Model and index for evaluating aging delaying effect of estrogen replacement therapy and application of model and index

Abstract

The invention belongs to the field of biomedicine, and particularly relates to a group of models, indexes or methods for evaluating an estrogen replacement treatment effect or screening estrogen replacement treatment drugs. The invention relates to a method for establishing a model for evaluating an estrogen replacement treatment effect or screening an estrogen replacement treatment drug, a model or an index for evaluating the estrogen replacement treatment effect or screening the estrogen replacement treatment drug, and a method, a device and electronic equipment for screening the estrogen replacement treatment drug. A non-volatile computer-readable storage medium and its use for assessing the effect of estrogen replacement therapy or for screening for drugs for estrogen replacement therapy.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to a group of models, indicators or methods for evaluating the effect of estrogen replacement therapy, and the use of the models, indicators or methods to evaluate the effect of estrogen replacement therapy in delaying aging. Background Art

[0002] Menopause is an inevitable process of female aging, marking the end of female reproductive ability. It may be accompanied by a variety of menopausal symptoms, such as hot flashes, insomnia, mood disorders, skin aging, vaginal atrophy and dryness, etc.; severe cases can aggravate osteoporosis, lead to cognitive decline and cardiovascular disease, etc., seriously affecting the health of middle-aged and elderly women. At present, hormone replacement therapy, which uses hormone supplements to make up for the decrease in hormone levels in menopausal women, is often used to treat female menopausal symptoms. This therapy has been shown to prevent bone loss in postmenopausal women, reduce fractures, reduce the incidence of coronary heart disease, etc., and relieve a variety of menopausal symptoms. Therefore, estrogen replacement therapy during the perimenopausal period is expected to delay the aging of middle-aged and elderly women to a certain extent. At present, there are few studies on the impact of hormone replacement therapy on the aging of the female body and its heterogeneity in the population. To solve these problems, a reliable evaluation system for estrogen replacement therapy to delay female aging is a decisive prerequisite, but such an evaluation system is still lacking.

[0003] Currently available methods for evaluating the effects of estrogen replacement therapy on aging mainly use a single clinical measurement index, such as measuring carotid artery compliance using ultrasound imaging. 1 , use a skin moisture tester to test skin moisture, use a skin elasticity tester to test skin elasticity, and use a high-frequency ultrasound device to test skin thickness 2,3 , the California Verbal Learning Test was used to test the subjects' verbal and visual memory abilities 4 .

[0004] In addition, a study has also preliminarily shown that by calculating the apparent age of the oral epithelium through the methylation clock, it is possible to reflect the delayed effect of estrogen replacement therapy on aging. 5 .

[0005] Existing technologies are limited to evaluating the effects of estrogen replacement therapy on the aging of a single tissue. However, human aging is a highly heterogeneous process, and the aging of a single tissue can hardly reflect the overall aging of a person. Therefore, current evaluation methods cannot judge the effect of estrogen replacement therapy on delaying the aging of subjects from a systematic perspective. Summary of the invention

[0006] In view of the main shortcomings of the current technology (for example, single evaluation index, inaccurate evaluation results, etc.), the main purpose of the present invention is to establish an evaluation system for measuring the effect of estrogen replacement therapy on delaying female aging from multiple levels and angles of human function.

[0007] Therefore, in a first aspect, the present application provides a method for establishing a model, comprising:

[0008] (1) Establish an age-related dataset;

[0009] (2) establishing an age estimation model based on the age-related data set obtained in step (1);

[0010] (3) Screening the age estimation models for an age estimation model that can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs.

[0011] In certain embodiments, the therapeutic effect is delaying or reversing aging, and / or preventing and / or treating aging-related diseases.

[0012] In certain embodiments, in step (3) of the method, an age estimation model that can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs is screened by the following method:

[0013] (i) providing: a set of subjects who have not received estrogen replacement therapy, and a set of subjects who have received the estrogen replacement therapy;

[0014] (ii) using the age estimation model of step (2) to determine the aging rates of the subjects in the two subject sets in (i);

[0015] (iii) comparing the measured aging rates of the two subject groups in (ii); if the measured aging rate of the subject group receiving the estrogen replacement therapy is lower than that of the subject group not receiving the estrogen replacement therapy, it indicates that the age estimation model used has the potential to be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs.

[0016] In certain embodiments, in step (iii) of the method, the measured aging rates of the two sets of subjects in (ii) are compared by using a Wilcoxon rank sum test.

[0017] In certain embodiments, the number of subjects in the subject set not receiving estrogen replacement therapy in (i) is ≥100, for example, ≥500, ≥1000, or ≥10,000; and / or the number of subjects in the subject set receiving estrogen replacement therapy in (i) is ≥100, for example, ≥500, ≥1000, or ≥10,000.

[0018] In certain embodiments, the subject is Asian.

[0019] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0020] 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%.

[0021] In some embodiments, the subject is within the age range of 45-60 years old.

[0022] In certain embodiments, the subject is female.

[0023] In certain embodiments, the aging rate of the subject is determined in step (ii) by the following method:

[0024] (a) using the age estimation model in step (2) to estimate the age of the naturally aging population in each age group to obtain a predicted age; and establishing a linear regression model between the predicted age and the actual age;

[0025] (b) applying the actual age of the subjects in the two subject sets in (i) to the linear regression model in (a) to obtain the theoretical predicted age;

[0026] (c) applying the age estimation model of step (2) to the subjects of the two subject sets in (i) to obtain predicted age;

[0027] (d) Subtract the theoretical predicted age obtained in (b) from the predicted age obtained in (c) to obtain the aging rate of the corresponding subject.

[0028] In certain embodiments, the age-related dataset in step (1) is established by the following method:

[0029] (a) performing one or more of the following analyses on multiple subjects, and obtaining a gene expression dataset, a protein abundance dataset, a metabolite abundance dataset, a cell ratio dataset, or a clinical phenotype measurement dataset based on the analysis results:

[0030] 1) Transcriptome sequencing to obtain the expression level of each gene in the biological samples of the multiple subjects;

[0031] 2) quantitative proteomics analysis to obtain the abundance of each protein in the biological samples of the multiple subjects;

[0032] 3) quantitative metabolomics analysis to obtain the abundance of each metabolite in the biological samples of the multiple subjects;

[0033] 4) quantitative analysis of cell ratios, obtaining the ratios of each type of cells in the biological samples of the multiple subjects;

[0034] 5) performing clinical phenotype detection on the multiple subjects to obtain a clinical phenotype measurement data set;

[0035] (b) Using age as a training label, construct the age-related dataset.

[0036] In certain embodiments, the subjects are ≥100 people, for example, ≥500 people, ≥1000 people, or ≥10000 people.

[0037] In certain embodiments, the subject is Asian.

[0038] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0039] 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%.

[0040] In certain embodiments, the subject is female.

[0041] In certain embodiments, the clinical phenotype detection includes a physical examination (for example, one or more of a general examination, an internal medicine examination, a surgical examination, an ultrasound examination, a body composition analysis, a routine blood test, glycosylated hemoglobin, six items of sex hormones, a routine urine test, a routine stool test, a lung function test, a bone density test, an electrocardiogram, and a biochemical analysis), and / or a mobility test (for example, one or more of a single-leg stand, a grip strength test, a 30-second sit-and-stand test, a Purdue pegboard test, and a stepping test).

[0042] In certain embodiments, the biological samples in the analyses 1)-4) are each independently selected from blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts.

[0043] In certain embodiments, the biological sample in analyses 2) and 3) is plasma; and / or, the biological sample in analyses 1) and 4) is whole blood.

[0044] In certain embodiments, the gene expression dataset is obtained by the following method:

[0045] i) obtaining immune cells from the biological sample;

[0046] ii) performing transcriptome sequencing on the immune cells;

[0047] iii) Based on the transcriptome sequencing results, the original expression values ​​of all genes in the immune cells are obtained.

[0048] In certain embodiments, in step iii), the method further comprises standardizing the original gene expression value to obtain a standardized gene expression value (e.g., a TPM gene expression value, a FPKM gene expression value, or a RPKM gene expression value). In certain embodiments, the standardization is a TPM conversion method, and the standardized gene expression value is a TPM gene expression value.

[0049] In certain embodiments, the biological sample is a blood sample (eg, a whole blood sample), and the immune cells are peripheral monocytes.

[0050] In certain embodiments, the protein abundance dataset is obtained by:

[0051] i) isolating and quantifying proteins from the biological sample;

[0052] ii) enzymatically hydrolyzing the protein to obtain a peptide sample;

[0053] iii) analyzing the peptide sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data;

[0054] 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.

[0055] 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.

[0056] 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).

[0057] In certain embodiments, the metabolite abundance dataset is obtained by:

[0058] i) analyzing the biological sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data;

[0059] ii) performing qualitative analysis based on the mass spectrum data, and screening the mass spectrum data for successful qualitative analysis.

[0060] In certain embodiments, the method further comprises normalizing the successfully characterized mass spectral data, for example, dividing the original intensity value of the successfully characterized mass spectral data by the median value of all metabolite abundance values ​​in each biological sample.

[0061] In certain embodiments, the cell proportion dataset is obtained by the following method:

[0062] (i) obtaining immune cells from the biological sample;

[0063] (ii) performing single-cell transcriptome sequencing or flow cytometry analysis on the obtained immune cells;

[0064] (iii) Based on the results of single-cell transcriptome sequencing or flow cytometry analysis, the proportion of each cell type in immune cells is obtained.

[0065] In certain embodiments, the biological sample is a blood sample (eg, a whole blood sample), and the immune cells are peripheral monocytes.

[0066] In certain embodiments, the method further comprises the following steps before step (b): performing a correlation analysis between the one or more data sets obtained in step (a) and age, and screening for age-related features.

[0067] In certain embodiments, a Pearson correlation analysis is performed between the one or more data sets obtained in step (a) and 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 age-related feature.

[0068] 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.

[0069] In certain embodiments, the method further comprises annotating the age-related features with biological functions, and classifying them based on the annotation results to obtain three sub-candidate data sets of hormone, lipid and immune.

[0070] In certain embodiments, the method further includes performing tissue-specific annotation on the age-related features, and classifying them based on the annotation results to obtain six sub-candidate data sets: liver module, muscle module, nervous system module, immune system module, hematopoietic system module, and pan-tissue module.

[0071] In certain embodiments, in step (2), the method further comprises the step of training the age estimation model, which comprises:

[0072] (a) calculating the estimated age of the subject corresponding to the age-related data set using the age estimation model;

[0073] (b) 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.

[0074] In certain embodiments, the training method is a method for training a biological age estimation model.

[0075] 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.

