, if the subject's phenotypic state score is less than -1.076, the subject's health state is at an excellent level; if the subject's phenotypic state score is greater than or equal to -1.076 or less than 0.336, the subject's health state is at a qualified level; if the subject's phenotypic state score is greater than or equal to 0.336 or less than 1.451, the subject's health state is at a minimally impaired ... If the subject's phenotypic status score is greater than or equal to 1.451, the subject's health status belongs to the impairment level; Preferably, if the health status of the subject belongs to the minimally damaged level or the damaged level, the subject has a risk of developing a metabolic disease (eg, hyperlipidemia).

11. The model of claim 8, wherein: When the model includes an amino acid metabolism coordinate axis, the model is used to predict the health status of a subject in amino acid metabolism (e.g., to predict the risk of a subject suffering from a metabolic disease); Preferably, the target markers of the amino acid metabolism coordinate axis and the corresponding weight coefficients are selected from: Threonine, -4.164; Vitamin-B5-Pantothenic-acid, 4.095; Cystine, 4.061; Glutamate, 3.147; Beta-alanine, -2.722; Taurine, 2.156; Asparagine, -2.14; Ornithine, 1.769; Aspartate, 1.4; Tyrosine, 0.892; Lysine, 0.618; Phenylalanine hydroxylase activity index 0.318, or any combination thereof; Preferably, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the above target markers after 0-1 standardization and their corresponding weight coefficients; Preferably, if the subject's phenotypic state score is less than -1.596, the subject's health state belongs to the excellent level; if the subject's phenotypic state score is greater than or equal to -1.596 or less than 0.313, the subject's health state belongs to the standard level; if the subject's phenotypic state score is greater than or equal to 0.313 or less than 1.938, the subject's health state belongs to the minimally impaired level; if the subject's phenotypic state score is greater than or equal to 1.938, the subject's health state belongs to the impaired level; Preferably, if the health status of the subject belongs to the minimally impaired level or the damaged level, the subject has a risk of suffering from a metabolic disease.

12. The model of claim 8, wherein: When the model includes a metabolic stress axis, the model is used to predict the health status of a subject under metabolic stress (e.g., to predict the risk of a subject suffering from a metabolic disease); Preferably, the target markers of the metabolic pressure coordinate axis and their corresponding weight coefficients are selected from: IL8, 6.755; Glucose, 4.739; ICAM-1, 4.522; SysBP, 3.818; Dopamine, 3.707; LDL, 3.457; E-selectin, 2.7; 5-Hydroxy-L-tryptophan, -2.548; GGTP, 2.291; IL6, 2.165; N6-acetyl lysine, 2.149; IL18, 2.148; Cortisol, -1.978; TSH, 1.776; Normetanephrine, 1.602; IL1b, 1.566; FT4, -1.366; GOLA, 1.326; Tyrosine, 1.127; DiaBP, 1.089; CEA, -1.048; Phenylalanine, -0.789; Adiponectin, -0.486; Vanillomandelic acid, 0.401; tChol, 0.22, or any combination thereof; Preferably, the phenotypic status score of the subject is obtained by accumulating the product of the levels of all the above target markers after 0-1 standardization and their corresponding weight coefficients; Preferably, if the subject's phenotypic state score is less than -2.662, the subject's health state belongs to the excellent level; if the subject's phenotypic state score is greater than or equal to -2.662 or less than 0.389, the subject's health state belongs to the standard level; if the subject's phenotypic state score is greater than or equal to 0.389 or less than 3.079, the subject's health state belongs to the minimally impaired level; if the subject's phenotypic state score is greater than or equal to 3.079, the subject's health state belongs to the impaired level; Preferably, if the health status of the subject belongs to the minimally impaired level or the damaged level, the subject has a risk of suffering from a metabolic disease.

