Biological age construction method and system based on clinical physiological and biochemical indexes and accelerated aging condition evaluation method

Abstract

The invention belongs to the technical field of life science, and particularly relates to a biological age construction method and system based on clinical physiological and biochemical indexes and an accelerated aging condition evaluation method. Initial clinical indexes and actual ages of the participants are collected, and outcome variables of the participants are recorded in a follow-up visit mode and comprise death or survival conditions, cardiovascular disease morbidity conditions and corresponding event occurrence time; screening key clinical indexes according to absolute Spearman correlation coefficients of the initial clinical indexes and actual ages; the key clinical indexes and the actual age are used as independent variables, death is used as a dependent variable, and a relational expression between the biological age and the key clinical indexes and the actual age is obtained through a Gompertz proportional risk model. According to the method, the biological age is calculated based on conventional clinical physiological and biochemical indexes, and compared with the biological age calculated based on omics and image data, the method can be more conveniently and efficiently applied to the majority of people, and aging evaluation is incorporated into conventional medical care items.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of life science, and particularly relates to a biological age construction method and system based on clinical physiological and biochemical indicators and an evaluation method for accelerated aging. BACKGROUND

[0002] Global aging has become a major challenge. Rapid changes in population structure exacerbate the burden of health problems related to aging, especially cardiovascular diseases and premature death. Aging is accompanied by physiological disorders in multiple systems, and key mechanisms such as chronic inflammation, telomere shortening, and cell aging are core factors leading to gradual deterioration of health status. Although aging is a universal phenomenon, there are significant differences in the rate of aging among different individuals, even among people of the same age. Biological age, as an indicator reflecting the physiological state of individuals, is an effective indicator for evaluating the aging state (such as accelerated aging). Accurate assessment of aging state is crucial for delaying aging and achieving healthy aging.

[0003] Accelerated aging refers to the accelerated functional decline caused by internal and external factors in living organisms, resulting in the premature appearance of physiological and pathological changes related to aging. In recent years, studies have suggested that accelerated aging is associated with an increase in cardiovascular disease and mortality. Clarifying these associations is crucial for developing strategies to prevent cardiovascular disease and premature death.

[0004] Existing technologies mainly identify aging markers such as plasma metabolites and proteins based on cross-sectional studies, case-control studies, or cohort studies. Existing technologies for screening aging-related markers mainly include: 1) univariate analysis, analyzing the differences between indicators through t-test or non-parametric test methods; 2) multivariate analysis, using logistic regression model, Cox regression model, etc. to evaluate the association between each indicator and aging; 3) machine learning methods, such as random forest algorithm, to screen indicators related to aging. Biological age is constructed based on aging markers. Existing biological age is mainly constructed based on omics and imaging data, which have problems such as difficulty in collecting, high cost, and high technical requirements in large-scale population applications.

[0005] Existing biological age construction and evaluation technologies include: dividing the population into training set and validation set, in the training set, based on linear regression model, etc. to construct a prediction model (with actual age as the dependent variable and aging-related markers as the independent variable), using K-fold cross-validation method to further optimize model parameters, after establishing the final model, using SHAP value, etc. to evaluate the importance of the prediction variable. In the validation set, the prediction performance is evaluated based on correlation coefficient, mean absolute error, etc. If conditions permit, the prediction effect of the model can be further verified in an independent external population. SUMMARY

[0006] The present application aims to provide a biological age construction method and system based on clinical physiological and biochemical indicators and an evaluation method of accelerated aging condition, which screens conventional clinical indicators that can reflect aging, constructs biological age, and is expected to be more convenient and efficient in application in a wide population.

[0007] To achieve the above-mentioned purpose, according to a first aspect of the present application, a biological age calculation method based on clinical physiological and biochemical indicators is provided, which collects clinical indicators and actual age of a tester, and then substitutes into the following formula to calculate the biological age of the tester:

[0008]

[0009] wherein, z 1 represents fasting blood glucose, unit: mmol / L; z 2 represents waist circumference, unit: cm; z 3 represents systolic blood pressure, unit: mmHg; z 4 represents high-density lipoprotein cholesterol, unit: mmol / L; z 5 represents total cholesterol, unit: mmol / L; z 6 represents blood urea nitrogen, unit: mmol / L; z 7 represents mean corpuscular hemoglobin content, unit: pg; z 8 represents platelet count, unit: 10 9 / L; z 9 represents red blood cell count, unit: 10 12 / L; z 10 represents actual age, unit: years; the unit of biological age is years. The actual age and biological age can be expressed in decimal, for example, 1 year and 6 months can be recorded as 1.5 years. The units of the indicators are only used to determine the size of the indicator values that need to be substituted into the formula, and only the values under the corresponding units are substituted into the formula, and xb is a dimensionless parameter.

