CN110392740A

CN110392740A - The method and its application for determining crowd's sample Biological indicators collection, predicting biological age

Info

Publication number: CN110392740A
Application number: CN201780084324.XA
Authority: CN
Inventors: 齐彦伟; 柴相花; 李伟阳; 聂超; 王书元; 陈志华; 张现东; 李尉; 甄贺富; 谭美华; 张爱萍; 张彩芬; 李睿; 赵昕
Original assignee: BGI Shenzhen Co Ltd
Current assignee: BGI Shenzhen Co Ltd
Priority date: 2017-01-25
Filing date: 2017-01-25
Publication date: 2019-10-29
Also published as: WO2018137203A1

Abstract

Provide the method and its application of determining test individual biological age, wherein method includes the following steps: according to the method that the biological age of mentioned-above determining crowd's sample predicts Biological indicators collection, determine that each biological age with Sex, Age classification of the affiliated crowd's sample of test individual predicts Biological indicators collection；Biological indicators collection is predicted based on the corresponding biological age with Sex, Age classification of test individual, calculates the biological age of test individual value DA according to a preliminary estimate_EC；And using the sample age distribution data of different crowd as reference, to biological age value DA according to a preliminary estimate_ECMaximum a posteriori probability calculation processing is carried out, to determine the prediction biological age DA of the test individual.

Description

Method for determining biological index set of population sample and predicting biological age and application thereof

PRIORITY INFORMATION

Is free of

Technical Field

The invention relates to the technical field of biology, in particular to the technical field of biological age prediction, and more particularly relates to a method for determining a population sample biological index set and predicting biological age and application thereof.

Background

The biological age is related to the occurrence time of certain events in the growth and development of human body, and is deduced according to the development state of normal human physiology and anatomy, which indicates the actual state of the tissue structure and physiological function of human body.

The biological age is a comprehensive index of the health condition of the human body and is an objective expression of the aging degree of the organism. The age of a living being may not correspond to the actual age, and there are various methods and modes for its determination. The function of the cardiovascular system is strongly dependent on the age of the human body and reflects the health condition of the body. The biological age of the individual is calculated, so that the aging state of the body can be effectively evaluated, and the health condition of the human body can be known.

However, the calculation and evaluation of the Biological Age (BA) in the prior art are too simple, and the individual physical indicators are obtained only through simple biochemical indicators, psychological test reports and the like, so that the method is too coarse, and is not scientific and strict.

Thus, current methods for determining the biological age of an individual remain to be further investigated.

Disclosure of Invention

The present invention is directed to solving at least one of the problems of the prior art. It is therefore an object of the present invention at least to provide a means that enables the accurate determination of the biological age of an individual and the assessment of the level of ageing thereof.

It should be noted that the present invention has been completed based on the following findings of the inventors:

aging in humans is a complex and slow process. Along with the growth and development of people, different physiological states can be generated in different periods (age groups), the number or types of indexes which react or influence the biological age of an organism can be different, and the contribution degree to the biological age can be changed; for example, in the biological indicators of sex difference, there is a great difference between males and females in androgen, and after the age of 40 years, there is a great change in various physiological indicators such as sex hormone level. Therefore, screening effective biological indicators to evaluate the biological ages of different sexes is a difficult point and an innovative point faced by the current technology.

The biological age calculation model is established by the strategy, namely the strategy that the collinearity problem generated by multi-index fitting is eliminated by providing an index algorithm related to screening of the biological age, the death age distribution data of large-scale crowds are used, and the biological age is corrected and calculated to serve as the biological age of an individual.

Furthermore, the inventors have found that some current studies or protocols predict biological age using statistically unprocessed sets of indicators, compare only the biological age of an individual with chronological age, and finally determine abnormal aging or a young state of the body using only chronological ages greater or less than that of the individual, which is too simple and arbitrary. An effective aging baseline is therefore a difficult and innovative point for assessing aging.

Thus, further, the inventors tried to achieve the goal of guiding or intervening in aging by establishing a baseline model for aging assessment, filtering outlier samples, calculating a biological age confidence interval for the population, and finally assessing the relative status of the individual at the baseline of aging for the population.

Through a series of scientific design and accurate experiments, the inventor successfully screens biological indexes related to age or aging by collecting biological index data of large-scale samples, calculates and obtains individual biological ages, establishes and obtains aging baselines of different groups (gender groups and age groups), and finally achieves the purpose of accurately and quantitatively evaluating the aging level of individuals.

Thus, in a first aspect of the invention, the invention provides a method of determining a set of biological age-predictive biomarkers for a sample of a population. According to an embodiment of the invention, the method comprises the steps of: obtaining data of alternative biological indicators of all individuals in the population sample; grouping the alternative biological index data of all individuals in the crowd sample according to gender so as to obtain a male alternative biological index set and a female alternative biological index set; respectively carrying out variance expansion factor algorithm filtering on the male alternative biological index set and the female alternative biological index set so as to respectively obtain a male effective alternative biological index set and a female effective alternative biological index set; grouping the male effective candidate biological index set and the female effective candidate biological index set according to age groups respectively so as to obtain a plurality of male effective candidate biological index sets of different age groups and a plurality of female effective candidate biological index sets of different age groups; determining effective biological index sets of each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets respectively so as to obtain a plurality of different age groups of male effective biological index sets and a plurality of different age groups of female effective biological index sets, wherein for each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets, when the sample amount thereof is greater than the number of effective alternative biological indexes, the effective alternative biological indexes thereof are subjected to variance expansion factor algorithm filtering so as to determine effective biological index sets; when the sample size is smaller than the number of the effective alternative biological indexes, directly taking the set of the effective alternative biological indexes as an effective biological index set; and performing age-related filtering on the male effective biological index sets of the different age groups and the female effective biological index sets of the different age groups respectively to obtain male biological age prediction biological index sets of the different age groups and female biological age prediction biological index sets of the different age groups, wherein each biological age prediction biological index set classified according to the gender age is the biological age prediction biological index set of the crowd sample.

The inventor surprisingly finds that the biological age prediction biological index set of the crowd sample can be effectively determined by using the method, the biological ages of the individuals and the aging evaluation baseline of the crowd sample can be accurately determined by using the biological index sets, the relative aging degree of the individuals can be effectively evaluated based on the obtained biological ages of the individuals and the aging evaluation baseline of the crowd sample, and the evaluation result is good in accuracy and high in reliability.

In a second aspect of the invention, the invention provides a method of determining the biological age of an individual to be tested. According to an embodiment of the invention, the method comprises the steps of: according to the method for determining the biological age prediction biological index set of the crowd sample, determining each biological age prediction biological index set classified by gender and age of the crowd sample to which the individual to be detected belongs; based on a biological age prediction biological index set which is classified by gender and age and corresponds to the individual to be detected, calculating a biological age preliminary estimation value of the individual to be detected and sample age distribution data of different crowds as references, and performing maximum posterior probability calculation processing on the biological age preliminary estimation value so as to determine the predicted biological age BA of the individual to be detected.

The inventor surprisingly finds that the biological age of an individual can be accurately and effectively determined by using the method, and further, the relative aging degree of the individual can be effectively evaluated based on the aging evaluation baseline of the crowd sample, and the evaluation result is good in accuracy and high in reliability.

In a third aspect of the invention, a method of determining a baseline for aging assessment for a sample of a population is provided. According to an embodiment of the invention, the method comprises the steps of: according to the method for determining the biological age prediction biological index set of the crowd sample, determining each biological age prediction biological index set classified by gender and age of the crowd sample; performing re-filtering processing on each biological age prediction biological index set classified by gender and age, wherein when the sample amount of each biological age prediction biological index set classified by gender and age is larger than the number of biological age prediction biological indexes, removing the sample with the highest Euclidean Distance of 5 percent; when the sample size is smaller than the number of the biological age prediction biological indexes, removing the sample with the highest Mahalanobis Distance of 5 percent, and filtering the biological age prediction biological indexes by using a variance expansion factor algorithm; performing linear fitting on all biological age prediction biological indexes by using age CA, removing samples with Cook's Distance >1, and simultaneously removing the biological age prediction biological indexes with the relevance of less than 0.1 to screen out samples and a biological age prediction biological index set which meet the established baseline initial standard; based on the samples meeting the established baseline initial standard and the biological age prediction biological index set, calculating a biological age preliminary estimation value of each individual of the population samples by taking sample age distribution data of different populations as reference, and performing maximum posterior probability calculation processing on the biological age preliminary estimation value so as to determine a predicted biological age BA of each individual; performing linear fitting on the chronological age CA by using the predicted biological age BA of each individual, removing samples with the Cook's Distance >1, and repeating the steps until no sample with the Cook's Distance >1 exists, so as to screen out samples and biological age prediction biological index sets which meet the requirement of establishing a baseline; and calculating a 95% confidence interval of the biological age prediction of each sex age group based on the sample meeting the established baseline requirement and the biological age prediction biological index set, wherein the 95% confidence interval of the biological age prediction is the aging assessment baseline of each sex age group.

According to the embodiment of the invention, the aging evaluation baseline of the crowd sample can be effectively determined by using the method, so that the biological age of the individual to be detected is compared with the aging evaluation baseline of the gender age group, the relative aging degree of the individual to be detected can be effectively evaluated, and the evaluation result has good accuracy and high reliability.

In a fourth aspect of the invention, the invention provides a method of determining the relative degree of aging in a test subject. According to an embodiment of the invention, the method comprises the steps of: determining the biological age BA of the individual to be tested according to the method for determining the biological age of the individual to be tested; determining the aging evaluation baseline of each sex age group of the population sample of the individual to be tested according to the method for determining the aging evaluation baseline of the population sample; comparing the biological age BA of the individual to be detected with the aging evaluation baseline of the gender age group of the individual to be detected so as to determine the relative aging degree of the individual to be detected, wherein when the biological age BA of the individual to be detected is within the range of the aging evaluation baseline of the gender age group of the individual to be detected, the individual to be detected is judged to be at a normal aging level relative to the sample of the population; and when the biological age BA of the to-be-detected individual deviates from the range of the aging evaluation baseline of the sex age group of the to-be-detected individual, judging that the to-be-detected individual is in an abnormal aging level relative to the sample of the group of the to-be-detected individual, wherein when the biological age BA of the to-be-detected individual is higher than the upper limit of a confidence interval, the to-be-detected individual tends to be in an aging state relative to the sample of the group of the to-be-detected individual, and when the biological age BA of the to-be-detected individual is lower than the lower limit of the confidence interval, the.

