Male senescence marker and male senescence clock determination method

Abstract

The invention relates to a determination method of a male aging marker and a male aging clock, and belongs to the technical field of medical examination. A group of male senescence markers comprises a plurality of cell factors related to male senescence, and the cell factors comprise but are not limited to transforming growth factors b1, carcinoembryonic antigen related cell adhesion molecules 1, capillary morphogenetic genes 2, dipeptidyl peptidase IV, growth differentiation factors 15, hepatocyte adhesion molecules, L-selectin and neurogenic locus Notch homologous proteins 3. The kit comprises a total prostate specific antigen, a regenerated pancreas islet derived protein 4, alpha-N-acetylneuraminic acid alpha-2, 8-aminoacyltransferase 1, a thromboregulatory protein and a vascular endothelial growth factor receptor 3. The method has the advantages that a group of specific senescence markers related to male senescence are confirmed, a male cytokine senescence clock determination model is constructed, and the biological age of a male individual is evaluated and the senescence speed is predicted through quantitative analysis of the biomarkers.

Description

Technical Field

[0001] The invention relates to a method for determining male aging markers and male aging clocks, and belongs to the technical field of medical testing. Background Art

[0002] With the increasing aging of society, research on aging biology has become a hot topic.

[0003] Aging markers refer to those biological molecules and cell characteristics that are closely related to the aging process. Common aging markers in current research include but are not limited to: telomere shortening, oxidative stress products, inflammatory markers, metabolites, protein aggregates, etc. The detection of these markers usually requires specific biochemical analysis techniques, such as ELISA, Western blot, mass spectrometry, etc. Despite this, a single marker is often difficult to fully reflect the aging status of an individual.

[0004] Research has shown that gender plays an important role in the aging process. There are significant differences between men and women in terms of physiology, hormone levels, genetic susceptibility, etc., and these differences affect the rate and characteristics of aging. Some aging markers show gender specificity. For example, a decrease in estrogen levels in women is closely related to the aging process, while changes in testosterone levels in men are also associated with aging. In addition, the risk of certain diseases and the rate of aging differ between the sexes.

[0005] In recent years, researchers have begun to focus on the role of gender in the aging process and explore gender-specific aging markers. For example, some studies have attempted to reveal the impact of gender on aging by analyzing gene expression profiles, proteome and metabolome data of different genders.

[0006] Although sex differences have received attention in aging research, current research is relatively limited in scope, focusing mainly on a few known sex-specific markers and lacking exploration of broader sex differences. Existing studies often focus on a single sex or a single marker, lacking research that integrates and analyzes aging marker data from different sexes, which limits the ability to fully understand sex differences.

[0007] The concept of the aging clock originates from the biological record of time in organisms. The aging clock refers to the biomarkers in the body that reflect age changes. Aging markers are changes at the molecular, cellular or tissue levels related to the aging process, and aging acceleration refers to the rate of change of an individual's aging rate. At present, the determination of the aging clock mainly relies on biomarkers, such as telomere length and DNA methylation levels. These markers can reflect the replication history and aging state of cells. However, existing aging clock research mostly focuses on a single marker, lacking an evaluation system that integrates multiple biomarkers.

[0008] Cytokines are small secretory proteins released by cells that play an important role in cell signaling and are crucial in the aging process, affecting inflammation, cell aging, and tissue repair mechanisms. Cytokines are messengers of intercellular communication and they play a key role in regulating biological processes such as immune responses, inflammatory processes, tissue repair, and cell proliferation. Using cytokines as indicators of the aging clock can reflect systemic biological changes, rather than just local or tissue-specific changes.

[0009] Changes in cytokine levels can provide real-time information about an individual's physiological state. By monitoring changes in cytokines, the aging process can be assessed dynamically rather than relying solely on static biomarkers. Many aging-related diseases, such as cardiovascular disease, neurodegenerative diseases, and cancer, are associated with abnormal levels of specific cytokines. Therefore, incorporating cytokines into the assessment of the aging clock will help to identify and predict the occurrence of these diseases early. Aging is associated with a chronic, low-grade inflammatory state (known as "inflammaging"). Cytokines, especially inflammatory cytokines such as interleukins (ILs), tumor necrosis factor (TNF), and interferons (IFNs), are key indicators of the inflammatory state. Monitoring the levels of these cytokines can serve as a useful tool for assessing the risk of aging- and inflammation-related diseases.

[0010] Many cytokines can be easily measured by blood tests and other biological sample detection methods. These detection methods are relatively mature and easy to standardize, making cytokines feasible and reliable biomarkers in aging clock research. Because cytokine levels are affected by individual lifestyle, genetic factors, and environmental factors, they can serve as the basis for personalized aging assessment and intervention. This helps to develop anti-aging strategies that target individual specific needs. As indicators of the aging clock, cytokines can connect multiple disciplines such as immunology, molecular biology, physiology, and pathology, promote interdisciplinary research, and thus have a more comprehensive understanding of the aging process. Cytokines not only serve as biomarkers of aging, but also as potential targets for intervention and treatment. Regulating the levels of specific cytokines or their signaling pathways may help slow the aging process or treat aging-related diseases. By analyzing the expression patterns and changing trends of cytokines, the aging rate and possible lifespan of an individual can be predicted, providing important information for personalized health management. Summary of the invention

[0011] The technical problem to be solved by the present invention is: in view of the deficiencies of existing research, a method for determining aging markers and aging clocks that comprehensively considers gender differences is proposed. The method provides male-specific aging markers and also constructs a more comprehensive male cytokine aging clock model based on cytokines, aiming to provide a more accurate scientific basis for male aging intervention.

[0012] To achieve the above objectives, the present invention adopts the following technical solutions.

[0013] A group of male aging markers includes multiple cytokines associated with male aging, including but not limited to transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin), vascular endothelial growth factor receptor 3 (VEGF R3).

[0014] Transforming growth factor b1 (bIG-H3) is a strong growth inhibitor in multicellular organisms. It can significantly inhibit the growth and proliferation of lymphocytes, hematopoietic stem cells, epithelial cells, endothelial cells, hepatocytes, keratinocytes and adipocytes, but it can promote fibroblast proliferation, extracellular matrix protein expression and bone matrix protein synthesis. At present, bIG-H3 is considered to be a tumor suppressor gene. Defects in its expression or activation can eliminate its growth inhibitory effect, leading to malignant cell proliferation. Clinical data show that it has an important association with the occurrence of cancers such as prostate cancer, breast cancer and rectal cancer. Therefore, the accurate determination of the content in the sample is of great significance for the study of related cancers and other related diseases.