[0076] In certain embodiments, in the age estimation model that can be used for age estimation obtained in step (b), the subject estimates age Among them, A x represents the age-related features in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0077] Through the training, each age-related feature A is determined x The coefficient a x and the constant b.

[0078] In certain embodiments, the age estimation model obtained in step (b) that can be used for age estimation has one or more features selected from the following:

[0079] (1) The age-related characteristic is the abundance of an age-related protein, and the age-related protein is selected from one or more of the following:

[0080] 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.

[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 P35443 THBS4 P04004 VTN Q9Y279 VSIG4 Q15113 PCOLCE Q6UY14 ADAMTSL4 Q7Z7M8 B3GNT8 P20851 C4B 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

[0083] (2) The age-related characteristic is the abundance of an age-related metabolite, and the age-related metabolite is selected from one or more of the following:

[0084]

[0085]

[0086] Each age-related metabolite has a meaning generally understood by those skilled in the art. In certain embodiments, the exemplary CAS RN corresponding to each age-related metabolite is as follows:

[0087]

[0088]

[0089] (3) The age-related feature is the abundance of age-related hormone-related metabolites or proteins, the measurement data of hormone-related clinical measurement indicators, or the expression level of hormone-related genes, and the hormone-related metabolites, proteins, clinical measurement indicators or genes are selected from one or more of the following:

[0090]

[0091]

[0092] The hormone-related metabolites, proteins, clinical measurement indicators, and genes have the meanings commonly understood by those skilled in the art. In certain embodiments, the exemplary CAS RNs corresponding to each hormone-related metabolite, the exemplary Uniprot numbers corresponding to each hormone-related protein, and the exemplary Ensembl numbers corresponding to each hormone-related gene are as follows:

[0093]

[0094] (4) The age-related feature is the abundance of immune-related metabolites or proteins related to age, the measurement data of immune-related clinical measurement indicators, or the expression level of immune-related genes, and the immune-related metabolites, proteins, clinical measurement indicators or genes are selected from one or more of the following:

[0095] Feature Name Feature Type CLU protein PROS1 protein KNG1 protein C8G protein C2 protein FGB protein TFPI protein NOTCH2 protein SPP1 protein THBS4 protein CTSS protein MRC1 protein L-kynurenine Metabolites Arachidonic acid(peroxide free) Metabolites VCAM1 Gene ARG2 Gene F2RL3 Gene ITGA2B Gene ITGB5 Gene KRT31 Gene CCL24 Gene CXCR1 Gene PF4 Gene CD70 Gene TNFSF9 Gene globulin Clinical measurement indicators albumin Clinical measurement indicators CMV IgG Clinical measurement indicators

[0096] The immune-related metabolites, proteins, clinical measurement indicators, and genes have the meanings commonly understood by those skilled in the art. In certain embodiments, the exemplary CAS RNs corresponding to each immune-related metabolite, the exemplary Uniprot numbers corresponding to each immune-related protein, and the exemplary Ensembl numbers corresponding to each immune-related gene are as follows:

[0097]

[0098]

[0099] (5) The age-related characteristic is the abundance of an age-related liver protein, and the age-related liver protein is selected from one or more of the following:

[0100] VTN, IGFALS, C4BPB, APOL1, ALDOB, APMAP, A1BG, CRP, APOF, APOB, MAN1A1, FGG, CFI, C9, F5, F13B, PZP, PCSK9, PRG4, SERPINA4, OAF.

[0101] The age-related liver proteins have the meanings commonly understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to the age-related liver proteins are as follows:

[0102]

[0103]

[0104] (6) The age-related characteristic is the abundance of an age-related immune protein, and the age-related immune protein is selected from one or more of the following:

[0105] CSF1R, CD163, CAMP, CTSS, LTA4H, IL18BP, MRC1, LCP1.

[0106] The age-related immune proteins have the meanings commonly understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to the age-related immune proteins are as follows:

[0107] Uniprot Number Protein name P07333 CSF1R Q86VB7 CD163 P49913 CAMP P25774 CTSS P09960 LTA4H O95998 IL18BP P22897 MRC1 P13796 LCP1

[0108] (7) The age-related characteristic is the abundance of age-related nervous system-related proteins, and the nervous system-related proteins are selected from one or more of the following:

[0109] PLXDC2, CRTAC1, IGDCC4, CDH2, ALCAM, CTSF, CLU, NEO1, SPP1, POMGNT1, PTPRZ1, RELN.

[0110] Each of the nervous system-related proteins has a meaning commonly understood by those skilled in the art. In certain embodiments, the exemplary Uniprot numbers corresponding to each of the nervous system-related proteins are as follows:

[0111]

[0112]

[0113] In certain embodiments, the age estimation model obtained in step (b) that can be used for age estimation has one or more features selected from the following:

[0114] (1) The age-related feature involved in the model is the abundance of age-related proteins, where the coefficient a and constant b of each age-related protein are as follows:

[0115]

[0116]

[0117] (2) The age-related feature involved in the model is the abundance of age-related metabolites, where the coefficient a and constant b of each age-related metabolite are as follows:

[0118]

[0119]

[0120] (3) The age-related characteristics involved in the model are the abundance of age-related hormone-related metabolites or proteins, the measurement data of hormone-related clinical measurement indicators, or the expression level of hormone-related genes, wherein the coefficient a and the constant b of each hormone-related metabolite, protein, clinical measurement indicator or gene are as follows:

[0121]

[0122] (4) The age-related characteristics involved in the model are the abundance of age-related immune-related metabolites or proteins, the measurement data of immune-related clinical measurement indicators, or the expression level of immune-related genes, wherein the coefficient a and the constant b of each immune-related metabolite, protein, clinical measurement indicator or gene are as follows:

[0123]

[0124]

[0125] (5) The age-related feature involved in the model is the abundance of age-related liver proteins, where the coefficient a and constant b of each age-related liver protein are as follows:

[0126]

[0127]

[0128] (6) The age-related feature involved in the model is the abundance of age-related immune proteins, where the coefficient a and constant b of each age-related immune protein are as follows:

[0129] Coefficient a Protein name -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

[0130] (7) The age-related feature involved in the model is the abundance of age-related nervous system-related proteins, where the coefficient a and constant b of each nervous system-related protein are as follows:

[0131] Coefficient a Protein name 54.63822238 Intercept (constant b) 2.881203502 PLXDC2 18.56339359 CRTAC1 -68.31602221 IGDCC4 3.668497532 CDH2 18.38590337 ALCAM 16.90591413 CTSF -20.04492797 CLU 16.54447164 NEO1 16.85483007 SPP1 -116.6472463 POMGNT1 8.468567757 PTPRZ1 18.72079179 RELN .

[0132] 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.

[0133] In certain embodiments, the abundance of the age-related metabolite is the abundance of the corresponding metabolite in a blood (e.g., whole blood, plasma, or serum) sample of the subject. In certain embodiments, the abundance of the age-related metabolite is the abundance of the corresponding metabolite in a plasma sample of the subject.

[0134] In certain embodiments, the expression value of the gene is the expression value of the corresponding gene in the subject's immune cells (eg, peripheral monocytes).

[0135] In a second aspect, the present application provides an age estimation model obtained by the method described in the first aspect, wherein the age estimation model can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs, and comprises characteristics that are sensitive to estrogen replacement therapy.

[0136] In certain embodiments, the characteristic of sensitivity to estrogen replacement therapy is an age-related characteristic as defined in the first aspect.

[0137] In certain embodiments, the age estimation model is as defined in the first aspect.

[0138] In certain embodiments, the age estimation model is a biological age estimation model.

[0139] In certain embodiments, the subject is Asian.

[0140] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0141] 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%.

[0142] In certain embodiments, the subject is within the age range of 45-60 years old.

[0143] In certain embodiments, the subject is female.

[0144] In a third aspect, the present application provides a device for evaluating the effect of estrogen replacement therapy or screening estrogen replacement therapy drugs, the device comprising:

[0145] An input unit, used for inputting characteristics of a subject being sensitive to estrogen replacement therapy, wherein the characteristics of being sensitive to estrogen replacement therapy are age-related characteristics defined in the first aspect, and the subject has received estrogen replacement therapy or has received treatment with a candidate drug for estrogen replacement therapy;

[0146] an estimation unit for calculating the estimated biological age of the subject based on the input data using the model described in the second aspect; and an optional comparison unit for comparing the estimated biological age of the subject with a reference value;

[0147] Wherein, the reference value is the estimated biological age (or average estimated biological age) calculated according to the estimation unit for individuals (or groups) in the same chronological age group as the subject who have not received the estrogen replacement therapy or the estrogen replacement therapy candidate drug and are in a normal aging state; or, the reference value is the estimated biological age calculated according to the estimation unit before the subject receives the estrogen replacement therapy or the estrogen replacement therapy candidate drug.

[0148] In certain embodiments, the therapeutic effect is delaying or reversing aging, and / or preventing and / or treating aging-related diseases.

[0149] In certain embodiments, the subject's characteristics of sensitivity to estrogen replacement therapy are obtained with reference to the method for establishing and processing the dataset described in the first aspect.

[0150] In certain embodiments, the subject is Asian.

[0151] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0152] In certain embodiments, the subject is within the age range of 45-60 years old.

[0153] In certain embodiments, the subject is female.

[0154] In a fourth aspect, the present application provides a method for screening estrogen replacement therapy drugs, comprising:

[0155] (1) providing a subject's characteristics of sensitivity to estrogen replacement therapy, wherein the characteristics of sensitivity to estrogen replacement therapy are age-related characteristics as defined in the first aspect, and the subject has received treatment with a candidate drug for estrogen replacement therapy;

[0156] (2) inputting the features into the model described in the second aspect or the device described in the third aspect to obtain the estimated biological age of the subject;

[0157] (3) comparing the estimated biological age of the subject with a reference value to evaluate the therapeutic effect of the candidate drug.

[0158] In certain embodiments, the reference value is the estimated biological age (or average estimated biological age) calculated according to the estimation unit for individuals (or a group) in the same chronological age group as the subject who have not received treatment with the candidate drug and are in a normal aging state; alternatively, the reference value is the estimated biological age calculated according to the estimation unit before the subject received treatment with the candidate drug.

[0159] In certain embodiments, in step (3), when the estimated biological age of the subject is lower than the reference value, it indicates that the candidate drug has the potential to be used as an estrogen replacement therapy drug.

[0160] In certain embodiments, the subject's characteristics of sensitivity to estrogen replacement therapy are obtained with reference to the method for establishing and processing the dataset described in the first aspect.

[0161] In certain embodiments, the subject is Asian. In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0162] In certain embodiments, the subject is within the age range of 45-60 years old.

[0163] In certain embodiments, the subject is female.

