Ageing evaluation system for healthy male

Abstract

The invention discloses an aging assessment system for healthy males, which is characterized by comprising a data acquisition module, a data preprocessing module, a biological age calculation module and an aging assessment module. According to the method, the biological age calculation model of the healthy male is constructed by using the clinical common markers, so that the evaluation of the body aging condition of the male in a non-illness state is realized, and when personalized health guidance and intervention measures are formulated for the male in sub-health transition, the aging quantitative indexes can be provided, and earlier and more effective health management can be assisted to be realized. Meanwhile, compared with DNA methylation and omics related detection, the method is low in cost, high in cost performance and more beneficial to clinical popularization, and helps to achieve earlier and more effective health management.

Description

Technical Field

[0001] The present invention relates to an aging assessment system for healthy men. For healthy men, the biological age is estimated through conventional test indicators related to aging, and compared with the chronological age to evaluate the true aging status of the healthy men's entire body. Background Art

[0002] As human life expectancy increases, the risk of developing late-onset diseases associated with aging also increases. The incidence of diabetes, hypertension, hyperlipidemia, coronary atherosclerosis, chronic respiratory diseases, and cancer is increasing and appearing at a younger age. Aging refers to the gradual deterioration of organ function and the accumulation of damage with age. It is a complex process occurring at the molecular and cellular levels, as well as at the organ and system levels. The mechanisms of aging are diverse and may be related to age-related genes, free radical-induced damage, changes in immune function, telomere shortening, and environmental factors. It is a major risk factor for disease and mortality. Quantifying aging is a key component of assessing physical function and the risk of related diseases.

[0003] Chronological age (CA), also known as calendar age, is a traditional metric used to quantify aging, based on date of birth. However, individuals with the same CA often have different disease risks and life expectancies due to differences in genetics and lifestyle. In response to this, researchers have recently begun exploring biological age (BA). BA is a more accurate model for measuring aging than CA. Aging biomarkers form the foundation for constructing BAs and are primarily divided into two categories: histological data, such as DNA methylation, metabolomics, and proteomics; and clinical biomarkers, such as blood chemistry, hematology, anthropometry, and organ function tests. Based on these aging biomarkers, BA models can be constructed using methods such as multiple linear regression (MLR), principal component analysis (PCA), the Klemera-Doubal method (KDM), and machine learning. DNA methylation and omics-related tests are expensive, which is not conducive to large-scale promotion among the population. However, the BA model based on common clinical indicators has low establishment costs, high public acceptance, and greater possibility of practical application.

[0004] Aging spans the entire process from health to sub-health, disease, and death. Aging-related biomarkers vary across these four different stages, leading to corresponding changes in aging assessment methods. However, previous aging assessment methods were mostly unified, lacking specific aging assessment methods for different health stages and not distinguishing between women and men. However, due to significant differences in chromosomes and hormones, men and women exhibit significant differences in aging mechanisms and phenotypes, leading to certain differences in aging-related markers and assessment methods. Therefore, it is necessary to establish corresponding aging assessment methods for different genders and different health stages. The present invention establishes an aging assessment method for the entire body of a healthy male. Summary of the Invention

[0005] The purpose of the present invention is to provide an aging assessment program for healthy men.

[0006] In order to achieve the above-mentioned object, the present invention provides a healthy male aging assessment system, which is characterized by comprising:

[0007] A data acquisition module is used to obtain the time series age CA and indicator data of healthy men, wherein the indicator data include systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, total cholesterol, triglyceride, alanine aminotransferase, aspartate aminotransferase, and serum urea;

[0008] Data preprocessing module, used to transform all indicator data into a new set of principal components with the same number;

[0009] Biological age calculation module, used to calculate the biological age BA of healthy men using the KDM-BA model;

[0010] The aging assessment module predicts the aging rate based on the difference between the biological age BA and the chronological age CA of the same healthy male, Δage = CA - BA, where:

[0011] If Δage>0, aging slows down;

[0012] If Δage < 0, aging is accelerated;

[0013] If Δage=0, it means healthy aging.

