Age estimation method based on proteome data

Abstract

The invention belongs to the field of biomedicine, particularly relates to an age estimation method based on proteome data, and more particularly relates to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment and a nonvolatile computer readable storage medium. And their use for assessing the biological age of a subject, the degree or rate of aging of the subject, the degree or rate of aging of a specific tissue / organ of the subject, the effect of aging intervention means, the risk of the subject to suffer from an aging-related disease and / or the therapeutic effect of the aging-related disease.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to an age estimation model and estimation method based on protein data, and the model or method is used to evaluate the biological age of a subject, the degree or rate of aging of a subject, the degree or rate of aging of a subject's specific tissue / organ, the effect of aging intervention measures, the risk of a subject suffering from aging-related diseases and / or the treatment effect of aging-related diseases. Background Art

[0002] Aging is a complex process affected by multiple factors, including genetics and the environment, and is considered to be the root cause of many chronic diseases (such as cardiovascular and neurodegenerative diseases). Therefore, quantifying the aging rate will help evaluate disease risk and health level and provide a basis for personalized medicine. In addition, a variety of aging intervention strategies have been reported to slow down the aging rate to a certain extent, such as dietary restrictions and exercise, and a method to evaluate the degree of aging (i.e., biological age) is the basis for verifying the effectiveness of aging intervention measures. However, aging is a highly heterogeneous process, both in the population and within individuals, and has no absolute correlation with chronological age (i.e., the year from birth to the time of recording), that is, human biological age is not equal to chronological age. Therefore, the accurate evaluation of aging has become an emerging key scientific issue in aging research, and the development of tools and methods that can effectively evaluate the degree of aging is of great significance for understanding and intervening in aging.

[0003] At present, there are two main methods for evaluating the degree of aging. The first is to collect relevant biological data within the population, perform correlation analysis on the collected data and the individual's chronological age, and obtain detection indicators that are highly correlated with age, which are used to preliminarily evaluate the individual's degree of aging. The second is to establish a prediction model for the aging clock based on multiple indicators within the population to achieve the purpose of accurately measuring aging. The data types commonly used to establish aging clocks include clinical measurement data, DNA methylation data, and proteomic data. Among them, proteomic data may become an important data type for measuring aging because of its close relationship with health and the readability of the results. However, the protein age models currently established based on proteomic data are all based on Western populations, and aging is closely related to the environment. Models suitable for Western populations cannot accurately measure the aging of Asian populations (for example, Southeast Asians or East Asians). Summary of the invention

[0004] Through strict screening of health conditions and a uniform diet before testing, the inventors of this application established a cohort of naturally aging people in Quzhou, Zhejiang, collected blood samples from the cohort volunteers, and further separated the plasma in the blood. By performing mass spectrometry analysis on the protein components of plasma, a plasma proteome map of the aging population in China was established, and a series of proteins that are correlated with age were identified. Based on the abundance of these proteins, the inventors of this application used an elastic network machine learning model to predict the actual age of the subjects and established a protein age model for population aging. The results showed that the model has good predictive performance.

[0005] Therefore, in a first aspect, the present application provides a method for establishing a data set, comprising:

[0006] (1) collecting biological samples from multiple subjects, performing proteomic analysis on the biological samples, obtaining the abundance of each protein in the biological samples, and obtaining a protein abundance data set;

[0007] (2) Using age as a training label, the dataset is constructed for training an age estimation model.

[0008] In certain embodiments, the subjects are ≥100 people, for example, ≥500 people, ≥1000 people, or ≥10000 people.

[0009] In certain embodiments, the biological sample is selected from blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts; preferably plasma.

[0010] In certain embodiments, the protein abundance dataset is obtained by:

[0011] i) isolating and quantifying proteins from the biological sample;

[0012] ii) enzymatically hydrolyzing the protein to obtain a peptide sample;

[0013] iii) analyzing the peptide sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data;

[0014] iv) performing qualitative analysis based on the mass spectrometry data (eg, importing the mass spectrometry data into MaxQuant, matching it with the SwissProt human database, and screening results with FDR < 1%) to screen the mass spectrometry data for successful qualitative analysis.

[0015] In certain embodiments, the method further comprises normalizing the successfully characterized mass spectrometry data, for example, dividing the original intensity value of the successfully characterized mass spectrometry data by the median value of all protein abundance values ​​in each biological sample.

[0016] In certain embodiments, the method further comprises a step of removing high-abundance proteins after isolating proteins from the biological sample and before quantifying the proteins in step i).

[0017] In certain embodiments, the method further comprises, before step (2), performing a correlation analysis between the protein abundance dataset and age to screen for age-related proteins.

[0018] In certain embodiments, the protein abundance dataset is subjected to Pearson correlation analysis with age, with BMI used as a covariate, the P value is corrected by the BH method, and the result with P<0.05 is selected as the protein associated with age.

[0019] In certain embodiments, the data set is divided into a training set and a test set. In certain embodiments, the data set is divided into a training set and a test set in a 1:1 ratio.

[0020] In certain embodiments, the subject is Asian.

[0021] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0022] In some embodiments, the subject is over 18 years old, preferably 18-70 years old. In some embodiments, the subject age distribution is balanced. In some embodiments, the difference in the number of subjects in each age group (for example, divided into 18-44 years old age group, 45-59 years old age group and 60 years old age group) is less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5% or less than 2%.

[0023] In a second aspect, the present application provides a method for establishing a tissue-specific dataset, comprising:

[0024] (1) establishing a data set according to the method of the first aspect;

[0025] (2) screening the data set for proteins expressed in tissue specificity to obtain a tissue specific protein abundance data set; the tissue being selected from the group consisting of liver, muscle, nervous system, immune system, and hematopoietic system;

[0026] (3) Using age as a training label, construct the tissue-specific dataset for training the age estimation model.

[0027] In certain embodiments, in step (2) of the method, the tissue-specifically expressed protein is screened by the following steps:

[0028] (a) Obtain human proteome map data from public databases, obtain the TPM values, FPKM values, or RPKM values ​​of the protein types expressed in each tissue and their corresponding genes, and construct a protein-tissue abundance matrix;

[0029] (b) dividing the protein-tissue abundance matrix obtained in step (a) into two sub-matrices that increase and decrease with age;

[0030] (c) The two sub-matrices were z-score transformed for TPM value, FPKM value or RPKM value, and K-Means clustering was performed using the ComplexHeatmap R package to determine the types of tissue-specifically expressed proteins.