[0164] In a fifth aspect, the present application provides a method for evaluating the effect of estrogen replacement therapy, comprising:

[0165] (1) providing a subject's characteristics of sensitivity to estrogen replacement therapy, wherein the characteristics of sensitivity to estrogen replacement therapy are age-related characteristics defined in the first aspect, and the subject has received estrogen replacement therapy;

[0166] (2) inputting the features into the model described in the second aspect or the device described in the third aspect to obtain the estimated biological age of the subject;

[0167] (3) comparing the estimated biological age of the subject with a reference value to evaluate the therapeutic effect of the candidate drug.

[0168] In certain embodiments, the reference value is the estimated biological age (or average estimated biological age) calculated according to the estimation unit for individuals (or a group) who are in the same chronological age group as the subject and have not received the estrogen replacement therapy and are in a normal aging state; alternatively, the reference value is the estimated biological age calculated according to the estimation unit before the subject receives the estrogen replacement therapy.

[0169] In certain embodiments, in step (3), when the estimated biological age of the subject is lower than the reference value, it indicates that the effect of the estrogen replacement therapy is good.

[0170] In certain embodiments, the subject's characteristics of sensitivity to estrogen replacement therapy are obtained with reference to the method for establishing and processing the dataset described in the first aspect.

[0171] In certain embodiments, the subject is Asian. In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0172] In certain embodiments, the subject is within the age range of 45-60 years old.

[0173] In certain embodiments, the subject is female.

[0174] In a sixth aspect, the present application provides an electronic device, including:

[0175] Memory; and

[0176] A processor coupled to the memory, the processor being configured to execute the training method defined in the first aspect, the method for screening estrogen replacement therapy drugs described in the fourth aspect, or the method for evaluating the effect of estrogen replacement therapy described in the fifth aspect based on instructions stored in the memory.

[0177] In the seventh 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 defined in the first aspect, the method for screening estrogen replacement therapy drugs described in the fourth aspect, or the method for evaluating the effect of estrogen replacement therapy described in the fifth aspect.

[0178] In an eighth aspect, the present application provides use of the model described in the second aspect, the device described in the third aspect, the electronic device described in the sixth aspect, or the non-volatile computer-readable storage medium described in the seventh aspect in preparing a kit, wherein the kit is used to evaluate the effect of estrogen replacement therapy in a subject or to screen estrogen replacement therapy drugs.

[0179] In certain embodiments, the kit is used to:

[0180] 1) To evaluate the effect of estrogen replacement therapy in delaying or reversing aging;

[0181] 2) To evaluate the effect of estrogen replacement therapy in delaying or reversing liver aging;

[0182] 3) evaluate the effectiveness of estrogen replacement therapy in delaying or reversing immune system aging;

[0183] 4) Evaluate the effectiveness of estrogen replacement therapy in delaying or reversing nervous system aging;

[0184] 5) Evaluate the potential of estrogen replacement therapy to prevent and / or treat age-related diseases; and / or,

[0185] 6) Screening of estrogen replacement therapy drugs.

[0186] In certain embodiments, the subject is Asian.

[0187] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0188] In certain embodiments, the subject is within the age range of 45-60 years old.

[0189] In certain embodiments, the subject is female.

[0190] In a ninth aspect, the present application provides the use of a substance in preparing a detection reagent, wherein the detection reagent is used to evaluate the effect of estrogen replacement therapy (e.g., the effect of delaying or reversing aging, and / or the effect of treating, preventing and / or treating aging-related diseases) or to screen estrogen replacement therapy drugs;

[0191] Wherein, the substance is selected from a reagent or a combination of reagents capable of detecting one or more of the following indicators:

[0192] (1) Alkaline phosphatase level;

[0193] (2) ALT level;

[0194] (3) aspartate aminotransferase level;

[0195] (4) cholinesterase level;

[0196] (5) follicle-stimulating hormone level;

[0197] (6) Glycated hemoglobin level;

[0198] (7) CD163 levels;

[0199] (8) LILRB1 levels;

[0200] (9) the number and / or proportion of naive CD8-positive T cells (e.g., the proportion in peripheral monocytes);

[0201] (10) the number and / or proportion of nonclassical monocytes (e.g., the proportion in peripheral monocytes);

[0202] (11) SATB1 expression;

[0203] (12) RCAN3 expression;

[0204] (13) CACHD1 expression;

[0205] (14) LRRN3 expression;

[0206] (15) Dl-β-homoleucine level;

[0207] (16) Phenylalanine level;

[0208] (17) Stachydrine level;

[0209] (18) 3-dehydrocarnitine levels;

[0210] (19) Gln-arg level;

[0211] (20) N-acetylhistamine level;

[0212] (21) L-arachidonic acid carnitine level;

[0213] (22) Stearoylcarnitine levels;

[0214] (23) 2,4,6-tri-tert-butylaniline levels;

[0215] (24) 4-pregnene-17α,20β-diol-3-mono-20-sulfate levels;

[0216] (25) Pregnenolone sulfate levels;

[0217] (26) Estrone sulfate levels;

[0218] (27) PC (16:0 / 18:0) level;

[0219] (28) Ethylmorphine levels.

[0220] It is easy for those skilled in the art to understand that the genes, proteins, and compounds described in this aspect have the meanings commonly understood by those skilled in the art.

[0221] In certain embodiments, the compounds described herein have exemplary CAS RNs as shown below:

[0222] Dl-β-homoleucine (CAS: 3653-34-7), phenylalanine (CAS: 63-91-2), stachydrine (CAS: 471-87-4), 3-dehydrocarnitine (CAS: 10457-99-5), N-acetylhistamine (CAS: 2497-02-1), L-arachidonic acid carnitine (CAS: 36816-11-2), stearoyl carnitine (CAS: 1976-27-8), 2,4, 6-Tri-tert-butylaniline (CAS: 961-38-6), 4-pregnene-17α, 20β-diol-3-mono-20-sulfate (CAS: 138949-80-1), pregnenolone sulfate (CAS: 1247-64-9), estrone sulfate (CAS: 481-97-0), PC (16:0 / 18:0) (CAS: 130614-06-1), ethylmorphine (CAS: 76-58-4).

[0223] In certain embodiments, exemplary Ensembl numbers corresponding to the genes described in this aspect are as follows:

[0224] SATB1: ENSG00000182568; RCAN3: ENSG00000117602; CACHD1: ENSG00000158966; LRRN3: ENSG00000173114.

[0225] In certain embodiments, exemplary Uniprot numbers corresponding to the proteins described in this aspect are as follows:

[0226] CD163: Q86VB7; LILRB1: Q8NHL6.

[0227] In certain embodiments, the reagent or reagent combination is used to measure the above-mentioned indicators by an in vitro detection method.

[0228] In certain embodiments, the evaluating or screening comprises the following steps:

[0229] (1) measuring one or more indicators as defined above in one or more samples from the subject, wherein the subject has received estrogen replacement therapy or has received treatment with a candidate drug for estrogen replacement therapy; (2) comparing the measured values ​​in (1) with their respective corresponding reference values; and (3) evaluating the effect of estrogen replacement therapy or treatment with the candidate drug on the subject (e.g., the effect of delaying or reversing aging, and / or, the effect of treating, preventing and / or treating aging-related diseases).

[0230] In certain embodiments, the one or more samples are each independently selected from blood (e.g., whole blood, plasma or serum), urine, saliva, cell and tissue extracts. In certain embodiments, the samples corresponding to the indicators (1) to (6) defined in claim 23 are plasma or serum samples, the samples corresponding to the indicators (7) to (8) and (15) to (28) are plasma samples, and / or the samples corresponding to the indicators (9) to (14) are samples containing immune cells (e.g., samples containing peripheral mononuclear cells, such as peripheral blood).

[0231] In certain embodiments, the reference value is the value of the corresponding indicator in the same type of sample of the subject before receiving the estrogen replacement therapy or the candidate drug treatment, or the reference value is the value (or average value) of the corresponding indicator in the same type of sample of individuals (or groups) who are not in the same chronological age group as the subject and have not received the estrogen replacement therapy or the candidate drug treatment and are in a normal aging state.

[0232] In certain embodiments, in step (3), if one or more of the following characteristics are present, it indicates that the estrogen replacement therapy is effective or the candidate drug has the potential to be used as an estrogen replacement therapy drug:

[0233] (a) the measured value of any one or more of the indicators (1) to (6) in the sample from the subject is lower than the reference value;

[0234] (b) the measured value of index (7) and / or (8) in the sample from the subject is lower than the reference value;

[0235] (c) the measured value of the indicator (9) in the sample from the subject is higher than the reference value;

[0236] (d) the measured value of the indicator (10) in the sample from the subject is lower than the reference value;

[0237] (e) the measured value of any one or more of the indicators (11) to (14) in the sample from the subject is higher than the reference value;

[0238] (f) the measured value of any one or more of the indicators (15)-(23) in the sample from the subject is lower than the reference value;

[0239] (g) the measured value of any one or more of the indicators (24)-(28) in the sample from the subject is higher than the reference value;

[0240] Among them, the indicator type corresponding to the indicator number is the same as above.

[0241] In certain embodiments, the subject is Asian; in certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0242] In certain embodiments, the subject is within the age range of 45-60 years old.

[0243] In certain embodiments, the subject is female.

[0244] In a tenth aspect, the present application provides a method for evaluating the effect of estrogen replacement therapy (e.g., the effect of delaying or reversing aging, and / or the effect of treating, preventing and / or treating aging-related diseases) or screening estrogen replacement therapy drugs; the method comprises the following steps:

[0245] (1) determining one or more indicators as defined in the ninth aspect in one or more samples from the subject, wherein the subject has received estrogen replacement therapy or has received treatment with a candidate drug for estrogen replacement therapy; (2) comparing the measured values ​​in (1) with their respective corresponding reference values; and (3) evaluating the effect of estrogen replacement therapy or the candidate drug treatment on the subject (e.g., the effect of delaying or reversing aging, and / or, the effect of treating, preventing and / or treating aging-related diseases).

[0246] In certain embodiments, the one or more samples are each independently selected from blood (e.g., whole blood, plasma or serum), urine, saliva, cell and tissue extracts. In certain embodiments, the samples corresponding to the indicators (1) to (6) defined in claim 23 are plasma or serum samples, the samples corresponding to the indicators (7) to (8) and (15) to (28) are plasma samples, and / or the samples corresponding to the indicators (9) to (14) are samples containing immune cells (e.g., samples containing peripheral mononuclear cells, such as peripheral blood).

[0247] In certain embodiments, the reference value is the value of the corresponding indicator in the same type of sample of the subject before receiving the estrogen replacement therapy or the candidate drug treatment, or the reference value is the value (or average value) of the corresponding indicator in the same type of sample of individuals (or groups) who are not in the same chronological age group as the subject and have not received the estrogen replacement therapy or the candidate drug treatment and are in a normal aging state.