[0014] Preferably, the KDM-BA model uses the following formula to calculate biological age BA:

[0015]

[0016] In the formula, m is the number of principal components, x is j is the jth principal component, q jis the regression intercept of the jth principal component on the time series age CA; k j is the regression slope of the jth principal component on the time series age CA, s j is the root mean square error of the regression of the jth principal component on the time series age CA, s BA is the root mean square error of the regression of all principal components on the time series age CA.

[0017] This method uses common clinical markers to construct a biological age calculation model for healthy men, enabling the assessment of physical aging in men without illness. This model can provide quantitative indicators of aging when developing personalized health guidance and intervention measures for men experiencing sub-health conditions, enabling earlier and more effective health management. Furthermore, compared to DNA methylation and omics-related tests, this method is less expensive and more cost-effective, making it more suitable for clinical application and enabling earlier and more effective health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart for model construction in an embodiment of the present invention;

[0019] Figure 2 Demonstrates the performance of the model constructed by the embodiment of the present invention;

[0020] Figure 3 The specific data used in the embodiments of the present invention are shown;

[0021] Figure 4 A density map used in an embodiment of the present invention;

[0022] Figure 5 The calculation result of y.true3 in the embodiment of the present invention is illustrated. DETAILED DESCRIPTION

[0023] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0024] This paper uses the Klemera-Doubal (KDM) method to establish a biological age model, namely KDM-BA. KDM is a new concept and method for calculating biological age, proposed by mathematicians Petr Klemera and Stanislav Doubal in 2006. It is based on the following assumption: a person's true age, namely biological age (BA), is equal to the sum of CA and its influencing factors, where the influencing factors are assumed to have a mean of zero and a constant variance.

[0025] like Figure 1 As shown, the present invention screens out clinical markers that significantly affect BA in healthy men through the following method, thereby establishing a KDM-BA model, which specifically includes the following steps:

[0026] Step 1: Data collection:

[0027] Inclusion criteria:

[0028] a) Aged ≥ 18 years;

[0029] b) Relevant inspection and testing information is complete;

[0030] Exclusion criteria:

[0031] a) History of hypertension, diabetes, coronary heart disease, or dyslipidemia;

[0032] b) history of cancer;

[0033] c) Patients with liver and renal insufficiency;

[0034] d) This health checkup indicates abnormal serological indicators, including:

[0035] 1) Systolic blood pressure ≥140 mmHg and / or diastolic blood pressure ≥90 mmHg (Guidelines for the Prevention and Treatment of Hypertension in China (2018 Revised Edition));

[0036] 2) Fasting blood glucose ≥7.0 mmol / L and / or glycosylated hemoglobin ≥6.5% (National Guidelines for Primary Diabetes Prevention and Management (2022 Edition));

[0037] 3) Total cholesterol > 6.2 mmol / L, triglycerides > 2.3 mmol / L;

[0038] 4) Obese individuals: Body mass index (BMI) ≥ 28 kg·m -2 Diagnosed as obese ("Guidelines for the Diagnosis and Treatment of Obesity in Primary Care (2019 Edition)"). Body Mass Index (BMI) = Weight (kg) x Height -2 (m -2 );

[0039] We collected data from 64,459 adult participants who underwent health examinations at the Physical Examination Center of Zhongshan Hospital, Affiliated to Fudan University, Shanghai, between January 2021 and December 2022. Based on their medical history and current examination results, we excluded 50,372 individuals with a history of hypertension, diabetes, dyslipidemia, obesity, cancer, and other medical conditions, as well as individuals with no prior medical history but abnormal results from the current examination. A total of 5,332 healthy males were included.

[0040] Step 2: Screen for indicators that are monotonically correlated with CA: Pearson correlation analysis was used to analyze the correlations between relevant indicators and CA, using a correlation coefficient of |r| > 0.1 and P < 0.05 as the screening criteria. Indicators that are monotonically correlated with CA were selected for modeling. The selected indicators were categorized into nine sections: basic indicators, anemia, inflammation, cardiovascular system, liver, kidney, metabolism, endocrine system, and tumor markers. The screened indicators with |r|>0.1 and P<0.05 included height, weight, BMI, systolic blood pressure, diastolic blood pressure, red blood cell count, hemoglobin, platelet count, hematocrit, mean corpuscular volume, mean hemoglobin, mean hemoglobin concentration, CV, white blood cell count, neutrophil ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, glycated albumin, total cholesterol, triglycerides, HDL-C, LDL-C, apolipoprotein A, ALT, SLR, lactate dehydrogenase, total protein, albumin, albumin-to-globulin ratio, serum urea, serum uric acid, FT3, FT4, oncofetal protein, CA19-9, CA125, Cyfra21-1, PSA and fPSA.

