Metabolism biology age construction method based on plasma metabonomics marker and related system thereof

Abstract

The invention belongs to the field of biological aging, and discloses a metabolic biology age construction method based on plasma metabonomics markers and a related system.The construction method comprises the following steps that S1, data sets including plasma metabonomics indexes, actual age data, death or not and death time are prepared, and a training set and a test set are obtained; s2, in the training set, screening specific plasma metabonomics indexes required for constructing metabolic biology ages to obtain 21 specific plasma metabonomics indexes in total; and S3, by utilizing the training set, taking the actual age and the specific plasma metabonomics index as independent variables, taking the death condition as a dependent variable, and obtaining a relational expression of the metabolic biological age through a Gompertz proportional risk model. On the basis of the plasma metabonomics marker, the metabolic biological age is constructed for predicting the death risk, and the aging condition of the metabolic function of an individual can be accurately reflected.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological aging, and more particularly relates to a metabolic biological age construction method based on plasma metabolomics markers and a related system thereof. BACKGROUND

[0002] The global population is rapidly aging, and it is estimated that the population aged 65 and over will reach 808 million by 2023, and 2.2 billion by 2070 [1, 2]. Aging is accompanied by a gradual decline in metabolic function [3], and metabolic aging acceleration significantly increases the risk of cardiovascular and metabolic diseases and death [4-7]. Therefore, accurately assessing metabolic health levels and identifying metabolic aging acceleration are crucial for preventing cardiovascular and metabolic diseases and promoting healthy aging.

[0003] Clinically, the aging state is evaluated by chronological age or clinical physiological and biochemical indicators. However, even among people of the same age, there are significant differences in the degree of aging. In addition, clinical physiological and biochemical indicators (such as blood routine indicators, blood glucose indicators, and blood lipid indicators, etc.) often reflect the macro health status of the body rather than the deep metabolic condition. These indicators have limitations in capturing the characteristics of metabolic aging at the microscopic level, such as relatively insufficient sensitivity and specificity, making it difficult to comprehensively and deeply assess the metabolic aging process of individuals. In contrast, metabolomics can more comprehensively and deeply reflect the metabolic state by systematically analyzing hundreds of metabolic indicators, thereby accurately assessing the metabolic aging of the body.

[0004] Metabolic biological age can be calculated based on plasma metabolomics data, which can more accurately reflect metabolic aging than chronological age and can be used to identify individuals with accelerated metabolic aging. Existing technologies have constructed some metabolic biological ages based on plasma metabolomics data [5-7]. Existing technologies mainly use cross-sectional studies, case-control studies, or cohort studies, and use univariate analysis (such as t-test or non-parametric test, etc.) or multivariate regression analysis (such as linear regression model, etc.) to identify metabolic aging markers. Metabolic biological age is constructed based on metabolic aging markers. Existing metabolic biological age construction and evaluation technologies include: dividing the population into training and validation sets, establishing a linear regression model (with chronological age as the dependent variable and metabolic aging markers as the independent variable) in the training set, further optimizing model parameters using K-fold cross-validation, and establishing the final model to calculate metabolic biological age. In the validation set, the reliability of metabolic biological age is evaluated based on correlation coefficients, mean absolute errors, etc.

[0005] However, the existing metabolic biology age is mainly constructed for the purpose of predicting the actual age, and the higher the fitting accuracy of the actual age model, the more limited the information independent of the actual age provided by the metabolic biology age calculated according to the model in predicting diseases, which is contrary to the expectation that the metabolic biology age can better predict the future disease or health status of individuals than the actual age. Therefore, the existing technology has limitations in constructing the method of metabolic biology age.

[0006] References: 1. United Nations, Department of Economic and Social Affairs, Population Division (2023). World Population Ageing 2023: Challenges and opportunities of population ageing in the least developed countries. UN DESA / POP / 2023 / TR / NO.5. https: / / desapublications.un.org / publications / world-population-ageing-2023-challenges-and-opportunities-population-ageing-least 2. United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Summary of Results. UN DESA / POP / 2024 https: / / population.un.org / wpp / Publications / . 3. López-Otín C, Blasco MA, Partridge L, et al. The hallmarks of aging. Cell 2013;153:1194-217. 4. Jia X, Fan J, Wu X, Cao X, Ma L, Abdelrahman Z, Zhao F, Zhu H, Bizzarri D, Akker EBVD, Slagboom PE, Deelen J, Zhou D, Liu Z. A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors. Adv Sci (Weinh). 2024 Nov;11(43):e2406670. doi: 10.1002 / advs.202406670. Epub 2024 Sep 27. PMID: 39331845; PMCID: PMC11578329. 5. Shang X, Liu J, Zhu Z, Zhang X, Huang Y, Liu S, Wang W, Zhang X, Ma S, Tang S, Hu Y, Ge Z, Yu H, He M. Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study. Aging Cell. 2024 May;23(5):e14125. doi: 8.1111 / acel.14125. Epub 2024 Feb 21. PMID:38380547; PMCID: PMC11113347. 6. Lau CE, Manou M, Markozannes G, Ala-Korpela M, Ben-Shlomo Y, Chaturvedi N, Engmann J, Gentry-Maharaj A, Herzig KH, Hingorani A, Jarvelin MR, Kahonen M, Kivimaki M, Lehtimaki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt AF, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modeling of age and lifespan: A multicohort analysis. Aging Cell. 2024 Jul;23(7):el4164. doi: 10.1111 / acel.14164. Epub 2024 Apr 18. PMID: 38637937; PMCID: PMC11258446. 7. Zeng X, Chen R, Shi D, Zhang X, Su T, Wang Y, Hu Y, He M, Yu H, Shang X. Association of metabolomic aging acceleration and body mass index phenotypes with mortality and obesity-related morbidities. Aging Cell. 2025 Apr;24(4):el4435. doi: 10.1111 / acel.14435. Epub 2024 Dec 12. PMID: 39663904; PMCID: PMC11984667. SUMMARY In view of the fact that the actual age, traditional clinical physiological and biochemical indexes (such as blood routine indexes, blood sugar indexes and blood lipid indexes, etc.) cannot accurately reflect the aging degree of metabolic function, and the ability to assess the risk of cardiovascular metabolic diseases and death is limited, the purpose of the present application is to provide a metabolic biological age construction method based on plasma metabolomics markers and a related system thereof. The metabolic biological age construction method based on plasma metabolomics markers is comprehensive and rigorous. 21 typical plasma metabolomics indexes (namely, the average diameter of very low density lipoprotein (VLDL) particles, the unsaturation degree of fatty acids, linoleic acid, the percentage of omega-3 fatty acids in total fatty acids, the percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large ultra-high density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, the percentage of triglyceride in large VLDL particles in total lipids, the percentage of phospholipid in medium density lipoprotein particles in total lipids, the percentage of cholesterol ester in low density lipoprotein particles in total lipids, and the percentage of free cholesterol in small HDL particles in total cholesterol) are used. Based on the Gompertz proportional risk model, the metabolic biological age is constructed for the purpose of predicting the risk of death, so as to accurately reflect the metabolic function aging condition of an individual. Moreover, the 21 typical plasma metabolic indexes obtained based on the present application can be used for developing a kit for estimating metabolic biological age and developing a kit for evaluating the risk of cardiovascular metabolic diseases and the risk of death.