13. The model according to any one of claims 8 to 12, wherein: When the model comprises a sugar metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis and a metabolic pressure coordinate axis, the model is used to predict the health status of the subject (e.g., to predict the risk of the subject suffering from a metabolic disease); Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis and their corresponding weight coefficients are as described in claims 9-12; Preferably, the phenotypic status score of the subject on each coordinate axis is obtained by accumulating the product of the level of all target markers after 0-1 standardization processing and the corresponding weight coefficient on each coordinate axis; then the score on each coordinate axis is independently standardized on 0-1, and the average value of the scores after 0-1 standardization processing obtained on all coordinate axes is the phenotypic status score; Preferably, the formula for 0-1 normalization of the target marker level is (x-min) / (max-min), wherein x is the level of a target marker in a subject, and min and max are the level of the target marker in all n healthy subjects. and the minimum and maximum values ​​of the levels in m healthy impaired or subhealthy subjects; Preferably, the formula for 0-1 normalization of the scores of each coordinate axis is (y-min) / (max-min), where y is the score of a certain coordinate axis, and min and max are the minimum and maximum values ​​of the score in the coordinate axis, respectively; Preferably, if the subject's phenotypic state score is less than 0.226, the subject's health state belongs to the excellent level; if the subject's phenotypic state score is greater than or equal to 0.226 or less than 0.401, the subject's health state belongs to the standard level; if the subject's phenotypic state score is greater than or equal to 0.401 or less than 0.558, the subject's health state belongs to the minimally impaired level; if the subject's phenotypic state score is greater than or equal to 0.558, the subject's health state belongs to the impaired level; Preferably, if the health status of the subject belongs to the minimally impaired level or the damaged level, the subject has a risk of suffering from a metabolic disease.

14. A method for constructing a model for assessing elastic homeostasis of a subject, the method comprising: (I) selecting multiple markers that participate in or affect the following four metabolic categories: (a) markers that participate in or affect glucose metabolism, (b) markers that participate in or affect lipid metabolism, (c) markers that participate in or affect amino acid metabolism, and (d) markers that participate in or affect metabolic stress; Select one to several (e.g., 2, 3, 4) categories from the four categories as coordinate axes for establishing the model (e.g., a sugar metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis and / or a metabolic pressure coordinate axis), and select x target markers from the markers under each coordinate axis; wherein x is any integer (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60); (II) selecting Y healthy subjects as reference subjects, collecting biological samples from each reference subject, and determining the level of the target marker in each biological sample at T time points before and after eating; wherein Y is an integer greater than or equal to 2 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), and T is an integer greater than or equal to 3 (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15); (III) Calculate the difference Δs of the curve area of ​​the target marker level between each two reference subjects at each two adjacent time points, obtain T-1 Δs for each two reference subjects, calculate the sum of T-1 Δs to obtain the overall difference ΔS of the curve area of ​​the target marker level between each two reference subjects, and obtain T-1 Δs for all reference subjects. ΔS; (IV) collecting a biological sample from the subject to be tested, determining the level of the target marker in the biological sample at the time point selected in step (II), calculating the difference Δh of the curve area of ​​the target marker level between the subject to be tested and each reference subject at every two adjacent time points, obtaining a total of T-1 Δhs for the subject to be tested and each reference subject, calculating the sum of the T-1 Δhs to obtain the overall difference ΔH of the curve area of ​​the target marker level between the subject to be tested and each reference subject, obtaining a total of Y ΔHs to construct a model for evaluating the elastic homeostasis of the subject; (V) By comparison The size of Y ΔS and Y ΔH is used to evaluate or judge the elastic homeostasis of the subject to be tested; Preferably, the greater the amount by which ΔH is greater than ΔS, the worse the elastic homeostasis of the subject to be tested; Preferably, the level of the target marker in each biological sample is measured at 0 h before eating and T-1 time points after eating; Preferably, when T is 6, the level of the target marker in each biological sample is measured at 0 h before eating, 0.5 h, 1 h, 2 h, 3 h, and 4 h after eating; Preferably, t represents any selected time point, and t+1 is the next time point adjacent to the time point, wherein t≤T-1; for example, when the selected time points are 0h before eating, 0.5h, 1h, 2h, 3h, 4ht after eating, and t is 0.5h, then t+1 is 1h. Preferably, the level of the marker can be directly measured, or further calculated from the measurement results; Preferably, the method for determining the level of the marker includes: biochemical analysis, physiological analysis, immunological method (such as enzyme-linked immunosorbent assay ELISA, colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immune lateral flow analysis), molecular biology detection method (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection method (such as light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis method, chromatography, mass spectrometry, electrophoresis, hybridization (such as Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy.