The inventor surprisingly finds that the relative aging degree of the individual to be tested can be effectively evaluated by using the method, and the evaluation result is good in accuracy and high in reliability.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 shows a flow chart of a biological age assessment model according to an embodiment of the invention;

FIG. 2 shows a flowchart of an aging baseline estimation model according to an embodiment of the invention;

FIG. 3 is a graph showing the results of baseline distribution of aging for different ages BA and age groups of different sexes according to an embodiment of the present invention;

FIG. 4 shows the results of indicators associated with biological ages in the 20-25 year old group of women, according to an embodiment of the present invention;

FIG. 5 shows the results of indicators associated with biological ages in the age group of women 25-30 years old, according to an embodiment of the present invention;

FIG. 6 shows the results of indicators associated with biological ages in the age group of women 30-35 years old, according to an embodiment of the present invention;

FIG. 7 shows the results of indicators associated with biological age in a female age group of 35-40 years according to an embodiment of the invention;

FIG. 8 shows the results of indicators associated with biological ages in a female 40-100 years old age group, according to an embodiment of the present invention;

FIG. 9 shows the results of indicators associated with biological age in a male 20-25 year old age group, according to an embodiment of the present invention;

FIG. 10 shows the results of indicators associated with biological age in a male 25-30 year old age group, according to an embodiment of the present invention;

FIG. 11 shows the results of indicators associated with biological age in a age group of men between 30-35 years according to an embodiment of the invention;

FIG. 12 shows the results of indicators associated with biological age in a male age group of 35-40 years according to an embodiment of the invention;

FIG. 13 shows the results of indicators associated with biological age in a male 40-100 year old age group, according to an embodiment of the present invention;

FIG. 14 shows the results of measuring biological age and aging level of sample No. 167 according to example 2 of the present invention.

Detailed Description

The following describes embodiments of the present invention in detail. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

Method for determining biological age prediction biological index set of population sample

In the prior art, Biological Age (BA) is simply evaluated, individual physical indexes are obtained only through biochemical indexes, psychological test reports and the like, indexes which really affect the Biological Age are not filtered, and people are directly subjected to fitting calculation to obtain the so-called psychological Age, immune Age and the like, so that the method is too coarse. In addition, in the selection of the indexes, scientific and rigorous algorithms are not used for screening the indexes related to the biological age. In addition, the difference of contribution degree of each index to the biological age under different sexes and age groups is not considered; thus evaluating the biological indicators. These differential factors, to some extent, influence the calculation of biological age and the assessment of aging.

Therefore, the inventor firstly provides a more scientific and rigorous method for screening and determining a biological index set for biological age prediction of a population sample compared with the prior art. In particular, in a first aspect of the invention, the invention provides a method of determining a set of biological age-predictive biomarkers for a sample of a population. According to an embodiment of the invention, the method comprises the steps of: obtaining data of alternative biological indicators of all individuals in the population sample; grouping the alternative biological index data of all individuals in the crowd sample according to gender so as to obtain a male alternative biological index set and a female alternative biological index set; respectively carrying out variance expansion factor algorithm filtering on the male alternative biological index set and the female alternative biological index set so as to respectively obtain a male effective alternative biological index set and a female effective alternative biological index set; grouping the male effective candidate biological index set and the female effective candidate biological index set according to age groups respectively so as to obtain a plurality of male effective candidate biological index sets of different age groups and a plurality of female effective candidate biological index sets of different age groups; determining effective biological index sets of each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets respectively so as to obtain a plurality of different age groups of male effective biological index sets and a plurality of different age groups of female effective biological index sets, wherein for each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets, when the sample amount thereof is greater than the number of effective alternative biological indexes, the effective alternative biological indexes thereof are subjected to variance expansion factor algorithm filtering so as to determine effective biological index sets; when the sample size is smaller than the number of the effective alternative biological indexes, directly taking the set of the effective alternative biological indexes as an effective biological index set; and performing age-related filtering on the male effective biological index sets of the different age groups and the female effective biological index sets of the different age groups respectively to obtain male biological age prediction biological index sets of the different age groups and female biological age prediction biological index sets of the different age groups, wherein each biological age prediction biological index set classified according to the gender age is the biological age prediction biological index set of the crowd sample.

The inventor surprisingly finds that the biological age prediction biological index set of the crowd sample can be effectively determined by using the method, the biological ages of the individuals and the aging evaluation baselines of the crowd sample can be accurately determined by using the biological index sets, the relative aging degree of the individuals can be effectively evaluated based on the obtained biological ages of the individuals and the aging evaluation baselines of the crowd sample, and the obtained result is good in accuracy and high in reliability.

According to some preferred embodiments of the invention, the alternative biological indicator is at least one selected from the group consisting of longevity genes, mitochondrial DNA copy number, telomere length, overall methylation level, hormone level. It should be noted that the "alternative biological indicators" described herein may be any measurable characteristic biological indicator in the population, including all known and unknown aging-related indicators, and are not limited to the above-mentioned several indicators. In addition, early studies to determine the association with aging, like biochemical blood, may include future detection of features. The range of indices should be broad and the method of the invention (sometimes also referred to as a model) can discriminate between processing known and unknown indices and whether age or age is correlated to determine a set of indices that calculate a biological age.

According to an embodiment of the invention, the data of the biological indicators of all individuals in the population sample comply with a standard quality control.

According to an embodiment of the present invention, all the biomarker data in the male candidate set and the female candidate set are the same batch test, or meet the requirement of testing CV value between batches, or do not meet the requirement of testing CV value between batches, which has been modified by using LMM algorithm, and the sample size of each group should be larger than the number of its candidate biomarkers. Therefore, the obtained biological age prediction biological index set of the crowd sample is reliable, and the method is applied to biological age calculation, crowd sample aging evaluation baseline determination and final individual relative aging degree determination, and has good accuracy and high reliability.

According to an embodiment of the invention, the variance inflation factor algorithm filtering is performed by:

(1) obtaining a regression coefficient a based on the following formula according to multiple linear regression₀，a₁，a₂，...，a_m-1，a_m：

(2) According to the regression coefficient a₀，a₁，a₂，...，a_m-1，a_mThe sum of squared deviations q is found based on the following equation:

(3) and solving a complex correlation coefficient R according to the deviation square sum q based on the following formula:

wherein, and

(4) and according to the complex correlation coefficient R, solving the VIF corresponding to each alternative biological index or effective alternative biological index based on the following formula:

and

(5) and filtering each alternative biological index or effective alternative biological index by adopting two thresholds of VIF & gt 5 or VIF & gt 10, wherein when the VIF & gt 5 or VIF & gt 10, the alternative biological index or the effective alternative biological index is rejected if the corresponding biological age related index and other biological age related indexes have strong collinearity.

Therefore, the method is beneficial to the implementation of the subsequent steps, the obtained biological age prediction biological index set of the crowd sample is reliable, and the method is applied to the biological age calculation, the crowd sample aging evaluation baseline determination and the final individual relative aging degree determination, and has good accuracy and high reliability.

According to an embodiment of the invention, said grouping by age group is performed at predetermined age group intervals. In addition, it should be noted that the age group interval scale of the age group is not limited in theory, and may be selected in actual operation according to the sample size to be studied and the accuracy of the evaluation. According to some embodiments of the invention, the predetermined age interval of the group may be 5 years or 10 years. According to some specific examples of the invention, the predetermined age interval of the grouping is selected to be 5 years, in particular between 20 and 40 years, grouped every 5 years, while individuals older than 40 years are grouped because: on one hand, the overall change of the age of the individual of the human is not too large on the scale of 3-5 years, and on the other hand, the number of samples is limited, and the finer the age groups are, the fewer the samples in each group are; for over 40 years of age, it is also theoretically age-grouped, but in practical embodiments older samples are sampled less often; while samples under 20 years of age were too small to be ignored.

According to an embodiment of the present invention, the chronological age correlation filtering is performed using Pearson correlation analysis based on the following formula:

wherein y represents the age CA, x represents the detection value corresponding to the effective biological index,

according to the principle of maximally retaining the effective biological indexes, when r is less than 0.1, filtering the corresponding effective biological indexes.

Therefore, the biological age prediction biological index set of the screened crowd sample is reliable, and the method is applied to biological age calculation, crowd sample aging evaluation baseline determination and final individual relative aging degree determination, and has good accuracy and high reliability.

Applications of

Furthermore, the inventor selects an index algorithm related to biological age based on the method for determining the biological age prediction biological index set of the population sample, eliminates the problem of collinearity caused by multi-index fitting, uses large-scale population death age distribution data, corrects and calculates the biological age as the individual biological age, and the strategy is the biological age calculation model established by the invention.

Thus, in a second aspect of the invention, there is provided a method of determining the biological age of an individual to be tested. According to an embodiment of the invention, the method comprises the steps of: according to the method for determining the biological age prediction biological index set of the crowd sample, determining each biological age prediction biological index set classified by gender and age of the crowd sample to which the individual to be detected belongs; based on a biological age prediction biological index set which is classified by gender and age and corresponds to the individual to be detected, calculating a biological age preliminary estimation value of the individual to be detected and sample age distribution data of different crowds as references, and performing maximum posterior probability calculation processing on the biological age preliminary estimation value so as to determine the predicted biological age BA of the individual to be detected.

According to the embodiment of the invention, the preliminary estimation value of the biological age of the individual to be tested is calculated by a KD model method based on the following formula

Wherein, it is the value of the forecast BA, C is the age CA, the variance of the biological age forecast biological index set BMs, j is the biological age forecast biological index BM, m is the kind of the biological age forecast biological index set BMs, k is the index of the biological age forecast biological index set BMs_jPredicting the slope of the fit of the biological index BM to CA, q, for each biological age_jPredicting for each biological age the intercept, x, of the fit of the biological index BM to the chronological age CA_jThe value of the biological index BM is predicted for the jth biological age of sample x.

Therefore, the subsequent steps are facilitated, and the finally determined biological age is accurate and reliable.

According to an embodiment of the present invention, k is determined according to the following steps_j、q_jAnd s:

(1) calculating a regression coefficient k according to unary linear regression based on the following formula_j，q_j(j＝0，1，...，m-1)：

y＝kx+q，

Wherein

(2) According to the regression coefficient k_j，q_j(j ═ 0, 1.., m-1), the deviation sum of squares q' is calculated based on the following formula:

(3) from the sum of squared deviations q', the mean standard deviation s is calculated based on the following formula:

According to an embodiment of the invention, the predicted biological age BA is determined by performing a maximum a posteriori probability calculation based on the following formula:

wherein, in the first estimation of physiological age, mu_CThe actual age C, σ and σ₀Respectively, a likelihood function and a prior function standard deviation. Therefore, the finally determined biological age is accurate and reliable.

Furthermore, in the prior art, some studies or protocols simply calculate the biological age, simply comparing the biological age to the chronological age, greater or less than the chronological age, indicates abnormal aging, less stringent and no quantitative range baseline. An effective aging baseline is therefore a difficult and innovative point for assessing aging.