[0248] In certain embodiments, in step (3), if one or more of the following characteristics are present, it indicates that the estrogen replacement therapy is effective or the candidate drug has the potential to be used as an estrogen replacement therapy drug:

[0249] (a) the measured value of any one or more of the indicators (1) to (6) in the sample from the subject is lower than the reference value;

[0250] (b) the measured value of index (7) and / or (8) in the sample from the subject is lower than the reference value;

[0251] (c) the measured value of the indicator (9) in the sample from the subject is higher than the reference value;

[0252] (d) the measured value of the indicator (10) in the sample from the subject is lower than the reference value;

[0253] (e) the measured value of any one or more of the indicators (11) to (14) in the sample from the subject is higher than the reference value;

[0254] (f) the measured value of any one or more of the indicators (15)-(23) in the sample from the subject is lower than the reference value;

[0255] (g) the measured value of any one or more of the indicators (24)-(28) in the sample from the subject is higher than the reference value;

[0256] Among them, the indicator type corresponding to the indicator number is the same as above.

[0257] In certain embodiments, the subject is Asian; in certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0258] In certain embodiments, the subject is within the age range of 45-60 years old.

[0259] In certain embodiments, the subject is female.

[0260] Definition of terms

[0261] 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.

[0262] 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."

[0263] 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.

[0264] 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.

[0265] 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.

[0266] As used herein, the term "estrogen replacement therapy" (HRT) refers to the treatment of postmenopausal or perimenopausal women with estrogen deficiency with supplementation of estrogen to prevent or alleviate their menopausal symptoms.

[0267] 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 6 or Table 8-10. 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.

[0268] As used herein, unless the context clearly indicates, the genes described herein have the meanings generally understood by those skilled in the art. In certain embodiments, the genes described herein include their wild type and natural or artificially generated mutant types. In certain embodiments, the genes described herein and their corresponding Ensembl numbers are shown in Table 5 or Table 9-10. In certain embodiments, the genes described herein have: (i) a nucleotide sequence corresponding to its corresponding Ensembl number, or (ii) a nucleotide sequence 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 compared to (i).

[0269] 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 Mol. 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.

[0270] As used herein, unless the context clearly indicates otherwise, the compounds described herein have the meanings generally understood by those skilled in the art, for example, the compounds used herein and their corresponding CAS numbers are shown in Table 7, Table 9-10. The compounds can be obtained by separation and purification from organisms or the environment, or by artificial preparation.

[0271] As used herein, the compound "Gly-Pro-Arg" has the meaning generally understood by those skilled in the art, and refers to a tripeptide having the amino acid sequence shown in Gly-Pro-Arg from the N-terminus to the C-terminus. In certain embodiments, the tripeptide exists in the form of a chain peptide or a cyclic peptide. In certain embodiments, the tripeptide contains or does not contain modified (e.g., naturally modified or artificially modified) amino acid residues.

[0272] As used herein, the compound "Gln-arg" has the meaning generally understood by those skilled in the art, and refers to a dipeptide having an amino acid sequence shown in Gln-arg from N-terminus to C-terminus. In certain embodiments, the dipeptide contains or does not contain modified (e.g., naturally modified or artificially modified) amino acid residues.

[0273] As used herein, the compound "Pc 38:4", i.e., phosphatidylcholine 38:4 (lecithin 38:4), has the meaning generally understood by those skilled in the art. Generally speaking, it refers to a 1,2-diacyl-sn-glycero-3-phosphocholine, wherein the acyl groups at C-1 and C-2 contain a total of 38 carbons and 4 double bonds. In certain embodiments, the structural formula of the compound Pc 38:4 is as follows:

[0274]

[0275] As used herein, the compound "5.alpha.-pregnan-3.alpha.,17-diol-20-one 3-sulfate" refers to 5α-pregnan-3α,17-diol-20-one 3-sulfate, which has the meaning generally understood by those skilled in the art. In certain embodiments, the structural formula of the compound is as follows

[0276]

[0277] Advantageous Effects of the Invention

[0278] The present invention establishes an evaluation system for estrogen replacement therapy to delay female systemic aging from multiple levels of clinical, tissue and organ, cell and molecule, making the evaluation system more comprehensive and systematic, and the evaluation results more accurate.

[0279] 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

[0280] Figure 1 :The expression of marker genes of different cell types in single-cell transcriptome maps.

[0281] Figure 2 : Cell type annotation results of human peripheral monocyte transcriptome atlas.

[0282] Figure 3 : K-means clustering was used to screen the multi-level data for stable changes with age, among which Group 1 and Group 2 were stable change groups.

[0283] Figure 4 : Figure 3 The trend graph of the characteristics of different groups changing with age also clearly shows that the characteristics in group 1 and group 2 change steadily with age.

[0284] Figure 5 : Age prediction results of four single-level aging clocks, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0285] Figure 6 : Clustering of tissue-origin modules of aging-related plasma proteins based on their expression levels in different tissues.

[0286] Figure 7 : Age prediction results of 5 tissue-specific protein aging clocks, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0287] Figure 8 : Functional annotation results of age-related molecular features. The pathways circled by different lines are three main types.

[0288] Fig. 9 : The multidimensional composite aging clock predicts age results, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0289] Fig.10 : Six clinical measurement indicators (alanine aminotransferase, alkaline phosphatase, aspartate aminotransferase, cholinesterase, follicle-stimulating hormone, and glycosylated hemoglobin) that can evaluate the anti-aging effect of estrogen replacement therapy, as well as control indicators (aldosterone, small nail board test, maximum vital capacity, blood sugar, low-density lipoprotein, and L3-bone mineral density).

[0290] Fig.11 : Two plasma proteins (CD163 and LILRB1) that can evaluate the anti-aging effect of estrogen replacement therapy, and control proteins (LRP1, APOC4, and APOC3).

[0291] Fig.12: 14 plasma metabolites and control metabolites that can evaluate the anti-aging effect of estrogen replacement therapy.

[0292] Fig.13 : Two blood cell types (naive CD8+T and non-classical monocytes) that can be used to evaluate the anti-aging effect of estrogen replacement therapy, as well as control cell types (naive CD4+T, toxic CD4+T, toxic CD8+T).

[0293] Fig.14 : Four genes (SATB1, RCAN3, CACHD1, LRRN3) that can evaluate the anti-aging effect of estrogen replacement therapy, and a control gene (NT5E).

[0294] Fig.15 : Four aging clocks (hormone clock, protein clock, metabolic clock, immune clock) and control clocks (phenotypic clock, transcriptional clock, complex clock, lipid clock) that can evaluate the anti-aging effect of estrogen replacement therapy.

[0295] Fig.16 : Three tissue protein aging clocks (immune protein clock, liver protein clock, nervous system protein clock) and control clocks (hematopoietic system protein clock, muscle protein clock) that can evaluate the anti-aging effect of estrogen replacement therapy. DETAILED DESCRIPTION

[0296] The invention will now be described with reference to the following examples which are intended to illustrate the invention rather than to limit the invention.

[0297] Unless otherwise specified, the molecular biology experimental methods and immunoassays used in the present invention are basically carried out with reference to the methods described in J. Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd Edition, Cold Spring Harbor Laboratory Press, 1989, and FM Ausubel et al., A Compendium of Molecular Biology Experiments, 3rd Edition, John Wiley & Sons, Inc., 1995; the use of restriction endonucleases is in accordance with the conditions recommended by the product manufacturer. It is understood by those skilled in the art that the embodiments describe the present invention by way of example and are not intended to limit the scope of the invention claimed.

[0298] Example 1: Volunteer recruitment and sample collection

[0299] 1) Natural aging population

[0300] 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: A) Inclusion conditions

[0301] a) Aged 18 or above, with no major bad habits;

[0302] 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:

[0303] i. Hypertension, well controlled (taking no more than 3 medications);

[0304] ii. Osteoporosis, osteopenia and / or osteoarthritis;

[0305] iii. Benign prostatic hypertrophy;

[0306] iv. Cataract, glaucoma, macular degeneration;

[0307] v.Dyslipidemia;

[0308] vi. Hypothyroidism;

[0309] vii. Prediabetes or abnormal fasting blood sugar (fasting blood sugar 100-126 mg / dL).

[0310] c) The subjects can communicate well with the researchers and cooperate with all tasks during the study;

[0311] i. Subjects must voluntarily sign a written informed consent.

[0312] B) Exclusion criteria

[0313] a) Age does not meet the inclusion criteria;

[0314] b) Participants are currently undergoing other clinical trials or are receiving any research drugs or devices for treatment within 30 days prior to enrollment;

[0315] c) The investigator reports a history or current diagnosis of significant chronic medical conditions, including:

[0316] i. Any cancer (except polycythemia vera, basal cell or squamous cell skin cancer);

[0317] ii. Coronary artery disease / myocardial infarction / clinically significant congestive heart failure;

[0318] iii. Stroke / transient ischemic attack;

[0319] iv. Deep vein thrombosis / pulmonary embolism;

[0320] v. Serum creatinine>1.5mg / dl (male);

[0321] vi. Poorly controlled hypertension (significant hypertension (systolic blood pressure > 160

[0322] mmHg, or diastolic blood pressure>100mmHg);

[0323] vii. History of active liver disease or metabolic acidosis;

[0324] viii. Chronic kidney disease / hemodialysis treatment, history of severe renal impairment and / or

[0325] eGFR≤45ml / min / 1.73m2;

[0326] ix. Severe autoimmune / inflammatory diseases, such as rheumatoid arthritis, lupus, Crohn's disease, etc.;

[0327] x. Neurological diseases such as dementia, such as Alzheimer's / Parkinson's, etc.;

[0328] xi. Diabetes (hemoglobin A1C>6.5% or fasting blood sugar>126 mg / dL or taking diabetes

[0329] medication or insulin therapy);

[0330] xii. Recent (within 3 months) cardiovascular disease events (myocardial infarction, coronary artery intervention, coronary artery bypass grafting);

[0331] xiii. Infectious diseases such as HIV, hepatitis, tuberculosis, etc.

[0332] d) Currently taking the following medications regularly:

[0333] i. Chemotherapy drugs (such as tamoxifen, doxorubicin, mitoxantrone, bleomycin);

[0334] ii. Antiplatelet drugs (e.g. clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine

[0335] ticlopidine / ticlopidine, except aspirin);

[0336] iii. Cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0337] e) Continuous alcohol or drug abuse;

[0338] f) Unable to provide informed consent;

[0339] g) The researcher believes that the physical factors of the participants may have an adverse effect on the research process or results.

