Age estimation method based on metabolic data

Abstract

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

Description

Technical Field

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

[0002] With the development of medical level and the improvement of public health quality, the average human life expectancy has gradually increased, and the burden of related chronic diseases has increased. Aging is a complex process that gradually leads to the decline of physiological functions of multiple organs and even the entire body. The complexity of aging and the differences in the environment in which individuals live lead to great differences in the aging rates of different individuals and populations, which are not completely consistent with chronological age. Although the aging mechanism is not fully understood at present, intervention measures such as calorie restriction and exercise can slow down the aging rate of individuals and improve health status to a certain extent. Therefore, the accurate prediction of physiological age and the accurate evaluation of the effect of aging intervention have become an emerging key scientific issue in aging research.

[0003] Fluctuations in the levels of endogenous metabolites can directly reflect the physiological state and pathological changes of the body. Therefore, metabolomics analysis can provide new insights into the prediction of aging rates and mechanism analysis. As the end product of cellular metabolism, metabolomics may provide more detailed information and have a stronger correlation with physiological age than other "omics features" such as transcriptomes. Currently, most studies are based on population data from European and American countries, and aging may be affected by environmental and population factors. These models may not be suitable for Asian populations (e.g., Southeast Asians or East Asians). Therefore, there is an urgent need for a metabolic age model suitable for Asian populations (e.g., Southeast Asians or East Asians). Summary of the invention

[0004] Through strict screening of health conditions and a uniform diet before testing, the inventors of this application established a cohort of naturally aging Chinese people, collected blood samples from the volunteers in the cohort, and further performed plasma metabolome analysis, and identified a series of metabolites that are correlated with age. Based on the abundance of these metabolites, the inventors of this application used an elastic network machine learning model to establish a metabolic age model for population aging, which can accurately predict the biological age of the subjects and is further used to evaluate the degree or rate of aging of the subjects, the effect of aging intervention measures, the risk of aging-related diseases in the subjects, and / or the treatment effect of aging-related diseases.

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

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

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

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

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

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

[0011] i) obtaining metabolites from the biological sample;

[0012] ii) determining the identity and abundance of said metabolites.

[0013] In certain embodiments, the metabolite abundance dataset is obtained by:

[0014] (a) isolating metabolites from the biological sample;

[0015] (b) analyzing the metabolite sample by liquid chromatography-mass spectrometry and collecting mass spectrometry data;

[0016] (c) performing qualitative analysis based on the mass spectral data to screen the mass spectral data for successful qualitative analysis;

[0017] Preferably, the method further comprises standardizing 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.

[0018] In certain embodiments, the method further comprises, before step (2), performing a correlation analysis between the metabolite abundance dataset and age to screen metabolites associated with age.

[0019] In certain embodiments, the metabolite abundance dataset is subjected to Pearson correlation analysis with age, with BMI used as a covariate, the P value is corrected by the BH method, and the result with P<0.05 is selected as the metabolite associated with age.

[0020] In certain embodiments, the data set is divided into a training set and a test set in a 1:1 ratio. In certain embodiments, the data set is divided into a training set and a test set in a 1:1 ratio.

[0021] In certain embodiments, the subject is Asian.

[0022] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0023] 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%.

[0024] In a second aspect, the present application also provides a method for training an age estimation model, comprising:

[0025] (1) establishing a data set according to the method of the first aspect;

[0026] (2) calculating the estimated age of the subject corresponding to the data using the age estimation model according to the data set;

[0027] (3) Training the age estimation model based on the difference between the estimation result and the age label of the data, thereby obtaining an age estimation model that can be used for age estimation.

[0028] In certain embodiments, the training method is a method for training a biological age estimation model.

[0029] 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.

[0030] In certain embodiments, the subject's estimated age is Among them, A x represents the abundance of age-related metabolites in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0031] Through the training, the abundance of each age-related metabolite A is determined. x The coefficient a x and the constant b.

[0032] In certain embodiments, the age-related metabolite abundance is the abundance of the corresponding metabolite in a subject's blood (e.g., whole blood, plasma, or serum) sample. In certain embodiments, the age-related metabolite abundance is the abundance of the corresponding metabolite in a subject's plasma sample.