[0010] The second aspect of the present application provides a biological age construction method based on clinical physiological and biochemical indicators, which is obtained by the following steps: S1, collecting initial clinical indicators and actual age of participants, and follow-up recording outcome variables of participants, the outcome variables being death or survival condition, cardiovascular disease incidence, and time of occurrence of corresponding events; S2, screening key clinical indicators from the clinical indicators of step S1 according to the absolute Spearman correlation coefficient of the initial clinical indicators and the actual age; S3. Using the aforementioned key clinical indicators and actual age as independent variables and mortality as the dependent variable, the relationship between biological age and key clinical indicators and actual age is obtained through the Gompertz proportional hazards model.

[0011] Furthermore, the screening of key clinical indicators in step S2 specifically includes the following steps: S21. Select 20-40 initial clinical indicators as candidate aging biomarkers, and screen out clinical indicators with an absolute Spearman correlation coefficient |r| > 0.1 with actual age from the initial clinical indicators. S22. Calculate the Spearman correlation coefficient r between each pair of clinical indicators obtained from step S21. Treat a pair of indicators with |r|>0.70 as two highly correlated indicators to obtain a set of highly correlated indicators and a set of non-highly correlated indicators. S23. From each pair of indicators in the highly correlated indicator set described in S22, select the indicator with a higher absolute Spearman correlation coefficient |r| with the actual age, and combine it with the non-highly correlated indicator set to constitute the key clinical indicator.

[0012] Furthermore, in step S21, before screening, the values ​​of each initial clinical indicator are sorted from smallest to largest, and the 0.1% quantile and the 99.9% quantile are set. The extreme values ​​of the initial clinical indicators that are less than the 0.1% quantile and greater than the 99.9% quantile are replaced with the set quantile values ​​through tail reduction processing to correct the skewed distribution and reduce the influence of extreme values.

[0013] Furthermore, the initial clinical indicators include fasting blood glucose, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, blood urea nitrogen, serum creatinine, serum uric acid, alanine aminotransferase, total bilirubin, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin content, mean corpuscular hemoglobin concentration, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, platelet count, mean platelet volume, platelet distribution width, waist circumference, and body mass index. The key clinical indicators include fasting blood glucose, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, total cholesterol, blood urea nitrogen, mean corpuscular hemoglobin, platelet count, and red blood cell count.

[0014] Furthermore, step S3 includes the following steps: S31. Using the aforementioned key clinical indicators and actual age as independent variables and mortality as the dependent variable, calculate the 10-year mortality risk d1 for each participant using the Gompertz proportional hazards model. S32. Using actual age as the independent variable and mortality as the dependent variable, calculate the 10-year mortality risk d2 for each participant using the Gompertz proportional hazards model: S33. Let the mortality risk d1 equal the mortality risk d2, and solve for the relationship between biological age, key clinical indicators, and actual age.

[0015] Furthermore, in step S1, the participants' actual age at the time of initial data collection was between 20 and 79 years old; the clinical indicators and actual age were collected at the start of the study.

[0016] The third aspect of this invention provides a method for assessing accelerated aging, which uses the biological age obtained by the biological age calculation method or the biological age obtained by the construction method to assess accelerated aging. Specifically, it includes: determining the residual through linear regression analysis of the test subject's biological age against their actual age. If the residual is >0, it indicates that the test subject is in accelerated aging; if the residual is ≤0, it indicates that the test subject is in non-accelerated aging.

[0017] The fourth aspect of this invention provides a method for constructing a cardiovascular disease risk and mortality risk assessment model, comprising: using actual age, gender, education level, smoking status, drinking status, diet, physical activity level, body mass index, sleep time and number of comorbidities as covariates, and determining the cardiovascular disease risk ratio and mortality risk ratio of the accelerated aging test subjects compared with non-accelerated aging test subjects through a Cox proportional hazards model.