[0031] In certain embodiments, the data set is divided into a training set and a test set. In certain embodiments, the data set is divided into a training set and a test set in a 1:1 ratio.

[0032] In certain embodiments, the subject is Asian.

[0033] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0034] In some embodiments, the subject is over 18 years old, preferably 18-70 years old. In some embodiments, the subject age distribution is balanced. In some embodiments, the difference in the number of subjects in each age group (for example, divided into 18-44 years old age group, 45-59 years old age group and 60 years old age group) is less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5% or less than 2%.

[0035] In a third aspect, the present application provides a method for training an age estimation model, comprising:

[0036] (1) establishing a data set according to the method of the first aspect;

[0037] (2) calculating the estimated age of the subject corresponding to the data using the age estimation model according to the data set;

[0038] (3) Training the age estimation model based on the difference between the estimation result and the age label of the data, thereby obtaining an age estimation model that can be used for age estimation.

[0039] In certain embodiments, the training method is a method for training a biological age estimation model.

[0040] In certain embodiments, the method uses the glmnet package for the training. In certain embodiments, the method uses the glmnet package, version 4.1.4 for the training, and the alpha value is selected from 0.1-0.9, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 or 0.9.

[0041] In certain embodiments, the subject's estimated age is Among them, A x represents the abundance of age-related proteins in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0042] Through the training, the abundance of each age-related protein A is determined x The coefficient a x and the constant b.

[0043] In certain embodiments, the abundance of the age-related protein is the abundance of the corresponding protein in a subject's blood (e.g., whole blood, plasma, or serum) sample. In certain embodiments, the abundance of the age-related protein is the abundance of the corresponding protein in a subject's plasma sample.

[0044] In certain embodiments, the age-related proteins involved in the age estimation model obtained in step (3) that can be used for age estimation are selected from one or more of the following:

[0045] THBS4, VTN, VSIG4, PCOLCE, ADAMTSL4, B3GNT8, C4BPB, CRTAC1, ALDOB, IGDCC4, BMP1, CRP, COMP, SLC4A1, CD163, ALCAM, CSP G4, APOB, CTSS, ANGPTL3, LRP1, LAMC1, IGFBP2, CILP2, F13B, CST6, IGF1, PZP, SPP1, PCSK9, POMGNT1, PRG4, SSC5D, ABI3BP.

[0046] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0047] Uniprot Number Protein name P35443 THBS4 P04004 VTN Q9Y279 VSIG4 Q15113 PCOLCE Q6UY14 ADAMTSL4 Q7Z7M8 B3GNT8 P20851 C4BP Q9NQ79 CRTAC1 P05062 ALDOB Q8 IGDCC4 P13497 BMP1 P02741 CRP P49747 COMP P02730 SLC4A1 Q86VB7 CD163 Q13740 ALCAM Q6UVK1 CSPG4 P04114 APOB P25774 CTSS Q9Y5C1 ANGPTL3 Q07954 LRP1 P11047 LAMC1 P18065 IGFBP2 Q8IUL8 CILP2 P05160 F13B Q15828 CST6 P05019 IGF1 P20742 PZQ P10451 SPP1 Q8NBP7 PCSK9 Q8WZA1 POMGNT1 Q92954 PRG4 A1L4H1 SSC5D Q7Z7G0 ABI3BP .

[0048] In certain embodiments, the coefficient a and constant b of each age-related protein in the age estimation model that can be used for age estimation obtained in step (3) are as follows:

[0049]

[0050]

[0051] In certain embodiments, the subject is Asian.

[0052] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0053] In some embodiments, the subject is over 18 years old, preferably 18-70 years old. In some embodiments, the subject age distribution is balanced. In some embodiments, the difference in the number of subjects in each age group (for example, divided into 18-44 years old age group, 45-59 years old age group and 60 years old age group) is less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5% or less than 2%.

[0054] In a fourth aspect, the present application provides a method for training a tissue-specific age estimation model, comprising:

[0055] (1) establishing a tissue-specific dataset according to the method of the second aspect;

[0056] (2) calculating the estimated age of the subject corresponding to the data using the age estimation model based on the tissue-specific data set;

[0057] (3) Training the age estimation model based on the difference between the estimation result and the age label of the data, thereby obtaining an age estimation model that can be used for age estimation.

[0058] In certain embodiments, the training method is a method for training a biological age estimation model.

[0059] In certain embodiments, the method uses the glmnet package for the training. In certain embodiments, the method uses the glmnet package, version 4.1.4 for the training, and the alpha value is selected from 0.1-0.9, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 or 0.9.

[0060] In certain embodiments, the subject's estimated age is Among them, A x represents the abundance of age-related proteins in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0061] Through the training, the abundance of each age-related protein A is determined x The coefficient a x and the constant b.

[0062] In certain embodiments, the abundance of the age-related protein is the abundance of the corresponding protein in a subject's blood (e.g., whole blood, plasma, or serum) sample. In certain embodiments, the abundance of the age-related protein is the abundance of the corresponding protein in a subject's plasma sample.

[0063] In certain embodiments, the age estimation model that can be used for age estimation obtained in step (3) is a tissue-specific age estimation model having a feature selected from the following:

[0064] (1) The age estimation model that can be used for age estimation is a liver-specific age estimation model, and the age-related proteins involved are selected from one or more of the following:

[0065] VTN, IGFALS, C4BPB, APOL1, ALDOB, APMAP, A1BG, CRP, APOF, APOB, MAN1A1, FGG, CFI, C9, F5, F13B, PZP, PCSK9, PRG4, SERPINA4, OAF.

[0066] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0067] Uniprot Number Protein name P04004 VTN P35858 IGFALS P20851 C4B O14791 APOL1 P05062 ALDOB Q9HDC9 APMAP P04217 A1BG P02741 CRP Q13790 APOF P04114 APOB P33908 MAN1A1 P02679 FGG P05156 CFI P02748 C9 P12259 F5 P05160 F13B P20742 PZQ Q8NBP7 PCSK9 Q92954 PRG4 P29622 SERPINA4 Q86UD1 OAF

[0068] (2) The age estimation model that can be used for age estimation is a muscle-specific age estimation model, and the age-related proteins involved are selected from one or more of the following:

[0069] SPTB, THBS4, ADAMTSL4, IGF2R.

