A new ecosystem for managing healthy aging

Abstract

none The present invention relates to a method for calculating biological age in a subject and across multiple subjects by performing polygenic DNA methylation on biomarkers, including measuring the methylation status of 13 CG sites located in the ElovL2AS1 region, a putative antisense region to the ElovL2 gene. Furthermore, the present invention provides a computer-implemented method for providing lifestyle change recommendations in the form of a self-learning "ecosystem" for lifestyle management of biological aging, using novel measurements of the DNA methylation clock as a continuous, dynamic outcome. The present invention also relates to a combination of DNA methylation biomarkers for calculating biological age and a kit for determining biological age. Furthermore, the present invention discloses the use of the disclosed method for calculating biological age in methods for assessing the effectiveness of biological interventions and screening for anti-aging agents.

Description

[Technical field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims priority pursuant to 35 U.S.C. §119(e) to provisional application Ser. No. 62 / 854,226, filed May 29, 2019, entitled “EPI-AGING: A NOVEL ECOSYSTEM FOR MANAGING HEALTHY AGING,” the contents of each of which are incorporated herein by reference. [Sequence table] This application contains a Sequence Listing that has been filed electronically in ASCII format and is incorporated herein by reference in its entirety. Said ASCII copy, created on May 28, 2020, is named TPC57505_Seq List_ST25.txt and is 4,096 bytes in size.

[0002] The present invention relates generally to epigenetics and DNA methylation signatures in human DNA, in particular to methods of determining epigenetic aging in a person and managing healthy aging based on DNA methylation signatures. More specifically, the present invention provides methods involving DNA methylation signatures for molecular diagnostics, health management, and lifestyle modification of personalized healthy aging using digital technology. [Background technology]

[0003] While chronological age is understood as the number of years a person has lived, biological age, also called physiological age, indicates how old an individual is. It is difficult to determine an individual's biological age because people age at different rates. Some people "look" and "feel" older than their chronological age, while others look younger than their chronological age. Overall human chronological age correlates with biological age, but this is not always the case. Biological age is a better parameter of an individual's health, well-being, and longevity than chronological age. There are several parallel studies comparing the physiological age equivalents: As such, biological age reflects and is influenced by several lifestyle factors, including diet, exercise, sleep habits, etc. However, the assessment of an individual's biological age remains a challenge. Importantly, the need to measure true biological age is driven by the idea that this could lead to the testing and design of interventions to slow down the rate of biological aging. During the past decades, significant efforts have been expended to identify different parameters that can predict biological aging and lifespan; for example, measurements of frailty (Ferrucci et al., 2002), hair graying, skin aging (Yanai, Budovsky, Tacutu, & Fraifeld, 2011), and levels of different types of white blood cells. However, most of these markers were found to offer no advantage over knowing chronological age.

[0004] Recently, advances in molecular biology have introduced novel molecular measures of aging: “telomere length” (Monaghan, 2010) and “metabolic measures” (Hertel (Hertel et al., 2016) has been used to predict biological age. However, although telomere length varies with age, the correlation between chronological age and telomere length was weak and the predictive power for lifespan was low. Furthermore, the techniques used to measure telomere length are technically demanding and technical errors confound the determination of age. Another measurement that has been used is the "metabolic age score," which measures different metabolites in urine. (Hertel et al., 2016) This technique requires sophisticated methods to measure the different urinary components.

[0005] The discovery of the "epigenetic clock" by Horvath has led to a paradigm shift in the search for biological age markers (Horvath, 2013). This clock is based on the measurement of the DNA methylation clock at the 353CG position of DNA. The degree of methylation of genes included in the methylation clock has been found to correlate better with chronological age than telomere length or other measures of aging such as hair, skin, or frailty. More importantly, while for most people, the DNA methylation clock is very close to the chronological clock, for certain people, the clock advances faster than the chronological clock. Thus, a person can have an epigenetic age that is much older than the chronological clock. Recent studies have suggested that such advances in the DNA methylation clock predict early death from various causes. A recent analysis of 13 different studies with a total of 13,089 people showed that the epigenetic clock was able to predict all-cause mortality, independent of several risk factors such as age, body mass index (BMI), education, smoking, physical activity, alcohol use, smoking, and certain comorbidities. (Chen et al., 2016)

[0006] A recent review by Jylhava, Pedersen, and Hagg in EBiomedicine concludes that "telomere length is the best-studied predictor of biological age, but many novel predictors are emerging, and the epigenetic clock is currently the best predictor of biological age, as it correlates well with age and predicts mortality" (Jylhava, Pedersen, & Hagg, 2017). "Technical bias in measuring telomere length may also contribute to the lack of consistent results." The authors conclude, "Simply put, telomere length has been widely validated but has low predictive power. Composite biomarkers are less well validated but may be stronger predictors than telomeres, as may metabolic age scores. The epigenetic clock currently performs best, taking both aspects into account" (Jylhava et al., 2017).

[0007] Telomere length and epigenetic cloning as measures of biological age in the 1895 Berlin Aging Study II by Valentin Max Vetteret et al. Locke's comparison concluded that "As previously mentioned, in the BASE-II cohort, telomere lengths in the younger population were significantly shorter than in the older population, but telomere length and chronological age were only very weakly negatively correlated in BASE-II (Rs2 = .013)." In contrast, this study found that "our results showed a positive significant correlation between DNA methylation (epigenetic clock) age estimates and chronological age RsRs2 = 0.47, which persisted after adjustment for covariates (sex, leukocyte distribution, alcohol, smoking)." The authors conclude that "In summary, as expected, DNAm age was found to be a much more accurate predictor of chronological age than telomere length." (Vetter et al., 2018)

[0008] A Scottish study of two birth cohorts found that telomere length explained 2.8% of the variance in age and the epigenetic clock explained 34.5% of the variance in age in a combined cohort analysis. In the same study, a 1 standard deviation increase in baseline epigenetic age was also associated with a 25% increased risk of death in the combined cohort analysis. In the same model, a 1 standard deviation increase in baseline telomere length was independently associated with an 11% decreased risk of death (P < 0.047) (Marioni et al., 2018). Summary of the Invention

[0009] While it is becoming clear that the "epigenetic clock" is the most accurate measurement of biological age to date, available tests require testing numerous sites using blood, an invasive and costly sample, which is not applicable for large-scale patient-triggered use. Available methods, while sufficient for research and clinically relevant studies, are not suitable for consumer-centric use of this test. Thus, there is a need for high-throughput, non-invasive tests that are accurate and robust.

[0010] As a solution to the aforementioned problems, the present invention provides a consumer-based, executable, repeatable test of the epigenetic clock integrated with a computer-readable medium in the form of a system that integrates an accurate, robust, saliva-based "epi-aging test" using a novel CG site in the entire health ecosystem for self-learning, self-developing healthy aging, thereby providing what may be referred to as an application (hereinafter also referred to as an "app") that enables data collection and communication with consumers, data sharing, and machine learning technology. Current methods are costly (requiring DNA methylation of many CG sites across different genomic regions), invasive (using blood), standalone, and do not provide guidance for improving age scores. Although general concepts of behaviors that positively impact health are recommended in the medical literature, the exact personalized combination of lifestyle changes that may help a particular person is unknown. The present invention discloses a system that integrates a consumer-based "DNA methylation age test" using saliva with an app-guided health and lifestyle management environment that combines data sharing, machine learning, and personalization of health style interventions managed by the consumer via an app. Data is kept completely personal and shared only among consumers, not with outsiders. The motivation for consumers to share data is that by participating in a sharing community they will receive higher quality advice to improve their health. Thus, the benefit of sharing data is dynamically and iteratively provided to consumers by obtaining higher quality data and lifestyle assessments and recommendations. Objectives of the invention

[0011] The main object of the present invention is to extract DNA from a substrate of a subject, measure DNA methylation in the DNA extracted from the substrate to obtain a DNA methylation profile, Polygenic Score Analyze DNA methylation profiles to obtain Polygenic The present invention relates to a method for calculating the biological age of a subject, comprising the step of determining the biological age of the subject from the score. Here, extracting DNA from a substrate includes extracting genomic DNA from saliva or blood obtained from a subject.

[0012] A further object of the present invention is to provide a method for the detection of methylation profiles of polygenic DNA methylation biomarkers, the method comprising: PolygenicThe present invention relates to a method for calculating the biological age of a subject from the score, which comprises measuring the methylation status of a CG site in any of the human CG sites located in the ElovL2 AS1 region, which is located in the putative antisense region to the ElovL2 gene and is shown in SEQ ID NO:1, and combinations thereof.

[0013] Another object of the present invention is to extract DNA from a plurality of substrates from a plurality of subjects, and measure DNA methylation in the DNA extracted from the plurality of substrates to obtain a DNA methylation profile; Polygenic Analyzing the DNA methylation profile to obtain a score, and Polygenic The present invention relates to a method of calculating biological age across multiple subjects, comprising determining the biological age of the subjects from the scores. Here, extracting DNA includes extracting genomic DNA from saliva or blood obtained from multiple subjects.

[0014] Yet another object of the present invention relates to a kit for determining the biological age of a subject, said kit comprising means and reagents for collection and stabilization of a substrate from a subject, a scanner for reading the barcode of the kit, instructions for collection and stabilization of said substrate, said substrate being saliva or blood of the subject, said stabilization of said substrate being for mailing the collected substrate for extraction of DNA for the purpose of measuring DNA methylation in the DNA extracted from the substrate, and a step of obtaining a DNA methylation profile of the subject to determine the biological age of the subject.

[0015] Yet another object of the present invention is a computer-implemented method for providing recommendations for lifestyle changes, comprising the steps of: evaluating entries in a computer readable medium obtained through sharing of user data from a subject; matching the entries to a kit for determining the biological age of a subject obtained from the subject for determining the biological age of the subject; performing statistical analysis using the evaluation of entries in a computer readable medium obtained through sharing of user data from the subject to obtain a calculated biological age of the subject using a method for calculating biological age or a method for calculating biological age across multiple subjects; integrating the calculated biological age into a machine learning model for the subject to obtain an integrated data report; creating a dynamic report for the subject by analyzing the integrated data report with the progress of responses to questionnaires obtained by sharing user data from the subject over time and comparing them with the recommendations of the National Association; and sharing the dynamic report in a computer readable medium with the subject to provide recommendations for lifestyle changes.

