Method for evaluating individual aging age group based on metabolite combination and application

Abstract

The invention belongs to the technical field of biomedicine and individual physiological function prediction intervention, and relates to a method for evaluating individual physiological functions and health intervention, in particular to a method for evaluating individual aging age groups based on metabolite combinations and application. According to the present invention, any one or a combination of a plurality of small molecule plasma metabolites of the following five metabolites such as Maltose / Lactase, Phenylacetylglutamine, Gluconolactone, AMP and Maltotriose is adopted to deduce the aging stage of an individual; the metabolite-based composition can better predict and deduce the aging stage of an individual, is expected to be applied to clinical practice for evaluating the aging process of a subject, and has positive significance for further establishing measures for promoting healthy aging and intervening aging.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine and individual physiological function prediction and intervention, and relates to a method for evaluating individual physiological functions and health interventions, and particularly to a method and application for evaluating the aging age range of an individual based on a metabolite combination. The metabolite combination of the present invention can preferably predict and infer the aging stage of an individual, and is expected to be applied to clinical practice to evaluate the aging process of a subject, which has a positive significance for further establishing interventions for promoting healthy aging and intervening in aging measures. Background Art

[0002] It is recorded in the literature that aging is defined as the systematic deterioration of physiological functions and is affected by various factors such as environment and genes. Aging is a risk factor for the susceptibility of many chronic diseases (such as neurodegenerative diseases, diabetes, and tumors, etc.). It is recorded in the literature that the occurrence and development of the chronic diseases related to the increase in age are related to age-related cumulative damage. At the same time, during the aging process, due to the changes in the physiological functions and adaptability of the body, it also causes a high susceptibility to diseases and a low "repair" ability. The industry recognizes that evaluating the degree and process of aging has a positive significance for understanding the functional aging of the body and promoting healthy aging. However, there is currently no relatively universal and easy-to-quantify set of indicators for evaluating the aging process and predicting aging, or a unique molecular map.

[0003] Research practice shows that blood is a clinically easily obtainable biological sample, and its biochemical indicators and cell states are widely used in clinical disease diagnosis. As one of the main components of blood, the content of metabolites in plasma directly reflects the physiological state of the body, and at the same time, these metabolites are also directly involved in the biochemical reactions of the body. Research also shows that during the aging process of the body, there is a decline in the function of tissue cells, and this decline in function is necessarily accompanied by passive changes in individual metabolites. In addition, metabolites are also affected by the physiological needs of the host and the external environment. Although the changes in these plasma metabolites are affected by exogenous factors, it is undeniable that they are mainly affected by the functional adaptation regulation of the host, and this functional adaptation regulation is also age-dependent. Therefore, metabolites are molecular indicators that can reflect the functional state of the host, especially the functional changes related to aging. Since during the general aging process, due to the influence of diseases or disease susceptibility, chronological age cannot fully reflect the aging situation of an individual, therefore, evaluating the aging stage and degree has a positive significance for promoting healthy aging and intervening in aging, and how to infer the aging stage of the body is an urgent problem to be solved in this technical field.

[0004] Based on the current state of the art, the inventors of the present application intend to provide a method for evaluating individual physiological functions and health interventions, specifically a method for evaluating the aging stage of an individual based on a metabolite combination and its applications. The metabolite-based combination of the present invention can better predict and infer the aging stage of an individual, and is expected to be applied in clinical practice to evaluate the aging process of subjects, which has a positive significance for further establishing health-promoting aging interventions and anti-aging measures.

[0005] The present invention uses any one or a combination of the following 5 small molecule plasma metabolites: Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose to infer the aging stage of an individual.

[0006] The metabolite-based combination of the present invention can better predict and infer the aging stage of an individual, and is expected to be applied in clinical practice to evaluate the aging process of subjects, which has a positive significance for further establishing health-promoting aging interventions and anti-aging measures. Summary of the Invention

[0007] The object of the present invention is to provide a method for evaluating individual physiological functions and health interventions based on the current state of the art, specifically a method for evaluating the aging stage of an individual based on a metabolite combination and its applications. The present invention uses any one or a combination of the following 5 small molecule plasma metabolites: Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose to infer the aging stage of an individual. The metabolite-based combination of the present invention can better predict and infer the aging stage of an individual, and is expected to be applied in clinical practice to evaluate the aging process of subjects, which has a positive significance for further establishing health-promoting aging interventions and anti-aging measures.

