Human muscle aging degree marker, and human muscle aging degree prediction model and construction method therefor

Abstract

Provided are a human muscle aging degree marker, and a human muscle aging degree prediction model and a construction method therefor. The human muscle aging degree marker comprises 119 CpG sites with different methylation levels, wherein the different methylation levels of the CpG sites correspond to different muscle aging degrees. The human muscle aging degree prediction model is expressed as: predicted age=Intercept+β1*CpG1+β2*CpG2+…+β119*CpG119, wherein Intercept is a constant term with a numerical value of 18.924314457, and β1-β119 are weights corresponding to CpG1-CpG119. This model can better explain a relationship between muscle aging and related gene expression, offering deeper insights for researchers and doctors.

Description

Human muscle aging degree marker, human muscle aging degree prediction model and construction method thereof Technical Field

[0001] The present invention belongs to the technical field of muscle age prediction, and particularly relates to a marker of human muscle aging degree, a human muscle aging degree prediction model and a construction method thereof. Background Art

[0002] Muscle tissue is a major component of the human body, and its degree of aging directly impacts human health and quality of life. One of the most significant effects of aging is the loss of skeletal muscle function. The sarcomere is the smallest functional unit of skeletal muscle. It is a repeating unit, each composed of two Z-lines and several myosin thick filaments and actin thin filaments. Although the impact of muscle aging on human health and quality of life is widely recognized, quantitative analysis of muscle aging remains a significant challenge. Currently, there is a lack of a biologically interpretable analytical method that can accurately and robustly assess the extent of muscle aging.

[0003] DNA methylation, as an epigenetic phenomenon, has been shown to be related to biological age prediction and has been widely used to construct aging clock models (Horvath, Steve. “DNA methylation age of human tissues and cell types.” Genome biology vol. 14, 10 (2013): R115.). However, existing aging clock models mainly rely on blood and other tissue samples, while muscle-based aging clock models are relatively lacking. Even the existing muscle aging clock model (Voisin, Sarah et al. “An epigenetic clock for human skeletal muscle.” Journal of cachexia, sarcopenia and muscle vol. 11, 4 (2020): 887-898) is only constructed through statistical screening of large-scale DNA methylation sites, and fails to consider the connection with muscle function-related genes. Therefore, its biological significance is poorly interpretable. The genotype of the gene ACTN3 has been shown to be closely related to muscle motor performance (Yang et al. 2003. AJ Hum Genet. 10: 1335-1346). ACTN3 is one of the primary functional genes on the sarcomere Z-line. However, while the relationship between ACTN3 gene expression and muscle performance has been intensively studied (over 1,000 related research papers have been published), there is a lack of information on how gene methylation levels affect the expression of ACTN3 and other sarcomere genes, and how this is related to age, exercise capacity, and muscle aging.

[0004] Therefore, existing technologies lack a robust and biologically interpretable quantitative analysis method for muscle aging based on functional gene methylation analysis. To address this issue, we propose a method for analyzing muscle aging based on the methylation levels of motor performance genes and sarcomere functional genes. We construct a model that links functional gene methylation level changes to the biological mechanisms of muscle aging, hoping to better predict the biological age of muscle and provide a new predictive tool for healthy aging.

[0005] Summary of the Invention

[0006] In view of this, the establishment process of the present invention's method for analyzing the degree of human muscle aging based on the methylation levels of sports performance genes and sarcomere function genes includes the following four parts:

[0007] 1. Establishment of Muscle Sample Methylation Dataset

[0008] We screened methylation data from healthy human muscle tissue on a large scale from public datasets such as GEO and ArrayExpress. The selected data came from the Infinium HumanMethylation450 BeadChip (450K) and the Infinium MethylationEPIC BeadChip (EPIC). The datasets are summarized as follows:

[0009] EPIC: GSE154980, GSE114763, GSE171140, E-MTAB-11282;

[0010] 450K: GSE48472, GSE52576, GSE60655, GSE61259, GSE61452, GSE64491, GSE69502, GSE78743, GSE50498, GSE87655.

