An evaluation system for screening exercise-related metabolic markers in obese patients and applications thereof

Abstract

The application belongs to the technical field of metabolic analysis, and particularly relates to an evaluation system for screening exercise-related metabolic markers of obese patients and application thereof. The application provides an evaluation system for screening exercise-related metabolic markers of obese patients, which comprises an exercise intervention module, a sample processing module, a data analysis module and a result output module. Different indexes of obese people after exercise are evaluated by using the evaluation system, and it is found that the comprehensive influence on the human body is reflected in the metabolic reconstruction of tissues and intertissue and potential metabolite pathways, and is accompanied by the improvement of plasma lipid spectrum, body composition and physiological function. The evaluation system can provide a new idea for the metabolic mechanism of exercise for improving the comprehensive health condition of the human body.

Description

Technical Field

[0001] This invention belongs to the field of metabolic analysis technology, specifically relating to an assessment system for screening exercise-related metabolic biomarkers in obese patients and its application. Background Technology

[0002] Regular exercise profoundly affects multiple tissues and is widely recognized for its health benefits, including reducing the risk of cardiovascular, metabolic, and neurological diseases. Researchers have created multi-organ metabolic atlases in mouse and rat models associated with obesity and exercise, revealing how these conditions influence metabolic pathways and physiological functions. While these atlases are valuable for understanding the underlying biological mechanisms, it remains unclear whether these results can be directly applied to improving human health and preventing disease.

[0003] The effects of exercise on metabolism extend beyond the skeletal muscle system. Exercise can influence almost all organ systems by prompting multiple tissues and organs to engage in synchronized and integrated activities. However, the coordination mechanisms by which tissues and organs perform these systemic physiological functions remain unclear. Traditionally, human exercise physiology research has focused on single tissues, most commonly skeletal muscle or plasma, by identifying key exercise-induced signaling pathways in skeletal muscle and plasma, including AMPK, PI3K / Akt, mTOR, and PGC-1α. However, few studies have considered changes in key signaling pathways in other tissues. Since exercise-induced metabolic regulation often requires the synergistic action of multiple organ systems, providing a comprehensive "skeletal muscle-centric" paradigm for human exercise metabolism is a pressing issue in this field. Summary of the Invention

[0004] To address the lack of a research method in the existing technology for studying the overall metabolic effects of exercise on the human body, this invention provides an assessment system for screening exercise-related metabolic biomarkers in obese patients and its application, specifically including the following technical solutions: This invention provides an assessment system for screening exercise-related metabolic biomarkers in obese patients, comprising an exercise intervention module, a sample processing module, a data analysis module, and a result output module. The exercise intervention module performs exercise intervention on obese patients. The sample processing module processes samples from obese patients, including tissue samples from obese patients before and after exercise intervention. The tissue samples include subcutaneous adipose tissue, omental adipose tissue, liver, stomach, and blood; metabolites are extracted from each tissue sample. The data analysis module, connected to the sample processing module, performs data analysis on the samples. The data analysis method includes: performing metabolomics analysis on the metabolites and performing data analysis on the metabolomics analysis results. The result output module is connected to the data analysis module and is used to determine and output the data analysis results. The method for determining and outputting the results is as follows: statistical analysis is performed on all results, and metabolites that show significant changes after exercise are selected as exercise-related metabolic markers.

[0005] Preferably, the exercise intervention includes aerobic exercise and anaerobic resistance exercise.

[0006] Preferably, the evaluation system for extracting metabolites from tissue samples includes: extracting metabolites from tissue samples using an extraction solution containing an internal standard; the internal standard includes L-Leucine-D7, the final concentration of which is 1 mg / L; the extraction solution includes acetonitrile and methanol, with a volume ratio of acetonitrile:methanol of 1:4.

[0007] Preferably, the evaluation system for metabolomics analysis of metabolites includes a TM broad-targeted metabolomics evaluation system; the evaluation system for data analysis of metabolomics analysis results includes analyzing metabolites using a database, extracting raw peak intensity data, and extracting peaks from MRM chromatograms.

[0008] Preferably, the data analysis further includes: performing dimensionality reduction analysis on the metabolomics analysis results; identifying differential metabolites, calculating the inter- and intra-tissue correlations of metabolites, and performing network statistics; and performing KEGG enrichment analysis on the metabolites.

[0009] Preferably, the dimensionality reduction analysis includes principal component analysis and t-SNE analysis on the log2 transformed metabolite abundance matrix.

[0010] Preferably, the differential metabolite identification includes performing a log2 transformation on all data and evaluating the data distribution; for normally distributed data, an unpaired two-tailed test is used. t - Test analysis: Mann-Whitney U test was used for non-normally distributed data; multiple comparisons were corrected to control for false discovery rate; metabolites were named and classified.

[0011] Preferably, the correlation between and within tissues of metabolites includes calculating the Spearman correlation coefficient using the metabolite abundance matrix after log2 transformation to estimate the pairwise correlation of metabolites between and within tissues; the network statistics include constructing an undirected communication network using metabolite pairs with P≤0.0001 and calculating node degree and compactness centrality.

[0012] Preferably, the KEGG enrichment analysis includes determining enrichment pathways through a hypergeometric test, setting a minimum number of enriched metabolites in each pathway to 1, and manually selecting significant pathways. P <0.05.

[0013] The present invention also provides the application of the assessment system described above in health intervention for obese populations, wherein the application is: screening exercise-related metabolic biomarkers using the assessment system described above for health intervention for obese populations.

[0014] The beneficial effects of this invention are as follows: This invention provides an assessment system for screening exercise-related metabolic biomarkers in obese patients, including an exercise intervention module, a sample processing module, a data analysis module, and a results output module. This invention involved two groups of obese women undergoing four-week aerobic and resistance training programs or a no-training control program, respectively, and conducted comprehensive global metabolomics analysis on the plasma, subcutaneous adipose tissue, omental adipose tissue, liver, and stomach tissues of the obese women. This invention reveals that physical exercise leads to tissue-specific metabolic responses, rather than simply acting on the blood or muscles. The comprehensive effects of exercise on the human body are manifested in intra- and inter-tissue metabolic remodeling and potential metabolite pathways, accompanied by improvements in plasma lipid profiles, body composition, and physiological function. The assessment system described in this invention can provide new insights into the metabolic mechanisms by which exercise improves overall human health. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below.

