EPI Aging: A Novel Ecosystem for Managing Healthy Aging

Abstract

A method for calculating the biological age of a subject and across multiple subjects by performing multi-gene DNA methylation on biomarkers is provided, the method comprising measuring the methylation status of 13 CG sites located in the putative antisense region of the ElovL2 gene, namely the ElovL2 AS1 region. Further, a computer-implemented method for providing recommendations for lifestyle changes in the form of a self-learning "ecosystem" is provided, the self-learning "ecosystem" using new measurements of the DNA methylation clock as continuous dynamic results for lifestyle management of biological aging. Also provided is a combination of DNA methylation biomarkers for calculating biological age and a kit for determining the biological age. Further, disclosed is the use of a disclosed method for calculating biological age in a method for evaluating the effect of biological interventions and a method for screening anti-aging agents.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority under section 119(e) to U.S. Provisional Application Serial No. 62 / 854,226, filed on May 29, 2019, entitled “EpiAging: Novel Ecosystem for Managing Healthy Aging,” the contents of each of which are incorporated herein by reference.

[0003] Sequence Listing

[0004] This application contains a sequence listing, which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. The ASCII copy, created on May 28, 2020, is named TPC57505_SeqList_ST25.txt and is 4,096 bytes in size. Technical Field

[0005] The present invention generally relates to epigenetic and DNA methylation signatures in human DNA, and more specifically to methods for determining epigenetic aging and managing healthy aging in individuals based on DNA methylation signatures. More specifically, the present invention provides a method involving DNA methylation signatures for molecular diagnostics, health management, and lifestyle changes for personalized healthy aging using App digital technologies. Background Art

[0006] While chronological age is understood as the number of years an individual has lived, biological age, also known as physiological age, indicates how old an individual appears to be. Because people age at different rates, determining an individual's biological age is a challenge. Some people "look" and "feel" older than their chronological age, while others appear younger than their actual age. Although a person's chronological age is generally correlated with biological age, this is not always the case. Compared to chronological age, biological age is a better parameter to reflect an individual's health, well-being, and lifespan. As an equivalent to physiological age, biological age reflects and is influenced by several lifestyle factors, including diet, exercise, and sleep habits. But assessing an individual's biological age remains a challenge. Importantly, the need to estimate true biological age is driven by the idea that this may lead to the testing and design of interventions that will slow the rate of biological aging. Over the past few decades, a great deal of effort has been invested in identifying different parameters that can predict biological aging and lifespan, such as measures 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, the discovery of most of these markers is no more advantageous than knowing a person's chronological age.

[0007] Recently, advances in molecular biology have introduced new molecular measures of aging. "Telomere length" (Monaghan, 2010) and "metabolic metrics" (Hertel et al., 2016) have been used to predict biological age. However, although the length of telomeres varies with age, it has been found that the correlation between chronological age and telomere length is weak, and the predictive ability for life span is low. In addition, the technology for measuring telomere length is technically demanding, and technical errors can confuse the determination of age. Another measure that has been used is the "metabolic age score" (Hertel et al., 2016) that measures different metabolites in urine. This technology requires complex methods for measuring different urine components.

[0008] The discovery of the "epigenetic clock" by Horvath (Horvath, 2013) has led to a paradigm shift in the study of biological markers of age. This clock is based on measuring the DNA methylation clock at 353 CG positions in DNA. The degree of methylation of genes included in the methylation clock has been found to correlate more strongly with a person's chronological age than any previous measure, including telomere length, as well as other measures of aging such as hair, skin, and frailty. Importantly, while the DNA methylation clock closely matches the chronological clock for most people, in some individuals it advances faster than the chronological clock, resulting in a person's epigenetic age potentially being significantly greater than their chronological clock. Recent studies have shown that this progression in the DNA methylation clock predicts premature death from various causes. A recent analysis of 13 different studies totaling 13,089 individuals demonstrated that the epigenetic clock can predict all-cause mortality independently of several risk factors, such as age, body mass index (BMI), education, smoking, physical activity, alcohol consumption, tobacco use, and certain comorbidities (Chen et al., 2016).

[0009] A recent review by Pedersen and Hagg in EBiomedicine concluded: "Although telomere length is the best-studied predictor of biological age, many new predictors are emerging. The epigenetic clock is currently the best predictor of biological age because it correlates well with age and predicts mortality ( Pedersen and Hagg, 2017). Technical biases in telomere length measurements may also contribute to the lack of consistent results. The authors conclude: "In short, telomere length has been extensively validated but has low predictive power. Composite biomarkers are not yet fully validated but have the potential to be stronger predictors than telomeres, as do metabolic age scores. Taking both aspects into account, epigenetic clocks currently perform best ( et al., 2017).

[0010] Valentin Max Vetter et al. compared telomere length and the epigenetic clock as a measure of biological age in 1895 individuals in the Berlin Aging Study II and concluded that although "as previously described, telomere length was significantly shorter in the younger than in the older cohort in the BASE-II cohort, in BASE-II there was a very weak inverse correlation between telomere length and chronological age (R 2 =0.013)”. In contrast, the study found that “the results showed a significant positive correlation between DNA methylation (epigenetic clock) age estimates and chronological age (R2 =0.47), and the significant positive association remained after adjusting for covariates (sex, leukocyte distribution, alcohol, and smoking). The authors concluded: "In conclusion, and as expected, DNAm age was found to be a more accurate predictor of chronological age than telomere length (Vetter et al., 2018)."

[0011] In a study of two Scottish birth cohorts, telomere length explained 2.8% of the age variance in the combined cohort analysis, while the epigenetic clock explained 34.5% of the age variance. In the same study, also in the combined cohort analysis, a one standard deviation increase in baseline epigenetic age was associated with a 25% increased risk of mortality, while in the same model, a one standard deviation increase in baseline telomere length was independently associated with an 11% decreased risk of mortality (P < 0.047) (Marioni et al., 2018).

[0012] While it is increasingly clear that the "epigenetic clock" is the most accurate measure of biological age to date, available tests require testing a large number of sites using blood, an invasive and costly sample, making it impractical for consumer-driven use in large numbers of patients. While available methods are adequate for research and clinically relevant studies, they are not feasible for consumer-focused use of this test. Therefore, there is a need for accurate, robust, high-throughput, and non-invasive tests.

[0013] The present invention provides a solution to the problem in the form of a system that integrates an accurate and robust saliva-based "Epi-Aging Test" using new CG sites within the entire health ecosystem for self-learning, self-empowered healthy aging using repeated testing of consumer-based and actionable epigenetic clocks integrated with a computer-readable medium, which can alternatively be referred to as an application (App) that enables data collection and communication with consumers, data sharing, and machine learning techniques. Current methods are costly (requiring DNA methylation analysis of many CG sites across different genomes) and invasive (using blood) and are stand-alone, and do not provide guidance for improving age scores. Although general concepts of behaviors that have a positive impact on health are recommended in the medical literature, the exact personalized combination of lifestyle changes that may be useful for a specific 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, which combines data sharing, machine learning, and personalization of health interventions managed by consumers through the App. Data is completely blinded and shared only between consumers and no other external parties. The consumer's motivation to share data is the fact that he / she gets higher quality advice to improve his / her health by participating in the sharing community, thus the benefits of sharing data are dynamically and repeatedly delivered to the consumer by getting higher quality lifestyle assessments and recommendations.

[0014] Purpose of the Invention

[0015] The main object of the present invention relates to a method for calculating the biological age of a subject, the method comprising the following steps: extracting DNA from a substrate of the subject; measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; analyzing the DNA methylation profile to obtain a polygenic score; and determining the biological age of the subject based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject.

[0016] Another object of the present invention relates to a method for calculating the biological age of a subject, wherein the biological age is derived from a polygenic score, which is obtained from a measured DNA methylation profile for a polygenic DNA methylation biomarker, the method comprising measuring the methylation status of CG sites within any one and combination of human CG sites, wherein the human CG sites are located in the putative antisense region of the ElovL2 gene as shown in SEQ ID NO: 1, i.e., the ElovL2AS1 region.

[0017] Another object of the present invention relates to a method for calculating biological age across multiple subjects, the method comprising the following steps: extracting DNA from multiple substrates from multiple subjects; measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; analyzing the DNA methylation profile to obtain a polygenic score; and determining the biological age across multiple subjects based on the polygenic score, wherein extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the multiple subjects.

[0018] Yet another object of the present invention relates to a kit for determining the biological age of a subject, the kit comprising: an apparatus and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and wherein stabilizing the substrate is for mailing the collected substrate for DNA extraction, so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject.

[0019] Yet another object of the present invention relates to a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the steps of: evaluating entries in a computer-readable medium as obtained by sharing user data from a subject; matching the entries with a kit for determining the biological age of a subject as obtained from the subject for determining 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 the biological age across multiple subjects to obtain a calculated biological age; integrating the calculated biological age for the subject in a machine learning model by performing statistical analysis using the evaluation of the entries in the computer-readable medium as obtained by sharing user data from the subject to obtain an integrated data report; preparing a dynamic report for the subject by analyzing the integrated data report and the progress of questionnaire responses as obtained by sharing user data from the subject over time and comparing the integrated data report and the questionnaire responses with the recommendations of a national association; and sharing the dynamic report on the computer-readable medium with the subject to provide recommendations for lifestyle changes.