15. The method of claim 14, wherein: In step (III), the calculation steps of the overall change curve area difference ΔS in each two reference subjects (e.g., subject a and subject b) are as follows: i. Through the formula Calculate separately and The value of ii. Calculate the difference in the curve area of ​​the target marker between two reference subjects at each two adjacent time points ΔS (t,t+1) ←; like That is, there is no intersection between the change curves at time points t and t+1, then Where Δt is the time interval between the tth and t+1th time points; like That is, there is an intersection between the change curves at time points t and t+1, then Where Δt is the time interval between the tth and t+1th time points; in, iii. According to the formula The area difference ΔS between the change curves of the target marker in the two reference subjects was obtained. Preferably, a t and b t are the measured values ​​of the target markers in reference subject a and reference subject b at the tth time point, a1 and b1 are the measured values ​​of the target markers in reference subject a and reference subject b 0 h before eating, respectively.

16. The method according to claim 14 or 15, wherein In step (IV), the calculation steps of the overall change curve area difference ΔH between the subject to be tested (e.g., subject d) and each reference subject (e.g., subject a) are as follows: i. Through the formula Calculate separately and The value of ii. Calculate the difference ΔH in the curve area of ​​the target marker between the tested subject and the reference subject at each two adjacent time points (t,t+1) ; like That is, there is no intersection between the change curves at time points t and t+1, then Where Δt is the time interval between the tth and t+1th time points; like That is, there is an intersection between the change curves at time points t and t+1, then in, iii. According to the formula The area difference between the change curves of the target marker in the tested subject d and the reference subject a is obtained. Preferably, a t and d t are the measured values ​​of the target markers in the reference subject a and the tested subject d at the t-th time point, respectively; a1 and d1 are the measured values ​​of the target markers in subjects a and d 0 h before eating, respectively.

17. The method according to any one of claims 14 to 16, when two reference subjects (e.g., reference subject A and reference subject B) are selected, step (V) of the method is achieved by the following method: (i) calculating the difference in the change curve area ΔS(a, b) between the reference subject a and the reference subject b on the target marker, and calculating the difference in the change curve area ΔH(a, d) between the subject to be tested d and the reference subject a, and the difference in the change curve area ΔH(b, d) between the subject to be tested d and the reference subject b; (ii) The elastic steady-state score of the subject to be tested on the target marker is m / n, where m is the number of subjects to be tested whose ΔH is greater than ΔS in the target marker, and n is the number of reference subjects; wherein, the larger the elastic homeostasis score of the subject, the worse the elastic homeostasis of the subject, that is, the current health state of the subject is more likely to enter a worse health state; For example, if ΔH(a, d) is greater than ΔS(a, b), and ΔH(b, d) is not greater than ΔS(a, b), then m is 1, n is 2, and the elastic steady-state fraction of the target marker for the subject d to be tested is 1 / 2; Optionally, the elastic steady-state score of the subject to be tested on one or several coordinate axes is calculated, and the elastic steady-state score of the subject to be tested on one or several coordinate axes is the weighted average of the elastic steady-state scores of all target markers selected on one or several coordinate axes (for example, the glucose metabolism coordinate axis includes target markers E and F, then the elastic steady-state score of the subject on the glucose metabolism coordinate axis is (β E ×Elastic steady-state fraction E +β F ×Elastic steady-state fraction F ) / 2, β is the weight coefficient); Preferably, an approach is adopted that gives equal weight to all molecules, i.e., β X and β Y Both are 1.

