Erythrocyte metabolism marker for biological age prediction, screening method, prediction model and application of erythrocyte metabolism marker

Abstract

The invention relates to the technical field of metabolome biologic detection, in particular to an erythrocyte metabolism marker for biological age prediction, a screening method, a prediction model and application of the erythrocyte metabolism marker. The erythrocyte metabolism marker comprises one or more of ornithine, hypoxanthine, ribose-1 / 5-phosphoric acid, ribose, glycine, glutathione, lactic acid, 6-glucose phosphate, 2, 3-diphosphoglyceric acid, dehydroepiandrosterone, glyceric acid, uric acid or inosine. The erythrocyte metabolism marker has the characteristics of strong correlation, simple collection and stable result, and the prediction precision is far higher than that of a conventional scheme; and the constructed KDM model can accurately and obviously predict the biological age change.

Description

Technical Field

[0001] This invention relates to the field of metabolomics biological detection technology, and in particular to a red blood cell metabolic biomarker for biological age prediction, a screening method, a prediction model, and its application. Background Technology

[0002] Benefiting from improved living standards, advancements in medical technology, and environmental improvements, the average life expectancy of the global population has significantly increased. It is projected that by 2050, the global elderly population (>60 years old) will reach 2.1 billion. However, increased life expectancy does not equate to healthy aging. Many elderly individuals often face one or more age-related chronic diseases or health problems, posing significant challenges to socioeconomic and healthcare resources. Therefore, identifying and defining key biomarkers that can predict and measure healthy aging, especially factors related to longevity, has become an important research direction for improving healthy life expectancy and reducing public healthcare costs.

[0003] Currently, numerous studies have identified a series of aging and longevity-related biomarkers based on plasma metabolomics, microbiome, and other characteristics. However, due to interference from external environmental factors and lifestyle, the stability and accuracy of these biomarkers are poor, making it difficult to comprehensively and stably reflect the body's intrinsic aging state. On the other hand, the KDM model is an advanced biological age estimation algorithm that conforms to biological principles. It determines the age corresponding to the physiological functional state of the human body by estimating biological age, acknowledging that different systems and indicators age at different rates, and exhibiting less bias and higher accuracy for both young and aged individuals. However, many existing prediction models predict aging and longevity based on single features, failing to comprehensively and systematically reflect the individual's aging process. While commonly used plasma and microbiome characteristics can be used for prediction, they are subject to significant fluctuations due to external environmental and temporary factors, resulting in insufficient stability.

[0004] In recent years, red blood cells, as a functionally stable and readily available biological sample in the circulatory system, have attracted researchers' attention. The multidimensional characteristics of red blood cells, including metabolism, protein modification, and enzyme activity, are closely related to aging status and longevity potential. Targeting these characteristics of red blood cells will provide more accurate and stable indicators for predicting aging and longevity. However, research and application in this area are still in their early stages, lacking systematic research and validation, and there are currently no biological age prediction methods based on red blood cell metabolic characteristics.

[0005] Therefore, developing an accurate predictive model based on erythrocyte metabolic characteristics can provide a more reliable and comprehensive tool for assessing aging and longevity, thereby promoting interventions and management related to healthy aging. Summary of the Invention

[0006] This invention provides a biochemical age prediction biomarker for erythrocyte metabolism, a screening method, a prediction model, and its application. The purpose is to target the metabolic characteristics of erythrocytes and construct a biochemical age prediction model to assess the metabolic transition of erythrocytes and its correlation with plasma biochemical age.

[0007] To achieve the above objectives, the present invention provides an application of erythrocyte metabolic markers in the preparation of diagnostic products for biological age prediction, wherein the erythrocyte metabolic markers include one or more of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, glucose-6-phosphate, 2,3-diphosphoglycerate, uric acid, glyceric acid, dehydroepiandrosterone, or inosine.

[0008] Preferably, the diagnostic product predicts biological age by detecting a significant increase or decrease in the relative content of erythrocyte metabolic markers in erythrocytes.