[0020] An alternative object of the present invention relates to a method for developing a computer-readable medium, the method comprising the steps of storing data derived from a plurality of subjects; analyzing the stored data; and building a model, wherein the step of storing data derived from a plurality of users comprises a cloud-based SQL database, wherein the step of analyzing the stored data comprises a method selected from the group consisting of deep machine learning, reinforcement learning, and machine learning, or a combination thereof, and wherein the step of building the model comprises correlating the input questionnaire measurements and the output as the difference between DNA methylation age and chronological age, as well as other physiological and psychological outputs such as pain, blood pressure, BMI, and mood. Summary of the Invention

[0021] Thus, the present invention provides methods and materials that can be used to assess the progression of age, the impact of lifestyle, and provide personalized lifestyle recommendations regarding lifestyle changes based on the calculation of biological age by analyzing DNA methylation at a CG site or CG position located upstream of the gene encoding antisense mRNA to the ElovL2 gene (ElovL2 AS1 region) in a substrate of DNA extracted from a substrate comprising blood and saliva from a subject or across multiple subjects.

[0022] An embodiment of the present invention relates to a method for calculating the biological age of a subject, the method comprising the following steps: extracting DNA from a substrate of the subject; measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; analyzing the DNA methylation profile to obtain a polygenic score; and determining the biological age of the subject based on the polygenic score, wherein extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject.

[0023] Because the present invention has discovered that age progression is highly correlated with methylation of CG positions or CG sites located in or mapped to the upstream region of the gene encoding the antisense mRNA for the ElovL2 gene (referred to as the ElovL2 AS1 region), another embodiment of the present invention relates to a method for calculating a subject's biological age, wherein the method includes a step of measuring DNA methylation, the step being performed for a multi-gene DNA methylation biomarker, the method including measuring the methylation status of a CG site within any one of the human CG sites described in Table 1, which provides CG positions and combinations thereof as disclosed herein, occurring on human chromosome 6, and which are mapped to the putative antisense region of the ElovL2 gene, i.e., the ElovL2 AS1 region, as shown in SEQ ID NO: 1. The present invention has discovered that targeted amplicon sequencing of this region revealed the aforementioned 13 novel CG site combinations as described in Table 1, which provides CG positions as disclosed herein, occurring on human chromosome 6, in the ElovL2 AS1 region, as shown in SEQ ID NO: 1, and that methylation at these positions is highly correlated with biological age in saliva. The linear regression equation of the present invention reveals the regression coefficients of these sites with age, wherein the combined weighted equation of these sites accurately predicts biological age.

[0024] Embodiments of the present invention disclose a method for calculating biological age across multiple subjects, the method comprising the following steps: extracting DNA from multiple substrates from multiple subjects; measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; analyzing the DNA methylation profile to obtain a polygenic score; and determining the biological age across multiple subjects based on the polygenic score, wherein extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subjects. The present invention discloses a method for accurately measuring DNA methylation age in saliva from hundreds of people simultaneously by amplifying target-specific primer sequences followed by barcoded primers in a single next-generation Miseq sequencing reaction, and performing multiplexed sequencing, data extraction, and methylation quantification, by determining DNA methylation in multiple genomes as described in Table 1, wherein 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 present invention also discloses the measurement of methylation of the DNA methylation CG sites as described in Table 1, which provides the CG positions in the ElovL2 AS 1 region as shown in SEQ ID NO: 1 and appearing on human chromosome 6, using pyrosequencing determination or methylation-specific PCR or digital PCR. The present invention also discloses the calculation of a polygenic weighted methylation score for predicting age.

[0025] An embodiment of the present invention discloses a kit for determining the biological age of a subject, the kit comprising: an apparatus and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and wherein stabilizing the substrate is for mailing the collected substrate for DNA extraction, so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject.

[0026] Embodiments of the present invention disclose a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the following steps: evaluating entries in a computer-readable medium, such as obtained by sharing user data from a subject; matching the entries with a kit as described herein and obtained from the subject for determining 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 the biological age across multiple subjects as described herein to obtain a calculated biological age; integrating the calculated biological age for the subject in a machine learning model by performing statistical analysis using the evaluation of the entries in the computer-readable medium to obtain an integrated data report; preparing a dynamic report for the subject by analyzing the integrated data report and the progress of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses with recommendations of a national association; and sharing the dynamic report on the computer-readable medium with the subject to provide recommendations for lifestyle changes. Thus, the present invention discloses a computer-implemented method as disclosed herein, which is a novel process for integrating repeated DNA methylation age measurements of biological age in saliva with dynamic lifestyle changes using a computer-readable medium, which may alternatively be referred to as an App to manage these changes. Since DNA methylation age determination only requires saliva, the disclosed method of the present invention provides a consumer-initiated test sequence via a computer-readable medium or App, where saliva is spit into a saliva collection kit, which is then mailed to a laboratory for a DNA extraction kit and subsequent DNA methylation analysis. Lifestyle changes are recorded in the App; methylation data, along with lifestyle data, are captured in a database and continuously and iteratively analyzed by machine learning programs such as neural networks.

[0027] Embodiments of the present invention disclose a method for developing a computer-readable medium, the method comprising the steps of: storing data derived from multiple subjects; analyzing the stored data; and constructing a model, wherein the step of storing the data derived from multiple users comprises a cloud-based SQL database, wherein the step of analyzing the stored data comprises a method selected from the group consisting of deep machine learning, reinforcement learning, and machine learning, or a combination thereof, and wherein the step of constructing the model comprises correlating input questionnaire measurements with output values ​​of the difference between DNA methylation age and chronological age, as well as other physiological and psychological outputs such as pain, blood pressure, BMI, and mood. The data shared by multiple consumers is continuously analyzed to construct a model that correlates input lifestyle changes with output values ​​of the difference between DNA methylation age or biological age and chronological age, provided by the methods disclosed herein. The model disclosed herein is applied to the individual's data, and the model generates recommendations regarding the individual's lifestyle changes. The input lifestyle changes and output values ​​of DNA methylation age or biological age disclosed herein are iteratively measured and used to further reinforce learning, while additional recommendations are broadcast to the consumer's app.

[0028] The present invention provides methods that can be used by those skilled in the art to measure biological age and the relationship between lifestyle changes and DNA methylation age. The DNA methylation markers (CGIDs) described in Table 1, which depicts selected CG positions in the upstream region of human chromosome 6 in the newly discovered gene ElovL2 AS1 as shown in SEQ ID NO: 1 disclosed herein, can be used in consumer-initiated saliva-based tests for determining DNA methylation aging or biological age, as well as for reporting and modifying lifestyle parameters using a "share" app or computer-readable medium and a machine learning system. The present invention can be used in consumer-initiated saliva-based tests. The present invention has been demonstrated to be useful for measuring "biological age" using a polygenic score based on the DNA methylation measurement method disclosed herein or by using other methods available to those skilled in the art for measuring DNA methylation, including simultaneous targeted amplicon sequencing of the antisense ELOVL2 AS1 region as shown in SEQ ID NO: 1 disclosed herein across hundreds of individuals or subjects, such as next-generation bisulfite sequencing, pyrosequencing, MeDip sequencing, Ion Torrent sequencing, Illumina 450K arrays, and Epic microarrays. The present invention also discloses the utility of the present invention for integrating DNA methylation measurements into a comprehensive plan of lifestyle changes using the "Epi-Aging" App disclosed herein, which can be developed by a person skilled in the art using open source and other programs such as Build Fire JS, Ionic, Appcelerator's Titanium SDK, Mobileangular UI, and Siberian CMS. The data, which can be processed by anyone skilled in the art, will be stored in a database such as MySQL, stored in a cloud server such as Azure Cloud or Amazon Cloud. The data will be analyzed using machine learning platforms such as neural networks using open source programs such as tensor flow or 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 and recommendations for combinations of lifestyle changes that may affect their healthy aging. The present invention also discloses the utility of the "Epi-Aging" DNA methylation test and App for measuring the impact of interventions on their biological age by sending saliva before and after recommended lifestyle changes to measure DNA methylation age.

[0029] For those skilled in the art, other objects, features and advantages of the present invention will become apparent from the following detailed description. However, it will be understood that although some embodiments of the present invention are indicated, the detailed description and specific examples are provided by way of illustration and not limitation. Many changes and modifications may be made within the scope of the present invention without departing from its spirit, and the present invention encompasses all such modifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 DNA methylation of a CG site in the upstream antisense region of the ElovL2 gene, known as the ElovL2 AS 1 region, is associated with age. An IGV browser view depicts the locations of two CGs, cgl6867657 and cg21572722, in the ElovL2 AS 1 region, as described in Table 1 disclosed herein, in the human genomic region surrounding the CG site. Pearson correlations between the methylation status of CGIDs in the blood cell genome on the publicly available Illumina 450K array and age revealed that the top CG was cgl6867657, with a Pearson product-moment correlation coefficient of r = 0.934 (p = 0), and that the adjacent site, cg21572722, had a correlation coefficient of r = 0.81004 (p = 0), indicating that the methylation status of the discovered CG sites is associated with age in the ElovL2 AS 1 region. Inspection of the genomic location of this CG revealed that it was a member of 13 CG (indicated) sequences located in a previously uncharacterized region, the ElovL2 AS1 region of the ElovL2 antisense gene.

[0031] Figure 2 ElovL2 AS1 region CG sites are highly correlated with age in saliva. The correlation between the methylation score of the weighted methylation levels of CG sites in the ElovL2 AS1 region and age in publicly available blood Illumina 450K arrays (GSE40279, n=656 and GSE2219, n=60) is depicted. The CG sites are cgl6867657, cg21572722, such as those located in chromosome 6, and cg09809672, such as those located in chromosome 1 (see Table 1 for the location in the genome). The analysis revealed a strong correlation between methylation and age across all ages.

[0032] Figure 3.Correlation of methylation at CG sites in the ElovL2 AS1 region with age in saliva, and comparison with the Horvath epigenetic clock. Depicted is the correlation of methylation at CG sites in the ElovL2 AS1 region, cgl687657 and cg21572722, with age in saliva, and comparison with the Horvath epigenetic clock. A. Correlation between the combined methylation score of cgl687657 and cg21572722 (HKG) using the DNA methylation profile of GSE78874 saliva and age. B. Correlation with the gold standard Horvath methylation clock score 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 average bias in predicting age compared to the gold standard Horvath clock.