[0015] Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1) is a cell surface transmembrane glycoprotein and a member of the carcinoembryonic antigen family. It belongs to the immunoglobulin superfamily adhesion molecule. CEACAM-1 is widely expressed on epithelial cells and vascular endothelial cells. CEACAM-1 can prevent tumor growth and epithelial cell proliferation, induce epithelial cell apoptosis, prevent T lymphocyte activation, stimulate B lymphocyte proliferation, inhibit T cell and NK cell cytotoxic effects, prevent tumor-infiltrating lymphocyte activity, delay granulocyte and monocyte apoptosis, promote tumor cell invasion, enhance endothelial cell activity, promote blood vessel formation, regulate blood vessel remodeling, etc., playing many important biological functions. CEACAM-1 is highly expressed in malignant tumors and can be used as a biomarker and a promising therapeutic target.

[0016] Capillary Morphogenesis Gene 2 (CMG-2) is a transmembrane protein, the full name is CapillaryMorphogenesis Gene 2. It was originally discovered in the process of vascular morphogenesis and is expressed in a variety of cell types. CMG-2 plays an important role in participating in intracellular and extracellular signal transduction, extracellular matrix assembly, cell adhesion and cell proliferation. In addition, CMG-2 is also a receptor for some important pathogens invading cells, such as anthrax toxin. Studies have shown that CMG-2 plays an important role in many diseases, such as cancer, infectious diseases, etc. Therefore, CMG-2 is a protein with important biological significance and potential clinical application value.

[0017] Dipeptidyl peptidase IV (DPPIV) is a highly conserved type II transmembrane glycoprotein distributed in a variety of cells. It is a cell membrane receptor and co-stimulatory molecule, and thus participates in the body's immune regulation, cell migration, cell adhesion and cell apoptosis. Therefore, it is related to the onset of a variety of diseases. Various DPPIV inhibitors are of great significance in the treatment of autoimmune diseases and cell proliferative diseases. DPPIV plays an important role in tumor biology and is an effective marker for a variety of cancers. Its level is increased on the cell surface or in the serum of some tumors and decreased in other tumors.

[0018] Growth differentiation factor 15 (GDF-15) is a cytokine produced by tumor cells, inflammatory cells and cardiomyocytes. GDF-15 has important biological significance in regulating cell growth, proliferation, apoptosis and inflammatory response. In recent years, studies have found that the expression level of GDF-15 in a variety of diseases is closely related to the occurrence and prognosis of the disease, such as cardiovascular disease, diabetes, chronic kidney disease, tumors, etc. In cardiovascular disease, GDF-15 is considered to be an important prognostic indicator and biomarker of disease activity, which can predict the occurrence of cardiovascular events and the risk of death of patients. In tumors, the expression of GDF-15 is closely related to the malignancy of the tumor, disease progression and prognosis, so it is considered to be a potential tumor biomarker and therapeutic target. In addition, GDF-15 is also involved in regulating physiological processes such as appetite and energy metabolism, so it also has certain biological significance in metabolic diseases such as obesity and metabolic syndrome.

[0019] HepaCAM is a cell adhesion molecule belonging to the immunoglobulin superfamily. It was originally discovered in the liver and has the function of regulating cell adhesion, migration and proliferation. It is expressed in a variety of tissues and is believed to have the effect of inhibiting certain tumors, especially in renal cancer and bladder cancer, where its reduced expression is associated with tumor development. Therefore, HepaCAM is of great significance in cell biology and cancer research.

[0020] L-Selectin is a type I transmembrane cell adhesion molecule. The binding of L-Selectin to endothelial-derived ligands initiates the adhesion and rolling behavior of neutrophils on the lumen wall of post-capillary venules, constituting the first step of a multi-step adhesion cascade. L-Selectin helps lymphocytes and neutrophils enter inflammatory sites. Anti-L-Selectin antibodies block lymphocyte homing to lymph nodes, and soluble chimeric molecules block the migration of leukocytes to peripheral lymph nodes and inflamed peritoneum. L-Selectin and its ligands are involved in the adhesion of blastocysts to the endometrium at the maternal-fetal interface. It is related to the diagnosis and prognosis of post-traumatic acute respiratory distress syndrome (ARDS). In the development of malignant tumors, selectins promote the interaction between tumor cells and blood components (platelets, endothelial cells, and leukocytes). The increase of selectin ligands on tumor cells is associated with tumor metastasis and poor prognosis.

[0021] Neurogenic locus Notch homologous protein 3 (Notch-3) is one of the four mammalian Notch proteins and an important member of the human Notch signaling pathway. It belongs to the Notch family of transmembrane receptors and is involved in a variety of biological processes such as cell growth, differentiation, and apoptosis. It acts as a signal receptor to control cell fate in many developmental and adult tissue environments. Notch signaling is still important for tissue maintenance and renewal in adult organisms. The Notch signaling pathway is a highly conserved cell signaling pathway that plays an important regulatory role in physiological processes such as development, growth, maturation, and tissue repair of the body, and misregulation of Notch is associated with many diseases. Overexpression, gene amplification, and misactivation of Notch-3 are associated with different cancers, especially triple-negative breast cancer and ovarian cancer. Notch-3 mutations are associated with the dominant genetic disease CADASIL (autosomal dominant cerebral arteriopathy with subcortical infarcts and leukoencephalopathy), and there is further evidence that Notch-3 dysregulation is associated with hypertensive diseases. Notch-3 is mainly expressed in the central nervous system and cardiovascular system, and is involved in the development, differentiation, and repair of neurons and cardiovascular cells. Studies have shown that Notch-3 is closely related to the occurrence and development of many diseases, such as cardiovascular diseases, nervous system diseases, tumors, etc. Therefore, Notch-3 is a biomarker and therapeutic target with potential clinical application value.

[0022] Total prostate-specific antigen (PSA-total) is a single-chain glycoprotein secreted by epithelial cells of prostate acini and ducts. It is a serine protease of the class of kallikrein in function. It participates in the liquefaction process of semen and is an important indicator for the diagnosis and differential diagnosis of benign and malignant prostate diseases. The normal physiological function of PSA in the human body is to help liquefy semen and is related to male reproductive function. As a specific marker for prostate cancer, it is also the only recognized organ-specific tumor marker. Elevated serum PSA generally indicates the presence of prostate lesions (prostatitis, benign hyperplasia or cancer). Serum PSA is one of the most important indicators for the detection and early detection of prostate cancer. The positive critical value of serum TPSA quantification is greater than 10 μg / L, and the diagnostic specificity of prostate cancer is 90% to 97%. Regenerating islet-derived protein 4 (Reg4) is a calcium-independent lectin with mannose binding specificity and can retain carbohydrate recognition activity even in an acidic environment. Its ability to function independently of calcium indicates that it has a strong and multifunctional lectin activity. REG4 is involved in inflammatory and metaplastic reactions within the gastrointestinal epithelium, implying a potential role in processes related to gastrointestinal inflammation and cell transformation. The unique property of maintaining mannose binding specificity under acidic conditions suggests that its adaptability may contribute to its functional significance in dynamic physiological environments, especially in the gastrointestinal environment. REG4 is secreted by pancreatic β cells and is a member of the Reg family, which has the effects of promoting cell proliferation and anti-apoptosis. In addition to pancreatic islet cells, the expression of REG4 is also found in a variety of tissues and cell types, such as the stomach, intestine, bile duct, liver, endometrium, and kidney. Studies have shown that REG4 plays an important role in a variety of diseases, such as digestive system tumors, pancreatic diseases, inflammatory diseases, and immune system diseases. In digestive system tumors, high expression of REG4 is closely related to the malignancy and prognosis of the tumor, so it is considered to be a tumor marker and therapeutic target with potential clinical application value.