[0070] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0071] Uniprot Number Protein name P11277 SPTB P35443 THBS4 Q6UY14 ADAMTSL4 P11717 IGF2R

[0072] (3) The age estimation model that can be used for age estimation is a nervous system-specific age estimation model, and the age-related proteins involved are selected from one or more of the following:

[0073] PLXDC2, CRTAC1, IGDCC4, CDH2, ALCAM, CTSF, CLU, NEO1, SPP1, POMGNT1, PTPRZ1, RELN.

[0074] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0075] Uniprot Number Protein name Q6UX71 PLXDC2 Q9NQ79 CRTAC1 Q8 IGDCC4 P19022 CDH2 Q13740 ALCAM Q9UBX1 CTSF P10909 CLU Q92859 NEO1 P10451 SPP1 Q8WZA1 POMGNT1 P23471 PTPRZ1 P78509 RELN

[0076] (4) The age estimation model that can be used for age estimation is an immune system-specific age estimation model, and the age-related proteins involved are selected from one or more of the following:

[0077] CSF1R, CD163, CAMP, CTSS, LTA4H, IL18BP, MRC1, LCP1.

[0078] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0079] Uniprot Number Protein name P07333 CSF1R Q86VB7 CD163 P49913 CAMP P25774 CTSS P09960 LTA4H O95998 IL18BP P22897 MRC1 P13796 LCP1

[0080] (5) The age estimation model that can be used for age estimation is an age estimation model specific to the hematopoietic system, and the age-related proteins involved are selected from SLC4A1, CILP2 or a combination thereof.

[0081] Each age-related protein has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each age-related protein are as follows:

[0082] Uniprot Number Protein name P02730 SLC4A1 Q8IUL8 CILP2 .

[0083] In certain embodiments, the tissue-specific age estimation model obtained in step (3) that can be used for age estimation has features selected from the following:

[0084] (a) The tissue-specific age estimation model is a liver-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows:

[0085] Coefficient a Age-related proteins 91.90145258 Intercept (constant b) -92.21095458 VTN -14.72952777 IGFALS 14.34699608 C4B -6.114763217 APOL1 15.43255484 ALDOB 1.711377123 APMAP -1.547596228 A1BG 0.291524898 CRP -4.311441915 APOF 27.05314791 APOB 2.02130645 MAN1A1 -7.157786211 FGG 16.08933787 CFI 3.070322617 C9 25.20853993 F5 7.018942492 F13B 6.039314797 PZQ 16.49938142 PCSK9 8.619798386 PRG4 12.1185648 SERPINA4 2.304829347 OAF

[0086] (b) The tissue-specific age estimation model is a muscle-specific age estimation model, and the coefficient a and constant b of each age-related protein involved are as follows:

[0087] Coefficient a Age-related proteins -9.519331126 Intercept (constant b) -73.78133039 SPTB 38.47048892 THBS4 7.741473788 ADAMTSL4 46.89315992 IGF2R

[0088] (c) The tissue-specific age estimation model is a nervous system-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows:

[0089]

[0090]

[0091] (d) The tissue-specific age estimation model is an immune system-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows:

[0092] Coefficient a Age-related proteins -12.02033782 Intercept (constant b) -0.301511671 CSF1R 23.74191112 CD163 7.774179805 CAMP 13.8126609 CTSS 23.89395493 LTA4H 11.33796179 IL18BP 26.66617282 MRC1 3.360641979 LCP1

[0093] (e) The tissue-specific age estimation model is a hematopoietic system-specific age estimation model, and the coefficient a and constant b of each age-related protein involved are as follows:

[0094] Coefficient a Age-related proteins 72.00964548 Intercept (constant b) -18.51968325 SLC4A1 -68.09865809 CILP2 .

[0095] In certain embodiments, the subject is Asian.

[0096] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0097] In some embodiments, the subject is over 18 years old, preferably 18-70 years old. In some embodiments, the subject age distribution is balanced. In some embodiments, the difference in the number of subjects in each age group (for example, divided into 18-44 years old age group, 45-59 years old age group and 60 years old age group) is less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5% or less than 2%.

[0098] In a fifth aspect, the present application provides an age estimation model, which is obtained according to the training method described in the third aspect.

[0099] In certain embodiments, the age estimation model is a biological age estimation model.

[0100] In a sixth aspect, the present application provides a tissue-specific age estimation model, which is obtained according to the training method described in the fourth aspect.

[0101] In certain embodiments, the tissue-specific age estimation model is a biological age estimation model.

[0102] In a seventh aspect, the present application provides an age estimation device, comprising:

[0103] An input unit, used for inputting the abundance of an age-related protein of a subject, wherein the protein is as defined in the third aspect or the fourth aspect;

[0104] An estimation unit is used to calculate the estimated age of the subject according to the input age-related protein abundance using the age estimation model described in the fourth aspect or the tissue-specific age estimation model described in the fifth aspect.

[0105] In certain embodiments, the device further comprises an output unit for outputting the estimated age of the subject.

[0106] In certain embodiments, the device is a biological age estimation device.

[0107] In certain embodiments, the abundance of the age-related protein of the subject is obtained by referring to the method for establishing and processing the protein abundance dataset described in the first aspect.

[0108] In certain embodiments, the subject is Asian.

[0109] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0110] In some embodiments, the subject is within the age range of 45-60 years old.

[0111] In an eighth aspect, the present application provides an age estimation method, comprising:

[0112] Providing the abundance of an age-related protein in a subject, wherein the age-related protein is as defined in the third aspect or the fourth aspect;

[0113] The age-related protein abundance is input into the age estimation model described in the fourth aspect, the tissue-specific age estimation model described in the fifth aspect, or the age estimation device described in the seventh aspect to estimate the age of the subject.

[0114] In certain embodiments, the method is a biological age estimation method.

[0115] In certain embodiments, the abundance of the age-related protein of the subject is obtained by referring to the method for establishing and processing the protein abundance dataset described in the first aspect.

[0116] In certain embodiments, the subject is Asian.

[0117] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0118] In some embodiments, the subject is within the age range of 45-60 years old.

[0119] In a ninth aspect, the present application provides an electronic device, including:

[0120] Memory; and

[0121] A processor coupled to the memory, the processor being configured to execute the age estimation model training method described in the third aspect or the fourth aspect, or the age estimation method described in the eighth aspect, based on instructions stored in the memory.

[0122] In the tenth aspect, the present application provides a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the training method of the age estimation model described in the third aspect or the fourth aspect, or the age estimation method described in the eighth aspect.