[0033] Figure 4 . Prediction of age in saliva using the 13CG ElovL2 AS1 polygenic score. The utility of the present invention is depicted. A. Using the methylation levels of saliva DNA from 65 people, the methylation score for predicting age was calculated using the linear regression equation for predicting age as a function of the weighted methylation levels of CG sites 1, 5, 6, and 9 in the ElovL2 AS1 region (for locations in the genome, see Table 1). Figure 1 The ElovL2 AS1 region described in

[15] was amplified from bisulfite-converted saliva DNA and multiplexed on a Miseq next-generation sequencer. B. Methylation score for predicted age calculated using the linear regression equation for predicted age as a function of weighted methylation levels of CG sites 1, 2, 5, 6, and 9 in the ElovL2 AS1 region (for positions in the genome, see Table 1). C. Methylation score for predicted age calculated using the linear regression equation for predicted age as a function of weighted methylation levels of CG sites 1 and 2 in the ElovL2 AS1 region (for positions in the genome, see Table 1). Methylation score for predicted age calculated using the linear regression equation for predicted age as a function of weighted methylation levels of all CG sites in the ElovL2 AS1 region (for positions in the genome, see Table 1). E. Comparison of predicted values ​​of different methylation scores. The equation containing all 13 CG sites outperforms any other combination.

[0034] Figure 5 Epi Aging App. Depicts the homepage of the Epi Aging App.

[0035] Figure 6A health ecosystem based on the Epi Aging App. This section describes the utility of the Epi Aging App and data-driven lifestyle management. The center of the health ecosystem is the Epi Aging App. The app allows customers to learn about DNA methylation, biological aging, and nutritional supplements. The app allows customers to input lifestyle, cardiovascular health, mood, nutrition, sleep, gender, and pain data. The app allows customers to order a saliva test kit through a marketplace. The saliva kit is delivered by mail, and the customer scans a barcode that assigns them an ID that links their phone ID to the test ID. The customer sends the saliva kit to the lab via prepaid mail. Methylation age data and iterative lifestyle data from the lab are broadcast from the app and the lab to an SQL database. Similarly, other customers send their lifestyle data and DNA methylation data to the database. Machine learning algorithms analyze the data using deep learning and iterative input data. A model is calculated that defines the weight of each input in determining the outcome. The model analyzes individual data and delivers the predicted outcome (delta DNA methylation minus chronological age) of changes in lifestyle to the app. Based on the direction and extent of change in DNA methylation age relative to chronological age, the customer makes lifestyle changes and orders a new saliva test with iterative analysis and additional recommendations, and the cycle repeats itself.

[0036] Figure 7 The Epi Healthy Ecosystem describes the Epi Healthy Ecosystem and its role in healthy aging. The Epi Aging App positions itself at the center of the healthy ecosystem. It allows for real-time delivery of health recommendations from prominent national medical associations. It creates a marketplace for health providers and lifestyle products, as well as a marketplace for different new tests. The app sends data to a universal data server, which iteratively analyzes all the information and, based on the analysis, provides recommendations for lifestyle changes, possible tests, and health provider and supplier information. DETAILED DESCRIPTION

[0037] In the description of each embodiment, reference may be made to the figures forming a part of the description, and in the accompanying drawings, specific embodiments of the present invention may be put into practice by way of illustration. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention. Those skilled in the art understand well and generally adopt many of the techniques and procedures described or referenced herein. Unless otherwise defined, all technical terms, symbols and other scientific terms or special words used herein are intended to have the meanings generally understood by those skilled in the art to which the present invention belongs. In some cases, for clarity and / or for ease of reference, the terms with generally understood meanings are defined herein, and including these definitions herein should not necessarily be interpreted as representing a substantial difference from the meanings generally understood in the art.

[0038] All descriptions of the figures are for the purpose of describing selected versions of the invention and are not intended to limit the scope of the invention.

[0039] All publications mentioned herein are incorporated herein by reference to disclose and describe aspects, methods and / or materials in connection with which the publications are cited.

[0040] DNA methylation refers to the chemical modification of DNA molecules. Technology platforms such as the Illumina Infinium microarray or DNA sequencing-based methods have been found to provide highly robust and reproducible measurements of human DNA methylation levels. There are over 28 million CpG or CG loci in the human genome. Therefore, certain loci have been assigned unique identifiers, such as those found in the Illumina CpG or CG locus database. Here, we use the CG locus designation identifier.

[0041] definition:

[0042] As used herein, the term "CG" refers to a dinucleotide sequence containing cytosine and guanosine bases in DNA. As used herein, a CG position referred to as a "CG site" is a position in the human genome defined by the chromosome and nucleotide position in the reference human genome hgl9.

[0043] As used herein, the term "β value" refers to the calculation of the methylation level at a CGID position obtained by normalization and quantification of an Illumina 450K or EPIC array using the following: the intensity ratio between methylated and unmethylated probes and the formula: β value = methylated C intensity / (methylated C intensity + unmethylated C intensity), wherein the value ranges between 0 and 1, where 0 is completely unmethylated and 1 is completely methylated.

[0044] As used herein, the term "decision tree" is a type of data mining algorithm that selects from among a number of variables and interactions between variables that are most predictive of the response or outcome to be explained (Mann et al., 2008).

[0045] 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 given outcome or response (Shi, Seligson, Belldegrun, Palotie, & Horvath, 2005; Svetnik et al., 2003).

[0046] As used herein, the term "lasso regression" is a method for variable selection for linear regression models that identifies the minimum subset of predictors required to predict the outcome (response variable) with minimal prediction error (Kim, Kim, Jeong, Jeong, & Kim, 2018).

[0047] As used herein, the term “K-means cluster analysis” is an unsupervised machine learning method that partitions observations into a smaller set of clusters, where each observation belongs to one cluster (Beauchaine and Beauchaine, 2002; Kakushadze and Yu, 2017).

[0048] As used herein, the term “reinforcement learning” involves receiving feedback from data analysis and learning through trial and error. A series of successful decisions will lead to a reinforcement process (Zhao, Kosorok, and Zeng, 2009).

[0049] As used herein, the term "penalized regression" refers to a statistical method that aims to identify the minimum number of predictors required to predict an outcome from a larger list of biomarkers, as implemented, for example, in the R statistical package "penalized" as described by Goeman, JJ, L1 penalized estimation in the Cox proportional hazards model, Biometrical Journal 52(1), 70-84.

[0050] As used herein, the term "clustering" refers to grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in one sense or another) to each other than objects in other groups (clusters).

[0051] As used herein, the terms "neural networks and deep learning" refer to machine learning methods that incorporate neural networks in several layers to iteratively learn from data. Neural networks treat different data inputs, such as lifestyle variables, as a collection of connected units or nodes called artificial neurons, which have multiple interactions like neurons in the brain (De Roach, 1989; Mupparapu, Wu, and Chen, 2018; Sherbet, Woo, and Dlay, 2018). These interactions drive the output of biological aging, measured as acceleration or deceleration relative to chronological age.

[0052] As used herein, the term "multiple or polygenic linear regression" refers to a statistical method that estimates the relationship between multiple "independent variables" or "predictors," such as the percentage of methylation in multiple CG IDs, and a "dependent variable," such as chronological age. When several "independent variables," such as CG IDs, are included in the model, this method determines the "weight" or coefficient for each CG ID in predicting the "outcome" (e.g., dependent variable, such as age).

[0053] As used herein, the term "Pearson correlation" refers to a statistical method for 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 measures the correlation between 0, indicating no correlation, and 1, indicating a perfect correlation (Hardy and Magnello, 2002).

[0054] The currently disclosed method is based on the discovery of sites in the human genome where methylation status is associated with age, which were discovered by performing a series of Pearson correlations between age and DNA methylation on 450K sites across the genome in available public datasets (GSE61496, GSE98876), and the DNA methylation markers so discovered were validated using data from GSE40729. The analysis identified cgl6867637 as the highest site associated with age (r=0.934827, p=0). The present invention discovered a fragment of the human genome on chromosome 6 that is the antisense sequence of the previously described age-associated ElovL2 gene, an ElovL2 AS1 region containing 13 CGs referred to herein as CG sites, the CG sites being the positions of dinucleotide sequences as described in Table 1 below as disclosed herein, the combined methylation measures of the positions calculated in the multiple linear regression equation providing a polygenic score for biological age in saliva with greater accuracy than previously reported positions in the genome. These sites have not been previously described because they are not included in Illumina arrays. Therefore, the present invention discloses novel CG sites whose combined weighted methylation levels are correlated with age. The present invention further demonstrates herein that by amplifying a single amplicon and measuring hundreds of people simultaneously using indexed next-generation sequencing, it is possible to accurately measure methylation at all 13 CG sites, thereby reducing costs and increasing throughput by using the "Epi-Aging" test disclosed herein. A subject or customer orders a saliva collection kit, spits into a kit collection tube and sends the kit back to a laboratory where DNA is extracted, converted by bisulfite, and the ElovL2 AS1 region and index are amplified. Amplicons from 200 subjects are sequenced in the same Miseq reaction. The FastQ files are analyzed and the methylation levels at the 13 CG sites are determined. Using an equation that relates the weighted methylation values ​​for the 13 CG sites to age, the biological age is calculated and shared with the customer.

