Age estimation method based on transcriptome sequencing

Abstract

The invention relates to an age estimation method based on transcriptome sequencing, in particular to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. A series of age-related immune cell proportion changes are identified by combining a single cell sequencing technology, and the age-related immune cell proportion changes can be used as a good aging marker.

Description

Technical Field

[0001] The invention belongs to the field of biomedicine, and in particular relates to an age estimation method based on transcriptome sequencing. Background Art

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

[0003] There are currently two main methods for evaluating the degree of aging. The first is to collect relevant biological data within the population, perform correlation analysis on the collected data and the individual's chronological age, and obtain detection indicators that are highly correlated with age, which are used to preliminarily evaluate the individual's degree of aging. The second is to establish a predictive model for the aging clock based on multiple indicators within the population, in order to achieve the purpose of accurately measuring aging. The types of data currently commonly used to establish aging clocks include clinical measurement data, DNA methylation data, and transcriptome data. Among them, transcriptomes may become an important data type for measuring aging because of the stability of their detection technology and the readability of the results. However, the transcriptome age model currently established based on the transcriptome may have shortcomings such as inconsistent detection platforms and sample sources. 1-3 Most importantly, the existing models are all based on Western populations, and aging is closely related to the environment. In order to accurately measure the aging of the Chinese population, it is necessary to establish an age prediction model based on Chinese population data. Summary of the invention

[0004] Through strict screening of health conditions and a uniform diet before testing, the present invention established a natural aging population cohort in Quzhou, Zhejiang, collected blood samples from the cohort volunteers, and further separated peripheral monocytes in the blood. By sequencing the transcriptome of peripheral monocytes, a blood cell transcriptome map of the aging population in China was established, and a series of genes correlated with age were identified. Based on the expression levels of these genes, the present invention used an elastic network machine learning model to predict the actual age of the subjects and established a transcriptional age model for the aging of the Chinese population. The results showed that the model has good predictive performance.

[0005] In a first aspect, the present invention provides a method for establishing a data set, comprising:

[0006] (1) collecting biological samples from multiple subjects, performing transcriptome sequencing (e.g., tissue transcriptome sequencing, single-cell transcriptome sequencing, microarray detection, or flow cytometric analysis) on the biological samples, obtaining the expression level of each gene in the biological samples, and obtaining a gene expression data set;

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

[0008] In some embodiments, the biological sample is selected from blood (eg, whole blood, plasma, or serum), urine, saliva, cells, and tissue extracts.

[0009] In some embodiments, the gene expression dataset is obtained by the following method:

[0010] i) obtaining immune cells (such as peripheral monocytes) from the biological sample;

[0011] ii) performing transcriptome sequencing on the immune cells;

[0012] iii) based on the transcriptome sequencing results, obtaining the expression values ​​of all genes in the immune cells.

[0013] In some embodiments, the immune cells are subjected to tissue transcriptome sequencing, single cell transcriptome sequencing, microarray detection, or flow cytometric analysis.

[0014] In some embodiments, RNA is extracted from the immune cells, an RNA-seq sequencing library is constructed (for example, using a VAHTS Universal V6 RNA-seq Library Prep Kit), and paired-end sequencing is performed (for example, using an Illumina Hisses X-Ten sequencing platform to perform the paired-end sequencing) to obtain a sequencing file with a read length of 150 bp.

[0015] In some embodiments, the sequencing file is processed as follows: removing adapters, aligning with a reference genome (eg, hg19 human reference genome), and quantifying gene expression.

[0016] In some embodiments, the method further comprises performing a normalization process on the gene expression value, for example, using a TPM, FPKM or RPKM conversion method to perform the normalization process; preferably, using a TPM conversion method to perform the normalization process to obtain a TPM gene expression value dataset:

[0017]

[0018] Here, n is the number of all genes.

[0019] In other embodiments, single-cell transcriptome sequencing is performed on the immune cells to analyze the changes in the proportion of the immune cells. Specifically, the single-cell transcriptome sequencing includes the following steps: sorting and counting living cells from the immune cells, preparing a cDNA single-cell library, performing double-end sequencing, and obtaining a sequencing file with a read length of 150 bp.

[0020] In some embodiments, the sequencing file is subjected to genome alignment and cell label splitting processing to obtain a gene-cell expression matrix, low-quality cells and multiple cells are screened based on the expression matrix, and the obtained high-quality single-cell data is further processed by the Seurat software package for standardization, integration, dimensionality reduction and clustering, and the clustered cell clusters are annotated according to the marker genes of the cell type.

[0021] In some embodiments, a subset of each cell type obtained above is taken, and its original expression matrix is ​​extracted as a feature matrix for deconvolution of the transcriptome data. This feature matrix and the transcriptome count data are input into the Impute Cell Fraction function module of the CIBERSORTx tool, the number of iterations is set to 500, and S-mode batch correction is performed, and finally the deconvolution result of the cell type ratio of the transcriptome data (cell ratio-cell type matrix) is obtained.

