Urolithin a intervention biological age evaluation method, marker and kit

Abstract

The application provides a method for constructing a prediction model for evaluating intervention of urolithin A on biological age and a kit for evaluating intervention of urolithin A on biological age. The application identifies specific markers related to molecular age by detecting methylation changes in blood samples before and after intervention of urolithin A, and constructs a prediction model by using the markers, so as to realize objective evaluation of the intervention effect of urolithin A.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a method for constructing a predictive model for evaluating the biological age of organisms based on DNA methylation data and urolithiasis A intervention. Background Technology

[0002] Urolithin A is a promising natural anti-aging ingredient. It is not found directly in food, but rather is a compound produced through the metabolism of ellagitannins from foods such as pomegranates, raspberries, and nuts, in conjunction with gut microbiota. Because it effectively induces mitophagy (the clearing of dysfunctional mitochondria) and promotes mitochondrial regeneration, urolithin A has been shown to extend lifespan in model organism studies, thus demonstrating its broad potential in the field of anti-aging.

[0003] The anti-aging potential of urolithin A is mainly based on its multifaceted mechanisms of action. Firstly, with age, the capacity for mitophagy in cells declines, leading to the accumulation of damaged mitochondria. Urolithin A can activate mitophagy through multiple pathways, including PINK1-Parkin-dependent and BNIP3-independent pathways, effectively clearing this "cellular waste," promoting mitochondrial regeneration, thereby improving overall cellular function and delaying the aging process. Secondly, a randomized, double-blind, placebo-controlled clinical trial reported in the literature showed that daily supplementation with 1000 mg of urolithin A significantly improved the composition and metabolism of immune cells in middle-aged individuals (45-70 years old) in just 4 weeks. Specifically, this manifested as an increase in younger, naïve CD8+ cells. + It increases the ratio of T cells to NK cells and reduces inflammatory phenotypes, thereby combating age-related immune decline. Furthermore, studies in individuals aged 40-65 have found that urolithin A supplementation can increase muscle strength by approximately 12% and optimize biomarkers of mitochondrial health, helping to slow age-related decline in muscle strength and endurance. Finally, research indicates that urolithin A can reduce amyloid protein levels. β (A) β ) sedimentation and tau Protein hyperphosphorylation is one of the two key pathological features of Alzheimer's disease. It has shown potential to improve memory impairment and cognitive function in animal models. Furthermore, research suggests that urolithin A may have positive effects on skin health (potentially by promoting collagen production and resisting oxidative stress), cardiovascular protection (through anti-inflammatory and antioxidant effects), and metabolic balance (such as improving insulin sensitivity).

[0004] Urolithin A, as an emerging anti-aging compound, exerts its anti-aging effects through mechanisms such as activating autophagy, improving mitochondrial function, and promoting mitochondrial regeneration, showing promising application prospects in delaying biological aging. There is an urgent need to develop assessment models for urolithin A intervention to improve the specificity and sensitivity of the assessment, providing a more accurate and reliable evaluation tool for the anti-aging effects of urolithin A. Summary of the Invention

[0005] To address the technical problems in existing technologies, such as insufficient sensitivity and specificity for specific interventions of urolithin A, low assessment sensitivity, lack of targeted biomarker discovery capabilities, and high application costs, this invention provides a method for constructing a predictive model for assessing the biological age of urolithin A intervention in order to build a highly sensitive assessment system for urolithin A.

[0006] In a first aspect, the present invention provides a method for constructing a predictive model for evaluating biological age in response to urolithiasis A intervention, the method comprising the following steps: 1) Collect DNA samples from subjects before and after taking urolithiasis A; 2) Perform methylation sequencing on the DNA sample to obtain methylation sequencing data; 3) Perform differential sequencing data analysis on the subjects before and after taking urolithin A to obtain differentially methylated sites; 4) Based on the methylation sequencing data obtained in step 2), predict the biological age of the subject before and after taking urolithin A using the aging clock, and obtain the subject's urolithin A response data according to the biological age, for example, dividing the subject into a high response group and a low response group. 5) Among the differentially methylated sites obtained in step 3), select sites whose differences in methylation levels before and after taking urolithin A are related to the differences in the subject's biological age before and after taking urolithin A, and use these sites as biological age-related sites; 6) The methylation levels of the biological age-related sites of the subjects before taking urolithin A and the urolithin A response data of the subjects were used as training set data to train the prediction model and obtain the prediction model.

[0007] In a second aspect, the present invention provides a predictive model for assessing biological age of urolithiasis A intervention, constructed using the method of the first aspect of the present invention.

[0008] In a third aspect, the present invention provides a method for assessing biological age of urolithin A intervention, the method comprising using a predictive model for assessing biological age of urolithin A intervention based on a second aspect of the present invention.

[0009] In a fourth aspect, the present invention provides biomarkers for assessing the biological age of urolithiasis A intervention, said biomarkers comprising the following 46 methylation sites: chr12, 6742665; chr16, 6031887; chr10, 48438524; chr17, 79700641; chr22, 37348963; chr1, 180040465; chr5, 24504913; chr13, 82739391; chr2, 149051919; chr5, 192650; chr1, 6420503; chr17, 17141286; chr17, 17141239; chr11, 85379785; chr7, 157609742; chr7, 67942679; chr11, 59353038; chr5, 69346576; chr20, 55299187; chr2, 8980613; chr6, 166511358; chr9, 132163449; chr2, 128408751; chr12, 12210462; chr12, 122492228; chr7, 153018404; chr7, 77400383; chr12, 132435947; chr19, 813408; chr6, 105131580; chr17, 72933858; chr12, 122177976; chrX, 39681330; chr4, 49139154; chr3, 11168561; chr1, 246853427; chr3, 13019970; chr1, 68134890; chr18, 60573853; chr10, 104888800; chr12, 111305902; chr3, 125622827; chr16, 2152231; chr2, 241580592; chr5, 77309759; chr11, 134709481; The locus is represented as "chromosome, location".

[0010] In a fifth aspect, the present invention provides a kit for assessing biological age of urolithiasis A intervention, the kit comprising 45 primer pairs as in SEQ ID NO:1-90, the 45 primer pairs being used to detect biomarkers of the fourth aspect of the present invention.