[0055] The present invention further discloses and addresses the utility of the currently disclosed methods for dynamically calculating biological age to improve healthy aging in clients by recommending 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. Because lifestyle data and methylation age data, or biological age, are dynamically collected from multiple subjects / users, machine learning can correlate combinations of lifestyle parameter changes with increases or decreases in DNA methylation and biological age. The present invention integrates DNA methylation testing with subject / consumer-centric sharing, learning, and lifestyle change. Subjects / consumers subscribe to and communicate their lifestyle decisions using the EpiAging App or computer-readable media disclosed herein. Improving health is a two-way partnership and collaborative effort, not a one-way flow of instructions from "know-it-all" health professionals (health providers) to "compliant" and passive patients (health consumers). Using the EpiAging App, consumers are presented with the best scientific advice, as distilled from the most prominent national medical associations. Consumers decide which recommendations to implement. Consumers share their decisions using the "blind" App. The consumer receives an ID linked to their mobile phone ID, but personal information such as address, name, email, etc. is "firewalled". The individual interventions and results of multiple users and the DNA methylation age test results are repeatedly analyzed to integrate physiological and psychological results. The data is analyzed using state-of-the-art machine learning algorithms such as neural networks, using, for example, tensor flow. A model is constructed that relates different input parameters and the output delta between DNA methylation age and chronological age. The consumer's personal data is used together with the model, and the recommendations for adjustments are personalized and delivered to the consumer. The present invention provides a grand platform for generating dynamic recommendation models that are constantly improving and inspired by science. The present invention proposes an "evolutionary" platform that dynamically improves with the use of an ever-expanding body of data. As disclosed in the present invention, the well-being of the customer and the learning environment both evolve together in the dynamic interaction between DNA methylation testing, lifestyle changes, shared data and machine learning.

[0056] The invention disclosed herein has multiple embodiments. In one aspect of the invention, the invention provides a polygenic DNA methylation marker set of biological age for lifestyle management of healthy aging, the polygenic DNA methylation marker set being derived using Pearson correlation analysis of the correlation between age and DNA methylation on whole genome DNA methylation across the genome, the whole genome DNA methylation being derived by mapping methods such as Illumina 450K or 850K arrays, whole genome bisulfite sequencing using various next generation sequencing platforms, methylated DNA immunoprecipitation (MeDIP) sequencing or hybridization to oligonucleotide arrays or a combination of these methods.

[0057] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject according to the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject. As disclosed herein, the method for calculating the biological age of a subject, wherein the measuring DNA methylation is performed using a method comprising: DNA pyrosequencing, mass spectrometry-based (Epityper TM ), PCR-based methylation assay, targeted amplicon next-generation bisulfite sequencing performed on a platform selected from the group of HiSeq, MiniSeq, MiSeq and NextSeq sequencers, Ion Torrent sequencing, methylated DNA immunoprecipitation (MeDIP) sequencing or hybridization to an oligonucleotide array. A method for calculating the biological age of a subject as disclosed herein, wherein the measuring DNA methylation is performed for a multi-gene DNA methylation biomarker, the method comprising measuring the methylation status of a CG site within any one of a human CG site and a combination thereof, the CG site being the position of a dinucleotide sequence as described in Table 1 below as disclosed herein, the position being located at SEQ ID NO: 1

[0058] The antisense region of the ElovL2 gene in human chromosome 6, namely the ElovL2 AS1 region, is shown in FIG.

[0059] Table 1: Locations of CG methylation sites (CG sites) corresponding to the 13 CG sites ElovL2AS1 region upstream of the antisense ElovL2 gene as shown in SEQ ID NO: 1 and useful in embodiments of the present invention.

[0060] The positions in the human genome of the selected 13 CG dinucleotides in the CG sites used in various examples herein are found in Table 1 contained herein, which also provides the CG positions in chromosome 1 used in the figures and examples of this application.

[0061]

[0062] * NA means not available

[0063] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, and the measuring DNA methylation is performed using DNA pyrosequencing, the DNA pyrosequencing comprising primers such as a forward biotinylated primer as shown in SEQ ID NO: 2 (AGGGGAGTAGGGTAAGTGAG), a reverse primer as shown in SEQ ID NO: 3 (ACCATTTCCCCCTAATATATACTT), and a pyrosequencing primer as shown in SEQ ID NO: 4 (GGGAGGAGATTTGTAGGTTT).

[0064] In one embodiment, the present invention provides a method for performing DNA pyrosequencing methylation determination using the ElovL2 AS 1 region containing CG sites and combinations thereof for DNA methylation age using primers as disclosed herein and standard conditions for pyrosequencing reactions recommended by the manufacturer (Pyromark, Qiagen), wherein the primers include a forward (biotinylated) primer as shown in SEQ ID NO: 2, an Elovl2_Rv primer as shown in SEQ ID NO: 3, and an Elovl2_Seq primer as shown in SEQ ID NO: 4.

[0065] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject according to the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, wherein the measuring DNA methylation is performed using targeted amplicon next-generation bisulfite sequencing performed on a platform selected from the group consisting of HiSeq, MiniSeq, MiSeq, and NextSeq sequencers, the targeted amplicon next-generation bisulfite sequencing comprising a forward primer as shown in SEQ ID NO: 5 (ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT) and a forward primer as shown in SEQ ID NO: 6 (ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT). Primers such as the reverse primer shown in No. 6 (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC).

[0066] In one embodiment, the present invention provides the use of a multi-gene multiplexed amplicon bisulfite sequencing DNA methylation assay for measuring DNA methylation age in saliva by using an ElovL2 AS 1 region containing CG sites and combinations thereof, wherein the CG sites are the positions of dinucleotide sequences as described in Table 1 as disclosed herein, using primers as disclosed herein and standard conditions involving the following: bisulfite conversion; sequential amplification including the use of: (a) target-specific primers (PCR1) and (b) barcode primers (PCR 2); and multiplexed sequencing in a single next-generation Miseq sequencer (Illumina); demultiplexing using Illumina software; data extraction and methylation quantification using standard methods for methylation analysis including Methylkit; and subsequent calculation of a weighted DNA methylation score for use in calculating the biological age of a subject, wherein the target-specific primers (PCR 1) are as described in SEQ ID NO: 1. The forward primer is as shown in SEQ ID NO:5 (ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT) and the reverse primer is as shown in SEQ ID NO:6 (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC), and wherein the barcode primer (PCR 2) is the forward primer as shown in SEQ ID NO:7 (AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC) and the reverse primer as shown in SEQ ID NO:8 (CAAGCAGAAGACGGCATATACGAGATAGTACATCGGTGACTGGAGTTCAGACGTG), i.e., the barcode index primer. In SEQ ID NO: 5ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT as disclosed herein, the black bases (1-45) are the adaptor and the red bases (46-62) are the targeting sequence. In SEQ ID NO: 6GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTACACRATACTACTTC TCCCC as disclosed herein, the black bases (1-34) are the adaptor and the red bases (35-58) are the targeting sequence.In SEQ ID NO: 8, barcode primer CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG as disclosed herein, the bases in red (25-32) are the index; up to 200 variants using this index were used.

[0067] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject based on the polygenic score, wherein the measuring DNA methylation is performed using a PCR-based methylation assay selected from the group consisting of methylation-specific PCR and digital PCR.

[0068] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject based on the polygenic score, wherein analyzing the DNA methylation profile to obtain the polygenic score comprises using a multiple linear regression equation or a neural network analysis.

[0069] In one embodiment, the present invention provides a method for calculating biological age across multiple subjects, the method comprising the following steps: (a) extracting DNA from multiple substrates from multiple subjects; (b) measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age across the multiple subjects based on the polygenic score, wherein extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subjects.

[0070] In one embodiment, the present invention provides a method for calculating biological age across multiple subjects, the method comprising the following steps: (a) extracting DNA from multiple substrates from multiple subjects; (b) measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age across the multiple subjects based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subjects, and wherein the measuring DNA methylation in the extracted DNA from the multiple substrates comprises the following steps: (i) amplifying the genomic DNA extracted from the multiple substrates with target-specific primers to obtain PCR product 1; (ii) amplifying the PCR product 1 of step (i) by barcode primers to obtain PCR product 2; (iii) performing multiplexed sequencing in a single next-generation Miseq sequencing reaction using the PCR product 2 of step (ii); (iv) extracting data from the multiplexed sequencing of step (iii); and quantifying DNA methylation based on the extracted data of step (iv) to obtain a DNA methylation profile for each substrate. In an alternative embodiment, the present invention provides a method for calculating biological age across multiple subjects, wherein the target-specific primers used to obtain PCR product 1 include a forward primer as shown in SEQ ID NO: 5 (ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNYGGGYGGYGATTTGTAGGTTTAGT) and a primer such as SEQ ID NO: 6 (GTGACTGGAGTTCAGACGTGTGCTGCTCTTCCGATCTCCCTACACRATACTACTTCTCCCC), and wherein the barcode primers used to obtain PCR product 2 include a forward primer as shown in SEQ ID NO: 7 (AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC) and a reverse primer as shown in SEQ ID NO: 8 (CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCAGACGTG), the reverse primer being a barcode index primer.

[0071] In one embodiment, the present invention provides a combination of DNA methylation biomarkers for calculating biological age, wherein the combination of DNA methylation biomarkers includes human CG sites and combinations thereof, and the human CG sites are located in the putative antisense region of the ElovL2 gene as shown in SEQ ID NO: 1, i.e., the ElovL2 AS 1 region. In one embodiment of the present invention, 13 CG sites located in the putative antisense region of the ElovL2 gene as shown in SEQ ID NO: 1, i.e., the ElovL2AS1 region, are described, and the sites can be used alone or in combination as a measure of biological age. In one embodiment, the present invention provides the use of CG sites and combinations thereof, wherein the sites are the positions of the dinucleotide sequences disclosed in the present invention as described in Table 1.

[0072] In one embodiment, the present invention provides a kit for determining the biological age of a subject, the kit comprising: a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and wherein the stabilizing the substrate is for mailing the collected substrate to extract DNA so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject. In an alternative embodiment, the present invention provides a kit for determining the biological age of a subject, wherein the kit is a saliva collection kit, the customer or subject orders the kit, spits saliva into a kit collection tube, and the kit collection tube is mailed to a laboratory via a DNA extraction kit, followed by DNA methylation analysis. In one embodiment, the present invention provides a kit for collecting a saliva sample from a customer, the kit comprising a device and reagents for collecting a substrate from a customer and stabilizing the substrate. In one embodiment, the present invention provides a kit comprising a device and reagents for measuring DNA methylation of CG sites and combinations thereof, wherein the sites are positions of dinucleotide sequences as described in Table 1 as disclosed herein.