[0016] Figure 1 This is a flowchart illustrating the evaluation system described in this invention; Among them, Sed represents the sedentary group; Exe represents the exercise group; Plasma represents plasma; SAT represents subcutaneous fat; OAT represents greater omentum fat; Liver represents liver; Stomach represents stomach. Figure 2 A diagram showing the overall characteristics of metabolites in five tissues; Where a~b show the distribution and classification of detected metabolites in different tissues, with different colors representing different metabolite categories; c is a schematic diagram showing the clear separation of the metabolite profiles of the five tissues after t-SNE analysis; d~h show the PCA analysis results of metabolites in each tissue. Figure 3 A graph showing the changes in differentially metabolites in various tissues; Wherein, a is a volcano plot of upregulated and downregulated differential metabolites in each tissue, wherein the differential metabolites have P<0.05 and FC>1 or <1; b is the classification and proportion of significantly differential metabolites in each tissue; Figure 4 These are tissue-specific and systemically different metabolites found in five different tissues. Where a represents tissue-specific differential metabolites; b represents differential metabolites across one or more tissues; c-f represent the relative abundance of four typical exerkines (lactic acid, AICAR, kynurenine, and Lac-Phe) in each tissue; nd indicates not detected; ns indicates no significant difference. This indicates that P < 0.05. This indicates that P < 0.01. This indicates that P < 0.001; Figure 5 This represents the tissue metabolite-related network under Sed and Exe states; Where, a is a heatmap of differential metabolite correlations between Sed and Exe in various tissues; b~e are Cytoscape displays of correlation networks specific to Sed, specific to Exe, and shared by both; f~g are statistical counts of the number of nodes (metabolites) and edges (correspondences) in different tissues; h is the node degree distribution display result; i is the node proximity display result; in the Spearman correlation analysis results, This indicates that P < 0.05. This indicates that P < 0.01. This indicates that P < 0.001; Figure 6 For inter-tissue metabolite correlation analysis; Where a~b are the Circos plots of inter-tissue metabolite correlations between Sed and Exe; c~f are the statistical results of differentially correlated metabolites and their correlations across tissues in Sed and Exe; g~h represent the results of node degree and proximity analysis; and Spearman correlation analysis results are also included. This indicates that P < 0.05. This indicates that P < 0.01. This indicates that P < 0.001; Figure 7 Figure showing the results of KEGG pathway enrichment analysis of differentially metabolites in various tissues; Note: Figure 7 Only pathways with P < 0.05 are displayed. Detailed Implementation

[0017] This invention provides an assessment system for screening exercise-related metabolic biomarkers in obese patients, comprising an exercise intervention module, a sample processing module, a data analysis module, and a result output module. The exercise intervention module performs exercise intervention on obese patients. The sample processing module processes samples from obese patients, including tissue samples from obese patients before and after exercise intervention. The tissue samples include subcutaneous adipose tissue, omental adipose tissue, liver, stomach, and blood; metabolites are extracted from each tissue sample. The data analysis module, connected to the sample processing module, performs data analysis on the samples. The data analysis method includes: performing metabolomics analysis on the metabolites and analyzing the results of the metabolomics analysis. The result output module, connected to the data analysis module, determines and outputs the data analysis results. The determination and output method involves statistically analyzing all results and screening metabolites that show significant changes after exercise as exercise-related metabolic biomarkers.

[0018] The exercise intervention module of this invention includes both aerobic exercise and anaerobic resistance exercise. As one implementation, the aerobic exercise includes dynamic stretching warm-up, brisk walking, and static stretching cool-down. As one implementation, the intensity of the brisk walking is set to 60% of the obese patient's maximum oxygen uptake. As one implementation, the anaerobic resistance exercise includes dumbbell curl resistance training. As one implementation, the resistance training intensity is set to 60% of the maximum weight for one repetition. As one implementation, the exercise intervention also includes nutritional optimization of the obese patient's diet. As one implementation, the nutritional optimization follows the guidelines of the American Society for Metabolic and Bariatric Surgery (ASMBS).

[0019] As one embodiment, the evaluation system for extracting metabolites from tissue samples includes: extracting metabolites from tissue samples using an extraction solution containing an internal standard. As one embodiment, the internal standard includes L-Leucine-D7, with a final concentration of 1 mg / L; the extraction solution includes acetonitrile and methanol, with a volume ratio of acetonitrile:methanol of 1:4. In one implementation, the evaluation system for metabolomics analysis of metabolites includes the TM broad-targeted metabolomics evaluation system. In another implementation, the evaluation system for data analysis of the metabolomics analysis results includes analyzing metabolites using the METWARE database, extracting raw peak intensity data from Analyst 1.63 software using MultiQuant 3.0.3 software, and extracting peaks from the MRM chromatogram.

[0020] As one implementation, the data analysis further includes: performing dimensionality reduction analysis on the metabolomics analysis results; identifying differentially expressed metabolites, calculating inter- and intra-tissue correlations of metabolites, and performing network statistics; and performing KEGG enrichment analysis on metabolites. As one implementation, the dimensionality reduction analysis includes using the R packages FactoMineR (v2.6) and Rtsne (v0.17) to perform principal component analysis and t-SNE analysis on the log2-transformed metabolite abundance matrix. As one implementation, the differentially expressed metabolite identification includes performing log2 transformation on all data, using the Shapiro-Wilk test to assess data distribution; and using unpaired two-tailed tails for normally distributed data. t - For analytical testing, the Mann-Whitney U test was used for non-normally distributed data. Multiple comparisons were corrected for false discovery rate using the Benjamini-Hochberg procedure. Metabolites were named and classified based on the METWARE database and LIPID MAPS. As one implementation, the inter-tissue and intra-tissue correlations of metabolites included calculating the Spearman correlation coefficient using a log2-transformed metabolite abundance matrix to estimate pairwise correlations between and within tissues. The network statistics included constructing an undirected communication network using metabolite pairs with P ≤ 0.0001, and calculating node degree and compactness centrality using Cytoscape (v3.10.2). As one implementation, the KEGG enrichment analysis included downloading KEGG metabolic pathways and related metabolites from the KEGG API database, using the R package ClusterProfiler (v4.4.4) to determine enriched pathways through hypergeometric tests, setting the minimum number of enriched metabolites in each pathway to 1, and manually selecting significant pathways (P < 0.05).