[0340] C) Termination criteria

[0341] a) Failure to follow the diet, work and rest schedule and physical examination requirements in accordance with the research content;

[0342] b) Experiencing abnormal physical conditions such as colds or fever during the study;

[0343] c) Accidental injury or physical discomfort during the research process;

[0344] d) Comply with the subject’s subjective wishes to terminate the program.

[0345] 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.

[0346] 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 examination and biological sample collection.

[0347] Table 1 Unified standardized diet menu

[0348]

[0349] 2) Estrogen replacement therapy cohort

[0350] The estrogen replacement therapy cohort inclusion criteria are as follows:

[0351] a) Female aged 48-55 years.

[0352] b) Having taken estrogen drugs for more than 2 years in accordance with doctor's advice;

[0353] c) The applicant must be in good health and have no major diseases or chronic diseases, such as: any cancer (except polycythemia vera / basal cell or squamous cell skin cancer); coronary artery disease / myocardial infarction; stroke / transient ischemic attack; deep vein thrombosis / pulmonary embolism; chronic kidney disease / hemodialysis treatment; autoimmune disease / inflammation, such as rheumatoid arthritis / lupus / Crohn's disease, etc.; Alzheimer's / Parkinson's disease; diabetes (taking diabetes medication or insulin treatment); aortic aneurysm / cerebral aneurysm.

[0354] d) No premature ovarian failure, hysterectomy / endometrial ablation, or polycystic ovary syndrome.

[0355] e) Does not have the following symptoms: known or suspected pregnancy; unexplained vaginal bleeding; known or suspected breast cancer and sex hormone-dependent malignant tumors; active venous or arterial thromboembolic disease; severe liver or kidney dysfunction; porphyria; otosclerosis; meningioma; uterine fibroids, endometriosis, history of endometrial hyperplasia, uncontrolled diabetes and severe hypertension, gallbladder disease, migraine, epilepsy, asthma, hyperprolactinemia.

[0356] f) Not currently taking the following drugs: chemotherapy drugs (such as tamoxifen, doxorubicin, etc.); antiplatelet drugs (such as clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine / Ticlopidine, except aspirin); cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0357] g) No study drugs or devices were used within 30 days before enrollment and no other clinical trials were participated in within 3 months before enrollment.

[0358] After the inclusion subjects are determined, other examinations and sample collection procedures are the same as those for the naturally aging population cohort.

[0359] Example 2: Data Collection

[0360] 1) Clinical phenotype detection

[0361] The physical examination included general examination, internal medicine examination, surgical examination, B-ultrasound examination, body composition analysis, blood routine test, glycosylated hemoglobin, six items of sex hormones, urine routine test, stool routine test, lung function test, bone density test, electrocardiogram and biochemical analysis. The specific indicators are shown in Table 2.

[0362] Table 2 Detailed indicators of physical examination

[0363]

[0364]

[0365]

[0366]

[0367]

[0368]

[0369]

[0370]

[0371] In addition, the clinical measurement indicators also include 5 mobility tests, as shown in Table 3.

[0372] Table 3 Action ability test detection method

[0373]

[0374] 2) Biological sample processing

[0375] Blood samples were obtained by venous blood sampling after the volunteers 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 (400g, 15min, 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. The steps for separating peripheral mononuclear cells are as follows:

[0376] a) Dilute EDTA anticoagulated peripheral blood with PBS at a ratio of 1:1 in a 15 mL centrifuge tube and invert

[0377] 4 to 5 times, mix thoroughly;

[0378] b) Take another 15 mL centrifuge tube, add 1 / 3 volume of lymphocyte separation solution, and carefully add the diluted peripheral blood vertically and slowly along the tube wall onto the lymphocyte separation solution, keeping the liquid surface clear;

[0379] (It is recommended to use a 200μL pipette to add blood sample at the beginning, and after a certain thickness is formed, use a 1mL pipette

[0380] Pipette)

[0381] c) Horizontal centrifugation, 400g, 30min, 4°C, the centrifuge lifting speed needs to be adjusted to the lowest, the lifting speed is 1, the lowering speed is 0, and the total centrifugation time is about 1h;

[0382] d) After centrifugation, it is divided into three layers: the upper layer is plasma and PBS, the middle layer is lymphocyte separation fluid, and the lower layer is mainly red blood cells and granulocytes. There is a narrow white cloudy band at the interface between the upper and middle layers, which is PBMC. First, use a 1mL pipette to discard most of the upper yellow liquid, and the remaining 2mL to the PBMC should be carefully transferred to a 15mL centrifuge tube with a 1mL or 200μL pipette;

[0383] e) Add 10 mL of PBS, mix gently, centrifuge (1500 rpm, 15 min), remove the supernatant, and repeat twice;

[0384] f) Resuspend in 6 mL PBS, take 1 mL aliquots into individual cryopreservation tubes, centrifuge at 400 g, 15 min, 4°C, remove the supernatant, resuspend in 1 mL Trizol, mix by gently pipetting, and freeze in a -80°C freezer for transcriptome sequencing;

[0385] g) Take another 2 mL of the PBS resuspended liquid in step f and use cell freezing solution (90% FBS + 10%

[0386] DMSO) to resuspend into single cells and cryopreserve the cells.

[0387] 3) Peripheral monocyte transcriptome sequencing

[0388] a) For PBMC samples treated with Trizol, thaw on ice and add 0.2 ml chloroform per ml sample, shake and place at room temperature for 3-5 minutes, centrifuge at 4°C for 15 minutes (10000g), transfer the colorless aqueous phase to a clean centrifuge tube, add 0.5 ml isopropanol and place at room temperature for 10 minutes. Centrifuge at 4°C for 10 minutes (10000g), discard the supernatant and add 1 ml pre-cooled 75% alcohol, resuspend and centrifuge at 4°C (8000g) for 5 minutes, discard the supernatant, dry at room temperature, dissolve the sample in 20 μl DEPC water and store at -80°C for later use.

[0389] b) After dissolving the RNA sample in the above step, dilute and pipette 1-3 μg RNA sample according to the concentration for

[0390] VAHTS Universal V6 RNA-seq Library Prep Kit (Illumina, NR604-01 / 02) was used to construct RNA-seq sequencing libraries;

[0391] c) The obtained RNA-seq library was sequenced with double ends using the Illumina Hisses X-Ten sequencing platform to obtain a sequencing file with a read length of 150 bp.

[0392] 4) Single-cell transcriptome sequencing of peripheral monocytes

[0393] a) Thaw the frozen cells quickly at 37°C, centrifuge and resuspend in 0.4% BSA / PBS.

[0394] PI staining, live cells were sorted using flow cytometry;

[0395] b) The sorted cells were stained with AO / PI dual fluorescence and counted automatically by Luna-FL instrument, with 14,000 cells collected for each sample;

[0396] c) Place cells and 10X Genomics reagents on the machine to prepare a single water-in-oil suspension;

[0397] d) reverse transcription into cDNA by PCR and freezing at -20°C;

[0398] e) Prepare single cell library from cDNA according to the protocol of Chromium Next GEM Single Cell 3′ Reagent Kits v3.1;

[0399] f) The obtained RNA-seq library was sequenced with the Nova-PE150 sequencing platform to obtain a sequencing file with a read length of 150 bp.

[0400] 5) Plasma proteome quantification

[0401] 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 14Abundant Protein Depletion Spin Columns Kit (Thermo Scientific)Instructions Remove high-abundance proteins. Protein concentration was determined using the BCA kit.

[0402] 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.

[0403] 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.

[0404] d) Liquid chromatography-mass spectrometry analysis:

[0405] 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 TM The 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.

[0406] 6) Plasma metabolome analysis

[0407] Plasma samples were thawed at 4°C, and 100 μL of the sample was mixed with 400 μL of ice-cold methanol / acetonitrile (volume ratio 1:1) to remove proteins. The mixture was centrifuged at 14,000 g for 15 min at 4°C, and the supernatant was removed. For LC-MS analysis, the sample was reconstituted in 100 μL of acetonitrile / water (volume ratio 1:1) solvent. The analysis was performed using high-performance liquid chromatography (UHPLC) (1290 Infinity LC from Agilent Technologies) coupled to a quadrupole time-of-flight mass spectrometer (AB Sciex TripleTOF 6600). For hydrophilic interaction liquid chromatography (HILIC) separation, the samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column (Waters, Ireland). For the first MS / MS acquisition, the instrument was set to acquire over the m / z range of 60–1000 Da, and the product ion scan time was set to 0.2 s / spectrum. The second MS / MS acquisition was performed in the m / z range of 25-1000Da with a time of 0.05 sec / spectrum. Product ion scans were performed using information-dependent acquisition (IDA) with high sensitivity mode selected. Briefly, the dissociation potential (DP) was fixed at ±60V and isotopes within the 4Da range were excluded. The collision energy (CE) was fixed at 35V, ±15eV. Ten candidate ions were monitored per cycle.

[0408] Example 3: Data preprocessing

[0409] 1) Peripheral monocyte transcriptome data

[0410] a) Use TrimGlalore software (version 0.4.5) to remove the adapter from the original fastq file of sequencing;

[0411] b) Use HISAT2 software to align the processed fastq file to the hg19 human reference genome to obtain a bam format alignment result file;

[0412] c) Use the samtools tool to convert the bam file into a sam file;

[0413] d) The alignment results with alignment scores > 20 were used to quantify gene expression using HTSeq software (version 0.11.0) to obtain raw count data;

[0414] e) The obtained quantitative matrix was processed according to the following formula to obtain the TPM value of all genes in each sample:

[0416]

[0417] Here, n is the number of all genes.

[0418] 2) Peripheral monocyte single-cell transcriptome data

[0419] a) The raw data were first processed by CellRanger software (version 4.0.0) to obtain the gene-cell expression matrix;

[0420] b) The expression matrix was read in using the Seurat package (version 3.2.3), and the cells were preliminarily screened to retain only cells with a mitochondrial gene content <20% and a gene number >400;

[0421] c) Then, the DoubletFinder package (version 2.0.2) was used to remove potential multicellularity;

[0422] d) After filtering, each sample was normalized by Seurat's SCTransform function and reduced in dimension by the RunPCA function. Based on the dimensionality reduction result, the SelectIntegrationFeatures function was used to select 3,000 highly variable genes for sample integration;

[0423] e) After running the PreSCTIntegration function, all samples were integrated according to the Seurat RPCA integration process;

[0424] f) The integrated samples are processed by RunUMAP, FindNeighbors, and FindClusters functions to complete the dimensionality reduction and clustering steps;

[0425] g) Based on the marker genes of cell types, the clustered cell clusters were further annotated to obtain single-cell transcriptome maps of 18 blood cell types ( Figure 1-2 );

[0426] h) Subset each cell type, retaining only 200 cells of each type, and extract the original expression matrix of these 3600 (200*18) cells as the feature matrix for deconvolution of transcriptome data;

[0427] i) Input the feature matrix obtained in the previous step and the original count data of the transcriptome obtained in step 1) into the Impute Cell Fraction function module of the CIBERSORTx tool, set the number of iterations to 500, and perform S-mode batch correction;

[0428] j) Finally, the deconvolution result of the cell type ratio of the transcriptome data is obtained (cell ratio-cell type matrix).