[0073] In one embodiment, the present invention provides an application (App) for managing the ordering, submission, receipt of test results and lifestyle management of DNA methylation age tests. In one embodiment, an open source development tool is used to develop the App to contain information about the test, a virtual shopping cart for ordering the test, a scanning function for scanning the barcode of the saliva test kit, and a function for receiving test results from the laboratory. In one embodiment, the present invention provides a questionnaire to be included in the App, which will explore lifestyle functions that may affect "healthy aging", the questionnaire containing basic physiological measurements, weight, height, blood pressure, heart rate, etc., emotional self-assessment, McGill pain questionnaire (McGill painquestionnaire), diet and nutrition questionnaire, exercise questionnaire and lifestyle questions including alcohol, drugs and smoking, and combinations thereof. In one embodiment, the method includes performing statistical analysis on the responses to the questionnaire and providing a dynamic report to the consumer on the App, which describes the progress of the responses to the questionnaire over time compared to the recommendations of national associations such as cancer, heart and stroke, and diabetes.

[0074] In one embodiment, the present invention provides for storing data derived from multiple users in a cloud-based SQL database and using "machine learning" to analyze the data and build a model that correlates the input questionnaire measurements and the output difference between DNA methylation age and chronological age, as well as other physiological and psychological outputs such as pain, blood pressure, BMI, and mood. In other embodiments, the present invention as disclosed herein wherein the machine learning is selected from a group of "deep machine learning" data mining methods comprises the following: a neural network; or "reinforcement learning" that reinforces the most effective lifestyle changes by utilizing feedback from consumers; or a "machine learning" data mining algorithm comprising a "random forest" analysis; or a "machine learning" data mining algorithm comprising a K-means cluster analysis; or a "machine learning" platform comprising Amazon Machine Learning (AML); or "machine learning" software comprising the H2O.ai product on platforms such as the Apache Hadoop Distributed File system, Amazon EC2 Google Compute Engine, and Microsoft Azure.

[0075] In one embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the following steps: (a) evaluating an entry in a computer-readable medium, such as obtained by sharing user data from a subject; (b) matching the entry in step (a) with a kit, such as a kit for determining the biological age of a subject, the kit comprising a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood from the subject, and wherein the stabilizing the substrate is for mailing the collected substrate for DNA extraction, so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject; (c) calculating the biological age of the subject using the method, the method comprising: (i) extracting DNA from the substrate from the subject; (ii) (i) measuring DNA methylation in extracted DNA from the substrate to obtain a DNA methylation profile; (iii) analyzing the DNA methylation profile to obtain a polygenic score; and (iv) determining the biological age of the subject based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject to obtain a calculated biological age; (d) integrating the calculated biological age for the subject of step (c) in a machine learning model by performing statistical analysis using the evaluation of step (a) to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progression of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses to recommendations of national associations; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes.In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, wherein the computer-readable medium includes an open source development tool to contain the following: information about a test for calculating biological age based on the method disclosed herein; a virtual shopping cart for ordering the test; a scanning function for scanning a bar code of a kit for determining the biological age of a subject as disclosed herein; and a function for receiving test results from a laboratory, and wherein the open source development tool includes a questionnaire contained in the computer-readable medium to explore lifestyle functions that influence healthy aging, the questionnaire including basic physiological measurements, weight, height, blood pressure, heart rate, mood self-assessment, McGill Pain Questionnaire, Diet and Nutrition Questionnaire, Exercise Questionnaire, and lifestyle questions including alcohol, drugs, and smoking, and combinations thereof.

[0076] In an alternative embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the following steps: (a) evaluating entries in a computer-readable medium, such as obtained by sharing user data from a subject; (b) matching the entries of step (a) with a kit, such as for determining the biological age of a subject, the kit comprising a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood from the subject, and wherein the stabilizing the substrate is for mailing the collected substrate for DNA extraction so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject; (c) calculating the biological age of the subject using the method, the method comprising: (i) extracting DNA from a plurality of substrates from a plurality of subjects; (ii) ) measuring DNA methylation in extracted DNA from multiple substrates to obtain a DNA methylation profile; (iii) analyzing the DNA methylation profile to obtain a polygenic score; and (iv) determining the biological age across multiple subjects based on the polygenic score, wherein the extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject to obtain a calculated biological age; (d) integrating the calculated biological age for the subject of step (c) in a machine learning model by performing statistical analysis using the evaluation of step (a) to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progress of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses to recommendations of a national association; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes.In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, wherein the computer-readable medium includes an open source development tool to contain the following: information about a test for calculating biological age based on the method disclosed herein; a virtual shopping cart for ordering the test; a scanning function for scanning a bar code of a kit for determining the biological age of a subject as disclosed herein; and a function for receiving test results from a laboratory, and wherein the open source development tool includes a questionnaire contained in the computer-readable medium to explore lifestyle functions that influence healthy aging, the questionnaire including basic physiological measurements, weight, height, blood pressure, heart rate, mood self-assessment, McGill Pain Questionnaire, Diet and Nutrition Questionnaire, Exercise Questionnaire, and lifestyle questions including alcohol, drugs, and smoking, and combinations thereof.

[0077] In yet another alternative embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the steps of: (a) evaluating entries in a computer-readable medium, such as obtained by sharing user data from a subject; (b) matching the entries of step (a) with a kit, such as for determining the biological age of a subject, the kit comprising a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting and stabilizing the substrate, wherein the substrate is saliva or blood from the subject, and wherein stabilizing the substrate is for mailing The method comprises the steps of: (i) extracting DNA from a plurality of substrates from a plurality of subjects; (ii) measuring DNA methylation in the extracted DNA from the plurality of substrates to obtain a DNA methylation profile; (iii) analyzing the DNA methylation profile to obtain a polygenic score; and (iv) determining the biological age across a plurality of subjects according to the polygenic score, wherein the extracting DNA comprises extracting DNA from saliva obtained from the subjects; (v) determining the biological age across a plurality of subjects according to the polygenic score. or blood to obtain a calculated biological age, wherein the measuring of DNA methylation in the extracted DNA from the plurality of substrates comprises the following steps: (1) amplifying the genomic DNA extracted from the plurality of substrates with target-specific primers to obtain PCR product 1; (2) amplifying the PCR product 1 of step (1) with barcode primers to obtain PCR product 2; (3) performing multiplex sequencing in a single next-generation Miseq sequencing reaction using the PCR product 2 of step (2); (4) extracting data from the multiplex sequencing of step (3); and (5) quantifying DNA methylation based on the extracted data of step (d) to obtain a calculated biological age. (d) integrating the calculated biological age of the subject of step (c) in a machine learning model by performing statistical analysis using the evaluation of step (a) to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progress of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses with recommendations of national associations; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes.In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, wherein the computer-readable medium includes an open source development tool to contain the following: information about a test for calculating biological age based on the method disclosed herein; a virtual shopping cart for ordering the test; a scanning function for scanning a bar code of a kit for determining the biological age of a subject as disclosed herein; and a function for receiving test results from a laboratory, and wherein the open source development tool includes a questionnaire contained in the computer-readable medium to explore lifestyle functions that influence healthy aging, the questionnaire including basic physiological measurements, weight, height, blood pressure, heart rate, mood self-assessment, McGill Pain Questionnaire, Diet and Nutrition Questionnaire, Exercise Questionnaire, and lifestyle questions including alcohol, drugs, and smoking, and combinations thereof.

[0078] In one embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the following steps: (a) evaluating an entry in a computer-readable medium, such as obtained by sharing user data from a subject; (b) matching the entry of step (a) with a kit, such as a kit for determining the biological age of a subject, the kit comprising a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and wherein the stabilizing the substrate is for mailing the collected substrate for DNA extraction so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject; (c) calculating the biological age of the subject using a method for calculating the biological age of a subject as disclosed herein; (d) calculating the biological age of the subject by using step (a) performing statistical analysis based on the evaluation of the subject's biological age, integrating the calculated biological age of step (c) for the subject in a machine learning model to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progress of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses with the recommendations of national associations; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes, wherein the method includes using Android or Apple or WeChat mini-programs for personalized lifestyle recommendations to create a health ecosystem focused on normalizing or slowing biological aging of the subject, or using standard data workflows and management systems such as cloud data pre-processing across multiple subjects to store data in an enterprise-class object storage device in a server or cloud server including Amazon, Ali cloud or Microsoft Azure. In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, wherein the method includes calculating the weighted contributions of different lifestyle measures to the biological age of a subject or across multiple subjects using a set of artificial intelligence algorithms, such as random forests (RF), support vector machines (SVM), linear discriminant analysis (LDA), generalized linear models (GLM) and deep learning (DL), and the weighted contributions are dynamically updated to provide personalized lifestyle recommendations for lifestyle changes.