[0022] In some embodiments, before step (2), the method further includes performing a correlation analysis between the gene expression dataset and age to screen for age-related genes.

[0023] In some embodiments, the gene expression dataset is subjected to Pearson correlation analysis with age, BMI is used as a covariate, the P value is corrected by the BH method, and the results with P<0.05 are selected as genes associated with age.

[0024] In some embodiments, the age-related genes are selected from genes related to immune cell activation, cell lysis or cell death pathways, and / or genes related to substance metabolism and biosynthesis, and T cell function degeneration.

[0025] In some embodiments, the age-related gene is selected from KRT31, COBL, CUX2, NPR3, KIF17, SFRP5, NR4A1, MT2A, TNFSF9, CD70, TGM3, GNAI1, CHN1, KCNA5, PLVAP, KREMEN2, SERPINF1, REG4, KRT7, EGR4, KCNK1, CIB2, GLCE, FRAS1, CMTM2, RASL10B, GATA6, TNFRSF11A, EPHA2, RORC, SLC4A10, KLHL8, TNFRSF21, IGFBP3 , SHROOM2, CHRNB3, CCDC3, BEND6, FAM154A, KIT, MYO1E, AFAP1L1, CD1B, CD1E, CPA5, LMNA, CYGB, FAM171A2, IL23R, VCAM1, XIRP2, HPGDS, FB LN2, HPGD, ZNF704, CYYR1, PLEKHA7, YPEL4, LDHD, KCTD19, AR, EFNA1, B3GALNT1, HSPB3, MT1E, MYO7B, CEACAM3, LRRC8D, MAP6, SPSB1, GATM, S NTB1, ARPP21, CD248, C5orf64, ALOX15B, FIGN, SNN, LRRC26, KPNA7, BPIFB4, NHLRC1, SPTSSB, PDLIM7, SLC2A7, LYPD2, DLL1, ZNF521, PAX9, SHISA4, LGR4, TRBV6-4, TRBV10-1, RP11-43F13.3, LL22NC03-75H12.2, IGLV9-49, MEG9, AC069394.1, RP11-123K19.1, LINC00694, AC00508 2.12, RP11-476B1.1, ST13P4, RP1-266L20.2, HSPA8P18, RP11-30P6.6, RP11-285G1.2, AC128709.3, TRHDE-AS1, LINC01134, UOX, MTHFD2P 1. RGMB-AS1, HHIP-AS1, CTD-2003C8.2, ZBED3-AS1, RNU6-1143P, CTB-37A13.1, HLA-AS1, ST13P3, RP11-111J6.2, GTSCR1 and RP11-439M11.1. .

[0026] In some embodiments, the dataset is split into a training set and a test set, for example, in a 1:1 ratio.

[0027] In some embodiments, the subjects are Asians (eg, East Asians or Southeast Asians), such as Chinese; preferably, the subjects are ≥100, eg, ≥500, ≥1000, or ≥10000.

[0028] 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%.

[0029] In a second aspect, the present invention provides a method for training an age estimation model, comprising:

[0030] (1) establishing a data set according to any method described in the first aspect;

[0031] (2) calculating the estimated age of the subject corresponding to the data using the age estimation model according to the data set;

[0032] (3) Training the age estimation model based on the difference between the estimation result and the age label of the data.

[0033] In some embodiments, the training is performed using the glmnet package (version 4.1.4). In some embodiments, the alpha value in the glmnet package 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.

[0034] In some embodiments, the estimated age y of the subject is calculated using the following formula:

[0035] A x represents the expression level of age-related genes in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0036] Through the training, the expression level A of each age-related gene is determined. x The coefficient a x and the constant b.

[0037] In some embodiments, the age-related gene is selected from KRT31, COBL, CUX2, NPR3, KIF17, SFRP5, NR4A1, MT2A, TNFSF9, CD70, TGM3, GNAI1, CHN1, KCNA5, PLVAP, KREMEN2, SERPINF1, REG4, KRT7, EGR4, KCNK1, CIB2, GLCE, FRAS1, CMTM2, RASL10B, GATA6, TNFRSF11A, EPHA2, RORC, SLC4A10, KLHL8, TNFRSF21, IGFBP3, SHROOM2, CHRNB3, CCDC3, BEND6, FAM154A, KIT, MYO1E, AFAP1L1, CD1B, CD1E, CPA5, LMNA, CYGB, FAM171A2, IL23R, VCAM1, XIRP2, HPGDS, FBL N2, HPGD, ZNF704, CYYR1, PLEKHA7, YPEL4, LDHD, KCTD19, AR, EFNA1, B3GALNT1, HSPB3, MT1E, MYO7B, CEACAM3, LRRC8D, MAP6, SPSB1, GATM, SN TB1, ARPP21, CD248, C5orf64, ALOX15B, FIGN, SNN, LRRC26, KPNA7, BPIFB4, NHLRC1, SPTSSB, PDLIM7, SLC2A7, LYPD2, DLL1, ZNF521, PAX9, S HISA4, LGR4, TRBV6-4, TRBV10-1, RP11-43F13.3, LL22NC03-75H12.2, IGLV9-49, MEG9, AC069394.1, RP11-123K19.1, LINC00694, AC00508 2.12, RP11-476B1.1, ST13P4, RP1-266L20.2, HSPA8P18, RP11-30P6.6, RP11-285G1.2, AC128709.3, TRHDE-AS1, LINC01134, UOX, MTHFD2P 1. RGMB-AS1, HHIP-AS1, CTD-2003C8.2, ZBED3-AS1, RNU6-1143P, CTB-37A13.1, HLA-AS1, ST13P3, RP11-111J6.2, GTSCR1 and RP11-439M11.1.