18. The method according to any one of claims 14 to 16, when 3 or more reference subjects are selected, step (V) of the method is achieved by the following method: (i) obtained by step (III) ΔS, construct the reference distribution of ΔS of the target marker, calculate the mean μ and standard deviation σ of all ΔS among the reference subjects, and calculate the normal distribution N(μ, σ 2 ) quantile value; where, For target markers whose ΔS among reference subjects is non-normally distributed, the ΔS is logarithmically transformed based on a constant e before constructing a reference distribution of the ΔS of the target marker; (ii) the elastic steady-state score of the target marker of the subject to be tested is m / n, wherein m is the number of ΔH greater than the quantile value, and n is the number of reference subjects; wherein a larger elastic steady-state score of a subject indicates a worse elastic steady-state of the subject, i.e., the current health state of the subject is more likely to enter a worse health state; For example, three reference subjects a, b and c are selected. If the ΔH(d, a) of the subject to be tested d and the reference subject a is greater than the quantile value, and the ΔH(d, b) of the subject to be tested d and the reference subject b is greater than the quantile value, and the ΔH(c, d) of the subject to be tested d and the reference subject c is not greater than the quantile value or the mean μ, then the number of values ​​greater than the quantile value or is 2; then m is 2, n is 3, and the elastic steady-state score of the target marker E of the subject to be tested d is 2 / 3; Optionally, the elastic steady-state score of the subject to be tested on one or several coordinate axes is calculated, and the elastic steady-state score of the subject to be tested on one or several coordinate axes is the weighted average of the elastic steady-state scores of all target markers selected on one or several coordinate axes (for example, the glucose metabolism coordinate axis includes target markers E and F, then the elastic steady-state score of the subject on the glucose metabolism coordinate axis is (β E ×Elastic steady-state fraction E +β F ×Elastic steady-state fraction F ) / 2, β is the weight coefficient); Preferably, an approach is adopted that gives equal weight to all molecules, i.e., β X and β Y Both are 1.

19. An elastic steady-state model, wherein: The model is constructed by the method described in any one of claims 14 to 18; wherein, the larger the elastic homeostasis score of the subject, the worse the elastic homeostasis of the subject, that is, the current health state of the subject is more likely to enter a worse health state; Preferably, the health status of the subject is obtained according to the health homeostasis model according to any one of claims 8 to 13; Preferably, the health status of the subject includes excellent level, standard level, slight damage level, and damage level from best to worst.

20. The model of claim 19, wherein: When the model includes a glucose metabolism axis, the model is used to assess and predict the risk of a subject entering a worse health state; Preferably, the target marker selected by the sugar metabolism coordinate axis is selected from: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, or any combination thereof; Preferably, the target markers selected for the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol; Preferably, the target markers selected for the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate and C-pept; Preferably, the target markers selected for the sugar metabolism coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine.

21. The model of claim 19 or 20, wherein: When the model includes a lipid metabolism axis, the model is used to predict the risk of the subject entering a worse health state; Preferably, the target marker selected by the lipid metabolism coordinate axis is selected from: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, or any combination thereof; Preferably, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate and 3-Hydroxybutanoic acid; Preferably, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1 and Carnitine C18; Preferably, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH and Deoxycholic acid; Preferably, the target markers selected for the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA and Carnitine C3; Preferably, the target markers selected by the lipid metabolism coordinate axis include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH and Carnitine C12.

22. The model of any one of claims 19 to 21, wherein: When the model includes an amino acid metabolism axis, the model is used to predict the risk of a subject entering a worse health state; Preferably, the target marker selected by the amino acid metabolism coordinate axis is selected from: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, or any combination thereof; Preferably, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine and Lysine; Preferably, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine; Preferably, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine and Phenylalanine; Preferably, the target markers selected by the amino acid metabolism coordinate axis include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine.

23. The model of any one of claims 19 to 22, wherein: When the model includes a metabolic stress axis, the model is used to predict the risk of a subject entering a worse health state; Preferably, the target marker selected by the metabolic pressure coordinate axis is selected from: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof; Preferably, the target markers selected for the metabolic pressure coordinate axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine and Metanephrine; Preferably, the target markers selected for the metabolic stress coordinate axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline and VCAM-1; Preferably, the target markers selected for the metabolic stress coordinate axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine and Dopamine; Preferably, the target markers selected by the amino acid metabolism coordinate axis include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1 and P-selectin.

