EpiAging ; novel ecosystem for managing healthy aging

1 . A method for calculating biological age of a subject, the method comprising the steps of:

(a) extracting DNA from a saliva sample obtained from the subject;

(b) measuring DNA methylation in the extracted DNA from the saliva sample 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 from the polygenic score,

wherein the measuring DNA methylation consists of measuring the methylation status of each CG site of a set of CG sites of the ElovL2 AS1 region as set forth in SEQ ID NO: 1, the set of CG sites consisting of the CG sites positioned 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 in the sequence as set forth in SEQ ID NO: 1;

wherein the measuring DNA methylation is performed using targeted-amplicon next generation bisulfite sequencing, which comprises amplifying genomic DNA extracted from the saliva sample with target specific primers as set forth in SEQ ID NO:5 for the forward primer and SEQ ID NO:6 for the reverse primer to obtain PCR product 1, amplifying the PCR product 1 with barcoding primers to obtain PCR product 2, performing multiplexed next generation bisulfite sequencing in a single reaction using the PCR product 2, extracting data from the multiplexed sequencing step, and quantifying DNA methylation from the extracted data to obtain the DNA methylation profile;

or using DNA pyrosequencing comprising primers as set forth in SEQ ID NO:2 for a forward, biotinylated primer, SEQ ID NO:3 for a reverse primer, and SEQ ID NO:4 for a pyrosequencing primer.

2 . The method of claim 1 , wherein the analyzing the DNA methylation profile to obtain a polygenic score comprises using multiple linear regression equations or neural network analysis.

3 . The method of claim 1 , further comprising performing the method of claim 1 on multiple subjects.

4 . The method of claim 3 , wherein the measuring the methylation status of each CG site of the set of CG sites of the ElovL2 AS1 region comprises

(a) amplifying genomic DNA extracted from multiple saliva samples with target specific primers to obtain PCR product 1;

(b) amplifying the PCR product 1 of step (a) with barcoding primers to obtain PCR product 2;

(c) performing multiplexed next generation bisulfite sequencing in a single reaction using the PCR product 2 of step (b);

(d) extracting data from the multiplexed sequencing of step (c); and

(e) quantifying DNA methylation from the extracted data of step (d) to obtain a DNA methylation profile for each saliva samples,

wherein the target specific primers to obtain PCR product 1 comprise primers as set forth in SEQ ID NO:5 for the forward primer and SEQ ID NO:6 for the reverse primer, and wherein the primers to obtain PCR product 2 comprise primers as set forth in SEQ ID NO:7 for the forward primer and SEQ ID NO:8 for the reverse primer.

5 . The method of claim 1 for use in a method of assessing the effect of a biological intervention upon the biological age of a subject, the method comprising the steps of:

(a) determining the biological age of a subject using the method of claim 1 to obtain the initial biological age before a biological intervention;

(b) performing a biological intervention upon said subject;

(c) repeating the step (a) on a subsequent saliva sample obtained from said subject after step (b) has been performed to obtain the biological age after the biological intervention;

(d) integrating the biological age after the biological intervention in a machine learning model for said subject to assess the effect of the biological intervention upon the biological age of said subject,

wherein the biological intervention of step (b) is selected from the group consisting of nutritional supplements, vitamins, therapy, administration of a test substance, dietary manipulation, metabolic manipulation, surgical manipulation, social manipulation, behavioural manipulation, environmental manipulations, sensory manipulations, hormonal manipulation and epigenetic manipulation, and combinations thereof, and

wherein the integrating the biological age after the biological intervention in the machine learning model for said subject comprises the biological age assessed in step (c) and physiological parameters obtained through sharing of user data from said subject.

6 . The method of claim 1 for use in a method of screening for an agent for being an anti-ageing agent, the method comprising the steps of:

(a) determining the biological age of a subject using the method of claim 1 to obtain the initial biological age before administration of a test agent;

(b) administering the test agent to said subject;

(c) repeating the step (a) on a subsequent saliva sample obtained from said subject after step (b) has been performed to obtain the biological age after the administration of the test agent;

(d) integrating the biological age after the administration of the test agent in a machine learning model for said subject to assess whether a reduction in age has been calculated to determine if the test agent is an anti-ageing agent for said subject, and

wherein the integrating the biological age after the administration of the test agent in the machine learning model for said subject comprises the biological age assessed in step (c) and physiological parameters obtained through sharing of user data from said subject.