[0007] To achieve the above-mentioned purpose, according to one aspect of the present application, a metabolic biological age construction method based on plasma metabolomics markers is provided, characterized by comprising the following steps: S1. Preparing a data set, wherein the data set comprises plasma metabolomics indexes of different detection individuals at the time of detection, actual age data at the time of detection, and whether death occurs before a preset subsequent time point and the corresponding death time, and dividing the overall data set into a training set and a test set according to a preset proportion; wherein if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; S2. Screening specific plasma metabolomics indexes required for constructing metabolic biology age in the training set, the specific plasma metabolomics indexes screened include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium-density lipoprotein particles in total lipids, percentage of cholesterol ester in low-density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, a total of 21 specific plasma metabolomics indexes; S3. Using the training set, taking actual age and 21 specific plasma metabolomics indexes screened in step S2 as independent variables, and taking death condition as dependent variable, obtaining a relationship between metabolic biology age and actual age and specific plasma metabolomics indexes by Gompertz proportional risk model; then, calculating metabolic biology age of the individual by using the relationship; wherein, the death condition includes whether to die and corresponding death time.

[0008] As a further preferred embodiment of the present application, it further comprises the following steps: S4. Identifying individuals in metabolic aging acceleration based on actual age and metabolic biology age obtained in step S3.

[0009] As a further preferred embodiment of the present application, the method for identifying individuals in metabolic aging acceleration in step S4 includes any one of the following two methods: (i) comparing metabolic biology age of the individual obtained in step S3 with actual age of the individual, if metabolic biology age of the individual is greater than actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has higher risk of cardiovascular metabolic diseases and death; if metabolic biology age of the individual is less than or equal to actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has lower risk of cardiovascular metabolic diseases and death; (ii) for a certain group whose sample size meets preset requirements, calculating metabolic biological ages of all individuals in the group by using the relationship in step S3, then fitting a linear regression model with the metabolic biological age as the dependent variable and the actual age as the independent variable, obtaining a residual, if the residual of a certain individual is greater than 0, it indicates that the individual is metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the residual of a certain individual is less than or equal to 0, it indicates that the individual is not metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; wherein the certain group is the whole set or a subset of the data set.

[0010] As a further preferred embodiment of the present application, the method further comprises the step of: S5. For the test set, calculating the metabolic biological age by using the relationship in step S3, and evaluating the reliability of the calculated metabolic biological age; the evaluation is specifically using: (1) the Spearman correlation coefficient between the actual age and the metabolic biological age, the mean absolute error, and / or (2) the prediction performance of the metabolic biological age on the risk of cardiovascular metabolic diseases and death, and / or (3) the association between the metabolic aging acceleration evaluated by the metabolic biological age and the risk of cardiovascular metabolic diseases and death.

[0011] As a further preferred embodiment of the present application, the obtaining of the relationship in step S3 specifically comprises the following steps: S31, taking the specific plasma metabolomics indicators and the actual age screened in step S2 as independent variables, and taking the death condition as the dependent variable, calculating the 10-year death risk d1 of each participant by the Gompertz proportional risk model; S32, taking the actual age as the independent variable, and taking the death as the dependent variable, calculating the 10-year death risk d2 of each participant by the Gompertz proportional risk model; S33, setting the death risk d1 equal to the death risk d2, and solving to obtain the relationship between the metabolic biological age and the specific plasma metabolomics indicators and the actual age; Preferably, in step S3, the relationship between the metabolic biological age and the actual age and the specific plasma metabolomics indicators is as follows:

[0012] wherein the metabolic biological age is counted in years,

[0013] z1 is the Z-normalized value of the average diameter of very low-density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetate, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in very large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglyceride in large VLDL particles in total lipids, z19 is the Z-normalized value of the percentage of phospholipid in medium-density lipoprotein particles in total lipids, z20 is the Z-normalized value of the percentage of cholesterol ester in low-density lipoprotein particles in total lipids, z21 is the Z-normalized value of the percentage of free cholesterol in small HDL particles in total cholesterol; chronological_age is the actual age in years.

[0014] As a further preferred embodiment of the present application, in step S1, the plasma metabolomics indicators are all Z-normalized values. The preset subsequent time point is preferably at least 10 years away from the detection.

[0015] According to another aspect of the present application, the present application provides a metabolic biology age prediction system based on plasma metabolomics markers, characterized in that it comprises: A data set construction module is configured to construct a data set comprising plasma metabolomics indicators of different detection individuals at the time of detection, actual age data at the time of detection, and whether death occurs before a preset subsequent time point and the corresponding death time, and divide the overall data set into a training set and a test set according to a preset ratio; wherein if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; The plasma metabolomics index screening module is used for screening specific plasma metabolomics indexes required for constructing the metabolic biological age in the training set, and the specific plasma metabolomics indexes screened include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium-density lipoprotein particles in total lipids, percentage of cholesterol ester in low-density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, and a total of 21 specific plasma metabolomics indexes; The metabolic biological age construction module is used for utilizing the training set, taking the actual age and the 21 specific plasma metabolomics indexes screened by the plasma metabolomics index screening module as independent variables, and taking the death condition as a dependent variable, to obtain a relationship between the metabolic biological age and the actual age and the specific plasma metabolomics indexes by a Gompertz proportional risk model; then, the metabolic biological age of an individual is calculated by using the relationship; wherein the death condition includes whether to die and the corresponding death time. Preferably, the relationship between the metabolic biological age and the actual age and the specific plasma metabolomics indexes is as follows:

[0016] wherein the metabolic biological age is counted in years,

[0017] z1 is the Z-normalized value of the average diameter of very low-density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetate, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in very large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglyceride in large VLDL particles, z19 is the Z-normalized value of the percentage of phospholipid in medium-density lipoprotein particles, z20 is the Z-normalized value of the percentage of cholesterol ester in low-density lipoprotein particles, z21 is the Z-normalized value of the percentage of free cholesterol in small HDL particles; chronological_age is the actual age in years.