[0011] In a sixth aspect, the present invention provides a method for assessing biological age of urolithiasis A intervention, the method comprising: 1) Collect DNA samples from the subjects to be tested and determine the methylation level at the markers in the fourth aspect of this invention; 2) Input the methylation level determined in step 1) into the prediction model of the second aspect of the present invention to obtain the prediction result of the subject to be tested.

[0012] In a seventh aspect, the present invention provides biomarkers for the biological age of subjects taking urolithiasis A, said biomarkers including methylation sites used by aging clocks and biomarkers of the fourth aspect of the present invention.

[0013] The beneficial effects of this invention are as follows: By performing whole-genome methylation screening on the same group of subjects before and after specific phenotypes of urolithin A intervention, methylation biomarkers that show significant changes under the influence of urolithin A and have the highest good fit with molecular age are directly screened. These biomarkers have the characteristic of "urolithin A responsiveness," which can more directly and sensitively capture the biological effects brought about by urolithin A, avoiding interference introduced by a large number of irrelevant sites in universal loci, making the evaluation results more accurate and reliable. A complete "from unbiased discovery to targeted validation" process is integrated. RRBS sequencing in the method serves as a hypothesis-free discovery tool, ensuring that no potential or unknown urolithin A-related biomarkers are missed. Targeted amplicon sequencing is performed on these newly discovered biomarkers, achieving low cost and high efficiency. This integrated approach, with its high-throughput and precise validation, ensures both comprehensive discovery and cost-effectiveness and convenience for subsequent applications. By comparing molecular age changes before and after intervention in the same subject, the effectiveness is assessed, effectively eliminating inherent epigenetic background differences between individuals. Furthermore, by combining scale phenotypic data and integrating targeted sequencing data into a universal biological age model, it achieves "reading personalized changes on a universal scale," significantly improving the accuracy and statistical power of the assessment. The identified biomarker set itself constitutes "urolithin A efficacy biomarkers." The assessment system and even reagent kits built based on these specific biomarkers can provide the most direct, objective, and quantifiable evidence chain for the research and efficacy verification of urolithin A-related products, possessing extremely high translational value and market application prospects. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the accompanying drawings in the specific embodiments will be briefly described below.

[0015] Figure 1 The data summary of subjects taking urolithin A in the examples is shown.

[0016] Figure 2 The performance of the regression model in the examples is shown.

[0017] Figure 3 The cross-validation results of the regression model in the example are shown.

[0018] Figure 4 The performance of the age prediction model using 116 (70+46) methylation sites in the examples is shown. Detailed Implementation

[0019] The present invention will be described in detail below. It should be understood that the following description is merely illustrative and is not intended to limit the scope of the invention; the scope of protection of the invention is defined by the appended claims. Furthermore, those skilled in the art will understand that modifications can be made to the technical solutions of the present invention without departing from its spirit and intent. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter pertains. Before a detailed description of the invention, the following definitions are provided to better understand it.

[0021] In the context of this invention, many embodiments use the expressions "comprising," "including," or "basically / mainly composed of." The expressions "comprising," "including," or "basically / mainly composed of" should be understood as open-ended expressions, indicating that they include not only the elements, components, parts, and method steps specifically listed after the expression, but also other elements, components, parts, and method steps. Additionally, in this document, the expressions "comprising," "including," or "basically / mainly composed of" may also be understood as closed-ended expressions in certain circumstances, indicating that they only include the elements, components, parts, and method steps specifically listed after the expression, and do not include any other elements, components, parts, or method steps. In this case, the expression is equivalent to the expression "composed of."

[0022] In this paper, the term "aging clock" refers to a time-series age predictor, a model for interpreting omics data in the context of aging. An aging clock is a machine learning model that learns patterns in molecular characteristics across a large sample, such as CpG methylation levels at specific gene loci in blood cells or protein concentrations in plasma, to estimate the age of the sample source; this estimated age is also referred to in this paper as "biological age" or "molecular age." The CpG loci used in general methylation age models are derived from large-scale natural population cohort screening, reflecting the "common" characteristics of aging. However, these loci may not be sensitive to specific intervention pathways of urolithin A, leading to low signal-to-noise ratios and insufficient sensitivity when assessing its effects. The method of this invention directly screens for methylation biomarkers that show significant changes under the influence of urolithin A and have the highest good fit to molecular age by performing genome-wide methylation screening (Reduced Representation Bisulfite Sequencing, RRBS) on the same cohort of subjects before and after specific phenotypes treated with urolithin A. These biomarkers are "urolithin A responsive," thus enabling more specific and sensitive capture of the biological effects of urolithin A, avoiding interference from a large number of irrelevant sites in the general site, thereby making the assessment results more accurate and reliable.

[0023] Most studies directly use readily available universal clocks for evaluation, which is a "black box" validation that fails to discover new mechanisms of action or biomarkers. Developing new biomarkers requires independently designed, complex, multi-stage experiments. This invention integrates a complete "from unbiased discovery to targeted validation" process. The first step, RRBS sequencing, serves as a hypothesis-free discovery tool, ensuring that no potential, unknown urolithin A-related biomarkers are overlooked. The second step, targeted amplicon sequencing of these newly discovered biomarkers, achieves low-cost, high-throughput, and accurate validation. This integrated approach ensures both comprehensive discovery and the cost-effectiveness and convenience of subsequent applications.

[0024] Directly applying a general biological age model to intervention studies results in predictions that reflect an individual's comparison with the "general population," rather than with their "pre-intervention self." Due to significant baseline differences between individuals, such cross-sectional comparisons dilute the intervention's effectiveness. The core design of this invention is a self-comparison before and after intervention. This method assesses effectiveness by comparing changes in molecular age before and after intervention in the same subject, effectively eliminating inherent epigenetic background differences between individuals and incorporating scale phenotypic data. Finally, targeted sequencing data is integrated into the general model for calculation. Essentially, this utilizes the calibration capabilities of the general model, but with the most responsive personalized biomarker data as input, thus achieving "reading personalized changes on a general scale," greatly improving the accuracy and statistical power of the assessment.