[0018] The fifth aspect of this invention provides a biological age construction system based on clinical physiological and biochemical indicators, comprising: The data acquisition module is used to collect the participants' initial clinical indicators and actual age, and to follow up and record the participants' outcome variables, including death or survival, cardiovascular disease incidence, and the time of occurrence of the corresponding events. The key clinical indicator screening module is used to screen key clinical indicators from the initial clinical indicators based on the absolute Spearman correlation coefficient between the initial clinical indicators and the actual age. The biological age construction module is used to construct the relationship between biological age and key clinical indicators and actual age using the key clinical indicators and actual age as independent variables and mortality as the dependent variable, through the Gompertz proportional hazards model.

[0019] In summary, compared with the prior art, the above-described technical solutions conceived by this invention mainly possess the following technical advantages: 1. Compared with existing biological age estimates based on omics or imaging data, this invention uses common clinical indicators to construct biological age. These indicators are more cost-effective, suitable for large-scale population applications, and can be used to develop biological age prediction software, monitor individuals with accelerated aging, identify individuals with a high risk of cardiovascular disease and premature death, and provide methods for personalized health interventions. It is expected to be applied more conveniently, efficiently, and sensitively in a wide range of populations, and to incorporate aging assessment into routine healthcare programs.

[0020] 2. Existing research primarily aims to predict chronological age when constructing biological age. The higher the fitting accuracy of the chronological age model, the less information the biological age calculated from it provides that is independent of chronological age when predicting diseases. This contradicts the expectation that biological age is a better predictor of an individual's future diseases or health conditions than chronological age. This invention aims to predict mortality risk when estimating biological age. Compared to chronological age models, this type of mortality risk prediction model will estimate a biological age that better reflects an individual's physiological condition and the risk of future diseases or death.

[0021] 3. This invention is based on a large-scale prospective cohort study. Compared to previous biological ages constructed based on cross-sectional studies or case-control studies, the research quality of this invention is higher. Attached Figure Description

[0022] Figure 1 To establish inclusion and exclusion processes for populations used to construct biological age. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0024] Example: This invention uses data from the Kailuan and Dongfeng Tongji cohorts to construct biological age using clinical physiological and biochemical indicators. Based on biological age, it assesses accelerated aging and explores the association between accelerated aging and cardiovascular disease and mortality risk, thereby assessing the cardiovascular disease and mortality risk of participants.

[0025] This invention constructs and validates biological ages based on the Kailuan Chinese Cohort and the Dongfeng Tongji Chinese Cohort. After excluding individuals with cardiovascular disease, cancer, diabetes, or chronic kidney disease, as well as participants missing more than 50% of clinical indicators, a total of 147,552 participants aged 20-79 years were included to construct biological ages.

[0026] For internal validation, after excluding individuals who were younger than 20 or older than 79 at baseline, individuals with cardiovascular disease at baseline, and individuals who lacked clinical data required to calculate biological age, a total of 107,830 participants from the Kailuan cohort were included to assess the predictive power of biological age for cardiovascular disease and mortality risk, and the association between baseline accelerated aging and cardiovascular disease and mortality risk.

[0027] For external validation, after excluding individuals younger than 20 years or older than 79 years at baseline, individuals with cardiovascular disease at baseline, and individuals lacking clinical data required to calculate biological age, a total of 14,032 participants from the Dongfeng Tongji cohort were included to assess the predictive power of biological age for cardiovascular disease and mortality risk, and the association between baseline accelerated aging and cardiovascular disease and mortality risk. The inclusion and exclusion process is as follows: Figure 1 The biological ages obtained in this invention and the generalizability and reliability of their assessed associations with accelerated aging, cardiovascular disease, and mortality risk were verified through different cohorts.