[0018] According to another aspect of the present application, the present application provides a metabolic aging acceleration identification system based on plasma metabolomics markers, characterized in that it comprises: A data set construction module is configured to construct a data set, wherein the data set comprises plasma metabolomics indicators of different detection individuals at the detection time, actual age data at the detection time, and whether death occurs before a preset subsequent time point and the corresponding death time, and the overall data set is divided into a training set and a test set according to a preset ratio; wherein if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; The plasma metabolomics index screening module is used for screening specific plasma metabolomics indexes required for constructing the metabolic biological age in the training set, and the specific plasma metabolomics indexes screened include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium-density lipoprotein particles in total lipids, percentage of cholesterol ester in low-density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, and a total of 21 specific plasma metabolomics indexes; The metabolic biological age construction module is used for utilizing the training set, taking the actual age and the 21 specific plasma metabolomics indexes screened by the plasma metabolomics index screening module as independent variables, and taking the death condition as a dependent variable, to obtain a relationship between the metabolic biological age and the actual age and the specific plasma metabolomics indexes by a Gompertz proportional risk model; then, the metabolic biological age of an individual is calculated by using the relationship; wherein the death condition includes whether to die and the corresponding death time. Preferably, the relationship between the metabolic biological age and the actual age and the specific plasma metabolomics indexes is as follows:

[0019] wherein the metabolic biological age is counted in years,

[0020] z1 is the Z-normalized value of the average diameter of very low density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetate, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in very large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglyceride in large VLDL particles, z19 is the Z-normalized value of the percentage of phospholipid in medium-density lipoprotein particles, z20 is the Z-normalized value of the percentage of cholesterol ester in low-density lipoprotein particles, z21 is the Z-normalized value of the percentage of free cholesterol in small HDL particles; chronological_age is the actual age in years; a metabolic aging acceleration identification module for identifying individuals in metabolic aging acceleration based on the actual age and the metabolic biological age obtained by the metabolic biological age construction module; Preferably, the metabolic aging acceleration is identified by comparing the metabolic biological age of an individual obtained by the metabolic biological age construction module with the actual age of the individual, and if the metabolic biological age of the individual is greater than the actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biological age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death. Alternatively, it is identified by fitting a linear regression model with the metabolic biological age as the dependent variable and the actual age as the independent variable according to the metabolic biological age and the actual age of a certain group of people whose sample size meets the preset requirements, obtaining the residual, and if the residual of an individual is greater than 0, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the residual of an individual is less than or equal to 0, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; wherein the certain group of people is the whole set or a subset of the data set.

[0021] According to still another aspect of the present application, the present application provides a cardiovascular metabolic disease risk and death risk early warning system based on plasma metabolomics markers, characterized in comprising: a data set construction module for constructing a data set comprising plasma metabolomics indexes of different detection individuals at the time of detection, actual age data at the time of detection, and whether death occurs before a preset subsequent time point and the corresponding death time, and dividing the overall data set into a training set and a test set according to a preset proportion; wherein if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; a plasma metabolomics index screening module for screening specific plasma metabolomics indexes required for constructing metabolic biological age in the training set, the specific plasma metabolomics indexes screened comprising: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium-density lipoprotein particles in total lipids, percentage of cholesterol ester in low-density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, a total of 21 specific plasma metabolomics indexes; a metabolic biological age construction module for using the training set, taking actual age and the 21 specific plasma metabolomics indexes screened by the plasma metabolomics index screening module as independent variables, and taking death condition as dependent variable, to obtain a relationship between metabolic biological age and actual age and specific plasma metabolomics indexes by Gompertz proportional risk model; then, calculating the metabolic biological age of the individual by using the relationship; wherein the death condition comprises whether death occurs and the corresponding death time; Preferably, the relationship between metabolic biological age and actual age and specific plasma metabolomics indexes is as follows:

[0022] wherein the metabolic biological age is counted in units of years,

[0023] z1 is the Z-normalized value of the average diameter of very low density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetate, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in very large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglyceride in large VLDL particles, z19 is the Z-normalized value of the percentage of phospholipid in medium-density lipoprotein particles, z20 is the Z-normalized value of the percentage of cholesterol ester in low-density lipoprotein particles, z21 is the Z-normalized value of the percentage of free cholesterol in small HDL particles; chronological_age is the actual age in years; a metabolic aging acceleration identification module for identifying individuals in metabolic aging acceleration based on the actual age and the metabolic biological age obtained by the metabolic biological age construction module; Preferably, the metabolic aging acceleration is identified by comparing the metabolic biological age of an individual obtained by the metabolic biological age construction module with the actual age of the individual, and if the metabolic biological age of the individual is greater than the actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biological age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death. Alternatively, it is identified by fitting a linear regression model with the metabolic biological age as the dependent variable and the actual age as the independent variable according to the metabolic biological age and the actual age of a certain group of people whose sample size meets the preset requirements, obtaining the residual, and if the residual of an individual is greater than 0, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the residual of an individual is less than or equal to 0, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; wherein the certain group of people is the whole set or a subset of the data set. A cardiovascular metabolic disease risk and death risk early warning module: for early warning cardiovascular metabolic disease risk and death risk of an individual based on whether the individual is metabolic aging acceleration identified by the metabolic aging acceleration identification module.

[0024] According to a last aspect of the present application, the present application provides application of specific plasma metabolomics indicators as biomarkers in preparation of a kit for estimating metabolic biological age or in preparation of a kit for evaluating cardiovascular metabolic disease onset risk and death risk, characterized in that the specific plasma metabolomics indicators simultaneously include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large ultra-high density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium density lipoprotein particles in total lipids, percentage of cholesterol ester in low density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol.