[0021] The present invention also provides the application of the assessment system described above in health intervention for obese populations, characterized in that the application is: screening exercise-related metabolic biomarkers using the assessment system described above for health intervention in obese populations.

[0022] To further illustrate the present invention, the following detailed description, in conjunction with the accompanying drawings and embodiments, provides an assessment system for screening exercise-related metabolic biomarkers in obese patients and its application, but these descriptions should not be construed as limiting the scope of protection of the present invention.

[0023] Example 1 Experimental Preparation Ethical Statement This embodiment was approved by the Ethics Committee for Experimental Sports Science of Beijing Sport University (No. 2020131H) and follows the guidance of the Declaration of Helsinki. All experimental procedures adhered to the guidelines and regulations of the Key Laboratory of Physical Education and Fitness of the Ministry of Education, Beijing Sport University. All participants were informed of the research procedures, potential risks, and benefits. They all agreed to contribute a small tissue sample (approximately 20 mg) from subcutaneous adipose tissue (SAT), omental adipose tissue (OAT), liver, stomach, and blood (approximately 5 mL). The experimental procedure of this embodiment is as follows: Figure 1 As shown.

[0024] (1) Participants and Groups Ten obese women who planned to undergo metabolic and weight loss surgery at the Metabolic and Weight Loss Center of China-Japan Friendship Hospital in Beijing were recruited to participate in this study as a sample.

[0025] The inclusion criteria for the sample were: 1) age between 20 and 40 years old; 2) BMI between 30 and 40 kg / m². 2 1) No concurrent systemic diseases, bone / joint or muscle injuries; 2) No antibiotic use in the past month; 3) Willing to participate in the study.

[0026] The exclusion criteria for the sample were: 1) chronic diseases, such as cardiovascular disease, chronic kidney disease or cancer; 2) being unable to exercise due to being overweight; and 3) using drugs that may interfere with metabolism.

[0027] Ten participants were randomly assigned equally to either the physical activity group (Exe) or the sedentary group (Sed). The Exe group was required to complete a 4-week physical activity program, while the Sed group maintained their daily activities without any additional physical activity. All participants were informed of the study procedures and potential risks, and informed consent was obtained from all participants.

[0028] The Exe group's training plan included concurrent training. Concurrent training consisted of low-intensity aerobic exercise and relatively high-intensity anaerobic resistance training, lasting for four weeks, three times a week. Each training session lasted 75 minutes, including 5 minutes of dynamic stretching warm-up, 30 minutes of dumbbell curl resistance training, 30 minutes of brisk walking (with heart rate monitoring), and 10 minutes of static stretching cool-down. The aerobic exercise (walking on a treadmill) intensity was set at 60% VO2max, and the resistance training (dumbbell curls) intensity was set at 60% of the maximum repetition weight (1RM). 1RM testing was conducted according to the procedures recommended in the American College of Sports Medicine (ACSM) guidelines, "Franklin BA, et al, Medicine ACoS. ACSM's guidelines for exercise testing and prescription. (NoTitle). 2000." In addition, all participants underwent preoperative nutritional optimization supervised by a registered dietitian one month prior to surgery. The reference protocol for the preoperative nutritional optimization method was “Mechanick JI, et al. Clinicalpractice guidelines for the perioperative nutrition, metabolic, and nonsurgical support of patients undergoing bariatric procedures–2019 update: cosponsored by American Association of Clinical Endocrinologists / American College of Endocrinology, The Obesity Society, American Society for Metabolic & Bariatric Surgery, Obesity Medicine Association, and American Society of Anesthesiologists. Surgery for Obesity and Related Diseases. 2020;16(2):175-247.” The preoperative nutritional optimization method followed the guidelines of the American Society for Metabolic & Bariatric Surgery (ASMBS).

[0029] (2) Assessment of body composition and physiological function Body composition and physiological function were assessed in obese patients in the Exe and Sed groups. In the Exe group, assessments were performed once before training (Exe-pre) and once after training and before surgery (Exe-post); in the Sed group, assessment was performed once before surgery. Body composition was assessed using InBody970 (InBody Co., Ltd.) on the morning after an overnight fast.

[0030] Physiological function assessments included a 6-minute walk test, a sit-to-stand test, a biceps curl test, and a VO2max test.

[0031] In the 6-minute walking test, participants were asked to walk as fast as possible for 6 minutes on a 40-meter square track and record the distance they walked.

[0032] In the sit-stand test, participants were asked to sit on a chair without armrests and repeatedly stand up and sit down, with the total number of repetitions recorded over 30 seconds.

[0033] In the biceps curl test, participants were asked to stand and perform biceps curls while holding 3 kg dumbbells, and the total number of repetitions was recorded over 30 seconds.

[0034] The VO2max was evaluated using an incremental load bicycle test (Monark, 893E, Sweden) and with the METALYZER® 3B system (Cortex, Germany).

[0035] (3) Collection of tissue samples Following an overnight fast, blood samples (5 mL) were collected in EDTA tubes via subcutaneous vein puncture in the forearm the morning after fasting. The Sed group received one blood sample, while the Exe group received samples before exercise training (Exe-pre) and after training (Exe-post), both before surgery. Total cholesterol, triglycerides, LDL cholesterol, and HDL cholesterol levels were analyzed using a Cobas 6000 (Roche Diagnostics International Ltd., California) according to a standard, evaluated clinical protocol.

[0036] Other tissue samples collected in the Exe and Sed groups included SAT, OAT, liver, and stomach, collected by cutting a small piece of tissue (20 mg each) during the metabolic and weight-loss surgery. The surgery for the Exe group was performed 48 hours after the last training session.