[0023] α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1) is one of the six members (ST8SiaⅠ-VI) of the ST8Sia subfamily of sialyltransferases, also known as ganglioside GD3 synthase (GD3s). ST8Sia1 is the only key enzyme in the biosynthesis of ganglioside GD3 and ganglioside GD2. The functional study of ST8Sia1 is of great significance for the biosynthesis of GD3 and its downstream products, as well as the occurrence and development of tumors. It has been reported in the literature that ST8Sia1 is highly expressed in tumors, especially in tumors of neuroectodermal origin, such as malignant gliomas and melanomas.

[0024] Thrombomodulin is a single-chain transmembrane glycoprotein that is mainly present in vascular endothelial cells and can also be expressed in hematopoietic progenitor cells, monocytes and macrophages. Thrombomodulin can prevent the occurrence and development of tumors by inhibiting epithelial-mesenchymal cell transformation, inhibiting mitogen-activated protein kinase or activated protein C and fibrinolysis inhibitors. In atherosclerosis, thrombomodulin can block thrombin-mediated PAR-1 activation, inhibit endothelial cell autophagy and apoptosis, and thus prevent the occurrence and development of atherosclerosis. The lectin-like domain of thrombomodulin can also bind to thrombin to inhibit its activity, thereby inhibiting thrombosis, pulmonary fibrosis and inflammatory response. Thrombomodulin is associated with the pathogenesis of diabetic nephropathy, preeclampsia and ischemia-reperfusion injury.

[0025] Vascular endothelial growth factor receptor 3 (VEGF R3), also known as FLT4 (Fms-like tyrosine kinase receptor 4), is a membrane-bound tyrosine kinase receptor that plays an important role in the growth and development of the lymphatic and vascular systems. After binding to its ligands VEGF-C and VEGF-D, VEGF R3 can regulate the proliferation, migration and survival of lymphatic and vascular endothelial cells, thereby affecting the occurrence and differentiation of lymphatic vessels and blood vessels. In addition, VEGF R3 also plays an important role in tumor lymphatic metastasis, inflammatory response and immune regulation. Therefore, VEGF R3 is considered to be an important biomarker and therapeutic target, and has potential clinical application value in the treatment of tumors and other related diseases.

[0026] Another aspect of the present invention provides a method for predicting the age of a male individual, the method comprising the following steps:

[0027] a. Obtain biological samples from male subjects;

[0028] b. determining the content of male aging markers in the biological sample;

[0029] c. inputting the content value of the male aging marker into a preset cytokine aging clock;

[0030] d. outputting the estimated age of the male individual based on the cytokine aging clock.

[0031] The male aging markers include but are not limited to: transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin), vascular endothelial growth factor receptor 3 (VEGF R3).

[0032] The biological sample is blood, serum, plasma or saliva; the determination step adopts immunoassay, mass spectrometry, biochemical analysis method or biochip technology.

[0033] The calculation formula of the cytokine aging clock is: predicted age = 70.80454 + bIG-H3 × 5.93294 + CEACAM-1 × -9.78756 + CMG-2 × -1.03379 + DPPIV × -13.82591 + GDF-15 × 33.1637 + HepaCAM × 4.31484 + L-Selectin × -22.57612 + Notch-3 × 8.31285 + PSA-total × 6.66175 + Reg4 × 8.59947 + ST8SIA1 × -0.47551 + Thrombomodulin × 0.77648 + VEGF R3 × 0.00077, wherein the cytokine values ​​are all detection concentrations in pg / mL.

[0034] The method for predicting the age of a male individual also includes calculating the aging acceleration, namely Δage.

[0035] The calculation formula of the aging acceleration Δage is:

[0036] ΔAge 初始 = Predicted age – Chronological age

[0037] Δage=Δage 初始 —loess(chronological age—Δage 初始 )

[0038] Among them, chronological age refers to our own age; "Δage" is the indicator finally obtained by our model that can be used to evaluate aging. A large Δage means accelerated aging and there may be health problems; Loess is a local polynomial regression fitting (Local Polynomial Regression Fitting), which uses the initial aging acceleration Δage minus the fitted age difference to correct the impact of age on aging acceleration.

[0039] The present invention adopts cytokine antibody chip technology, and on the basis of screening 1000 cytokines, identifies 18 cytokines that are closely related to male aging, and uses Lasso regression to construct a male aging clock model. Subsequently, after verification and optimization of 610 people, a combination of 13 cytokines was finally determined as candidate cytokines for aging markers in male plasma. Through the calculation of the aging clock model, the correlation coefficient between the predicted age and the chronological age is 0.98, and the root mean square error RMSE = 3.64. The predicted age is subtracted from the chronological age to obtain the difference, and the age is corrected by LOESS regression to obtain the individual's aging acceleration. The results show that the aging acceleration and the individual's laboratory test indicators such as liver and kidney function, intrinsic function and somatic function have a significant correlation, and the aging acceleration can be used to use the Logistic regression method to determine whether the individual has a disease. This study established an aging clock for men, accurately predicted age, and the calculated aging acceleration can be used to assess the health status of individuals.

[0040] A system for predicting the age of a male individual, comprising:

[0041] a. Biological sample collection module, which is used to collect biological samples from male individuals, such as blood, urine, saliva or other body fluids. The module can include various sampling devices, such as needles, blood collection tubes, swabs, etc. The collected biological samples can be stored in appropriate containers for subsequent processing and analysis.

[0042] b. A polypeptide content determination module, which is used to determine the content of at least 13 male aging markers in the collected biological sample. These markers may include but are not limited to: transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin), vascular endothelial growth factor receptor 3 (VEGF R3). The module can use various biochemical methods to measure the content of these markers, such as antibody array, enzyme-linked immunosorbent assay (ELISA), chemiluminescence assay (CLIA), mass spectrometry (MS), etc.

[0043] c. A cytokine aging clock module, which is used to bring the measured polypeptide content into a prediction algorithm to predict the age of the male individual. This module can use a machine learning model or a statistical model to predict age.