[0033] In certain embodiments, the age-related metabolites involved in the age estimation model that can be used for age estimation obtained in step (3) of the method are selected from one or more of the following:

[0034]

[0035]

[0036] 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:

[0037]

[0038]

[0039] In certain embodiments, the coefficient a and constant b of each age-related metabolite in the age estimation model that can be used for age estimation obtained in step (3) of the method are as follows:

[0040]

[0041]

[0043] In certain embodiments, the subject is Asian.

[0044] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0045] 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%.

[0046] In a third aspect, the present application also provides an age estimation model, which is obtained according to the training method described in the second aspect.

[0047] In certain embodiments, the age estimation model is a biological age estimation model.

[0048] In a fourth aspect, the present application provides an age estimation device, comprising:

[0049] An input unit, used to input the abundance of age-related metabolites of a subject, wherein the age-related metabolites are as defined in the second aspect;

[0050] An estimation unit is used to calculate the estimated age of the subject according to the input age-related metabolite abundance using the age estimation model described in the third aspect.

[0051] In certain embodiments, the device further comprises an output unit for outputting the estimated age of the subject.

[0052] In certain embodiments, the device is a biological age estimation device.

[0053] In certain embodiments, the abundance of metabolites associated with age in the subject is obtained by referring to the method for establishing and processing the metabolite abundance dataset described in the first aspect. In certain embodiments, the subject is Asian.

[0054] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0055] In a fifth aspect, the present application provides an age estimation method, comprising:

[0056] Providing age-related metabolite abundance of a subject, wherein the age-related metabolite is as defined in the second aspect;

[0057] The age-related metabolite abundance is input into the age estimation model as described in the third aspect or the age estimation device as described in the fourth aspect to estimate the age of the subject.

[0058] In certain embodiments, the age-related metabolite abundance of the subject is obtained by referring to the method for establishing and processing the metabolite abundance dataset described in the first aspect.

[0059] In certain embodiments, the method is a biological age estimation method.

[0060] In certain embodiments, the subject is Asian.

[0061] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0062] In a sixth aspect, the present application provides an electronic device, including:

[0063] Memory; and

[0064] A processor coupled to the memory, the processor being configured to execute the training method of the age estimation model as described in the second aspect, or the age estimation method as described in the fifth aspect, based on instructions stored in the memory.

[0065] In a 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 for the age estimation model as described in the second aspect, or the age estimation method as described in the fifth aspect.

[0066] In an eighth aspect, the present application provides a use of the age estimation model according to the third aspect, the age estimation device according to the fourth aspect, the electronic device according to the sixth aspect, or the non-volatile computer-readable storage medium according to the seventh aspect in preparing a kit, wherein the kit is used for one or more of the following:

[0067] 1) Estimation of subject age (e.g., biological age);

[0068] 2) Evaluate the aging rate or degree of the subjects;

[0069] 3) Evaluate the effectiveness of aging interventions;

[0070] 4) Evaluate the risk of subjects developing aging-related diseases;

[0071] 5) Evaluate the therapeutic effects of aging-related diseases.

[0072] In certain embodiments, the evaluations described in 1) to 5) are each independently a secondary evaluation.

[0073] In certain embodiments, the subject is Asian.

[0074] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0075] In a ninth aspect, the present application provides a method, wherein the method is used for one or more of the following:

[0076] 1) Evaluate the aging rate or degree of the subjects;

[0077] 2) Evaluate the effectiveness of aging interventions;

[0078] 3) Evaluate the risk of subjects developing aging-related diseases;

[0079] 4) Evaluate the therapeutic effects of aging-related diseases;

[0080] The method comprises the following steps: (1) estimating the biological age of the subject according to the method described in the fifth aspect; (2) comparing the biological age with a reference value; and (3) evaluating the aging rate or degree of the subject, the effect of aging intervention measures, the risk of the subject developing aging-related diseases and / or the treatment effect of aging-related diseases.