[0037] (4) Extraction of metabolites from tissue samples Blood sample preparation: Take 50 µL of blood sample and vortex for 10 s, then add 300 µL of extraction solution containing the internal standard 4-nitrophenol (acetonitrile:methanol = 1:4, v / v). Vortex the mixture for 3 min, then centrifuge at 12,000 rpm for 10 min at 4 °C. Collect 200 µL of the supernatant, incubate at -20 °C for 30 min, then centrifuge again at 12,000 rpm for 3 min at 4 °C. Collect 180 µL of the supernatant for LC-MS analysis.

[0038] For other tissue samples, 20 mg of each tissue was homogenized for 20 s using a homogenizer (30 Hz). After centrifugation at 3,000 rpm for 30 s at 4 °C, 400 µL of extraction solution containing the internal standard 4-nitrophenol (methanol:water = 7:3, v / v) was added. The sample was shaken at 1,500 rpm for 5 min, placed on ice for 15 min, and then centrifuged at 12,000 rpm for 10 min at 4 °C. 300 µL of the supernatant was collected, incubated at -20 °C for 30 min, and then centrifuged again at 12,000 rpm for 3 min at 4 °C. 200 µL of the supernatant was collected for LC-MS analysis.

[0039] (5) Metabolomics analysis and data analysis and processing This embodiment employs a broad-target metabolomics approach, which integrates the advantages of non-targeted (high resolution, broad coverage) and targeted (high sensitivity, precise quantification) metabolomics. Initially, high-resolution TOF-MS is used for MS / MS scanning to extract MRM ion-pair information. Based on this, a sample-specific database is constructed by integrating data from the METWARE broad-target library. Subsequently, QTRAP triple quadrupole mass spectrometry is used for precise MRM-based quantification of metabolites in the database, significantly improving data depth and quality.

[0040] Metabolites in the samples were identified using non-targeted metabolomics methods, followed by analysis using the METWARE database. Chromatographic separation was performed on an ultra-high performance liquid chromatography (UHPLC) system (ExionLC AD, SCIEX, Framingham, MA, USA) using an ACQUITY UPLC HSS T3 C18 column (2.1 mm × 100 mm, 1.8 μm, Waters Ltd., UK). The HPLC gradient settings were as follows: 0–10 min 95% A; 10–11 min 10% A; 11–11.1 min 95% A; 11.1–14.1 min 95% A. Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid. The flow rate was 0.35 mL / min, and the injection volume was 5 µL. The column temperature was maintained at 40 °C. Untargeted metabolomics analysis was performed using a quadrupole-time-of-flight mass spectrometer (TripleTOF 6600, SCIEX, Framingham, MA, USA). ESI source conditions were set as follows: ion source gas 1: 50 Psi, ion source gas 2: 50 Psi, curtain gas: 25 Psi, source temperature: 500℃, and ion spray voltage fluctuation (ISVF) of 5500V or -4500V in positive or negative ion modes, respectively. Broadly targeted metabolomics data acquisition was performed using a QTRAP triple quadrupole mass spectrometer (SCIEX, Framingham, MA, USA) in MRM mode. ESI source parameters were as follows: source temperature 500℃, ion spray voltage (IS) 5500 V for positive ions and -4500 V for negative ions; ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were 50, 50, and 25 Psi, respectively; collision gas (CAD) was set to high.

[0041] Raw peak intensity data were extracted from Analyst 1.63 software (SCIEX, Framingham, MA, USA) using MultiQuant 3.0.3 software (SCIEX, Framingham, MA, USA). Peaks in the MRM chromatogram were extracted using the following parameters: Gaussian smoothing width (0 point), retention time half-window (30 s), minimum peak width (2 points), minimum peak height (0), baseline noise percentage (70%), baseline subtraction window (1.0 min), and peak splitting (2 points). The minimum intensity threshold for peak filtering was 1000 cps, the minimum signal-to-noise ratio (S / N) was 3, and the retention time deviation was less than 0.2 min. Peak integration and correction were performed within the software, and the peak area (Area) of each peak was used to represent the relative abundance of the corresponding metabolite. All peak area data were exported and saved for further analysis. Missing values ​​were filled using one-fifth of the minimum value for each metabolite (row direction). The coefficient of variation (CV) of the quality control (QC) samples was calculated, and metabolites with a detection rate below 50% or a CV greater than 0.3 were excluded to obtain the final data matrix.

[0042] (6) Dimensionality reduction analysis of metabolomics data Principal component analysis (PCA) and t-SNE analysis were performed on the log2-transformed metabolite abundance matrix using the R packages FactoryMineR (v2.6) and Rtsne (v0.17), respectively. Ellipses represent 95% confidence intervals, calculated using the mean and covariance of each group of points.

[0043] (7) Identification of differential metabolites Before statistical analysis, missing values ​​(if any) were imputed, and all data were transformed using a log2 transformation. The distribution of the data was evaluated using the Shapiro-Wilk test. Unpaired two-tailed tests were used for normally distributed data. t - Statistical analysis was performed using the Mann-Whitney U test for non-normally distributed data. Metabolites with a fold change >1 and p < 0.05 were considered statistically significant. Results are expressed as mean ± standard deviation (mean ± SD). False discovery rate (FDR) was controlled using the Benjamini-Hochberg procedure to correct for multiple comparisons. Metabolites were named and classified based on the METWARE database and LIPID MAPS (https: / / www.lipidmaps.org / ), including amino acids, carbohydrates, energy, peptides, lipids, nucleotides, cofactors, vitamins, and exogenous substances. Exogenous substances were excluded from further correlation and pathway enrichment analyses.

[0044] (8) Metabolite correlation and network statistics Spearman correlation coefficients were calculated using the log2-transformed metabolite abundance matrix to estimate pairwise correlations between and within tissues. An undirected communication network was constructed using metabolite pairs with p ≤ 0.0001, and parameters such as node degree and compactness centrality were calculated using Cytoscape (v3.10.2) software.

[0045] (9) KEGG enrichment analysis Download KEGG metabolic pathways and related metabolites from the KEGG API (https: / / www.kegg.jp / kegg / rest / keggapi.html). Enriched pathways were identified using the hypergeometric test with the R package ClusterProfiler (v4.4.4), setting the minimum number of enriched metabolites in each pathway to 1. Significant pathways (P < 0.05) were manually selected for further analysis.