In yet a third aspect of the invention, a method of determining a baseline for aging assessment for a sample of a population is provided. The method is scientific and rigorous, and the determined aging evaluation baseline of the population sample has good reliability and high practical value.

Specifically, according to an embodiment of the present invention, the method comprises the steps of: according to the method for determining the biological age prediction biological index set of the crowd sample, determining each biological age prediction biological index set classified by gender and age of the crowd sample; performing re-filtering processing on each biological age prediction biological index set classified by gender and age, wherein when the sample amount of each biological age prediction biological index set classified by gender and age is larger than the number of biological age prediction biological indexes, removing the sample with the highest Euclidean Distance of 5 percent; when the sample size is smaller than the number of the biological age prediction biological indexes, removing the sample with the highest Mahalanobis Distance of 5 percent, and filtering the biological age prediction biological indexes by using a variance expansion factor algorithm; performing linear fitting on all biological age prediction biological indexes by using age CA, removing samples with Cook's Distance >1, and simultaneously removing the biological age prediction biological indexes with the relevance of less than 0.1 to screen out samples and a biological age prediction biological index set which meet the established baseline initial standard; based on the samples meeting the established baseline initial standard and the biological age prediction biological index set, calculating a biological age preliminary estimation value of each individual of the population samples by taking sample age distribution data of different populations as reference, and performing maximum posterior probability calculation processing on the biological age preliminary estimation value so as to determine a predicted biological age BA of each individual; performing linear fitting on the chronological age CA by using the predicted biological age BA of each individual, removing samples with the Cook's Distance >1, and repeating the steps until no sample with the Cook's Distance >1 exists, so as to screen out samples and biological age prediction biological index sets which meet the requirement of establishing a baseline; and calculating a 95% confidence interval of the biological age prediction of each sex age group based on the sample meeting the established baseline requirement and the biological age prediction biological index set, wherein the 95% confidence interval of the biological age prediction is the aging assessment baseline of each sex age group.

According to an embodiment of the present invention, the preliminary estimate of biological age of each individual was calculated by the KD model method based on the following formula

According to some specific examples of the present invention, k is determined according to the following steps_j、q_jAnd s:

y＝kx+q，

Wherein

wherein, in the first estimation of physiological age, mu_CThe actual age C, σ and σ₀The standard deviation of the likelihood function and the standard deviation of the prior function are respectively.

According to an embodiment of the present invention, screening the population sample based on the linear fitting result comprises: samples of Cook's Distance >1 were removed. Therefore, the screening effect is good, the finally determined aging evaluation baseline of the population sample is reliable, and the application value is high.

As mentioned above, the method can effectively determine the biological age of the individual to be detected and the aging evaluation baseline of the crowd sample, and further compare the biological age of the individual to be detected with the aging evaluation baseline of the gender age group of the individual to be detected, so that the relative aging degree of the individual to be detected can be effectively evaluated, and the evaluation result has good accuracy and high reliability.

Furthermore, in another aspect of the present invention, the present invention also provides apparatus, devices and systems adapted to implement the methods described above, for example, corresponding to the method of determining a set of biological age prediction biological indicators of a sample of a population of people of the present invention, an apparatus adapted to implement the method for determining a set of biological age prediction biological indicators of a sample of a population of people; corresponding to the method for determining the biological age of an individual to be tested according to the invention, a device suitable for implementing the method for determining the biological age of an individual to be tested; corresponding to the method of determining a baseline for aging assessment of a population sample of the present invention, an apparatus adapted to perform the method for determining a baseline for aging assessment of a population sample; a system for determining the relative degree of aging of an individual to be tested, adapted to carry out the method, corresponding to the method of determining the relative degree of aging of an individual to be tested of the invention. It is to be understood that the above-described devices, apparatuses and systems adapted for carrying out the methods each comprise a corresponding unit, apparatus or device adapted for carrying out the respective step.

Specifically, taking the method for determining the biological age of the individual to be tested of the present invention as an example, the corresponding device, i.e. the device for determining the biological age of the individual to be tested, comprises: the device is used for determining a biological age prediction biological index set of the crowd sample, and is used for determining each biological age prediction biological index set classified by gender and age of the crowd sample to which the individual to be detected belongs according to the method for determining the biological age prediction biological index set of the crowd sample; preliminary estimate value computational device of biology age, the preliminary estimate value computational device of biology age with be used for confirming the device of the biological age prediction biological indicator set of crowd's sample links to each other, is used for calculating based on the preliminary estimate value of biology age and the biological age prediction device of the individual that awaits measuring that corresponds with gender age classification's biological age prediction biological indicator set, the biological age prediction device with the preliminary estimate value computational device of biology age links to each other for sample age distribution data with different crowds is the reference, right the preliminary estimate value of biology age carries out maximum posterior probability computational processing, so that confirm the prediction biology age BA of the individual that awaits measuring.

The features and advantages of the methods of the present invention described above are also applicable to the apparatus for determining a biological age prediction biological index set of a population sample, the device for determining a biological age of a subject to be tested, the device for determining an aging assessment baseline of a population sample, and the system for determining a relative aging degree of a subject to be tested, which are corresponding to the above-mentioned features and advantages of the methods of the present invention, and will not be described in detail again.

According to the embodiment of the present invention, it should be further noted that the present invention has at least one of the following effects:

(1) the developed liquid biopsy technology (saliva and blood) can stably and efficiently detect biological indexes;

(2) based on data of a large population sample, a rigorous biological age calculation model is used, so that the biological age can be accurately calculated;

(3) based on data of a population large sample, aging assessment baselines of different genders and different age groups can be established, and relative aging states of individuals in the population can be assessed, so that recommended intervention measures for abnormal aging can be scientifically and normatively provided.

The scheme of the invention will be explained with reference to the examples. It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are carried out according to techniques or conditions described in literature in the art (for example, refer to molecular cloning, a laboratory Manual, third edition, scientific Press, written by J. SammBruke et al, Huang Petang et al) or according to product instructions. The reagents or apparatus used are not indicated by the manufacturer, but are conventional products available commercially, for example from Illumina.

The general method comprises the following steps:

the method of the invention for determining the relative degree of aging of a test subject generally comprises the steps of:

according to the method for determining the biological age prediction biological index set of the crowd sample, disclosed by the invention, each biological age prediction biological index set classified by gender and age of the crowd sample to which the individual to be detected belongs is determined;

calculating a preliminary estimation value of the biological age of the individual to be detected and a biological age prediction biological index set classified by gender and age corresponding to the individual to be detected

Taking sample age distribution data of different crowds as reference, and carrying out maximum posterior probability calculation processing on the preliminary biological age estimation value so as to determine the predicted biological age BA of the individual to be detected;

performing re-filtering processing on each biological age prediction biological index set classified by gender and age, wherein when the sample amount of each biological age prediction biological index set classified by gender and age is larger than the number of biological age prediction biological indexes, removing the sample with the highest Euclidean Distance of 5 percent; when the sample size is smaller than the number of the biological age prediction biological indexes, removing the sample with the highest Mahalanobis Distance of 5 percent, and filtering the biological age prediction biological indexes by using a variance expansion factor algorithm;

performing linear fitting on all biological age prediction biological indexes by using age CA, removing samples with Cook's Distance >1, and simultaneously removing the biological age prediction biological indexes with the relevance of less than 0.1 to screen out samples and a biological age prediction biological index set which meet the established baseline initial standard;

calculating an initial estimate of biological age for each individual of the population sample based on the sample satisfying the established baseline initial criteria and the set of biological age prediction biomarkers

Taking sample age distribution data of different populations as reference, and carrying out maximum posterior probability calculation processing on the preliminary biological age estimation value so as to determine the predicted biological age BA of each individual;

performing linear fitting on the chronological age CA by using the predicted biological age BA of each individual, removing samples with the Cook's Distance >1, and repeating the steps until no sample with the Cook's Distance >1 exists, so as to screen out samples and biological age prediction biological index sets which meet the requirement of establishing a baseline;

calculating 95% confidence intervals of biological age predictions of the age groups of each gender based on the samples meeting the established baseline requirements and the biological age prediction biological index set, wherein the 95% confidence intervals of the biological age predictions are aging assessment baselines of the age groups of each gender; and

comparing the biological age BA of the individual to be detected with the aging assessment baseline of the gender age group of the individual to be detected so as to determine the relative aging degree of the individual to be detected, wherein when the biological age BA of the individual to be detected is within the range of the aging assessment baseline of the gender age group of the individual to be detected, the individual to be detected is judged to be at a normal aging level relative to the sample of the population; and when the biological age BA of the to-be-detected individual deviates from the range of the aging evaluation baseline of the sex age group of the to-be-detected individual, judging that the to-be-detected individual is in an abnormal aging level relative to the sample of the group of the to-be-detected individual, wherein when the biological age BA of the to-be-detected individual is higher than the upper limit of a confidence interval, the to-be-detected individual tends to be in an aging state relative to the sample of the group of the to-be-detected individual, and when the biological age BA of the to-be-detected individual is lower than the lower limit of the confidence interval, the.

Example 1

In the embodiment, the detection methods of the aging-related indexes are respectively explained from the aspects of data acquisition technologies or processes; and elaborates models for assessing aging: the biological age calculation model and the aging evaluation baseline model comprise the following specific steps:

first, the contribution degree of longevity gene to biological age

The latest research progress of the longevity gene at home and abroad is integrated, and the longevity gene locus is used for calculating the genetic background risk evaluation index of the individual biological age. Therefore, a series of classical longevity genes which are researched and verified in Chinese population are selected from longevity gene databases. By combining large-scale genotyping data of Chinese Han people, SNP loci on a plurality of longevity genes (such as SIRT1, APOE, FOXO1,3, IL6, TOMM40 and APOC1) establish an OR-GRS risk assessment model through a Bayesian prior probability formula, calculate the risk contribution coefficient of each locus genotype to longevity and obtain an OR-GRS logistic regression expression. The longevity risk index for each individual is calculated using a regression equation.

Table 1-1 specific information on the function and related sites of longevity genes is as follows:

table 1-2 PCR primer amplification sequence information for long-lived gene associated sites is as follows:

a) the experimental process comprises the following steps:

DNA preparation

Preparing Blood DNA by referring to the DNA Blood Mini Kit instruction of a QIAGEN Blood DNA extraction Kit, and determining the concentration;

PCR amplification

PCR amplification was performed under the following conditions:

and (3) PCR system:

PCR procedure:

instruments and consumables: 9700, 96-well plate.

PCR product pooling.