[0008] Based on the support of existing theories and the state of the art, the present invention obtained a series of common metabolite content spectra through UPLC / TQMS analysis, and on this basis, obtained time nodes of the aging stage according to the similarity of the metabolome to divide the aging stage of the population. To obtain the simplest aging stage prediction model, the present invention used the cross-validation method algorithm to screen out 5 metabolites as plasma prediction indicators for the aging stage combination, and established a corresponding random forest prediction model using these 5 metabolites for predicting the aging stage. Data calculations show that the prediction models of the 5 metabolites exhibit high age prediction capabilities in both the training set and the validation set.

[0009] Specifically, to achieve the above object, the present invention has taken the following technical solutions:

[0010] A screening method for dividing the aging stage age division points of individuals based on individual plasma metabolites is provided. The screening method mainly includes: obtaining the time points of the aging stage, performing cluster analysis on the original quantitative metabolite data, grouping healthy people according to metabolite characteristics, and obtaining 60 and 80 years old as the age points for dividing the aging stage.

[0011] In the present invention, healthy population data (chronological age is approximately similar to the aging process) is used to provide an age point for dividing the aging stage. The age point is selected from one or two of the following age points:

[0012] The division with 60 years old and 80 years old as the age nodes. Preferably, the healthy aging stage is divided into:

[0013] Young / middle-aged stage (18 - 59 years old), elderly stage (60 - 79 years old), very old stage (80 years old).

[0014] In the present invention, a series of blood metabolite markers for predicting the aging stage are provided. The blood metabolite markers are selected from one or more of the following metabolites:

[0015] Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose.

[0016] In the present invention, the blood metabolites and the corresponding aging stage algorithms mainly include any one or several combinations of the following 5 small molecule metabolites: Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose.

[0017] Any one or several combinations of the 5 small molecule plasma metabolites described in the present invention can be further used to prepare a detection product for evaluating the aging stage.

[0018] In the present invention, a prediction model for the aging stage based on the composition of blood metabolites is provided. The screening method of the prediction model includes:

[0019] Blood metabolite screening method, cross-validating the blood metabolites of healthy people to obtain the minimal blood biomarkers for predicting the aging stage, and using the screened biomarkers to establish an aging stage prediction model using a random forest model, and detecting the reliability and sensitivity of the model through a test set.

[0020] In the present invention, the biomarkers for screening the minimalist aging stage prediction model are obtained by using the cross-validation screening method for the minimalist blood metabolites predicting the aging stage.

[0021] In the present invention, a model or system for predicting aging is provided, and the model is a random forest regression model constructed by using the randomForest method in the randomForest package.

[0022] In the present invention, a computing device is provided, and the computing device includes a memory, a processor, and a computer program that can run on the processor. The executed program includes the following steps:

[0023] Step 1: Obtain the quantitative levels of the metabolites described in the present invention in an individual;

[0024] Step 2: Use the constructed model to predict the probability of the age group by using the metabolite levels obtained in Step 1;

[0025] Step 3: Identify the corresponding group according to the probability predicted in Step 2.

[0026] Preferably, the identification method in the above steps is as follows: The probability of the sample belonging to each aging stage is obtained through the model. If the probability that the sample falls into the young / middle-aged stage calculated by the model is higher than the probability of falling into the old or very old stage, it is determined that the sample is in the young / middle-aged stage; if the probability that the sample falls into the old stage calculated by the model is higher than the probability of falling into the young / middle-aged or very old stage, it is determined that the sample is in the old stage; if the probability that the sample falls into the very old stage calculated by the model is higher than the probability of falling into the young / middle-aged or old stage, it is determined that the sample is in the very old stage.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention provides a method for predicting the aging stage by using blood metabolites. By using the minimalist metabolite combination in blood as the measurement object, the detection cost and detection cycle are reduced, making the whole process convenient and efficient; using the quantitative data of the minimalist metabolite combination for model prediction can obtain the aging situation of an individual and can effectively explain the process and level of aging. The model has been verified by an independent population and has high prediction accuracy and universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 . Selection of the features of the minimum metabolite model based on the random forest algorithm.

[0030] Figure 2 . Performance graph of the random forest model based on the minimum metabolite combination.