[0009] Preferably, among the erythrocyte metabolic markers, the relative content of one or more of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, or glucose-6-phosphate increases significantly with age, with a significance P value < 0.05 and R > 0.1;

[0010] Among the erythrocyte metabolic markers, the relative content of one or more of 2,3-diphosphoglyceric acid, dehydroepiandrosterone, glyceric acid, uric acid, or inosine decreased significantly with age, with a significance P value < 0.05 and R < -0.15.

[0011] Under the same technical concept, the present invention also provides a kit for predicting biological age, the kit comprising the diagnostic products described above.

[0012] Under the same technical concept, the present invention also provides a method for screening erythrocyte metabolic markers used in the above applications, comprising the following steps:

[0013] (1) Collect red blood cell samples from healthy aging people and long-lived people, perform non-targeted metabolomics detection on the red blood cell samples, construct a linear regression model for analysis, and screen out metabolic biomarkers that change significantly with age.

[0014] (2) Pathway enrichment analysis, mapping analysis and LASSO regression analysis were performed on the metabolic markers in sequence to screen and determine the erythrocyte metabolic markers for biological age prediction.

[0015] Under the same technical concept, the present invention also provides a method for constructing a biological age prediction model based on erythrocyte metabolic markers, comprising the following steps:

[0016] S1. Collect and analyze samples; based on non-targeted and / or targeted metabolomics detection methods, perform metabolomics analysis on the erythrocyte metabolic markers described in each analytical sample to obtain metabolite information data of the analytical sample;

[0017] S2. Based on metabolite information data, construct a KDM model for biological age prediction. The formula for the KDM model for biological age prediction is shown below:

[0018]

[0019] in, Indicates biological age, Indicates actual age. Indicates selecting the first Metabolic markers of species Indicates the first Information data on metabolic biomarkers of the species, Indicates the first The slope of changes in metabolic markers of a species with age. Indicates the first The intercept of metabolic markers of a species, This represents the root mean square error. This represents the variance in the estimated age explained by all selected metabolic biomarkers. When multiple metabolic biomarkers are selected, , and For inclusion A vector of numerical values.

[0020] Preferably, the biological age prediction model uses metabolite information data of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactic acid, glucose-6-phosphate, 2,3-diphosphoglycerate, dehydroepiandrosterone, glyceric acid, uric acid, and inosine to construct a KDM model for biological age.

[0021] Under the same technical concept, the present invention also provides a biological age prediction model, which is obtained by the above-described construction method.

[0022] Under the same technical concept, the present invention also provides an application of a biological age prediction model in clinical screening or auxiliary diagnosis for predicting biological age.

[0023] The above-described solution of the present invention has the following beneficial effects:

[0024] (1) This invention establishes a rapid non-target detection analysis method based on liquid chromatography-mass spectrometry. Through linear regression model, pathway enrichment analysis and mapping analysis of aging metabolic characteristics and LASSO regression model, red blood cell metabolic markers related to biological aging are screened out, and the predictive potential of metabolic markers for biological age is evaluated.

[0025] (2) This invention screens a group of red blood cell metabolic markers for biological age prediction. The metabolic markers can be used individually or in combination. Combining 13 red blood cell metabolic features can accurately predict biological age. Red blood cell metabolic markers have the characteristics of strong correlation, simple collection and stable results, and the prediction precision is much higher than conventional methods.

[0026] (3) The present invention establishes a KDM model for predicting biological age based on erythrocyte metabolic markers, which can accurately and significantly predict changes in biological age. Attached Figure Description

[0027] Figure 1 These are 13 erythrocyte metabolic markers for biological age prediction identified by LASSO regression analysis in this embodiment of the invention.

[0028] Figure 2 This is a linear regression analysis graph of erythrocyte metabolic markers for biological age prediction in an embodiment of the present invention, as a function of biological age.