[0038] In some embodiments, the coefficient a and constant b of each age-related gene in the age estimation model are:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] In a third aspect, the present invention provides an age estimation model, which is trained by the method described in any one of the second aspects.

[0045] In a fourth aspect, the present invention provides an age estimation device, comprising:

[0046] An input unit, used for inputting a subject and an age-related gene, wherein the age-related gene is as defined in any one of the second aspect;

[0047] An estimation unit, used to estimate the estimated age of the subject according to the input age-related genes using the age estimation model;

[0048] and an optional output unit for outputting the estimated age of the subject.

[0049] In a fifth aspect, the present invention provides an age estimation method, comprising:

[0050] Providing data on the expression of genes associated with age in a subject, wherein the age-associated genes are as defined in any one of the second aspect;

[0051] The age-related gene expression data is input into the age estimation model described in any one of the fourth aspects to estimate the age of the subject.

[0052] The gene expression data are obtained by referring to the method for establishing and processing the gene expression data set described in the first aspect above.

[0053] In a sixth aspect, the present invention provides an electronic device, comprising:

[0054] Memory; and

[0055] A processor coupled to the memory, the processor being configured to execute, based on instructions stored in the memory, the training method for the age estimation model described in any one of the second aspects, or the age estimation method described in any one of the fifth aspects.

[0056] In a seventh aspect, the present invention provides a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the training method for the age estimation model described in any one of the second aspect, or the age estimation method described in any one of the fifth aspect.

[0057] In an eighth aspect, the present invention provides a method for establishing a data set according to any one of the first aspect, a training method for an age estimation model according to any one of the second aspect, an age estimation model according to any one of the third aspect, an age estimation device according to any one of the fourth aspect, an age estimation method according to any one of the fifth aspect, an electronic device according to any one of the sixth aspect, or a non-volatile computer-readable storage medium according to any one of the seventh aspect, in the preparation of a kit, wherein the kit is used for one or more of the following:

[0058] 1) Estimate the age of the subjects;

[0059] 2) Evaluate or assist in evaluating the aging rate or degree of subjects;

[0060] 3) Evaluate or assist in evaluating the effectiveness of aging interventions;

[0061] 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease);

[0062] 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases;

[0063] 6) Evaluate or assist in evaluating the risk of age-related death;

[0064] 7) Evaluate or assist in evaluating the degree of immune senescence;

[0065] 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration;

[0066] 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells;

[0067] 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

[0068] In a ninth aspect, the present invention provides a kit for detecting the proportion of the following immune cells in peripheral mononuclear cells in a biological sample: toxic CD4-positive T cells, toxic CD8-positive T cells, CD56-low-expressing natural killer cells, megakaryocytes, immature CD4-positive T cells, immature CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells and CD56-high-expressing natural killer cells, wherein the kit is used for:

[0069] 1) Estimate the age of the subjects;

[0070] 2) Evaluate or assist in evaluating the aging rate or degree of subjects;

[0071] 3) Evaluate or assist in evaluating the effectiveness of aging interventions;

[0072] 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease);

[0073] 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases;

[0074] 6) Evaluate or assist in evaluating the risk of age-related death;

[0075] 7) Evaluate or assist in evaluating the degree of immune senescence;

[0076] 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration;

[0077] 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells;

[0078] 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

[0079] In a tenth aspect, the present invention provides a use of a substance having the following activity in preparing a drug: down-regulating the proportion of the following immune cells in peripheral monocytes of a subject: toxic CD4-positive T cells, toxic CD8-positive T cells, natural killer cells with low CD56 expression and / or megakaryocytes, and / or up-regulating the proportion of the following immune cells in peripheral monocytes: immature CD4-positive T cells, immature CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells and / or natural killer cells with high CD56 expression,

[0080] Wherein, the drug is used to reduce the biological aging rate of the subject, and / or delay the onset of adverse age-related phenotypes, or reduce their severity, and / or prevent aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease) to prevent or treat aging-related diseases.