[0044] The prediction algorithm module is based on a machine learning model or a statistical model.

[0045] The machine learning models include but are not limited to Lasso regression, random forest, XGBoost and SVM.

[0046] Lasso Regression: A linear regression model that uses L1 regularization to select feature variables. Lasso regression is able to effectively select important features through L1 regularization, which is particularly important when dealing with high-dimensional data sets. When predicting the age of men, it can automatically remove irrelevant variables, thereby simplifying the model and improving accuracy. Because Lasso tends to compress the coefficients of certain features to zero, the results of the model are easier to interpret, allowing analysts to clearly understand which factors have the greatest impact on age prediction.

[0047] Random Forest: An ensemble learning model that uses multiple decision trees to predict outcomes. Random Forest reduces the risk of overfitting that may be caused by a single tree by integrating multiple decision trees for prediction. This allows the model to maintain a high level of generalization when faced with complex data. Random Forest provides an assessment of feature importance to help analysts identify the variables that have the greatest impact on predicting male age. For example, features such as gender, occupation, or education level may be identified as key factors. Random Forest has a good tolerance for missing values ​​and can still make effective predictions even when the data is incomplete.

[0048] XGBoost: A gradient boosting model that uses decision trees to predict results. XGBoost is known for its efficient computing speed and excellent prediction performance, and is particularly suitable for processing large-scale data sets. In the prediction of male age, it can quickly iterate and optimize the model to improve prediction accuracy. XGBoost's built-in regularization mechanism can effectively prevent overfitting, making the model perform better on complex data sets. Supports multiple loss functions and evaluation indicators, allowing users to adjust the model according to specific needs to adapt to different data characteristics and business scenarios.

[0049] SVM: A support vector machine model that uses kernel functions to predict results. SVM performs classification by constructing a maximum margin hyperplane, and can also effectively classify linearly inseparable data through kernel functions. In male age prediction, SVM can handle complex nonlinear relationships, thereby improving prediction accuracy. SVM is highly robust to outliers, which allows the model to maintain stable performance in noisy data sets. SVM can still perform well when the number of samples is relatively small, which may be particularly important for some male age prediction data sets.

[0050] Statistical models may include, but are not limited to:

[0051] Linear regression: A linear model that uses the method of least squares to predict an outcome.

[0052] Logistic regression: A linear model that uses the method of least squares to predict binary outcomes.

[0053] The module can be implemented using various programming languages, such as Python, R, MATLAB, etc. Prediction algorithms can be trained and validated using public datasets, such as NCBI's Gene Expression Omnibus (GEO) database.

[0054] The system can be used to predict the age of male individuals, especially middle-aged and elderly men. The system can be used in the fields of healthcare, insurance, sports, etc. For example, healthcare providers can use the system to predict the age of patients and then develop personalized treatment plans. Insurance companies can use the system to predict the age of policyholders and then determine insurance premiums. Sports coaches can use the system to predict the age of athletes and then develop personalized training plans.

[0055] In clinical applications, the cytokine aging clock of the present invention has important academic and application value:

[0056] First, personalized aging assessment: The clock can provide patients with personalized aging assessments by measuring specific cytokines to assess biological age. This can help doctors accurately understand the aging rate of individuals and provide more accurate health management advice, especially in preventing aging-related diseases such as cardiovascular disease and diabetes.

[0057] Second, early disease screening and intervention: Changes in cytokines are often closely related to pathological processes such as inflammation and decreased immune function. The aging clock can be used as a screening tool for early diseases, helping doctors to detect potential health problems early and conduct early intervention based on biological age changes to delay the aging process and the occurrence of diseases.

[0058] Third, dynamic health monitoring and efficacy evaluation: In clinical practice, the aging clock can be used for long-term health monitoring, by dynamically tracking changes in cytokines and observing the effects of interventions (such as drug treatment or lifestyle changes) on individual biological age. This can provide an objective biological basis for efficacy evaluation and help adjust treatment plans.

[0059] Finally, aging-related disease risk assessment: Cytokines are closely related to the risk of a variety of elderly diseases, such as Alzheimer's disease, arteriosclerosis, etc. By regularly evaluating the patient's cytokine aging clock, doctors can assess the risk of these diseases in advance and take preventive measures.

[0060] The present invention has industrial applicability: the present invention has broad application potential in personalized medicine, health management and aging mechanism research, and can significantly improve the ability to accurately monitor and intervene in the aging process, as shown in the following:

[0061] 1. Accurate screening of aging markers: By screening 13 key cytokines from the male population as aging markers, the clock can accurately assess the biological age of an individual. Compared with other more generalized biomarker screenings, this clock model focuses on cytokine changes in men, enhancing the specificity and accuracy of the model.

[0062] 2. Multi-dimensional monitoring of aging status: By combining multiple cytokines, this model can not only predict the aging process in a single dimension, but also integrate the changes in multiple biological pathways to provide multi-dimensional aging progress information. This makes it a powerful tool for comprehensive monitoring of individual aging status.

[0063] 3. Application in personalized health management: The male cytokine aging clock can be used as an important tool for personalized health management, especially in predicting the risk of aging-related diseases and formulating health intervention measures. It can provide clinicians with a more scientific basis for individual aging assessment and promote early intervention and optimization of health management decisions.

[0064] 4. Promote the development of basic research: The application of this clock model is not only valuable in health management, but also can further promote basic research in aging biology. By long-term tracking and studying the changing patterns of cytokines, we can deepen our understanding of the aging mechanism and promote the development of aging intervention therapies.

[0065] The present invention uses cytokine antibody array (biochip) technology to identify a group of specific biomarkers associated with male aging by analyzing blood samples from different populations. This process not only confirms the existence of these markers, but also lays the foundation for the construction of a male cytokine aging clock determination model.

[0066] Cytokine antibody arrays can detect multiple biomarkers simultaneously, providing comprehensive physiological status information. This high-throughput detection method enables researchers to quickly obtain a large amount of data, thereby improving research efficiency. This technology has high specificity and sensitivity, and can accurately identify cytokines related to aging, thereby providing reliable data support for subsequent analysis.

[0067] By identifying a set of specific aging markers, the present invention provides a scientific basis for assessing the biological age of male individuals. These markers may include cytokines related to immune response, inflammatory state and metabolic process. Through quantitative analysis of these biomarkers, the aging rate of individuals can be predicted, providing a reference for personalized health management.

[0068] The present invention successfully constructed a male cytokine aging clock model: the constructed biological aging clock model can evaluate the biological age of an individual by quantitatively analyzing biomarkers. This method provides a new perspective for traditional age assessment and can more accurately reflect the true health status of an individual. This model can not only be used to assess current health conditions, but also provide a scientific basis for anti-aging interventions. By monitoring changes in biomarkers, researchers can evaluate the effectiveness of different interventions and thus optimize anti-aging strategies.

