Method and system for evaluating aging of males with abnormal metabolism

Abstract

One aspect of the invention provides an aging assessment method for males with abnormal metabolism. On the other hand, the invention provides an aging evaluation system for males with abnormal metabolism. Compared with the prior art, the method has the advantages that the biological age calculation model of the males with the abnormal metabolism is constructed by using the clinical common markers, so that the body aging condition of the males in the abnormal metabolism state is evaluated, quantitative aging indexes can be provided when personalized health guidance and intervention measures are made for the males with the abnormal metabolism, and the evaluation accuracy is improved. And earlier and more effective health management can 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 method and system for men with metabolic abnormalities. For men with metabolic abnormalities, their biological age is estimated through conventional test indicators related to aging, and compared with their chronological age to assess the aging status of the entire body of the men with metabolic abnormalities. 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 progresses through a continuous process, from health to sub-health, disease, and death. Aging-related biomarkers vary across these four stages, leading to corresponding changes in aging assessment methods. Metabolic abnormalities such as obesity, diabetes, hypertension, and hyperlipidemia accelerate the progression of aging. However, previous aging assessment methods have mostly been standardized, lacking specific approaches for different health stages or differentiating between men and women. However, due to significant differences in chromosomes and hormones, men and women exhibit significant differences in aging mechanisms and phenotypes, leading to discrepancies in aging-related biomarkers and assessment methods. Developing appropriate aging assessment methods tailored to different sexes and health stages is crucial. With the development of society and the aging of the population, the number of people with metabolic abnormalities is increasing. Therefore, there is an urgent need to develop aging assessment methods for men with metabolic abnormalities. Summary of the Invention

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

[0006] To achieve the above objectives, one aspect of the present invention is to provide a method for assessing aging in men with metabolic abnormalities, characterized by comprising the following steps:

[0007] Step 1: Obtain the chronological age CA of men with metabolic abnormalities;

[0008] Step 2: Obtain indicator data for men with metabolic abnormalities, including systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, serum glycosylated albumin ratio, total cholesterol, triglycerides, alanine aminotransferase, serum albumin, serum urea, and serum uric acid;

[0009] Step 3: All indicator data are transformed into a new set of principal components with the same number, and input into the KDM-BA model together with the time series age CA to calculate the biological age BA;

[0010] Step 4: Based on the difference between the biological age BA and the chronological age CA of the same metabolically abnormal male, Δage=CA-BA, the aging rate is predicted, 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, in step 3, 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 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.

[0017] Another aspect of the present invention is to provide an aging assessment system for men with metabolic abnormalities, characterized by comprising:

[0018] A data acquisition module is used to obtain the time series age CA and indicator data of males with metabolic abnormalities, wherein the indicator data include systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, serum glycosylated albumin ratio, total cholesterol, triglycerides, alanine aminotransferase, serum albumin, serum urea, and serum uric acid;

[0019] Data preprocessing module, used to transform all indicator data into a new set of principal components with the same number;

[0020] Biological age calculation module, used to calculate and predict the biological age BA of men with metabolic abnormalities using the KDM-BA model;

[0021] 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 metabolically abnormal male, Δage = CA - BA, where:

[0022] If Δage>0, aging slows down;

[0023] If Δage < 0, aging is accelerated;

[0024] If Δage=0, it means healthy aging.

[0025] Preferably, the KDM-BA model uses the following formula to calculate biological age BA:

[0026]

[0027] 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 jis 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.

[0028] Compared to existing solutions, this method utilizes common clinical markers to construct a biological age calculation model for men with metabolic abnormalities. This model allows for the assessment of physical aging in men with metabolic abnormalities. This model can provide quantitative indicators of aging when developing personalized health guidance and intervention measures for men with metabolic abnormalities, 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 promotion and enabling earlier and more effective health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a method for assessing aging in men with metabolic abnormalities disclosed in an embodiment of the present invention;

[0030] Figure 2 The performance of the model constructed by the embodiment of the present invention is demonstrated;

[0031] Figure 3 The specific data used in the embodiments of the present invention are shown;

[0032] Figure 4 A density map used in an embodiment of the present invention;

[0033] Figure 5 The calculation result of y.true3 in the embodiment of the present invention is illustrated. DETAILED DESCRIPTION

[0034] 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.