[0081] In certain embodiments, the reference value is the chronological age of the subject; alternatively, the reference value is the biological age (or average biological age) estimated according to the method described in aspect 5 for individuals (or a group) who are in the same chronological age group as the subject and in a normal aging state; alternatively, the subject is a subject who has received aging intervention or treatment for an aging-related disease, and the reference value is the biological age (or average biological age) estimated according to the method described in aspect 5 for individuals (or a group) who are in the same chronological age group as the subject and have not received the aging intervention or treatment for an aging-related disease and are in a normal aging state; alternatively, the subject is a subject who has received aging intervention or treatment for an aging-related disease, and the reference value is the biological age estimated according to the method described in aspect 5 before receiving the aging intervention or before receiving the aging-related disease treatment.

[0082] In certain embodiments, in step (2), when the biological age is higher than the reference value, it indicates that the subject is in a state of accelerated aging, the effect of the aging intervention measure is not significant, the subject has a higher risk of suffering from an aging-related disease, and / or the treatment effect of the aging-related disease is not significant.

[0083] In certain embodiments, the evaluations described in 1) to 5) are each independently a secondary evaluation.

[0084] In certain embodiments, the extent of aging includes normal aging, accelerated aging, and / or delayed aging.

[0085] In certain embodiments, the subject is Asian.

[0086] In certain embodiments, the subject is Southeast Asian or East Asian (eg, Chinese, Japanese, Korean, Mongolian).

[0087] Definition of terms

[0088] 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.

[0089] 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."

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 2. The compounds can be obtained by separation and purification from organisms or the environment, or can be obtained by artificial preparation.

[0094] 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.

[0095] 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:

[0096]

[0097] Advantageous Effects of the Invention

[0098] The metabolic age model for predicting biological age provided in the present application can be used to predict the biological age of Southeast Asians or East Asians (e.g., Chinese, Koreans, Japanese, Mongolians), and to evaluate their degree or rate of aging, the risk of developing aging-related diseases, and the effectiveness of aging intervention measures and / or treatments for aging-related diseases they have received.

[0099] 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

[0100] Figure 1 : Flowchart for establishing an aging clock based on plasma metabolomics.

[0101] Figure 2 : The percentage of different classes of metabolites in the total identified metabolites (left) and age-related metabolites (right).

[0102] Figure 3 : Age-related plasma metabolites (Pearson correlation, Benjamini

[0103] Hochberg adjusted P value < 0.05, corrected for BMI), the upper bar graph is the individual age. The right column is the metabolite class annotation.

[0104] Figure 4 : Dot plot of age-related change scores for different metabolite classes. Positive numbers indicate that metabolites of the indicated class accumulate more with age, while negative numbers indicate a trend of decreasing change with age. The size of the dots indicates the number of metabolites associated with age.

[0105] Figure 5 : Metabolic aging clock predicts age results, R is the Pearson correlation coefficient, MAE is the mean absolute error. DETAILED DESCRIPTION

[0106] The present invention is further described in detail below in conjunction with specific embodiments, and the examples provided are only for illustrating the present invention, rather than limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art, and do not constitute a limitation of the present invention in any way.

[0107] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0108] The main implementation route of the present invention involves cohort establishment and sample collection, plasma metabolome sequencing, data processing and model establishment ( Figure 1 ).

[0109] Example 1. Cohort establishment and sample collection

[0110] 1. Queue establishment

[0111] 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:

[0112] 1) Enrollment conditions

[0113] a) Aged 18 or above, with no major bad habits;

[0114] 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:

[0115] i. Hypertension, well controlled (taking no more than 3 medications);

[0116] ii. Osteoporosis, osteopenia and / or osteoarthritis;

[0117] iii. Benign prostatic hypertrophy;

[0118] iv. Cataract, glaucoma, macular degeneration;

[0119] v.Dyslipidemia;

[0120] vi. Hypothyroidism;

[0121] vii. Prediabetes or abnormal fasting blood sugar (fasting blood sugar 100-126 mg / dL).

[0122] c) The subjects can communicate well with the researchers and cooperate with all tasks during the study;

[0123] i. Subjects must voluntarily sign a written informed consent.