[0069] This invention plays an important role in the growing body of knowledge on biomarkers of aging. By systematically identifying and analyzing specific markers associated with male aging, it provides a new tool for future age-related research and clinical evaluation. This will not only help deepen the understanding of the mechanisms of aging, but also promote the development of related fields and provide support for public health strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Diagram of the serial dilution of cytokine standards

[0071] Figure 2A It shows that among the 1000 cytokines in males, 44 are linearly positively correlated with age, and 58 are negatively correlated.

[0072] Figure 2B Shown: Bootstrap-Lasso screening of aging markers in men

[0073] Figure 2C Shown: Cross-validation curve of Lasso regression of 18 cytokines in men

[0074] Figure 2D Shown: Lasso male 18 cytokine regression coefficient path diagram

[0075] Figure 2E Shown: The 18-factor aging clock for men

[0076] Figure 3A Shown: Validation of 18 aging markers in a large male population

[0077] Figure 3B Shown: Cytokines with consistent expression trends in both cohorts

[0078] Figure 3C Shown: The newly established aging clock

[0079] Figure 3D Shown: New aging clock validated in large population

[0080] Figure 3E Shown: Lasso coefficient of each cytokine of the new aging clock

[0081] Figure 4A Shown: Predicted age for each age group for males

[0082] Figure 4B Shown: ROC curve of the self-reported disease discrimination model based on male Δ age

[0083] Figure 4CThe following table shows the correlation analysis between male cytokines and laboratory test indicators. The abbreviations in the figure mean: HGB, hemoglobin; PLT, platelet; RBC, red blood cell; D3-H, D3-hydroxybutyrate; GLU, blood glucose; IGF, insulin-like growth factor; INS, insulin; FOL, folic acid; HCY, homocysteine; hsCRP, high-sensitivity C-reactive protein; IGM, immunoglobulin M; SOD, superoxide dismutase; LDL-C, low-density lipoprotein cholesterol; NFFA, free fatty acid; TG, triglyceride; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CHE, cholinesterase; GGT, gamma-glutamyl transferase; LDH, lactate dehydrogenase; PAB, prealbumin; TBA, total bile acid; CRE, creatinine; Cystatin-C, cystatin C; GFR, glomerular filtration rate; UREA, urea nitrogen; URIC, uric acid.

[0084] Figure 4D Figure 2 shows the correlation analysis between male cytokines and laboratory test indicators above the reference interval. The abbreviations in the figure are: GLU, blood glucose; HbA1c, glycosylated hemoglobin; INS, insulin; HCY, homocysteine; hsCRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides; Cystatin-C, cystatin C; GFR, glomerular filtration rate.

[0085] Figure 4E Shown is the correlation analysis between male cytokines and laboratory test indicators below the reference interval. The abbreviations in the figure are: FOL, folic acid; GFR, glomerular filtration rate.

[0086] Figure 4F Shown: Correlation analysis between laboratory test indicators and Δage.

[0087] Figure 4G Shown: Correlation analysis between intrinsic function and physical function and Δage.

[0088] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0089] Example 1: Screening 18 aging markers from 1000 cytokines in men

[0090] 1. Sample Collection

[0091] The age distribution of male subjects was 26-92 years old, and they were divided into four age groups: young group, young-middle-aged group, middle-aged group and old group, with average ages of 30.8±1.68, 44.67±1.89, 58.67±1.74 and 79.73±5.72 years, respectively, with 15 people in each group.

[0092] The sample collection time was from December 2019 to January 2020. The samples were collected at the Beijing Hospital Physical Examination Center, No. 1 Dahua Road, Dongdan, Beijing. Fasting blood samples were collected from the subjects at 8 am every day. Anticoagulant tubes were used to collect venous blood from the subjects' elbows. The operators were professionally trained nurses. After the blood samples were collected, they were centrifuged at 4°C, 3000×g for 10 minutes, and the supernatant was collected into a cryopreservation tube and stored in a -80°C refrigerator. Collector: Zeng Lutao, contact number: 010-58115080

[0093] 2. Cytokine Detection

[0094] Cytokine aging markers were detected using a cytokine antibody chip, which was a chip kit for detecting 1,000 cytokines (QAH-CAA-X00) purchased from Ruiboao (Guangzhou) Biotechnology Co., Ltd.

[0095] Information on the 1000 cytokines used in this example is shown in Table 1.

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

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[0130] 3. Specific testing steps:

[0131] 3.1 Complete drying of the slide

[0132] Take the glass slide out of the box, equilibrate at room temperature for 20-30 minutes, open the packaging bag, remove the sealing strip, and then place the chip in a vacuum dryer or dry it at room temperature for 1-2 hours.

[0133] 3.2 Configuration of standard products

[0134] 3.2.1 Schematic diagram of the gradient dilution of cytokine standards is shown in Figure 1 shown.

[0135] 3.2.2 Add 500 μL of sample diluent to the cytokine standard mixture tube to re-dissolve the standard. Before opening the tube, centrifuge quickly and gently pipette up and down to dissolve the powder. Mark this tube as Std1.

[0136] 3.2.3 Label 6 clean centrifuge tubes as Std2, Std3 to Std7 respectively, and add 200 μL of sample diluent to each tube.

[0137] 3.2.4 Take 100 μL of Std1 and add it to Std2 and mix gently. Then take 100 μL of Std2 and add it to Std3. Continue to dilute in gradient to Std7.

[0138] 3.2.5 Take 100 μL of the sample dilution into another new centrifuge tube and mark it as CNTRL as a negative control.

[0139] Note: Because the starting concentration of each cytokine is different, the concentration series of each cytokine is different after the gradient dilution from Std1 to Std7. Please refer to the kit instructions.

[0140] 3.3 Chip operation process

[0141] 3.3.1 Add 100 μL of sample diluent to each well, incubate on a shaker at room temperature for 1 hour, and block the quantitative antibody chip.

[0142] 3.3.2 Draw out the buffer in each well, add 60 μL of standard solution and sample (sample diluted 2 times, 100 μL sample loaded) to the well, and incubate at 4°C overnight.

[0143] 3.3.3 Wash the slides using the Thermo Scientific Wellwash Versa chip washer in two steps. First, wash with 1× Wash Solution I, 250 μL of 1× Wash Solution I per well, wash 10 times, shake for 10 seconds each time, select high shaking intensity, and dilute 20× Wash Solution I with deionized water. Then switch to 1× Wash Solution II for washing, 250 μL of 1× Wash Solution II per well, wash 6 times, shake for 10 seconds each time, select high shaking intensity, and dilute 20× Wash Solution II with deionized water.