[0123] In an eleventh aspect, the present application provides a use of the age estimation model described in the fifth aspect, the tissue-specific age estimation model described in the sixth aspect, the age estimation device described in the seventh aspect, the electronic device described in the ninth aspect, or the non-volatile computer-readable storage medium described in the tenth aspect in preparing a kit, wherein the kit is used for one or more of the following:

[0124] 1) Estimation of subject age (e.g., biological age);

[0125] 2) Evaluate the aging rate or degree of the subjects;

[0126] 3) Evaluate the rate or degree of liver aging in subjects;

[0127] 4) Evaluate the rate or degree of muscle aging in subjects;

[0128] 5) Evaluate the rate or degree of aging of the subjects’ nervous system;

[0129] 6) Evaluate the rate or degree of aging of the subject's immune system;

[0130] 7) Evaluate the aging rate or degree of the hematopoietic system of the subjects;

[0131] 8) Evaluate the effectiveness of aging interventions;

[0132] 9) Evaluate the risk of subjects suffering from aging-related diseases;

[0133] 10) Evaluate the therapeutic effects of aging-related diseases.

[0134] In certain embodiments, the evaluations described in 1) to 10) are each independently a secondary evaluation.

[0135] In certain embodiments, the subject is Asian.

[0136] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0137] In a twelfth aspect, the present application provides a method, wherein the method is used for one or more of the following:

[0138] 1) Evaluate the aging rate or degree of the subjects;

[0139] 2) Evaluate the rate or degree of liver aging in subjects;

[0140] 3) Evaluate the rate or degree of muscle aging in subjects;

[0141] 4) Evaluate the rate or degree of aging of the subjects’ nervous system;

[0142] 5) Evaluate the rate or degree of aging of the subject's immune system;

[0143] 6) Evaluate the aging rate or degree of the hematopoietic system of the subjects;

[0144] 7) Evaluate the effectiveness of aging interventions;

[0145] 8) Evaluate the risk of subjects suffering from aging-related diseases;

[0146] 9) Evaluate the therapeutic effects of aging-related diseases;

[0147] The method comprises the following steps: (1) estimating the biological age of the subject according to the method described in aspect 8; (2) comparing the biological age with a reference value; and (3) evaluating the aging rate or degree of the subject, evaluating the aging rate or degree of the subject's liver, evaluating the aging rate or degree of the subject's muscles, evaluating the aging rate or degree of the subject's nervous system, evaluating the aging rate or degree of the subject's immune system, evaluating the aging rate or degree of the subject's hematopoietic system, the effectiveness of aging intervention measures, the subject's risk of developing aging-related diseases and / or the effectiveness of treatment of aging-related diseases.

[0148] In certain embodiments, the reference value is the chronological age of the subject; or, the reference value is the biological age (or average biological age) estimated according to the method described in aspect 8 for individuals (or a group) who are in the same chronological age group as the subject and in a normal aging state; or, the subject is a subject who has received aging intervention or treatment for an aging-related disease, and the reference value is the biological age (or average biological age) estimated according to the method described in aspect 8 for individuals (or a group) who are in the same chronological age group as the subject and have not received the aging intervention or treatment for an aging-related disease and are in a normal aging state; or, the subject is a subject who has received aging intervention or treatment for an aging-related disease, and the reference value is the biological age estimated according to the method described in aspect 8 before receiving the aging intervention or before receiving the aging-related disease treatment.

[0149] In certain embodiments, in step (2), when the biological age is higher than the reference value, it indicates that the subject is in a state of accelerated aging, the subject's liver is in a state of accelerated aging, the subject's muscles are in a state of accelerated aging, the subject's nervous system is in a state of accelerated aging, the subject's immune system is in a state of accelerated aging, the subject's hematopoietic system is in a state of accelerated aging, the effect of the aging intervention measure is not significant, the subject has a higher risk of suffering from an aging-related disease, and / or the treatment effect of the aging-related disease is not significant.

[0150] In certain embodiments, the evaluations described in 1) to 5) are each independently a secondary evaluation.

[0151] In certain embodiments, the extent of aging includes normal aging, accelerated aging, and / or delayed aging.

[0152] In certain embodiments, the method is used to assess the rate or extent of liver aging in a subject, and the coefficient a and constant b of the age-related protein used in step (1) of the method to estimate the biological age of the subject according to the method described in the eighth aspect are as shown in (a) in the fourth aspect.

[0153] In certain embodiments, the method is used to assess the rate or extent of muscle aging in a subject, and the coefficient a and constant b of the age-related protein used in step (1) of the method to estimate the biological age of the subject according to the method described in the eighth aspect are as shown in (b) in the fourth aspect.

[0154] In certain embodiments, the method is used to assess the rate or extent of aging of the nervous system of a subject, and the coefficient a and constant b of the age-related protein used in step (1) of the method to estimate the biological age of the subject according to the method described in the eighth aspect are as shown in (c) of the fourth aspect.

[0155] In certain embodiments, the method is used to assess the rate or extent of aging of a subject's immune system, and the coefficient a and constant b of the age-related protein used in step (1) of the method to estimate the biological age of the subject according to the method described in the eighth aspect are as shown in (d) in the fourth aspect.

[0156] In certain embodiments, the method is used to assess the rate or extent of aging of the hematopoietic system of a subject, and the coefficient a and constant b of the age-related protein used in step (1) of the method to estimate the biological age of the subject according to the method described in the eighth aspect are as shown in (e) in the fourth aspect.

[0157] In certain embodiments, the subject is Asian.

[0158] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0159] Definition of terms

[0160] In the present invention, unless otherwise specified, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art.

[0161] When the terms "for example," "such as," "including," "comprising," or variations thereof are used herein, these terms will not be considered as limiting terms, but will be interpreted to mean "but not limited to" or "not limited to."

[0162] The terms "a" and "an" and "the" and similar referents in the context of describing the invention (especially in the context of the following claims) should be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0163] As used herein, the term "aging" has the meaning commonly understood by those skilled in the art, and is a common biological phenomenon in the growth and development of the body. Aging can be considered as a process of body function decline accompanied by aging, which is usually accompanied by a continuous decline in physical health and an increase in the risk of aging-related diseases, functional disorders and death.

[0164] As used herein, the term "aging-related diseases" refers to aging-related diseases whose risk of occurrence increases significantly with aging. In certain embodiments, the aging-related diseases include, but are not limited to, neurodegenerative diseases (such as Alzheimer's disease), diabetes, heart disease, cardiovascular and cerebrovascular diseases, hypertension, hyperlipidemia, osteoporosis, osteoarthritis, chronic kidney disease, cancer, and vision loss.