24. The model of any one of claims 19 to 23, wherein: When the model comprises a sugar metabolism coordinate axis, a lipid metabolism coordinate axis, an amino acid metabolism coordinate axis and a metabolic stress coordinate axis, the model is used to predict the risk of the subject entering a worse health state; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis are selected from the following: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Pr oline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP,Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine and Glyceraldehyde-3-phosphate; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate and L-Malate; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6 and Carnitine C14:2; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH and Carnitine C14; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1 and Carnitine C16:1; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creati nine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH and 3-Methylhistidine; Preferably, the target markers selected for the sugar metabolism coordinate axis, lipid metabolism coordinate axis, amino acid metabolism coordinate axis and metabolic pressure coordinate axis include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate, Valine, TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Pr oline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP,Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1 and P-selectin., 25. A method for evaluating the health status of a subject, wherein: The method comprises: using the phenotypic state model according to any one of claims 8 to 13 to obtain the health state of the subject; preferably, the health state is selected from the group consisting of excellent level, standard level, minimally damaged level or damaged level; Preferably, the method further comprises: based on obtaining the health status of the subject, using the elastic homeostasis model described in any one of claims 19-24 to predict the risk of the subject entering a worse health status.

26. A method for assessing elastic homeostasis of a subject, wherein: The method comprises: using the elastic homeostasis model described in any one of claims 19-24 to evaluate the elastic homeostasis of the subject; wherein, the greater the elastic homeostasis score of the subject, the worse the elastic homeostasis of the subject.

27. A method for assessing a subject's risk of developing a metabolic disease, wherein: The method comprises: using the phenotypic state model of any one of claims 8 to 13 to obtain the health state of the subject, and / or using the elastic homeostasis model of any one of claims 19 to 24 to obtain the elastic homeostasis of the subject; Preferably, the health status is selected from excellent level, standard level, slightly damaged level or damaged level; Preferably, if the health status of the subject belongs to the minimally damaged level or the damaged level, the metabolic health status of the subject is close to that of a subject suffering from a metabolic disease; Preferably, if the health status of the subject belongs to the minimally damaged level or the damaged level, the subject has a risk of suffering from a metabolic disease, or has suffered from a metabolic disease; Preferably, the worse the elastic homeostasis of the subject, the higher the risk of the subject suffering from metabolic disease; Preferably, the metabolic disease is selected from diabetes, hypertension, coronary artery atherosclerotic heart disease, hyperlipidemia and hyperuricemia.

28. A method for evaluating whether a therapy for treating a metabolic disease in a subject is effective, the method comprising: Measuring the health state and / or elastic homeostasis of the subject before and after metabolic disease therapy by the phenotypic state model according to any one of claims 8 to 13 and / or the elastic homeostasis model according to any one of claims 19 to 24; Preferably, if the health state and / or elastic homeostasis of the subject after the metabolic disease therapy is better than the health state and / or elastic homeostasis of the subject before the metabolic disease therapy, the therapy is evaluated to be effective; Preferably, the subject obtains the phenotypic state score a after metabolic disease therapy and the phenotypic state score b of the subject before metabolic disease therapy through the phenotypic state model described in any one of claims 8-13, and if the phenotypic state score a is lower than the phenotypic state score b, the therapy is evaluated to be effective. Preferably, the subject obtains the elasticity steady-state score a after metabolic disease therapy and the elasticity steady-state score b of the subject before metabolic disease therapy through the elasticity steady-state model described in any one of claims 19-24, and if the elasticity steady-state score a is lower than the elasticity steady-state score b, the therapy is evaluated to be effective.

29. A method for evaluating whether an intervention program for improving a subject's metabolic health is effective, the method comprising: Measuring the health state and / or elastic homeostasis of the subject before and after the intervention by the phenotypic state model according to any one of claims 8 to 13 and / or the elastic homeostasis model according to any one of claims 19 to 24; Preferably, the intervention is evaluated to be effective if the health status and / or elastic homeostasis of the subject after the intervention is better than the health status and / or elastic homeostasis of the subject before the intervention; Preferably, the subject obtains the phenotypic state score a after the metabolic health intervention and the phenotypic state score b of the subject before the metabolic health intervention through the phenotypic state model described in any one of claims 8-13, and if the phenotypic state score a is lower than the phenotypic state score b, the therapy is evaluated to be effective. Preferably, the subject obtains the elasticity steady-state score a after the metabolic health intervention and the elasticity steady-state score b before the metabolic health intervention through the elasticity steady-state model described in any one of claims 19-24, and if the elasticity steady-state score a is lower than the elasticity steady-state score b, the therapy is evaluated to be effective.