4. The 10 PCR products from each sample were mixed together at 1:1, then mixed at 1:1 with 5 differently labeled PCR mixtures, and the corresponding sample for each mixed product was recorded.

post-Pooling PCR product purification

After each pooling, the samples were purified with 1.5 volumes of AmpureXP Beads and finally dissolved in 39.5. mu.l EB, following the specific procedure:

5.1 shaking and uniformly mixing the magnetic beads, and standing for 30min at room temperature;

5.2 adding magnetic beads with the volume 1-1.5 times of the sample volume, blowing, uniformly mixing (or sealing film and shaking), standing and adsorbing for 5-10min, placing a magnetic frame for adsorbing for 2-3min, and absorbing waste liquid;

5.3 adding 500ul 75% ethanol (adding 100ul of plate), sealing, washing for 10 times, placing magnetic frame for adsorbing for 2-3min, and removing waste liquid;

5.4 adding 500ul 75% ethanol (adding 100ul of plate), sealing, washing for 10 times, placing magnetic frame for adsorbing for 2-3min, and removing waste liquid;

drying at 5.537 deg.C for 3-5min (observing the magnetic beads to crack);

5.6 adding 30-50ul water or Elution Buffer for dissolving, shaking after 5000rpm, short centrifuging again, standing for 5min, and adsorbing for 2-3min by a magnetic frame.

5.7 transfer the supernatant to a new EP tube for storage.

6. Library construction

Taking 200ng of each purified pooling sample, and performing library building according to a library building process:

6.1 End repair (End-Repaired)

a) 10 XNEBNext ER Buffer and 10mM dNTPs mix were removed from the kit stored at-20 ℃ in advance, placed on ice for thawing and mixed well with 10 XNEBNext ER Buffer.

b) Prepare the end repair reaction system in a 1.5ml centrifuge tube:

DNA (purified product from 4)	10μL
10x NEBNext ER Buffer	7μL
T4 DNA Pol(3U/ul)	1.87μL
T4 PNK(10U/ul)	1.40μL
BSA(10mg/ml)	0.8μL
Water (W)	48.93μL
Total volume	70μL

c) After the above Mix was added, the mixture was shaken gently and homogenized, centrifuged briefly, and incubated at 20 ℃ for 30 min.

d) Purification was performed using 1.5 volumes of Axygen beads, which were redissolved in 40. mu.l TE and the procedure was as above.

6.2 adding "A" to the end (A-Tailing)

a) 10 XNEBuffer 2 and 100mM dATP were removed from the kit previously stored at-20 ℃ and placed on ice to thaw and mix well.

b) Prepare the end-addition "a" reaction system in a 1.5ml centrifuge tube:

H2O	11.45μL
10x NEBuffer2	6μL

100mM dATP	0.15μL
Klenow Exo-(5U/ul)	2.4μL
total volume	20μL

c) The mixture was incubated in a Thermomixer at 37 ℃ for 30 min.

d) Purification was performed using 1.5 volumes (75. mu.l) of Axygen beads, which were redissolved in 38. mu.l of TE, and the procedure was as above.

6.3 attachment of Adapter (Adapter Ligation)

a) 3 XHB buffer and T-tailed omega adapter153 were removed from the kit previously stored at-20 ℃ and placed on ice to thaw and mix well.

b) Prepare the Adapter ligation reaction in a 1.5ml centrifuge tube:

DNA	38μL
25μM T-tailedΩadapter153	2μL
H2O	1.24μL
3x HB buffer	25μL
8bp Index PE PCR-free Adapter oligo mix(50uM)	2μL
T4 DNA Ligase(600U/ul)	3.76μL
total volume	70μL

c) The mixture was incubated in a Thermomixer at 20 ℃ for 60 min.

d) The reaction was stopped with 35. mu.L EDTA. Then purified with 0.5 volumes of Axygen beads and redissolved in 25. mu.l TE, and the procedure was as above.

6.4 ligation product amplification (PCR)

Preparing PCR mixed solution for vortex about 5 minutes in advance, and uniformly mixing:

note: the Buffer needs to be taken out in advance and melted, and is shaken and uniformly mixed before use and centrifuged at low speed. After the reaction mixture is prepared, the mixture is placed at normal temperature.

If multiple sample reactions are to be prepared, the reaction mixture is prepared with a 10% loss.

To 80. mu.L of the PCR mixture, 20. mu.l of the purified adaptor ligation product was added and the pipette tip was pipetted 8 times to mix well.

The sample was placed in a PCR machine and subjected to a PCR reaction (note: the PCR machine requires a hot lid) in a reaction system of 100. mu.L, and the reacted sample was placed in a refrigerator at-20 ℃ if necessary for storage.

6.5 purification of the amplified sample (PCR purification)

The sample with AdA amplification reaction completed is transferred to a new 1.5ml non-stick tube, 100 μ L of mixed solution of XP/Tween20 is added, the mixed solution of magnetic beads and sample is blown and uniformly mixed by a gun head for 7-10 times, after being combined for 5 minutes at room temperature, the mixed solution is blown and uniformly mixed by the gun head for 7-10 times again, after being combined for 5 minutes at room temperature, the mixed solution is placed on a magnetic frame for 2 minutes (until the liquid is clear), and the supernatant is carefully discarded.

Adding 500 μ L of 75% ethanol into the non-stick tube on MPC, covering the tube cover tightly, turning upside down, mixing for 5 times, and discarding the supernatant; the 500 μ L75% ethanol was washed 1 time again, the residual ethanol was discarded as much as possible with a small range pipette, and the solution was dried at room temperature.

Resuspending the magnetic beads with 47. mu.L of TE/Tween, blowing and mixing with a gun head for 7-10 times, combining with room temperature for 5 minutes, placing on a magnetic frame, combining for 2 minutes (until the liquid is clear), carefully sucking out 45. mu.L of supernatant to a new 1.5mL EP tube, and preparing for the next reaction or storing in a refrigerator at-20 DEG C

6.6 quality control of amplified samples (PCR quality control)

3 μ L of PCR product was delivered to the library quality control group for Agilent 2100 detection.

7. Establishing a risk assessment OR-GRS model

Genetic Risk model assessment (Odds Ratio-Genetic Risk Scores, OR-GRS) model calculation based on Odds Ratio:

(1) calculating the OR weight Risk factor ω for each SNP

ω_ORi＝ln(ORi)

(2) Calculating the frequency of occurrence of the at-risk allele of each SNP G_iAnd GRS

(3) Sample conditional probability P (D ═ 1| G) at the time of occurrence of each SNP genotype was calculated_i)

For example: when i is 1, i.e. SNP1 genotype is GA,

(4) model building

Wherein G is_iIndicates the degree of genotype contribution (based on OR value, take G_i0, 1, 2), α is a constant term, β is a multiple regression coefficient of each locus, and ω is an OR weight risk factor.

(5) Calculating parameters of a model

(Notes:1.Signif.codes:0'***'0.001'**'0.01'*'0.05'.'0.1”1；2.Dispersion parameter for Gaussian family taken to be 0.01856851)

Computational expression of the contribution of longevity genotypes to biological age:

LogitP(D＝1|G)＝0.032-0.49*G1+7.96*G2+0.64*G3-1.72*G4-1.70*G5-0.19*G6-0.96*G7+1.90*G8+0.71*G9+0.86*G10

second, mitochondrial DNA copy number

Mitochondria are in central position in metabolism and bioenergy conversion. Mitochondrial DNA (mtdna) deletions or mutations can lead to abnormal oxidative phosphorylation and energy supply, defects in electron transport complexes or other mitochondrial-disorder-causing substances can cause excessive Reactive Oxygen Species (ROS) production, and ROS reacts with DNA, RNA, proteins or lipids to easily cause many pathological changes, which can lead to aging or the development of various diseases, even cancers. Thus, the instability of the mitochondrial genome reflects the level of senescence in the body to some extent, and studies have shown that mitochondrial DNA copies serve as biological indicators of senescence.

2.1 mitochondrial copy number detection-Mi/N method

1) Primer:

2) and (3) standard substance:

3) the detection method and the principle are as follows:

ct values of mitochondrial gene and reference gene (GAPDH gene) were measured by real-time fluorescent quantitative PCR. And respectively calculating the copy numbers of the mitochondrial gene and the internal reference gene according to the curve simulated by the standard substance. Finally, the ratio of the copy number of the mitochondria (Mi) to the single copy gene (N), namely the Mi/N ratio, is calculated to obtain the relative copy number of the mitochondria. The standard must be set in each on-machine reaction, so that the stability of different batches of experiments can be evaluated by the curve formed by the standard.

4) Reagent: KAPA SYBR Fast ABI5ml

5) The instrument comprises the following steps: StepOnePlus^TMReal-time fluorescent quantitative PCR system

6) The experimental operation flow comprises the following steps:

DNA preparation and dilution to 5-20 ng/. mu.L;

system preparation and Q-PCR processing machine

QPCR System (10. mu.L System):

reagent/sample	Dosage (Single reaction)
KAPASYBR FASTqPCR Kit Master Mix(2X)	5μL
Primer Mix(1P/μL)	1μL
DNA	2μL
Water (W)	2μL

7) Instruments and consumables: 9700, 96-well plate.

8) Typesetting: 5, repeating the reaction for 1 time by each pair of primers of the standard substance and the sample, namely 1 sample with 4 wells in total.

Third, telomere Length quantification

Telomere length detection-T/S method

1) Primer:

2) the detection method and the principle are as follows:

the real-time fluorescence quantitative PCR is carried out in two parts, and Ct values of telomeres and reference genes RPLPO (ribosome large subunit PO protein gene) are respectively measured. The ratio of the repeat copy number of the telomere (T) to the single copy gene (S), namely the T/S ratio, can obtain the relative length of the telomere, and the T/S ratio is in direct proportion to the length of the telomere.

The T/S calculation formula is as follows:

T/S＝[2^{CT(telomeres)}/2^{CT(single copy gene)}]＝2^-ΔCT。

after introduction of the standard, T/S is 2^-ΔΔCTThe numerical value of the standard telomere length is the comparison short message of the telomere length of the sample and the standard, the telomere length is smaller than that of the standard when the numerical value is smaller than 1, and the telomere length is larger than that of the standard when the numerical value is larger than 1. The stability between different batches of experiments can be evaluated by introducing multiple standards through the T/S value of the standard.

3) Reagent: KAPA SYBR Fast ABI5 ml.

4) The instrument comprises the following steps: StepOnePlus^TMReal-time fluorescent quantitative PCR system.

5) The experimental operation flow comprises the following steps: DNA preparation and dilution to 5-20 ng/. mu.L;

system preparation and Q-PCR processing machine

QPCR System (10. mu.L System):

reagent/sample	Dosage (Single reaction)
KAPASYBR FASTqPCR Kit Master Mix(2X)	5μL
Primer Mix(1P/μL)	2μL
DNA	2μL
Water (W)	1μL

6) Instruments and consumables: 9700, 96-well plate.