[0081] In an eleventh aspect, the present invention provides an evaluation method comprising:

[0082] (1) measuring the proportion of a first type of immune cell in peripheral monocytes in a biological sample of the subject to obtain a first measurement value, wherein the first type of immune cell is selected from one or more of toxic CD4-positive T cells, toxic CD8-positive T cells, CD56-low-expressing natural killer cells, and megakaryocytes;

[0083] (2) measuring the proportion of the second type of immune cells in peripheral monocytes in the biological sample of the subject to obtain a second measurement value, wherein the second type of immune cells is selected from one or more of naive CD4-positive T cells, naive CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells, and natural killer cells with high CD56 expression;

[0084] (3) performing at least one of the following comparisons based on the subject's age and the first measurement value and / or the second measurement value:

[0085] a. Comparison of cell ratios:

[0086] 1) The first measurement value and / or the second measurement value vs. the proportion of the same cell type in peripheral monocytes of healthy people of the same age as the subject; or

[0087] 2) the first measurement value and / or the second measurement value vs. the proportion of the same cell type in peripheral mononuclear cells of the subject at a certain time before or after the measurement; or

[0088] b. Age comparison: the current age of the subject vs. the average age of the healthy population when the proportion of the same cell type in peripheral monocytes of the healthy population is the same as the measured value;

[0089] (4) Based on the comparison results of step (3), the following evaluation is performed:

[0090] 1) Estimate the age of the subjects;

[0091] 2) Evaluate or assist in evaluating the aging rate or degree of subjects;

[0092] 3) Evaluate or assist in evaluating the effectiveness of aging interventions;

[0093] 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease);

[0094] 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases;

[0095] 6) Evaluate or assist in evaluating the risk of age-related death;

[0096] 7) Evaluate or assist in evaluating the degree of immune senescence;

[0097] 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration;

[0098] 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells;

[0099] 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

[0100] In some embodiments, the first type of immune cells is positively correlated with aging of the subject; and the second type of immune cells is negatively correlated with aging of the subject.

[0101] In this article, the subject is a mammal. In some embodiments, the subject is an Asian. In some embodiments, the subject is an East Asian or Southeast Asian. In some embodiments, the subject is a Chinese.

[0102] Herein, the biological sample is selected from blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts.

[0103] 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.

[0104] As used herein, the term "aging-related diseases" refers to aging-related diseases whose risk of occurrence increases significantly with aging of the body. 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. In certain embodiments, the aging-related diseases are selected from aging, cardiovascular (such as hypertension, coronary heart disease, heart failure or stroke) and neurodegenerative diseases (such as Alzheimer's disease or Parkinson's disease).

[0105] As used herein, the term "gene" refers to a fragment of DNA associated with the production of a polypeptide chain. It may include regions before and after the coding region (leader and non-transcribed tail), as well as intervening sequences (introns) between individual coding segments (exons). The term gene is used interchangeably with nucleic acids, cDNA, mRNA, small non-coding RNA, microRNA (miRNA), Piwi-interacting RNA, and short hairpin RNA (shRNA) encoded by a gene or locus. The terms "nucleic acid" and "polynucleotide" refer to deoxyribonucleic acid (DNA) or ribonucleic acid "RNA" and polymers thereof in single-stranded or double-stranded form. Unless otherwise specified, the term includes nucleic acids containing known analogs of natural nucleotides, which have similar binding properties to the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise specified, a specific nucleic acid sequence also implicitly includes conservatively modified variants thereof (such as degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences as well as sequences explicitly indicated. Specifically, the replacement of degenerate codons can be achieved by generating a sequence in which the third position of one or more selected (or all) codons is replaced by mixed bases and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19: 5081 (1991); Ohtsuka et al., J. Biol. Chem. 260: 2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8: 91-98 (1994)). "Genome" refers to the sum of all genetic material of an organism, including DNA or RNA. Each gene mentioned herein is known in the art and has the meaning generally understood in the art.

[0106] Advantageous Effects of the Invention

[0107] The technical solution of the present invention is based on a high-quality cohort of naturally aging healthy people in China, which eliminates the influence of factors other than aging on the results to the greatest extent. By sequencing the transcriptome of easily available blood cells and establishing a transcriptome age model, it will help to accurately measure the degree of aging in the Chinese population and be used to evaluate aging intervention measures. It will be an important tool for the transformation of basic aging research into clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 :Expression of marker genes of different cell types in single-cell transcriptome maps

[0109] Figure 2 :Cell type annotation results of human peripheral monocyte transcriptome atlas

[0110] Figure 3 : Genes up-regulated and down-regulated with age identified by correlation analysis

[0111] Figure 4 : Cell types whose proportions are significantly correlated with age

[0112] Figure 5 : Pathway enrichment results of age-related genes

[0113] Figure 6 : Prediction results of transcription age, R is the Pearson correlation coefficient, MAE is the mean absolute error DETAILED DESCRIPTION

[0114] The embodiments of the present invention will be described in detail below in conjunction with the examples, but those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific conditions are specified in the examples, they are carried out according to normal conditions or the conditions recommended by the manufacturer. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be obtained commercially.