[0016] Another object of the present invention relates to a method for developing a computer readable medium. The method includes the steps of storing data obtained from a plurality of subjects, analyzing the stored data, and constructing a model; and storing the data obtained from the plurality of subjects includes storing the data in a cloud-based SQL database. The step of analyzing the stored data may include deep machine learning, reinforcement learning, and machine learning. or a combination thereof, and the step of constructing said model is a method for developing a computer readable medium comprising as input questionnaire measurements and as output the difference between DNA methylation age and chronological age, and correlations of other physiological and psychological outputs such as pain, blood pressure, BMI, mood, etc.

[0017] Thus, the present invention provides methods and materials useful for assessing the impact of age progression, lifestyle, and provides personalized lifestyle recommendations regarding lifestyle changes based on the calculation of biological age by methods analyzing DNA methylation of CG sites or CG positions upstream of a gene encoding an antisense mRNA directed against the ElovL2 gene (ElovL2 AS1 region) in DNA extracted from a subject or across multiple subjects or from matrices including blood and saliva. [Means for solving the problem]

[0018] One embodiment of the present invention relates to a method for calculating the biological age of a subject, the method comprising: extracting DNA from a matrix of the subject; measuring DNA methylation in the DNA extracted from the matrix to obtain a DNA methylation profile; and analyzing the DNA methylation profile. Polygenic Get the score, and Polygenic determining the biological age of the subject from the score. Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from the subject.

[0019] The present invention has found that age progression is highly correlated with methylation of CG positions or CG sites present or located in the region upstream of the gene encoding the antisense mRNA directed against the ElovL2 gene (referred to as the ElovL2 AS1 region). Thus, another embodiment of the present invention relates to a method for calculating the biological age of a subject, comprising a step of measuring DNA methylation performed on a polygenic DNA methylation biomarker comprising measuring the methylation status of any one of the human CG sites listed in Table 1. Table 1 provides putative antisense regions for the ElovL2 gene, CG positions appearing on human chromosome 6 as disclosed herein located in the ElovL2 AS1 region as shown in SEQ ID NO:1, and combinations thereof. The present invention has found that targeted amplicon sequencing of the region reveals combinations of the aforementioned 13 novel CG sites as listed in Table 1. Table 1 provides CG positions appearing on human chromosome 6 as disclosed herein in the ElovL2 AS1 region as shown in SEQ ID NO:1, the methylation of which was highly correlated with biological age in saliva. The linear regression equation of the present invention reveals the regression coefficients of these sites with age, and a combined weighted equation of these sites accurately predicts biological age.

[0020] One embodiment of the present invention discloses a method for calculating biological age across multiple subjects, the method comprising: extracting DNA from multiple substrates of multiple subjects; measuring DNA methylation in the DNA extracted from the multiple substrates to obtain a DNA methylation profile; and analyzing the DNA methylation profile to calculate biological age across multiple subjects. Polygenic Get the score, Polygenic The present invention relates to a method for determining biological ages across multiple subjects from the scores, and the DNA extraction includes extracting genomic DNA from saliva or blood obtained from the subjects. PolygenicWe disclose a method to accurately measure DNA methylation age in saliva by determining DNA methylation in a set of CG positions appearing on human chromosome 6, as disclosed herein, in the ElovL2 AS1 region, shown in SEQ ID NO:1, simultaneously in hundreds of individuals, by sequential amplification with target-specific primers, followed by barcoded primers, and multiplex sequencing, data extraction, and quantification of methylation in a single next-generation Miseq sequencing reaction. The present invention also relates to an ElovL2 AS having SEQ ID NO: 1 as described in Table 1. Provided are CG positions appearing on human chromosome 6 as disclosed herein in a region and disclose measuring the methylation of said DNA methylation CG sites by using a pyrosequencing assay or a methylation specific PCR or digital PCR. The present invention predicts age Polygenic The calculation of the weighted methylation score is disclosed.

[0021] One embodiment of the present invention discloses a kit for determining the biological age of a subject, the kit comprising means and reagents for collecting and stabilizing a substrate of the subject, a scanner for reading the barcode of the kit, and instructions for collecting and stabilizing the substrate, the substrate being saliva or blood of the subject, the stabilization of the substrate being for mailing the collected substrate to extract DNA for measuring DNA methylation in the DNA extracted from the substrate, and obtaining a DNA methylation profile of the subject to determine the biological age of the subject.

[0022] One embodiment of the present invention discloses a computer-implemented method for providing recommendations for lifestyle changes. The method includes the steps of: evaluating the entries in a computer-readable medium obtained through sharing user data from a subject; matching the entries to a kit disclosed herein and obtained from the subject to determine the biological age of the subject; calculating the biological age of the subject using the method for calculating the biological age of a subject or the method for calculating biological age across multiple subjects disclosed herein; integrating the calculated biological age into a machine learning model of the subject by performing statistical analysis using the evaluation of the entries in the computer-readable medium to obtain an integrated data report; creating a dynamic report of the subject by analyzing the integrated data report with the progress of answering a questionnaire obtained by sharing user data from the subject over time and comparing them with the recommendations of the National Association; and sharing the dynamic report in a computer-readable medium with the subject to provide recommendations for lifestyle changes. Thus, the present invention discloses a computer-implemented method, which is a novel process that integrates repeated DNA methylation age measurements of biological age in saliva with dynamic lifestyle changes using a computer readable medium, also called an app, to manage these changes. Because DNA methylation age determination requires only saliva, the disclosed method of the present invention provides consumer-driven ordering of the test via a computer readable medium or app, where the saliva is spit into a saliva collection kit and a DNA extraction kit is mailed to a lab for DNA methylation analysis. Lifestyle changes are recorded in the app, and the methylation data and lifestyle data are captured in a database and continuously and iteratively analyzed by machine learning programs such as neural networks.

[0023] One embodiment of the present invention discloses a method for developing a computer readable medium. The method includes storing data obtained from a plurality of subjects, analyzing the stored data, and building a model, the storing data obtained from a plurality of users includes a cloud-based SQL database. The analyzing the stored data includes a group selected from deep machine learning, reinforcement learning, machine learning, or a combination thereof, and the building the model includes correlation of input questionnaire measurements and the difference between DNA methylation age and chronological age as output, and other physiological and psychological outputs such as pain, blood pressure, BMI, mood, etc. Data shared by a plurality of consumers is continuously analyzed to build a model that correlates input lifestyle changes with the output of the difference between DNA methylation age or biological age and chronological age provided by the method disclosed in the present invention. The model disclosed herein is applied to the personal data, and the model derives recommendations regarding personal changes in lifestyle. The input lifestyle changes and output DNA methylation age or biological age disclosed herein are repeatedly measured and provided to the consumer's app. This is used for further reinforcement learning along with additional advice directed at the

[0024] The present invention provides a method that can be used by those skilled in the art to measure the relationship between biological age and lifestyle changes and DNA methylation age. The DNA methylation markers (CGIDs) listed in Table 1 depict selected CG positions in the upstream region of human chromosome 6 of the newly discovered gene ElovL2 AS1 as set forth in SEQ ID NO:1 disclosed herein. The present invention is useful for consumer-driven, saliva-based tests to determine DNA methylation aging or biological age, and for reporting and correcting lifestyle parameters using a "share" app or computer-readable medium and machine learning systems. The present invention provides a method for the detection of SEQ ID NOs. 1-5, 2-6, 2-7, 2-8, 3-9, 3-10, 3-11, 3-12, 3-14, 3-15, 3-16, 3-17, 3-18, 3-20, 3-21, 3-22, 3-23, 3-24, 3-25, 3 Based on the DNA methylation measurement method disclosed herein, which includes targeted amplicon sequencing of the antisense ELOVL2 AS1 region described in NO:1. Polygenic It has been shown to be useful to measure "biological age" using the Score or other methods of measuring DNA methylation available to those of skill in the art, such as next generation bisulfite sequencing, pyrosequencing, MeDip sequencing, Ion Torrent sequencing, Illumina 450K arrays, Epic microarrays, etc. The present invention also discloses the utility of the present invention for integrating DNA methylation measurements in a comprehensive plan for lifestyle changes using the "epi-aging" app disclosed herein, which can be developed by those skilled in the art using open source and other programs such as Build Fire JS, Ionic, Titanium SDK from Appcelerator, Mobile angular UI, and Siberian CMS. The data can be handled by anyone skilled in the art. It is stored in a database such as MySQL on a cloud server such as Azure cloud or Amazon cloud. On these servers, the data is analyzed by machine learning platforms such as neural networks using open source programs such as Tensor flow and R statistics available to those skilled in the art. The present invention discloses the utility of the present invention in providing customers with dynamic "personalized" reports containing recommendations regarding combinations of lifestyle changes that may impact healthy aging. The present invention also discloses the utility of an "epi-aging" DNA methylation test and app to measure the effect of interventions on biological age by sending saliva to measure DNA methylation age before and after recommended lifestyle changes.