[0028] Blood samples were collected from all enrolled participants, and clinical biochemical indicators were tested. Physical function indicators were measured during cohort follow-up. Clinical data underwent quality control and preprocessing. Outcome information was obtained from questionnaires, hospital records, insurance records, and death certificates during cohort follow-up. Correlation analysis was used to screen for aging-related biomarkers, and biological age was constructed based on these biomarkers. Accelerated aging was assessed based on biological age, and the association between accelerated aging and cardiovascular disease and mortality risk was explored, thereby assessing participants' cardiovascular disease and mortality risks. The research project involved in this invention has been approved by the Ethics Committee of Kailuan General Hospital and the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. All participants in the cohort signed informed consent forms. The specific process is as follows: (1) Participant inclusion, questionnaire survey, physical examination The standardized questionnaires are designed and reviewed by professional researchers. After undergoing standardized training, investigators collect questionnaire information from participants, including basic demographics, socioeconomic status, lifestyle, and medical history, and measure participants' physical indicators such as weight and waist circumference.

[0029] (2) Identification of patients who die and develop new cardiovascular disease Cardiovascular diseases include coronary heart disease / myocardial infarction and stroke. Deaths and new-onset cardiovascular diseases are identified through follow-up surveys, hospital discharge summaries, insurance records from insurance companies, and death certificates.

[0030] (3) Sample collection and testing Participants had their blood drawn intravenously by nurses who had received standardized training. Afterward, they underwent clinical biochemical tests at the hospital's laboratory, including complete blood count, glucose metabolism indicators, blood lipid indicators, liver function tests, and kidney function tests.

[0031] (4) Clinical indicator data processing Thirty clinical indicators were selected as candidate aging biomarkers in the Kailuan cohort, including: fasting blood glucose, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, blood urea nitrogen, serum creatinine, serum uric acid, alanine aminotransferase, total bilirubin, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin content, mean corpuscular hemoglobin concentration, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, platelet count, mean platelet volume, platelet distribution width, waist circumference, and body mass index. The values ​​below the 0.1% quantile and above the 99.9% quantile of these 30 indicators were replaced with the 0.1% and 99.9% quantiles, respectively, to perform tail reduction processing, thereby correcting for skewed distributions and reducing extreme values. For example, for a certain indicator, sort its values ​​from smallest to largest. If the total number of participants is 10,000, then replace all values ​​in the indicator below the 10th position with the 10th position value, and replace all values ​​above the 9990th position with the 9990th position value.

[0032] (5) Screening of aging biomarkers From 30 candidate clinical indicators, those with an absolute Spearman correlation coefficient |r| > 0.1 with respect to actual age were selected. Then, pairwise Spearman correlation coefficients (r) between the selected indicators were calculated, where |r| > 0.70 was considered a high correlation between the two indicators. If a selected indicator did not have a highly correlated counterpart, it was directly retained as a final clinical indicator. Within each pair of highly correlated indicators, the indicator with the higher |r| with actual age was selected, and the other indicator was removed. Ultimately, nine clinical indicators were selected, including fasting blood glucose, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, total cholesterol, blood urea nitrogen, mean corpuscular hemoglobin level, platelet count, and red blood cell count.

[0033] (6) Construction and assessment of biological age Biological age was constructed in the Kailuan cohort. Biological age was calculated using a Gompertz proportional hazards model, with mortality as the dependent variable and nine selected clinical indicators and chronological age as independent variables. Specifically, the nine clinical indicators and chronological age were first incorporated into the Gompertz proportional hazards model, and based on this model, the 10-year (120-month) mortality risk d1 for each individual was calculated.

[0034] In the above formula It is an auxiliary parameter, estimated from the data. It is a natural constant. This represents a linear combination of nine clinical indicators and actual age. ,in It is the intercept, estimated from the data. For 9 clinical indicators and actual age, The regression coefficients representing nine clinical indicators and actual age are estimated from the data.

[0035] Subsequently, actual age was separately incorporated into the Gompertz proportional hazards model, and based on this model, the 10-year (120-month) mortality risk d2 for each individual was calculated:

[0036] In the above formula It is an auxiliary parameter, estimated from the data. It is a natural constant. ,in It is the intercept, estimated from the data. The regression coefficients, representing the actual age, are estimated from the data.

[0037] Let the mortality risk d1 equal the mortality risk d2, and the formula for calculating biological age is as follows:

[0038] .

[0039] After constructing the biological age in the Kailuan cohort, in the Dongfeng Tongji cohort, the biological age was calculated using the above formula after tailing of nine key clinical indicators, including fasting blood glucose, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, total cholesterol, blood urea nitrogen, mean corpuscular hemoglobin content, platelet count, and red blood cell count.