[0115] The technical route of the present invention mainly involves seven steps: cohort establishment, data collection, data processing, age-related analysis, aging clock establishment, and aging clock evaluation and application.

[0116] 1. Volunteer recruitment and sample collection

[0117] The "Preliminary Screening Questionnaire" was published through the WeChat Mini Program to collect basic information of potential volunteers who are interested in joining this study based on the inclusion criteria, and to screen volunteers who meet the inclusion criteria; the inclusion criteria are as follows:

[0118] 1) Enrollment conditions

[0119] a) Aged 18 or above, with no major bad habits;

[0120] 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:

[0121] i. Hypertension, well controlled (taking no more than 3 medications);

[0122] ii. Osteoporosis, osteopenia and / or osteoarthritis;

[0123] iii. Benign prostatic hypertrophy;

[0124] iv. Cataract, glaucoma, macular degeneration;

[0125] v.Dyslipidemia;

[0126] vi. Hypothyroidism;

[0127] vii. Prediabetes or abnormal fasting blood sugar (fasting blood sugar 100-126 mg / dL).

[0128] c) The subjects can communicate well with the researchers and cooperate with all tasks during the study;

[0129] i. Subjects must voluntarily sign a written informed consent.

[0130] 2) Exclusion criteria

[0131] a) Age does not meet the inclusion criteria;

[0132] b) Participants are currently undergoing other clinical trials or are receiving any research drugs or devices for treatment within 30 days prior to enrollment;

[0133] c) The investigator reports a history or current diagnosis of significant chronic medical conditions, including:

[0134] i. Any cancer (except polycythemia vera, basal cell or squamous cell skin cancer);

[0135] ii. Coronary artery disease / myocardial infarction / clinically significant congestive heart failure;

[0136] iii. Stroke / transient ischemic attack;

[0137] iv. Deep vein thrombosis / pulmonary embolism;

[0138] v. Serum creatinine>1.5mg / dl (male);

[0139] vi. poorly controlled hypertension (significant hypertension (systolic blood pressure > 160 mmHg, or diastolic blood pressure > 100 mmHg) despite treatment);

[0140] vii. History of active liver disease or metabolic acidosis;

[0141] viii. Chronic kidney disease / hemodialysis treatment, history of severe renal damage and / or eGFR≤45ml / min / 1.73m2;

[0142] ix. Severe autoimmune / inflammatory diseases, such as rheumatoid arthritis, lupus, Crohn's disease, etc.;

[0143] x. Neurological diseases such as dementia, such as Alzheimer's / Parkinson's, etc.;

[0144] xi. Diabetes (hemoglobin A1C>6.5% or fasting blood glucose>126 mg / dL or taking diabetes medication or insulin treatment);

[0145] xii. Recent (within 3 months) cardiovascular disease events (myocardial infarction, coronary artery intervention, coronary artery bypass grafting);

[0146] xiii. Infectious diseases such as HIV, hepatitis, tuberculosis, etc.

[0147] d) Currently taking the following medications regularly:

[0148] i. Chemotherapy drugs (such as tamoxifen, doxorubicin, mitoxantrone, bleomycin);

[0149] ii. Antiplatelet drugs (e.g. clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine / Ticlopidine, excluding aspirin);

[0150] iii. Cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0151] e) Continuous alcohol or drug abuse;

[0152] f) Unable to provide informed consent;

[0153] g) The researcher believes that the physical factors of the participants may have an adverse effect on the research process or results.

[0154] 3) Termination criteria

[0155] a) Failure to follow the diet, work and rest schedule and physical examination requirements in accordance with the research content;

[0156] b) Experiencing abnormal physical conditions such as colds or fever during the study;

[0157] c) Accidental injury or physical discomfort during the research process;

[0158] d) Comply with the subject’s subjective wishes to terminate the program.

[0159] 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 take other foods.

[0160] 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.

[0161] Table 1 Unified standardized diet menu

[0162]

[0163]

[0164] 2. Data Collection

[0165] 1) Biological sample processing

[0166] 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:

[0167] a) Dilute EDTA anticoagulated peripheral blood with PBS in a 1:1 ratio in a 15 mL centrifuge tube and invert 4 to 5 times to mix thoroughly;

[0168] b) Take another 15mL 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; (It is recommended to use a 200μL pipette to add blood sample at the beginning, and use a 1mL pipette after a certain thickness is formed)

[0169] 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;

[0170] 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;

[0171] e) Add 10 mL of PBS, mix gently, centrifuge (1500 rpm, 15 min), and remove the supernatant. This is the first wash;

[0172] f) Resuspend in 6 mL PBS, take 1 mL aliquots into individual cryovials, centrifuge at 400 g, 15 min, 4°C, and discard the supernatant;

[0173] g) Resuspend in 1 mL Trizol and mix by gently pipetting;

[0174] h) Store in a -80℃ freezer.