[0025] Other objects, features and advantages of the present invention will become apparent to those skilled in the art from the following detailed description. It should be understood, however, that the detailed description and specific examples, while indicating certain embodiments of the invention, are given by way of illustration and not limitation. Many changes and modifications within the scope of the invention can be made without departing from the spirit thereof, and the invention includes all such modifications. [Brief description of the drawings]

[0026] [Figure 1] DNA methylation at CG sites in the antisense region upstream of the ElovL2 gene, referred to as the ElovL2 AS1 region, correlates with age. An IGV browser view of the human genomic region surrounding the CG sites and the locations of two CGs in the ElovL2 AS1 region, namely cg16866757 and cg21572722, listed in Table 1 disclosed herein, are shown. Pearson correlation between methylation status of genome-wide CGIDs of blood cells from publicly available Illumina 450K arrays and age revealed that the top CG is cg16867657 with a Pearson product moment correlation coefficient of r=0.934 (p=0) and the correlation coefficient of the adjacent site cg21572722 with age is r=0.81004 (p=0), indicating that the methylation status of the discovered CG sites correlates with age in the ElovL2AS1 region. Examination of the genomic location of this CG revealed that it is a member of a sequence of 13 CGs (shown) present in a previously uncharacterized region, the ElovL2 antisense gene ElovL2 AS1 region. [Diagram 2] CG sites in the ElovL2 AS1 region are highly correlated with age in saliva. Methylation scores of weighted methylation levels of CG sites in the ElovL2 AS1 region, namely cg16867657, cg21572722 located on chromosome 6, and cg09809672 located on chromosome 1 (see location in Table 1 of the genome), are correlated with age on publicly available blood Illumina 450K arrays (GSE40279 n=656 and GSE2219, n=60). The analysis revealed a strong correlation between methylation and age across all ages. [Diagram 3] Correlation of methylation at CG sites in the ElovL2 AS1 region with age in saliva and comparison with the Horvath epigenetic clock. Correlation of methylation at CG sites in the ElovL2 AS1 region, namely cg1687657 and cg21572722, with age in saliva and comparison with the Horvath epigenetic clock are shown. A. Correlation of the combined methylation score (HKG) of cg1687657 and cg21572722 with age using DNA methylation profiles from saliva of GSE78874. B. Correlation between the gold standard Horvath methylation clock scores using the same Illumina 450K data. C. Comparison of the accuracy of the two tests. The combined score of these two sites has a lower mean deviation in predicted age than the gold standard Horvath clock. [Figure 4]Prediction of age using 13CG ElovL2AS1 polygenic scores in saliva. The utility of the present invention is shown. A. Methylation scores predicting age calculated with a linear regression equation predicting age as a function of weighted methylation levels of CG sites 1, 5, 6, 9 of the ElovL2 AS1 region using methylation levels of salivary DNA from 65 individuals (see Table 1 for location in the genome). The ElovL2 AS1 region described in FIG. 1 was amplified from bisulfite-converted salivary DNA and subjected to multiplex next-generation sequencing on a Miseq next-generation sequencer. B. Methylation scores predicting age calculated with a linear regression equation predicting age as a function of weighted methylation levels of CG sites 1, 2, 5, 6, 9 of the ElovL2 AS1 region (see Table 1 for location in the genome). C. Methylation scores predicting age calculated with a linear regression equation predicting age as a function of weighted methylation levels of CG sites 1, 2 of the ElovL2 AS1 region (see Table 1 for location in the genome). D. Methylation scores predicting age calculated with a linear regression equation predicting age as a function of weighted methylation levels of all CG sites in the ElovL2 AS1 region (see Table 1 for location in the genome). E. Comparison of the predictive value of different methylation scores. The equation including all 13 CG sites outperforms all other combinations. [Diagram 5] Epi-Aging App. The homepage of the Epi-Aging App is depicted. [Figure 6]Epi-Aging app-based health ecosystem. The utility of the epi-aging app; data-guided lifestyle management is depicted. At the center of the health ecosystem is the epi-aging app. The app allows customers to learn about DNA methylation, biological aging, and dietary supplements. The app allows customers to input data on lifestyle, cardiovascular health, mood, nutrition, gender-specific sleep, and pain. The app allows customers to order saliva test kits from the marketplace. The saliva kits are mailed and the customer scans a barcode, which assigns the customer an ID that connects the phone ID and the test ID. The customer sends the saliva kit to the lab with postage prepaid. The methylation age data from the lab and the repetitive lifestyle data are sent from the app and the lab to a database programmed in SQL. Similarly, other customers are sending their lifestyle data and DNA methylation data to the database. Machine learning algorithms analyze the data using deep learning and repetitive input data. A model is calculated that defines the weight of each input in determining the outcome. Personalized data is analyzed by the model and lifestyle changes (delta DNA methylation minus chronological age) predicted to change the outcome are delivered to the app. The customer makes lifestyle changes and orders a new saliva test, and the cycle is repeated, with further analysis and recommendations based on the direction and extent of DNA methylation age changes relative to chronological age. [Figure 7] Health Epi Ecosystem. The Health Epi Ecosystem and its usefulness in healthy aging are depicted. EpiAging app sits at the center of the health ecosystem. It enables live streaming of health advice from reputed national medical associations. It creates a marketplace for health providers and lifestyle products, as well as a marketplace for various novel tests. The app sends the data to a common data server that repeatedly analyzes all the information and provides recommendations on lifestyle changes, feasible tests, and information for health providers and vendors based on the analysis. Detailed Description of the Invention

[0027] In describing the embodiments, reference may be made to the accompanying figures, which form a part of this specification and which show, by way of illustration, specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the invention. Many of the techniques and procedures described or referenced herein are well understood and commonly used by those skilled in the art. Unless otherwise defined, all technical terms, notations, and other scientific or technical terms used herein are intended to have the meanings commonly understood by those skilled in the art to which the invention pertains. In some cases, terms with commonly understood meanings are defined herein for clarity and / or ready reference, and the inclusion of such definitions herein should not necessarily be interpreted as representing a substantial difference from what is commonly understood in the art.

[0028] All figures in the drawings are intended to illustrate selected versions of the invention and are not intended to limit the scope of the invention.

[0029] All publications mentioned herein are incorporated by reference to disclose and describe the aspects, methods, and / or materials in connection with which the publications are cited.

[0030] DNA methylation refers to the chemical modification of DNA molecules. Technology platforms such as Illumina Infinium microarrays and DNA sequencing-based methods have been shown to lead to highly robust and reproducible measurements of DNA methylation levels in people. There are over 28 million CpG or CG loci in the human genome. As a result, specific loci are given unique identifiers, such as those in the Illumina CpG or CG loci database. These CG locus designation identifiers are used herein. [Definition]

[0031] The term "CG" as used herein refers to a dinucleotide sequence in DNA that contains cytosine and guanosine bases. As used herein, a CG position, referred to as a "CG site", is a position in the human genome and is defined by a chromosome and nucleotide position in the reference human genome hg19. The term "beta value" as used herein refers to the ratio of intensities between methylated and unmethylated probes and is derived by normalization and quantification of Illumina 450K or EPIC arrays using the formula: beta value = 0 is fully unmethylated and 1 is fully methylated. Methylation C intensity / (methylation C intensity + unmethylation C intensity) between 0 and 1. This refers to the calculation of methylation levels at the extracted CGID positions. As used herein, the term "decision tree" is a type of data mining algorithm that selects, from many variables and their interactions, those that are most predictive of a explained response or outcome (Mann et al., 2008). As used herein, the term "random forest" is a type of data mining algorithm that can select the variables that are most important in determining a particular outcome or response (Shi, Seligson, Belldegrun, Palotie, & Horvath, 2005; Svetnik et al., 2003). The term “lasso regression,” as used herein, is a method for selecting variables in a linear regression model that identifies the minimal subset of predictors that are necessary to predict an outcome (response variable) with minimized prediction error (Kim, Kim, Jeong, Jeong, & Kim, 2018). As used herein, the term “K-means cluster analysis” is an unsupervised machine learning technique that partitions observations into a set of smaller clusters, with each observation belonging to one cluster (Beauchaine & Beauchaine, 2002; Kakushadze & Yu, 2017). As used herein, the term “reinforcement learning” involves receiving feedback from data analysis and learning through trial and error. A series of successful decisions leads to a reinforcement of the process (Zhao, Kosorok, & Zeng, 2009). The term "penalized regression" as used herein refers to a statistical method that aims to identify the minimum number of predictors needed to predict an outcome from a larger list of biomarkers as implemented in the R statistical package, as described in "Estimating L1 Penalization in Cox Proportional Hazards Models" by Goeman, JJ (Biometrical Journal 52(1), 70-84). As used herein, the term "clustering" refers to the grouping of a set of objects in such a way that objects in the same group (called a cluster) are more similar (in various senses) to each other than to objects in other groups (clusters). As used herein, the term "neural networks and deep learning" refers to machine learning methods that incorporate neural networks in several layers to iteratively learn from data. Neural networks examine various data inputs, such as lifestyle variables, as a collection of connected units or nodes called artificial neurons that have multiple interactions like neurons in the brain (De Roach, 1989; Mupparapu, Wu, & Chen, 2018; Sherbet, Woo, & Dlay, 2018). These interactions drive an output of biological aging, measured as acceleration or deceleration compared to chronological age. As used herein, the term "multiple or polygenic linear regression" refers to a regression model using multiple CGs. It refers to a statistical method of estimating the relationship between multiple "independent variables" or "predictors," such as percentage of methylation in ID, and a "dependent variable," such as chronological age, in which a "weight" or coefficient for each CG ID is determined in predicting an "outcome" (dependent variable, such as age) when multiple "independent variables," such as CG ID, are included in the model. As used herein, the term "Pearson correlation" refers to a statistical method of estimating the correlation between an "independent variable" or "predictor," such as the percentage of methylation in CG ID, and a "dependent variable," such as chronological age. The Pearson product-moment correlation coefficient, r, quantitatively weights the correlation between 0, indicating no correlation, and 1, indicating perfect correlation (Hardy & Magnello, 2002).

[0032] The currently disclosed method is based on the discovery of sites in the human genome where methylation status correlates with age, by performing a series of Pearson correlations between age and DNA methylation at 450K sites in the genome from available publicly available data sets (GSE61496, GSE98876). The DNA methylation markers were discovered by cloning and validated using data from GSE40729. The analysis identified cg16867637 as the top site correlated with age (r = 0.934827, p = 0). The present invention has discovered a fragment of the human genome on chromosome 6 that is an antisense sequence to the aforementioned age-associated ElovL2 gene. The ElovL2 AS1 region, containing the 13 CG, referred to herein as the CG site, is a dinucleotide sequence location as set forth in Table 1 below, as disclosed herein, and calculated with a multiple linear regression equation, and the combined methylation measurements provide a higher accuracy of predicting biological age in saliva than previously reported genomic locations. Polygenic These sites were not previously described because they were not included in the Illumina array. Thus, the present invention discloses novel CG sites whose combined weighted methylation levels correlate with age.The present invention further demonstrates herein that methylation of all 13 CG sites can be accurately measured by amplifying a single amplicon and measuring hundreds of individuals simultaneously using indexed next generation sequencing. Thus, by using the "epi-aging" test disclosed herein, costs are reduced and throughput is increased. The subject or customer orders a saliva collection kit, spits into the kit collection tube, and sends the kit back to the lab. At the lab, DNA is extracted, bisulfite converted, and the ElovL2 AS1 region is amplified and indexed. Amplicons from 200 subjects are sequenced in the same Miseq reaction. The FastQ files are analyzed to determine methylation levels at the 13 CG sites. Using an equation correlating weighted methylation values ​​of the 13 CG sites with age, biological age is calculated and shared with the customer.