7) Typesetting: there were 2 control samples, each with duplicate wells for each primer pair, for a total of 4 wells.

Four, Total methylation level detection

4.1 DNA hydrolysis

a) Preparing a hydrolysis reaction solution: an amount of deionized water was taken and added to it 2 moles per liter Potassium Acetate (Potassium Acetate), 1 mole per liter Magnesium Acetate (Magnesium Acetate), 0.05 moles per liter Dithiothreitol (DTT), 1 mole per liter Tris-acetic acid (Tris-Acetate) at pH 7.9. The proportions of methyl acetate, magnesium acetate, dithiothreitol, Tris-acetic acid and deionized water are respectively 1:40, 1:100, 1:50 and 1: 50. Then, a hydrolysis reaction solution was prepared by adding 2U of Deoxyribonuclease I (DNase I), 2U of Lambda Exonuclease (Lambda Exonuclease) and 2U of Shrimp Alkaline Phosphatase (SAP) to 50. mu.l of the above solution.

b) Adding a certain amount of DNA into the hydrolysis reaction solution, and uniformly mixing. The ratio of DNA to the hydrolysis reaction solution was such that every 1ug of DNA was dissolved in 100ul of the hydrolysis reaction solution.

c) The mixed solution is incubated for 2h at 37 ℃.

d) Hydrolysis was complete and the hydrolysis products were checked by electrophoresis on a 1% agarose gel.

e) Storing the hydrolysate at-20 deg.C, and detecting with mass spectrometer.

4.2 liquid chromatography-mass spectrometry

a) And optimizing the mass spectrum condition. A Q-Trap4500 instrument is selected, and the parameters are set as follows:

ion source	Turbo V
Mode(s)	Positive ion mode
IS	4500
CUR	30
CAD	Medium
GS1	45
GS2	40
Tem	400

b) And (3) constructing compound parent ion/daughter ion pairs by taking a standard product of a single nucleotide compound as a sample:

c) and optimizing the liquid phase condition. ACQUITYI-Class apparatus, ACQUITYHSS T3 column (2.1X 100mm,1.7 u). A, B two reagents are configured as mobile phases. A: 0.1% formic acid (water soluble), B: 0.1% formic acid (methanol soluble). A. And B, mixing according to a gradient to prepare a mobile phase.

And (5) configuring a standard curve. mdC, hmdC, dG 5 gradient concentration points final concentration as follows. A, T, C, G, dA, U, dC, mC were kept constant against background and the final concentrations were as follows.

d) And (4) processing the standard curve, QC and the sample. And (3) loading 5ul of sample per needle, firstly repeating two needles of wash, then loading the labeled koji according to the sequence from std1 to std5, and loading QC and samples on a machine by one needle of wash (QC is inserted into the samples at intervals).

4.3 off-line data processing

MultiQuant (AB SCIEX) peak-integrating software was selected. Setting: algorithm MQ4, gaussian smoothing of 2 points. Quality control standard: S/N > 3; accuracy >80& < 120; CV < 15%; r2> 0.90.

The concentration calculated according to the quality control standard is exported, and the data stability and the DNA global methylation level are analyzed through a Perl script. The calculation formula is as follows:

fifth, hormone index quantification

5.1 saliva pretreatment

a) Sample preparation

1) Placing all samples on corresponding positions of an EP pipe frame in sequence according to the arranged sample table;

2) calculating according to the saliva sample to be detected, taking 2 tubes of deionized water to prepare a blank and a plurality of mixed saliva samples which are packaged in a split charging mode within the validity period, wherein each tube is 800 mu L and is used for preparing QC;

3) saliva samples were thawed and centrifuged, or 850ul of supernatant was removed and centrifuged for 25,000rcf 10 min.

4) Taking 800 mu L of centrifuged saliva sample, transferring to a corresponding EP tube on a No. 1 EP tube frame for standby, and temporarily storing the residual saliva sample-80 until a result is obtained;

5) one person is responsible for sampling samples, the other person is responsible for placing samples and rechecking, and inconsistent parts need to be marked in a sample table;

6) adding 400 mu L of ultrapure water and 10 mu L of isotope internal standard (ISTD, the internal standard is not added in double blank holes) into the obtained sample, QC and blank, and blowing and uniformly mixing by using a suction head;

7) the number of lining tubes corresponding to all samples, sample bottles with label paper attached, and bottle caps with new liners replaced were prepared.

b) SPE plate activation and equilibration

1) And (3) activation: open the extraction kit at normal temperature, take out the SPE plate, place in 2mL waste liquid collecting plate. Sequentially adding 1mL of acetonitrile and methanol into the holes to be used, and respectively passing through the column at normal temperature and normal pressure (or passing through the column at low pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second);

2) balancing: putting the SPE plate on a 2mL waste liquid collecting plate, adding 1mL of ultrapure water into a hole to be used, passing through a column at normal temperature and normal pressure (or passing through the column at low air pressure under a 96-hole positive pressure extraction device, and preferably at the liquid outflow speed of 1-2 drops/second), repeating for 2 times, and passing through 1mL of ultrapure water for 3 times in total;

c) sample loading and wash

1) Sample preparation and Loading: transferring the mixture to a corresponding hole of an SPE plate, passing through a column at normal temperature and normal pressure (or passing through the column at low air pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second), and giving high pressure to completely flow out the liquid after the liquid completely flows out; waste liquid is discarded; care was taken to maintain the wet state of the column.

2) Wash: adding 200 mu L of ultrapure water into each hole, passing through a column at normal temperature and normal pressure (or passing through a column at low pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second), and finally giving a large pressure of 1s to allow the liquid to completely flow out; the 96-well SPE plate was placed under a 96-well positive pressure extraction device and the SPE column was completely dried using high air pressure.

d) Extraction of

1)600uL ddH₂O: changing a 2mL waste liquid collecting plate below the completely dried SPE plate into a 2mL receiving plate 1, adding 600 μ L ultrapure water into each hole, and passing through a column at normal temperature and normal pressure (or passing through a column at low pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second); finally, giving a large pressure for 1s to ensure that the liquid completely flows out;

2)600uL ddH₂o Wash: changing a receiving plate 1 below an SPE plate into a 2mL waste liquid collecting plate, adding 600 mu L of ultrapure water into each hole, passing through a column at normal temperature and normal pressure (or passing through the column at low air pressure under a 96-hole positive pressure extraction device, preferably at the liquid outflow speed of 1-2 drops/second), and finally giving large pressure of 1s to allow the liquid to completely flow out, and then passing through the column at high air pressure to completely dry the SPE column;

3)600uLDCM (dichloromethane): changing a 2mL waste liquid collecting plate below a completely dried SPE plate into a 2mL receiving plate 2, adding 600 mu LDCM into each hole, and passing through a column at normal temperature and normal pressure (or passing through a column at low air pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second); finally, giving a large pressure for 1s to ensure that the liquid completely flows out; then, using high air pressure to pass through the column to completely dry the SPE column;

4)600uL of acetonitrile: changing the collecting plate 2 below the completely dried SPE plate into a collecting plate 3, adding 600 mu L acetonitrile into each hole, and passing through the column at normal temperature and normal pressure (or passing through the column at low pressure under a 96-hole positive pressure extraction device, preferably at a liquid outflow speed of 1-2 drops/second); finally, giving a large pressure for 1s to ensure that the liquid completely flows out; then, using high air pressure to pass through the column to completely dry the SPE column;

e) pumping out, re-dissolving

1) And (3) draining: transferring the extract liquor in the receiving plate to a new numbered 1.5mL centrifuge tube or directly in the collecting plate, and placing the centrifuge tube in a freezing vacuum concentrator for centrifugal pumping;

2) redissolving: after draining, 600uL ddH₂Adding 25ul ammonium citrate into the O solution; respectively adding 30uL of ammonium citrate complex solution into 600uLDCM and 600uL of acetonitrile, shaking for 5min on a vortex mixer, simply centrifuging, and mixing.

f) Combining liquids

1) A hydrophobic phase: centrifuging the mixture of DCM and acetonitrile for 10min at 25,000rcf, and placing 22ul in a sample injection bottle; waiting for the detection on the computer.

2) Hydrophilic phase: mixing 20 μ L DCM and acetonitrile, adding 15ul ddH2O, shaking for 1min on a vortex mixer, centrifuging for 10min at 25,000rcf, and placing 33 μ L into a sample injection bottle; waiting for the detection on the computer.

5.2 Mass spectrometric detection

a) Hydrophobic hormone detection

The saliva hydrophobic hormones tested included 8 species, namely 25OHV 3, DHEA, DHEAS, E1, E2, F, T, P

1) Selecting a QTRAP5500 mass spectrometer, replacing a chromatographic column Hydro-RP with the mass spectrometer of 150mm multiplied by 2mm and 4 mu m, and adding a protective column;

2) preparing mobile phase and needle washing liquid

3) Placing the sample in a sample feeding disc of an automatic sample feeder, and recording the position of the bottle

4) After the quality control of the instrument is confirmed to be qualified, the ion source is replaced by a Turbo Spray APCI source, hardware is activated, a hormone detection method is called, and the LC-MS/MS system is balanced for 20 min;

setting parameters:

5) continuously feeding three pins of STD, and starting to load the sample if the STD meets the quality standard and the MRM Intensity and column efficiency loading quality control standard in evaluation;

6) creation of batch and run samples

7) After the computer is operated, the mobile phase is changed into ddH₂O (phase A) and methanol (phase B), washing chromatographic column with high water phase (90% phase A) for 20min, washing with high organic phase (90% phase B) for 20min, and storing

8) And (3) data uploading: when the quality control data is normal, the quality control data and the sample data are uploaded to the mainframe

b) Hydrophilic parahormone detection

The hydrophilic hormones tested in saliva include 3, i.e. 8-OHdG, CML, Melatonin

1) Selecting QTRAP5500 mass spectrometer, replacing with chromatographic column C8(kinetexR 2.6 mu m C8100A), and adding protective column

2) Preparing mobile phase and needle washing liquid

4) After the quality control of the instrument is confirmed to be qualified, the ion source is changed into an ESI source, hardware is activated, a saliva hydrophilicity index detection method is called, and the LC-MS/MS system is balanced for 20 min;

setting parameters:

6) batch and run samples were created.

7) After the computer is operated, the mobile phase is changed into ddH₂O (phase A) and methanol (phase B), washing the column with high water phase (90% phase A) for 20min, washing with high organic phase (90% phase B) for 20min, and storing.

8) And (3) data uploading: when the quality control data is normal, the quality control data and the sample data are uploaded to the mainframe.