[0025] Overall, compared with the prior art, the above technical solutions conceived by the present application mainly have the following technical advantages: 1. Actual age cannot accurately evaluate the aging condition, and even among people of the same age, there are significant differences in the degree of aging. The clinical physiological and biochemical indicators (such as blood routine indicators, blood glucose indicators, and blood lipid indicators, etc.) often used in the construction of biological age in the past often reflect the macro health state of the body, rather than the deep metabolic state. These indicators have limitations such as relatively insufficient sensitivity and specificity in capturing the metabolic aging characteristics at the microscopic level, and are difficult to comprehensively and deeply evaluate the metabolic aging process of individuals. In contrast, the present application uses metabolomics indicators to construct metabolic biological age. Metabolomics can more comprehensively and deeply reflect the metabolic state by systematically analyzing hundreds of metabolic indicators, thereby accurately evaluating the metabolic aging of the body.

[0026] The 21 specific plasma metabolomics indexes required for constructing the metabolic biology age are screened (the screening process is for the training set, with the death condition as the dependent variable, and the metabolomics indexes as the independent variable, the blood plasma metabolomics indexes related to the risk of death are screened through the least absolute shrinkage and selection operator algorithm combined with the Cox proportional risk model, as the specific plasma metabolomics indexes), which are: the average diameter of very low density lipoprotein (VLDL) particles, the unsaturation of fatty acids, linoleic acid, the percentage of omega-3 fatty acids in total fatty acids, the percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, the percentage of triglyceride in large VLDL particles, the percentage of phospholipid in medium-density lipoprotein particles, the percentage of cholesterol ester in low-density lipoprotein particles, the percentage of free cholesterol in small HDL particles. (a) The average diameter of very low density lipoprotein (VLDL) particles, the unsaturation of fatty acids, linoleic acid, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, and free cholesterol in large high-density lipoprotein (HDL) particles are directly measured indexes. (b) The percentage of omega-3 fatty acids in total fatty acids, the percentage of linoleic acid in total fatty acids, the percentage of triglyceride in large VLDL particles, the percentage of phospholipid in medium-density lipoprotein particles, the percentage of cholesterol ester in low-density lipoprotein particles, and the percentage of free cholesterol in small HDL particles are indirectly obtained from directly measured indexes; that is, in order to obtain these indirect indexes, omega-3 fatty acids, total fatty acids, triglyceride in large VLDL particles, total lipids in large VLDL particles, phospholipids in medium-density lipoprotein particles, total lipids in medium-density lipoprotein particles, cholesterol ester in low-density lipoprotein particles, total lipids in low-density lipoprotein particles, free cholesterol in small HDL particles, and total cholesterol in small HDL particles also need to be supplemented.

[0027] 2, The present application constructs the relationship between the metabolic biology age and the actual age and the specific plasma metabolomics indexes, that is, the metabolic biology age in years can be calculated by the relationship (this relationship is obtained by Gompertz proportional risk model in the training set, with the 21 specific plasma metabolomics indexes screened and the actual age as the independent variable, and the death condition as the dependent variable; the 21 specific plasma metabolomics indexes appearing in the formula are Z standardized values corresponding to them):

[0028]

[0029] The prior art is mainly aimed at predicting the actual age when constructing the metabolic biological age. The higher the fitting accuracy of the actual age model, the more limited the information provided by the metabolic biological age calculated according to the model in predicting diseases, which is contrary to the expectation that the metabolic biological age can better predict the future disease or health status of the individual than the actual age. The present application is aimed at predicting the death risk when estimating the metabolic biological age. Compared with the actual age model, the metabolic biological age estimated by such a death risk model will better reflect the physiological condition and the risk of future disease or death of the individual.

[0030] 3、The method of the present application can further identify individuals with accelerated metabolic aging by using metabolic biological age and actual age, and warn the risk of cardiovascular metabolic diseases and death risk for individuals with accelerated metabolic aging. Specifically, it can be achieved by any one of the following two methods: (i) comparing the metabolic biological age of an individual with the actual age of the individual, if the metabolic biological age of the individual is greater than the actual age of the individual, it indicates that the individual is accelerated metabolic aging, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biological age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not accelerated metabolic aging, and has a lower risk of cardiovascular metabolic diseases and death; (ii) collecting the metabolic biological age and actual age of a certain population, taking the metabolic biological age as the dependent variable and the actual age as the independent variable, fitting a linear regression model, and obtaining the residual, if the residual of a certain individual is greater than 0, it indicates that the individual is accelerated metabolic aging, and has a higher risk of cardiovascular metabolic diseases and death; if the residual of a certain individual is less than or equal to 0, it indicates that the individual is not accelerated metabolic aging, and has a lower risk of cardiovascular metabolic diseases and death. The certain population is the whole set or a subset of the data set.

[0031] Individuals with accelerated metabolic aging have a higher risk of cardiovascular metabolic diseases and death, which is reflected by the significant correlation between accelerated metabolic aging and the risk of cardiovascular metabolic diseases and death. After considering factors such as age, gender, race, education level, socioeconomic status, body mass index, dietary pattern, physical activity level, smoking status, drinking status, glomerular filtration rate, history of hypertension, use of antihypertensive drugs, history of dyslipidemia, use of lipid-lowering drugs, etc., compared with non-accelerated metabolic aging participants, accelerated metabolic aging participants have a higher incidence of cardiovascular metabolic diseases and mortality.

[0032] 4、The present application is based on a large sample size of prospective cohort study. Compared with the metabolic biological age constructed by previous small sample size of cross-sectional study or case-control study, the research quality of the present application is higher. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 Flowchart of the method for constructing metabolic biological age based on plasma metabolomics markers.

[0034] Figure 2 Figure for the association between metabolic aging acceleration and the risk of cardiovascular and metabolic diseases and death.