[0033] The present invention further discloses and addresses the utility of the presently disclosed methods in calculating biological age in a dynamic manner to improve healthy aging of customers by suggesting lifestyle changes. The present invention discloses that effective interventions can be derived from "machine learning" of the relationships between multiple lifestyle variables and the differences between DNA methylation age and biological changes. As lifestyle data and methylation age data or biological age are dynamically collected from multiple subjects / users, the machine learns how combinations of changes in lifestyle parameters relate to increases or decreases in the difference between DNA methylation and biological age. The present invention integrates DNA methylation testing with subject / consumer-centric sharing, learning and lifestyle changes. As disclosed herein in the present invention, subjects / consumers use the epi-aging app or computer readable medium to order and communicate lifestyle decisions. Improving health is a two-way partnership and collaborative effort, not a one-way flow of instructions from a "learned, omniscient" medical professional (healthcare provider) to a "docile" and passive patient (healthcare consumer). The best advice from scientific knowledge curated from the most reputable national medical associations is presented to the consumer using the epi-aging app. The consumer decides which recommendation to act on. The consumer shares the decision using the "totally blind" app. The consumer receives an ID that is linked to their mobile ID but "firewalled" from personal information such as address, name, email, etc. The personal interventions and results of multiple users, as well as the results of the DNA methylation age test, are analyzed iteratively, integrating both physical and mental results. The data is analyzed using state-of-the-art machine learning algorithms, for example neural networks such as Tensorflow. A model is established that correlates different input parameters with the output delta between DNA methylation age and chronological age. The consumer's personal data is used in the model and tuning suggestions are personalized and delivered to the consumer. The present invention provides a grand platform for generating perpetually evolving dynamic recommendation models inspired by scientific knowledge. The present invention proposes an "evolutionary" platform that dynamically improves with use of an ever-expanding body of data. As disclosed in the present invention, both customer well-being and the learning environment co-evolve in a dynamic interplay between DNA methylation testing, lifestyle changes, shared data, and machine learning.

[0034] The invention disclosed herein has several embodiments. In one aspect of the present invention, the present invention provides polygenic DNA methylation markers of biological age for lifestyle management of healthy aging. The polygenic DNA methylation marker set is derived using Pearson correlation analysis of the correlation between age and DNA methylation across the genome on genome-wide DNA methylation derived by mapping methods, such as Illumina 450K or 850K arrays, genome-wide bisulfite sequencing using various next generation sequencing platforms, methylated DNA immunoprecipitation (MeDIP) sequencing, hybridization with oligonucleotide arrays, or a combination of these methods.

[0035] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The score determines the subject's biological age. wherein extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, and wherein the DNA methylation measurement is performed by a method such as DNA pyrosequencing, mass spectrometry-based (Epityper et al., 2011) or a combination of methods. TM ), performed using methods including PCR-based methylation assays, targeted amplicon next-generation bisulfite sequencing on a platform selected from the group of HiSeq, MiniSeq, MiSeq, and NextSeq sequencers, ion torrent sequencing, methylated DNA immunoprecipitation (MeDIP) sequencing, or hybridization with oligonucleotide arrays. A method for calculating the biological age of a subject as disclosed herein, wherein the DNA methylation measurement is performed on a polygenic DNA methylation biomarker comprising measuring the methylation status of a CG site in any one of human CG sites, a combination of which is located in the antisense region to the ELOVL2 gene in human chromosome 6, the ELOVL2 AS1 region being represented by SEQ ID NO:1 (CGCCCTCGCGTCCGCGGCGTCCCCTGCCGGCCGGGCGGCGATTTGCAGGTCCAGCCGGCGCCGGTTTCGCGCGGGCGGCTCAACGTCCACGGAGCCCCAGGAATACCCACCCGCTGCCCAGATCGGCAGCCGCTGCTGCGGGGAGAAGCAGTATCGTGCAGGGCGGGCACGCTGGTCTTGCTTACAGTTGGGCTTCGGTGGGTTTGAAGCACACATTAGGGGGAAATGGCTCTGTTCCTGCAGGTTTGCGCAGTCTGGGTTTCTTAG).

[0036] Table 1: 13 CG sites upstream of the antisense ElovL2 gene, positions corresponding to the ElovL2 AS1 region shown in SEQ ID NO:1 and harboring CG methylation sites (CG sites) useful in embodiments of the present invention. The locations in the human genome of the selected 13 CG dinucleotides at the CG sites used in the various embodiments herein are found in Table 1 of the present application, which also provides the CG positions in chromosome 1 used in the figures and examples of the present application. [Table 1] *NA means not available.

[0037] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The score determines the subject's biological age. Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the DNA methylation measurement is performed using DNA pyrosequencing including primers set forth in SEQ ID NO:2 (AGGGGAGTAGGGTAAGTGAG) as a biotinylated forward primer, SEQ ID NO:3 (ACCATTTCCCCCTAATATATACTT) as a reverse primer, and SEQ ID NO:4 (GGGAGGAGATTTGTAGGTTT) as a pyrosequencing primer.

[0038] In one embodiment of the present invention, there is provided the use of DNA pyrosequencing methylation assay for DNA methylation age using ElovL2 AS1 region containing CG sites, in combination with the primers disclosed herein and the standard conditions of pyrosequencing reaction recommended by the manufacturer (Pyromark, Qiagen), and the positions of dinucleotide sequences described in Table 1 disclosed herein. The primers are primers including biotinylated forward primer set to SEQ ID NO:2, Elov1_Seq primers shown in SEQ ID NO:3 and SEQ ID NO:4.

[0039] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The score determines the subject's biological age. The extracting of DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the DNA methylation measurement is performed by sequencing on a platform selected from the group of HiSeq, MiniSeq, MiSeq, and NextSeq sequencers. The primers comprise a forward primer (ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT) shown in SEQ ID NO:5 and a reverse primer (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC) shown in SEQ ID NO:6, and are performed using targeted amplicon next-generation bisulfite sequencing.

[0040] In one embodiment of the present invention, a method for measuring DNA methylation age in saliva by using the ElovL2 AS1 region containing the CG site is described. Polygenic The present invention provides a use of a multiplex amplicon bisulfite sequencing DNA methylation assay, the combination of which is the location of the dinucleotide sequence as described in Table 1 disclosed herein, using sequential amplification including the use of primers disclosed herein and standard conditions including bisulfite conversion (a) target specific primers (PCR1) and (b) barcode primers (PCR2) and multiplex sequencing in a single next generation Miseq (Illumina), demultiplexing using Illumina software, data extraction and quantification of methylation using standard methods for methylation analysis including Methyl Kit, followed by calculation of a weighted DNA methylation score for calculation of the biological age of the subject, wherein the target specific primers (PCR1) are: forward primer is shown in SEQ ID NO: 5 (ACACTCTTTCTCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT); The reverse primer is shown in SEQ ID NO:6 (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTCTCTCCCC), for the forward primer is shown in SEQ ID NO:7 (AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC) and for the reverse primer is shown in SEQ ID NO:8 (CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG), which is the barcode index primer (PCR2). In SEQ ID NO:5 disclosed herein, ACACTCTTTCTCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT, the bases in black are the adapter and the bases in red are the targeted sequence. In SEQ ID NO:6 disclosed herein, GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC, the black bases are the adapter and the red bases are the targeted sequence. In the SEQ ID NO:8 disclosed herein, the barcode primer is CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG, the bases in red are the index, and up to 200 of this index A variation of is used.

[0041] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) PolygenicThe score determines the subject's biological age. The method, wherein the DNA methylation measurement is performed using a PCR-based methylation assay selected from the group of methylation-specific PCR and digital PCR.

[0042] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The score determines the subject's biological age. Polygenic The method, wherein analyzing the DNA methylation profile to obtain a score comprises using multiple linear regression equations or neural network analysis.

[0043] In one embodiment of the present invention, there is provided a method for calculating biological age across multiple subjects, said method comprising the steps of: (a) Extract DNA from multiple substrates from multiple subjects. (b) DNA methylation of DNA extracted from multiple substrates is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. Polygenic The scores are used to determine biological age across subjects. The method, wherein the DNA extraction comprises extracting genomic DNA from saliva or blood obtained from the subject.

[0044] In one embodiment of the present invention, there is provided a method for calculating biological age across multiple subjects, said method comprising the steps of: (a) Extract DNA from multiple substrates from multiple subjects. (b) DNA methylation of DNA extracted from multiple substrates is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score, Polygenic The scores are used to determine biological age across subjects. Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and measuring DNA methylation of the extracted DNA from the multiple substrates includes the steps of: (i) Genomic DNA extracted from multiple substrates is amplified with target-specific primers to obtain PCR product 1. (ii) amplifying PCR product 1 from step (i) by barcoding primers to obtain PCR product 2; (iii) PCR product 2 from step (ii) is used to perform multiplex sequencing in a single next-generation Miseq sequencing reaction. (iv) extracting data from the multiplexed sequencing of step (iii); quantifying DNA methylation from the extracted data of step (iv) to obtain DNA methylation profiles for each substrate; Get your profile. In another embodiment of the present invention, a method for calculating biological age across multiple subjects is provided, wherein the target specific primers for obtaining PCR product 1 include a primer set forth in SEQ ID NO: 5 (ACACTCTTTCTCCTACACGACGCTCTTCTACCACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT) as a forward primer and a primer set forth in SEQ ID NO: 6 (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC) as a reverse primer, and the barcoding primers for obtaining PCR product 2 include a primer set forth in SEQ ID NO: 7 (AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC) as a forward primer and a primer set forth in SEQ ID NO: 8 (CAAGCA GAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG ) which is a barcode index primer.