[0025] Whole-genome bisulfite sequencing (WGBS) is costly and unsuitable for large-scale sample screening or clinical monitoring; while microarray technology using only fixed sites lacks flexibility and cannot discover new biomarkers. This invention employs RRBS as a compromise, achieving sufficient data depth and breadth at a lower cost than WGBS. More importantly, after the biomarker discovery phase, subsequent evaluation can be completed solely through targeted amplicon sequencing. This method is extremely low-cost, high-throughput, and has low requirements for DNA sample quality, making it ideal for future application in larger-scale clinical trials, consumer health testing, or clinical monitoring. The kits developed based on this method also have significant cost advantages and market competitiveness.

[0026] Using a universal clock to evaluate a specific product often raises questions about the direct relevance and persuasiveness of the results, making it difficult to support clear product efficacy claims. The set of biomarkers identified in this invention constitutes itself a "biomarker of urolithin A efficacy." The evaluation system and even the kits constructed based on these specific biomarkers can provide the most direct, objective, and quantifiable chain of evidence for the research and efficacy verification of urolithin A-related products, possessing extremely high translational value and market application prospects. Furthermore, it can be promoted and practiced in establishing methods for evaluating other similar components.

[0027] As previously stated, the present invention aims to provide a method for assessing biological age of urolithiasis A intervention.

[0028] In a first aspect, the present invention provides a method for constructing a predictive model for evaluating biological age in response to urolithiasis A intervention, the method comprising the following steps: 1) Collect DNA samples from subjects before and after taking urolithiasis A; 2) Perform methylation sequencing on the DNA sample to obtain methylation sequencing data; 3) Perform differential sequencing data analysis on the subjects before and after taking urolithin A to obtain differentially methylated sites; 4) Based on the methylation sequencing data obtained in step 2), predict the biological age of the subject before and after taking urolithin A using the aging clock, and obtain the subject's urolithin A response data according to the biological age, for example, dividing the subject into a high response group and a low response group. 5) Among the differentially methylated sites obtained in step 3), select sites whose differences in methylation levels before and after taking urolithin A are related to the differences in the subject's biological age before and after taking urolithin A, and use these sites as biological age-related sites; 6) The methylation levels of the biological age-related sites of the subjects before taking urolithin A and the urolithin A response data of the subjects were used as training set data to train the prediction model and obtain the prediction model.

[0029] In one implementation, the subject takes an oral dose of 520 mg / day of urolithin A.

[0030] In one implementation, the subject takes urolithiasis A orally for at least 2 consecutive months.

[0031] In one implementation, the DNA sample is obtained from the subject's peripheral blood.

[0032] In one embodiment, the aging clock is constructed using methylation data. In another embodiment, the aging clock is constructed using methylation data obtained from healthy individuals. In yet another embodiment, an existing constructed aging clock can be directly used to calculate biological age in this invention.

[0033] In one implementation, in step 4), subjects are divided into a high-response group and a low-response group by the following method: the high-response group is defined as the group whose biological age difference before and after taking urolithin A is greater than the median of the biological age difference before and after taking urolithin A for all subjects, and the low-response group is defined as the group whose biological age difference is less than or equal to the median.

[0034] In one implementation, the prediction model is a LASSO regression model.

[0035] In a second aspect, the present invention provides a predictive model for assessing biological age of urolithiasis A intervention, constructed using the method of the first aspect of the present invention.

[0036] In a third aspect, the present invention provides a method for assessing biological age of urolithin A intervention, the method comprising using a predictive model for assessing biological age of urolithin A intervention based on a second aspect of the present invention.

[0037] In a fourth aspect, the present invention provides biomarkers for assessing biological age of urolithiasis A intervention, the biomarkers comprising the following 46 methylation sites (Table 1).

[0038] Table 1

[0039] In one implementation, the inventors screened more biomarkers by relaxing the threshold, and further introduced an additional 72 methylation sites on top of the above 46 methylation sites (a total of 118 methylation sites, see Table 2). Using more biomarkers can obtain a more robust model and achieve better prediction results.

[0040] Table 2

[0041] In one implementation, one or more of the aforementioned markers can be used for prediction, which can also achieve good prediction results.

[0042] In a fifth aspect, the present invention provides a kit for assessing biological age of urolithiasis A intervention, the kit comprising 45 primer pairs as in SEQ ID NO:1-90, the 45 primer pairs being used to detect the 46 biomarkers mentioned in the fourth aspect of the present invention.

[0043] In one embodiment, the kit further comprises 71 primer pairs such as SEQ ID NO:91-232, for a total of 116 primer pairs (Table 3) for detecting the 118 biomarkers mentioned in the fourth aspect of the present invention.

[0044] Table 3

[0045] In one embodiment, the kit further comprises DNA polymerase, dNTPs, and buffer.

[0046] In a sixth aspect, the present invention provides a method for assessing biological age of urolithiasis A intervention, the method comprising: 1) Collect DNA samples from the subjects to be tested and determine the methylation level at the markers in the fourth aspect of this invention; 2) Input the methylation level determined in step 1) into the prediction model of the second aspect of the present invention to obtain the prediction result of the subject to be tested.

[0047] In one implementation, the DNA sample is obtained from the subject's peripheral blood.

[0048] In one embodiment, in step 1), the methylation level at the marker of the fourth aspect of the invention is determined using the kit of the fifth aspect of the invention.

[0049] In a seventh aspect, the present invention provides biomarkers of the biological age of subjects taking urolithiasis A, said biomarkers including methylation sites used by the aging clock (e.g., those used in step 4 of the method of the first aspect) and biomarkers of the fourth aspect of the present invention (i.e., the 46 sites in Table 1 or the total of 118 sites in Tables 1 and 2).

[0050] Example The present invention and its technical effects will be clearly and completely described below with reference to embodiments and accompanying drawings, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0051] Example 1: Constructing an aging clock This embodiment utilizes methylation data to construct an aging clock for biological age prediction, specifically including the following steps: I. Experimental Design and Data Preprocessing 1. Sample Collection We collected peripheral blood samples from over 400 healthy donors, all of whom signed informed consent forms. Donors ranged in age from 18 to 85 years, with a roughly balanced sex ratio across each age group to minimize gender bias.

[0052] 2. Targeted bisulfite sequencing CpG methylation levels were quantified using the Acegen multiplex targeted bisulfite sequencing panel (Acegen TBS). The amplicone covered all 353 CpG sites utilized by the Horvath clock (doi: 10.1186 / gb-2013-14-10-r115) and 293 sites utilized by the Li clock (doi: 10.1289 / EHP2773). The libraries were sequenced to ensure a median sequencing depth >100× for each CpG.