[0035] 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.

[0036] The present invention aims to screen out clinical markers that have a significant impact on BA in men with metabolic abnormalities, thereby establishing a KDM-BA model, such as Figure 1 As shown, the specific steps include:

[0037] Step 1: Data collection:

[0038] Inclusion criteria:

[0039] a) Aged ≥ 18 years;

[0040] b) The relevant examination and testing information of the physical examination is complete;

[0041] c) History of hypertension, diabetes, obesity (BMI ≥ 28 kg·m -2 ), history of hyperlipidemia or abnormal metabolic indicators indicated by the current health check-up, including:

[0042] 1) Systolic blood pressure ≥140 mmHg and / or diastolic blood pressure ≥90 mmHg (Guidelines for the Prevention and Treatment of Hypertension (2018 Revised Edition));

[0043] 2) Fasting blood glucose ≥7.0 mmol / L and / or glycosylated hemoglobin ≥6.5% (Guidelines for Primary Care Diabetes Prevention and Management (2022 Edition));

[0044] 3) Total cholesterol > 6.2 mmol / L, triglycerides > 2.3 mmol / L (Guidelines for Blood Lipid Management (2023 Edition));

[0045] 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 ).

[0046] Exclusion criteria:

[0047] a) Patients with liver dysfunction and renal insufficiency;

[0048] b) history of cancer;

[0049] We collected data from 64,459 adult participants who underwent physical examinations at the Physical Examination Center of Zhongshan Hospital Affiliated to Fudan University in Shanghai from January 2021 to December 2022. Based on the patients' medical history and the results of the current physical examinations, 14,601 men with metabolic abnormalities were ultimately included.

[0050] Step 2: Pearson correlation analysis:

[0051] Pearson correlation analysis was performed between the relevant indicators and CA, and the correlation coefficient |r|>0.1 and P<0.05 were used as screening conditions to screen out indicators that were monotonically correlated with CA for subsequent adjustment of the model.

[0052] Step 3: Fill in missing values:

[0053] All indicators 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.

[0054] Step 4: Principal component analysis (PCA):

[0055] 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.

[0056] Step 5. Calculate KDM-BA: Substitute the selected indicators into the following formula:

[0057]

[0058] 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 j is 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.

[0059] 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 2The 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, glycated albumin ratio, total cholesterol, triglycerides, ALT, serum albumin, serum urea, and serum uric acid.

[0060] Step 6. Evaluate KDM-BA: Compare all obtained BAs 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 by methods such as and residual distribution.

[0061] The constructed KDM-BA model calculated the Pearson correlation coefficient between BA and CA to be 0.940 (0.938, 0.941), the standard deviation in the Bland Altman analysis was 0, the MAE was 3.889, the MSE was 26.122, the RMSE was 5.111, the slope of the linear regression was 1, and R 2 is 0.883, see Figure 2 .

[0062] Therefore, the first aspect of the embodiment of the present invention discloses a method for assessing aging in men with metabolic abnormalities, comprising the following steps:

[0063] Step 1: Obtain the chronological age (CA) of males with metabolic abnormalities (calculated based on date of birth) in years;

[0064] Step 2: Obtain indicator data for men with metabolic abnormalities, including:

[0065] Systolic blood pressure is used to indicate the systolic blood pressure value measured in resting state in men with metabolic disorders, and the unit is mmHg;

[0066] Diastolic blood pressure is used to indicate the diastolic blood pressure value measured in resting state in men with metabolic disorders, and the unit is mmHg;

[0067] Height, used to indicate the measured net height of men with metabolic abnormalities, in meters (m);

[0068] Weight, used to express the weight measured in men with metabolic abnormalities, in kilograms (Kg);

[0069] Hematocrit (Hct) is used to indicate the hematocrit index measured in men with metabolic abnormalities;

[0070] Neutrophil ratio is used to indicate the neutrophil index measured in men with metabolic abnormalities, in %;

[0071] Lymphocyte ratio, used to indicate the lymphocyte ratio index measured in men with metabolic abnormalities, the unit is (%);

[0072] Monocyte ratio is used to indicate the monocyte ratio index measured in men with metabolic abnormalities, in (%);

[0073] Fasting blood glucose is used to indicate the blood glucose index measured in men with metabolic disorders when fasting, and the unit is mmol / L;

[0074] Serum glycosylated albumin ratio (GA%) is used to indicate the glycosylated albumin ratio in serum of men with metabolic abnormalities under fasting conditions, in %.