[0045] In one embodiment of the present invention, there is provided a combination of DNA methylation biomarkers for calculating biological age, said combination of DNA methylation biomarkers comprising human CG sites and combinations thereof, which are mapped to the putative antisense region to the ElovL2 gene, the ElovL2 AS1 region, as shown in SEQ ID NO:1. In one embodiment of the present invention, 13 CG sites are depicted located in the ElovL2 AS1 region, a putative antisense region to the ElovL2 gene shown in SEQ ID NO: 1, which can be used alone or in combination as a measurement of biological age. In one embodiment of the present invention, the use of CG sites and their combinations are the positions of dinucleotide sequences as described in Table 1 disclosed in the present invention is provided.

[0046] In one embodiment of the present invention, a kit is provided for determining the biological age of a subject, said kit comprising means and reagents for collection and stabilization of a substrate from a subject, a scanner for reading the barcode of the kit, and instructions for collection and stabilization of the substrate, said substrate being saliva or blood from a subject, said stabilization of said substrate being for mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation of the DNA extracted from the substrate, and obtaining a DNA methylation profile of the subject to determine the biological age of the subject. In another embodiment of the present invention, a kit for determining the biological age of a subject is provided, said kit being a saliva collection kit that is ordered by a customer or subject and spat into a kit collection tube and mailed to a laboratory for DNA methylation analysis with a DNA extraction kit.

[0047] In one embodiment of the present invention, a kit for collecting a saliva sample from a customer is provided, comprising means and reagents for collecting and stabilising saliva from a customer. In one embodiment of the present invention, a kit is provided comprising means and reagents for measuring DNA methylation of CG sites, the combinations of which are positions of dinucleotide sequences as described in Table 1 disclosed herein. In one embodiment of the present invention, an application (app) is provided for managing DNA methylation age test ordering, submission, receiving test results, and lifestyle management. In one embodiment of the present invention, the app is developed using open source development tools and includes information about the test, a virtual shopping cart for ordering the test, a scanning function for scanning the barcode on the saliva kit, and a function for receiving the test results from the lab. In one embodiment of the present invention, the app provides questionnaires included to explore lifestyle features that may impact "healthy aging" including basic physiological measures such as weight, height, blood pressure, heart rate, self-rating of mood, McGill Pain Questionnaire, diet and nutrition questionnaire, exercise questionnaire, and lifestyle questionnaires such as alcohol, drugs, and smoking. One embodiment of the present invention includes a method for performing statistical analysis on the questionnaire responses and providing the consumer on the app with dynamic reports that describe the progress of their questionnaire responses over time compared to national association recommendations for cancer, heart disease and stroke, diabetes, etc.

[0048] One embodiment of the present invention provides for storing data from multiple users in a cloud-based SQL database and using "machine learning" to analyze the data and build models that correlate the difference between chronological age and DNA methylation age as input questionnaire measurements and outputs with other physiological and psychological outputs such as chronological age, pain, blood pressure, BMI, mood, etc. In further embodiments of the invention disclosed herein, the machine learning includes "deep machine learning" data mining methods including neural networks, or "reinforcement learning" with consumer feedback utilized to reinforce the most effective lifestyle changes, or "random forest" analysis. The "machine learning" data mining algorithms, or K-Means cluster analysis, or Amazon Machine Learning (AML), are selected from the group of "machine learning" software including H2O.ai products on platforms such as Apache Hadoop Distributed File System, Amazon EC2 Google Compute Engine, Microsoft Azure, etc.

[0049] In one embodiment of the present invention, a computer-implemented method for providing recommendations for lifestyle changes is provided, said method comprising the following steps. (a) Evaluate entries in computer readable media obtained through sharing of user data from subjects. (b) matching the entry of step (a) with means and reagents for collection and stabilization of a substrate from a subject, a kit for determining the biological age of the subject, including a scanner for reading the barcode of the kit, and instructions for collection and stabilization of the substrate, wherein the substrate is saliva or blood of the subject, and the stabilization of the substrate is for mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation of the DNA extracted from the substrate, thereby obtaining a DNA methylation profile of the subject to determine the biological age of the subject. (c) Calculate the subject's biological age using a method that includes: (i) Extracting DNA from a substrate from a subject. (ii) measuring DNA methylation of DNA extracted from the substrate to obtain a DNA methylation profile; (iii) Analyzing DNA methylation profiles Polygenic Get the score. (iv) Polygenic The score determines the subject's biological age. Extracting the DNA may include extracting genomic DNA from saliva or blood obtained from the subject to obtain a calculated biological age. (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing statistical analysis using the assessment of step (a) to obtain an integrated data report. (e) creating a dynamic report for the subject by analyzing the integrated data report of step (d) along with the progression of responses to the questionnaire obtained by sharing user data from the subject over time and comparing them with the recommendations of the National Association. and (f) a computer-implemented system for providing recommendations regarding lifestyle changes. The dynamic report of step (e) is shared with the subject in a readable medium. In a further embodiment of the present invention, there is provided a computer implemented method for providing recommendations for lifestyle changes, said computer readable medium including an open source development tool including information regarding a test for calculating biological age based on the methods disclosed herein, a virtual shopping cart for ordering said test, a scanning function for scanning a barcode of a kit for determining the biological age of a subject as disclosed herein, and a function for receiving test results from a laboratory, said open source development tool including questionnaires including basic physiological measurements, weight, height, blood pressure, heart rate, self-assessment of mood, McGill Pain Questionnaire, diet and nutrition questionnaire, exercise questionnaire and lifestyle questionnaire including alcohol, drugs, smoking, and combinations thereof, included in the computer readable medium for investigating lifestyle features that affect healthy aging.

[0050] In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, said method comprising the steps of: (a) Evaluate entries in computer readable media obtained through sharing of user data from subjects. (b) matching the entry from step (a) with a kit for determining the biological age of the subject, comprising means and reagents for collecting and stabilizing a substrate from the subject, a scanner for reading the barcode of the kit, and instructions for collecting and stabilizing the substrate, said substrate being saliva or blood of the subject, and for mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation of the DNA extracted from the substrate, to obtain a DNA methylation profile of the subject to determine the biological age of the subject; (c) calculating the biological age of the subject using a method comprising: (i) Extract DNA from multiple substrates from multiple subjects. (ii) measuring DNA methylation of DNA extracted from multiple substrates to obtain a DNA methylation profile; (iii) Analyzing DNA methylation profiles Polygenic Get the score. (iv) Polygenic The scores are used to determine biological age across subjects. Extracting the DNA may include extracting genomic DNA from saliva or blood obtained from the subject to obtain a calculated biological age. (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing a statistical analysis using the assessment of step (a) to obtain an integrated data report; (e) creating a dynamic report of said subject by analyzing the integrated data report of step (d) with the progress of the responses to the questionnaire obtained by sharing user data from said subject over time and comparing them with the recommendations of the National Association; and (f) sharing the dynamic report of step (e) in a computer readable medium with the subject to provide recommendations regarding lifestyle changes.

[0051] In a further embodiment of the present invention, a computer implemented method for providing recommendations for lifestyle changes is provided, the computer readable medium includes an open source development tool including information regarding a test for calculating biological age based on the methods disclosed herein, a virtual shopping cart for ordering the test, a scanning function for scanning a barcode of a kit for determining the biological age of a subject as disclosed herein, and a function for receiving test results from a laboratory, the open source development tool includes basic physiological measurements, weight, height, blood pressure, heart rate, self-assessment of mood, included in the computer readable medium for investigating lifestyle features that affect healthy aging. questionnaires including the Pain Assessment Questionnaire, McGill Pain Questionnaire, Diet and Nutrition Questionnaire, Exercise Questionnaire and lifestyle questionnaire including alcohol, drugs, and smoking, and combinations of these.

[0052] In yet another embodiment of the present invention, there is provided a computer-implemented method for providing recommendations for lifestyle changes, said method comprising the steps of: (a) Evaluate entries in computer readable media obtained through sharing of user data from subjects. (b) matching the entry in step (a) with a kit for determining the biological age of the subject, including means and reagents for collecting and stabilizing a substrate from the subject, a scanner for reading the barcode of the kit, and instructions for collecting and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and the stabilization of the substrate is for mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation in the DNA extracted from the substrate, to obtain a DNA methylation profile of the subject and determine the biological age of the subject. (c) Calculate the subject's biological age using a method that includes: (i) Extract DNA from multiple substrates from multiple subjects. (ii) measuring DNA methylation of DNA extracted from multiple substrates to obtain a DNA methylation profile; (iii) Analyzing DNA methylation profiles Polygenic Get the score. (iv) Polygenic determining biological age across subjects from the scores; wherein extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject to obtain a calculated biological age, and measuring DNA methylation in the DNA extracted from the multiple substrates includes the steps of: (1) Genomic DNA extracted from multiple substrates is amplified with target-specific primers to obtain PCR product 1. (2) amplifying PCR product 1 from step (1) by barcoding primers to obtain PCR product 2; (3) Use PCR product 2 from step (2) to perform multiplex sequencing in a single next-generation Miseq sequencing reaction. (4) Extracting data from the multiplexed sequencing of step (3). (5) Quantifying DNA methylation from the extracted data in step (d) to obtain a DNA methylation profile for each substrate. (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing a statistical analysis using the assessment of step (a) to obtain an integrated data report; (e) creating a dynamic report of said subject by analyzing the integrated data report of step (d) with the progress of the responses to the questionnaire obtained by sharing user data from said subject over time and comparing them with the recommendations of the National Association; and (f) sharing the dynamic report of step (e) in a computer readable medium with the subject to provide recommendations regarding lifestyle changes.