[0041] Step 3: Fill in missing values:

[0042] The selected indicators that were monotonic with CA were further cleaned. If the missing values for a particular indicator exceeded 20% of the total, the indicator was deleted. Missing values were filled using multiple imputation. Based on the data distribution shown in the density plot of the imputed dataset, the imputation model that most closely matched the distribution of the original dataset was selected.

[0043] Step 4: Principal component analysis (PCA):

[0044] PCA is used to reduce the dimensionality of the indicator data included in the model, transforming the original data into a new set of variables with the same number, namely principal components, thereby removing redundant information from the original data set and improving the effectiveness and efficiency of the analysis.

[0045] Step 5. Calculate KDM-BA: Substitute the selected indicators into the following formula:

[0046]

[0047] Where: m is the number of principal components (PC), that is, the number of main influencing factors; x j is the jth PC; q j is the regression intercept of the jth PC on BA. Since BA is unknown, it is replaced by CA, the same below; k j is the regression slope of the jth P on BA; s jis the root mean square error of the regression of the jth PC to BA; s BA is the root mean square error of the regression of CA to BA, which can be replaced by the root mean square error of the regression of all PCs of unknown BA to CA.

[0048] All obtained BAs were compared with CAs using Pearson correlation coefficient, Bland Altman analysis, mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), linear regression analysis, and its determination coefficient R 2 The KDM-BA calculated by the constructed model was evaluated using methods such as statistic analysis and residual distribution. While ensuring that the basic modules (basic indicators, anemia, inflammation, cardiovascular system, liver, kidney, and metabolism) were not missing, non-essential indicators were continuously removed, ultimately retaining systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, neutrophil ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, total cholesterol, triglycerides, ALT, AST, and serum urea.

[0049] Step 6: The constructed KDM-BA model has a Pearson correlation coefficient of 0.967 (0.964, 0.968) between BA and CA. The standard deviation in the Bland Altman analysis is 4.629, the MAE is 3.641, the MSE is 21.427, the RMSE is 4.629, the slope of the linear regression is 1, and R 2 is 0.933, see Figure 2 .

[0050] Based on the clinical markers screened above that significantly affect BA in healthy men, an embodiment of the present invention discloses an aging assessment system for healthy men, comprising:

[0051] The data acquisition module is used to obtain the time series age CA and indicator data of healthy men, where the indicator data includes:

[0052] Systolic blood pressure is used to indicate the systolic blood pressure value measured in a healthy male at rest, in mmHg;

[0053] Diastolic blood pressure is used to indicate the diastolic blood pressure value measured in a healthy male at rest, in mmHg;

[0054] Height: used to indicate the net height measured by a healthy male, in meters (m);

[0055] Weight is used to indicate the weight measured by a healthy male in kilograms (Kg);

[0056] Hematocrit (Hct) is used to indicate the hematocrit index measured in healthy men;

[0057] Neutrophil ratio is used to indicate the neutrophil index measured in healthy men, in %;

[0058] Lymphocyte ratio is used to indicate the lymphocyte ratio index measured in healthy men, in units of (%);

[0059] Monocyte ratio is used to indicate the monocyte ratio index measured in healthy men, in %;

[0060] Fasting blood glucose is used to indicate the blood glucose index measured in healthy men when fasting, with the unit being mmol / L;

[0061] Total cholesterol is used to indicate the total cholesterol index measured in healthy men under fasting conditions, with the unit being mmol / L;

[0062] Triglyceride is used to indicate the triglyceride index measured in healthy men under fasting conditions, with the unit being mmol / L;

