Method and equipment for evaluating senescence of healthy women

Abstract

The invention discloses an aging assessment method for healthy women. According to the method, a healthy female biological age calculation model is constructed by using clinical common markers, so that the evaluation of the body aging condition of the female in a non-illness state is realized, and when personalized health guidance and intervention measures are made for the sub-health transition female, an aging quantitative index 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 a method and device for assessing aging of healthy women. For healthy women, their biological age is estimated through conventional test indicators related to aging, and compared with their chronological age to evaluate the true aging status of the healthy women'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 progresses through the entire 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. However, previous aging assessment methods were mostly standardized, lacking specific methods for different health stages or distinguishing between women and men. However, due to significant differences in chromosomes, hormones, and other factors, 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 appropriate aging assessment methods for different sexes and health stages. Summary of the Invention

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

[0006] To achieve the above object, one aspect of the present invention is to provide a method for assessing aging in healthy women, characterized by comprising the following steps:

[0007] Step 1: Obtain the time series age CA of healthy women;

[0008] Step 2: Obtaining indicator data of healthy women, wherein the indicator data include systolic blood pressure, diastolic blood pressure, body mass index (BMI), hematocrit, granulocyte ratio, monocyte ratio, fasting blood glucose, triglyceride, alanine aminotransferase, aspartate aminotransferase, serum creatinine, and serum urea;

[0009] Step 3: Convert all indicator data into a new set of principal components with the same number;

[0010] Step 4: All principal components and time series age CA are input into the KDM-BA model to calculate the biological age BA of healthy women;

[0011] Step 5: Based on the difference between the biological age BA and the chronological age CA of the same healthy woman, Δage=CA-BA, the aging rate is predicted, where:

[0012] If Δage>0, aging is decelerated;

[0013] If Δage < 0, aging is accelerated;

[0014] If Δage=0, it means healthy aging.

[0015] Preferably, in step 4, the KDM-BA model uses the following formula to calculate biological age BA:

[0016]

[0017] In the formula, m is the number of principal components, x is jis 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.

[0018] Another aspect of the present invention is to disclose an electronic device, comprising:

[0019] one or more processors;

[0020] A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute software to implement the above-mentioned aging assessment method for healthy women.

[0021] This method uses common clinical markers to construct a biological age calculation model for healthy women, enabling the assessment of women's physical aging in a healthy state. This model can provide quantitative indicators of aging when developing personalized health guidance and intervention measures for women 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 promotion and enabling earlier and more effective health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for assessing aging in healthy women disclosed in an embodiment of the present invention;

[0023] Figure 2 The performance of the model constructed by the embodiment of the present invention is demonstrated;

[0024] Figure 3 The specific data used in the embodiments of the present invention are shown;

[0025] Figure 4 A density map used in an embodiment of the present invention;

[0026] Figure 5 The calculation result of y.true3 in the embodiment of the present invention is illustrated. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] like Figure 1 As shown, one aspect of the embodiments of the present invention is to disclose a method for assessing aging in healthy women, aiming to screen out clinical markers that significantly affect BA in healthy women, thereby establishing a KDM-BA model, which specifically includes the following steps:

[0030] Step 1: Data collection:

[0031] Inclusion criteria:

[0032] a) Aged ≥ 18 years;

[0033] b) Relevant inspection and testing information is complete;

[0034] Exclusion criteria:

[0035] a) History of hypertension, diabetes, coronary heart disease, or dyslipidemia;

[0036] b) history of cancer;

[0037] c) Patients with liver and renal insufficiency;

[0038] d) This health checkup indicates abnormal serological indicators, including:

[0039] 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));

[0040] 2) Fasting blood glucose ≥7.0 mmol / L and / or glycosylated hemoglobin ≥6.5% (National Guidelines for Primary Diabetes Prevention and Management (2022 Edition));

[0041] 3) Total cholesterol > 6.2 mmol / L, triglycerides > 2.3 mmol / L;

[0042] 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 );

[0043] We collected data from 64,459 adult participants who underwent health checkups at the Zhongshan Hospital Affiliated to Fudan University in Shanghai between January 2021 and December 2022. Based on their medical history and current examination results, we excluded 50,372 participants with a history of hypertension, diabetes, dyslipidemia, obesity, cancer, and other medical conditions, as well as those with no prior medical history but abnormal results from the current examination. A total of 8,755 healthy women were included.

[0044] 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, BMI, systolic blood pressure, diastolic blood pressure, homocysteine, red blood cell count, platelet count, hematocrit, mean corpuscular volume, mean hemoglobin, centrocyte ratio, lymphocyte ratio, monocyte ratio, fasting blood glucose, glycated albumin, triglycerides, HDL-C, apolipoprotein A, apolipoprotein B, ALT, AST, lactate dehydrogenase, alkaline phosphatase, glutathione reductase, total protein, albumin, albumin-to-globulin ratio, serum creatinine, serum urea, serum uric acid, FT3, FT4, oncofetal protein, CA125, Cyfra21-1, and SCC.

[0045] Step 3: Fill in missing values:

[0046] 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.

[0047] Step 4: Principal component analysis (PCA):

[0048] 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.

[0049] Step 5. Calculate KDM-BA: Substitute the selected indicators into the following formula:

[0050]

[0051] 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.