[0079] In one embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, the method comprising the following steps: (a) evaluating an entry in a computer-readable medium, such as obtained by sharing user data from a subject; (b) matching the entry of step (a) with a kit, such as for determining the biological age of a subject, the kit comprising a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva or blood of the subject, and wherein the stabilizing the substrate is for mailing the collected substrate for DNA extraction so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject; (c) calculating the biological age of the subject using a method for calculating biological age across multiple subjects as disclosed herein; (d) calculating the biological age of the subject by using step ( a) performing statistical analysis on the evaluation, integrating the calculated biological age of the subject of step (c) in a machine learning model to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progress of questionnaire responses over time, such as obtained by sharing user data from the subject, and comparing the integrated data report and the questionnaire responses with the recommendations of national associations; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes, wherein the method includes using Android or Apple or WeChat mini-programs for personalized lifestyle recommendations to create a health ecosystem focused on normalizing or slowing the biological aging of the subject, or using standard data workflows and management systems such as cloud data pre-processing across multiple subjects to store data in an enterprise-class object storage device in a server or cloud server including Amazon, Ali cloud or Microsoft Azure. In another embodiment, the present invention provides a computer-implemented method for providing recommendations for lifestyle changes, wherein the method includes calculating the weighted contributions of different lifestyle measures to the biological age of a subject or across multiple subjects using a set of artificial intelligence algorithms, such as random forests (RF), support vector machines (SVM), linear discriminant analysis (LDA), generalized linear models (GLM) and deep learning (DL), and the weighted contributions are dynamically updated to provide personalized lifestyle recommendations for lifestyle changes.

[0080] In one embodiment, the present invention provides a method for developing a computer-readable medium, the method comprising the steps of: (a) storing data derived from a plurality of subjects; (b) analyzing the stored data of step (a); and (c) building a model, wherein the step of storing the data derived from the plurality of users comprises a cloud-based SQL database, wherein the step of analyzing the stored data comprises a method selected from the group consisting of deep machine learning, reinforcement learning, and machine learning, or a combination thereof, and wherein the step of building the model comprises correlating input questionnaire measurements with outputs of the difference between DNA methylation age and chronological age, as well as other physiological and psychological outputs such as pain, blood pressure, BMI, and mood. In another embodiment, the present invention provides a method for developing a computer-readable medium, wherein the machine learning comprises a method selected from the group consisting of a data mining algorithm comprising random forest analysis, a data mining algorithm comprising K-means cluster analysis, a platform comprising Amazon Machine Learning (AML), software comprising the H2O.ai product on a platform comprising the Apache Hybrid Distributed File System, Amazon EC2 Google Compute Engine, and Microsoft Azure, or a combination thereof.

[0081] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, the method being used in a method for evaluating the effect of a biological intervention on the biological age of a subject, the method comprising the following steps: (i) calculating the biological age of the subject using the method as disclosed herein to obtain an initial biological age before the biological intervention; (ii) performing the biological intervention on the subject; and (iii) performing the biological intervention on the subject after step (ii) has been performed. Repeating step (i) with subsequent substrates obtained from the subject to obtain the biological age after the biological intervention; and (iv) integrating the biological age for the subject in a machine learning model after the biological intervention to evaluate the effect of the biological intervention on the biological age of the subject, wherein the biological intervention of step (ii) is selected from the group consisting of nutritional 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 a combination thereof, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, and wherein after the biological intervention, the integrating the biological age for the subject in the machine learning model comprises the biological age evaluated in step (iii) and the physiological parameters obtained by sharing user data from the subject.

[0082] In one embodiment, the present invention provides a method for calculating biological age across multiple subjects, the method comprising the following steps: (a) extracting DNA from multiple substrates from multiple subjects; (b) measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age across multiple subjects based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, the method being used in a method for evaluating the effect of a biological intervention on the biological age of a subject, the method comprising the following steps: (i) calculating the biological age of the subject using a method as disclosed herein to obtain an initial biological age before the biological intervention; (ii) performing the biological intervention on the subject; and (iii) after step (ii) has been performed, performing the biological intervention on the subject. Repeating step (i) with subsequent substrates obtained from the subject to obtain the biological age after the biological intervention; (iv) integrating the biological age of the subject in a machine learning model after the biological intervention to evaluate the effect of the biological intervention on the biological age of the subject, wherein the biological intervention of step (ii) is selected from the group consisting of nutritional 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 a combination thereof, wherein extracting DNA includes extracting genomic DNA from saliva or blood obtained from the subject, and wherein after the biological intervention, integrating the biological age of the subject in the machine learning model includes the biological age evaluated in step (iii) and physiological parameters obtained by sharing user data from the subject.

[0083] In one embodiment, the present invention provides a method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age of the subject based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, the method being used in a method for screening an agent as an anti-aging agent, the method comprising the following steps: (i) calculating the age of the substrate obtained from the subject using the method as disclosed herein to obtain an initial biological age before biological intervention; (ii) administering a test to the subject. agent; (iii) after step (ii) has been performed, repeating step (i) on a subsequent substrate obtained from the subject to obtain the biological age after the administration of the test agent; and (iv) after the administration of the test agent, integrating the biological age for the subject in a machine learning model to evaluate the reduction in age that has been calculated by the integration in the machine learning model, so as to determine the test agent as an anti-aging agent for the subject, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, and wherein after the administration of the test agent, the integrating the biological age for the subject in the machine learning model comprises the biological age evaluated in step (iii) and a physiological parameter obtained by sharing user data from the subject.

[0084] In one embodiment, the present invention provides a method for calculating biological age across multiple subjects, the method comprising the following steps: (a) extracting DNA from multiple substrates from multiple subjects; (b) measuring DNA methylation in the extracted DNA from the multiple substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profile to obtain a polygenic score; and (d) determining the biological age across multiple subjects based on the polygenic score, wherein the extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, the method being used in a method for screening an agent as an anti-aging agent, the method comprising the following steps: (i) calculating the age of the substrate obtained from the subject using the method as disclosed herein to obtain an initial biological age before biological intervention; (ii) administering to the subject with a test agent; (iii) after step (ii) has been performed, repeating step (i) on a subsequent substrate obtained from the subject to obtain the biological age after said administering of the test agent; and (iv) after said administering of the test agent, integrating the biological age for the subject in a machine learning model to evaluate the reduction in age that has been calculated by the integration in the machine learning model so as to identify the test agent as an anti-aging agent for the subject, wherein said extracting DNA comprises extracting genomic DNA from saliva or blood obtained from the subject, and wherein after said administering of the test agent, said integrating the biological age for the subject in the machine learning model comprises the biological age evaluated in step (iii) and physiological parameters obtained by sharing user data from the subject.

[0085] Examples

[0086] The following examples are given to illustrate the present invention and therefore should not be construed as limiting the scope of the present invention.

[0087] Example 1: It was found that the weighted DNA methylation levels of 13 CG sites contained in the upstream DNA region of the ElovL2 gene, namely the ElovL2 AS1 region, predicted the age of saliva DNA.

[0088] In this example, the present invention relates to an "epigenetic clock", which is considered to be the most accurate measure of biological age to date. However, the tests available to date require the use of blood to measure DNA methylation at many sites (about 350), which is an invasive and high-cost method that is not suitable for widely distributed consumer products. Although the available methods are sufficient for research and clinical related studies, it is not feasible for consumer-driven public use of this test. Therefore, the present invention provides a method for accurate, robust, high-throughput and non-invasive testing of biological aging based on an "epigenetic clock", specifically DNA methylation. In this example, the present invention provides a multi-CG DNA methylation marker of biological age for lifestyle management of healthy aging.

[0089] Discovery of age-related CG sites in blood

[0090] The present invention performed Pearson correlation analysis on a publicly available 450K Illumina whole genome DNA methylation array (GSE40729) from blood. A small number of previously unreported CG sites were selected and analyzed. Two of these CG sites were found to be located upstream of the antisense region of the ElovL2 gene, which is referred to as the ElovL2 AS 1 region, as shown in the physical map disclosed herein. Figure 1 As depicted in the representative example of , where as shown therein, the site was found to be highly correlated with age (with a Pearson correlation coefficient r>0.9 and p=0). As disclosed herein, the present invention then determined that the combined weighted DNA methylation measurement of the two CG sites accurately predicted the following: Figure 2 Depicted are the age of blood DNA in independent cohorts (GSE40279, n=656 and GSE2219, n=60). It can be seen that methylation of the CG sites in the ElovL2 AS1 region, as shown in SEQ ID NO: 1, disclosed herein, progresses from nearly 0% in fetuses to nearly 90% in nonagenarians. Thus, this ElovL2 AS1 region, as shown in SEQ ID NO: 1, and the CG sites found within said region, which are the positions of the dinucleotide sequences disclosed herein and described in Table 1, alone show an almost perfect correlation with age, suggesting that a small number of said CG sites may be sufficient to determine biological age.

[0091] ElovL2 CG sites in the AS1 region predict age in saliva samples

[0092] To assess the broad applicability of the disclosed DNA methylation age test, it is important that it does not require qualified health professionals to derive biological material. In this example, the present invention determined whether it is possible to use the disclosed highly correlated CG sites as age predictors in saliva by testing publicly available 450K Illumina array methylation data for saliva (GSE78874, n=259), which are the positions of dinucleotide sequences as described in Table 1 located in the ElovL2 AS1 region disclosed herein as shown in SEQ ID NO: 1. The present invention discloses a methylation score composed of the following weighted methylation measurements: cgl6867657 and cg21572722, which are the positions of dinucleotide sequences as described in Table 1 and are the positions of CG sites in the region as shown in SEQ ID NO: 1; the ElovL2 AS1 region together with cg09809672, which is a CG site in chromosome 1 as disclosed herein as described in Table 1, predicted age with a mean deviation of 5.62 years and a median deviation of 4.74 years. The present invention then compares the accuracy of the model disclosed herein with the gold standard Horvath clock. Figure 3 As shown, the performance of the ElovL2 AS1 region site is slightly better than that of 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 discloses that this previous knowledge ignores the fact that two CG sites (i.e., cgl6867657 and cg21572722) that were thought to be located in the ElovL2 gene are actually located upstream of a different gene, the ElovL2-AS1 gene, in the antisense orientation of the ElovL2 gene (see, as disclosed herein). Figure 1 ), wherein the upstream region is referred to as the ElovL2AS1 region and as disclosed herein is shown in SEQ ID NO: 1. This region upstream of the ElovL2-AS1 gene contains selected 13 CG sites (see Table 1 as disclosed above).