[0053] 3. Quality Control After removing the adapter, the reads were aligned to hg38 using BSMAP. The methylation rate of each target CpG was calculated. β Value). Remove sites and samples with a call-rate < 90%. Missing values. β Value through k -NN ( k = 19) was used for estimation. Finally, the ComBat empirical Bayesian method was applied to remove the technical batch and gender effects while preserving the age-related variance.

[0054] II. Feature Filtering 1. Calculate the methylation level of each CpG site relative to actual age. Pearson Correlation coefficient, and remove | r | CpG sites < 0.35.

[0055] 2. Adjacent CpG sites (≥3 within 100 bp) are merged into a single DMR mean to reduce redundancy.

[0056] 3. The remaining candidate probes are used for model building.

[0057] III. Core Modeling: Resilient Network + Least Squares Method (en-ls) 1. Training / Test Split: 80% of the samples are used for training and 20% for testing. The split is randomized but stratified by age decimal and gender.

[0058] 2. Hyperparameter tuning. Using... R The package "glmnet" is used for elastic network regression ( α = 0.5). Ten-fold cross-validation was used, and the selection was based on the "1 standard error" criterion. λ The value is adjusted to maximize model simplicity.

[0059] 3. Second-stage least squares method. Following the Horvath method, CpG sites with non-zero regression coefficients in the elastic network are refitted using ordinary least squares to reduce bias and make the slope between the predicted age and the actual age approximately equal to 1.

[0060] 4. Model "locking". The final clock consists of 70 CpG sites, with fixed intercepts and coefficients: Methylation age = β 0 + Σ i β i × Methylation ratio i IV. External Validation and Calibration 1. Use independent queues (N≥100) to estimate MAE, RMSE, R 2 And the regression slope.

[0061] 2. The error was that the data was stratified by gender and age group (<30, 30-60, >60).

[0062] 3. If a systematic deviation is observed, perform linear calibration again: Age adjustment value = a + b × Original age in, a and b The results were obtained by least squares estimation on the validation set and applied to all future samples.

[0063] V. Conclusion The above process generates a methylation clock with 70 CpG sites, which preserves the cross-platform and cross-laboratory portability of the Horvath framework, while achieving MAE ≤ 3.5 years in both internal and external blood datasets.

[0064] Example 2: Randomized, double-blind enrollment and intervention This embodiment collected blood samples from subjects before and after urolithiasis A intervention to ensure the objectivity and reliability of the experiment and avoid bias. The specific steps included: 1. Intervention: Thirty healthy individuals were randomly and double-blindedly enrolled. All participants signed informed consent forms and took TimeBeacon daily. ® Brand MitoPhy ® Two capsules of urolithin A cell energy supplement were administered, with a daily dose of 520 mg of urolithin A. A 5 mL blood sample was collected before administration and again after two months of continuous use. During this period, subjects maintained normal diet and lifestyle habits, avoiding other factors that might affect methylation.

[0065] 2. Phenotypic Data: All subjects completed a phenotypic scale after taking urolithin A for two consecutive months. Qualitative grading data on phenotypic significance before and after the intervention were obtained. The phenotypic scale included 20 items: increased energy, reduced fatigue, improved concentration, enhanced memory, improved mood, reduced anxiety, improved circadian rhythm, improved sleep, neurological recovery, increased muscle strength, improved exercise endurance, weight loss, fat reduction, increased appetite, improved bowel movements and constipation relief, enhanced alcohol metabolism, increased skin radiance, faster wound healing, enhanced immunity, improved vision, promoted hair and nail growth, and enhanced libido. Each phenotype was categorized into three characteristics: "highly significant," "perceived," and "not significant." Figure 1 ).

[0066] 3. Sample Collection: 5 mL blood samples were collected via venous blood collection before intervention (baseline) and 2 months after intervention. Samples were preserved in standardized anticoagulant tubes (EDTA tubes) and immediately frozen at -80°C to prevent DNA degradation.

[0067] This embodiment uses a randomized double-blind design to control experimental variables, obtain paired samples before and after the intervention, and complete phenotypic scales to provide basic data for subsequent methylation analysis, ensuring the comparability and statistical significance of the results.

[0068] Example 3: DNA extraction, RRBS methylation library construction, sequencing, and bioinformatics analysis This embodiment extracts DNA from blood samples, performs RRBS whole-genome methylation sequencing, and uses bioinformatics analysis to obtain statistically significant methylation sites and regions before and after urolithin A intervention. The specific steps include: 1. DNA Extraction: High-quality genomic DNA was extracted from frozen blood samples using a commercial DNA extraction kit (Qiagen DNeasy Blood Kit). The extraction process included cell lysis, protein removal, and DNA purification steps to ensure that the DNA concentration and purity met sequencing requirements (A260 / A280 ratio between 1.8 and 2.0).

[0069] 2. RRBS methylation library preparation and sequencing: Library construction principle: RRBS technology uses restriction endonucleases (such as MspI) to digest DNA, enrich CpG-dense regions, and then performs bisulfite treatment to convert unmethylated cytosine into uracil, thereby distinguishing the methylation status through sequencing.

[0070] Specific procedures: RRBS library construction was performed on the DNA of each sample, including enzyme digestion, end repair, adapter ligation, bisulfite conversion, and PCR amplification. After library construction, paired-end sequencing was performed using the Illumina NovaSeq high-throughput sequencing platform to generate raw sequencing data (FASTQ format).

[0071] 3. Bioinformatics analysis: 3.1 Data Preprocessing The raw sequencing data were quality controlled (using the FastQC tool) to remove low-quality reads and adapter sequences. Then, the reads were aligned to the reference genome (hg38) using alignment software (Bismark), and methylation site information was extracted (to generate a CpG methylation level file).

[0072] Data quality control parameters: To ensure data quality, the raw methylation data undergoes a two-stage filtering process: Raw data filtering criteria (based on missing rate): retain sites with a missing rate of no more than 20%.

[0073] Site filtering criteria before differential analysis (based on variability and mean): Sites with low variability and extreme methylation levels (too high or too low) are filtered out. Sites with a variance threshold <0.01 are filtered out, while sites with a mean methylation level between 0.05 and 0.95 are retained.