[0075] Total cholesterol is used to indicate the total cholesterol index measured in men with metabolic disorders under fasting conditions, with the unit being mmol / L;

[0076] Triglyceride is used to indicate the triglyceride index measured in men with metabolic abnormalities under fasting conditions, with the unit being mmol / L;

[0077] Alanine aminotransferase (ALT) is used to indicate the ALB index measured in men with metabolic abnormalities, with the unit being g / L;

[0078] Serum albumin (ALB), used to represent the ALT index measured in men with metabolic abnormalities, the unit is U / L;

[0079] Serum urea is used to indicate the serum urea index measured in men with metabolic abnormalities, with the unit being mmol / L;

[0080] Serum uric acid (UA) is used to indicate the serum urea index measured in men with metabolic abnormalities, and the unit is μmol / L;

[0081] Step 3: All indicator data are transformed into a new set of variables with the same number of variables, that is, the principal components are obtained, and the biological age BA is calculated based on the KDM-BA model:

[0082]

[0083] Where: m is the number of principal components, x 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 CA of time series age;

[0084] Step 4: Based on the difference between the biological age BA and the chronological age CA of the same metabolically abnormal male, Δage=CA-BA, the aging rate is predicted, where:

[0085] If Δage>0, aging slows down;

[0086] If Δage < 0, aging is accelerated;

[0087] If Δage=0, it means healthy aging.

[0088] 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, serum glycosylated albumin ratio, total cholesterol, triglyceride, ALT, serum albumin, serum urea and serum uric acid, the method of the present invention comprises the following steps:

[0089] Arrange the data, the column name is indicator, the row name is the examiner number (data frame format, such as Figure 3 shown);

[0090] Check missing values and remove those whose physical examination items are missing more than 20%;

[0091] Multiple interpolation methods are used to fill missing values. The most appropriate interpolation method is selected based on the density map, for example Figure 4 As shown, the interpolation method closest to the actual distribution curve (blue) is selected as the best choice (the red curve pointed by the arrow);

[0092] Principal component analysis to reduce the dimensionality of the data;

[0093] The TrueTrait function of the WGCNA package in R language is used to calculate the corresponding biological age and export it to Excel, such as Figure 5 As shown, (y is the chronological age, y.true3 is the biological age, which is used for subsequent calculation of age acceleration);

[0094] Calculate Δage: Δage = CA - BA. For example, the first examinee has a chronological age of 91 and a biological age of 84.63. His Δage is 6.37 years, which is greater than 0, indicating that his aging has slowed down and his health is good. The 11th examinee has a Δage of -5.49 years, which is less than 0, indicating that his aging has accelerated, and there may be a situation of accelerated aging.

[0095] The above technical solution can be used to calculate the biological age of men with metabolic abnormalities, including:

[0096] ①Indicator acquisition: age (date of birth), systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, neutrophil ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, serum glycosylated albumin ratio, total cholesterol, triglycerides, ALT, serum albumin, serum urea, and serum uric acid were collected from men with metabolic abnormalities during physical examination;

[0097] 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).