[0035] Figure 3 Figure for the predictive effect of metabolic biological age on the risk of cardiovascular and metabolic diseases and death. DETAILED DESCRIPTION

[0036] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0037] Example: The data used in the embodiments of the present application come from a publicly available large prospective cohort study in the UK, UK Biobank (https: / / www.ukbiobank.ac.uk / ), which has been approved by the Northwest Multi-Center Research Ethics Committee in the UK, and all cohort participants have signed the informed consent form. From 2006 to 2010 (baseline of the cohort), 203491 participants without coronary heart disease, stroke, heart failure, and type 2 diabetes at baseline left blood samples at the time of cohort recruitment, and the plasma metabolomics indicators of the baseline samples were quantitatively detected by using a high-throughput nuclear magnetic resonance platform. During the follow-up of the cohort, new coronary heart disease, stroke, heart failure, type 2 diabetes patients and death individuals were identified by linking hospital registration systems, primary care systems, death registration systems and self-reporting. The research cutoff time of the present embodiment is set to April 1, 2022. If a participant develops new coronary heart disease, stroke, heart failure or type 2 diabetes during the follow-up, the onset time of the new patient is recorded, and if the participant has not developed the above diseases before the research cutoff, the research cutoff time of the participant is recorded. Similarly, if a participant dies during the follow-up, the death time is recorded, and if the participant has not died before the research cutoff, the research cutoff time of the participant is recorded. The least absolute shrinkage and selection operator algorithm combined with the Cox proportional hazards regression model is used to screen and construct the plasma metabolomics indicators of metabolic biology age. With the death condition (including whether to die and the corresponding time) as the dependent variable, and the actual age and the screened metabolomics indicators as the independent variables, the Gompertz proportional risk model is fitted to construct the metabolic biology age. Based on the metabolic biology age and the actual age, the acceleration of individual metabolic aging is identified, and the risk of cardiovascular and metabolic diseases and the risk of death of the individual are evaluated.

[0038] The specific steps are as follows: (1) Participant inclusion, questionnaire survey and physical examination The standardized questionnaire is designed and audited by professional researchers, and the investigators collect the questionnaire information of the participants after unified training, including basic demographic, socioeconomic status, lifestyle and disease history, and measure the physical function indicators of the participants, such as body weight and waist circumference. These specific information includes: age, gender, race, education level, socioeconomic status, body mass index, dietary pattern, physical activity level, smoking status, drinking status, glomerular filtration rate, history of hypertension, use of antihypertensive drugs, history of dyslipidemia, and use of lipid-lowering drugs.

[0039] (2) Sample collection, preservation and transportation The plasma samples of the participants are collected by personnel who have been trained uniformly. The plasma samples are transported on dry ice to the Nuffield Health Laboratory, and then stored in a -80℃ ultra-low temperature refrigerator for detection.

[0040] (3) Plasma metabolome measurement The metabolomics indicators of plasma samples were measured by 6 nuclear magnetic resonance spectrometers (Bruker AVANCE III HD). During the measurement, the plasma samples were always kept at 6°C. Two nuclear magnetic resonance spectra were recorded for each plasma sample: one was the over-saturated proton spectrum, mainly caused by the resonance of proteins and lipids in various lipoprotein particles; the other was the Carr-Purcell-Meiboom-Gill (CPMG) T2 relaxation spectrum, in which most of the high-molecular lipoprotein, lipid signals were suppressed, so that the signals of low-molecular-weight metabolites (such as amino acids, glycolysis products, ketone bodies, etc.) were enhanced.

[0041] The original metabolomics data contained 249 metabolite indicators, of which 168 were directly measured indicators, and 81 were indirectly obtained indicators (such as ratios, percentages, etc.) from the directly measured indicators. In addition, we further calculated 76 indirectly obtained indicators that were not included in the original data set but had potential biological significance. Finally, a total of 325 metabolomics indicators were obtained, as shown in Table 1.

[0042] Table 1 List of 325 metabolomics indicators

[0043] (4) Identification of new-onset cardiovascular and metabolic disease patients and death individuals Cardiovascular and metabolic diseases include coronary heart disease, stroke, heart failure, and type 2 diabetes. New-onset cardiovascular and metabolic disease patients and death individuals were identified by linking hospitalization registration systems, primary care systems, death registration systems, and self-reported methods.

[0044] (5) Plasma metabolomics data preprocessing Before analysis, the missing values of metabolite indicators were filled with the mean value (for example, if there are y missing values in the detection values of this metabolite indicator of all 203491 people, that is, the detection values of this metabolite indicator of y people are empty, then the detection values of this metabolite indicator of these y people are filled with the average value of the detection values of this metabolite indicator of the remaining (203491-y) people). Subsequently, all metabolomics indicators were subjected to ln(x+1) transformation, then subtracted from the mean and divided by the standard deviation (Z-standardized).

[0045] (6) Screening of metabolomics indicators required for constructing metabolic biological age The analysis process is shown in Figure 1 , and is described as follows: 203491 participants were randomly divided into an 80% training set (sample size: 162793) and a 20% test set (sample size: 40698). In the training set, the least absolute shrinkage and selection operator algorithm combined with the Cox proportional hazards model was used to screen metabolomics indicators related to mortality risk. The least absolute shrinkage and selection operator algorithm is based on the L1 regularization principle, adds an L1 norm penalty term to the objective function of the model, and realizes the automatic selection of variables and the shrinkage of coefficients in the model. The least absolute shrinkage and selection operator algorithm will shrink the coefficients of metabolomics indicators, and metabolomics indicators with coefficients of 0 will be excluded, and metabolomics indicators with non-zero coefficients will be retained. Finally, 21 indicators were selected from 325 metabolomics indicators to construct metabolic biological age (Table 2).

[0046] Table 2. Metabolomics indicators for constructing metabolic biological age screened by the least absolute shrinkage and selection operator algorithm combined with the Cox proportional hazards model

[0047] (7) Construction of metabolic biological age In the training set, the metabolic biological age was constructed based on the Gompertz proportional hazards model, which takes the death condition (including whether to die and the corresponding time) as the dependent variable, and the 21 selected metabolomics indicators and the actual age as the independent variables. Specifically, first, the 21 metabolomics indicators and the actual age are included in the Gompertz proportional hazards model, and based on this model, the 10-year (120-month) mortality risk di of each individual is calculated:

[0048] In the above formula, is an auxiliary parameter, which is estimated from the data, is a natural constant, represents the linear combination of the 21 metabolomics indicators and the actual age, , where is the intercept, which is estimated from the data, is the 21 metabolomics indicators and the actual age, represents the regression coefficient corresponding to the 21 metabolomics indicators and the actual age, which is estimated from the data.

[0049] Subsequently, only the actual age is included in the Gompertz proportional hazards model, and based on this model, the 10-year (120-month) mortality risk d2 of each individual is calculated:

[0050] In the above formula It is an auxiliary parameter, estimated from the data. It is a natural constant. ,in It is the intercept, estimated from the data. The regression coefficients, representing the actual age, are estimated from the data.