[0046] (10) Statistical analysis All data analyses were performed using R (v4.3.2, R Development Core Team, Vienna, Austria). Before statistical analysis, missing values ​​were manually imputed (if present) using one-fifth of the minimum value for each metabolite (row direction), followed by a log2 transformation. The Shapiro-Wilk test was used to assess the data distribution. Unpaired two-tailed tests were applied to normally distributed data. t - Test; otherwise, use the Mann-Whitney U test to compare differences between groups. Perform correlation analysis using Spearman rank correlation. Group values ​​are expressed as mean (M) and standard deviation (SD). Unless otherwise stated in the legend or methods section, statistical significance is set at P < 0.05. Significance levels are expressed as follows: ns indicates no significant difference (P > 0.05); This indicates that P ≤ 0.05; This indicates that P < 0.01; This means P < 0.001.

[0047] (11) Body composition, physiological function tests and plasma biochemical analysis The results of body composition, physiological function tests, and plasma biochemical analysis are shown in Table 1.

[0048] Table 1. Baseline and post-intervention clinical parameters of obese female participants.

[0049] As shown in Table 1, no significant differences were observed between the Sed and Exe-pre groups in any parameters. However, a comparison between Exe-pre and Exe-post revealed significant improvements in body composition, including reductions in BMI, body fat mass, and visceral fat area, but with an increase in skeletal muscle mass. Exe-post also showed significant improvements in basal metabolic rate and exercise capacity (including sit-to-stand test, bicep curl test, and 6-minute walk test). Nevertheless, there was no significant difference in VO2max between Exe-pre and Exe-post. Plasma biochemical analysis showed that Exe-post had significantly lower levels of fasting blood glucose, triglycerides, and low-density lipoprotein cholesterol compared to Exe-pre, while there were no significant differences in insulin, total cholesterol, or high-density lipoprotein cholesterol between Exe-pre and Exe-post.

[0050] The results of metabolomics analysis are as follows Figure 2 As shown. By Figure 2 As can be seen, metabolomics analysis identified 1415 different metabolites in plasma, 1338 in SAT, 1112 in stomach, 1109 in liver, and 1109 in OAT. Of these, 687 metabolites were plasma-specific, 155 were SAT-specific, 1 was stomach-specific, and no liver or OAT-specific metabolites were found. Among the five tissues, plasma showed the highest number of total metabolites and tissue-specific metabolites (687), while liver and OAT showed the lowest numbers of both total metabolites (1109 each) and tissue-specific metabolites (absent in both tissues). Dimensionality reduction analysis of metabolites in all five tissues resulted in clear separation between different tissue types. Similarly, within each tissue, dimensionality reduction analysis distinguished between Sed and Exe tissues, showing clear separation within each tissue.

[0051] Quantification results of differential metabolites, such as Figure 3 As shown. By Figure 3 As can be seen, the quantitative calculation of metabolite changes in each tissue was the ratio of the relative intensity of each metabolite in the Exe group to that in the Sed group, and expressed as differential metabolites. These differential metabolites were divided into nine different categories, further categorized as increased and decreased differential metabolites. Quantification of these differential metabolites showed that OAT had the most differential metabolites (324), followed by plasma (179), SAT (104), liver (92), and stomach (90). Furthermore, OAT showed the second highest proportion of decreased differential metabolites (83.64%), second only to liver (83.70%). Further analysis of the nine categories of differential metabolites in the five tissues showed that lipid metabolites showed the most significant changes in plasma (39.67%), OAT (23.15%), and SAT (21.16%), and the second most significant changes in liver (22.83%) and stomach (20%).

[0052] The results of the analysis of the remaining eight tissue-specific and total differential metabolites are as follows: Figure 4 As shown, by Figure 4 As can be seen, after excluding exogenous substances, the distribution of the remaining eight tissue-specific and total differential metabolites in the five tissues is as follows: Figure 4 As shown. Quantification of tissue-specific differentially expressed metabolites in the five tissues revealed that the number of tissue-specific differentially expressed metabolites (175) and the total number of differentially expressed metabolites (256) were highest in OAT, followed by plasma (116, 148, respectively), SAT (55, 88, respectively), stomach (32, 71, respectively), and liver (31, 69, respectively). Lipids and amino acids / peptides showed the greatest variation in the five tissues, decreasing in liver and OAT but increasing in SAT and plasma.

[0053] This embodiment also specifically examined four motor factors—lactic acid, AICAR, kynurenine, and Lac-Phe—and the results are as follows: Figure 4 As shown, it can be seen that Figure 4 The results showed significant changes in the Exe group compared to the Sed group in the corresponding tissues. Lactate was detected only in plasma and SAT, with a significant increase observed only in plasma-Exe. AICAR was detected only in plasma, with a significant decrease observed in plasma-Exe. Kynuremin and Lac-Phe were detected in all five tissues. However, kynuremin showed a significant decrease only in OAT-Exe, while Lac-Phe showed a significant decrease only in gastric-Exe.

[0054] This embodiment further analyzed the five significantly increased and decreased differential metabolites in five tissues, and the results are as follows: Figure 5 As shown, among the eight differential metabolites, only lipids and amino acids showed dominant changes. Four different lipids in plasma increased significantly, and three different amino acids in OAT increased significantly, while two decreased.