[0029] Figure 3 This refers to the training and validation sets of the KDM model for biological age prediction in this embodiment of the invention, as well as the validation results for long-lived individuals; wherein, Figure 3 (A) shows the computational results of the KDM model for biological age prediction on the training set of healthy aging population; Figure 3 (B) shows the calculation results of the KDM model for biological age prediction on a test set of healthy aging population; Figure 3 (C) Results of the KDM model for biological age prediction in aging and long-lived populations; Figure 3 (D) is a chart comparing the calculated biological age with the actual age of long-lived individuals; Figure 3 (E) is a pie chart comparing the calculated biological age with the actual age in the long-lived population. Detailed Implementation

[0030] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0032] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a locking connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0033] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] Example 1:

[0035] This embodiment performs metabolomics analysis on red blood cell samples from healthy aging individuals and long-lived individuals to screen for metabolic biomarkers for predicting biological age.

[0036] 1. Acquisition of human sample data

[0037] This invention collected red blood cell samples from healthy aging individuals aged 20-90 (301 cases) and long-lived individuals over 90 years of age (260 cases), and collected their clinical baseline characteristics. These individuals did not include those with serious illnesses, infections, who had undergone surgery, or who were pregnant.

[0038] 2. Red blood cell sample collection, separation, and preservation

[0039] 1 mL of peripheral venous blood was drawn from each subject into an EDTA anticoagulant tube. After mixing, the blood was centrifuged at 2000 g for 5 minutes at 4°C. The supernatant plasma was aspirated and aliquoted into 1.5 mL EP tubes at 200 μL each. These tubes were then flash-frozen in liquid nitrogen and stored at -80°C. The intermediate layer (leukocytes and platelets) was aspirated and aliquoted into 1.5 mL EP tubes. These tubes were then flash-frozen in liquid nitrogen and stored at -80°C. The lower layer of red blood cells was aspirated and aliquoted into 1.5 mL EP tubes at 200 μL each. These tubes were then flash-frozen in liquid nitrogen and stored at -80°C.

[0040] 3. Rapid detection and analysis of non-targeted metabolomics in erythrocytes based on LC-MS

[0041] 1) Sample pretreatment: Thaw the red blood cell samples on ice. Transfer 20 μL of red blood cells to a 1.5 mEP tube, add 180 μL of pre-chilled extraction buffer (methanol:acetonitrile:water = 5:3:2) at -20°C, shake for a few seconds, then place in a 4°C shaker and shake for 30 minutes. Centrifuge at 18000g for 10 minutes at 4°C. Transfer 100 μL of the supernatant to the sample insert tube. Additionally, take 20 μL of the supernatant from each sample and mix them to form a quality control (QC) sample for instrumental analysis.

[0042] 2) Analytical Testing: Metabolomics analysis was performed using a Thermo Fisher Dionex UltiMate 3000 UHPLC and a Qexactive HFMS ultra-high performance liquid chromatography-mass spectrometry system. The chromatographic column was a Kinetex C18 column (2.1 x 150 mm, 1.7 μm), the flow rate was 0.45 mL / min, the column oven temperature was 45 °C, the sample temperature was 4 °C, and the injection volume was 10 mL. In the positive ion mode liquid chromatography, mobile phase A (0.1% formic acid aqueous solution) and mobile phase D (0.1% formic acid acetonitrile solution) were used. The elution gradient was as follows: 0-0.5 min, 5% D; 0.5-1.1 min, 5%-95% D; 1.1-2.75 min, 95% A; 2.75-3 min, 95%-5% A; 3-5 min, 5% A. The negative ion mode liquid chromatography section used mobile phase B (95% acetonitrile aqueous solution / 1mM ammonium acetate solution) and mobile phase C (5% acetonitrile aqueous solution / 1mM ammonium acetate solution). The elution gradient was as follows: 0-0.5 min, 100% C; 0.5-1.1 min, 100% B; 1.1-2.75 min, 100% B; 2.75-3 min, 100%-0% B; 3-5 min, 100% C.

[0043] Mass spectrometry parameters were set as follows: resolution 60,000, scan range 65-900 m / z; maximum injection time 200 ms, automatic gain control (AGC) 3x10. 6 Ions, sheath gas flow rate 45, auxiliary gas flow rate 15, purge gas flow rate 0. All samples were randomly injected, and a QC sample was injected every 10 samples.