[0074] In subsequent differential analysis, at least 5 valid samples are required for both the pre-intervention group and the post-intervention group at each site.

[0075] result: Original data filtering results: Number of sites retained after quality control: 901,525 Filtering criteria: Missing rate ≤ 20% Pre-analysis site filtering results: All sites after quality control were screened by variability and mean (variance > 0.01, mean between 0.05 and 0.95).

[0076] The final number of loci included in the differential analysis was 901,525. Sample size statistics: Sample size in the pre-intervention group: 25 Sample size in the after-intervention group: 21 For each locus, in the differential analysis, both the pre-intervention group and the post-intervention group met the requirement of having at least 5 valid samples.

[0077] Perform interpolation of missing values ​​in adjacent intervals: A distance-weighted interpolation method is used, processed in parallel on a chromosome basis: Interpolation method: Distance-weighted average. Neighboring non-deleted sites are searched within a 1000 bp range upstream and downstream of the target site. Weights are then calculated using the following formula, and a weighted average is calculated based on these weights. After calculating the average, boundary constraints are applied to ensure the value is valid. If there are no valid neighbors (or no neighbors at all) within the window, the median of all non-deleted sites on this chromosome is used for interpolation.

[0078] Weighting formula: Weight = 1 / (Distance + 1) result: After interpolation, the missing data rate was significantly reduced, meeting the requirements for subsequent analysis.

[0079] 3.2 Differential methylation analysis use Wilcoxon The rank-sum test was used to compare methylation levels before and after the intervention, and a normal approximation was used to handle large samples or cases with knots. Variability was also checked to ensure that at least one set had a variance greater than 1. e -8, to avoid testing for constant values.

[0080] result: Number of sites that completed valid tests: 901,525 All sites passed the variability test (at least one set of variance > 1). e -8).

[0081] The difference in methylation levels before and after intervention at each site was quantified by the effect size ( Cohen's dQuantification. First, calculate the pooled standard deviation, then divide the locus mean by the pooled standard deviation to obtain the effect size.

[0082] Effect size distribution: Calculated for all sites Cohen's d Effect size, ranging from -1.696 to +1.662.

[0083] Example 4: Model integration to mine aging-related biomarkers and validate them with targeted sequencing This embodiment integrates pre- and post-intervention phenotypic data from differentially expressed methylation sites and uses machine learning algorithms to fit and discover biomarkers related to molecular age. These biomarkers are then validated using targeted amplicon sequencing to achieve accurate prediction of molecular age after urolithin A intervention. The specific steps include: 1. Stratification and Correlation Analysis First layer: Strong signal filtering. That is, simultaneously satisfying... P The location of the value (statistical significance) and effect size threshold.

[0084] P Value threshold: Wilcoxon test P Value < 0.001 Effect size threshold: |Effect size| > 0.5 result: Number of candidate sites selected through strong signal screening: 513 Screening criteria: simultaneously meet P Value < 0.001 and |effect size| > 0.5 Screening site characteristics: All 513 sites showed significant changes in methylation levels, with absolute effect sizes greater than 0.5, indicating moderate to large biological effects.

[0085] Second layer: FDR correction. (Using...) Benjamini-Hochberg The method employs multiple test corrections, and sites that meet the criteria are considered to have significant differences.

[0086] FDR threshold: Corrected FDR < 0.1 When the number of sites with FDR < 0.1 is less than 10, the following criteria are applied: P Value threshold: <0.01 Effect size threshold: | Cohen's d | > 0.3 Mean difference threshold: |mean difference| > 0.1 result: All 513 strong signal candidate sites were corrected by FDR (FDR < 0.1).

[0087] Final number of significantly differentiating sites: 513 The FDR values ​​for all significant sites were <0.001, which is far below the threshold of 0.1.

[0088] 2. Correlation analysis use Spearman Rank correlation coefficient analysis was used to analyze the correlation between "site methylation change value" and "biological age change value".

[0089] Analysis variables: Variable X: The difference between the methylation level at the site before and after taking urolithin A. Variable Y: Change in biological age, the difference between age before and after administration, wherein the subject's biological age before and after administration is predicted using the aging clock obtained in Example 1. Correlation analysis is performed only when there are at least two valid paired sample data.

[0090] Paired sample information: Number of successfully paired samples: 14 pairs All paired samples contained complete pre- and post-intervention methylation data and biological age data.

[0091] result: Correlation analysis was performed on 513 significantly different loci with biological age changes, and all loci met the requirement of at least two valid paired samples.

[0092] Screening of strongly correlated sites: Correlation coefficient threshold: | Spearman Correlation coefficient | > 0.6 P Value threshold: Correlation test P Value < 0.1 After filtering, sort the results in descending order by the absolute value of the correlation coefficient.

[0093] result: Number of loci strongly correlated with biological age changes: 13 Features of strongly correlated sites: Correlation coefficient range: | r | = 0.60 to 0.84 The strongest correlated locus: chr12:6742665, correlation coefficient = -0.84. P Value = 0.00015 All 13 loci showed a moderate to strong correlation with changes in biological age.

[0094] Top 5 strongly correlated loci: chr12:6742665, correlation coefficient = -0.84 P Value = 0.00015 chr16:6031887, correlation coefficient = -0.78 P Value = 0.00092 chr10:48438524, correlation coefficient = 0.75 P Value = 0.00182 chr17:79700641, correlation coefficient = -0.74 P Value = 0.00247 chr22:37348963, correlation coefficient = -0.69 P Value = 0.00591 Set the correlation coefficient threshold to ±0.5. P The value was 0.2, and 46 methylation sites were obtained through screening (Table 1). Set the correlation coefficient threshold to ±0.35. P The value was 0.2, and 118 methylation sites were obtained through screening (Table 2).

[0095] 3. Machine learning modeling This step constructs two independent machine learning models, one for predicting biological age changes and the other for classifying intervention responses.

[0096] 3.1 Age Change Prediction Model Model task: Predict biological age change (continuous dependent variable Y) using “methylation change value” (feature X).

[0097] Model type: L1 regularized LASSO regression ( α = 1), select the regularization coefficient that minimizes the mean squared error (MSE) of cross-validation. At the same time, enable leave-one-out cross-validation (LOOCV), that is, the number of cross-validation folds equals the number of samples.