[0055] Spearman correlation analysis was used to identify the correlations between differentially metabolites within each tissue in the Sed and Exe groups, and the results are illustrated in a heatmap, where red and blue represent positive and negative correlations, respectively. Figure 6 As shown in Figure a, in OAT, liver, and stomach, the Exe group exhibited a more defined and tightly packed correlation pattern, characterized by distinct clusters or structured “plaques,” indicating tighter metabolic regulation. In contrast, the Sed group in these tissues showed a more dispersed and disorganized correlation pattern. In SAT and plasma, the correlation structure showed a comparable pattern between the Sed and Exe groups. Visualization of the correlation networks of the Sed and Exe groups in each tissue, Sed-specific, Exe-specific, and common correlations shared by the Sed and Exe groups are shown in Figure a. Figure 6As shown in Figures b-g, the quantification of the correlation network reveals that the total number of nodes and edges between Sed-specific and Exe-specific networks remains relatively stable for each organization, but varies significantly across different organizations. Among the five organizations, OAT exhibits a significantly higher total number of nodes and edges in both Sed-specific and Exe-specific networks, while the total number of edges is significantly higher in Sed-specific networks than in Exe-specific networks. Further analysis of node degree (i.e., the number of edges directly connected to a node) is shown below. Figure 6 As shown in h and i, the nodal density of the Exe group was significantly higher than that of the Sed group in plasma, but lower in OAT and stomach, while no difference was observed in SAT and liver. Consistent with the nodal density results, the nodal density of the Exe group was significantly higher than that of the Sed group in plasma, but lower in SAT, OAT and stomach, while no difference was observed in liver.

[0056] Circos results of inter-tissue differential metabolite correlations in response to physical exercise, as shown in... Figure 7 As shown, by Figure 7 As can be seen, the inter-tissue networks of all relevant differentially expressed metabolites were observed in the Sed and Exe groups. The Exe group showed denser and more widely distributed inter-tissue connections, while the Sed group showed a more aggregated or less dynamic cross-tissue interaction network. Quantification of the correlation network showed that the number of nodes and edges, as well as the positive / negative correlations (edges), remained relatively stable from the Sed group to the Exe group across the five tissues. Further analysis of the correlation network revealed significant differences in the number of nodes and edges in the Sed-specific and Exe-specific networks across the ten tissue pairs, with the plasma-OAT pair exhibiting the highest number of nodes and edges in both the Sed-specific and Exe-specific networks. Furthermore, the number of nodes and edges remained relatively stable between the Sed and Exe groups across all ten tissue pairs. All four tissues involving OAT exhibited the highest number of nodes and edges in the Sed-specific, Exe-specific, and common networks. Of the ten tissue pairs, three of the four pairs showing a relatively higher number of Exe-specific edges than Sed-specific edges involved the liver.

[0057] The analysis results of nodal degree and nodal density of ten organizational pairs are as follows: Figure 7 As shown in g~h, in plasma-OAT, SAT-OAT, OAT-liver, and liver-stomach pairs, the node density of Exe specificity is significantly higher than that of Sed specificity. However, in OAT-stomach pairs, the node density of Exe specificity is lower than that of Sed specificity. Regarding node tightness, in plasma-liver and liver-stomach pairs, Exe specificity shows a significantly higher value than Sed specificity, but in SAT-OAT, SAT-liver, SAT-stomach, OAT-liver, and OAT-stomach pairs, its value is lower than that of Sed specificity.

[0058] To reveal potential metabolic pathways associated with significantly altered metabolites in exercise response, KEGG enrichment analysis was performed on each tissue, and the results are as follows: Figure 7 As shown, plasma exhibits the most significant metabolic response among all tissues, likely due to its integration within the circulatory system, where metabolites from various tissues accumulate. Amino acid metabolism is enriched in all tissues, as shown in the results. Figure 7 As shown, amino acid-related metabolites are increased in plasma and SAT, but decreased in OAT, liver, and stomach. Carbohydrate metabolism is also enriched in plasma, SAT, OAT, and stomach. Notably, carbohydrate metabolites are increased in plasma and SAT, showing a mixed trend in OAT and stomach. Lipid metabolism is mainly enriched in plasma and liver, with lipid metabolites increasing in plasma but decreasing in liver, indicating tissue-specific mobilization and utilization of lipids. Nucleotide metabolism is enriched in plasma, SAT, and OAT, with metabolite levels increasing in plasma and SAT but decreasing in OAT. Furthermore, protein digestion and absorption pathways are enriched in all tissues. Corresponding metabolites are generally increased in plasma and SAT, but decreased in OAT, liver, and stomach, indicating enhanced protein turnover efficiency during physical exercise. These findings collectively suggest that exercise induces coordinated, tissue-specific metabolic remodeling. The observed metabolite turnover reflects the mobilization and redistribution of energy substrates, highlighting the synergistic contribution of different tissues to exercise-induced metabolic adaptation.

[0059] (12) Results Analysis Compared to Exe-pre, Exe-post showed significant improvements in body composition, exercise capacity, and plasma metabolic parameters (Table 1), demonstrating the effectiveness of physical exercise in reducing central obesity and improving physiological function. However, despite significant reductions in fasting blood glucose, triglycerides, and LDL cholesterol levels, insulin, total cholesterol, and HDL cholesterol levels did not change significantly. Similar reports have previously indicated that short-term and long-term exercise interventions effectively improved specific biomarkers; however, VO2max did not change significantly. This is attributed to the relatively short duration of training.

[0060] Metabolic profiling revealed significant differences in the amount of metabolites in the five tissues (see [link]). Figure 2 This demonstrates tissue-specific metabolic responses to physical exercise. While metabolic profiling of five different tissues has not been previously performed in humans, the metabolic responses of specific tissues (such as skeletal muscle, adipose tissue, and plasma) to physical exercise have been extensively studied in humans and animals. This example shows that exercise training has significant metabolic effects on all five tissues, as evidenced by the clear separation of Exe and Sed in all five tissues (see [link to relevant documentation]). Figure 2Notably, OAT exhibited the most significant metabolic response (see...). Figure 4 This suggests that OAT may contribute to fat reduction during physical exercise. This result is consistent with previous observations in animals that exercise training induces significant changes in OAT metabolites and a reduction in intra-abdominal fat in humans.

[0061] Of the nine differentially expressed metabolites identified in five tissues, lipid metabolites showed the most significant changes (see [link to relevant documentation]). Figure 3 (Table 2). The results indicate the effectiveness of physical exercise on lipid metabolism, i.e., weight control or cardiovascular health. Our results are consistent with previous observations that regular physical activity leads to a reduction in adipose tissue mass and overall metabolic improvement, as well as a reduction in total abdominal fat and subcutaneous abdominal fat in obese women with metabolic syndrome.