[0429] 3) Plasma proteome data

[0430] 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:

[0431] i. Lysing enzyme is Trypsin / P

[0432] ii. A maximum of 2 missing cleavages are allowed

[0433] iii. For the precursor ion, the mass tolerance is 20ppm and 5ppm in the first and main searches respectively

[0434] iv. For fragment ions, the mass tolerance is 0.02Da

[0435] v. Fixed modification is carbamidomethylation on Cys

[0436] vi. Variable modifications include acetylation of the protein N-terminus and oxidation of Met

[0437] b) The screening criteria for search results was FDR < 1%;

[0438] c) The raw intensity values ​​will be divided by the median value of each sample to obtain a normalized protein abundance matrix.

[0439] 4) Plasma metabolome data

[0440] a) First, the mass spectrometry raw data was converted into an MzXML file using ProteoWizard MSConvert (version 3.0.6428);

[0441] b) Then, peak screening was performed using XCMS software (version 3.14.1), with key parameters including: centWave m / z = 10 ppm, peakwidth = c (10, 60), prefilter = c (10, 100);

[0442] c) Then the screened peaks are grouped, and the key parameters include: bw = 5, mzwid = 0.025, minfrac = 0.5;

[0443] d) Then, the isotopes and adducts were annotated using CAMERA software (version 3.6), and only the extracted ion features with non-zero values ​​exceeding 50% in all samples were retained;

[0444] e) Identification of metabolite species was performed by comparing the m / z values ​​(<10 ppm) with the mass spectra in the database (from Sino-Science New Life Science Company);

[0445] f) The raw intensity values ​​will be divided by the median value of each sample to obtain a normalized metabolite abundance matrix.

[0446] Example 4: Age-related feature calculation

[0447] a) firstly, performing age correlation analysis on the clinical measurement value matrix and the TPM gene expression matrix, cell ratio matrix, protein abundance matrix, and metabolite abundance matrix obtained in Example 3, i.e., performing Pearson correlation analysis on the value of each clinical measurement value / gene / cell type / protein / metabolite and the age of the individual, and using BMI as a covariate;

[0448] b) The results were screened and only those with a BH-corrected P value less than 0.05 were selected as clinical measurements / genes / cell types / proteins / metabolites associated with age.

[0449] Example 5: Establishment of the Aging Clock

[0450] 1) Screening for characteristics that change steadily with age

[0451] a) merging the matrices of age-related features obtained in Example 4 together (features as rows, individuals as columns, except for cell proportions), and performing z-score conversion on the rows;

[0452] b) The transformed feature-individual matrix will be further clustered by the ComplexHeatmap R package to obtain 6 feature groups with different abundance patterns in the age trajectory ( Figure 3 );

[0453] c) Select the two feature groups (group 1 and group 2) that are most stable with age as the features that change stably with age ( Figure 3-4 ).

[0454] 2) Establishment of a single-level aging clock

[0455] This section includes the construction of phenotypic (clinical measurements), transcriptional (peripheral monocyte transcriptome), protein (plasma proteome), and metabolic (plasma metabolome) clocks.

[0456] a) First, the clinical measurement value matrix, gene expression matrix, protein abundance matrix, and metabolite abundance matrix are screened for features, and only the features that change stably with age are retained;

[0457] b) Then the four matrices are screened for missing values, where the clinical measurement matrix only retains samples with less than 10 missing values, and the other three matrices only retain features detected in all samples;

[0458] c) Split the selected matrix into 1:1 training and test sets according to the random assignment in advance; d) Use age as the training label and use the glmnet package (version 4.1.4) to build the model.

[0459] We used 9 alpha values ​​between 0.1 and 0.9 to build the model and tested it in the test set. Finally, we selected the alpha with the highest accuracy as the parameter of the final model.

[0460] e) The final alpha value is 0.7 (phenotypic clock), 0.1 (transcription clock), 0.5 (protein clock),

[0461] 0.4 (metabolic clock), the final model is shown in Table 4-7;

[0462] f) The final model was applied to the test set to obtain the predicted biological age of the individuals in the test set and compared with their corresponding actual age. The results showed that all four single-level aging clocks had good biological age prediction capabilities ( Figure 5 ). In theory, 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, the biological age and the 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 the chronological age, which shows that the model has a high accuracy in assessing biological age.

[0463] Table 4. Final model of phenotypic clock

[0464]

[0465]

[0466] Table 5. Final model of transcriptional clock

[0467]

[0468]

[0469] Table 6. Final protein clock model

[0470]

[0471]

[0472] Table 7. Final metabolic clock model

[0473]

[0474]

[0475] 3) Establishment of tissue-specific protein clock

[0476] In order to further measure aging from multiple tissues, 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.

[0477] 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;

[0478] b) extracting the "RNA tissuespecific nTPM" column of the table merged in step 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;

[0479] 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;

[0480] 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 6 );

[0481] 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;

[0482] f) The obtained proteins with tissue-specific expression were respectively used with reference to steps b) to d) in step 2) of Example 5 to establish five tissue-specific protein clocks for liver, muscle, nervous system, immune system, and hematopoietic system;

[0483] g) The final alpha value was 0.9 (liver, muscle, nervous system) and 0.1 (immune, hematopoietic system), and the final model is shown in Table 8;

[0484] h) The final model was applied to the test set to obtain the predicted biological age of the individuals in the test set and compared with their corresponding actual age. The results showed that the five tissue-specific protein clocks all had good biological age prediction capabilities, but the prediction capabilities of different tissues were different, which indirectly reflected the differences in the aging rates of different tissues ( Figure 7 ).

[0485] Table 8. Final model of tissue-specific protein clock

[0486]

[0487]

[0488] 4) Establishment of a multidimensional composite aging clock

[0489] The aging clocks established in this step include the total multidimensional composite aging clock and the composite aging clock with biological function annotations. The specific steps are as follows:

[0490] a) firstly annotate the age-related genes / proteins / metabolites obtained in 1) using the MetaboAnalyst tool (version 5.0);

[0491] b) Pathways with similar functions were manually merged into one major category, and finally the three major categories of hormones, lipids, and immunity were determined ( Figure 8 );

[0492] c) Classify age-related clinical indicators into hormone, lipid, and immune categories, and annotate clinical indicators including:

[0493] i. Lipids: cholesterol, low-density lipoprotein, apolipoprotein A, triglycerides

[0494] ii. Immunity: globulin, albumin, cytomegalovirus IgG

[0495] iii. Hormones: anti-Mullerian hormone, dehydroepiandrosterone sulfate, aldosterone, prolactin, luteinizing hormone, follicle-stimulating hormone

[0496] d) First, the clinical measurement value matrix and the TPM gene expression matrix, protein abundance matrix, and metabolite abundance matrix obtained in Example 3 were respectively normalized to 0-1, that is, the following processing was performed:

[0497]

[0498] Matrix is ​​all the values ​​of the four matrices, and missing values ​​will not be included in the calculation of the maximum / minimum values.

[0499] e) merging the four standardized matrices and retaining only the age-related features screened in Example 4 to obtain a screened full matrix;

[0500] f) Use the full matrix (corresponding to the total composite clock) and the three sub-matrices (corresponding to the hormone, lipid, and immune composite clocks) composed of the features included in the three major categories obtained in c for subsequent operations;

[0501] g) Filter out samples with missing values ​​greater than or equal to 10;

[0502] h) establishing a composite clock model by referring to steps c)-d) in step 2) of embodiment 5 respectively;

[0503] i) The final alpha values ​​were 0.3 (total composite clock), 0.9 (hormone composite clock), 0.9 (lipid composite clock), and 0.7 (immune composite clock). The final models are shown in Tables 9-10;

[0504] j) The final model was applied to the test set to obtain the predicted biological age of the individuals in the test set and compared with their corresponding actual ages. The results showed that the total composite clock had the best prediction effect, indicating that the input of more levels of information can effectively improve the prediction effect of the model ( Fig. 9 ). The three sub-composite clocks with biological function annotations also achieved good biological age prediction results, and the accuracy of different sub-composite clocks varied, which may reflect the different changes in different functions with age, that is, hormone aging changes most obviously with age, while immune and lipid aging are slower than hormone aging ( Fig. 9 ).

[0505] Table 9. Total composite clock final model

[0506]

[0507]

[0508]

[0509] Table 10. Functionally annotated sub-complex clock final model

[0510]

[0511]

[0512]

[0513] Example 6: Evaluation of the effect of estrogen replacement therapy

[0514] 1) Evaluation of the effects of estrogen replacement therapy on age-related characteristics

[0515] The aim of this section was to identify age-related characteristics of treatment effects by comparing volunteers treated with estrogen replacement therapy (hereafter referred to as the aged-HRT group) with control volunteers of the same age range (hereafter referred to as the aged group).

[0516] For age-related clinical measurements, proteins, metabolism, and cellular proportions, the values ​​of the young, old, and old-HRT groups were compared using the Wilcoxon rank sum test to identify indicators that changed oppositely from the young group to the old group in the old-HRT group.

[0517] Among the clinical measurements, six effective indicators were identified, including alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, cholinesterase, follicle-stimulating hormone, and glycated hemoglobin ( Fig.10 ), including alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, and cholinesterase as markers of liver function and liver damage, indicating that the concentration of these four enzymes in the blood can be used to focus on evaluating the effect of estrogen replacement therapy on delaying liver aging. In addition, compared with other clinical indicators that are also related to age, such as aldosterone level, small nail board test, maximum vital capacity, blood sugar, low-density lipoprotein, and L3-bone density, the above six effective indicators have significantly better evaluation effects ( Fig.10 ).

[0518] Among plasma proteins, two effective indicators were identified, namely CD163 and LILRB1 ( Fig.11 ), both of which are related to the activation of immune inflammation. Compared with lipid-related proteins, such as LRP1, APOC4, and APOC3, which are also highly correlated with age, they are more sensitive to the effects of estrogen replacement therapy, indicating that the concentrations of these two proteins in plasma can be used to evaluate the effect of estrogen replacement therapy on delaying the increase in inflammation associated with aging ( Fig.11 ).

[0519] Among the plasma metabolites, 14 effective indicators were identified, including multiple acylcarnitines and their derivatives. Compared with 10 randomly selected age-related metabolites, these 14 metabolites showed greater changes in the old-HRT group ( Fig.12 ).