[0093] Example 2: Bisulfite conversion, multiplexed amplification and next generation sequencing and methylation calculation of 13 CGs in the ElovL2 AS1 region.

[0094] The present disclosure further provides for incubating saliva with proteinase K (200 micrograms at 37°C for 30 minutes) at the laboratory, wherein the saliva is collected by the subject or customer in a DNA stabilization buffer (Tris 10mM EDTA 10mM, SDS 1%) mailed to the laboratory. The genomic DNA is then purified using a Qiagen kit. The purified DNA is treated with sodium bisulfite using, for example, the EZ DNA Bisulfite Treatment Kit. In a standard Taq polymerase reaction, a targeted sequence library is generated by a two-step PCR reaction using the following primers:

[0095] For PCR 1 - amplify the amplicon corresponding to the sequence shown in SEQ ID NO: 1:

[0096] Forward primer as shown in SEQ ID NO: 5:

[0097]

[0098] The reverse primer is shown in SEQ ID NO: 6:

[0099]

[0100] For PCR 2 - barcoding samples, perform a second PCR reaction using the following primers:

[0101] Forward primer as shown in SEQ ID NO:7:

[0102]

[0103] Barcode (reverse) primer as shown in SEQ ID NO: 8:

[0104] 5'CAAGCAGAAGACGGCATACGAGATAGTCATCGGTGACTGGAGTTCA GACGTG 3' (bases 25-32 are indexed in red; up to 200 variants use this index).

[0105] The second set of primers introduced an index as well as a reverse sequencing primer and a forward sequencing primer for each sample. PCR products 2 from all samples were merged and purified on AMPpure-XP beads (NEB). The library was quantified by QPCR and loaded into a MiSeq flow cell. Quick Q files were aligned to the relevant genomic regions using BisMark or other editing software.

[0106] Example 3: Superior performance of 13 CG sites in the upstream region of the ElovL2-AS1 gene region, such as the ElovL2 AS1 region shown in SEQ ID NO: 1.

[0107] Next, the present disclosure determined whether the combined weighted methylation score of the disclosed 13 CG sites provides superior predictive performance when compared to 2 or 3 CG sites. Saliva samples were collected from 65 volunteers at Hong Kong Science Park, and the methylation levels at the 13 CG sites (see Table 1 as disclosed above) were determined as described in Example 2. The present disclosure performed a series of multivariate linear regressions using different combinations of CG sites. Figure 4 The results shown in show that the combination of 13 CG sites (see Figure 4 D) than the combination of 4 CG sites (see Figure 4 Part A of ), a combination of 5 CGs (see Figure 4 Part B of ) or a combination of Illumina 2 CG sites (see Figure 4 The results that the combination of 13 CG sites outperformed smaller combinations of 4, 5, or 2 CG sites are shown in Figure 2. Figure 4 The statistical comparison data shown in Figure E further demonstrated that the Pearson product-moment correlation coefficient r of the 13CG methylation score was 0.95 (p = 1.8×10-33).

[0108] Example 4: Determining biological age in a saliva sample from a customer.

[0109] As discussed above, biological age is an important parameter of health. However, since the test disclosed herein is intended for use by homebound individuals outside the professional healthcare system, it is important that the test be simple and not require blood drawing by a healthcare professional. This is preferred because blood collection itself can be a low-risk procedure and can be delivered to centers with existing laboratory facilities via regular mail. Therefore, the present invention discloses that 13 CG sites in the ElovL2AS1 region form the basis of the EpiAging test, which provides this opportunity. In the present invention, as disclosed, a saliva test kit containing a stabilization buffer that stabilizes DNA for up to one month is ordered via the EpiAging app, online, or via email. The stabilization buffer contains 20 mM Tris-HCl (pH 8.0), 20 mM EDTA, 0.5% SDS, and 1% Triton X-100. The barcoded kit is delivered to the subject's residence by mail. The customer scans the barcode with their phone's scanner and registers their barcode with the app, which links the barcode to their phone's internal ID. Following the instructions provided in the Epi Aging Test Kit, the customer spits into a collection tube and then transfers the saliva to a tube containing stabilization buffer, places the tube in a prepaid postage envelope, and sends it to the laboratory. In the laboratory, DNA is extracted, bisulfite converted (chemical bisulfite conversion converts unmethylated C to T, while methylated C remains as C), and the ElovL2 AS1 region is amplified as described in Example 3 and analyzed on a MiSeq 111u mi Sequencing was performed on a genomic DNA sequencer using samples from other patients. The fastQ files were analyzed and the methylation values ​​(m) of the 13 CG sites were calculated: mCGn = CGnC counts / (CGnT counts + CGnC counts).

[0110] These values ​​are then entered into the following equation to calculate biological age:

[0111] Biological age = (CG1*87.5643+CG2*6.3301+CG3*-

[0112] 0.8691+CG4*1.9468+CG5*40.0336+CG6*49.4303+CG7*-

[0113] 14.7868+CG8*22.9042+CG9*-

[0114] 49.7942+CG10*111.7467+CG11*41.8108+CG12*0.4144+CG13*-150.8005)-71.6872

[0115] The biological age is then sent to the client via their Epi Aging App, or the client can retrieve the results using their barcode ID. A biological age that is significantly greater than chronological age (+5 years) is a "red flag" for lifestyle changes. Clients measure their biological age regularly (every 6-12 months) and assess their progress in closing the gap between biological and chronological age.

[0116] Example 5: Epi Aging App for managing biological age tests and lifestyle data.

[0117] The present invention discloses an Epi aging app that complies with Apple and Android operating systems (for the homepage of the app, see Figure 5 ), and provides information about the "Epi Aging Test", how to order, a shopping cart for ordering, and links to electronic payments such as PayPal or Alipay. The innovative aspect of the disclosed App of the present invention is that it combines client-based management of the "Epi Aging Test" with a client-driven lifestyle change management system based on dynamic recommendations from renowned national and international medical groups, the system is "self-reporting", shared data, machine learning, iterative changes driven by iterative machine learning, providing personalized reports to the client, and repeated assessments (see Figure 6 The "reinforcement learning" system guides lifestyle changes that have the greatest impact on slowing the acceleration of aging, as determined by the difference between DNA methylation age and chronological age. The apps provided herein are built using open-source programs known to those skilled in the art, such as Build Fire JS, Ionic, Appcelerator's Titanium SDK, Mobile Angular UI, and Siberian CMS.

[0118] The app is downloaded from the Apple Store, Google Play Store and website. The app requires registration and allocation of a customer ID. The app activates a scanner that scans barcodes and links the test barcode to the customer ID. The data will be linked to these "blind" IDs. Personal data and customer data are separated by a "firewall" and tokenized to ensure complete blinding of "aging" and lifestyle data. The data management system cannot access personal data. A system has been built to recover personal IDs, which can only be activated by the customer using their email account, but is completely independent of the data management system. Blinding of data is a fundamental feature of the app.

[0119] The home page of the app contains several buttons (see Figure 5 ). A button links to basic information and scientific citations about "aging" testing and public medicine links for further and deeper understanding of the field.

[0120] This information provides information on the link between lifestyle and aging. A second button links to a page containing a series of buttons linking to lifestyle and well-being domains, such as "Mood," "Chronic Pain," "Nutrients," including intake of nutritional supplements like SAMe and vitamins, "Physiological Measures," including blood pressure, heart rate, weight, height, fasting glucose levels, and other metabolic tests, medications, substance abuse, alcohol, smoking, and client-entered exercise data. Each section is preceded by recommendations compiled from reputable associations such as the National Heart and Stroke Association, the Diabetes Association, and the American Cancer Society. The recommendations section contains links to these associations so that clients can make their own judgments and decisions. The philosophy behind the lifestyle management section is self-empowerment and client control over their lifestyle decisions. Data entry is accomplished by moving a numerical scale. Yes-no entries are marked with a scale of 0 for no and 1 for yes. Other quantifiable entries are entered by their quantity. At the top of each data entry scale, an expanded representation of the recommendation is presented, providing the client with an estimate of their performance relative to the color-coded recommendation. The recommended range is indicated in green. Deviations from the recommendation are indicated by red above the range and blue below. Customers can save their data by clicking the Save button at the end of each data entry section. Once data is entered, a summary analysis report is provided. A chart depicting progress over time relative to national recommendations is also provided. Once laboratory aging testing is completed, the test is remotely delivered to the app. Customer data, along with data from other customers, is stored in a cloud-based database for further analysis.

[0121] Example 6: Machine learning-driven analysis of health and DNA methylation age data and personalized recommendations for lifestyle improvements.

[0122] Data from multiple users is stored in a cloud-based SQL database (see Figure 7 Machine learning algorithms are being used to analyze the data, and models are being developed that correlate input questionnaire measures such as pain, blood pressure, BMI, and mood with outputs such as differences in DNA methylation age and chronological age. For example, methods such as neural networks, decision trees, random forest lasso regression, K-means cluster analysis, reinforcement learning, and penalized regression are being used.

[0123] The method as disclosed in the present invention includes performing statistical analysis on the responses to the questionnaire and providing the consumer with a dynamic report on the App that describes the progress of the responses to the questionnaire over time compared to the recommendations of national associations such as cancer, heart and stroke, and diabetes.

[0124] Although the present invention has been explained in conjunction with the preferred embodiments thereof, it is to be understood that numerous other possible modifications and variations can be made without departing from the spirit and scope of the invention.

[0125] advantage

[0126] The novel aspect of the method disclosed in this invention relative to methods known in the art is that the combination of 13 previously undescribed CG sites in the ElovF2 AS 1 region provides extremely high-accuracy prediction of the age of a saliva sample from a single amplicon. Accuracy and simplicity are enhanced by multiplexing and the use of robust next-generation sequencing. This approach significantly reduces costs and makes the test suitable for use as a consumer product.