[0144] 3.3.4 Incubation of detection antibody mixture

[0145] Centrifuge the detection antibody mixture tube, then add 1.4 ml of sample diluent, mix well and centrifuge again. Add 80 μL of detection antibody to each well and incubate on a shaker at room temperature for 2 hours.

[0146] 3.3.5 Cleaning, same as step 3.3.3

[0147] 3.3.6 Incubation of Cy3-streptavidin

[0148] Centrifuge the Cy3-streptavidin tube, then add 1.4 ml of sample diluent, mix well and centrifuge quickly again. Add 80 μL of Cy3-streptavidin to each well, cover the slide with aluminum foil and incubate in the dark, and incubate on a shaker at room temperature for 1 hour.

[0149] 3.3.7 Cleaning, same as step 3.3.3

[0150] 3.3.8 Fluorescence detection

[0151] Scan the signal using a laser scanner such as InnoScan 300, using Cy3 or green channel (excitation frequency = 532nm)

[0152] 1) Instrument model: InnoScan 300 Microarray Scanner

[0153] 2) Manufacturer: Innopsys

[0154] 3) Origin: Parc d'Activités Activestre; 31390 Carbonne–France

[0155] 4) Scanning parameters: Wavelength: 532nm; Resolution: 10μm

[0156] 3.4 Use data analysis software to perform data analysis.

[0157] The GAL file dedicated to the array and the microarray analysis software GenePix were used for data extraction, and the data analysis software Q-Analyzer of QAH-CAA-X00 was used for data analysis.

[0158] The linear correlation of each factor on age was analyzed using linear regression using the R lm() function according to the following formula:

[0159] Cytokine level~α+β1 age+β2sex+ε

[0160] Gender was used as a covariate, and a Q value < 0.05 after Bonferroni correction was considered to be linearly correlated.

[0161] To determine the relative proportion of variation explained by age and sex, we calculated PartialEta2 using the following formula:

[0162]

[0163] The results are as follows Figure 2A As shown, among the 1000 cytokines in males, 44 were positively correlated and 58 were negatively correlated with age.

[0164] The cytokines with linear correlation screened out by linear regression were subjected to 1000 Boostrap self-sampling using the R sample() function. Each time, 1200 samples were randomly selected from the original data with replacement. The 1200 samples were then subjected to Lasso regression for variable screening (α=1) using the R glmnet package. The cv.glmnet function was used for 10× cross validation to find the minimum lambda value (λ). After obtaining the regression coefficients of all cytokines, the cytokines with a coefficient of 0 were removed, and the remaining cytokines were aging markers. The regularization coefficient λ was adjusted, and finally 18 cytokines were screened out from the cytokines in men as potential aging markers (Table 2). Namely, bIG-H3, CEACAM-1, CMG-2, DPPIV, GDF-15, HepaCAM, IGFBP-2, IL-23R, L-Selectin, Notch-3, NrCAM, PSA-total, Reg4, ST8SIA1, Thrombomodulin, TREM-1, VCAM-1 and VEGF R3.

[0165] Table 2. Composition of male aging markers

[0166]

[0167]

[0168] 4. Establishing a male cytokine aging clock

[0169] Figure 2BThe figure shows the initial screening of aging markers by Bootstrap-Lasso for males. As shown in the figure, Bootstrap-Lasso regression analysis was performed on male cytokines, and 1000 Boostrap self-service samplings were performed. Each sampling was performed with replacement and 1200 samples were randomly selected from the original data, and then Lasso regression was performed on these 1200 samples for variable screening. Among the 1000 samplings, 55 cytokines were screened out 900 times, so these 55 cytokines were used as preliminary aging markers.

[0170] Figure 2C The cross-validation curve of Lasso regression is shown, showing the effect of different regularization parameters λ on the mean square error (MSE). As shown in the figure, when the number of features is 18, the mean square error is the smallest.

[0171] Figure 2D This figure shows how the various regression coefficients change as the regularization parameter λ changes. This figure shows how Lasso regression gradually shrinks some unimportant feature coefficients to zero by imposing penalties as λ increases, thereby performing feature selection.

[0172] The above 18 aging markers were used to establish an aging clock, and the formula was: predicted age = 53.46667 + GDF-15 × 3.96232 + HepaCAM × 2.76149 + IL-23R × 2.2133 + VCAM-1 × 2.12014 + Reg4 × 1.81386 + Notch-3 × 1.77581 + IGFBP-2 × 1.1288 + CMG-2 × 1.12624 + PSA-total × 0.93189 + TREM-1 × -0.12598 + DPPIV × -0.6904 + Thrombomodulin × -1.08519 + VEGF R3×-1.26547+NrCAM×-1.48816+bIG-H3×-1.92235+L-Selectin×-1.99946+ST8SIA1×-2.3392. The correlation coefficient between the predicted age and the chronological age is 0.9804, and the root mean square error is 3.6408, which means the prediction is very accurate. Figure 2E shown.

[0173] We also used other machine learning methods to build models based on these 18 cytokines, including random forest, XGBoost, and support vector machine (SVM). The correlation coefficient between predicted age and chronological age was used to evaluate the prediction effect of the model.

[0174] The random forest model was built using the R language "randomForest" package. Random forest is an ensemble learning method for classification and regression tasks. It works by building multiple decision trees during training and outputting the category mode (classification) or average prediction value (regression) of each decision tree.

[0175] The XGBoost model was built using the R package “xgboost”. XGBoost is a powerful machine learning algorithm for regression, classification, and ranking tasks, known for its high performance in predictive models.

[0176] The R language "e1071" software package was used to construct a linear (kernel = linear) support vector machine (SVM) model. Support vector machine (SVM) is a powerful classifier that divides data into different categories by finding the optimal hyperplane, and is effective even in high-dimensional space.

[0177] Table 3. Predictive effects of different machine learning methods in establishing aging clocks

[0178]

[0179] As shown in the table above, Lasso regression has the best prediction effect.

[0180] Example 2: Validation of male cytokine aging clock in a large population

[0181] 1. Sample Collection

[0182] There were 610 male validation subjects (hereinafter referred to as the "adult population"), with an age distribution of 25-91 years old and an average age of 53.01±15.11 years old. The samples were collected in two batches. The first batch was collected from December 2019 to January 2020 at No. 1, Dongdan Dahua Road, Dongcheng District, Beijing, and the Beijing Hospital Physical Examination Center (No. 1, Dongdan Dahua Road, Dongcheng District, Beijing). The second batch was collected in October 2021 at the Central Blood Station of Tongzhou District, Beijing (No. 21 Tonghu Street, Tongzhou District, Beijing). Fasting blood samples from subjects were collected at 8 am every day, and anticoagulant tubes were used to collect venous blood from the cubital fossa of the subjects. The operators were professionally trained nurses. After the blood samples were collected, they were centrifuged at 4°C, 3000×g for 10 minutes, and the supernatant was collected into a cryopreservation tube and stored in a -80°C refrigerator.