[0175] 2) Peripheral monocyte transcriptome sequencing

[0176] 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.

[0177] b) After dissolving the RNA sample in the above step, dilute according to the concentration and take 1-3 μg RNA sample for use in VAHTSUniversal V6 RNA-seq Library Prep Kit (Illumina, NR604-01 / 02) to construct RNA-seq sequencing library;

[0178] 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.

[0179] 3) Single-cell transcriptome sequencing of peripheral monocytes

[0180] a) The frozen cells were rapidly thawed and revived at 37°C, centrifuged and resuspended in 0.4% BSA / PBS, stained with PI, and live cells were sorted using a flow cytometer;

[0181] 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;

[0182] c) Place cells and 10X Genomics reagents on the machine to prepare a single water-in-oil suspension;

[0183] d) reverse transcription into cDNA by PCR and freezing at -20°C;

[0184] e) Prepare single cell library from cDNA according to the protocol of Chromium Next GEM Single Cell 3′ Reagent Kits v3.1;

[0185] f) The obtained cDNA library was sequenced with double ends using the Nova-PE150 sequencing platform to obtain a sequencing file with a read length of 150 bp.

[0186] 3. Data Preprocessing

[0187] 1) Peripheral monocyte transcriptome data

[0188] a) Use TrimGlalore software (version 0.4.5) to remove the adapter from the original fastq file of sequencing;

[0189] b) Use HISAT2 software to align the processed fastq file to the hg19 human reference genome to obtain a bam format alignment result file;

[0190] c) Use the samtools tool to convert the bam file into a sam file;

[0191] d) The alignment results with alignment scores > 20 were used to quantify gene expression using HTSeq software (version 0.11.0);

[0192] e) The obtained quantitative matrix was processed according to the following formula to obtain the TPM value of all genes in each sample:

[0193]

[0194] Here, n is the number of all genes.

[0195] 2) Peripheral monocyte single-cell transcriptome data

[0196] a) The raw data were first processed by CellRanger software (version 4.0.0) to obtain the gene-cell expression matrix;

[0197] 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;

[0198] c) Then, the DoubletFinder package (version 2.0.2) was used to remove potential multicellularity;

[0199] 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;

[0200] e) After running the PreSCTIntegration function, all samples were integrated according to the Seurat RPCA integration process;

[0201] f) The integrated samples are processed by RunUMAP, FindNeighbors, and FindClusters functions to complete the dimensionality reduction and clustering steps;

[0202] 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 );

[0203] 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;

[0204] i) Input the feature matrix obtained in the previous step and the original count data of the transcriptome obtained in 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;

[0205] j) Finally, the deconvolution result of the cell type ratio of the transcriptome data is obtained (cell ratio-cell type matrix).

[0206] 4. Age-related feature screening

[0207] a) First, age correlation analysis was performed on the TPM gene expression matrix and the cell ratio matrix, that is, the expression of each gene was analyzed with the individual's age by Pearson correlation analysis, and BMI was used as a covariate;

[0208] b) The results were screened and only the results with a BH-corrected P value less than 0.05 were selected as age-related genes and cell types. The results showed that these genes and cell types increased or decreased with age ( Figure 2-3 );

[0209] c) Regarding age-related genes, further annotation using the Metascape tool revealed that these upregulated genes were related to immune cell activation, cell lysis, and cell death pathways, while the downregulated pathways were mainly involved in the metabolism and biosynthesis of various substances and were related to the degeneration of T cell function ( Figure 4 );

[0210] d) Regarding age-related cell types, the present invention found that the cell types that increase with age include toxic CD4-positive T cells, toxic CD8-positive T cells, CD56 low-expressing natural killer cells and megakaryocytes, and the cell types that decrease with age include immature CD4-positive T cells, immature CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells and CD56 high-expressing natural killer cells. The proportions of these cell types are highly correlated with age ( Figure 3 ), illustrating their usefulness as markers of aging.

[0211] 5. Establishment of Transcriptional Age Model

[0212] a) First, the gene expression matrix is ​​screened for features, and only features related to age are retained;

[0213] b) Split the selected matrix into 1:1 training and test sets according to the random allocation in advance;

[0214] c) Age was used as the training label and the glmnet package (version 4.1.4) was used to build the model. Nine alpha values ​​between 0.1 and 0.9 were used for modeling and tested in the test set. Finally, the alpha with the highest accuracy was selected as the parameter of the final model. The final alpha value was 0.1. The final model is shown in Table 2.