[0052] 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 the residual distribution. While ensuring that the basic modules were not missing (basic indicators, anemia, inflammation, cardiovascular system, liver, kidney, metabolism), unnecessary indicators were continuously removed, and the following indicators were finally retained:

[0053] Systolic blood pressure is used to indicate the systolic blood pressure value measured in a healthy woman at rest, in mmHg;

[0054] Diastolic blood pressure is used to indicate the diastolic blood pressure value measured in a healthy woman at rest, in mmHg;

[0055] Body Mass Index (BMI): used to indicate the height and weight of healthy women. -2 (m -2 )” to calculate BMI;

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

[0057] Neutrophil ratio is used to express the neutrophil index measured in healthy women, the unit is (%);

[0058] Monocyte ratio is used to indicate the monocyte ratio index measured in healthy women, in (%);

[0059] Fasting blood glucose is used to indicate the blood glucose index measured in healthy women on a fasting basis, in mmol / L;

[0060] Triglyceride is used to indicate the triglyceride index measured in healthy women under fasting conditions, with the unit being mmol / L;

[0061] Alanine aminotransferase (ALT) is used to represent the ALT index measured in healthy women, with the unit being U / L;

[0062] Aspartate aminotransferase (AST) is used to represent the AST index measured in healthy women, with the unit being U / L;

[0063] Serum creatinine: used to indicate the serum creatinine index measured in healthy women, the unit is μmol / L;

[0064] Serum urea: used to indicate the serum urea index measured in healthy women, the unit is mmol / L.

[0065] Step 6: The constructed KDM-BA model has a Pearson correlation coefficient of 0.909 (0.906, 0.913) between BA and CA. The standard deviation in the Bland Altman analysis is 6.841, the MAE is 5.019, the MSE is 46.799, the RMSE is 6.841, the slope of the linear regression is 1, and R 2 is 0.827, see Figure 2 .

[0066] If there is a healthy physical examination data set (according to the inclusion and exclusion criteria above), which includes the patient's number, age, systolic blood pressure, diastolic blood pressure, BMI, hematocrit, neutrophil ratio, monocyte ratio, fasting blood glucose, triglycerides, ALT, AST, serum creatinine and serum urea, then a specific implementation of the method disclosed in the present invention may include the following steps:

[0067] Arrange the data, the column name is indicator, the row name is the examiner number (data frame format, such as Figure 3 shown);

[0068] Check missing values and remove those whose physical examination items are missing more than 20%;

[0069] Multiple imputation methods are used to fill missing values. The most appropriate imputation method is selected based on the density map. For example, the interpolation method closest to the actual distribution curve (blue) is selected as the best choice (the red curve indicated by the arrow);

[0070] Principal component analysis to reduce the dimensionality of the data;

[0071] 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;

[0072] Calculate Δage: Δage = CA - BA. For example, the first examinee has a chronological age of 59 and a biological age of 56.67. His Δage is 2.33 years, which is greater than 0, indicating that his aging has slowed down and his health is good. The third examinee has a Δage of -1.81 years, which is less than 0, indicating that his aging has accelerated, and there may be a situation of accelerated aging.

[0073] The above technical solution can be used to calculate the biological age of individuals in the health check population dataset:

[0074] ①Indicator acquisition: age (date of birth), systolic blood pressure, diastolic blood pressure, BMI, hematocrit, neutrophil ratio, monocyte ratio, fasting blood glucose, triglycerides, ALT, AST, serum creatinine, and serum urea were collected from the participants.

[0075] 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).

[0076] A second aspect of an embodiment of the present invention is to disclose an electronic device including a processor that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). The processor may, for example, include a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may include a single processing unit or multiple processing units for performing different actions of the method for assessing aging in healthy women according to an embodiment of the present disclosure.

[0077] The RAM stores various programs and data required for the operation of the electronic device. The processor, ROM, and RAM are connected to each other via a bus. The processor implements the above-mentioned method for assessing aging in healthy women by executing the programs in the ROM and / or RAM. It should be noted that the programs may also be stored in one or more memories other than the ROM and RAM. The processor may also perform various operations of the method for assessing aging in healthy women according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0078] According to an embodiment of the present disclosure, the electronic device may further include an input / output (I / O) interface, which is also connected to the bus. The electronic device may also include one or more of the following components connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, etc.; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs communication processing via a network such as the Internet. The drive is also connected to the I / O interface as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed.

Claims

1. A method for assessing aging in healthy women, characterized in that: The method comprises the following steps, characterized in that the method comprises the following steps: Step 1: Obtain the time series age CA of healthy women; Step 2: Obtaining indicator data of healthy women, wherein the indicator data include systolic blood pressure, diastolic blood pressure, body mass index (BMI), hematocrit, granulocyte ratio, monocyte ratio, fasting blood glucose, triglyceride, alanine aminotransferase, aspartate aminotransferase, serum creatinine, and serum urea; Step 3: Convert all indicator data into a new set of principal components with the same number; Step 4: All principal components and time series age CA are input into the KDM-BA model to calculate the biological age BA of healthy women; Step 5: Based on the difference between the biological age BA and the chronological age CA of the same healthy woman, 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 healthy women according to claim 1, wherein: In step 4, 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. An electronic device comprising: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute software to implement the aging assessment method for healthy women according to claim 1.

Images