[0051] Let the mortality risk d1 equal the mortality risk d2, and the formula for calculating biological age is as follows, where the specific metabolomics index is a dimensionless Z-standardized value:

[0052]

[0053] (8) Metabolic aging accelerates individual identification. After constructing the metabolic biological age (MBA) formula in the training set, the MBA calculation formula was obtained. In the test set, the MBA of each participant was calculated. Subsequently, a linear regression model was fitted to the test set with the MBA as the dependent variable and the actual age as the independent variable, and the residuals were obtained. If the residual of an individual is greater than 0, it indicates that the individual is experiencing accelerated metabolic aging; if the residual of an individual is less than or equal to 0, it indicates that the individual is experiencing non-accelerated metabolic aging.

[0054] (9) Evaluation of the reliability of metabolic biological age In the test set, the reliability of metabolic biological age was assessed by at least one of the following indicators: (1) Spearman correlation coefficient and mean absolute error between metabolic biological age and actual age; (2) the association between accelerated metabolic aging identified by metabolic biological age and cardiovascular metabolic diseases (coronary heart disease, stroke, heart failure, type 2 diabetes) and mortality risk; and (3) the predictive effect of metabolic biological age on cardiovascular metabolic diseases (coronary heart disease, stroke, heart failure and type 2 diabetes) and mortality.

[0055] In the test set, metabolic biological age was strongly correlated with actual age (Spearman correlation coefficient = 0.890, mean absolute error = 3.460).

[0056] A Cox proportional hazards regression model was established to assess the association between metabolic aging acceleration and the risk of cardiovascular metabolic diseases and the risk of death, with cardiovascular metabolic diseases and the corresponding time, death and the corresponding time as dependent variables, metabolic aging acceleration as independent variable, and actual age, gender, race, education level, social economic status, body mass index, dietary pattern, physical activity level, smoking status, drinking status, glomerular filtration rate, history of hypertension, use of antihypertensive drugs, history of dyslipidemia, and use of lipid-lowering drugs as covariates. Figure 2 It was shown that metabolic aging acceleration was significantly associated with coronary heart disease, stroke, heart failure, type 2 diabetes, and death, and the risk of coronary heart disease, stroke, heart failure, type 2 diabetes, and death in individuals with metabolic aging acceleration was increased by 25%, 30%, 57%, 46%, and 78%, respectively, compared with individuals without metabolic aging acceleration.

[0057] In the Cox proportional hazards model with gender, race, education level, social economic status, body mass index, dietary pattern, physical activity level, smoking status, drinking status, glomerular filtration rate, history of hypertension, use of antihypertensive drugs, history of dyslipidemia, and use of lipid-lowering drugs as covariates, actual age and metabolic biological age were added, respectively. Figure 3 It was shown that metabolic biological age significantly improved the prediction effect on cardiovascular metabolic diseases (coronary heart disease, heart failure, type 2 diabetes) and death compared with actual age, and the Harrell's C index of the model for predicting coronary heart disease, heart failure, type 2 diabetes, and death was significantly improved when metabolic biological age was used to replace actual age in the Cox proportional hazards model.

[0058] It is easily understood by those skilled in the art that the above description is only the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement, and improvement within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for constructing a metabolic biological age based on plasma metabolomics indicators, characterized in that, Comprising the following steps: S1. Preparing a data set comprising plasma metabolomics indicators from different detection individuals at the time of detection, actual age data at the time of detection, and whether death occurs before a preset subsequent time point and the corresponding death time, and dividing the overall data set into a training set and a test set according to a preset ratio; wherein if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; S2. In the training set, screening specific plasma metabolomics indicators required for constructing metabolic biological age, the specific plasma metabolomics indicators screened include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large ultra-high density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium density lipoprotein particles in total lipids, percentage of cholesterol ester in low density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, a total of 21 specific plasma metabolomics indicators; S3. Using the training set, taking actual age and the 21 specific plasma metabolomics indicators screened in step S2 as independent variables, and taking death condition as dependent variable, obtaining the relationship between metabolic biological age and actual age and specific plasma metabolomics indicators by Gompertz proportional risk model; then, calculating the metabolic biological age of the individual using the relationship; wherein the death condition includes whether death occurs and the corresponding death time.

2. The method of constructing a metabolic biological age based on plasma metabolomics markers according to claim 1, wherein, Further comprising the step: S4. Identifying individuals in metabolic aging acceleration based on actual age and metabolic biological age obtained in step S3.

3. The method for constructing metabolic biological age based on plasma metabolomics indicators as described in claim 2, characterized in that, The specific method for identifying individuals in metabolic aging acceleration in step S4 includes any one of the following two methods: (i) Comparing the metabolic biological age of the individual obtained in step S3 with the actual age of the individual, if the metabolic biological age of the individual is greater than the actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biological age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; (ii) For a certain group whose sample size meets the preset requirement, calculate the metabolic biological age of all individuals in the group using the relationship in step S3, then fit a linear regression model taking metabolic biological age as dependent variable and actual age as independent variable, obtain the residual, if the residual of a certain individual is greater than 0, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; If the residual of a certain individual is less than or equal to 0, it indicates that the individual is not metabolic aging accelerated, and has a lower risk of cardiovascular metabolic diseases and death; wherein the certain group is the whole set or a subset of the data set.

4. The method of constructing a metabolic biological age based on plasma metabolomics markers according to claim 2, wherein, The method further comprises the steps of: S5. For the test set, the metabolic age is calculated using the relationship in step S3, and the reliability of the calculated metabolic age is evaluated; the evaluation is specifically using: (1) the Spearman correlation coefficient between the actual age and the metabolic age, the mean absolute error, and / or (2) the prediction performance of the metabolic age on the risk of cardiovascular metabolic diseases and death, and / or (3) the association between the metabolic aging acceleration evaluated by the metabolic age and the risk of cardiovascular metabolic diseases and death.