[0053] In a further embodiment, the present invention provides a computer implemented method for providing recommendations for lifestyle changes, the computer readable medium comprising an open source development tool including information regarding a test for calculating biological age based on the methods disclosed herein, a virtual shopping cart for ordering said test, a scanning function for scanning a barcode of a kit for determining the biological age of a subject as disclosed herein, a function for receiving test results from a laboratory, and the open source development tool includes basic physiological measurements, weight, height, blood pressure, heart rate, self-assessment of mood, and a computer readable medium for investigating lifestyle features that affect healthy aging. , the McGill Pain Questionnaire, the diet and nutrition questionnaire, the exercise questionnaire and a lifestyle questionnaire including alcohol, drugs and smoking, and any combination of these.

[0054] In one embodiment of the present invention, a computer-implemented method for providing recommendations for lifestyle changes is provided, said method comprising the following steps. (a) Evaluate entries in computer readable media obtained through sharing of user data from subjects. (b) matching the entry of step (a) with means and reagents for collection and stabilization of a substrate from a subject, a kit for determining the biological age of the subject, including a scanner for reading the barcode of the kit, and instructions for collection and stabilization of the substrate, wherein the substrate is saliva or blood of the subject, and the stabilization of the substrate is for mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation of the DNA extracted from the substrate, thereby obtaining a DNA methylation profile of the subject to determine the biological age of the subject. (c) calculating the biological age of the subject using the methods for calculating the biological age of a subject disclosed herein. (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing statistical analysis using the assessment of step (a) to obtain an integrated data report. (e) creating a dynamic report for the subject by analyzing the integrated data report of step (d) along with the progression of responses to the questionnaire obtained by sharing user data from the subject over time and comparing them with the recommendations of the National Association. and (f) sharing the dynamic report of step (e) in a computer readable medium with the subject to provide recommendations regarding lifestyle changes. wherein the method includes creating a health ecosystem focused on personalized lifestyle recommendations, normalizing or delaying biological aging of a subject, or using an Android or Apple or We Chat mini program to store data in object storage enterprise on a server or cloud server including Amazon, Ali cloud, or Microsoft Azure using a standard data pipeline and management system such as Cloud dataprep across multiple subjects.

[0055] In a further embodiment of the present invention, there is provided a computer implemented method for providing recommendations for lifestyle changes, wherein said method comprises use of a set of artificial intelligence algorithms such as Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Generalized Linear Model (GLM) and Deep Learning (DL) for calculating weighted contribution of various lifestyle measures across a subject's biological age or across multiple subjects, which is dynamically updated to provide personalized lifestyle recommendations for lifestyle changes.

[0056] In one embodiment of the present invention, a computer-implemented method for providing recommendations for lifestyle changes is provided, said method comprising the following steps. (a) Evaluate entries in computer readable media obtained through sharing of user data from subjects. (b) matching the entries of step (a) with means and reagents for collection and stabilization of a substrate from a subject, a kit for determining the biological age of the subject, a scanner for reading the barcode of the kit, and instructions for collection and stabilization of the substrate, wherein the substrate is saliva or blood of the subject, and the stabilization of the substrate is performed by extracting DNA from the saliva or blood of the subject for the purpose of measuring DNA methylation of the DNA extracted from the substrate. The substrate is mailed to obtain a DNA methylation profile of the subject and to determine the biological age of the subject. (c) calculating the biological age of the subject using the methods for calculating biological age across subjects disclosed herein; (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing statistical analysis using the assessment of step (a) to obtain an integrated data report. (e) creating a dynamic report for the subject by analyzing the integrated data report of step (d) along with the progression of responses to the questionnaire obtained by sharing user data from the subject over time and comparing them with the recommendations of the National Association. and (f) sharing the dynamic report of step (e) in a computer readable medium with the subject to provide recommendations regarding lifestyle changes. wherein the method includes creating a health ecosystem focused on personalized lifestyle recommendations, normalizing or delaying biological aging of a subject, or using an Android or Apple or We Chat mini program to store data in object storage enterprise on a server or cloud server including Amazon, Ali cloud, or Microsoft Azure using a standard data pipeline and management system such as Cloud dataprep across multiple subjects.

[0057] In a further embodiment of the present invention, there is provided a computer implemented method for providing recommendations for lifestyle changes, wherein said method comprises use of a set of artificial intelligence algorithms such as Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Generalized Linear Model (GLM) and Deep Learning (DL) for calculating weighted contribution of various lifestyle measures across a subject's biological age or across multiple subjects, which is dynamically updated to provide personalized lifestyle recommendations for lifestyle changes.

[0058] In one embodiment of the present invention, a method for developing a computer-readable medium is provided, the method comprising the following steps. (a) Store data from multiple subjects. (b) analyzing the stored data from step (a); (c) building a model where storing data obtained from a plurality of users includes a cloud-based SQL database; The step of analyzing the stored data includes a group selected from deep machine learning, reinforcement learning, and machine learning, and the step of building a model includes correlations of input questionnaire measurements with the difference between DNA methylation age and chronological age as outputs, and other physiological and psychological outputs such as pain, blood pressure, BMI, mood, etc.

[0059] In a further embodiment of the present invention, a method is provided for developing a computer readable medium, wherein the machine learning comprises a group selected from a data mining algorithm comprising random forest analysis, or a data mining algorithm comprising K-Means cluster analysis, or software comprising the Amazon Machine Learning (AML) platform, or H2O.ai products on platforms including Apache Hadoop Distributed File System, Amazon EC2 Google Compute Engine, and Microsoft Azure, or combinations thereof.

[0060] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The biological age of the subject is determined from the score. Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from the subject for use in a method for evaluating the effect of a biological intervention on the biological age of the subject, the method including the steps of: (i) Calculate the subject's biological age using the methods disclosed herein to obtain an initial biological age before the biological intervention. (ii) administering a biological intervention to said subject; (iii) repeating step (i) on subsequent substrates obtained from said subject after step (ii) has been performed to obtain a biological age following the biological intervention. (iv) integrating the biological age after the biological intervention into a machine learning model for the subject to assess the effect of the biological intervention on the biological age of the subject. The biological intervention is selected from the group of dietary supplements, vitamins, therapies, administration of test substances, dietary manipulation, metabolic manipulation, surgical manipulation, social manipulation, behavioral manipulation, environmental manipulation, sensory manipulation, hormonal manipulation and epigenetic manipulation or combinations thereof. The extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the integration of the biological age after the biological intervention in the machine learning model of the subject includes the biological age assessed in step (iii) and physiological parameters obtained through sharing of user data from the subject.

[0061] In one embodiment of the present invention, there is provided a method for calculating biological age across multiple subjects, said method comprising the steps of: (a) Extract DNA from multiple substrates from multiple subjects. (b) DNA methylation of DNA extracted from multiple substrates is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. Polygenic determining biological age across subjects from the scores; Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from the subject for use in a method for assessing the effect of a biological intervention on the biological age of the subject, the method comprising the steps of: (i) Calculate the subject's biological age using the methods disclosed herein to obtain an initial biological age before the biological intervention. (ii) administering a biological intervention to said subject; (iii) repeating step (i) on subsequent substrates obtained from said subject after step (ii) has been performed to obtain a biological age following the biological intervention. (iv) integrating the biological age after the biological intervention into a machine learning model for the subject to assess the effect of the biological intervention on the biological age of the subject. The biological intervention is selected from the group of dietary supplements, vitamins, therapies, administration of test substances, dietary manipulation, metabolic manipulation, surgical manipulation, social manipulation, behavioral manipulation, environmental manipulation, sensory manipulation, hormonal manipulation and epigenetic manipulation or combinations thereof. The extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the integration of the biological age after the biological intervention in the machine learning model of the subject includes the biological age assessed in step (iii) and physiological parameters obtained through sharing of user data from the subject.

[0062] In one embodiment of the present invention, there is provided a method for calculating the biological age of a subject, said method comprising the steps of: (a) Extract DNA from the subject's matrix. (b) DNA methylation of DNA extracted from the substrate is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. (d) Polygenic The score determines the subject's biological age. Extracting the DNA includes extracting genomic DNA from saliva or blood obtained from a subject for use in a method for screening drugs for being anti-aging agents, the method comprising the steps of: (i) Calculate the age of substrate obtained from the subject using the methods disclosed herein to obtain an initial biological age before biological intervention. (ii) administering a test agent to said subject. (iii) repeating step (i) on subsequent substrates obtained from said subject after step (ii) has been carried out to obtain the biological age following administration of the test agent. (iv) integrating the biological age after administration of the test agent into the subject's machine learning model to evaluate whether age reduction has been calculated by integrating into the machine learning model, and determining the test agent as an anti-aging agent for the subject, and the extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the integration of the biological age after biological intervention in the subject's machine learning model includes the biological age assessed in step (iii) and physiological parameters obtained through sharing of user data from the subject.

[0063] In one embodiment of the present invention, there is provided a method for calculating biological age across multiple subjects, said method comprising the steps of: (a) Extract DNA from multiple substrates from multiple subjects. (b) DNA methylation of DNA extracted from multiple substrates is measured to obtain a DNA methylation profile. (c) Analyzing DNA methylation profiles Polygenic Get the score. Polygenic determining biological age across the plurality of subjects from the scores, and extracting said DNA includes extracting genomic DNA from saliva or blood obtained from the subjects for use in a method of screening for drugs that are anti-aging agents; The method includes the following steps. (i) Calculate the age of substrate obtained from the subject using the methods disclosed herein to obtain an initial biological age before biological intervention. (ii) administering a test agent to the subject. (iii) repeating step (i) on subsequent substrates obtained from said subject after step (ii) has been carried out to obtain the biological age following administration of the test agent. (iv) integrating the biological age after administration of the test agent into the subject's machine learning model to evaluate whether a decrease in age has been calculated by integrating into the machine learning model, and determining the test agent as an anti-aging agent for the subject, and the extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and the integrating the biological age after administration of the test agent into the subject's machine learning model includes the biological age evaluated in step (iii) and physiological parameters obtained through sharing user data from the subject. EXAMPLES

[0064] The following examples are given as illustrations of the present invention and therefore should not be construed as limiting the scope of the present invention. [Example 1]

[0065] Discovery of 13 CG sites in the DNA region upstream of the ElovL2 gene and their weighted D ElovL2 AS1 region in which NA methylation levels predict age in salivary DNA In this example, the invention relates to the "epigenetic clock", which has been recognized as the most accurate measurement of biological age to date. However, the tests available so far require measuring DNA methylation at many sites (~350) using blood, an invasive and costly method that is not applicable as a widely distributed consumer product. The available methods, although sufficient for research and clinically relevant studies, are not suitable for consumer-driven general use of this test. Thus, the present invention provides a method that is an accurate, robust, high-throughput, non-invasive test of biological aging based on the "epigenetic clock", in particular DNA methylation. The invention in this embodiment provides polyCG DNA methylation markers of biological age for lifestyle management of healthy aging.