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[0152] Zhao, Y., Kosorok, MR, & Zeng, D. (2009). Reinforcement learning design for cancer clinical trials. StatMed, 28(26), 3294-3315. doi:10.1002 / sim.3720 Sequence Listing <110> Epimed Global <120> Epiaging: Novel Ecosystem for Managing Healthy Aging <130> TPC57505 <150> US 62 / 854,226 <151> 2019-05-29 <160> 8 <170> PatentIn version 3.5 <210> 1 <211> 266 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 1 cgccctcgcg tccgcggcgt cccctgccgg ccgggcggcg atttgcaggt ccagccggcg 60 ccggtttcgc gcggcggctc aacgtccacg gagccccagg aatacccacc cgctgcccag 120 atcggcagcc gctgctgcgg ggagaagcag tatcgtgcag ggcgggcacg ctggtcttgc 180 ttacagttgg gcttcggtgg gtttgaagca cacattaggg ggaaatggct ctgttcctgc 240 aggtttgcgc agtctgggtt tcttag 266 <210> 2 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 2 aggggagtag ggtaagtgag 20 <210> 3 <211> twenty four <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 3 accatttccc cctaatatat actt 24 <210> 4 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 4 gggaggagat ttgtaggttt 20 <210> 5 <211> 62 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <220> <221> misc_features <222> (34)..(38) <223> n is a, c, g or t <400> 5 acactctttc cctacacgac gctcttccga tctnnnnnyg ggyggygatt tgtaggttta 60 gt 62 <210> 6 <211> 58 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 6 gtgactggag ttcagacgtg tgctcttccg atctccctac acratactac ttctcccc 58 <210> 7 <211> 45 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 7 aatgatacgg cgaccaccga gatctacact ctttccctac acgac 45 <210> 8 <211> 52 <212> DNA <213> Artificial sequence <220> <223> Description of artificial sequences: synthetic polynucleotides <400> 8 caagcagaag acggcatacg agatagtcat cggtgactgg agttcagacg tg 52

Claims

1. A method for calculating the biological age of a subject, the method comprising the following steps: (a) extracting DNA from a substrate from the subject; (b) measuring DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile; (c) analyzing the DNA methylation profiles of 13 human CG sites located in the putative antisense region of the ElovL2 gene as shown in SEQ ID NO: 1, i.e., the ElovL2 AS1 region, wherein 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, to obtain a polygenic score using a multiple linear regression equation; and (d) determining the biological age of the subject from the polygenic score, wherein extracting DNA comprises extracting genomic DNA from saliva obtained from the subject.

2. The method according to claim 1, wherein the measuring of DNA methylation is performed using a method comprising: DNA pyrosequencing, mass spectrometry-based Epityper TM Methylation assays can be performed by PCR, 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 to oligonucleotide arrays.

3. The method of claim 1 , wherein the measuring DNA methylation is performed using DNA pyrosequencing, the DNA pyrosequencing comprising a forward biotinylated primer as shown in SEQ ID NO: 2, a reverse primer as shown in SEQ ID NO: 3, and a pyrosequencing primer as shown in SEQ ID NO:

4.

4. The method of claim 1 , wherein the measuring DNA methylation is performed using targeted amplicon next-generation bisulfite sequencing performed on a platform selected from the group consisting of HiSeq, MiniSeq, MiSeq, and NextSeq sequencers, the targeted amplicon next-generation bisulfite sequencing comprising a forward primer as shown in SEQ ID NO: 5 and a reverse primer as shown in SEQ ID NO:

6. 5 . The method of claim 1 , wherein said measuring DNA methylation is performed using a PCR-based methylation assay selected from the group consisting of methylation-specific PCR and digital PCR.

6. A method for calculating biological age across a plurality of subjects, the method comprising the steps of: (a) extracting DNA from multiple substrates from multiple subjects; (b) measuring DNA methylation in the extracted DNA from multiple substrates to obtain a DNA methylation profile; (c) analyzing the DNA methylation profiles of 13 human CG sites located in the putative antisense region of the ElovL2 gene as shown in SEQ ID NO: 1, i.e., the ElovL2 AS1 region, wherein 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, to obtain a polygenic score using a multiple linear regression equation; and (d) determining the biological age across a plurality of subjects from the polygenic score; Wherein said extracting DNA comprises extracting genomic DNA from saliva obtained from the subject.

7. The method of claim 6, wherein said measuring DNA methylation in said extracted DNA from a plurality of substrates comprises the steps of: (a) amplifying genomic DNA extracted from the plurality of substrates using target-specific primers to obtain PCR product 1; (b) amplifying the PCR product 1 of step (a) using barcoded primers to obtain PCR product 2; (c) performing multiplex sequencing in a single next-generation Miseq sequencing reaction using the PCR product 2 of step (b); (d) extracting data from the multiplexed sequencing of step (c); and (e) quantifying DNA methylation based on the extracted data of step (d) to obtain a DNA methylation profile for each substrate, Wherein said extracting DNA comprises extracting genomic DNA from saliva obtained from the subject.

8. The method according to claim 7, wherein the target-specific primers used to obtain PCR product 1 include a forward primer as shown in SEQ ID NO: 5 and a reverse primer as shown in SEQ ID NO: 6, and wherein the barcode primers used to obtain PCR product 2 include a forward primer as shown in SEQ ID NO: 7 and a reverse primer as shown in SEQ ID NO: 8, and the reverse primer as shown in SEQ ID NO: 8 is a barcode index primer.

9. A method for evaluating the effect of a biological intervention on a subject's biological age, comprising the steps of: (a) calculating the biological age of the subject using the method according to claim 1 or 6 to obtain an initial biological age before performing a biological intervention; (b) performing the biological intervention on the subject; (c) repeating said step (a) on a subsequent substrate obtained from said subject after step (b) has been performed, to obtain said biological age after performing said biological intervention; (d) integrating the biological age of the subject after the biological intervention into a machine learning model to assess the effect of the biological intervention on the biological age of the subject, wherein the biological intervention of step (b) is selected from the group consisting of nutritional supplements, therapies and test substances or combinations thereof, wherein said extracting DNA comprises extracting genomic DNA from saliva obtained from a subject, and Wherein integrating the biological age of the subject after the biological intervention into the machine learning model includes the biological age assessed in step (c) and physiological parameters obtained by sharing user data from the subject.

10. A method for screening an agent as an anti-aging agent, comprising the steps of: (a) calculating the biological age of a substrate obtained from a subject or subjects using the method according to claim 1 or 6 to obtain an initial biological age prior to administration of a test agent; (b) administering a test agent to the subject; (c) repeating said step (a) on a subsequent substrate obtained from said subject after said step (b) has been performed, to obtain said biological age after said administration of said test agent; (d) integrating the biological age of the subject in a machine learning model after the administration of the test agent to assess whether a reduction in age has been calculated by the integration in the machine learning model, so as to identify the test agent as an anti-aging agent for the subject, wherein said extracting DNA comprises extracting genomic DNA from saliva obtained from said subject, and Wherein, after the administering of the test agent, the integrating the biological age for the subject in the machine learning model includes the biological age assessed in step (c) and physiological parameters obtained by sharing user data from the subject.

11. A computer-implemented method for providing recommendations for lifestyle changes, the method comprising the steps of: (a) evaluating entries in a computer-readable medium obtained by sharing user data from a subject; (b) matching the items of step (a) with a kit obtained from the subject for determining the biological age of the subject, the kit comprising: a device and reagents for collecting a substrate from the subject and stabilizing the substrate; a scanner for reading a barcode on the kit; and instructions for collecting the substrate and stabilizing the substrate, wherein the substrate is saliva of the subject, and wherein the stabilizing the substrate is for mailing the collected substrate for DNA extraction, so as to measure DNA methylation in the extracted DNA from the substrate to obtain a DNA methylation profile of the subject, thereby determining the biological age of the subject; (c) calculating the biological age of the subject using the method according to claim 1 or 6 to obtain a calculated biological age; (d) integrating the calculated biological age for the subject of step (c) in a machine learning model by performing a statistical analysis using the evaluation of step (a) to obtain an integrated data report; (e) preparing a dynamic report for the subject by analyzing the integrated data report of step (d) and the progression of questionnaire responses over time as obtained by sharing user data from the subject and comparing the integrated data report and the questionnaire responses with national association recommendations; and (f) sharing the dynamic report of step (e) with the subject on the computer-readable medium to provide recommendations for lifestyle changes.

12. The computer-implemented method of claim 11 , wherein the computer-readable medium comprises an open source development tool to contain information about a test for calculating biological age based on the method of claim 1 or 6 ; a virtual shopping cart for ordering the test; a scanning function for scanning a barcode of the kit of claim 11 ; and a function for receiving test results from a laboratory, and wherein the open source development tool includes a questionnaire contained in the computer-readable medium to explore lifestyle factors that influence healthy aging, the questionnaire comprising basic physiological measurements, weight, height, mood self-assessment, a McGill pain questionnaire, a diet and nutrition questionnaire, an exercise questionnaire, and lifestyle questions including alcohol, drug, and smoking, and combinations thereof.

13. The computer-implemented method of claim 12, wherein the method comprises providing personalized lifestyle recommendations using an Android, Apple, or WeChat mini-program to create a health ecosystem focused on normalizing or slowing biological aging in a subject, or storing data in an enterprise-class object storage device in a server or cloud server including Amazon, Alibaba Cloud, or Microsoft Azure using a data pre-processing workflow and management system across multiple subjects.

14. The computer-implemented method of claim 13, wherein the method comprises calculating weighted contributions of different lifestyle measures to the biological age of a subject or across multiple subjects 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, the weighted contributions being dynamically updated to provide personalized lifestyle recommendations regarding lifestyle changes.

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