[0520] In the cell ratio, two effective indicators were identified, including naive CD8-positive T cells and non-classical monocytes. Compared with toxic CD8-positive T cells and naive / toxic CD4-positive T cells, which are also age-related, the above two cell types are significantly more sensitive to the effects of estrogen replacement therapy ( Fig.13 ).

[0521] Regarding age-related gene expression, the present invention verified the age-related genes identified in transcriptome sequencing in three groups by fluorescence quantitative PCR, and identified four effective indicators including SATB1, RCAN3, CACHD1 and LRRN3 ( Fig.14), compared with NT5E, which is also an age-related gene, these four genes have a significantly better evaluation effect on estrogen replacement therapy. The specific operation steps of fluorescence quantitative PCR are as follows:

[0522] a) 1-2 μg RNA was taken and reverse transcribed using a reverse transcription kit (R323, Vazyme), and the obtained product was stored at -80°C for future use;

[0523] b) The above product was treated with ddH 2 After diluting 200 times, 2.3 μl was taken and mixed with 2.5 μl of SYBR mix (Novoprotein, E096-01S) and 0.2 μl of primers (0.1 μl each for forward and reverse primers) to prepare a 5 μl system, and detected using a real-time quantitative PCR instrument (Bio-Rad). The primer sequences are shown in Table 11.

[0524] Table 11. Primer sequences used for fluorescence quantitative PCR

[0525] name Sequence (5'-3') SEQ ID NO: LRRN3-Forward AAGCCTCTTATCAATCTTCGCAG 1 LRRN3-Reverse CCAGTCCAACCAAGGCGTTA 2 CACHD1-Forward CATGCAGCGGATATTCAACTCC 3 CACHD1-Reverse TGCTTGTTCCGATTGACCACA 4 RCAN3-Forward CTTCACGCGGGAACAGAGTC 5 RCAN3-Reverse GCCTCGTCTGGGCAATTTT 6 SATB1-Forward GATCATTTGAACGAGGCAACTCA 7 SATB1-Reverse TGGACCCTTCGGATCACTCA 8 NT5E-Forward GCCTGGGAGCTTACGATTTTG 9 NT5E-Reverse TAGTGCCCTGGTACTGGTCG 10

[0526] 2) Evaluation of the effect of estrogen replacement therapy on the aging clock rate

[0527] This section aims to identify the aging clock that is sensitive to estrogen replacement therapy by calculating the different aging clock rates of the subjects and comparing the old-HRT group with the old group. The specific steps are as follows:

[0528] a) Establish a linear regression model for the predicted age and actual age of volunteers of various age groups in the non-treatment group;

[0529] b) Apply the actual age to the model established in the previous step to obtain the theoretical predicted age;

[0530] c) Subtract the predicted age from the theoretically predicted age to obtain the corresponding aging clock rate;

[0531] The rates of the old group and the old-HRT group were compared using the Wilcoxon rank sum test, and seven effective indicators were identified, namely protein clock, metabolic clock, hormone clock, immune clock, liver protein clock, immune protein clock and nervous system protein clock ( Figure 15-16 ), compared with other aging clocks with equally high age-predictive abilities, the seven clocks are significantly more sensitive to the effects of estrogen replacement therapy. These results suggest that the seven aging clocks can be used as indicators for systematically evaluating the effects of estrogen replacement therapy in delaying aging.

[0532] 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.

[0533] References

[0534] 1. Moreau, KL, Donato, AJ, Seals, DR, DeSouza, CA, and Tanaka, H. (2003). Regular exercise, hormone replacement therapy and the age-related decline in carotid arterial compliance in healthy women. Cardiovasc Res 57, 861-868.10.1016 / s0008-6363(02)00777-0.

[0535] 2. Sator, PG, Schmidt, JB, Sator, MO, Huber, JC, and Honigsmann, H. (2001). The influence of hormone replacement therapy on skin ageing: apilotstudy. Maturitas 39, 43-55.10.1016 / s0378-5122(00)00225-5.

[0536] 3.Sator, PG, Sator, MO, Schmidt, JB, Nahavandi, H., Radakovic, S., Huber, JC, and Honigsmann, H. (2007). A prospective, randomized, double-blind, placebo-controlled study on the influence of a hormone replacement therapy on skinaging in postmenopausal women.Climacteric10,320-334.10.1080 / 13697130701444073.

[0537] 4.Resnick,S.M.,and Maki,P.M.(2001).Effects of hormone replacementtherapy on cognitive and brain aging.Ann N Y Acad Sci 949,203-214.10.1111 / j.1749-6632.2001.tb04023.x.

[0538] 5.Levine,M.E.,Lu,A.T.,Chen,B.H.,Hernandez,D.G.,Singleton,A.B.,Ferrucci,L.,Bandinelli,S.,Salfati,E.,Manson,J.E.,Quach,A.,et al.(2016).Menopause accelerates biological aging.Proc Natl Acad Sci U S A 113,9327-9332.10.1073 / pnas.1604558113。

Claims

1. A method for establishing a model, comprising: (1) Establish an age-related dataset; (2) establishing an age estimation model based on the age-related data set obtained in step (1); (3) Screening the age estimation models for an age estimation model that can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs.

2. The method of claim 1, wherein The therapeutic effect is to delay or reverse aging, and / or prevent and / or treat aging-related diseases.

3. The method according to claim 1 or 2, wherein in step (3), the age estimation model that can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs is screened by the following method: (i) providing: a set of subjects who have not received estrogen replacement therapy, and a set of subjects who have received the estrogen replacement therapy; (ii) using the age estimation model of step (2) to determine the aging rates of the subjects in the two subject sets in (i); (iii) comparing the measured aging rates of the two subject groups in (ii); if the measured aging rate of the subject group receiving the estrogen replacement therapy is lower than that of the subject group not receiving the estrogen replacement therapy, it indicates that the age estimation model used has the potential to be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs.

4. The method of claim 3, wherein: In step (iii), the measured aging rates of the two subject sets in (ii) are compared by using the Wilcoxon rank sum test.

5. The method according to claim 3 or 4, wherein In step (ii), the aging rate of the subject is determined by the following method: (a) using the age estimation model in step (2) to estimate the age of the naturally aging population in each age group to obtain a predicted age; and establishing a linear regression model between the predicted age and the actual age; (b) applying the actual age of the subjects in the two subject sets in (i) to the linear regression model in (a) to obtain the theoretical predicted age; (c) applying the age estimation model of step (2) to the subjects of the two subject sets in (i) to obtain predicted age; (d) Subtract the theoretical predicted age obtained in (b) from the predicted age obtained in (c) to obtain the aging rate of the corresponding subject.

6. The method according to any one of claims 1 to 5, wherein The age-related dataset in step (1) is established by the following method: (a) performing one or more of the following analyses on multiple subjects, and obtaining a gene expression dataset, a protein abundance dataset, a metabolite abundance dataset, a cell ratio dataset, or a clinical phenotype measurement dataset based on the analysis results: 1) Transcriptome sequencing to obtain the expression level of each gene in the biological samples of the multiple subjects; 2) quantitative proteomics analysis to obtain the abundance of each protein in the biological samples of the multiple subjects; 3) quantitative metabolomics analysis to obtain the abundance of each metabolite in the biological samples of the multiple subjects; 4) quantitative analysis of cell ratios, obtaining the ratios of each type of cells in the biological samples of the multiple subjects; 5) performing clinical phenotype detection on the multiple subjects to obtain a clinical phenotype measurement data set; (b) Using age as a training label, construct the age-related dataset.

7. The method of claim 6, wherein the clinical phenotype detection comprises a physical examination (for example, one or more of a general examination, an internal medicine examination, a surgical examination, an ultrasound examination, a body composition analysis, a blood routine test, glycosylated hemoglobin, six items of sex hormones, a urine routine test, a stool routine test, a lung function test, a bone density test, an electrocardiogram test, and a biochemical analysis), and / or a mobility test (for example, one or more of a single-leg stand, a grip strength test, a 30-second sit-and-stand test, a Purdue pegboard test, and a stepping test).

8. The method of claim 6, wherein: The biological samples in the analyses 1)-4) are each independently selected from blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts; Preferably, the biological sample in analyses 2) and 3) is plasma; and / or, the biological sample in analyses 1) and 4) is whole blood.

9. The method according to any one of claims 6 to 8, which has one or more features selected from the following: (1) The gene expression dataset is obtained by the following method: i) obtaining immune cells from the biological sample; ii) performing transcriptome sequencing on the immune cells; iii) based on the transcriptome sequencing results, obtaining the original expression values ​​of all genes in the immune cells; Preferably, in step iii), the method further comprises standardizing the original gene expression value to obtain a standardized gene expression value (for example, a TPM gene expression value, a FPKM gene expression value or a RPKM gene expression value); preferably, the standardization process is a TPM conversion method, and the standardized gene expression value is a TPM gene expression value; Preferably, the biological sample is a blood sample (e.g., a whole blood sample), and the immune cells are peripheral monocytes; (2) The protein abundance dataset was 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 performing a standardization process on 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; (3) The metabolite abundance dataset was obtained by the following method: i) analyzing the biological sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data; ii) performing qualitative analysis based on the mass spectrum data, and screening the mass spectrum data for successful qualitative analysis; Preferably, the method further comprises performing a standardization process on the successfully identified mass spectral data, such as dividing the original intensity value of the successfully identified mass spectral data by the median value of all metabolite abundance values ​​in each biological sample; (4) The cell ratio dataset is obtained by the following method: (i) obtaining immune cells from the biological sample; (ii) performing single-cell transcriptome sequencing or flow cytometry analysis on the obtained immune cells; (iii) obtaining the proportion of each cell type in immune cells based on single-cell transcriptome sequencing or flow cytometry analysis results; Preferably, the biological sample is a blood sample (eg, a whole blood sample), and the immune cells are peripheral monocytes.

10. The method according to any one of claims 6 to 9, further comprising the following steps before step (b): Performing correlation analysis between the one or more data sets obtained in step (a) and age to screen for age-related features; Preferably, a Pearson correlation analysis is performed on the one or more data sets obtained in step (a) and 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 age-related feature.

11. The method according to any one of claims 6 to 10, 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.

12. The method according to any one of claims 6 to 11, wherein The method also includes annotating the age-related features with biological functions, and classifying them based on the annotation results to obtain three sub-candidate data sets of hormones, lipids and immunity.

13. The method according to any one of claims 6 to 12, wherein: The method also includes performing tissue-specific annotation on the age-related features and classifying them based on the annotation results to obtain six sub-candidate data sets: liver module, muscle module, nervous system module, immune system module, hematopoietic system module, and pan-tissue module.