[0124] 2) Exclusion criteria

[0125] a) Age does not meet the inclusion criteria;

[0126] b) Participants are currently undergoing other clinical trials or are receiving any research drugs or devices for treatment within 30 days prior to enrollment;

[0127] c) The investigator reports a history or current diagnosis of significant chronic medical conditions, including:

[0128] i. Any cancer (except polycythemia vera, basal cell or squamous cell skin cancer);

[0129] ii. Coronary artery disease / myocardial infarction / clinically significant congestive heart failure;

[0130] iii. Stroke / transient ischemic attack;

[0131] iv. Deep vein thrombosis / pulmonary embolism;

[0132] v. Serum creatinine>1.5mg / dl (male);

[0133] vi. poorly controlled hypertension (significant hypertension (systolic blood pressure > 160 mmHg, or diastolic blood pressure > 100 mmHg) despite treatment);

[0134] vii. History of active liver disease or metabolic acidosis;

[0135] viii. Chronic kidney disease / hemodialysis treatment, history of severe renal impairment and / or

[0136] eGFR≤45ml / min / 1.73m2;

[0137] ix. Severe autoimmune / inflammatory diseases, such as rheumatoid arthritis, lupus, Crohn's disease, etc.;

[0138] x. Neurological diseases such as dementia, such as Alzheimer's / Parkinson's, etc.;

[0139] xi. Diabetes (hemoglobin A1C>6.5% or fasting blood glucose>126 mg / dL or taking diabetes medication or insulin treatment);

[0140] xii. Recent (within 3 months) cardiovascular disease events (myocardial infarction, coronary artery intervention, coronary artery bypass grafting);

[0141] xiii. Infectious diseases such as HIV, hepatitis, tuberculosis, etc.

[0142] d) Currently taking the following medications regularly:

[0143] i. Chemotherapy drugs (such as tamoxifen, doxorubicin, mitoxantrone, bleomycin);

[0144] ii. Antiplatelet drugs (e.g. clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine / Ticlopidine, excluding aspirin);

[0145] iii. Cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0146] e) Continuous alcohol or drug abuse;

[0147] f) Unable to provide informed consent;

[0148] g) The researcher believes that the physical factors of the participants may have an adverse effect on the research process or results.

[0149] 3) Termination criteria

[0150] a) Failure to follow the diet, work and rest schedule and physical examination requirements in accordance with the research content;

[0151] b) Experiencing abnormal physical conditions such as colds or fever during the study;

[0152] c) Accidental injury or physical discomfort during the research process;

[0153] d) Comply with the subject’s subjective wishes to terminate the program.

[0154] 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.

[0155] 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.

[0156] Table 1 Unified standardized diet menu

[0157]

[0158] 2. Plasma Collection

[0159] Blood samples were obtained from volunteers by venous blood sampling after they fasted overnight. 5 ml of blood was collected and placed in an anticoagulant tube covered with EDTA and immediately transported to the laboratory's 4°C refrigerator for subsequent processing. The liquid samples were centrifuged (400 g, 15 min, 4°C) to separate the plasma samples, which were stored in cryopreservation tubes and placed in a -80°C refrigerator for long-term storage.

[0160] Example 2: Plasma metabolome analysis

[0161] 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 re-dissolved in 100 μL of acetonitrile / water (volume ratio 1:1). High-performance liquid chromatography (UHPLC) (Agilent

[0162] Technologies' 1290 Infinity LC) and quadrupole time-of-flight mass spectrometer (AB Sciex

[0163] The analysis was performed using a TripleTOF 6600 coupled to a HPLC-MS / MS instrument. For hydrophilic interaction liquid chromatography (HILIC) separation, a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column (Waters, Ireland) was used to analyze the samples. For the first MS / MS acquisition, the instrument was set to acquire over the m / z range of 60–1000 Da, and the time for the product ion scan was set to 0.2 sec / spectrum. The second MS / MS acquisition was over the m / z range of 25–1000 Da, with a time of 0.05 sec / spectrum. Information dependent acquisition (IDA) was used for the product ion scan, and the high sensitivity mode was selected. Briefly, the dissociation potential (DP) was fixed at ±60 V, and isotopes within the 4 Da range were excluded. The collision energy (CE) was fixed at 35 V, ±15 eV. Ten candidate ions were monitored per cycle.

[0164] Example 3: Data processing and model building

[0165] 1. Plasma metabolomics data preprocessing

[0166] a) First, the mass spectrometry raw data was converted into an MzXML file using ProteoWizard MSConvert (version 3.0.6428);

[0167] 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);

[0168] c) Then the screened peaks are grouped, and the key parameters include: bw = 5, mzwid = 0.025, minfrac = 0.5;

[0169] 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;

[0170] 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);

[0171] f) The raw intensity values ​​will be divided by the median value of each sample to obtain a normalized metabolite abundance matrix.