[0098] Model stability: After the model calculation is completed, the 95% confidence interval of the model coefficients is calculated through 1000 Bootstrap resampling.

[0099] Using a fixed random seed ensures the reproducibility of model training and bootstrap.

[0100] result: Number of input features: 13 (methylation change values ​​from strongly correlated sites) Final number of selected features: 7 Sample size: 14 paired samples Model performance metrics: training set R 2 : 0.791 (explains 79.1% of the variance) Figure 2 ) Cross-validation R 2 (LOOCV): -0.011 (indicating that the model performed poorly in cross-validation, possibly due to overfitting) Figure 3 ) Root mean square error (RMSE): 1.05 years Mean absolute error (MAE): 0.74 years Model coefficients and confidence intervals: The 95% confidence intervals for all coefficients were calculated using 1000 Bootstrap resampling iterations. The seven sites selected in the model and their coefficients: chr12:6742665, coefficient = -4.34, correlation coefficient with age change = -0.84 chr10:48438524, coefficient = 3.64, correlation coefficient with age change = 0.75 chr22:37348963, coefficient = -0.44, correlation coefficient with age change = -0.69 chr1:180040465, coefficient = -1.12, correlation coefficient with age change = -0.65 chr2:149051919, coefficient = -0.08, correlation coefficient with age change = -0.63 chr17:17141286, coefficient = 0.90, correlation coefficient with age change = 0.62 chr17:17141239, coefficient = 0.43, correlation coefficient with age change = 0.60 Model evaluation: The training set performs well. R 2 = 0.791), but cross-validation R 2 A negative value suggests that the model may be overfitting; the average prediction error is approximately 0.74 years.

[0101] 3.2 Intervention Response Classification Model Model task: Predict the intervention response category (binary dependent variable Y) using the baseline methylation level before intervention (feature X).

[0102] Dependent variable Y is defined as follows: Classification criteria: based on the median change in biological age.

[0103] High response (label=1): Biological age change calculated based on aging clock > median.

[0104] Low response (label=0): Biological age change calculated based on aging clock ≤ median.

[0105] Model type: Binomial logistic regression with LASSO regularization ( α = 1), select the regularization coefficient with the fewest features within a standard error range that minimizes the error. Use 10-fold cross-validation; if the number of samples is less than 10, automatically switch to LOOCV. A predicted probability > 0.5 is considered a high response.

[0106] result: Number of input features: 513 (baseline methylation levels from significantly different sites) Final number of key features selected: 2 Sample size: 14 paired samples Category labels: 7 high responders, 7 low responders (based on median biological age change) Model performance metrics: Classification accuracy: 92.9% (13 / 14 correctly classified) Sensitivity: 100% (7 out of 7 high responders correctly identified all cases) Specificity: 85.7% (6 / 7 low responders correctly identified) AUC value: 1.0 (perfect discrimination ability) Confusion matrix: True negative (TN): 6 cases (low responders were correctly identified as low responders). False positive (FP): 1 case (a low responder was mistakenly identified as a high responder). False negatives (FN): 0 (high-responder individuals were misdiagnosed as low-responders) True positive (TP): 7 cases (high-responders were correctly identified as high-responders). The model selected two key sites: chr6:158243715, coefficient = 1.21 (positive correlation, the higher the methylation level, the greater the probability of high response). Although this site is not among the 118 biomarkers mentioned above, it is beneficial for the prediction model. chr18:60573853, coefficient = -6.53 (negative correlation, the higher the methylation level, the lower the probability of a high response), this site is among the 118 biomarkers mentioned above.

[0107] Model evaluation: The classification accuracy is 92.9%, and the AUC is 1.0, indicating strong discriminative ability. With 100% sensitivity, it can identify all high-responders; The specificity was 85.7%, with one case of low response being misdiagnosed. High-accuracy classification can be achieved using only 2 loci.

[0108] In addition, two intervention response classification models were constructed using the 46 and 118 markers mentioned above in a similar manner: intervention response classification model-46 and intervention response classification model-118.

[0109] Performance metrics for the two models (Intervention Response Classification Model-46 / Intervention Response Classification Model-118): Classification accuracy: 100% (14 out of 14 correctly classified) / 92.9% (13 out of 14 correctly classified) Sensitivity: 100% (7 / 7 high responders correctly identified all) / 100% (7 / 7 high responders correctly identified all) Specificity: 100% (correctly identified in 7 / 7 low responders) / 85.7% (correctly identified in 6 / 7 low responders) AUC value: 1.0 (perfect discrimination ability) / 1.0 (perfect discrimination ability) These predictive models can accurately predict an individual's response to urolithin A intervention by detecting the methylation level of specific methylation sites before the subject takes urolithin A, providing a scientific basis for personalized anti-aging intervention.

[0110] 4. Characteristic Stability Analysis 4.1 Repeated validation parameters (for an intervention response classification model with 513 input features) This step is used to evaluate the consistency of feature selection in the intervention response classification model across multiple random resampling operations. The sample order is randomly shuffled in each resampling, and the process is repeated 100 times.

[0111] Single-repeated training parameters: Model type: Binomial logistic regression LASSO model, selecting the regularization coefficient with the smallest cross-validation error in this iteration, and enabling 5-fold cross-validation.

[0112] result: Variation in the number of selected features: The number of features selected by the model fluctuated somewhat across 100 repetitions.

[0113] 4.2 Characteristic Stability Evaluation Criteria The stability of a selected feature is evaluated by the proportion of times it is selected by the model in 100 repeated training iterations.

[0114] Feature selection frequency statistics: Total number of features selected in 100 repetitions: 2 Feature selection frequency: chr18:60573853, selection frequency = 10% (selected 10 times out of 100 repetitions) chr6:158243715, selection frequency = 6% (selected 6 times out of 100 repetitions) Stable feature recognition: Select a stable number of features with a frequency > 50%; no features are found when the 50% threshold is reached. Most stable feature: chr18:60573853 (selection frequency = 10%) Stability analysis conclusions: In 100 repeated validations, the two loci (chr6:158243715 and chr18:60573853) selected by the intervention response classification model had a relatively low selection frequency. This may be related to the small sample size (n=14), leading to a certain degree of randomness in feature selection. Despite the low stability, the final model performed excellently on the complete dataset (accuracy 92.9%, AUC=1.0).