5. The method of constructing a metabolic biological age based on plasma metabolomics markers according to claim 1, wherein, The obtaining of the relationship in step S3 specifically comprises the following steps: S31. Taking the specific plasma metabolomics indicators and the actual age screened in step S2 as independent variables, and taking the death condition as the dependent variable, the 10-year death risk d1 of each participant is calculated by the Gompertz proportional risk model; S32. Taking the actual age as the independent variable, and taking the death as the dependent variable, the 10-year death risk d2 of each participant is calculated by the Gompertz proportional risk model; S33. Let the death risk d1 be equal to the death risk d2, and solve the relationship between the metabolic age and the specific plasma metabolomics indicators and the actual age; Preferably, in step S3, the relationship between the metabolic age, the actual age and the specific plasma metabolomics indicators is as follows: wherein the metabolic age is measured in years, z1 is the Z-standardized value of the average diameter of very low density lipoprotein (VLDL) particles, z2 is the Z-standardized value of the unsaturation of fatty acids, z3 is the Z-standardized value of linoleic acid, z4 is the Z-standardized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-standardized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-standardized value of leucine, z7 is the Z-standardized value of valine, z8 is the Z-standardized value of phenylalanine, z9 is the Z-standardized value of tyrosine, z10 is the Z-standardized value of lactic acid, z11 is the Z-standardized value of acetate, z12 is the Z-standardized value of acetoacetic acid, z13 is the Z-standardized value of acetone, z14 is the Z-standardized value of albumin, z15 is the Z-standardized value of glycoprotein acetyl, z16 is the Z-standardized value of free cholesterol in large high-density lipoprotein (HDL) particles, z17 is the Z-standardized value of cholesterol ester in small HDL particles, z18 is the Z-standardized value of the percentage of triglyceride in large VLDL particles, z19 is the Z-standardized value of the percentage of phospholipid in medium-density lipoprotein particles, z20 is the Z-standardized value of the percentage of cholesterol ester in low-density lipoprotein particles, z21 is the Z-standardized value of the percentage of free cholesterol in small HDL particles; chronological_age is the actual age measured in years. 6.The method for constructing metabolic biology age based on plasma metabolomics index according to claim 1, wherein, In step S1, the plasma metabolomics indexes are all Z-standardized values; The preset subsequent time point is preferably at least 10 years away from the detection.

7. A metabolic biology age prediction system based on plasma metabolomics markers, characterized by, Comprise: A dataset construction module for constructing a dataset comprising plasma metabolomics indexes of different detection individuals at the time of detection, actual age data at the time of detection, and whether death occurs before a preset subsequent time point and the corresponding death time, and dividing the overall dataset into a training set and a test set according to a preset ratio; wherein, if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; A plasma metabolomics index screening module for screening specific plasma metabolomics indexes required for constructing metabolic biological age in the training set, the specific plasma metabolomics indexes screened comprising: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles in total lipids, percentage of phospholipid in medium-density lipoprotein particles in total lipids, percentage of cholesterol ester in low-density lipoprotein particles in total lipids, percentage of free cholesterol in small HDL particles in total cholesterol, a total of 21 specific plasma metabolomics indexes; A metabolic biological age construction module for using the training set, taking actual age and the 21 specific plasma metabolomics indexes screened by the plasma metabolomics index screening module as independent variables, and taking death condition as dependent variable, obtaining a relationship between metabolic biological age and actual age and specific plasma metabolomics indexes by Gompertz proportional risk model; then, calculating the metabolic biological age of the individual by using the relationship; wherein, the death condition comprises whether death occurs and the corresponding death time; Preferably, the relationship between metabolic biological age and actual age and specific plasma metabolomics indexes is as follows: wherein, the metabolic biological age is counted in years, z1 is the Z-normalized value of the average diameter of very low-density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetate, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglyceride in total lipids in large VLDL particles, z19 is the Z-normalized value of the percentage of phospholipid in total lipids in medium-density lipoprotein particles, z20 is the Z-normalized value of the percentage of cholesterol ester in total lipids in low-density lipoprotein particles, z21 is the Z-normalized value of the percentage of free cholesterol in total cholesterol in small HDL particles; chronological_age is the actual age in years.

8. A metabolic aging acceleration identification system based on plasma metabolomics markers, characterized by, Comprise: a data set construction module for constructing a data set comprising plasma metabolomics indicators from different detection individuals at the time of detection, actual age data at the time of detection, and whether death before a preset subsequent time point and the corresponding death time, and dividing the overall data set into a training set and a test set according to a preset ratio; wherein, if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; a plasma metabolomics indicator screening module for screening specific plasma metabolomics indicators required for constructing metabolic biological age in the training set, the specific plasma metabolomics indicators screened including: average diameter of very low-density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetate, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in total lipids in large VLDL particles, percentage of phospholipid in total lipids in medium-density lipoprotein particles, percentage of cholesterol ester in total lipids in low-density lipoprotein particles, percentage of free cholesterol in total cholesterol in small HDL particles, a total of 21 specific plasma metabolomics indicators; a metabolic biology age construction module: for using a training set, taking 21 specific plasma metabolomics indexes screened by the plasma metabolomics index screening module as independent variables, and taking death as a dependent variable, obtaining a relationship between metabolic biology age and actual age and specific plasma metabolomics indexes by a Gompertz proportional risk model; then, calculating the metabolic biology age of an individual by using the relationship; wherein, the death includes whether death and corresponding death time; Preferably, the relationship between metabolic biology age and actual age and specific plasma metabolomics indexes is as follows: wherein, the metabolic biology age is calculated in years, z1 is a Z-standardized value of the average diameter of very low density lipoprotein (VLDL) particles, z2 is a Z-standardized value of the unsaturation of fatty acids, z3 is a Z-standardized value of linoleic acid, z4 is a Z-standardized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is a Z-standardized value of the percentage of linoleic acid in total fatty acids, z6 is a Z-standardized value of leucine, z7 is a Z-standardized value of valine, z8 is a Z-standardized value of phenylalanine, z9 is a Z-standardized value of tyrosine, z10 is a Z-standardized value of lactic acid, z11 is a Z-standardized value of acetate, z12 is a Z-standardized value of acetoacetate, z13 is a Z-standardized value of acetone, z14 is a Z-standardized value of albumin, z15 is a Z-standardized value of glycoprotein acetyl, z16 is a Z-standardized value of free cholesterol in large high-density lipoprotein (HDL) particles, z17 is a Z-standardized value of cholesterol ester in small HDL particles, z18 is a Z-standardized value of the percentage of triglyceride in large VLDL particles, z19 is a Z-standardized value of the percentage of phospholipid in medium-density lipoprotein particles, z20 is a Z-standardized value of the percentage of cholesterol ester in low-density lipoprotein particles, z21 is a Z-standardized value of the percentage of free cholesterol in small HDL particles; chronological_age is the actual age calculated in years; a metabolic aging acceleration identification module: for identifying an individual in metabolic aging acceleration based on actual age and metabolic biology age obtained by the metabolic biology age construction module; Preferably, the metabolic aging acceleration is identified by comparing the metabolic biology age of an individual obtained by the metabolic biology age construction module with the actual age of the individual, if the metabolic biology age of the individual is greater than the actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biology age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death. Or, by the following way: according to the metabolic biology age and the actual age of a certain group of individuals whose sample size meets the preset requirements, taking the metabolic biology age as the dependent variable and the actual age as the independent variable, fitting a linear regression model, obtaining the residual, if the residual of a certain individual is greater than 0, it indicates that the individual is metabolic aging acceleration, and there is a higher risk of cardiovascular metabolic diseases and death; if the residual of a certain individual is less than or equal to 0, it indicates that the individual is not metabolic aging acceleration, and there is a lower risk of cardiovascular metabolic diseases and death; wherein the certain group is the whole set or a subset of the data set.