14. The method according to any one of claims 1 to 13, wherein In step (2), the method further includes the step of training the age estimation model, which includes: (a) calculating the estimated age of the subject corresponding to the age-related data set using the age estimation model; (b) 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.

15. The method of claim 14, wherein the training is performed using the glmnet package.

16. The method of claim 14 or 15, wherein the age estimation model obtained in step (b) can be used for age estimation, and the subject estimates the age in, A x represents the age-related features in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, each age-related feature A is determined x The coefficient a x and the constant b.

17. The method of claim 16, wherein: The age estimation model that can be used for age estimation obtained in step (b) has one or more features selected from the following: (1) The age-related characteristic is the abundance of an age-related protein, and the age-related protein is 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; (2) The age-related characteristic is the abundance of an age-related metabolite, and the age-related metabolite is selected from one or more of the following: (3) The age-related feature is the abundance of age-related hormone-related metabolites or proteins, the measurement data of hormone-related clinical measurement indicators, or the expression level of hormone-related genes, and the hormone-related metabolites, proteins, clinical measurement indicators or genes are selected from one or more of the following: (4) The age-related feature is the abundance of immune-related metabolites or proteins related to age, the measurement data of immune-related clinical measurement indicators, or the expression level of immune-related genes, and the immune-related metabolites, proteins, clinical measurement indicators or genes are selected from one or more of the following: (5) The age-related characteristic is the abundance of an age-related liver protein, and the age-related liver protein is 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; (6) The age-related characteristic is the abundance of an age-related immune protein, and the age-related immune protein is selected from one or more of the following: CSF1R, CD163, CAMP, CTSS, LTA4H, IL18BP, MRC1, LCP1; (7) The age-related characteristic is the abundance of age-related nervous system-related proteins, and the nervous system-related proteins are selected from one or more of the following: PLXDC2, CRTAC1, IGDCC4, CDH2, ALCAM, CTSF, CLU, NEO1, SPP1, POMGNT1, PTPRZ1, RELN.

18. The method of claim 16 or 17, wherein: The age estimation model that can be used for age estimation obtained in step (b) has one or more features selected from the following: (1) The age-related feature involved in the model is the abundance of age-related proteins, where the coefficient a and constant b of each age-related protein are as follows: (2) The age-related feature involved in the model is the abundance of age-related metabolites, where the coefficient a and constant b of each age-related metabolite are as follows: (3) The age-related characteristics involved in the model are the abundance of age-related hormone-related metabolites or proteins, the measurement data of hormone-related clinical measurement indicators, or the expression level of hormone-related genes, wherein the coefficient a and the constant b of each hormone-related metabolite, protein, clinical measurement indicator or gene are as follows: (4) The age-related characteristics involved in the model are the abundance of age-related immune-related metabolites or proteins, the measurement data of immune-related clinical measurement indicators, or the expression level of immune-related genes, wherein the coefficient a and the constant b of each immune-related metabolite, protein, clinical measurement indicator or gene are as follows: (5) The age-related feature involved in the model is the abundance of age-related liver proteins, where the coefficient a and constant b of each age-related liver protein are as follows: (6) The age-related feature involved in the model is the abundance of age-related immune proteins, where the coefficient a and constant b of each age-related immune protein are as follows: (7) The age-related feature involved in the model is the abundance of age-related nervous system-related proteins, where the coefficient a and constant b of each nervous system-related protein are as follows:

19. An age estimation model obtained by the method according to any one of claims 1 to 18, wherein the age estimation model can be used to evaluate the effect of estrogen replacement therapy or screen estrogen replacement therapy drugs, and comprises features that are sensitive to estrogen replacement therapy; Preferably, the characteristic of being sensitive to estrogen replacement therapy is an age-related characteristic as defined in claim 17; Preferably, the age estimation model is as defined in any one of claims 16-18; Preferably, the age estimation model is a biological age estimation model.

20. A device for evaluating the effect of estrogen replacement therapy or screening estrogen replacement therapy drugs, the device comprising: An input unit, for inputting a feature of a subject being sensitive to estrogen replacement therapy, wherein the feature of being sensitive to estrogen replacement therapy is an age-related feature as defined in claim 17, and the subject has received estrogen replacement therapy or has received treatment with a candidate drug for estrogen replacement therapy; An estimation unit for calculating the estimated biological age of the subject based on the input data using the model of claim 19; and, optionally, a comparison unit for comparing the estimated biological age of the subject with a reference value; Wherein, the reference value is the estimated biological age (or average estimated biological age) calculated by the estimation unit for individuals (or groups) in the same chronological age group as the subject who have not received the estrogen replacement therapy or the estrogen replacement therapy candidate drug and are in a normal aging state; or, the reference value is the estimated biological age calculated by the estimation unit before the subject receives the estrogen replacement therapy or the estrogen replacement therapy candidate drug; Preferably, the therapeutic effect is delaying or reversing aging, and / or preventing and / or treating aging-related diseases.

21. A method for screening estrogen replacement therapy drugs, comprising: (1) providing a subject's characteristic of being sensitive to estrogen replacement therapy, wherein the characteristic of being sensitive to estrogen replacement therapy is an age-related characteristic as defined in claim 17, and the subject has been treated with a candidate drug for estrogen replacement therapy; (2) inputting the features into the model described in claim 19 or the device described in claim 20 to obtain the estimated biological age of the subject; (3) comparing the estimated biological age of the subject with a reference value to evaluate the therapeutic effect of the candidate drug; Preferably, the reference value is the estimated biological age (or average estimated biological age) calculated by the estimation unit for individuals (or groups) in the same chronological age group as the subject who have not received treatment with the candidate drug and are in a normal aging state; or, the reference value is the estimated biological age calculated by the estimation unit before the subject receives treatment with the candidate drug; Preferably, in step (3), when the estimated biological age of the subject is lower than the reference value, it indicates that the candidate drug has the potential to be used as an estrogen replacement therapy drug; Preferably, the subject is Asian; preferably, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

22. An electronic device, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the training method defined in any one of claims 14-18, or the method for screening estrogen replacement therapy drugs as described in claim 21, based on instructions stored in the memory.

23. A non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the training method defined in any one of claims 14 to 18, or the method for screening estrogen replacement therapy drugs as described in claim 21.

24. Use of the model of claim 19, the apparatus of claim 20, the electronic device of claim 22, or the non-volatile computer-readable storage medium of claim 23 in preparing a kit, wherein the kit is used to evaluate the effect of estrogen replacement therapy in a subject or to screen estrogen replacement therapy drugs; Preferably, the kit is used for: 1) To evaluate the effect of estrogen replacement therapy in delaying or reversing aging; 2) To evaluate the effect of estrogen replacement therapy in delaying or reversing liver aging; 3) evaluate the effectiveness of estrogen replacement therapy in delaying or reversing immune system aging; 4) Evaluate the effectiveness of estrogen replacement therapy in delaying or reversing nervous system aging; 5) Evaluate the potential of estrogen replacement therapy to prevent and / or treat age-related diseases; and / or, 6) Screening of estrogen replacement therapy drugs.

25. Use of a substance in the preparation of a detection reagent for evaluating the effect of estrogen replacement therapy (e.g., the effect of delaying or reversing aging, and / or the effect of treating, preventing and / or treating aging-related diseases) or screening estrogen replacement therapy drugs; in, The substance is selected from a reagent or a combination of reagents capable of detecting one or more of the following indicators: (1) Alkaline phosphatase level; (2) ALT level; (3) aspartate aminotransferase level; (4) cholinesterase level; (5) follicle-stimulating hormone level; (6) Glycated hemoglobin level; (7) CD163 levels; (8) LILRB1 levels; (9) the number and / or proportion of naive CD8-positive T cells (e.g., the proportion in peripheral monocytes); (10) the number and / or proportion of nonclassical monocytes (e.g., the proportion in peripheral monocytes); (11) SATB1 expression; (12) RCAN3 expression; (13) CACHD1 expression; (14) LRRN3 expression; (15) Dl-β-homoleucine level; (16) Phenylalanine level; (17) Stachydrine level; (18) 3-dehydrocarnitine levels; (19) Gln-arg level; (20) N-acetylhistamine level; (21) L-arachidonic acid carnitine level; (22) Stearoylcarnitine levels; (23) 2,4,6-tri-tert-butylaniline levels; (24) 4-pregnene-17α,20β-diol-3-mono-20-sulfate levels; (25) Pregnenolone sulfate levels; (26) Estrone sulfate levels; (27) PC (16:0 / 18:0) level; (28) Ethylmorphine levels.

26. The use of claim 25, wherein The evaluation or screening includes the following steps: (1) measuring one or more indicators as defined in claim 25 in one or more samples from the subject, wherein the subject has received estrogen replacement therapy or has received treatment with a candidate drug for estrogen replacement therapy; (2) comparing the measured values ​​in (1) with their respective corresponding reference values; and (3) evaluating the effect of estrogen replacement therapy or treatment with the candidate drug on the subject (e.g., the effect of delaying or reversing aging, and / or the effect of treating, preventing and / or treating aging-related diseases); Preferably, the one or more samples are each independently selected from blood (e.g., whole blood, plasma or serum), urine, saliva, cell and tissue extracts; preferably, the samples corresponding to indicators (1) to (6) defined in claim 25 are plasma or serum samples, the samples corresponding to indicators (7) to (8) and (15) to (28) are plasma samples, and / or the samples corresponding to indicators (9) to (14) are samples containing immune cells (e.g., samples containing peripheral mononuclear cells, such as peripheral blood); Preferably, the reference value is the value of the corresponding index in the same type of sample of the subject before receiving the estrogen replacement therapy or the candidate drug treatment, or the reference value is the value (or average value) of the corresponding index in the same type of sample of individuals (or groups) who are in the same chronological age group as the subject and have not received the estrogen replacement therapy or the candidate drug treatment and are in a normal aging state; Preferably, in step (3), if one or more of the following characteristics are present, it indicates that the estrogen replacement therapy is effective or the candidate drug has the potential to be used as an estrogen replacement therapy drug: (a) the measured value of any one or more of the indicators (1) to (6) in the sample from the subject is lower than the reference value; (b) the measured value of index (7) and / or (8) in the sample from the subject is lower than the reference value; (c) the measured value of the indicator (9) in the sample from the subject is higher than the reference value; (d) the measured value of the indicator (10) in the sample from the subject is lower than the reference value; (e) the measured value of any one or more of the indicators (11) to (14) in the sample from the subject is higher than the reference value; (f) the measured value of any one or more of the indicators (15)-(23) in the sample from the subject is lower than the reference value; (g) The measured value of any one or more of the indicators (24)-(28) in the sample from the subject is higher than the reference value Wherein, the indicator type corresponding to the indicator number is defined as in claim 25; Preferably, the subject is Asian; preferably, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

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