[0115] 5. Primer synthesis and targeted amplification Primer design and synthesis: Based on the identified methylation markers (specific CpG fragments), specific primers were designed using primer design software (Primer3). Primer design ensured coverage of the target methylation region and avoided non-specific amplification. Each primer pair included a forward and a reverse primer, with a primer length of 18-25 nucleotides, a Tm value controlled within the range of 55-65℃, and a GC content of 40-60%. Primer design considered changes in DNA sequence after bisulfite treatment and ensured specific recognition of methylated and unmethylated cytosine sites. The synthesized primers underwent quality control (HPLC purification) before being used in subsequent experiments.

[0116] Targeted amplicon amplification and sequencing: Amplification principle: PCR amplification technology is used. The sample DNA is treated with bisulfite to convert unmethylated cytosine to uracil, while methylated cytosine remains unchanged. Then, PCR amplification is performed using primer pairs, and the methylation status of the amplified products is detected by high-throughput sequencing.

[0117] Specific procedures: Targeted amplicon PCR was performed on the DNA of each sample using designed primers and a high-fidelity DNA polymerase (KAPA HiFi HotStart ReadyMix). The amplification products were verified for size and purity by gel electrophoresis, then an amplicon library was constructed and sequenced using the high-throughput sequencing platform Illumina MiSeq to generate targeted sequencing data.

[0118] 6. Model Validation Twenty healthy subjects were randomly and double-blindly enrolled. All subjects signed informed consent forms, and 5 mL blood samples were collected from each subject via venous blood collection. Methylation library construction, sequencing, and bioinformatics data processing were performed using the same methods as in Example 3, obtaining methylation level data for 118 methylation sites as shown in Table 2. The data from these healthy subjects were input into intervention response classification model-46 and intervention response classification model-118, classifying the subjects into high-response and low-response groups.

[0119] 20 healthy subjects took TimeBeacon daily. ® Brand MitoPhy ® Two capsules of urolithin A cell energy supplement were administered, with a daily dose of urolithin A of 520 mg. After two months of continuous administration, 5 mL of blood was drawn intravenously. During this period, subjects maintained normal diet and lifestyle, avoided other factors that might affect methylation, and their individual well-being (e.g., [missing information]) was monitored. Figure 1 The phenotypes shown in the figure were recorded. Similarly, methylation library construction, sequencing, and bioinformatics data processing were performed on samples after urolithin A intervention.

[0120] Using data before and after urolithin A intervention, biological age was calculated based on the methylation clock obtained in Example 1, and the change in biological age of the subjects was calculated. Those with a change in biological age greater than the median were identified as high-response groups, and those without were identified as low-response groups.

[0121] The classification results of the subjects obtained using intervention response classification model-46 and intervention response classification model-118 are shown in Tables 4 and 5, respectively. The classification accuracy, sensitivity, and specificity of intervention response classification model-46 were 85%, 90.9%, and 77.8%, respectively, while those of intervention response classification model-118 were 80%, 81.8%, and 77.8%, respectively. The results show that both models can effectively predict whether the samples will benefit from urolithin A, demonstrating the reliability of the 46 / 118 biomarkers of this invention and the excellent predictive performance of the models constructed using these biomarkers. Furthermore, subjects predicted to have a high response by the models generally reported increased energy, concentration, and appetite after taking urolithin A, further illustrating the reliability of the biomarkers provided by this invention.

[0122] Table 4

[0123] Table 5

[0124] Example 5: Construction and Validation of a Composite Aging Prediction Model Based on "General-Response" Dual Features This embodiment aims to verify the predictive performance of a composite feature model that integrates the general aging clock features used in Example 1 with the urolithin A-specific response features screened in Example 4, in order to improve the sensitivity and robustness of urolithin A intervention assessment while ensuring the accuracy of biological age prediction.

[0125] 1. Model Building Methylation sequencing data from >400 healthy donors in Example 1 were used as the training set.

[0126] Feature selection: In this embodiment, a total of 116 methylation sites were selected as feature variables, specifically including: The 70 CpG sites associated with the universal aging clock identified in Example 1 (providing basic accuracy of biological age); The 46 CpG sites screened in Example 4 that showed high responsiveness to urolithin A intervention (providing specificity and sensitivity for intervention assessment).

[0127] Training process: Methylation level data of the above 116 sites were extracted, and a composite biological age prediction model was trained using the ElasticNet Regression algorithm. The optimal model parameters were determined by 10-fold cross-validation.

[0128] 2. Independent verification In addition, 23 subjects were selected as an independent validation set (Table 6), and all subjects signed informed consent forms. Peripheral blood DNA was extracted from these subjects, and the methylation levels of the aforementioned 116 sites were detected using targeted amplicon sequencing technology. The detection data were input into a trained composite prediction model to calculate the predicted biological age of each subject (Table 6).

[0129] 3. Results Analysis The predicted biological age was compared and analyzed with the subject's actual age. Figure 4 As can be seen, the 23 sample points in the validation set are closely distributed around the diagonal dashed line, exhibiting a very strong linear positive correlation. The vast majority of data points are very close to the diagonal (i.e., the prediction error), indicating that the model has stable predictive performance across different age groups.

[0130] Table 6

[0131] Among them, samples 10102410111 and 10102410121 were paired samples of a 43-year-old male before and after two months of continuous urolithin A treatment. For the paired samples 10102410111 and 10102410121, aging rate prediction was performed using 46 loci obtained from Example 4. The aging rate value before intervention was 0.73, and the aging rate value after intervention was 0.34, indicating that the aging rate of this individual was significantly reduced after urolithin A intervention.

[0132] The formula for calculating the aging rate value is: Aging rate = Age calculated by aging clock / Actual age For paired samples numbered 10102410111 and 10102410121, biological ages were predicted using models involving only 70 loci and 46 loci, respectively, without the involvement of urolithin A intervention. The predicted ages before and after intervention were 43.8 years and 42.4 years, respectively, indicating a 1.4-year reduction in biological age after urolithin A administration. In this embodiment, a model integrating 116 loci after urolithin A intervention was used to perform a second-order fit between predicted and actual ages. The predicted and actual biological ages before and after intervention were 43.6 years and 40.2 years, respectively, indicating a 3.4-year reduction in biological age after urolithin A administration.