Sixthly, establishing an Aging Evaluation Model (AEM)

The aging assessment model is specifically divided into two sub-models: (a) firstly, establishing a Biological Age Calculation Model (BACM) to calculate the Biological Age of each individual; (b) secondly, an Aging Baseline Estimation Model (ABEM) is established to calculate the Aging Baseline range of the sample, and the relative Aging degree of each individual is evaluated.

(a) The biological age calculation model is implemented by the following steps:

6.1, firstly, obtaining detection data corresponding to a series of samples through experiments and sequencing, and judging whether the detection data meet a first-level quality control standard;

6.2 grouping the detected data meeting the standard quality control according to the gender and the age group according to the following principle:

1. some indexes have different effects on samples of different sexes;

2. the aging-related indicators do not change linearly throughout life;

3. the same age interval is used for the division of the age groups.

6.3 firstly grouping according to gender, performing quality inspection on data obtained in batches, judging whether each BMs in each group accords with the same batch detection, performing inter-batch CV calculation on the data obtained in different batches, and if the CV value does not meet the requirement, correcting the inter-batch data by using a Linear Mixed Model (LMM), wherein the use premise of the model requires that samples in the batches meet the LMM requirement, and the LMM algorithm can remove inter-batch errors. The specific method is to add a correction term to each point. The value of the correction term is the difference between the full sample fit and the fit within the batch for which the data point is located.

6.4 use samples and BMs that meet the same batch test, or meet the required CV values between batches, or are finally corrected by using the LMM algorithm, when the sample amount is more than the BMs number, carry out Variance Inflation Factor algorithm filtering (VIF), otherwise, prompt that the sample amount is too small and interrupt the analysis.

6.5 for each gender group, performing VIF filtering to obtain an effective gender group BMs set, namely BS, and adopting the following specific algorithm:

(1) according to Multiple Linear Regression (MLR), i.e.:

obtaining a regression coefficient a₀，a₁，a₂，...，a_m-1，a_m；

(2) From the regression coefficients, the sum of squared deviations q is found, namely:

(3) and (3) solving a complex correlation coefficient R according to the square sum of the deviations, namely:

wherein, and

(4) and solving the VIF corresponding to each BMS according to the complex correlation coefficient, namely:

(5) according to the characteristics of actual data, two thresholds of VIF & gt 5 or VIF & gt 10 are adopted for filtering, namely when VIF & gt 5 or VIF & gt 10, strong collinearity exists between the corresponding biorarker and other biorarkers, and the BM is removed.

6.6 age group division is carried out on the samples after the gender grouping respectively, and whether the sample amount in different age groups is more than the BMs number is judged. If yes, performing VIF filtering on the effective gender grouping BMs set by using the method; otherwise, the BMs set is grouped using the effective gender.

6.7 for each Age group, filtering of Chronological Age (CA) associations is continued, the specific algorithm being mainly Pearson correlation analysis, as follows:

wherein y represents CA and x represents the detection value corresponding to BMs.

The corresponding BMs are filtered when r <0.1, according to the principle of maximally preserving valid biomarkers.

6.8 for each age group, using filtered BMs of each age group to calculate a physiological age preliminary estimation value, wherein a core algorithm is a KD model, and the calculation steps are as follows:

wherein is the value of the predicted BA; c is age CA; variance of BMs; j is BM; m is the BMs type; k is a radical of_jA slope fitted to CA for each BM; q. q.s_jIntercept, x, fitted to CA for each BM_jIs the value of the jth BM for sample x.

(1) Calculating a regression coefficient k according to unary linear regression_j，q_j(j ═ 0, 1.., m-1), i.e.:

y＝kx+q

wherein

(2) From the regression coefficients, the sum of squared deviations q is calculated, i.e.:

(3) from the sum of the squared deviations, the mean standard deviation is calculated, i.e.:

6.9 after calculating the physiological age estimation value at 6.8, in order to more accurately calculate the biological age estimation value under different populations, the data of the sample age distribution of different populations are used as a reference population to carry out maximum posterior probability calculation (MAP), so that the overfitting condition can be eliminated; finally, the predicted biological age BA is obtained. The calculation method is as follows:

wherein, the physiological age preliminary estimation value is mu₀The actual age C, σ and σ₀Respectively, a likelihood function and a prior function standard deviation. BA is the final calculated biological age. Please refer to fig. 1 for a flowchart of a biological age calculation model.

(b) The baseline model for aging assessment was performed as follows:

6.11 sample data for the age group selected at step 6.7 in the biological age assessment model is used. Judging whether the number of samples is larger than the number of BMs in each age group; if yes, removing the sample with the highest Euclidean Distance (5%) and using alternative BMs under the gender grouping to perform subsequent calculation; and if not, removing the sample with the highest 5% of the Mahalanobis Distance (Mahalanobis Distance), filtering the BMs by using the VIF algorithm, and performing subsequent calculation by using the filtered biosearers.

6.12 fitting all BMs with CA, after removing samples of Cook's Distance >1, BMs with CA relevance <0.1 were removed at the same time. At this point, the samples meeting the established baseline are selected.

6.13 the same algorithm as step 6.8 is used to complete the calculation of the physiological age and calculate the standard deviation of the physiological age. Meanwhile, a death age database of a research sample population is obtained, and a priori physiological age standard deviation is calculated.

6.14 calculate the physiological age BA using the maximum a posteriori probability (MAP).

6.15 use BA to match CA, judge whether the sample is greater than the threshold set by Cook's Distance. If so, the sample is removed.

6.16 repeat step 6.15 until there are no more samples than the Cook's Distance threshold, then calculate 95% confidence intervals for physiological age predictions for different age groups. The biological age confidence interval is the calculated baseline range for aging assessment. Please refer to fig. 2 for a flowchart of the aging baseline estimation model.

Example 2

With reference to the "general method" and example 1, screening of biological indicators and evaluation of the relative degree of aging of test individuals were performed based on blood and urine samples of 294 normal individuals, specifically:

this example is intended to evaluate the physiological age and aging rate of a living body using a plurality of biological indicators. Specifically, the inventors collected blood and urine samples of 294 normal individuals, detected nearly 60 biological indicators, and successfully screened aging biological indicators based on the aforementioned "general method" of the present invention, screened biological indicators linearly related to age using the biological age algorithm of the aging assessment model of the present invention, and calculated biological ages using these indicators, rejected outlier samples using the aging assessment baseline algorithm, and established biological age baselines of different genders and different age groups for assessing the aging state of individuals.

The biological indexes selected in this embodiment cover aging-affecting factors such as genomic instability, telomere deletion, mitochondrial dysfunction, hormone metabolism, protein homeostasis, and cell senescence. The first biological indicators tested by technical development were methylation level (mcDNA-level), telomere length (MTL), mitochondrial copy number (mtCN), mitochondrial cumulative mutation (mtHP), 8-hydroxydeoxyguanosine (8-OHdG), Carboxymethyllysine (CML), Dehydroepiandrosterone (DHEA), Dehydroepiandrosterone Sulfate (DHEAs), 25 hydroxyvd 3(25OHVD3), hydrocortisone (F), estrone (E1), estradiol (E2), progesterone (P), testosterone (T). The method comprises the steps of simultaneously obtaining 39 biochemical indexes of a conventional physical examination, specifically, white blood cell count (WBC), lymphocyte ratio (LYMR), intermediate cell ratio (MID), neutrophil ratio (GRANR), total number of Lymphocytes (LYM), total number of intermediate cells (MTD), total number of neutrophils (GRAN), total number of Red Blood Cells (RBC), Hemoglobin (HGB), Hematocrit (HCT), mean volume of red blood cells (MCV), mean hemoglobin content (MCH), mean hemoglobin concentration (MCHC), red blood cell distribution width CV (RDW-CV), red blood cell distribution width SD (RDW-SD), total number of Platelets (PLT), Mean Platelet Volume (MPV), Platelet Distribution Width (PDW), platelet volume (PCT), large platelet ratio (P _ LCR), aspartate Aminotransferase (AST), alanine Aminotransferase (ALT), Gamma-glutamyl transpeptidase (GGT), Creatinine (creatine), Fasting Blood Glucose (FBG), Triglycerides (TRIG), Total Cholesterol (TCHO), Low Density Lipoprotein Cholesterol (LDLC), High Density Lipoprotein Cholesterol (HDLC), Albumin (Albumin), Globulin (Globulin), Albumin/Globulin ratio (a/G), Indirect Bilirubin (IBIL), Total Bilirubin (TBIL), alkaline phosphatase (ALP), Uric Acid (UA), Direct Bilirubin (DBIL), Total Protein (TP), Urea (Urea).

First, the contribution degree of longevity gene to biological age

Blood samples of 10 volunteers were randomly selected, the extracted DNA was subjected to second-generation sequencing, BGISEQ500SE100 sequencing was used, and genotyping results were obtained as shown in the following table. In addition, the longevity locus contribution degree OR-GRS model is calculated. Longevity risk score values were obtained for each individual from S1-S10.

Second, mitochondrial DNA copy number and mitochondrial DNA accumulation mutation

2.1 mitochondrial copy number

1. Data export and data analysis.

In this embodiment, a total of 5 samples are tested, and the copy number of the mitochondrial copy number Mi pore and the copy number of the single gene copy number N pore are given after each sample system is completed by using a Q-PCR program, and then the quotient of the average value of the composite pores Mi-1 and Mi-2 and the average copy number of the composite pores N-1 and N-2 is taken, which is the relative mitochondrial copy number of the sample.

2. Data collation (table K above is listed as Quantity Mean value, i.e. average copy number of two duplicate wells):

TABLE 2-1

3. Mitochondrial relative copy number calculation

Tables 2 to 2

Third, telomere Length quantification

In this example, two standards and eight samples were detected in total, and CT values of Telomere (TEL) and reference gene RPLPO (ribosomal large subunit PO protein gene) were measured by real-time fluorescence quantitative PCR, respectively. And exporting the data of the detection instrument, and sorting and calculating the average CT value corresponding to each primer of each sample.

Firstly, calculating the ratio of the repeated copy number of the telomere (T) to the single copy gene (S), namely the T/S ratio can obtain the relative length of the telomere, and the T/S ratio is in direct proportion to the length of the telomere. The T/S calculation formula is as follows:

T/S＝[2^{CT(telomeres)}/2^{CT(single copy gene)}]＝2^-ΔCT。

after introduction of the standard, T/S is 2^-ΔΔCTThe numerical value of the standard telomere length is the comparison short message of the telomere length of the sample and the standard, the telomere length is smaller than that of the standard when the numerical value is smaller than 1, and the telomere length is larger than that of the standard when the numerical value is larger than 1. The stability between different batches of experiments can be evaluated by introducing multiple standards through the T/S value of the standard. And finishing the quantitative detection of the telomere length.

Telomere length quantification data are in the following table.

TABLE 3-1

Tetra, Total methylation level

In this embodiment, on one hand, the stability of the method is determined, and on the other hand, batch testing is performed.