[0031] Figure 3 . Receiver operating characteristic (ROC) curve of the minimal metabolite combination in the test set data.

[0032] Figure 4 . Receiver operating characteristic (ROC) curve of the minimal metabolite combination in the independent population data. Detailed implementation manner

[0033] Example 1

[0034] 1. To establish the correlation between age and the aging process, plasma samples of 132 healthy individuals were collected.

[0035] Among them, the definition of healthy individuals is as follows: the blood routine is within the normal range, and they have not suffered from cardiovascular and cerebrovascular diseases, metabolic diseases (such as hyperglycemia, hyperlipidemia, etc.), malignant tumors, or other chronic diseases (chronic gastroenteritis, chronic kidney disease, inflammatory bowel disease, etc.) currently or in the past. Ultra-high performance liquid chromatography tandem triple quadrupole mass spectrometry (UPLC / TQMS) was used to quantitatively analyze the blood metabolites of these 132 healthy individuals, and the blood metabolite values of each healthy individual were obtained.

[0036] 2. Hierarchical clustering analysis was used to divide the population based on the quantitative spectral data of metabolites. It was found that healthy individuals could be divided into 3 aging stages by 60 years old and 80 years old, namely: young / middle-aged, elderly, and very elderly stages.

[0037] 3. To predict the age group of the population using fewer metabolite combinations, a cross-validation algorithm was used to screen the metabolite set, and 5 biomarkers for aging stage prediction were obtained as follows: Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose.

[0038] 4. The data of these 132 healthy individuals were randomly sampled according to 7:3 and used as the training set and the test set respectively. A random forest model was used to construct a model for the 5 aging prediction biomarkers in the training set data. The test set was used to verify the accuracy and sensitivity of the model, and the receiver operating characteristic curve (ROC) was plotted, as well as the area under the curve (AUC) and the overall accuracy of the model prediction.

[0039] 5. The verification results of the test dataset show that the out-of-bag rate (OOB rate) of the model in the test set is 9.52%. The ROC curve shows that the model has a high ability to predict the aging stage. Among them, the areas under the curve for the young, old, and very old stages are 0.9318, 0.8588, and 0.9929 respectively, and the overall accuracy is 81.48%.

[0040] 6. To evaluate the universality of the model, 80 independent healthy people aged 30 - 90 were recruited as the validation cohort. The concentrations of Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, and Maltotriose in the blood were detected using UPLC / TQMS. The model constructed in Method 4 was used to predict the aging stage of the 80 healthy people.

[0041] 7. The verification results of the independent dataset show that the model has a high prediction accuracy. The ROC curve shows that the model has a high ability to predict the aging stage. Among them, the areas under the curve for the young, old, and very old stages are 0.8300, 0.7650, and 0.9547 respectively, and the overall prediction precision is 71.25%.

Claims

1. A method for evaluating the aging age range of an individual based on a metabolite combination, characterized in that, mainly including: Obtaining the time points of the aging stage; Cluster analysis of the original quantitative metabolite data; Dividing the healthy population into groups according to the metabolite profile characteristics; and obtaining 60 and 80 years old as the age points for dividing the aging stage.

2. The method according to claim 1, characterized in that, The aging stage is used to divide any one or two age points; Aging stage division: Division with 60 and 80 years old as the age nodes; among them, young / middle-aged segment: 18 - 59 years old; elderly segment: 60 - 79 years old; very elderly segment: > 80 years old.

3. The method according to claim 1, characterized in that, This method is: A blood metabolite screening method, cross-validating the blood metabolites of the healthy population to obtain biomarkers for minimally predicting the aging stage in blood, and using the screened biomarkers, establishing an aging stage prediction model using a random forest model, and detecting the reliability and sensitivity of the model through a test set.

4. The method according to claim 3, characterized in that, The prediction model is a random forest regression model constructed using the randomForest method in the randomForest package.

5. The method according to claim 3, characterized in that, The blood metabolites and the corresponding aging stage algorithms mainly include any one or several combinations of 5 small molecule metabolites: Maltose / Lactose, Phenylacetylglutamine, Gluconolactone, AMP, Maltotriose.

6. The method according to claim 5, characterized in that, The application of any one or several combinations of the 5 small molecule metabolites in the preparation of a detection product for evaluating the aging stage.

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