[0062] In this embodiment, the levels of four exercise factors—lactic acid, AICAR, kynurenine, and Lac-Phe—were examined in five tissues. These exercise factors are metabolic biomarkers commonly used to assess the metabolic effects of exercise. Lactic acid significantly increased in plasma and SAT after exercise training, consistent with previous observations that exercise induces a significant increase in circulating lactate in humans and mice, and is considered important in promoting browning of subcutaneous white adipose tissue, facilitating tissue-specific adaptation, and interorgan metabolic communication in response to exercise.

[0063] Following exercise intervention, Lac-Phe levels were significantly reduced in the stomach, with no significant changes in other tissues. Previous studies in mice, horses, and humans have shown that plasma Lac-Phe levels are significantly elevated after acute exercise, acting as a signaling metabolite to suppress appetite and contribute to energy homeostasis, ultimately helping to prevent obesity. This example shows a slight increase in plasma Lac-Phe, contrasting with previously observed significant increases after exercise. This difference may be due to exercise intensity, metabolic clearance, or tissue-specific regulation of Lac-Phe production. Considering the role of the stomach in endocrine signaling, particularly through ghrelin and other appetite-related hormones, this suggests a potential interaction between Lac-Phe and the gastric pathway in regulating post-exercise satiety. AICAR was detected only in plasma and was significantly reduced after exercise training. This reduction may indicate increased AICAR uptake in other metabolically active tissues, such as the heart and skeletal muscle, during exercise. Kynurenine was detected in all five tissues; however, a significant reduction was observed only in OAT after exercise training, consistent with previous findings in mice. Physical exercise is thought to reduce adipose tissue inflammation by downregulating indoleamine 2,3-dioxygenase (IDO) expression, thereby reducing the conversion of tryptophan to kynurenine. The significant reduction in kynurenine observed in OAT in this example may contribute to a reduction in the risk of adipose tissue inflammation.

[0064] Intra-tissue metabolic remodeling in response to physical exercise This study analyzed the correlations among differentially metabolites within each tissue in the Sed and Exe groups. The results showed that the number of significantly correlated differentially metabolites remained relatively stable across the five tissues, both Sed-specific and Exe-specific. Among the five tissues, OAT exhibited significantly higher numbers of differentially metabolites and total correlations in both Sed-specific and Exe-specific tests compared to other tissues. Notably, the total number of significantly correlated OAT samples was significantly higher in Sed-specific tests than in Exe-specific tests. These results indicate that OAT undergoes significant metabolic adaptation in response to physical exercise, highlighting its potential central role in exercise-induced metabolic remodeling. This is consistent with previous research demonstrating significant changes in adipose tissue metabolism induced by exercise training.

[0065] Further analysis of nodality provides more insights into intra-tissue metabolic remodeling in response to physical exercise. Specifically, in plasma, the mean nodality in the Exe group was significantly higher than that in the Sed group (see...). Figure 5 This indicates increased connectivity between circulating metabolites and potentially enhanced coordination. This finding is consistent with studies demonstrating that exercise affects serum metabolite networks, suggesting enhanced systemic metabolic integration. Conversely, compared to the Sed group, the OAT and stomach showed significantly lower nodality in the Exe group, indicating reduced metabolite interconnectivity and potentially the formation of more simplified or functionally specialized metabolic networks in these tissues after exercise. No significant differences in nodality were observed in the SAT or liver, suggesting that these reservoirs possess a more stable intratissue network structure in response to exercise intervention. These findings are further supported by node density analysis—a metric for measuring the efficiency of differentially metabolites communicating with other metabolites within a network. In plasma, the node density of the Exe group was significantly higher than that of the Sed group (see [link to analysis]). Figure 5This reinforces the idea that exercise training enhances the systemic integration and coordination of metabolic pathways in the circulatory system. Conversely, in the Exe group, the node density of SAT, OAT, and stomach was significantly reduced, suggesting that exercise may lead to the formation of more compartmentalized or refined intra-tissue metabolic patterns in these tissues. These observations further support the concept of tissue-specific metabolic plasticity responding to exercise. Increased network centrality in plasma may reflect systemic adaptations that promote inter-organ communication and metabolic homeostasis. Meanwhile, the reduced centrality observed in visceral tissues such as OAT and stomach may indicate metabolic optimization, i.e., redundant or inefficient interactions are pruned to support more targeted and functionally relevant metabolic pathways. Notably, the reduced node density and compactness in OAT reinforces earlier findings of reduced global relevance density in this tissue after training, highlighting its dynamics and responsiveness. In summary, these network-level adaptations contribute to a more nuanced understanding of how exercise reprograms tissue metabolism to promote systemic metabolic health.

[0066] Quantification of the correlation network of differentially metabolites in the five tissues showed that the number of significantly correlated and related differentially metabolites was relatively stable, and the ratio of positive to negative correlations was balanced between the Sed and Exe groups (see...). Figure 6 These findings suggest that exercise-induced metabolic reprogramming maintains a consistent, fundamental inter-tissue correlation structure, despite tissue-specific modifications. This is consistent with studies indicating that exercise induces systemic metabolic changes without completely disrupting the underlying metabolic network.

[0067] Interestingly, significant correlations and significant differences in the number of relevant differential metabolites were observed across the ten tissue pairings (see [link to relevant data]). Figure 6 Plasma-OAT pairings showed the highest correlation and number of differentially expressed metabolites in both Sed-specific and Exe-specific networks. This is noteworthy because OATs are particularly metabolically active and sensitive to systemic cues such as physical activity. In contrast, significant correlations and the number of relevant differentially expressed metabolites remained relatively stable between the Sed and Exe groups across all ten tissue pairs (see [link to relevant data]). Figure 6 This indicates that while exercise affects specific tissue interactions, it does not drastically alter the overall connectivity within inter-tissue metabolic networks. This stability may suggest the body's ability to maintain metabolic homeostasis while adjusting individual tissue responses to exercise training, a concept supported by previous research demonstrating the adaptive nature of metabolic networks to physiological changes.