[0044] 4. Data Preprocessing

[0045] The raw data was converted into mzXML format using RawConverter software and then imported into Maven software for peak extraction. The data underwent log10 transformation, range normalization, and Pareto standardization.

[0046] 5. Metabolomics analysis

[0047] 1) Linear Regression Model Analysis

[0048] A linear regression model was constructed after adjusting for sex to assess the correlation between erythrocyte metabolic markers and age. The linear model is as follows:

[0049] Metabolite level = α + β1age + β2sex + ε

[0050] Where α is the intercept of the linear regression model; β1 is the regression coefficient of the first independent variable age; β2 is the regression coefficient of the second independent variable sex; and ε is the residual.

[0051] We calculated the p-values ​​of the sum of squares and multiple hypothesis testing under the linear model, and determined age-related erythrocyte metabolic biomarkers based on p < 0.05. Through linear model analysis, we identified 192 biologically age-related erythrocyte metabolic biomarkers.

[0052] 2) Pathway enrichment analysis and corresponding pathway mapping analysis

[0053] Pathway enrichment and mapping analyses were performed on the 192 age-related erythrocyte metabolic biomarkers identified in section 1). The 192 erythrocyte metabolic features were input into the online website MetaboAnalys (https: / / www.metaboanalyst.ca / ) for pathway enrichment analysis. Subsequently, key metabolic pathways were obtained based on the corrected P<0.05 criterion used in the pathway analysis. The key metabolic pathways and the 192 metabolites were imported into R software, and pathway mapping analysis was performed on the 192 metabolites using the KEGGREST package. Key metabolic features were extracted by combining the key metabolic pathways. Finally, 31 erythrocyte metabolic biomarkers were found to be closely related to biological age and aging processes.

[0054] 3) LASSO regression analysis

[0055] For the 31 erythrocyte metabolic markers identified in 2), further LASSO regression analysis was used to determine 13 important age-related erythrocyte metabolic markers based on their significance and R-value. These 13 age-predicting erythrocyte metabolic markers are as follows: Figure 1 As shown;

[0056] 4) Linear analysis of age-related erythrocyte metabolic markers

[0057] The following is an analysis graph showing the changes in erythrocyte metabolic markers with biological age. Figure 2As shown, among the 13 characteristic metabolites, the levels of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, and glucose-6-phosphate metabolites increased significantly with age (R>0.1, p<0.05), while the levels of uric acid, 2,3-diphosphoglycerate, glyceric acid, dehydroepiandrosterone, and inosine metabolites decreased significantly with age (R<-0.15, p<0.05).

[0058] 6. Construction of KDM Model for Biological Age Prediction

[0059] Based on the 13 erythrocyte metabolic biomarkers obtained for biological age prediction, a KDM model for biological age prediction was constructed:

[0060] The formula for the KDM model for biological age prediction is shown below:

[0061]

[0062] in, Indicates biological age, Indicates actual age. Indicates selecting the first Metabolic markers of species Indicates the first Information data on metabolic biomarkers of the species, Indicates the first The slope of changes in metabolic markers of a species with age. Indicates the first The intercept of metabolic markers of a species, This represents the root mean square error. This indicates that the variance in the estimated age explained by all selected metabolic biomarkers is therefore... , and For inclusion A vector of numerical values.

[0063] Human sample data from healthy aging individuals were divided into training and validation sets in a 3:2 ratio. Based on the erythrocyte metabolomics dataset and 13 erythrocyte metabolic features, the biological age prediction model was input, and calculations were performed accordingly. , , The regression parameters of the KDM model were obtained, and their specific values ​​are shown in Table 1 below:

[0064] Table 1. Regression parameters of each metabolite in the KDM biological age prediction model.