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

Claims

1. A method for establishing a data set, comprising: (1) collecting biological samples from multiple subjects, performing transcriptome sequencing (e.g., tissue transcriptome sequencing, single-cell transcriptome sequencing, microarray detection, or flow cytometric analysis) on the biological samples, obtaining the expression level of each gene in the biological samples, and obtaining a gene expression data set; (2) Using age as a training label, the dataset is constructed for training an age estimation model.

2. The method of claim 1, wherein the biological sample is selected from the group consisting of blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts.

3. The method according to claim 1 or 2, wherein 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) obtaining the expression values ​​of all genes in the immune cells based on the transcriptome sequencing results; Preferably, the method further comprises normalizing the gene expression value, for example, using TPM, FPKM or RPKM conversion method to perform the normalization.

4. The method according to any one of claims 1 to 3, before step (2), further comprising performing a correlation analysis between the gene expression dataset and age to screen for age-related genes; Preferably, the gene expression dataset is subjected to Pearson correlation analysis with age, BMI is used as a covariate, the P value is corrected by the BH method, and the result with P<0.05 is selected as the gene associated with age.

5. The method according to any one of claims 1 to 4, wherein the data set is split into a training set and a test set, for example, splitting the data set in a 1:1 ratio.

6. The method according to any one of claims 1 to 5, wherein the subject is an Asian (eg, East Asian or Southeast Asian) person, such as a Chinese person; preferably, the number of the subjects is ≥100, for example, ≥500, ≥1000, or ≥10000.

7. A method for training an age estimation model, comprising: (1) establishing a data set according to the method described in any one of claims 1 to 6; (2) calculating the estimated age of the subject corresponding to the data using the age estimation model according to the data set; (3) Training the age estimation model based on the difference between the estimation result and the age label of the data.

8. The method of claim 7, wherein the training is performed using the glmnet package.

9. The training method of claim 7 or 8, wherein the subject estimates age A x represents the expression level of age-related genes in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, the expression level A of each age-related gene is determined. x The coefficient a x and the constant b.

10. The training method of claim 9, wherein the age-related gene is selected from KRT31, COBL, CUX2, NPR3, KIF17, SFRP5, NR4A1, MT2A, TNFSF9, CD70, TGM3, GNAI1, CHN1, KCNA5, PLVAP, KREMEN2, SERPINF1, REG4, KRT7, EGR4, KCNK1, CIB2, GLCE, FRAS1, CMTM2, RASL10B, GATA6, TNFRSF11A, EPHA2, RORC, SLC4A10, KLHL8, TNFRSF21, IG FBP3, SHROOM2, CHRNB3, CCDC3, BEND6, FAM154A, KIT, MYO1E, AFAP1L1, CD1B, CD1E, CPA5, LMNA, CYGB, FAM171A2, IL23R, VCAM1, XIRP2, HPGDS , FBLN2, HPGD, ZNF704, CYYR1, PLEKHA7, YPEL4, LDHD, KCTD19, AR, EFNA1, B3GALNT1, HSPB3, MT1E, MYO7B, CEACAM3, LRRC8D, MAP6, SPSB1, GAT M, SNTB1, ARPP21, CD248, C5orf64, ALOX15B, FIGN, SNN, LRRC26, KPNA7, BPIFB4, NHLRC1, SPTSSB, PDLIM7, SLC2A7, LYPD2, DLL1, ZNF521, PAX 9. SHISA4, LGR4, TRBV6-4, TRBV10-1, RP11-43F13.3, LL22NC03-75H12.2, IGLV9-49, MEG9, AC069394.1, RP11-123K19.1, LINC00694, AC005 082.12, RP11-476B1.1, ST13P4, RP1-266L20.2, HSPA8P18, RP11-30P6.6, RP11-285G1.2, AC128709.3, TRHDE-AS1, LINC01134, UOX, MTHFD2 P1, RGMB-AS1, HHIP-AS1, CTD-2003C8.2, ZBED3-AS1, RNU6-1143P, CTB-37A13.1, HLA-AS1, ST13P3, RP11-111J6.2, GTSCR1 and RP11-439M11.

1.

11. The training method according to claim 9 or 10, wherein the coefficient a and the constant b of each age-related gene in the age estimation model are:

12. An age estimation model, which is trained by the method according to any one of claims 7 to 11.

13. An age estimation device, comprising: An input unit, for inputting a subject and an age-related gene, wherein the age-related gene is as defined in claim 11; an estimating unit, configured to estimate the estimated age of the subject based on the input age-related genes using the age estimation model according to claim 12; and an optional output unit for outputting the estimated age of the subject.

14. A method for estimating age, comprising: Providing data on the expression of genes associated with age in a subject, wherein the age-associated genes are as defined in claim 11; The age-related gene expression data is input into the age estimation model described in claim 12 or the age estimation model described in claim 13 to estimate the age of the subject.