[0011] The entire dataset contains methylation data from 479 muscle tissue samples.

[0012] 2. Screening of functional genes in muscle sarcomeres

[0013] ACTN3 is the main structural gene of the Z-line on the sarcomere. In addition to it, we also selected other major sarcomere functional genes for analysis:

[0014] Z-line genes: "ACTN2", "ACTN3", "MYOT", "LDB3", "MYOZ1", "DES";

[0015] Myosin (heavy chain) thick line gene: "MYH1", "MYH2", "MYH3", "MYH4", "MYH6", "MYH7", "MYH7B", "MYH8", "MYH9", "MYH10", "MYH11", "MYH13", "MYH14", "MYH15", "MYH16";

[0016] Actin gene: "ACTA1".

[0017] 3. Screening of methylation sites corresponding to related genes

[0018] Based on the location of the relevant genes on DNA, we matched a total of 428 methylation sites shared by the Infinium HumanMethylation450 BeadChip (450K) and the Infinium MethylationEPIC BeadChip (EPIC).

[0019] 4. Establishment of a Muscle Aging Clock Model

[0020] Elastic Net Model

[0021] For the Elastic Net model, we chose this model because it combines both L1 and L2 regularization, which helps reduce model complexity and improve predictive performance. We used the cv.glmnet function in the glmnet package and determined the optimal value of the regularization parameter lambda through 10-fold cross-validation. Setting the alpha parameter to 0.5 balanced the weights between Ridge Regression and Lasso Regression (LASSO). This approach not only effectively handles multicollinearity in the data but also allows for feature selection when the feature dimension is high, thus avoiding overfitting.

[0022] Elastic Net model formula and parameters:

[0023] The model screened the methylation levels of 119 significant methylation sites, and the age prediction formula was:

[0024] Predicted age = Intercept + β1*CpG1 + β2*CpG2 + … + β119*CpG119; where Intercept is a constant term with a value of 18.924314457.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Enhanced biological significance: The prediction model of the present invention is based on the methylation levels of motor performance genes (such as ACTN3) and is closely linked to the biological mechanisms of muscle function and the aging process. This method not only provides an estimate of muscle biological age, but also its results are highly biologically interpretable. Compared with traditional aging clock models that rely on statistical screening, this model can better explain the relationship between muscle aging and the expression of related genes, thereby providing researchers and doctors with deeper insights.

[0027] 2. Potential for Clinical Application: Because this model focuses on the methylation levels of genes closely related to athletic performance, its predicted muscle age not only reflects the general biological aging process but also addresses the specific phenotypes of athletic performance and muscle aging. This association enables the model to be useful in individual health management and the development of exercise training plans, providing clinicians with quantitative data support and personalized recommendations.

[0028] 3. Potential for Continuous Monitoring: This predictive tool is suitable for long-term monitoring of individual muscle aging. By regularly measuring and analyzing muscle tissue methylation levels, it is possible to track the dynamics of muscle aging, promptly identify signs of accelerated aging, and adjust lifestyle or treatment plans accordingly. This provides a new method and tool for continuous clinical tracking of muscle aging, facilitating personalized health management and intervention. DETAILED DESCRIPTION

[0029] In order to allow those skilled in the art to understand the present invention more clearly and intuitively, the present invention will be further described below.

[0030] 1. Sample Extraction

[0031] The appropriate muscle tissue or blood sample is usually selected from the area that the doctor has assessed as most representative of the individual's health. For muscle, this might be the upper arm or thigh muscle; for blood, it is usually drawn from a vein.

[0032] Use aseptic technique for sampling: For muscle samples, remove a small piece of muscle tissue, such as with a needle prick. For blood samples, use a sterile lancet to draw blood.

[0033] The samples were kept frozen to prevent DNA degradation until further processing.

[0034] 2. DNA Extraction

[0035] Thaw frozen muscle tissue samples or blood samples and process them under sterile conditions.