(1) Multiple replicates were performed using the same sample to determine the stability of the assay. The QC samples were used for 6 replicates, and the concentrations of mononucleotide compounds (a, T, C, G, dA, U, dC, mC, mdC, dG, hmdC) obtained according to standard quality control procedures were analyzed for stability using mdC and dG concentration data, and the global methylation level for each sample was calculated from the concentrations of mdC and dG. The CV values calculated in 6 repeated experiments are all less than 15%, and the stability is good. The specific data are shown in the table below.

TABLE 4-1

(2) In addition, the batch test shows that the total methylation level stability of 12 saliva samples is better. There were 2 replicates per sample with CV values less than 15%. The specific data are as follows

TABLE 4-2

Sample (I)	1	2	3	4	5	6	7	8	9	10	11	12
Repetition of 1	0.902	0.882	1.109	0.807	0.837	1.233	1.426	1.270	1.276	1.436	1.408	1.281
Repetition 2	0.960	0.882	1.212	0.766	0.852	1.176	1.262	1.316	1.257	1.452	1.242	1.367
CV％	4.444	0.035	6.283	3.695	1.237	3.357	8.614	2.502	1.107	0.820	8.839	4.561

Fifth, hormone index quantification

5.1 MultiQuant (AB SCIEX) peak-integrating software is selected. Quality control standard: S/N > 3; accuracy >80& < 120; CV < 15%; r2> 0.90.

5.2 the concentrations calculated according to the quality control standards were derived, the stability of the data was analyzed by Perl script, and 34 samples of each hormone data were tested in batches, as shown in Table 5-1 below.

TABLE 5-1

Sixthly, calculating an aging evaluation model

6.1, first, 294 samples are detected, and 58 biological index data which accord with a first-level quality control standard are obtained;

6.2 samples 294 were grouped by age, 154 and 140 for male and female, respectively.

6.3 we first normalize each biological index data to eliminate batch-to-batch errors in the sample sets of both gender groups. (1) And (4) not processing the detection data in the same batch. (2) And (3) performing CV calculation among different batches on the obtained data, and correcting the biological index data among the batches by using a Linear Mixed Model (LMM) when the CV value does not meet < 15%.

And 6.4, carrying out number statistics by using the biological indexes meeting the treatment of 6.3, analyzing the relationship between the sample volumes of the male group and the female group and the BM number, wherein the sample volumes of the two groups are larger than that of the BM data, and filtering the biological indexes by using a VIF algorithm to respectively obtain effective alternative BM sets of the male group and the female group.

6.6 the age group is divided into two stages, the first stage is between 20 and 40 years old, and every five years is used as an interval of age groups; the second stage is individuals older than 40 years. The two gender groups were divided into 5 age groups. If the number of samples in the age group is less than the BM number, filtering is not performed; and filtering the age group with the sample number larger than the BM number by using a VIF algorithm to obtain an effective alternative BM set for biological age calculation.

6.7 biological age assessment was performed for each age group using the valid alternative BM set. The BACM model is used to calculate the relative biological age BA of each individual in the belonged gender group and the age group, the calculated BA is fitted with CA, and the calculated BA and CA are calculated to obtain that R ^2 is more than 0.6 and even reaches 0.98, and the significance is P <1e-4, as shown in figure 3, the solid line in the biological age BA of different genders and the aging baseline distribution graph of different age groups represents the fitting result of the biological age BA and the age CA.

6.8 correlation results of biological indicators and biological ages show that in different age groups, women in the 20-25 years old age group, indicators correlated with biological ages are TRIG, Lymph, HDL, PCT, BMI, Globulin, mtCN, UA, RDW-CV, Urea, MTL, Albumin, Alkphosph, FBG, P, T, PLCR, AST, GGT, E1, MCHC, Height, CML, DHEA, RDW-SD, Meldonin, mcDNA-level, 25OHVD3, LDL, etc.; FIG. 4 is an indicator associated with biological age in the 20-25 year old population of women; in the female 25-29 years old age group, the biological age-related indices are combined with LYM, LYMR, RDW-CV, LDL, UA, TRIG, BMI, CML, F, mt-CN, MCHC, MSIZODRATE, Creatinine, Meldonin, 8-OHdG, HDL, PLCR, T, mcDNA-level, TBIL, E1, MCV, Height, P, FBG, Alkphosph, RBC, GGT, DHEAS, AST, PCT, etc., as shown in FIG. 5 female 25-30 years old age group; in the age group of 30-35 years of female, the index associated with the biological age is combined with MCV, RDW-CV, PCT, RDW-SD, Globulin, msiederate, UA, DHEA, Urea, MCHC, LYMR, Albumin, DHEAS, TRIG, LYM, LDL, 8-OhdG, P, TBIL, Height, mcDNA-level, F, AST, BMI, HDL, mtCN, GGT, etc., as shown in the index associated with the biological age in the age group of 30-35 years of female in FIG. 6; in women 35-40 years of age, the biological age-associated indices are combined with RBC, Height, MCV, P, FBG, E1, Albumin, Urea, DHEA, 8-DHdG, MCHC, Creatinine, 25OHV 3, DHEAS, MTL, Melatonin, TRIG, LYMR, T, Globulin, Alkphosph, RDW-SD, HDL, LYM, GGT, LDL, F, PCT, PLCR, CML, BMI, etc., see FIG. 7 for biological age-associated indices in women 35-40 years of age; in women older than 40 years, the indicators associated with biological age are DHEA, P, Urea, MCHC, UA, msidizedrate, FBG, creatine, TRIG, LDL, MCV, TBIL, HDL, mtCN, LYMR, BMI, Height, RBC, LYM, RDW-SD, PLCR, DHEAs, Albumin, E1, Melatonin, F, Globulin, etc., see fig. 8 for the indicators associated with biological age in women older than 40-100 years; in summary, the types of the biological age-related indicators are different in different age groups of women, and the contribution degree of the same indicator to the biological age is also different in different age groups.

In addition, in the 20-24 years old group of men, the biological age-related indicators are combined with BMI, PDW, Height, PLCR, LDL, Melatonin, MTL, TRIG, DHEAS, mtCN, AST, PCT, CML, RDW-CV, P, Hemoglobin, WBC, UA, FBG, 25OHV 3, RDW-SD, GGT, MCHC, Albumin, LYMR, etc., as detailed in FIG. 9 for the biological age-related indicators in the 20-25 years old group of men; in the age group of 25-29 years of men, the biological age-related indicators are 8-OHdG, Albumin, GGT, Globulin, PCT, AST, LDL, TRIG, BMI, E2, Hemoglobin, HDL, RDW-SD, Melanonin, UA, DHEAS, MTL, IBIL, T, CML, Alkphosph, Creatinine, LYMR, 25OHV 3, DHEA, etc., as shown in FIG. 10 for the biological age-related indicators in the age group of 25-30 years of men; in the age group of men 30-34 years old, the index associated with the biological age is RDW-SD, WBC, Hemoglobin, Height, Creatinine, RDW-CV, CML, FBG, LYMR, Urea, Albumin, Alkphosph, F, Globulin, UA, BMI, 8-OhdG, LDL, P, mtCN, mcDNA-level, MTL, TRIG, GGT, etc., and the index associated with the biological age in the age group of men 30-35 years old is shown in FIG. 11; in men 35-39 years of age, GGT, PCT, IBIL, FBG, AST, UA, TRIG, BMI, HDL, mtCN, Albumin, LDL, LYMR, MCHC, E1, F, Globulin, alkphosh, Height, Melatonin, Hemoglobin, DHEA, PDW, creatine, PLCR, T, MTL, P, CML, DHEA, mcda-level, etc., in combination with the biological age index, see fig. 12 for biological age-related indices in men 35-40 years of age;

in men over 40 years of age, the biological age-related indicators are Albumin, T, IBIL, Height, MCHC, GGT, LDL, UA, MTL, BMI, Alkphosph, F, PCT, mtCN, Meldonin, Hemoglobin, PWD, PLCR, AST, Creatine, Globulin, DHEA, 8-OhdG, E1, FBG, etc., as shown in FIG. 13 for the biological age-related indicators in men over 40-100 years of age. In summary, the types of the biological age-related indicators are different in different age groups of men, and the contribution degree of the same indicator to the biological age is also different in different age groups.

The comparison and analysis of the indexes related to the biological ages of the two groups of males and females show that the types of the indexes related to the biological ages of the males and the females in different ages are greatly different, and the contribution degrees of the different indexes to the aging are different in different ages. The biological age-related indexes are the screened aging indexes, and the results also prove that the strategy of the aging assessment model is feasible, and effectively indicate that the biological age-related indexes are different along with the change of gender and age.

6.9 Prior to baseline calculation for aging assessment, first, sample data for the age group selected at step 6.7 in the biological age calculation model is used. Judging whether the number of samples in each age group is larger than that of the BMs, and removing the samples with the highest Euclidean Distance (Euclidean Distance) of 5 percent, thereby obtaining an effective alternative BM set under the gender grouping; when the number of samples is less than the BM number, the samples with the highest Mahalanobis Distance (Mahalanobis Distance) of 5% are removed, and the BM are filtered using the above-mentioned VIF algorithm.

6.10 fitting all BMs with CA, after removing samples of Cook's Distance >1, BMs with CA relevance <0.1 were removed at the same time. At this point, the samples meeting the established baseline are selected.

6.11 calculate biological age using the biological age calculation model and calculate sample biological age standard deviation. Meanwhile, death age distribution data of the people with the Chinese statistical yearbook are obtained, and the prior biological age standard deviation is calculated.

6.12 calculate the physiological age BA using the maximum a posteriori probability (MAP).

6.13 use BA to fit CA, judge whether the sample is greater than the threshold set by Cook's Distance. If so, the sample is removed.

6.14 repeat step 6.13 until there are no more samples than the Cook's Distance threshold, then calculate 95% confidence intervals for physiological age predictions for different age groups. The biological age confidence interval is the calculated baseline range for aging assessment. When an individual develops abnormal aging, it appears outside the aging baseline range, an outlier deviating from the baseline in fig. 3. The biological age tends to an aging state when the individual is above the upper confidence interval and the physiological age tends to a young state when the individual is below the lower confidence interval.

Seventhly, calculating the biological age of the individual and evaluating the aging state

Taking 167 st male as an example of a large sample, the age of the male is 38.13 years according to the above steps, and the content or score of each biological index is obtained as shown in the following table. BA calculated using KD algorithm calculated using biological age calculation model was 38.01 years old, within 95% confidence interval; the final BA was calculated using the MAP algorithm to be 37.51 years old, within a 95% confidence interval. Baseline model of aging assessment was used to establish baseline range observations of aging in male age groups 35-40 years, which did not deviate from the overall range and were in better physiological health (see the sample labeled d in figure 3, and figure 14). This means that the biological age of the sample is within the normal range, the condition of the body is good, and the aging level is within the normal range in the aging baseline of the population.