[0063] Alanine aminotransferase (ALT) is used to represent the ALT index measured in healthy men, with the unit being U / L;

[0064] Aspartate aminotransferase (AST) is used to represent the AST index measured in healthy men, with the unit being U / L;

[0065] Serum urea: used to indicate the serum urea index measured in healthy men, the unit is mmol / L;

[0066] Data preprocessing module, used to transform all indicator data into a new set of principal components with the same number;

[0067] Biological age prediction module, used to calculate the biological age BA of healthy men using the KDM-BA model:

[0068]

[0069] Where: m is the number of principal components, x j is the jth principal component, q j is the regression intercept of the jth principal component on the time series age CA; k j is the regression slope of the jth principal component on the time series age CA, s j is the root mean square error of the regression of the jth principal component on the time series age CA, sBA is the root mean square error of the regression of all principal components on the CA of time series age;

[0070] The aging assessment module predicts the aging rate based on the difference between the biological age BA and the chronological age CA of healthy men of the same generation, Δage = CA - BA, where:

[0071] If Δage>0, aging slows down;

[0072] If Δage < 0, aging is accelerated;

[0073] If Δage=0, healthy aging

[0074] If there is a data set of healthy subjects (according to the inclusion and exclusion criteria above), which includes the patient's number, age, systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, neutrophil ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, total cholesterol, triglyceride, ALT, AST and serum urea, the method for implementing the system of the present invention includes the following steps:

[0075] Arrange the data, the column name is indicator, the row name is the examiner number (data frame format, such as Figure 3 shown);

[0076] Check missing values and remove those whose physical examination items are missing more than 20%;

[0077] Multiple imputation is used to fill missing values. The most appropriate imputation method is selected based on the density map. For example, in the figure below, the interpolation method closest to the actual distribution curve (blue) is selected as the best choice (the red curve pointed by the arrow);

[0078] Principal component analysis to reduce the dimensionality of the data;

[0079] Use the TrueTrait function of the WGCNA package in R language to calculate and select the best biological age model KDM-BA3. Three biological ages will be obtained. Select the model with the best performance BA3 and export it to Excel, such as Figure 5 As shown, y is the chronological age and y.true3 is the biological age;

[0080] Calculate Δage: Δage = CA - BA. For example, the first examinee has a chronological age of 70 and a biological age of 65.31. His Δage is 4.69 years, which is greater than 0, indicating that his aging has slowed down and his health is good. The seventh examinee has a Δage of -0.69 years, which is less than 0, indicating that his aging has accelerated, and there may be a situation of accelerated aging.

[0081] The above technical solution can be used to calculate the biological age of healthy men in the health examination population dataset:

[0082] ① Index acquisition: Age (date of birth), systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, neutrophil ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, total cholesterol, triglycerides, ALT, AST, and serum urea were collected from healthy males during physical examination;

[0083] ② Calculate KDM-BA: Incorporate the above collected indicators into the modeling set, use R language software to calculate the biological age of the examinee, and evaluate their aging rate (Δage = CA-BA).

Claims

1. A healthy male aging assessment system, characterized in that: include: A data acquisition module is used to obtain the time series age CA and indicator data of healthy men, wherein the indicator data include systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, total cholesterol, triglyceride, alanine aminotransferase, aspartate aminotransferase, and serum urea; Data preprocessing module, used to transform all indicator data into a new set of principal components with the same number; Biological age calculation module, used to calculate the biological age BA of healthy men using the KDM-BA model; The aging assessment module predicts the aging rate based on the difference between the biological age BA and the chronological age CA of the same healthy male, Δage = CA - BA, where: If Δage>0, aging slows down; If Δage < 0, aging is accelerated; If Δage=0, it means healthy aging.

2. The aging assessment system for healthy men according to claim 1, wherein: The KDM-BA model uses the following formula to calculate biological age BA: In the formula, m is the number of principal components, x is j is the jth principal component, q j is the regression intercept of the jth principal component on the time series age CA; k j is the regression slope of the jth principal component on the time series age CA, s j is the root mean square error of the regression of the jth principal component on the time series age CA, s BA is the root mean square error of the regression of all principal components on the time series age CA.

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