[0066] Discovery of a CG site that correlates with blood age The present invention subjected a publicly available 450K Illumina genome-wide DNA methylation array (GSE40729) from blood to Pearson correlation analysis. A small number of previously unreported CG sites were selected and analyzed. Two of those CG sites were found to be upstream of the antisense region of the ElovL2 gene, called the ElovL2 AS1 region, shown as a representative example in Figure 1, as disclosed herein for the physical map. As shown there, they were found to be highly correlated with age (Pearson correlation coefficient r>0.9 and p=0). The present invention then determined that a combined weighted DNA methylation measurement of both of said CG sites, as disclosed herein, shown in Figure 2, accurately predicted blood DNA age in an independent cohort (GSE40279 n=656 and GSE2219, n=60). As shown in SEQ ID NO:1 disclosed herein, methylation of the CG site of the ElovL2 AS1 region progressed from nearly 0% in fetuses to nearly 90% in people aged 90 years. Thus, this ElovL2 AS1 region as shown in SEQ ID NO:1 and the CG sites found within said region are the dinucleotide sequence positions as described in Table 1 disclosed herein, which alone show an almost perfect correlation with age, suggesting that a small number of the aforementioned CG sites may be sufficient to determine biological age.

[0067] CG site of ElovL2 AS1 region predicts age in saliva samples It is important to note that no qualified medical professional is required to derive biological material to assess the broad applicability of the disclosed DNA methylation age test. In this example, the present invention determined whether the disclosed highly correlated CG sites, which are the dinucleotide sequence positions as described in Table 1 located in the ElovL2 AS1 region as shown in SEQ ID NO:1 disclosed herein, can be used as age predictors in saliva by testing publicly available 450K Illumina array methylation data of saliva (GSE78874, n = 259). The present invention disclosed a methylation score composed of weighted methylation measurements of cg16867657 and cg21572722, which are the dinucleotide sequence positions as described in Table 1, which are the CG site positions within the region as described in SEQ ID NO:1, the ElovL2 AS1 region and cg09809672 is a CG site on chromosome 1 as described in Table 1 disclosed herein, which predicted age with a mean deviation of 5.62 years and a median deviation of 4.74 years. Next, the present invention compared the accuracy of the model disclosed herein with the gold standard Horvath clock. As shown in FIG. 3, the performance of the ElovL2 AS1 region site was slightly better than the Horvath clock. It should be noted that the value of the ElovL2 gene in age detection is known in the art. However, the present invention is based on the fact that the previous knowledge has led to the identification of two CG sites (i.e., cg16866757 and cg2 We disclose that the ElovL2 gene (1572722) overlooked the fact that in its antisense orientation to the ElovL2 gene ElovL2-AS1 gene is actually in the upstream region of a different gene (see physical representation shown in FIG. 1 as disclosed herein), said upstream region now being referred to as the ElovL2 AS1 region and disclosed herein as set forth in SEQ ID NO: 1. This region upstream of the ElovL2 AS1 gene contains the 13 selected CG sites (see Table 1 above).

[0068] [Example 2] Bisulfite conversion, multiplex amplification, next-generation sequencing and methylation calculation of 13 CGs in the ElovL2 AS1 region The disclosure further discloses that saliva collected by subjects or customers and mailed to the lab in DNA stabilizing buffer (Tris 10 mM, EDTA 10 mM, SDS 1%) was incubated with proteinase K in the lab (30 minutes at 37°C for 200 micrograms). Genomic DNA was then purified using a Qiagen kit. The purified DNA was treated with sodium bisulfite, for example, using the EZ DNA bisulfite kit. A library of target sequences was generated by a two-step PCR reaction using the following primers in a standard Taq polymerase reaction: For PCR1--an amplicon corresponding to the sequence depicted in SEQ ID NO:1 was amplified. Forward primer shown in SEQ ID NO:5: 5'ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNYGGGYGGYGATTTGTAGGTTTAGT3' Reverse primer shown in SEQ ID NO:6: 5'GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC3' For PCR2 - To barcode the samples, a second PCR reaction was performed using the following primers: Forward primer shown in SEQ ID NO:7: 5'AATgATACggCgACCACCgAgATCTACACTCTTTCCCTACACgAC3' Barcoding reverse primer shown in SEQ ID NO:8: 5'CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG3' (the bases in red are the index, up to 200 variations of this index are used). The second set of primers introduced an index for each sample, as well as reverse and forward sequencing primers. PCR products 2 from all samples were combined and purified with AMPpure-XP beads (NEB). Libraries were quantified by QPCR and loaded onto a MiSeq flow cell. Rapid Q files were aligned to relevant genomic regions using BisMark or other editing software.

[0069] [Example 3] The region upstream of the ElovL2 AS1 gene region, the superior performance of 13 CG sites in the ElovL2 AS1 region shown in SEQ ID NO:1 Next, the present disclosure determined whether the combined weighted methylation score of the disclosed 13 CG sites provided superior predictive performance when compared to either 2 or 3 CG sites. Saliva samples were collected from 65 volunteers at the Hong Kong Science Park and methylation levels at the 13 CG sites (see Table 1 above) were determined as described in Example 2. The present disclosure performed a series of multivariate linear regressions with different combinations of CG sites. The results, shown in Figure 4 disclosed herein, demonstrate that the combined weighted methylation score of the disclosed 13 CG sites (part D of Figure 4) provides a predictive performance that is comparable to that of the 13 CG sites (part D of Figure 4). The results showed that the combination of 13 CG sites (see Figure 4, part A) performed better than any combination of 4 CG sites (see Figure 4, part B) or Illumina2 CG sites (see Figure 4, part C). The results regarding the superiority of the combination of 13 CG sites over smaller combinations of 4, 5, or 2 CG sites were further substantiated with statistical comparison data, as shown in Figure 4E. The Pearson product moment correlation coefficient r of the 13 CG methylation scores was 0.95 (p = 1.8x10-33).

[0070] [Example 4] Determining the biological age of saliva samples from customers As explained above, biological age is an important parameter of our health. However, since the test disclosed in the present invention is intended to be performed by the subject, it is important that the test is simple for the customer at home outside of the specialized medical system and does not require blood to be drawn by a medical professional as a preferred embodiment. As blood drawing itself may be a mildly risky procedure, it can also be delivered to a state-of-the-art central laboratory facility by regular sea mail. Thus, the present invention discloses that the 13 CG site of the ElovL2 AS1 region forms the basis of an epiaging test that provides such an opportunity. In the disclosed invention, the customer orders a saliva test kit through the epiaging app, web or email, which contains a stabilizing buffer that keeps the DNA stable for up to one month. The stabilizing buffer includes 20 mM Tris-HCl (pH 8.0), 20 mM EDTA, 0.5% SDS, and 1% Triton X-100. The barcode kit is mailed to the customer's residence. The customer scans the barcode with the scanner on his phone and registers in the app that links the barcode to his phone's internal ID. Following the instructions included with the epi-aging test kit, the customer spits into a collection tube, transfers the saliva to a tube containing stabilizing buffer, and sends the tube in a postage-prepaid envelope to the lab. At the lab, DNA is extracted, bisulfite converted (a chemical bisulfite conversion process converts unmethylated C to T and methylated C to remain C), and the ElovL2 AS1 region is amplified as described in Example 3 and sequenced with samples from other patients on a MiSeqIllumina sequencer. The fastQ files are analyzed and the methylation value (m) of the 13 CG sites is calculated: mCGn = CGnC counts / (CGnT counts + CGnC counts). The values ​​are then input into the following formula to calculate biological age: Biological age = (CG1 * 87.5643 + CG2 * 6.3301 + CG3 * -0.8691 + CG4 * 1.9468 + CG5 * 40.0336 + CG6 * 49.4303 + CG7 * -14.7868 + CG8 * 22.9042 + CG9 * -49.7942 + CG10 * 111.7467 + CG11 * 41.8108 + CG12 * 0.4144 + CG13 * -150.8005)-71.6872 The biological age is then sent to the customer in the EpiAging app or the customer can retrieve the result using their barcode ID. A significantly older biological age than chronological age (+5 years) serves as a "red flag" for lifestyle changes. Customers measure their biological age periodically (every 6-12 months) to evaluate their progress in reducing the gap between their biological and chronological ages.