9. A pre-warning system for cardiovascular metabolic disease risk and mortality risk based on plasma metabolomics markers, characterized in that, Comprise: A data set construction module: for constructing a data set, the data set comprising plasma metabolomics indicators from different detection individuals at the time of detection, actual age data at the time of detection, and whether death before a preset subsequent time point and the corresponding death time, and dividing the whole data set into a training set and a test set according to a preset proportion; wherein, if the detection individual does not die before the preset subsequent time point, the corresponding death time is counted as the preset subsequent time point; A plasma metabolomics indicator screening module: for screening specific plasma metabolomics indicators required for constructing metabolic biology age in the training set, the specific plasma metabolomics indicators screened including: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large high-density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles, percentage of phospholipid in total lipids in medium-density lipoprotein particles, percentage of cholesterol ester in total lipids in low-density lipoprotein particles, percentage of free cholesterol in total cholesterol in small HDL particles, a total of 21 specific plasma metabolomics indicators; A metabolic biology age construction module: for using the training set, taking the actual age and the 21 specific plasma metabolomics indicators screened by the plasma metabolomics indicator screening module as independent variables, and taking the death condition as the dependent variable, obtaining the relationship between metabolic biology age and actual age, specific plasma metabolomics indicators by Gompertz proportional risk model; then, calculating the metabolic biology age of the individual by using the relationship; wherein, the death condition includes whether death and the corresponding death time; Preferably, the relationship between the metabolic biology age and the actual age, specific plasma metabolomics indicators is as follows: Wherein, the metabolic biology age is counted in years, z1 is the Z-normalized value of the average diameter of very low density lipoprotein (VLDL) particles, z2 is the Z-normalized value of the unsaturation of fatty acids, z3 is the Z-normalized value of linoleic acid, z4 is the Z-normalized value of the percentage of omega-3 fatty acids in total fatty acids, z5 is the Z-normalized value of the percentage of linoleic acid in total fatty acids, z6 is the Z-normalized value of leucine, z7 is the Z-normalized value of valine, z8 is the Z-normalized value of phenylalanine, z9 is the Z-normalized value of tyrosine, z10 is the Z-normalized value of lactic acid, z11 is the Z-normalized value of acetate, z12 is the Z-normalized value of acetoacetic acid, z13 is the Z-normalized value of acetone, z14 is the Z-normalized value of albumin, z15 is the Z-normalized value of glycoprotein acetyl, z16 is the Z-normalized value of free cholesterol in very large high-density lipoprotein (HDL) particles, z17 is the Z-normalized value of cholesterol ester in small HDL particles, z18 is the Z-normalized value of the percentage of triglycerides in total lipids in large VLDL particles, z19 is the Z-normalized value of the percentage of phospholipids in total lipids in medium-density lipoprotein particles, z20 is the Z-normalized value of the percentage of cholesterol ester in total lipids in low-density lipoprotein particles, z21 is the Z-normalized value of the percentage of free cholesterol in total cholesterol in small HDL particles; chronological_age is the actual age in years; a metabolic aging acceleration identification module: for identifying individuals in metabolic aging acceleration based on the actual age and the metabolic biological age obtained by the metabolic biological age construction module; Preferably, the metabolic aging acceleration is identified by comparing the metabolic biological age of the individual obtained by the metabolic biological age construction module with the actual age of the individual, and if the metabolic biological age of the individual is greater than the actual age of the individual, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the metabolic biological age of the individual is less than or equal to the actual age of the individual, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; Alternatively, it is identified by fitting a linear regression model with the metabolic biological age as the dependent variable and the actual age as the independent variable according to the metabolic biological age and the actual age of a certain group of individuals whose sample size meets the preset requirements, and obtaining the residual, if the residual of a certain individual is greater than 0, it indicates that the individual is in metabolic aging acceleration, and has a higher risk of cardiovascular metabolic diseases and death; if the residual of a certain individual is less than or equal to 0, it indicates that the individual is not in metabolic aging acceleration, and has a lower risk of cardiovascular metabolic diseases and death; wherein the certain group of individuals is the whole set or a subset of the data set; a cardiovascular metabolic disease risk and death risk early warning module: for early warning the cardiovascular metabolic disease risk and death risk of an individual based on whether the individual is in metabolic aging acceleration identified by the metabolic aging acceleration identification module.

10. Use of specific plasma metabolomic markers as biomarkers in the preparation of a kit for estimating the metabolic biological age or in the preparation of a kit for evaluating the risk of onset and mortality of cardiovascular-metabolic diseases, characterized by the fact that, The specific plasma metabolomics indicators include: average diameter of very low density lipoprotein (VLDL) particles, unsaturation of fatty acids, linoleic acid, percentage of omega-3 fatty acids in total fatty acids, percentage of linoleic acid in total fatty acids, leucine, valine, phenylalanine, tyrosine, lactic acid, acetate, acetoacetic acid, acetone, albumin, glycoprotein acetyl, free cholesterol in large ultra-high density lipoprotein (HDL) particles, cholesterol ester in small HDL particles, percentage of triglyceride in large VLDL particles, percentage of phospholipid in total lipids in medium density lipoprotein particles, percentage of cholesterol ester in total lipids in low density lipoprotein particles, percentage of free cholesterol in total cholesterol in small HDL particles.

Images