Sample number 167 biological indicator content or score

Industrial applicability

The method for determining the biological age prediction biological index set of the crowd sample can be effectively used for determining the biological age prediction biological index set of the crowd sample, further the biological ages of the individuals and the aging evaluation baseline of the crowd sample can be accurately determined by utilizing the biological index sets, further, the relative aging degree of the individuals can be effectively evaluated based on the obtained biological ages of the individuals and the aging evaluation baseline of the crowd sample, and the evaluation result is good in accuracy and high in reliability.

Although specific embodiments of the invention have been described in detail, those skilled in the art will appreciate. Various modifications and substitutions of those details may be made in light of the overall teachings of the disclosure, and such changes are intended to be within the scope of the present invention. The full scope of the invention is given by the appended claims and any equivalents thereof.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

A method of determining a set of biological age predictive biological indicators for a sample of a population, comprising the steps of:

obtaining data of alternative biological indicators of all individuals in the population sample;

grouping the alternative biological index data of all individuals in the crowd sample according to gender so as to obtain a male alternative biological index set and a female alternative biological index set;

respectively carrying out variance expansion factor algorithm filtering on the male alternative biological index set and the female alternative biological index set so as to respectively obtain a male effective alternative biological index set and a female effective alternative biological index set;

grouping the male effective candidate biological index set and the female effective candidate biological index set according to age groups respectively so as to obtain a plurality of male effective candidate biological index sets of different age groups and a plurality of female effective candidate biological index sets of different age groups;

determining effective biological index sets of each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets respectively so as to obtain a plurality of different age groups of male effective biological index sets and a plurality of different age groups of female effective biological index sets, wherein for each of the plurality of different age groups of male effective alternative biological index sets and the plurality of different age groups of female effective alternative biological index sets, when the sample amount thereof is greater than the number of effective alternative biological indexes, the effective alternative biological indexes thereof are subjected to variance expansion factor algorithm filtering so as to determine effective biological index sets; when the sample size is smaller than the number of the effective alternative biological indexes, directly taking the set of the effective alternative biological indexes as an effective biological index set; and

and respectively carrying out age-related filtering on the male effective biological index sets of the different age groups and the female effective biological index sets of the different age groups so as to obtain male biological age prediction biological index sets of the different age groups and female biological age prediction biological index sets of the different age groups, wherein each biological age prediction biological index set classified according to the gender age is the biological age prediction biological index set of the crowd sample.
The method of claim 1, wherein the alternative biomarker is at least one selected from the group consisting of longevity genes, mitochondrial DNA copy number, telomere length, overall methylation level, and hormone level.
The method of claim 1, wherein the data for the biological indicators for all individuals in the population sample meets a standard quality control.
The method of claim 1, wherein all the biomarker data in the male candidate set and the female candidate set are for the same batch test, or meet the requirement for a test CV value between batches, or do not meet the requirement for a test CV value between batches and have been modified using an LMM algorithm, and wherein the sample size for each group should be greater than the number of its candidate biomarkers.
The method of claim 1, wherein the variance inflation factor algorithm filtering is performed by:

(1) obtaining a regression coefficient a based on the following formula according to multiple linear regression₀，a₁，a₂，...，a_m-1，a_m：

(2) According to the regression coefficient a₀，a₁，a₂，...，a_m-1，a_mThe sum of squared deviations q is found based on the following equation:

(3) and solving a complex correlation coefficient R according to the deviation square sum q based on the following formula:

wherein, and

(4) and according to the complex correlation coefficient R, solving the VIF corresponding to each alternative biological index or effective alternative biological index based on the following formula:

and

(5) and filtering each alternative biological index or effective alternative biological index by adopting two thresholds of VIF & gt 5 or VIF & gt 10, wherein when the VIF & gt 5 or VIF & gt 10, the alternative biological index or the effective alternative biological index is rejected if the corresponding biological age related index and other biological age related indexes have strong collinearity.
The method of claim 1, wherein said grouping by age group is at predetermined age group intervals.
The method of claim 1, wherein the chronological age correlation filtering is performed using Pearson correlation analysis based on the following formula:

wherein y represents the age CA, x represents the detection value corresponding to the effective biological index,

according to the principle of maximally retaining the effective biological indexes, when r is less than 0.1, filtering the corresponding effective biological indexes.
A method of determining the biological age of an individual to be tested, comprising the steps of:

the method according to any one of claims 1 to 7, wherein a set of biological age predictive biological indicators classified by gender and age is determined for a sample of a population to which the subject to be tested belongs;

calculating a preliminary biological age estimation value BA of the individual to be detected based on a biological age prediction biological index set which is corresponding to the individual to be detected and classified by gender and age_SC(ii) a And

taking sample age distribution data of different populations as reference, and performing initial estimation on the biological age BA_SCPerforming maximum posterior probability calculation processing to determine the predicted biological age BA of the tested individual.
The method according to claim 8, wherein the BA as the preliminary estimate of the biological age of the test subject is calculated by KD model method based on the following formula_SC：

Wherein, BA_SCIs the value of the forecast BA, C is the age CA, the variance of the biological age forecast biological index set BMs, j is the biological age forecast biological index BM, m is the kind of the biological age forecast biological index set BMs, k_jFor each living beingSlope of the Association of the academic age predictor biological indicators BM to CA, q_jPredicting for each biological age the intercept, x, of the fit of the biological index BM to the chronological age CA_jThe value of the biological index BM is predicted for the jth biological age of sample x.
The method of claim 8, wherein k is determined according to the following steps_j、q_jAnd s:

(1) calculating a regression coefficient k according to unary linear regression based on the following formula_j，q_j(j＝0，1，...，m-1)：

y＝kx+q，

Wherein

(2) According to the regression coefficient k_j，q_j(j ═ 0, 1.., m-1), the deviation sum of squares q' is calculated based on the following formula:

(3) from the sum of squared deviations q', the mean standard deviation s is calculated based on the following formula:
the method according to claim 9, wherein the predicted biological age BA is determined by performing a maximum a posteriori probability calculation based on the following formula:

wherein, BA_SCFor preliminary estimation of physiological age, μ₀The actual age C, σ and σ₀Respectively, a likelihood function and a prior function standard deviation.
A method of determining a baseline for aging assessment for a sample of a population, comprising the steps of:

the method of any one of claims 1-7, determining a set of biological age predictive biomarkers for each of a sample of the population classified by gender and age;

performing re-filtering processing on each biological age prediction biological index set classified by gender and age, wherein when the sample amount of each biological age prediction biological index set classified by gender and age is larger than the number of biological age prediction biological indexes, removing the sample with the highest Euclidean Distance of 5 percent; when the sample size is smaller than the number of the biological age prediction biological indexes, removing the sample with the highest Mahalanobis Distance of 5 percent, and filtering the biological age prediction biological indexes by using a variance expansion factor algorithm;

performing linear fitting on all biological age prediction biological indexes by using age CA, removing samples with Cook's Distance >1, and simultaneously removing the biological age prediction biological indexes with the relevance of less than 0.1 to screen out samples and a biological age prediction biological index set which meet the established baseline initial standard;

calculating a preliminary estimate of biological age BA for each individual of the population sample based on the sample satisfying the established baseline initial criteria and the set of biological age prediction biomarkers_SC；

Taking sample age distribution data of different populations as reference, and performing initial estimation on the biological age BA_SCPerforming a maximum posterior probability calculation process to determine a predicted biological age BA for each individual;

performing linear fitting on the chronological age CA by using the predicted biological age BA of each individual, removing samples with the Cook's Distance >1, and repeating the steps until no sample with the Cook's Distance >1 exists, so as to screen out samples and biological age prediction biological index sets which meet the requirement of establishing a baseline; and

calculating 95% confidence intervals of biological age predictions of the age groups of each gender based on the samples meeting the established baseline requirements and the biological age prediction biological index set, wherein the 95% confidence intervals of the biological age predictions are aging assessment baselines of the age groups of each gender.
The method according to claim 12, wherein the preliminary estimate BA of biological age of each individual is calculated by KD model method based on the following formula_SC：

Wherein, BA_SCIs the value of the forecast BA, C is the age CA, is the variance of the biological age forecast biological index set BMs, j is the biological age forecast biological index BM,m is the category of biological age prediction biological index set BMs, k_jPredicting the slope of the fit of the biological index BM to CA, q, for each biological age_jPredicting for each biological age the intercept, x, of the fit of the biological index BM to the chronological age CA_jThe value of the biological index BM is predicted for the jth biological age of sample x.
The method of claim 13, wherein k is determined according to the following steps_j、q_jAnd s:

(1) calculating a regression coefficient k according to unary linear regression based on the following formula_j，q_j(j＝0，1，...，m-1)：

y＝kx+q，

Wherein

(2) According to the regression coefficient k_j，q_j(j ═ 0, 1.., m-1), the deviation sum of squares q' is calculated based on the following formula:

(3) from the sum of squared deviations q', the mean standard deviation s is calculated based on the following formula:
the method according to claim 12, wherein the predicted biological age BA is determined by performing a maximum a posteriori probability calculation based on the following formula:

wherein, BA_SCFor preliminary estimation of physiological age, μ₀The actual age C, σ and σ₀The standard deviation of the likelihood function and the standard deviation of the prior function are respectively.
The method of claim 12, wherein screening the population sample based on the linear fit comprises: samples of Cook's Distance >1 were removed.
A method of determining the relative degree of aging of an individual to be tested, comprising the steps of:

determining the biological age BA of the test subject according to the method of any one of claims 8-11;

the method according to any one of claims 12-16, determining aging assessment baselines for each gender age group of a sample of a population to which the test individual belongs; and

comparing the biological age BA of the test individual with the baseline for aging assessment of the gender age group in which the test individual is located to determine the relative degree of aging of the test individual,

wherein the content of the first and second substances,

when the biological age BA of the individual to be detected is within the range of the aging evaluation baseline of the sex age group of the individual to be detected, judging that the individual to be detected is in a normal aging level relative to the sample of the population to which the individual to be detected belongs;

and when the biological age BA of the to-be-detected individual deviates from the range of the aging evaluation baseline of the sex age group of the to-be-detected individual, judging that the to-be-detected individual is in an abnormal aging level relative to the sample of the group of the to-be-detected individual, wherein when the biological age BA of the to-be-detected individual is higher than the upper limit of a confidence interval, the to-be-detected individual tends to be in an aging state relative to the sample of the group of the to-be-detected individual, and when the biological age BA of the to-be-detected individual is lower than the lower limit of the confidence interval, the.