[0065]

[0066] The constructed KDM model for predicting biological age was validated using validation set data from healthy aging individuals and red blood cell sample data from long-lived individuals. The calculated biological ages were highly correlated with actual ages, and the calculated biological ages for most long-lived individuals were lower than their actual ages. The validation results are shown in the following figures. Figure 3 This may indicate that long-lived individuals possess youthful red blood cell metabolic characteristics.

[0067] Figure 3 (A) shows the computational results of the KDM model for biological age prediction on the training set of healthy aging population; Figure 3 (B) shows the calculation results of the KDM model for biological age prediction on a test set of healthy aging population; Figure 3 (C) Results of the KDM model for biological age prediction in aging and long-lived populations; Figure 3 (D) is a chart comparing the calculated biological age with the actual age of long-lived individuals; Figure 3 (E) is a pie chart comparing the calculated biological age with the actual age in the long-lived population (the pie chart indicates that 17% of people have a calculated biological age that is greater than their actual age, and 83% of people have a calculated biological age that is less than their actual age).

Claims

1. The application of a erythrocyte metabolic marker in the preparation of diagnostic products for biological age prediction, characterized in that, The erythrocyte metabolic markers include one or more of the following: ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, glucose-6-phosphate, 2,3-diphosphoglycerate, dehydroepiandrosterone, glyceric acid, uric acid, or inosine.

2. The application as described in claim 1, characterized in that, The diagnostic product predicts biological age by detecting a significant increase or decrease in the relative content of erythrocyte metabolic markers in erythrocytes.

3. The application as described in claim 1, characterized in that, Among the erythrocyte metabolic markers, the relative content of one or more of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, or glucose-6-phosphate increased significantly with age, with a significance P value < 0.05 and R > 0.1; Among the erythrocyte metabolic markers, the relative content of one or more of 2,3-diphosphoglyceric acid, dehydroepiandrosterone, glyceric acid, uric acid, or inosine decreased significantly with age, with a significance P value < 0.05 and R < -0.

15.

4. A reagent kit for predicting biological age, characterized in that, The kit includes diagnostic products used in any one of claims 1-3.

5. A method for screening erythrocyte metabolic markers in any one of claims 1-3, characterized in that, Includes the following steps: (1) Collect red blood cell samples from healthy aging people and long-lived people, perform non-targeted metabolomics detection on the red blood cell samples, construct a linear regression model for analysis, and screen out metabolic biomarkers that change significantly with age. (2) Pathway enrichment analysis, mapping analysis and LASSO regression analysis were performed on the metabolic markers in sequence to screen and determine the erythrocyte metabolic markers for biological age prediction.

6. A method for constructing a biological age prediction model based on erythrocyte metabolic biomarkers, characterized in that, Includes the following steps: S1. Collect and analyze samples; based on non-targeted and / or targeted metabolomics detection methods, perform metabolomics analysis on erythrocyte metabolic markers in each analytical sample as described in any one of claims 1-3 to obtain metabolite information data of the analytical sample. S2. Based on metabolite information data, construct a KDM model for biological age prediction. The formula for the KDM model for biological age prediction is shown below: in, Indicates biological age, Indicates actual age. Indicates selecting the first Metabolic markers of species Indicates the first Information data on metabolic biomarkers of the species, Indicates the first The slope of changes in metabolic markers of a species with age. Indicates the first The intercept of metabolic markers of a species, This represents the root mean square error. This represents the variance in the estimated age explained by all selected metabolic biomarkers. When multiple metabolic biomarkers are selected, , and For inclusion A vector of numerical values.

7. The construction method as described in claim 6, characterized in that, The biological age prediction model uses metabolite information data of ornithine, hypoxanthine, ribose-1 / 5-phosphate, ribose, glycine, glutathione, lactate, glucose-6-phosphate, 2,3-diphosphoglycerate, dehydroepiandrosterone, glyceric acid, uric acid, and inosine to construct a KDM model of biological age.

8. A biological age prediction model, characterized in that, The biological age prediction model is obtained using the construction method described in any one of claims 6-7.

9. The application of the biological age prediction model as described in claim 8 in clinical screening or auxiliary diagnosis for predicting biological age.

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