[0068] Notably, all four tissues involved in OAT exhibited the highest significant correlations and number of relevant differentially expressed metabolites in Sed-specific, Exe-specific, and common networks (see Table 4). This reinforces the importance of OAT in exercise-induced metabolic remodeling.

[0069] The results of this embodiment show that the liver exhibited the greatest reduction in lipids (see Table 3). This is consistent with existing literature that exercise training can induce a reduction in hepatic lipid accumulation, attributed to enhanced β-oxidation, downregulated lipogenesis enzymes, or improved insulin sensitivity. However, this embodiment shows that in three out of the four pairs showing a relatively higher number of Exe-specific correlations than Sed-specific correlations, the liver was involved (see Table 4). Furthermore, the liver showed significant correlations with all other tissues on differential metabolites (see Table 5). Figure 6 This suggests that exercise promotes metabolite correlation / communication between the liver and other tissues. Therefore, the metabolic benefits of physical exercise to the liver are presumed to be due to increased lipid communication or output between the liver and other tissues, most likely via plasma to skeletal muscle. This may well explain the role of physical exercise in reducing the incidence of fatty liver disease.

[0070] The findings of this invention reveal that physical exercise induces coordinated yet tissue-specific metabolic adaptations, highlighting the complexity of systemic metabolic remodeling. The convergence of these changes in plasma underscores their integrative role in mediating inter-organ metabolic communication. Notably, amino acid metabolism is activated in all five tissues, but exhibits distinct trends—increasing in plasma and SAT, while decreasing in OAT, liver, and stomach (see [link to study]). Figure 7 This broad but heterogeneous response suggests that amino acid metabolism plays a central and conserved role in exercise-induced physiological remodeling, potentially supporting protein synthesis, energy production, and tissue repair. Carbohydrate and lipid metabolism also exhibited compartmentalized changes, with increased lipid metabolites in plasma but decreased in the liver, reflecting differentiated substrate utilization and clearance. The enrichment of nucleotide metabolism and protein digestion pathways further reflects enhanced biosynthetic activity, protein turnover, and amino acid cycling—processes crucial for supporting muscle repair and adaptation to training stimuli.

[0071] In summary, this invention reveals that exercise triggers a network of metabolically active tissues, where different organs play specialized roles in energy mobilization, substrate redistribution, and homeostasis maintenance. This inter-tissue coordination is crucial for adapting to the increased energy and biosynthetic demands imposed by physical exercise, as exemplified by the tissue-specific regulation of lipid metabolism in OAT and the conserved activation of amino acid metabolism in all examined tissues. This systemic integration may represent a key mechanism underlying the broad physiological benefits of exercise.

[0072] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, and not all embodiments. People can obtain other embodiments without creative effort, as shown in these embodiments, and these embodiments all fall within the protection scope of the present invention.

Claims

1. An assessment system for screening exercise-related metabolic biomarkers in obese patients, characterized in that, It includes a motion intervention module, a sample processing module, a data analysis module, and a results output module; The exercise intervention module provides exercise intervention for obese patients; The sample processing module is used to process samples from obese patients; the samples include tissue samples from obese patients before exercise intervention and tissue samples from obese patients after exercise intervention; the tissue samples include subcutaneous adipose tissue, omental adipose tissue, liver, stomach, and blood; metabolites are extracted from the tissue samples respectively; The data analysis module, which is connected to the sample processing module, is used to perform data analysis on the above-mentioned samples; the data analysis method includes: performing metabolomics analysis on metabolites, and performing data analysis on the metabolomics analysis results; The result output module is connected to the data analysis module and is used to determine and output the data analysis results. The method for determining and outputting the results is as follows: statistical analysis is performed on all results, and metabolites that show significant changes after exercise are selected as exercise-related metabolic markers.

2. The evaluation system as described in claim 1, characterized in that, The exercise intervention includes aerobic exercise and anaerobic resistance exercise.

3. The evaluation system as described in claim 1, characterized in that, The evaluation system for extracting metabolites from tissue samples includes: extracting metabolites from tissue samples using an extraction solution containing an internal standard; The internal standard includes L-Leucine-D7, with a final concentration of 1 mg / L; the extraction solution includes acetonitrile and methanol, with a volume ratio of acetonitrile:methanol of 1:

4.

4. The evaluation system as described in claim 1, characterized in that, The evaluation system for metabolomics analysis of metabolites includes the TM broad-targeted metabolomics evaluation system; The evaluation system for data analysis of metabolomics analysis results includes using a database to analyze metabolites, extracting raw peak intensity data, and extracting peaks from MRM chromatograms.

5. The evaluation system as described in claim 1, characterized in that, The data analysis also includes: dimensionality reduction analysis of the metabolomics analysis results; identification of differential metabolites, calculation of inter- and intra-tissue correlations of metabolites, and network statistics; and KEGG enrichment analysis of metabolites.

6. The evaluation system as described in claim 5, characterized in that, The dimensionality reduction analysis includes principal component analysis and t-SNE analysis on the log2 transformed metabolite abundance matrix.

7. The evaluation system as described in claim 5, characterized in that, The differential metabolite identification process includes performing a log2 transformation on all data and evaluating the data distribution; for normally distributed data, an unpaired two-tailed test is used. t - Test analysis: Mann-Whitney U test was used for non-normally distributed data; multiple comparisons were corrected to control for false discovery rate; metabolites were named and classified.

8. The evaluation system as described in claim 5, characterized in that, The correlations between and within tissues of metabolites include calculating Spearman correlation coefficients using a log2-transformed metabolite abundance matrix to estimate pairwise correlations between and within tissues of metabolites. The network statistics include constructing an undirected communication network using metabolite pairs with P ≤ 0.0001, and calculating node degree and compact centrality.

9. The evaluation system as described in claim 5, characterized in that, The KEGG enrichment analysis included identifying enrichment pathways through a hypergeometric test, setting a minimum number of metabolites enriched in each pathway to 1, and manually selecting significant pathways. P <0.

05.

10. The application of the assessment system according to any one of claims 1 to 9 in health intervention for obese populations, characterized in that, The application is as follows: using the evaluation system described in any one of claims 1 to 9 to screen exercise-related metabolic biomarkers for health intervention in obese individuals.

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