[0165] As used herein, unless the context clearly indicates, otherwise, the protein described herein has the meaning generally understood by those skilled in the art. In certain embodiments, the protein described herein comprises its wild type and natural or artificially produced mutant type. In certain embodiments, the protein described herein and its corresponding Uniprot number are as shown in Table 2 or Table 3. In certain embodiments, the protein described herein has: (i) as the amino acid sequence corresponding to its corresponding Uniprot number, or, (ii) compared with (i) having at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity amino acid sequence.

[0166] As used herein, the term "identity" is used to refer to the matching of sequences between two polypeptides or between two nucleic acids. When a position in both sequences being compared is occupied by the same base or amino acid monomer subunit (e.g., a position in each of the two DNA molecules is occupied by adenine, or a position in each of the two polypeptides is occupied by lysine), then the molecules are identical at that position. The "percent identity" between two sequences is a function of the number of matching positions shared by the two sequences divided by the number of positions compared x 100. For example, if 6 out of 10 positions in two sequences match, then the two sequences have 60% identity. For example, the DNA sequences CTGACT and CAGGTT share 50% identity (3 out of a total of 6 positions match). Typically, the comparison is made when the two sequences are aligned to produce maximum identity. Such an alignment can be achieved by using, for example, the method of Needleman et al. (1970) J. Mol. Biol. 48: 443-453, which can be conveniently performed by a computer program such as the Align program (DNAstar, Inc.). The percent identity between two amino acid sequences can also be determined using the algorithm of E. Meyers and W. Miller (Comput. Appl Biosci., 4: 11-17 (1988)), which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12, and a gap penalty of 4. In addition, the percent identity between two amino acid sequences can be determined using the Needleman and Wunsch (J MoI Biol. 48: 444-453 (1970)) algorithm, which has been incorporated into the GAP program in the GCG software package (available at www.gcg.com), using a Blossum 62 matrix or a PAM250 matrix and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6.

[0167] Advantageous Effects of the Invention

[0168] The technical solution of the present invention is based on a high-quality cohort of naturally aging healthy people in China, which eliminates the influence of factors other than aging on the results to the greatest extent. By performing proteomic analysis on easily available plasma and establishing a total protein age model and a tissue-specific protein age model, it will help to accurately measure the degree of aging of Southeast Asians or East Asians (for example, Chinese, Koreans, Japanese, Mongolians) and the degree of aging of tissues or organs, and be used to evaluate the effects of aging intervention measures or treatments of aging-related diseases. It will be an important tool for the transformation of basic aging research into clinical practice.

[0169] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings and examples, but it will be appreciated by those skilled in the art that the following drawings and examples are only used to illustrate the present invention, rather than to limit the scope of the present 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

[0170] Figure 1 : Proteins that accumulate and decrease with age identified by correlation analysis.

[0171] Figure 2 : Pathway enrichment results of age-related proteins.

[0172] Figure 3 : Aging-related plasma proteins were clustered into tissue-origin modules according to their expression abundance in different tissues.

[0173] Figure 4 : The prediction results of total protein age, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0174] Figure 5 : The prediction results of tissue-specific protein age, R is the Pearson correlation coefficient, MAE is the mean absolute error. DETAILED DESCRIPTION

[0175] The embodiments of the present invention will be described in detail below in conjunction with the examples, but those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific conditions are specified in the examples, they are carried out according to normal conditions or the conditions recommended by the manufacturer. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be obtained commercially.

[0176] The technical route of the present invention mainly involves seven parts: cohort establishment, data collection, data processing, age-related analysis, aging clock establishment, and evaluation and application of the aging clock.

[0177] Example 1: Volunteer recruitment and sample collection

[0178] The "Preliminary Screening Questionnaire" was published through the WeChat applet to collect basic information of potential volunteers who are interested in joining this study based on the inclusion criteria, which was used to screen volunteers who met the inclusion criteria; the inclusion criteria are as follows: 1) Inclusion conditions

[0179] a) Aged 18 or above, with no major bad habits;

[0180] b) The applicant is in good health and has not undergone any major surgery within the past six months. Routine conditions related to the normal aging process are permitted, including:

[0181] i. Hypertension, well controlled (taking no more than 3 medications);

[0182] ii. Osteoporosis, osteopenia and / or osteoarthritis;

[0183] iii. Benign prostatic hypertrophy;

[0184] iv. Cataract, glaucoma, macular degeneration;

[0185] v.Dyslipidemia;

[0186] vi. Hypothyroidism;

[0187] vii. Prediabetes or abnormal fasting blood sugar (fasting blood sugar 100-126 mg / dL).

[0188] c) The subjects can communicate well with the researchers and cooperate with all tasks during the study;

[0189] i. Subjects must voluntarily sign a written informed consent.

[0190] 2) Exclusion criteria

[0191] a) Age does not meet the inclusion criteria;

[0192] b) Participants are currently undergoing other clinical trials or are receiving any research drugs or devices for treatment within 30 days prior to enrollment;

[0193] c) The investigator reports a history or current diagnosis of significant chronic medical conditions, including:

[0194] i. Any cancer (except polycythemia vera, basal cell or squamous cell skin cancer);

[0195] ii. Coronary artery disease / myocardial infarction / clinically significant congestive heart failure;

[0196] iii. Stroke / transient ischemic attack;

[0197] iv. Deep vein thrombosis / pulmonary embolism;

[0198] v. Serum creatinine>1.5mg / dl (male);

[0199] vi. Poorly controlled hypertension (significant hypertension (systolic blood pressure > 160

[0200] mmHg, or diastolic blood pressure>100mmHg);

[0201] vii. History of active liver disease or metabolic acidosis;

[0202] viii. Chronic kidney disease / hemodialysis treatment, history of severe renal impairment and / or

[0203] eGFR≤45ml / min / 1.73m2;

[0204] ix. Severe autoimmune / inflammatory diseases, such as rheumatoid arthritis, lupus, Crohn's disease, etc.;

[0205] x. Neurological diseases such as dementia, such as Alzheimer's / Parkinson's, etc.;

[0206] xi. Diabetes (hemoglobin A1C>6.5% or fasting blood sugar>126 mg / dL or taking diabetes

[0207] medication or insulin therapy);

[0208] xii. Recent (within 3 months) cardiovascular disease events (myocardial infarction, coronary artery intervention, coronary artery bypass grafting);

[0209] xiii. Infectious diseases such as HIV, hepatitis, tuberculosis, etc.