[0098] A second aspect of the present invention is to disclose an aging assessment system for men with metabolic abnormalities, comprising:

[0099] The data acquisition module is used to obtain the time series age CA and indicator data of males with metabolic abnormalities, wherein the indicator data includes:

[0100] Systolic blood pressure is used to indicate the systolic blood pressure value measured in resting state in men with metabolic disorders, and the unit is mmHg;

[0101] Diastolic blood pressure is used to indicate the diastolic blood pressure value measured in resting state in men with metabolic disorders, and the unit is mmHg;

[0102] Height, used to indicate the measured net height of men with metabolic abnormalities, in meters (m);

[0103] Weight, used to express the weight measured in men with metabolic abnormalities, in kilograms (Kg);

[0104] Hematocrit (Hct) is used to indicate the hematocrit index measured in men with metabolic abnormalities;

[0105] Neutrophil ratio is used to indicate the neutrophil index measured in men with metabolic abnormalities, in %;

[0106] Lymphocyte ratio, used to indicate the lymphocyte ratio index measured in men with metabolic abnormalities, the unit is (%);

[0107] Monocyte ratio is used to indicate the monocyte ratio index measured in men with metabolic abnormalities, in (%);

[0108] Fasting blood glucose is used to indicate the blood glucose index measured in men with metabolic disorders when fasting, and the unit is mmol / L;

[0109] Serum glycosylated albumin ratio (GA%) is used to indicate the glycosylated albumin ratio in serum of men with metabolic abnormalities under fasting conditions, in %.

[0110] Total cholesterol is used to indicate the total cholesterol index measured in men with metabolic disorders under fasting conditions, with the unit being mmol / L;

[0111] Triglyceride is used to indicate the triglyceride index measured in men with metabolic abnormalities under fasting conditions, with the unit being mmol / L;

[0112] Alanine aminotransferase (ALT) is used to indicate the ALB index measured in men with metabolic abnormalities, with the unit being g / L;

[0113] Serum albumin (ALB), used to represent the ALT index measured in men with metabolic abnormalities, the unit is U / L;

[0114] Serum urea is used to indicate the serum urea index measured in men with metabolic abnormalities, with the unit being mmol / L;

[0115] Serum uric acid (UA) is used to indicate the serum urea index measured in men with metabolic abnormalities, and the unit is μmol / L;

[0116] Data preprocessing module, used to transform all indicator data into a new set of principal components with the same number;

[0117] The biological age prediction module is used to calculate and predict the biological age (BA) of men with metabolic abnormalities using the KDM-BA model:

[0118]

[0119] 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, s BA is the root mean square error of the regression of all principal components on the CA of time series age;

[0120] 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 metabolically abnormal male, Δage = CA - BA, where:

[0121] If Δage>0, aging slows down;

[0122] If Δage < 0, aging is accelerated;

[0123] If Δage=0, it means healthy aging.

Claims

1. A method for assessing aging in men with metabolic abnormalities, characterized in that: The following steps are involved: Step 1: Obtain the chronological age CA of men with metabolic abnormalities; Step 2: Obtain indicator data for men with metabolic abnormalities, including systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, serum glycosylated albumin ratio, total cholesterol, triglycerides, alanine aminotransferase, serum albumin, serum urea, and serum uric acid; Step 3: All indicator data are transformed into a new set of principal components with the same number, and input into the KDM-BA model together with the time series age CA to calculate the biological age BA; Step 4: Based on the difference between the biological age BA and the chronological age CA of the same metabolically abnormal male, Δage=CA-BA, the aging rate is predicted, where: If Δage>0, aging slows down; If Δage < 0, aging is accelerated; If Δage=0, it means healthy aging.

2. The method for assessing aging in men with metabolic abnormalities according to claim 1, wherein: In step 3, 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.

3. A system for assessing aging in men with metabolic abnormalities, characterized in that: include: A data acquisition module is used to obtain the time series age CA and indicator data of males with metabolic abnormalities, wherein the indicator data include systolic blood pressure, diastolic blood pressure, height, weight, hematocrit, granulocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, serum glycosylated albumin ratio, total cholesterol, triglycerides, alanine aminotransferase, serum albumin, serum urea, and serum uric acid; 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 and predict the biological age BA of men with metabolic abnormalities 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 metabolically abnormal male, Δage = CA - BA, where: If Δage>0, aging slows down; If Δage < 0, aging is accelerated; If Δage=0, it means healthy aging.

4. The aging assessment system for men with metabolic abnormalities according to claim 3, 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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