[0172] 2. Age-related feature screening

[0173] a) First, the metabolite abundance matrix was subjected to age correlation analysis, that is, the abundance of each metabolite was subjected to Pearson correlation analysis with the individual's age, and BMI was used as a covariate. A total of 99 and 46 metabolites were identified that were positively correlated and negatively correlated with age, respectively. Among them, about half of the age-related metabolites were lipids and lipid molecules, including metabolite molecules belonging to glycerophospholipids, fatty acyl groups, steroids, and steroid derivatives ( Figure 2 , 3 , 4);

[0174] b) The results were screened and only the results with BH-corrected P values ​​less than 0.05 were selected as age-related metabolites (Table 2);

[0175] 3. Establishment of metabolic age model

[0176] a) First, the metabolite abundance matrix is ​​screened for features, and only features related to age are retained;

[0177] b) Split the selected matrix into 1:1 training and test sets according to the random allocation in advance;

[0178] 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.

[0179] Table 2. Final metabolic clock model

[0180]

[0181]

[0182] 4. Evaluation of metabolic aging clock

[0183] The final model was applied to the test set to obtain the predicted biological age of the test set individuals and compared with their corresponding actual age. The results showed that the correlation coefficient between the predicted biological age and the actual age reached 0.87 (Pearson correlation, with BMI as a covariate), and the average absolute error was 5.14 years, which was highly accurate ( Figure 5 ).

[0184] 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 group, the biological age and the actual age (chronological age) are significantly correlated. As mentioned above, when the metabolic clock 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 metabolic clock model has a high accuracy in assessing biological age.

[0185] Although the specific embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that various modifications and changes may be made to the details according to all the teachings that have been published, and these changes are within the scope of protection of the present invention. The entire invention is given by the attached claims and any equivalents thereof.

Claims

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

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

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

4. The method according to any one of claims 1 to 3, wherein the metabolite abundance dataset is obtained by the following method: i) obtaining metabolites from the biological sample; ii) determining the identity and abundance of said metabolites.

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

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

7. A method for 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 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.

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 in, A x represents the abundance of age-related metabolites in the dataset, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, the abundance of each age-related metabolite A is determined. x The coefficient a x and constant b; Preferably, the age-related metabolite abundance is the abundance of the corresponding metabolite in a blood sample (eg, whole blood, plasma or serum) of a subject.

10. The training method according to claim 9, wherein: The age-related metabolites involved in the age estimation model obtained in step (3) and capable of being used for age estimation are selected from one or more of the following:

11. The training method according to claim 9 or 10, wherein: The coefficient a and constant b of each age-related metabolite in the age estimation model that can be used for age estimation obtained in step (3) are as follows:

12. An age estimation model, obtained by the training method according to any one of claims 7 to 11; Preferably, the age estimation model is a biological age estimation model.

13. An age estimation device, comprising: An input unit, for inputting the abundance of a subject's age-related metabolite, wherein the age-related metabolite is as defined in claim 10; an estimation unit, configured to calculate the estimated age of the subject according to the input age-related metabolite abundance using the age estimation model according to claim 12; Preferably, the device further comprises an output unit for outputting the estimated age of the subject; Preferably, the device is a biological age estimation device.

14. A method for estimating age, comprising: Providing an age-related metabolite abundance in a subject, wherein the age-related metabolite is as defined in claim 10; Inputting the age-related metabolite abundance into the age estimation model according to claim 12 or the age estimation device according to claim 13 to estimate the age of the subject; Preferably, the method is a biological age estimation method.

15. An electronic device, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the age estimation model training method according to any one of claims 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 age estimation model according to claim 12, the age estimation device according to claim 13, 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) Estimation of subject age (e.g., biological age); 2) Evaluate the aging rate or degree of the subjects; 3) Evaluate the effectiveness of aging interventions; 4) Evaluate the risk of subjects developing aging-related diseases; 5) Evaluate the therapeutic effects of aging-related diseases; Preferably, the evaluations described in 1) to 5) are each independently auxiliary evaluations.

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