[0210] d) Currently taking the following medications regularly:

[0211] i. Chemotherapy drugs (such as tamoxifen, doxorubicin, mitoxantrone, bleomycin);

[0212] ii. Antiplatelet drugs (e.g. clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine

[0213] ticlopidine / ticlopidine, except aspirin);

[0214] iii. Cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0215] e) Continuous alcohol or drug abuse;

[0216] f) Unable to provide informed consent;

[0217] g) The researcher believes that the physical factors of the participants may have an adverse effect on the research process or results.

[0218] 3) Termination criteria

[0219] a) Failure to follow the diet, work and rest schedule and physical examination requirements in accordance with the research content;

[0220] b) Experiencing abnormal physical conditions such as colds or fever during the study;

[0221] c) Accidental injury or physical discomfort during the research process;

[0222] d) Comply with the subject’s subjective wishes to terminate the program.

[0223] All volunteers who meet the inclusion criteria and are confirmed to be included in the group signed an informed consent form and joined the volunteer WeChat group for management and information communication. Within one month before the start of the project, volunteers need to maintain a normal schedule, eat a proper diet, avoid strenuous exercise, and refrain from excessive smoking or drinking. The day before the project, volunteers received a unified standardized diet (menu see Table 1) at the implementation hospital and were no longer allowed to eat other foods.

[0224] On the day of sample collection and physical examination, volunteers are required to wear clothes and shoes that are convenient for movement, are not allowed to wear makeup, and must bring identification documents (such as ID cards, etc.) to the hospital for physical examinations and biological sample collection.

[0225] Table 1 Unified standardized diet menu

[0226]

[0227] Example 2: Data Collection

[0228] 1) Biological sample processing

[0229] Blood samples were collected from volunteers after they fasted overnight. The collected blood was placed in two anticoagulant tubes covered with EDTA (for the separation of plasma and peripheral mononuclear cells, respectively), and immediately transported to the laboratory's 4°C refrigerator for subsequent processing. One tube of blood sample (approximately 5 ml) was centrifuged (400 g, 15 min, 4°C) to separate the plasma sample, which was stored in a cryopreservation tube and placed in a -80°C refrigerator for long-term storage.

[0230] 2) Plasma proteome quantification

[0231] a) Protein extraction: The sample was taken out from -80℃ and centrifuged at 12000g for 10 minutes at 4℃ to remove cell debris. The supernatant was transferred to a new centrifuge tube and centrifuged using Pierce TM Top 14 Abundant Protein Depletion Spin Columns Kit (Thermo Scientific) Instructions Remove high-abundance proteins. Protein concentration was determined using the BCA kit.

[0232] b) Trypsin hydrolysis: Take an equal amount of each sample protein for hydrolysis, add an appropriate amount of standard protein, adjust the volume to the same with lysis buffer, and then add dithiothreitol (DTT) to a final concentration of 5mM, and reduce at 56°C for 30min. Then add iodoacetamide (IAA) to a final concentration of 11mM, and incubate at room temperature in the dark for 15min. Transfer the alkylated sample to an ultrafiltration tube, centrifuge at 12000g for 20min at room temperature, replace with 8M urea 3 times, and then replace urea with replacement buffer 3 times, add trypsin at a ratio of 1:50 (protease: protein, m / m), and hydrolyze overnight. Centrifuge at 12000g for 10min at room temperature to recover the peptides, and then use ultrapure water to recover the peptides once, and combine the two peptide solutions.

[0233] c) HPLC fractionation: Agilent 300Extend C18 column was used to separate the samples. The column was 4.6×250 in size and the wavelength was 214 nm. The column oven temperature was 35°C. The column was equilibrated with 95% buffer A for 30 min. After the baseline was leveled, the graded gradient method was used. The peptide samples were loaded onto the HPLC and the method was started to separate the samples. The samples were collected at 1 min / tube. Tubes 11 to 46 were collected, a total of 36 tubes, which were combined into 12 components and dried under vacuum.

[0234] d) Liquid chromatography-mass spectrometry analysis:

[0235] Construction of spectral library: The peptides were dissolved in liquid chromatography mobile phase A and separated using the EASY-nLC 1200 ultra-high performance liquid phase system. Mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile; mobile phase B was an acetonitrile solution containing 0.1% formic acid. Liquid phase gradient settings: 0-55min, 4%~20%B; 55-67min, 20%~32%B; 67-71min, 32%~80%B; 71-75min, 80%B, and the flow rate was maintained at 500nL / min. After the peptides were separated by the ultra-high performance liquid phase system, they were injected into the Capillary ion source for ionization and then into the Orbitrap Exploris TM 480 mass spectrometer for data collection. DIA data collection: Liquid phase parameters are consistent with those used in library construction. After peptides are separated by ultra-high performance liquid phase system, OrbitrapExploris TMThe ion source voltage was set to 1.6 kV, and the peptide parent ion and its secondary fragments were detected and analyzed using TOF. The data acquisition mode used the data-independent parallel accumulation serial fragmentation (dia-PASEF) mode, and the primary mass spectrometry scanning range was set to 390-810 m / z. After one primary mass spectrum was acquired, 10 PASEF mode acquisitions were performed, and the secondary mass spectrometry scan was in the range of 100-1700 m / z, with every 25 m / z as a window.

[0236] Example 3: Data preprocessing

[0237] a) The raw data of mass spectrometry were first imported into MaxQuant software (version 1.6.15.0) and then searched based on the SwissProt human database. The key parameters included:

[0238] i. Lysing enzyme is Trypsin / P

[0239] ii. A maximum of 2 missing cleavages are allowed

[0240] iii. For the precursor ion, the mass tolerance is 20 in the first and main searches respectively.

[0241] ppm and 5ppm

[0242] iv. For fragment ions, the mass tolerance is 0.02Da

[0243] v. Fixed modification is carbamidomethylation on Cys

[0244] vi. Variable modifications include acetylation of the protein N-terminus and oxidation of Met

[0245] b) The screening criteria for search results was FDR < 1%;

[0246] c) The raw intensity values ​​will be divided by the median value of each sample to obtain a normalized protein abundance matrix.

[0247] Example 4: Age-related feature screening

[0248] a) First, the protein abundance matrix was subjected to age correlation analysis, that is, the abundance of each protein was subjected to Pearson correlation analysis with the individual's age, and BMI was used as a covariate;

[0249] b) The results were screened and only the results with a BH-corrected P value less than 0.05 were selected as age-related proteins. The results showed that these genes had an increase or decrease with age ( Figure 1 );

[0250] c) Further annotation using the Metascape tool revealed that these accumulated proteins were associated with complement, apolipoprotein, and immune inflammation activation, while the reduced proteins were mainly involved in platelet pathways, cell structure, and metabolic processes, which is consistent with previous understanding of aging ( Figure 2 ).