[0036] Chemical or mechanical methods are used to disrupt the cell and nuclear membranes, releasing the DNA.

[0037] Isolate pure DNA using a DNA extraction kit, such as one that contains a protease and DNA binding buffer.

[0038] The purity and concentration of the extracted DNA were tested to ensure that it was suitable for the next analysis.

[0039] 3. Methylation analysis using Illumina 450K or EPIC chips

[0040] Methylation levels of extracted DNA samples were analyzed using the Illumina Infinium HumanMethylation450 BeadChip or the higher-density EPIC BeadChip.

[0041] 4. Idat data preprocessing and normalization to obtain beta matrix

[0042] Use dedicated software to read the idat file obtained from the chip scanning results.

[0043] Perform preprocessing steps such as background correction, color balance adjustment, and probe intensity adjustment.

[0044] Methylation data were normalized using quantitative normalization methods (e.g., BMIQ) to eliminate batch effects and technical variations.

[0045] The methylation level of each CpG site is calculated and usually expressed as a beta value (between 0 and 1, representing the ratio of unmethylated to fully methylated).

[0046] 5. Extraction of methylation sites corresponding to related genes

[0047] From the normalized beta matrix, methylation data of CpG sites of genes were extracted based on a predetermined list of genes associated with muscle aging.

[0048] This may include identifying these genes through literature review, genomics databases, and prior biological knowledge.

[0049] 6. Input Elastic Net model

[0050] The extracted methylation data were input as features into the prediction model.

[0051] 7. Predicted muscle age as an outcome

[0052] The integrated model combines the input methylation data and outputs the predicted muscle biological age.

[0053] This output can be compared to chronological age to assess the degree of aging of muscle tissue.

[0054] Furthermore, the model can provide insights into biomarkers of muscle aging, providing a foundation for future research and therapeutic strategies.

[0055] Elastic Net model formula and parameters:

[0056] The model selected the methylation levels of 119 effective sites, and the age prediction formula was:

[0057] Predicted age = Intercept + β1*CpG1 + β2*CpG2 + … + β119*CpG119; where Intercept is a constant term with a value of 18.924314457.

[0058] The above description of the embodiments is intended to facilitate understanding and application of the present invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the embodiments described herein, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention should fall within the scope of protection of the present invention.

Claims

1. A marker of human muscle aging, characterized in that: Contains 119 CpG sites with different methylation levels, and the different methylation levels of the CpG sites correspond to different degrees of muscle aging; The CpG sites are as follows: cg00109451, cg00111977, cg00134787, cg00458754, cg00989575, cg01142475, cg01597727, cg01741041, cg01806972, cg02181220, cg021 92974, cg02652072, cg02970384, cg03634947, cg03755566, cg03844894, c g03918703, cg03991188, cg04030615, cg04077141, cg04573702, cg0466319 4. cg04897932, cg05358439, cg05426160, cg05432213, cg05462208, cg054 88523, cg05744229, cg06048973, cg06344211, cg06995966, cg07135408, cg 08012287, cg08155908, cg08314543, cg08691842, cg08722383, cg0992617 8. cg10072590, cg10316922, cg10523820, cg10662871, cg10819259, cg1121 5976, cg11231913, cg11362596, cg11589139, cg11691093, cg11955845, cg 11992783, cg12296532, cg12362629, cg12744888, cg13109654, cg13373361 , cg13660086, cg13984289, cg14281922, cg14316944, cg14606858, cg1485 2384, cg15046388, cg15578091, cg15950761, cg16010261, cg16212534, cg1 6386079, cg16836028, cg16842280, cg16862260, cg17242351, cg17383272 , cg17889586, cg17944891, cg18041123, cg18080670, cg18119735, cg18286 285, cg18343474, cg18412431, cg18432105, cg19107504, cg19113201, cg1 9381766, cg19415364, cg19477236, cg19558848, cg19573567, cg20335959,cg20482145、cg20618996、cg21009747、cg21301148、cg21376148、cg21376883、cg21553154、cg21828716、cg21871213、cg22144486、cg22588546、cg22897522、cg23109559、cg23240355、cg24082573、cg24371075、cg24374505、cg24746594、cg25117505、cg25256099、cg25311666、cg25420502、cg25851803、cg25921609、cg25930644、cg26512226、cg26515755、cg26857837、cg27380459。、 2. The human muscle aging degree marker according to claim 1, characterized in that The 119 CpG sites with different methylation levels were derived from the following genes: Z-line genes: ACTN2, ACTN3, MYOT, LDB3, MYOZ1, DES; Myosin thick line genes: MYH1, MYH2, MYH3, MYH4, MYH6, MYH7, MYH7B, MYH8, MYH9, MYH10, MYH11, MYH13, MYH14, MYH15, MYH16; Actin gene: ACTA1.