[0183] 2. Cytokine Detection

[0184] Cytokine antibody chip detection was performed using the Raybiotech QAH-CUST customized kit (purchased from Raybio (Guangzhou) Biotechnology Co., Ltd.).

[0185] The kit detects 18 cytokines screened out in Example 1, namely, bIG-H3, CEACAM-1, CMG-2, DPPIV, GDF-15, HepaCAM, IGFBP-2, IL-23R, L-Selectin, Notch-3, NrCAM, PSA-total, Reg4, ST8SIA1, Thrombomodulin, TREM-1, VCAM-1 and VEGF R3 in males.

[0186] 3. Detection: The detection method is the same as in Example 1.

[0187] The 18 cytokines screened above were verified using a cytokine antibody array in a large male population (610 people). The results are shown in Figure 3A .

[0188] Comparing the expression trends of the two tests in Example 1 and Example 2, it was found that 13 cytokines showed consistent expression trends with aging. Figure 3B .

[0189] Therefore, we used the above 13 cytokines to re-establish the aging clock in the population of Example 1. The correlation coefficient between the predicted age and the chronological age was 0.924, and the mean square error was 8.340. The prediction was still very accurate. Figure 3C A newly established male cytokine aging clock.

[0190] Substituting the data of the large population into the model calculation, the prediction accuracy can also be maintained at a high level. The correlation coefficient between the predicted age and the chronological age is 0.680, and the mean square error is 8.677. Figure 3D . Figure 3E Table 4 shows the Lasso coefficient of each cytokine of the new aging clock.

[0191] Table 4. Lasso coefficients of each cytokine in the new aging clock

[0192]

[0193] The calculation formula for the predicted age based on 13 cytokines is as follows (the detection concentration of cytokines is pg / mL):

[0194] Predicted age = 70.80454 + bIG-H3 × 5.93294 + CEACAM-1 × -9.78756 + CMG-2 × -1.03379 + DPPIV × -13.82591 + GDF-15 × 33.1637 + HepaCAM × 4.31484 + L-Selectin × -22.57612 + Notch-3 × 8.31285 + PSA-total × 6.66175 + Reg4 × 8.59947 + ST8SIA1 × -0.47551 + Thrombomodulin × 0.77648 + VEGF R3 × 0.00077

[0195] Example 3: Application of male cytokine aging clock

[0196] 1. Calculate the acceleration of aging

[0197] The aging acceleration Δage is calculated using the following formula:

[0198] ΔAge 初始 = Predicted age – Chronological age

[0199] Δage=Δage 初始 —loess(chronological age—Δage 初始 )

[0200] Loess is a local polynomial regression fitting, which uses the initial aging acceleration Δage minus the fitted age difference to correct the effect of age on aging acceleration. People with Δage greater than the 75% quantile are defined as the accelerated aging population, those with Δage less than the 25% quantile are defined as the decelerated aging population, and those in between are defined as the normal aging population.

[0201] The male adult population was divided into a healthy combination and a self-reported disease group (self-reported history of heart disease, diabetes, stroke and cerebrovascular disease, chronic nephritis, Parkinson's disease, dementia, cancer and other major life-threatening diseases) and compared with Δ age.

[0202] Table 5. Proportion of aging status in the healthy group of men with self-reported diseases *

[0203]

[0204] *Chi-square test: p<0.001

[0205] 2. ΔAge-based self-reported disease

[0206] Figure 4AThe predicted age of men in each age group is shown. As shown in the figure, among men over 40 years old, the median predicted age of the self-reported disease group is higher than that of the healthy group (there are too few people in the disease group among people under 40 years old), considering that the self-reported disease group ages faster than the healthy group.

[0207] The R language "pROC" package was used to make the ROC curve using Δage, and the area under the curve AUC = 0.820, see Figure 4B Logistic regression analysis was performed using the R language glm function, with self-reported disease defined as 1 and no self-reported disease defined as 0 as the dependent variable, and aging acceleration Δage as the independent variable. The odds ratio (OR) was 1.14, and the p value was less than 0.001.

[0208] 3. Correlation between cytokines, Δage and laboratory test indicators

[0209] Cholinesterase (CHE), albumin (ALB), prealbumin (PAB), γ-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), serum lactate dehydrogenase (LDH), homocysteine ​​(HCY), folate (FOL), GLU, D-3-hydroxybutyrate (D-3H), high-sensitivity C-reactive protein (hsCRP), superoxide dismutase (SOD), serum creatinine (Scr), blood urea nitrogen (UREA), uric acid (URIC), cystatin C (Cystatin C), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), free fatty acids (NFFA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), and total bile acid (TBA) were measured using an automatic biochemical analyzer (Hitachi LABOSPECT008AS; Hitachi, Tokyo, Japan). The levels of red blood cells (RBC), white blood cells (WBC), hemoglobin (HGB), and platelets (PLT) were determined by the cyanide hemoglobin method using a fully automated modular blood and body fluid analyzer (Sysmex XN-20 [A1], Sysmex Corporation, Kobe, Japan). HbA1c was measured by high-performance liquid chromatography (Tosoh G8 high-performance liquid chromatography analyzer, Tosoh Corporation, Tokyo, Japan). Immunoglobulin M (IgM) was measured using Mindray BS-2800M (Mindray, Shenzhen, China). Insulin-like growth factor (IGF) and insulin (INS) were detected by chemiluminescence. Plasma samples were obtained after centrifugation at 4°C and 1500 × g and stored at −80°C before testing. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI creatinine equation (2021).

[0210] Figure 4CThis is a correlation analysis between male cytokines and laboratory test indicators.

[0211] Figure 4D This is a correlation analysis between male cytokines and laboratory test indicators above the reference range.

[0212] Figure 4E This is a correlation analysis between male cytokines and laboratory test indicators below the reference interval.

[0213] FIG. 4C to FIG. 4E The results show that the correlation analysis of male cytokines and laboratory test indicators shows that GDF-15 has a significant correlation with most laboratory test indicators, and Reg4 and Notch-3 are also correlated with most laboratory test indicators. Male indicators above the reference interval, such as cystatin C and insulin, are significantly positively correlated with GDF-15, while high-sensitivity CRP and triglycerides are significantly negatively correlated with ST8SIA1; male glomerular filtration rate below the reference interval is negatively correlated with GDF-15, HepaCAM, IL-23R, and TREM-1; correlation analysis of Δ age and laboratory test indicators shows that male Δ age is significantly positively correlated with cystatin C, D3 hydroxybutyrate, fasting blood sugar and other indicators, and is significantly negatively correlated with IGF, albumin, glomerular filtration rate and other indicators.