[0251] Example 5: Establishment of protein age model

[0252] 1) Establishment of total protein age model

[0253] a) First, the protein abundance matrix is ​​screened for features, and only features related to age are retained;

[0254] b) Split the selected matrix into 1:1 training and test sets according to the random allocation in advance;

[0255] c) Age was used as the training label and the glmnet package (version 4.1.4) was used to build the model. Nine alpha values ​​between 0.1 and 0.9 were used for modeling and tested in the test set. Finally, the alpha with the highest accuracy was selected as the parameter of the final model. The final alpha value was 0.5. The final model is shown in Table 2.

[0256] Table 2. Final model of total protein age

[0257]

[0258]

[0259] 2) Establishment of tissue-specific protein age model

[0260] In order to further measure aging from multiple tissues and perspectives, the present invention further combines the expression data of different genes in different tissues from the Human Proteome Atlas Database (https: / / www.proteinatlas.org / ) to construct a tissue-specific protein clock.

[0261] a) Data download: Download protein information of five protein categories from the “Tissue” module of the Human Proteome Atlas Database, and merge the five tables, for a total of 20,090 proteins;

[0262] b) extracting the "RNA tissue specific nTPM" column of the combined table in a according to the age-related proteins of Example 4 as the expression abundance values ​​of different proteins in each tissue, and obtaining a protein name-tissue abundance matrix;

[0263] c) Divide the above matrix into two sub-matrices, one increasing and one decreasing with age, and perform z-score transformation on the rows respectively;

[0264] d) The two transformed sub-matrices were clustered by k-means using the ComplexHeatmap R package, and 7 (upregulated) and 8 (downregulated) protein modules with different abundance patterns in different tissues were obtained, respectively ( Figure 3 );

[0265] e) Based on the types of high-expression tissues, these modules are defined as liver module, muscle module, nervous system module, immune system module, hematopoietic system module, and pan-tissue module;

[0266] f) The obtained proteins with tissue-specific expression were subjected to steps b to c in the "Establishment of the total protein age model" to establish five tissue-specific protein clocks for the liver, muscle, nervous system, immune system, and hematopoietic system;

[0267] g) The final alpha values ​​were 0.9 (liver, muscle, nervous system) and 0.1 (immune, hematopoietic system). The final model is shown in Table 3.

[0268] Table 3. Final model of tissue-specific protein age

[0269]

[0270]

[0271] Example 6: Evaluation of proteomic age model

[0272] The final model was applied to the test set to obtain the predicted biological age of the test set individuals and compared with their corresponding actual age. The results showed that the correlation coefficient between the predicted biological age and the actual age of the total protein age model reached 0.88 (Pearson correlation, with BMI as a covariate), and the average absolute error was 5.09 years, which was highly accurate ( Figure 4 ). As for the tissue-specific protein age model, the results showed that the five tissue-specific protein clocks all had good age prediction capabilities, but the prediction capabilities of different tissues were different, which indirectly reflects the differences in the aging rates of different tissues ( Figure 5 ).

[0273] Theoretically, since the test set sample types are diverse and the number of samples is large enough, it can be basically considered that in the data of the test set, biological age and actual age (chronological age) are significantly correlated. As mentioned above, when the above model of the present application is applied to the test set for biological age assessment, it shows a significant correlation with chronological age, which shows that the above clock model has a high accuracy in assessing biological age.

[0274] Although the specific embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that various modifications and changes may be made to the details according to all the teachings that have been published, and these changes are within the scope of protection of the present invention. The entire invention is given by the attached claims and any equivalents thereof.

Claims

1. A method for establishing a data set, comprising: (1) collecting biological samples from multiple subjects, performing proteomic analysis on the biological samples, obtaining the abundance of each protein in the biological samples, and obtaining a protein abundance data set; (2) Using age as a training label, the dataset is constructed for training an age estimation model.

2. The method of claim 1, wherein: The number of subjects is ≥100, for example, ≥500, ≥1000, or ≥10000.

3. The method according to claim 1 or 2, wherein the biological sample is selected from blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts; preferably plasma.

4. The method according to any one of claims 1 to 3, wherein the protein abundance dataset is obtained by the following method: i) isolating and quantifying proteins from the biological sample; ii) enzymatically hydrolyzing the protein to obtain a peptide sample; iii) analyzing the peptide sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data; iv) performing qualitative analysis based on the mass spectrometry data (e.g., importing the mass spectrometry data into MaxQuant, matching it with the SwissProt human database, and screening results with FDR < 1%) to screen the mass spectrometry data for successful qualitative analysis; Preferably, the method further comprises standardizing the successfully characterized mass spectrometry data, for example, dividing the original intensity value of the successfully characterized mass spectrometry data by the median value of all protein abundance values ​​in each biological sample.

5. The method according to any one of claims 1 to 4, before step (2), further comprising performing a correlation analysis between the protein abundance dataset and age to screen for age-related proteins; Preferably, the protein abundance dataset is subjected to Pearson correlation analysis with age, BMI is used as a covariate, the P value is corrected by the BH method, and the result with P<0.05 is selected as the protein associated with age.

6. The method according to any one of claims 1 to 5, wherein the data set is divided into a training set and a test set, for example, divided into a training set and a test set in a 1:1 ratio.

7. A method for establishing a tissue-specific dataset, comprising: (1) establishing a data set according to any one of the methods of claims 1 to 6; (2) screening the data set for tissue-specifically expressed proteins to obtain a tissue-specific protein abundance data set; the tissue being selected from the group consisting of liver, muscle, nervous system, immune system, and hematopoietic system; (3) Using age as a training label, construct the tissue-specific dataset for training the age estimation model.

8. The method according to claim 7, wherein in step (2), the tissue-specifically expressed protein is screened by the following steps: (a) Obtain human proteome map data from public databases, obtain the TPM values, FPKM values, or RPKM values ​​of the protein types expressed in each tissue and their corresponding genes, and construct a protein-tissue abundance matrix; (b) dividing the protein-tissue abundance matrix obtained in step (a) into two sub-matrices that increase and decrease with age; (c) The two sub-matrices were subjected to z-score transformation for TPM value, FPKM value or RPKM value, and K-Means clustering was performed using the ComplexHeatmap R package to determine the types of tissue-specifically expressed proteins.