[0040] We used Spearman correlation coefficient and mean absolute error to assess the correlation between biological age and chronological age. The results are shown in Table 1. In the Kailuan cohort (internal validation cohort) and the Dongfeng Tongji cohort (external validation cohort), the Spearman correlation coefficients between biological age and chronological age were 0.986 and 0.954, respectively, and the mean absolute errors were 1.808 and 2.278, respectively, indicating that the two are highly correlated.

[0041] Table 1. Correlation and Mean Absolute Error between Biological Age and Chronological Age

[0042] (7) Definition of accelerated aging The residuals were determined by linear regression analysis of biological age against actual age. A residual > 0 indicates accelerated aging, while a residual ≤ 0 indicates non-accelerated aging.

[0043] (8) The association between accelerated aging and cardiovascular disease and mortality risk, and the predictive power of biological age for cardiovascular disease and mortality risk. Multivariate Cox proportional hazards models were used to assess the association between accelerated aging and the risk of cardiovascular disease and mortality. Controlled covariates in the multivariate Cox proportional hazards models included: chronological age, sex, education level, smoking status, alcohol consumption, diet, physical activity level, body mass index, sleep duration, and number of comorbidities. To evaluate the predictive performance of chronological age on the risk of mortality and cardiovascular disease, chronological age was replaced in the covariates in the Cox proportional hazards models, and Harrell's C-index was calculated for each model.

[0044] As shown in Table 2, in the Kailuan cohort, compared with the non-accelerated aging group, the accelerated aging group had a 60% increased risk of cardiovascular disease and a 46% higher risk of death; in the Dongfeng Tongji cohort, the accelerated aging group also had an increased risk of cardiovascular disease and death, indicating that accelerated aging as determined by biological age is significantly associated with an increased risk of cardiovascular disease and death.

[0045] As shown in Table 3, biological age combined with covariates exhibited better predictive performance than chronological age combined with covariates in predicting the risk of mortality and cardiovascular disease. Specifically, in both the Kailuan and Dongfeng Tongji cohorts, the Harrell's C index for biological age combined with covariates was significantly higher than that for chronological age combined with covariates. These results demonstrate that biological age can effectively predict the risk of mortality and cardiovascular disease.

[0046] Table 2. Associations between accelerated aging and cardiovascular disease and mortality risk

[0047] Table 3. Predictive power of biological age on mortality and cardiovascular disease risk

[0048] In summary, compared with existing biological age estimates based on omics or imaging data, this invention uses common clinical indicators to construct biological age. These indicators are more cost-effective, suitable for large-scale population applications, and can be used to develop biological age prediction software, monitor individuals with accelerated aging, identify individuals at high risk of cardiovascular disease and premature death, provide methods for personalized health interventions, and are expected to be applied more conveniently and efficiently in a wide population, integrating aging assessment into routine healthcare programs.

[0049] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating biological age based on clinical physiological and biochemical indicators, characterized in that, Based on the test subject's clinical indicators and actual age, the test subject's biological age is calculated using the following formula: in, z 1 represents fasting blood glucose, in mmol / L; z 2 indicates waist circumference, in cm; z 3 indicates systolic blood pressure, measured in mmHg; z 4 represents high-density lipoprotein cholesterol, with units of mmol / L; z 5 indicates total cholesterol, expressed in mmol / L; z 6 represents blood urea nitrogen, with the unit being mmol / L; z 7 represents the mean corpuscular hemoglobin content, expressed in pg. z 8 represents the platelet count, in units of 10. 9 / L; z 9 indicates the red blood cell count, with units of 10. 12 / L; z 10 The actual age is expressed in years; the biological age is expressed in years.

2. A method for constructing biological age based on clinical physiological and biochemical indicators, characterized in that, It is obtained through the following steps: S1. Collect the participants' initial clinical indicators and actual age, and follow up to record the participants' outcome variables, including death or survival, cardiovascular disease incidence, and the time of occurrence of the corresponding events. S2. Based on the absolute Spearman correlation coefficient between the initial clinical indicators and the actual age, key clinical indicators are selected from the initial clinical indicators in step S1. S3. Using the aforementioned key clinical indicators and actual age as independent variables and mortality as the dependent variable, the relationship between biological age and key clinical indicators and actual age is obtained through the Gompertz proportional hazards model.