[0133] The results of this embodiment show that after integrating 46 specific sites for urolithin A intervention, the composite model predicts that the biological age stability of urolithin A intervention is stronger and the specificity is higher. This is reflected in the greater biological age reversal, with a difference of 2 years.

[0134] The above provides a detailed description of the urolithiasis A intervention biological age assessment method, biomarkers, and reagent kits provided by this invention. Specific embodiments are used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for constructing a predictive model to assess the biological age of urolithiasis A intervention, the method comprising the following steps: 1) Collect DNA samples from subjects before and after taking urolithiasis A; 2) Perform methylation sequencing on the DNA sample to obtain methylation sequencing data; 3) Perform differential sequencing data analysis on the subjects before and after taking urolithin A to obtain differentially methylated sites; 4) Based on the methylation sequencing data obtained in step 2), predict the biological age of the subject before and after taking urolithin A using the aging clock, and obtain the subject's urolithin A response data according to the biological age. For example, the subject is divided into a high response group and a low response group (for example, the high response group is defined as the difference in biological age before and after taking urolithin A that is greater than the median difference in biological age before and after taking urolithin A for all subjects, and the low response group is defined as the difference that is less than or equal to the median). 5) Among the differentially methylated sites obtained in step 3), select sites whose differences in methylation levels before and after taking urolithin A are related to the differences in the subject's biological age before and after taking urolithin A, and use these sites as biological age-related sites; 6) The methylation levels of the biological age-related sites of the subjects before taking urolithin A and the urolithin A response data of the subjects were used as training set data to train the prediction model and obtain the prediction model.

2. The method according to claim 1, wherein, The subjects were given an oral dose of 520 mg / day of urolithin A.

3. The method according to claim 1 or 2, wherein, The subjects were given oral administration of urolithiasis A for at least 2 consecutive months; Preferably, the DNA sample is obtained from the peripheral blood of the subject; Preferably, the aging clock is constructed using methylation data; Preferably, the prediction model is a LASSO regression model.

4. A predictive model for assessing biological age of urolithiasis A intervention constructed using the method of any one of claims 1-3.

5. A method for assessing biological age of urolithin A intervention, the method comprising using the predictive model for assessing biological age of urolithin A intervention as described in claim 4.

6. Assess biomarkers for urolithiasis A intervention in biological age, wherein the biomarkers contain the following 46 methylation sites: chr12, 6742665; chr16, 6031887; chr10, 48438524; chr17, 79700641; chr22, 37348963; chr1, 180040465; chr5, 24504913; chr13, 82739391; chr2, 149051919; chr5, 192650; chr1, 6420503; chr17, 17141286; chr17, 17141239; chr11, 85379785; chr7, 157609742; chr7, 67942679; chr11, 59353038; chr5, 69346576; chr20, 55299187; chr2, 8980613; chr6, 166511358; chr9, 132163449; chr2, 128408751; chr12, 12210462; chr12, 122492228; chr7, 153018404; chr7, 77400383; chr12, 132435947; chr19, 813408; chr6, 105131580; chr17, 72933858; chr12, 122177976; chrX, 39681330; chr4, 49139154; chr3, 11168561; chr1, 246853427; chr3, 13019970; chr1, 68134890; chr18, 60573853; chr10, 104888800; chr12, 111305902; chr3, 125622827; chr16, 2152231; chr2, 241580592; chr5, 77309759; chr11, 134709481; in, Loci are represented as "chromosome, location".

7. The marker according to claim 6, wherein, The marker also includes the following 72 methylation sites: chr1, 17223246; chr1, 21034798; chr1, 32958143; chr1, 151572948; chr1, 185291026; chr1, 236669286; chr2, 11838161; chr2, 105490297; chr2, 110372798; chr2, 232506656; chr3, 50206058; chr3, 61188001; chr3, 72751093; chr3, 78735482; chr3, 128417976; chr3, 160070018; chr3, 173162834; chr3, 176973476; chr5, 32040188; chr5, 77309754; chr5, 149978541; chr5, 174240293; chr6, 35761821; chr6, 144441682; chr7, 71040574; chr7, 130807654; chr8, 141057355; chr8, 143600493; chr9, 35490521; chr9, 95840845; chr9, 98119385; chr9, 120741060; chr9, 140031311; chr10, 440229; chr10, 35202465; chr10, 45169266; chr10, 49554511; chr11, 66758725; chr11, 68050325; chr12, 122481395; chr13, 19786641; chr13, 27649406; chr13, 88562690; chr13, 109152872; chr14, 23783161; chr14, 58715398; chr14, 74907015; chr14, 81980125; chr15, 71081611; chr16, 15952421; chr16, 18066476; chr16, 29058733; chr16, 67687001; chr16, 88040108; chr17, 7287139; chr17, 68209097; chr17, 79699160; chr17, 81052172; chr18, 125249; chr19, 1281519; chr19, 40940491; chr19, 41336984; chr19, 48947121; chr19, 57349205; chr20, 31077485; chr20, 47664239; chr20, 49251438; chr21, 27245205; chr21, 45770684; chr22, 42410171; chr22, 50528621; chrX, 152765013; The locus is represented as "chromosome, location".

8. A kit for assessing biological age of urolithiasis A intervention, the kit comprising 45 primer pairs as in SEQ ID NO:1-90, the 45 primer pairs being used to detect the biomarker of claim 6.

9. The kit according to claim 8, wherein, The kit also contains 71 primer pairs, such as SEQ ID NO:91-232; Preferably, the kit further comprises DNA polymerase, dNTPs, and buffer.

10. A method for assessing the biological age of urolithiasis A intervention, the method comprising: 1) Collect DNA samples from the subjects to be tested and determine the methylation level at the markers described in claim 6 or 7; 2) Input the methylation level determined in step 1) into the prediction model of claim 4 to obtain the prediction result of the subject to be tested.

11. The method according to claim 10, wherein, The DNA sample was obtained from the subject's peripheral blood. Preferably, in step 1), the methylation level at the marker of claim 6 or 7 is determined using the kit of claim 8 or 9.

12. A biomarker for predicting the biological age of a subject taking urolithiasis A, said biomarker comprising methylation sites used by aging clocks and the biomarker of claim 6, predicting beneficial effects.

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