[0214] Figure 4F This is the correlation analysis between laboratory test indicators and Δage. Figure 4F The results showed that multiple laboratory test indicators were significantly correlated with Δage and were consistent with the predicted trends.

[0215] 4. Correlation between intrinsic function, physical function and Δ age

[0216] We constructed an intrinsic function assessment system using five dimensions, namely sensory (hearing), motor (gait speed), vitality (BMI and grip strength), psychological score (DASS-21 scale, self-assessment of life and self-assessment of health) and cognitive score (Montreal Cognitive Assessment (MoCA)).

[0217] Figure 4G This is the correlation analysis between intrinsic function and physical function and Δage. Figure 4G It shows that each dimension of intrinsic function is also strongly correlated with cytokines. The Δ age of men is negatively correlated with intrinsic function, that is, the older the predicted age, the lower the intrinsic function score and the worse the function. Moreover, Δ age is significantly correlated with multiple physical function indicators.

[0218] The above formula is used to calculate the aging acceleration (Δage). Its significant correlation with self-reported diseases, laboratory test indicators and intrinsic function suggests that it can be used as an early warning indicator of aging and health status and can be used to assess aging status. The larger the Δage, the greater the individual's aging acceleration, the greater the probability of suffering from disease, and the lower the intrinsic function score. Specifically:

[0219] (1) Association with self-reported diseases:

[0220] ΔOlder individuals are more likely to report having various chronic diseases, such as cardiovascular disease, diabetes, or arthritis.

[0221] This association suggests that Δage may reflect undetected pathological processes and help to detect potential health risks early.

[0222] (2) Correlation with laboratory test indicators:

[0223] ΔAge was significantly correlated with multiple biochemical indices, such as inflammatory markers (such as C-reactive protein), blood glucose levels, and blood lipid profiles.

[0224] This suggests that Δage may capture physiological changes at a subclinical level, providing opportunities for preclinical intervention.

[0225] (3) Relationship with intrinsic functions:

[0226] ΔOlder individuals tend to have lower scores in cognitive function, physical status, and daily living abilities.

[0227] This association reveals that Δage may indicate functional decline, which could help to take preventive measures early.

[0228] (4) Advantages of Δage as a comprehensive indicator:

[0229] Compared with a single indicator, Δage integrates information from multiple biomarkers and may provide a more comprehensive assessment of health status.

[0230] It provides new tools for personalized health management and precision medicine.

[0231] (5) Predictive value of Δage:

[0232] The older the age, the worse the current health condition may be, and the higher the risk of disease and functional decline may be in the future.

[0233] This makes Δage a valuable predictive tool that can be used to guide preventive interventions.

[0234] (6) Application value of Δage:

[0235] In clinical practice, Δage can be used to screen high-risk populations and guide personalized health management plans.

[0236] In the research field, it can be used as a new endpoint indicator to evaluate the effectiveness of anti-aging interventions.

[0237] As a comprehensive indicator, Δage not only reflects the current state of aging, but may also predict future health trajectories. Its application is expected to promote the development of preventive medicine and promote the paradigm shift from disease treatment to health management. However, further prospective studies are needed to verify its long-term predictive ability and applicability in different populations.

[0238] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Those skilled in the art can design many other modifications and implementation methods, which will fall within the scope and spirit of the principles disclosed in this application.

Claims

1. A set of male aging markers, characterized in that: The invention comprises a plurality of cytokines associated with male aging, including but not limited to transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin), and vascular endothelial growth factor receptor 3 (VEGF R3).

2. A method for predicting the age of a male individual, characterized in that: The method comprises the following steps: a. Obtain biological samples from male subjects; b. determining the content of male aging markers in the biological sample; c. inputting the content value of the male aging marker into a preset cytokine aging clock; d. outputting the estimated age of the male individual based on the cytokine aging clock.

3. A method for predicting the age of a male individual according to claim 2, characterized in that: The male aging markers include but are not limited to: transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin), vascular endothelial growth factor receptor 3 (VEGF R3).

4. A method for predicting the age of a male individual according to claim 2, characterized in that: The biological sample is blood, serum, plasma or saliva; the determination step adopts immunoassay, mass spectrometry, biochemical analysis method or biochip technology.

5. A method for predicting the age of a male individual according to claim 4, characterized in that: The calculation formula of the cytokine aging clock is: predicted age = 70.80454 + bIG-H3 × 5.93294 + CEACAM-1 × -9.78756 + CMG-2 × -1.03379 + DPPIV × -13.82591 + GDF-15 × 33.1637 + HepaCAM × 4.31484 + L-Selectin × -22.57612 + Notch-3 × 8.31285 + PSA-total × 6.66175 + Reg4 × 8.59947 + ST8SIA1 × -0.47551 + Thrombomodulin × 0.77648 + VEGF R3 × 0.00077, wherein the cytokine values ​​are all detection concentrations in pg / mL.

6. A method for predicting the age of a male individual according to claim 4, characterized in that: It also includes the calculation of the acceleration of aging, namely Δage.

7. A method for predicting the age of a male individual according to claim 6, characterized in that: The calculation formula of the aging acceleration (Δage) is: ΔAge 初始 = Predicted age – Chronological age Δage=Δage 初始 —loess(chronological age—Δage 初始 ) Wherein, Loess is a local polynomial regression fitting, which uses the initial aging acceleration Δage minus the fitted age difference to correct the effect of age on aging acceleration.

8. A system for predicting the age of a male individual, characterized in that: include: a. A biological sample collection module, used to collect biological samples from male individuals; b. A polypeptide content determination module, used to determine the content of at least 13 male aging markers in the collected biological sample, wherein the 13 male aging markers are transforming growth factor b1 (bIG-H3), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1), capillary morphogenesis gene 2 (CMG-2), dipeptidyl peptidase IV (DPPIV), growth differentiation factor 15 (GDF-15), hepatocyte adhesion molecule (HepaCAM), L-selectin (L-Selectin), neurogenic locus Notch homologous protein 3 (Notch-3), total prostate specific antigen (PSA-total), regenerating islet-derived protein 4 (Reg4), α-N-acetylneuraminic acid α-2,8-aminoacyltransferase 1 (ST8SIA1), thrombomodulin (Thrombomodulin) and vascular endothelial growth factor receptor 3 (VEGF R3); c. A cytokine aging clock module, used to input the measured polypeptide content into a prediction algorithm to predict the age of the male individual.

9. A method for predicting the age of a male individual according to claim 8, characterized in that: The prediction algorithm module is based on a machine learning model or a statistical model.

10. A method for predicting the age of a male individual according to claim 9, characterized in that: The machine learning models include but are not limited to Lasso regression, random forest, XGBoost and SVM.

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