3. The method for constructing biological age based on clinical physiological and biochemical indicators according to claim 2, characterized in that, The specific screening steps for the key clinical indicators mentioned in step S2 are as follows: S21. Select 20-40 initial clinical indicators as candidate aging biomarkers, and screen out clinical indicators with an absolute Spearman correlation coefficient |r| > 0.1 with actual age from the initial clinical indicators. S22. Calculate the Spearman correlation coefficient r between each pair of clinical indicators obtained from step S21. Treat a pair of indicators with |r|>0.70 as two highly correlated indicators to obtain a set of highly correlated indicators and a set of non-highly correlated indicators. S23. From each pair of indicators in the highly correlated indicator set described in S22, select the indicator with a higher absolute Spearman correlation coefficient |r| with the actual age, and combine it with the non-highly correlated indicator set to constitute the key clinical indicator.

4. The method for constructing biological age based on clinical physiological and biochemical indicators according to claim 3, characterized in that, In step S21, before screening, the values ​​of each initial clinical indicator are sorted from smallest to largest, and the 0.1% quantile and the 99.9% quantile are set. The extreme values ​​of the initial clinical indicators that are less than the 0.1% quantile and greater than the 99.9% quantile are replaced with the set quantile values ​​through tail reduction processing to correct the skewed distribution and reduce the influence of extreme values.

5. The method for constructing biological age based on clinical physiological and biochemical indicators according to claim 3, characterized in that, The initial clinical indicators include fasting blood glucose, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, blood urea nitrogen, serum creatinine, serum uric acid, alanine aminotransferase, total bilirubin, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin content, mean corpuscular hemoglobin concentration, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, platelet count, mean platelet volume, platelet distribution width, waist circumference, and body mass index. The key clinical indicators include fasting blood glucose, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, total cholesterol, blood urea nitrogen, mean corpuscular hemoglobin, platelet count, and red blood cell count.

6. The method for constructing biological age based on clinical physiological and biochemical indicators according to any one of claims 2-5, characterized in that, Step S3 includes the following steps: S31. Using the aforementioned key clinical indicators and actual age as independent variables and mortality as the dependent variable, calculate the 10-year mortality risk d1 for each participant using the Gompertz proportional hazards model. S32. Using actual age as the independent variable and mortality as the dependent variable, calculate the 10-year mortality risk d2 for each participant using the Gompertz proportional hazards model. S33. Let the mortality risk d1 equal the mortality risk d2, and solve for the relationship between biological age, key clinical indicators, and actual age.

7. The method for constructing biological age based on clinical physiological and biochemical indicators according to claim 2, characterized in that, In step S1, the participants' initial actual age was between 20 and 79 years old; the clinical indicators and actual age were collected at the start of the study.

8. A method for assessing accelerated aging, characterized in that, The biological age obtained by the biological age calculation method described in claim 1, or the biological age obtained by the construction method described in any one of claims 2-7, is used to assess accelerated aging. Specifically, it includes: determining the residual through linear regression analysis of the test subject's biological age against their actual age. If the residual is >0, it indicates that the test subject is in accelerated aging; if the residual is ≤0, it indicates that the test subject is in non-accelerated aging.

9. A method for constructing a cardiovascular disease risk and mortality risk assessment model, characterized in that, include: Using actual age, gender, education level, smoking status, drinking status, diet, physical activity level, body mass index, sleep time and number of comorbidities as covariates, the cardiovascular disease risk ratio and mortality risk ratio of accelerated aging test subjects compared with non-accelerated aging test subjects were constructed using the Cox proportional hazards model according to the assessment method described in claim 8.

10. A biological age construction system based on clinical physiological and biochemical indicators, characterized in that, include: The data acquisition module is used to collect the participants' initial clinical indicators and actual age, and to follow up and record the participants' outcome variables, including death or survival, cardiovascular disease incidence, and the time of occurrence of the corresponding events. The key clinical indicator screening module is used to screen key clinical indicators from the initial clinical indicators based on the absolute Spearman correlation coefficient between the initial clinical indicators and the actual age. The biological age construction module is used to construct the relationship between biological age and key clinical indicators and actual age using the key clinical indicators and actual age as independent variables and mortality as the dependent variable, through the Gompertz proportional hazards model.

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