30. A system for executing the method of any one of claims 25 to 29, the system comprising: (1) a storage component for storing data, wherein the storage component has instructions for determining an assessment of a subject stored therein; (2) a computer processor for processing data, wherein the computer processor is coupled to the storage component and configured to execute instructions stored in the storage component to receive the subject data and analyze the subject data according to one or more coordinate axes; (3) A display component for displaying evaluation information of the subject. Preferably, the computer executes the phenotypic state model according to any one of claims 8-13. Preferably, the computer executes the elastic steady-state model according to any one of claims 19-24.

31. A kit comprising reagents for determining the level of a marker in a biological sample, wherein the marker is selected from one or more of the following four metabolic categories: (a) a marker involved in or affecting carbohydrate metabolism, (b) a marker involved in or affecting lipid metabolism, (c) a marker involved in or affecting amino acid metabolism, (d) a marker involved in or affecting metabolic stress; Preferably, the kit is used to assess the health status (e.g., metabolic status) and / or elastic homeostasis (e.g., metabolic homeostasis) of a subject; Preferably, the kit is used to assess a subject's risk of suffering from a metabolic disease; Preferably, the metabolic syndrome is selected from diabetes, hypertension, coronary artery atherosclerotic heart disease, hyperlipidemia and hyperuricemia.

32. The kit according to claim 31, wherein The kit has one or more features selected from the following: (1) The marker involved in glucose metabolism is selected from the group consisting of: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI, Insulin, or any combination thereof; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3- phosphate, HbA1c, Glucose and Pyruvate; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid and C-pept; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate and 3-phosphoglycerate; Preferably, the markers involved in or affecting glucose metabolism include: Glycerol-3-phosphate, HbA1c, Glucose, Pyruvate, 4-Methyl-2-oxovaleric acid, C-pept, Glyceraldehyde-3-phosphate, 3-phosphoglycerate, Valine, GLP1, Isoleucine, L-Lactate, GIP, Glucagon, HIRI and Insulin; (2) The marker involved in or affecting lipid metabolism is selected from the group consisting of SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12, Carnitine C6, or any combination thereof; Preferably, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3 and Deoxycholic acid; Preferably, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2 and Succinate; Preferably, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate and tChol; Preferably, the markers involved in or affecting lipid metabolism include: SysBP, TG, Leptin, Carnitine C3, Deoxycholic acid, Carnitine, Isocitrate, Carnitine C14:2, Succinate, tChol, LDL, DiaBP, Carnitine C10:1, Carnitine C16OH, L-Malate, Carnitine C16:1OH, Carnitine C12 and Carnitine C6; (3) The markers involved in or affecting amino acid metabolism are selected from: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine, Phenylalanine hydroxylase activity index, or any combination thereof; Preferably, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine and Glutamate; Preferably, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine and Taurine; Preferably, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine and Ornithine; Preferably, the markers involved in or affecting amino acid metabolism include: Threonine, Vitamin-B5-Pantothenic-acid, Cystine, Glutamate, Beta-alanine, Taurine, Asparagine, Ornithine, Aspartate, Tyrosine, Lysine and Phenylalanine hydroxylase activity index; (4) the marker involved in or affecting metabolic stress is selected from the group consisting of IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid, tChol, or any combination thereof; Preferably, the markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP and Dopamine; Preferably, the markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine and IL18; Preferably, the markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b and FT4; Preferably, the markers involved in or affecting metabolic stress include: IL8, Glucose, ICAM-1, SysBP, Dopamine, LDL, E-selectin, 5-Hydroxy-L-tryptophan, GGTP, IL6, N6-acetyl lysine, IL18, Cortisol, TSH, Normetanephrine, IL1b, FT4, GOLA, Tyrosine, DiaBP, CEA, Phenylalanine, Adiponectin, Vanillomandelic acid and tChol.