[0071] [Example 5] Epi-Aging app for managing your biological age test and lifestyle data The present invention discloses an Epi-Aging app (see FIG. 5 for the app's homepage), which is compatible with either the Apple or Android operating system and provides information about the "Epi-Aging Test", how to order, an ordering cart, and links to electronic payment methods such as PayPal and Alipay. The innovative aspect of the present application is to combine the customer-based administration of the “Epi-Aging Test” with a system for dynamic recommendation “self-reporting” by reputable national and international medical groups, sharing of data, machine learning, iterative changes driven by iterative machine learning, personalized reporting to the customer, and iterative evaluation (see Figure 6). A system of “reinforcement learning” prescribes the lifestyle changes that will have the greatest impact on reducing accelerated aging as determined by DNA methylation age and chronological age differences. The app provided herein is open source and built by programs known to those skilled in the art such as Build Fire JS, Ionic, Appcelerator’s Titanium SDK, Mobile angular UI, Siberian CMS, etc. The app is downloaded from either the Apple store, Google Play store, and website. The app requires registration and assignment of a customer ID. The app activates a scanner that scans the barcode to link the test barcode with the customer’s ID. The data is linked to these “blind” IDs. Personal and customer data are separated by a "firewall" and tokenized to protect the complete blinding of "aging" and lifestyle data. The data management system has no access to personal data. The system is built in such a way that the customer can only initiate it by using their email account, but recovers the personal identity, completely blinded from the data management system. This is the basic functionality of the app, data blinding. There are several buttons on the front page of the app (see Figure 5). One button links to basic information about the "aging" test and scientific citations, and a PubMed link allows a wider and deeper knowledge of the area. This information provides information about the link between lifestyle and "aging". The second button links to a page containing a series of buttons linking to lifestyle and wellbeing domain buttons such as "Mood", "Chronic Pain", "Physiological Measurements" such as blood pressure, heart rate, weight, height, fasting sugar and other metabolic tests, medications, drugs of abuse, alcohol, smoking, and exercise data entered by the customer (including intake of dietary supplements such as SAMe, vitamins, etc.), and "Nutrition". Each section is preceded by recommendations collected from reputable associations such as the American Heart and Stroke Association, the Diabetes Association, and the American Cancer Society. The recommendations section contains links to these associations so that the customer can make their own judgments and decisions. The idea behind the lifestyle management section is self-development and taking control of the customer's lifestyle decisions. Data entry is done by moving a numerical scale. YES-NO entries are scaled as 0 for NO and 1 for YES. Other quantifiable entries are entered by quantity. Along with each data entry scale, a scaled presentation of the recommendations is presented to provide the customer with an assessment of their performance relative to the color-coded recommendations. The range of recommendations is shown in green. Deviations from the recommendations are shown in red above the range and blue below. A save button is clicked by the customer at the end of data entry in each section, allowing the data to be saved. Once the data is entered, a summary analysis report is provided. A chart is also provided illustrating progress over time in relation to the country recommendations. Once the lab aging test is complete, the test is delivered remotely to the app. The customer data, along with other customers' data, is stored in a cloud-based database for further analysis.

[0072] [Example 6] Machine learning-driven analysis of health and DNA methylation age data and personalized recommendations for lifestyle improvements The data from multiple users is stored in a cloud-based SQL database (see FIG. 7). “Machine learning” algorithms are used to analyze the data, building models that correlate input questionnaire measurements such as pain, blood pressure, BMI, mood, etc., with the difference between DNA methylation age and chronological age as output. For example, methods such as “neural networks”, decision trees, random forest lasso regression, K-Means cluster analysis, reinforcement learning, and “penalized regression” are used. The method disclosed in the present invention includes performing statistical analysis on the questionnaire responses and providing dynamic reports to the consumer on the app, describing the progression of the questionnaire responses over time compared to national association recommendations for cancer, heart disease, stroke, diabetes, etc.

[0073] Although the invention has been described in relation to its preferred embodiments, it will be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention.

[0074] [advantage] The innovative aspect of the method disclosed in this invention over what is known in the art is that the combination of 13 CG sites in the ElovL2 AS1 region, previously undescribed, provides highly accurate age prediction from one single amplicon saliva sample. Multiplexing and use of robust next generation sequencing improves accuracy and simplicity. This approach significantly reduces costs and makes the test feasible for as-consumer product applications.

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Claims

1. Steps below: (a) extracting DNA from a subject matrix; (b) measuring DNA methylation of DNA extracted from the substrate to obtain a DNA methylation profile; (c) obtaining a polygenic score by analyzing the DNA methylation profiles of 13 human CG sites located in the ElovL2 AS1 region, which is a putative antisense region to the ElovL2 gene, as shown in SEQ ID NO: 1, using multiple linear regression equations or neural network analysis; and (d) determining the biological age of the subject from the polygenic score; 1. A method for calculating the biological age of a subject, comprising: extracting the DNA includes extracting genomic DNA from saliva obtained from the subject; The 13 human CG sites are located at genomic coordinates 56-57, 59-60, 62-63, 68-69, 70-71, 72-73, 75-76, 83-84, 89-90, 111-112, 123-124, 130-131, and 138-139, with reference to SEQ ID NO:1; method.

2. The DNA methylation measurement is performed using a method comprising DNA pyrosequencing, mass spectrometry-based methods, PCR-based methylation assays, targeted amplicon next-generation bisulfite sequencing on a platform selected from the group of HiSeq™, MiniSeq™, MiSeq™ and NextSeq™ sequencers, Ion Torrent sequencing, methylated DNA immunoprecipitation sequencing, or hybridization to an oligonucleotide array, or The DNA methylation measurements were performed on 13 human CGs located in the ElovL2 AS1 region, which is a putative antisense region for the ElovL2 gene, as shown in SEQ ID NO:

1. performed on polygenic DNA methylation biomarkers, including measuring the methylation status of CG sites within a site; or The DNA methylation measurement was performed using a biotinylated forward primer shown in SEQ ID NO: 2, a reverse primer shown in SEQ ID NO: 3, and a primer sequence shown in SEQ ID NO:

4. or carried out using DNA pyrosequencing with the pyrosequencing primer designated NO: 4; or The DNA methylation measurement is performed using next-generation bisulfite sequencing of the targeted amplicon comprising a forward primer shown in SEQ ID NO:5 and a reverse primer shown in SEQ ID NO:6 on a platform selected from the group of HiSeq, MiniSeq, MiSeq, and NextSeq sequencers; or 2. The method of claim 1, wherein the DNA methylation measurement is performed using a PCR-based methylation assay selected from the group of methylation-specific PCR and digital PCR.

3. Steps below: (a) extracting DNA from a plurality of substrates from a plurality of subjects; (b) measuring DNA methylation of DNA extracted from said plurality of substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profiles of 13 human CG sites located in the ElovL2 AS1 region, which is a putative antisense region to the ElovL2 gene, as shown in SEQ ID NO: 1, using multiple linear regression equations or neural network analysis to obtain a polygenic score; and (d) determining biological age across multiple subjects from the polygenic scores; 1. A method for calculating biological age across multiple subjects, comprising: extracting the DNA includes extracting genomic DNA from saliva obtained from the subject; The 13 human CG sites are located at genomic coordinates 56-57, 59-60, 62-63, 68-69, 70-71, 72-73, 75-76, 83-84, 89-90, 111-112, 123-124, 130-131 and 138-139 with respect to SEQ ID NO: 1; method.

4. The DNA methylation measurement of the DNA extracted from the plurality of substrates comprises the following steps: (a) amplifying genomic DNA extracted from a plurality of substrates with target-specific primers to obtain a PCR product 1; (b) amplifying PCR product 1 of step (a) by barcoding primers to obtain PCR product 2; (c) performing multiplex sequencing using PCR product 2 of step (b) in a single next-generation Miseq sequencing reaction; (d) extracting data from the multiplexed sequencing of step (c); and (e) quantifying DNA methylation from the extracted data of step (d) to obtain a DNA methylation profile for each substrate. Including, extracting the DNA includes extracting genomic DNA from saliva obtained from the subject; The target-specific primers for obtaining the PCR product 1 include a forward primer shown in SEQ ID NO: 5 and a reverse primer shown in SEQ ID NO: 6, and the barcode primers for obtaining the PCR product 2 are barcode index primers, and include a forward primer shown in SEQ ID NO: 7 and a reverse primer shown in SEQ ID NO:

8. and a reverse primer set forth in SEQ ID NO:

8.

5. The method of claim 2, wherein the DNA methylation biomarkers include a combination of two or more of the 13 human CG sites.

6. Steps below: (a) evaluating entries in a computer readable medium obtained through the sharing of user data from subjects; (b) matching the entries of step (a) to a kit obtained from the subject to determine the biological age of the subject, the kit comprising: Means and reagents for collection and stabilization of the subject's substrate; A scanner to read the barcode on the kit; Instructions for collection and stabilization of the substrate; Including, the substrate is saliva of a subject; The stabilization of said substrate is performed by mailing the collected substrate to extract DNA for the purpose of measuring DNA methylation of the DNA extracted from the substrate, to obtain a DNA methylation profile of the subject and to determine the biological age of the subject. (c) calculating the biological age of the subject using the method of claim 1 or claim 3 to obtain a calculated biological age; (d) integrating the calculated biological age of step (c) into a machine learning model for said subject by performing a statistical analysis using the assessment of step (a) to obtain an integrated data report; (e) generating a dynamic report for the subject by analyzing the integrated data report of step (d) along with the progress of the responses to the questionnaire obtained by sharing user data from the subject over time; and (f) sharing the dynamic report of step (e) in a computer readable medium with the subject to provide recommendations regarding lifestyle changes; Including, A computer-implemented method for providing lifestyle change recommendations.

7. The computer readable medium comprises: An open source development tool comprising information about a test for calculating biological age based on the method of claim 1 or claim 3. a virtual shopping cart for ordering said tests; A scanning function for scanning the barcode of the kit according to claim 5; and the ability to receive test results from the lab; Including, 7. The computer-implemented method of claim 6, wherein the open source development tool includes questionnaires including basic physiological measurements, weight, height, blood pressure, heart rate, self-assessment of mood, McGill Pain Questionnaire, diet and nutrition questionnaire, exercise questionnaire and lifestyle questionnaire including alcohol, drugs, smoking, and combinations thereof contained in a computer readable medium to investigate lifestyle features that impact healthy aging.

8. Creating a health ecosystem focused on normalizing or slowing biological aging in subjects using Android™, Apple™, or We Chat™ mini-programs for personalized lifestyle recommendations, or using standard data pipelines across multiple subjects and management systems such as Cloud data prep™ to deliver data to servers or Amazon™ servers. Storing data in object storage enterprise on cloud servers such as Ali Cloud™, Microsoft Azure™, etc.; 8. The computer-implemented method of claim 7, comprising:

9. 8. The computer-implemented method of claim 7, comprising using a set of artificial intelligence algorithms such as Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Generalized Linear Model (GLM), and Deep Learning (DL) to calculate the weighted contribution of different lifestyle measures to biological age across a subject or multiple subjects, which is dynamically updated and provides personalized lifestyle recommendations for lifestyle changes.

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