3. A reagent or kit for predicting human muscle age, characterized in that: The method comprises a reagent capable of detecting the methylation levels of the 119 different CpG sites according to claim 1.

4. A prediction model for the degree of human muscle aging, characterized in that: The model is: Predicted age = Intercept + β1*CpG1 + β2*CpG2 + … + β119*CpG119; where Intercept is a constant term with a value of 18.924314457, and β1-β119 is the weight corresponding to CpG1-CpG119.

5. The prediction model according to claim 4, characterized in that The weights corresponding to the features CpG1-CpG 119 are as follows:

6. A method for predicting the degree of human muscle aging using the prediction model according to claim 5, characterized in that: include: The methylation levels of 119 biomarker CpGs in the genomic DNA of human muscle tissue are measured, and the prediction model described in claim 4 is used to determine the age of the muscle, which is compared with the actual age to obtain a muscle aging degree assessment result.

7. The method according to claim 6, wherein The methylation level of the biomarker CpG is measured by determining the methylation level of CpG in the genome of a biological sample, and the biological sample is a human muscle tissue or blood sample.

8. A method for constructing a human muscle aging degree prediction model according to claim 4, characterized in that: The steps include:

1. Establishment of Muscle Sample Methylation Dataset A large-scale screening of methylation data from healthy human muscle tissue was conducted using the GEO and ArrayExpress database platforms. The selected data came from the Infinium HumanMethylation450 BeadChip and the Infinium MethylationEPIC BeadChip platforms. The datasets are summarized as follows: EPIC: GSE154980, GSE114763, GSE171140, E-MTAB-11282; 450K: GSE48472, GSE52576, GSE60655, GSE61259, GSE61452, GSE64491, GSE69502, GSE78743, GSE50498, GSE87655; The entire dataset contains methylation data from 479 muscle tissue samples; 2. Screening of functional genes in muscle sarcomeres The following sarcomere function genes were selected for analysis: Z-line genes: "ACTN2", "ACTN3", "MYOT", "LDB3", "MYOZ1", "DES"; Myosin thick line gene: "MYH1", "MYH2", "MYH3", "MYH4", "MYH6", "MYH7", "MYH7B", "MYH8", "MYH9", "MYH10", "MYH11", "MYH13", "MYH14", "MYH15", "MYH16"; Actin thin line gene: "ACTA1"; 3. Screening of methylation sites corresponding to related genes Based on the location of the relevant genes on DNA, a total of 428 methylation sites shared by the Infinium HumanMethylation450 BeadChip and the Infinium MethylationEPIC BeadChip were matched; 4. Establishment of a Muscle Aging Clock Model Elastic Net model formula and parameters: The model selects the methylation levels of 119 effective methylation sites, and the age prediction formula is: Predicted age = Intercept + β1*CpG1 + β2*CpG2 + … + β119*CpG119; where Intercept is a constant term with a value of 18.924314457.

9. The method for constructing a prediction model according to claim 8, wherein: The weights corresponding to the features CpG1-CpG119 are as follows:

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