15. An electronic device, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the training method of the age estimation model according to any one of claims 7 to 11, or the age estimation method according to claim 14, based on instructions stored in the memory.

16. A non-volatile computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the program implements the training method of the age estimation model described in any one of claims 7 to 11, or the age estimation method described in claim 14.

17. Use of the method for establishing a data set according to any one of claims 1 to 6, the method for training an age estimation model according to any one of claims 7 to 11, the age estimation model according to claim 12, the age estimation model or device according to claim 13, the age estimation method according to claim 14, the electronic device according to claim 15, or the non-volatile computer-readable storage medium according to claim 16 in preparing a kit, wherein the kit is used for one or more of the following: 1) Estimate the age of the subjects; 2) Evaluate or assist in evaluating the aging rate or degree of subjects; 3) Evaluate or assist in evaluating the effectiveness of aging interventions; 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease); 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases; 6) Evaluate or assist in evaluating the risk of age-related death; 7) Evaluate or assist in evaluating the degree of immune senescence; 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration; 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells; 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

18. A kit for detecting the proportion of the following immune cells in peripheral mononuclear cells in a biological sample: toxic CD4-positive T cells, toxic CD8-positive T cells, natural killer cells with low CD56 expression, megakaryocytes, immature CD4-positive T cells, immature CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells and natural killer cells with high CD56 expression, wherein the kit is used for: 1) Estimate the age of the subjects; 2) Evaluate or assist in evaluating the aging rate or degree of subjects; 3) Evaluate or assist in evaluating the effectiveness of aging interventions; 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease); 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases; 6) Evaluate or assist in evaluating the risk of age-related death; 7) Evaluate or assist in evaluating the degree of immune senescence; 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration; 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells; 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

19. Use of a substance having the following activity in the preparation of a drug: down-regulating the proportion of the following immune cells in peripheral monocytes of a subject: toxic CD4-positive T cells, toxic CD8-positive T cells, natural killer cells with low CD56 expression and / or megakaryocytes, and / or up-regulating the proportion of the following immune cells in peripheral monocytes: immature CD4-positive T cells, immature CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells and / or natural killer cells with high CD56 expression, in, The drug is used to reduce the biological aging rate of the subject, and / or delay the onset of adverse age-related phenotypes, or reduce their severity, and / or prevent aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease) to prevent or treat aging-related diseases.

20. An evaluation method comprising: (1) measuring the proportion of a first type of immune cell in peripheral monocytes in a biological sample of the subject to obtain a first measurement value, wherein the first type of immune cell is selected from one or more of toxic CD4-positive T cells, toxic CD8-positive T cells, CD56-low-expressing natural killer cells, and megakaryocytes; (2) measuring the proportion of the second type of immune cells in peripheral monocytes in the biological sample of the subject to obtain a second measurement value, wherein the second type of immune cells is selected from one or more of naive CD4-positive T cells, naive CD8-positive T cells, plasmacytoid dendritic cells, classical dendritic cells, and natural killer cells with high CD56 expression; (3) performing at least one of the following comparisons based on the subject's age and the first measurement value and / or the second measurement value: a. Comparison of cell ratios: 1) The first measurement value and / or the second measurement value vs. the proportion of the same cell type in peripheral monocytes of healthy people of the same age as the subject; or 2) The first measurement value and / or the second measurement value vs. the proportion of the same cell type in peripheral monocytes of the subject at a certain time before or after the measurement; or b. Age comparison: the current age of the subject vs. the average age of the healthy population when the proportion of the same cell type in peripheral monocytes of the healthy population is the same as the measured value; (4) Based on the comparison results of step (3), the following evaluation is performed: 1) Estimate the age of the subjects; 2) Evaluate or assist in evaluating the aging rate or degree of subjects; 3) Evaluate or assist in evaluating the effectiveness of aging interventions; 4) Evaluate or assist in evaluating the risk of aging-related diseases (such as cardiovascular, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease); 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases; 6) Evaluate or assist in evaluating the risk of age-related death; 7) Evaluate or assist in evaluating the degree of immune senescence; 8) Evaluate or assist in evaluating the degree of aging-related T cell degeneration; 9) Evaluate or assist in evaluating the accumulation of senescence-associated toxic T cells; 10) Evaluate or assist in the evaluation of aging-related immune cell imbalance.

21. The use according to claim 17 or 19, the kit according to claim 18, or the method according to claim 20, wherein the subject is a mammal, preferably a human, more preferably an Asian, such as an East Asian or Southeast Asian, and more preferably a Chinese.

22. The kit of claim 18 or the method of claim 20, wherein the biological sample is selected from the group consisting of blood (eg, whole blood, plasma or serum), urine, saliva, cell and tissue extracts.

23. The method of claim 20, wherein the first type of immune cells is positively correlated with aging of the subject; and the second type of immune cells is negatively correlated with aging of the subject.

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