33. The kit of claim 32, wherein The kit has one or more features selected from the following: (1) The marker involved in or affecting glucose metabolism is selected from the group consisting of: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine, or any combination thereof; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate and 1,5-anhydroglucitol; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate and C-pept; Preferably, the markers involved in or affecting sugar metabolism include: Glycerol-3-phosphate, Insulin, Glucose, Isoleucine, Pyruvate, 1,5-anhydroglucitol, 3-Methyl-2-oxovaleric acid, L-Lactate, C-pept, GIP, Leucine, Glyceraldehyde-3-phosphate and Valine. (2) The markers involved in or affecting lipid metabolism are selected from the group consisting of: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH, Carnitine C12, or any combination thereof; Preferably, the markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate and 3-Hydroxybutanoic acid; Preferably, the markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1 and Carnitine C18; Preferably, the markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH and Deoxycholic acid; Preferably, the markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA and Carnitine C3; Preferably, the markers involved in or affecting lipid metabolism include: TG, Carnitine C4DC, Carnitine C5, Isocitrate, L-Malate, 3-Hydroxybutanoic acid, Carnitine, Aconitate, Citrate, Carnitine C10, Carnitine C12:1, Carnitine C18, Carnitine C6, Carnitine C14:2, Carnitine C5OH, Carnitine C8, Carnitine C18:2OH, Deoxycholic acid, Carnitine C10:OH, Carnitine C8:1, Carnitine C16OH, Carnitine C12OH, Carnitine C14, Carnitine C18:1, Carnitine C10:1, FFA, Carnitine C3, tChol, Carnitine C16:1OH, LDL, Carnitine C14:1, Carnitine C16:1, HDL, Carnitine C14OH and Carnitine C12. (3) The markers involved in or affecting amino acid metabolism are selected from: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine, or any combination thereof; Preferably, the markers affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine and Lysine; Preferably, the markers that affect amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine and Creatinine; Preferably, the markers affecting amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine and Phenylalanine; Preferably, the markers that affect amino acid metabolism include: Aspartate, Threonine, Vitamin-B5-Pantothenic-acid, Glutamate, Taurine, Lysine, Serine, Isoleucine, Histidine, Arginine, Cystine, Creatinine, Asparagine, Tryptophan, Proline, Ornithine, Leucine, Phenylalanine, Valine, Tyrosine, Beta-alanine, Methionine; (4) the marker involved in or affecting metabolic stress is selected from the group consisting of: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin, or any combination thereof; Preferably, the markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine and Metanephrine; Preferably, the markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline and VCAM-1; Preferably, the markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine and Dopamine; Preferably, markers involved in or affecting metabolic stress include: TSH, 3-Methylhistidine, Glucose, GALA, L-Thyronine, Metanephrine, SAA1, MODIC, N6-acetyl lysine, GGTP, Citrulline, VCAM-1, L-Lactate, ALAT, Cortisol, FT3, Phenylalanine, Dopamine, Tyrosine, E-selectin, FT4, tChol, LDL, 5-Hydroxy-L-tryptophan, ICAM-1, P-selectin.

34. The kit according to any one of claims 31 to 33, wherein The reagent measures the level of the marker in the biological sample by the following methods: biochemical analysis, physiological analysis, immunological method (e.g., enzyme-linked immunosorbent assay ELISA, colloidal gold, colloidal silver, colloidal carbon, turbidimetry, protein chip, immune lateral flow analysis), molecular biology detection method (PCR, RT-PCR, LAMP, Real-time PCR, digital PCR), optical analysis detection method (e.g., light scattering analysis, SPR, Raman spectroscopy, fluorescent marker molecular analysis), electrochemical analysis method, chromatography, mass spectrometry, electrophoresis, hybridization (e.g., Southern hybridization, Northern hybridization, gene chip), and / or nuclear magnetic resonance spectroscopy; Preferably, the sample is whole blood, serum, plasma, urine, feces, body fluid obtained from a subject; Preferably, the sample is whole blood obtained from a subject; Preferably, the subject is a mammal, for example, mouse, pig, rabbit, monkey, or human.

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