9. The method according to claim 7 or 8, wherein the data set is divided into a training set and a test set, for example, divided into a training set and a test set in a 1:1 ratio.

10. A method for training an age estimation model, comprising: (1) establishing a dataset according to the method described in any one of claims 1 to 6, or establishing a tissue-specific dataset according to the method described in any one of claims 7 to 9; (2) calculating the estimated age of the subject corresponding to the data using the age estimation model based on the data set or tissue-specific data set; (3) training the age estimation model according to the difference between the estimation result and the age label of the data, thereby obtaining an age estimation model that can be used for age estimation; Preferably, the training method is a training method for a biological age estimation model.

11. The training method of claim 10, wherein the training is performed using a glmnet package.

12. The training method of claim 10 or 11, wherein the subject estimates his age in, A x represents the abundance of age-related proteins in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, the abundance of each age-related protein A is determined x The coefficient a x and constant b; Preferably, the abundance of the age-related protein is the abundance of the corresponding protein in a blood sample (eg, whole blood, plasma or serum) of a subject; preferably, the abundance of the age-related protein is the abundance of the corresponding protein in a plasma sample of a subject.

13. The training method according to claim 12, wherein: The age-related proteins involved in the age estimation model obtained in step (3) and capable of being used for age estimation are selected from one or more of the following: THBS4, VTN, VSIG4, PCOLCE, ADAMTSL4, B3GNT8, C4BPB, CRTAC1, ALDOB, IGDCC4, BMP1, CRP, COMP, SLC4A1, CD163, ALCAM, CSP G4, APOB, CTSS, ANGPTL3, LRP1, LAMC1, IGFBP2, CILP2, F13B, CST6, IGF1, PZP, SPP1, PCSK9, POMGNT1, PRG4, SSC5D, ABI3BP.

14. The training method according to claim 12 or 13, wherein: The coefficient a and constant b of each age-related protein in the age estimation model that can be used for age estimation obtained in step (3) are as follows:

15. The training method according to claim 12, wherein: The age estimation model that can be used for age estimation obtained in step (3) is a tissue-specific age estimation model, which has the following features: (1) The age estimation model that can be used for age estimation is a liver-specific age estimation model, and the age-related proteins involved are selected from one or more of the following: VTN, IGFALS, C4BPB, APOL1, ALDOB, APMAP, A1BG, CRP, APOF, APOB, MAN1A1, FGG, CFI, C9, F5, F13B, PZP, PCSK9, PRG4, SERPINA4, OAF; (2) The age estimation model that can be used for age estimation is a muscle-specific age estimation model, and the age-related proteins involved are selected from one or more of the following: SPTB, THBS4, ADAMTSL4, IGF2R; (3) The age estimation model that can be used for age estimation is a nervous system-specific age estimation model, and the age-related proteins involved are selected from one or more of the following: PLXDC2, CRTAC1, IGDCC4, CDH2, ALCAM, CTSF, CLU, NEO1, SPP1, POMGNT1, PTPRZ1, RELN; (4) The age estimation model that can be used for age estimation is an immune system-specific age estimation model, and the age-related proteins involved are selected from one or more of the following: CSF1R, CD163, CAMP, CTSS, LTA4H, IL18BP, MRC1, LCP1; (5) The age estimation model that can be used for age estimation is an age estimation model specific to the hematopoietic system, and the age-related proteins involved are selected from SLC4A1, CILP2 or a combination thereof.

16. The training method according to claim 15, wherein: The tissue-specific age estimation model that can be used for age estimation obtained in step (3) has the following features: (a) The tissue-specific age estimation model is a liver-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows: (b) The tissue-specific age estimation model is a muscle-specific age estimation model, and the coefficient a and constant b of each age-related protein involved are as follows: (c) The tissue-specific age estimation model is a nervous system-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows: (d) The tissue-specific age estimation model is an immune system-specific age estimation model, and the coefficients a and constants b of the age-related proteins involved are as follows: (e) The tissue-specific age estimation model is a hematopoietic system-specific age estimation model, and the coefficient a and constant b of each age-related protein involved are as follows: 。 17. An age estimation model, obtained by the training method according to any one of claims 10 to 14; Preferably, the age estimation model is a biological age estimation model.

18. A tissue-specific age estimation model, obtained according to the method according to any one of claims 10-12, 15-16; Preferably, the tissue-specific age estimation model is a biological age estimation model.

19. An age estimation device, comprising: An input unit for inputting the abundance of an age-related protein of a subject, wherein the age-related protein is as defined in claim 13 or 15; An estimation unit, configured to calculate the estimated age of the subject according to the input age-related protein abundance using the age estimation model according to claim 17 or the tissue-specific age estimation model according to claim 18; Preferably, the device further comprises an output unit for outputting the estimated age of the subject; Preferably, the device is a biological age estimation device.

20. A method for estimating age, comprising: Providing the abundance of an age-related protein in a subject, wherein the age-related protein is as defined in claim 13 or 15; Inputting the age-related protein abundance into the age estimation model of claim 17 or the tissue-specific age estimation model of claim 18, or the age estimation device of claim 19 to estimate the age of the subject; Preferably, the method is a biological age estimation method.

21. An electronic device, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the age estimation model training method according to any one of claims 10 to 16, or the age estimation method according to claim 20, based on instructions stored in the memory.

22. A non-volatile computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the program implements the training method of the age estimation model described in any one of claims 10 to 16, or the age estimation method described in claim 20.

23. Use of the age estimation model of claim 17, the tissue-specific age estimation model of claim 18, the age estimation device of claim 19, the electronic device of claim 21, or the non-volatile computer-readable storage medium of claim 22 in preparing a kit, wherein the kit is used for one or more of the following: 1) Estimation of subject age (e.g., biological age); 2) Evaluate the aging rate or degree of the subjects; 3) Evaluate the rate or degree of liver aging in subjects; 4) Evaluate the rate or degree of muscle aging in subjects; 5) Evaluate the rate or degree of aging of the subjects’ nervous system; 6) Evaluate the rate or degree of aging of the subject's immune system; 7) Evaluate the aging rate or degree of the hematopoietic system of the subjects; 8) Evaluate the effectiveness of aging interventions; 9) Evaluate the risk of subjects suffering from aging-related diseases; 10) Evaluate the therapeutic effects of aging-related diseases; Preferably, the evaluations described in 1) to 10) are each independently auxiliary evaluations.

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