Abnormal fatty acid and amino acid metabolism in patients with sarcopenia

Abnormal fatty acid and amino acid metabolism in patients with sarcopenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Abnormal fatty acid and amino acid metabolism in patients with sarcopenia Xinbo Ma, Ailin Bian, Shimin Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863000/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND Age-associated skeletal muscle loss, a serious global health problem, causes undeniable distress to older people and communities. It can lead to disability and significant economic burden, with serious implications for people's quality of life and physical health. Relevant metabolic studies have shown that loss of skeletal muscle is closely associated with abnormalities in amino acid and fatty acid metabolism. A comprehensive study was conducted to delve into the factors associated with sarcopenia and the role of amino acid and fatty acid metabolism in the development of sarcopenia. METHODS In this study, we screened 650 patients with skeletal muscle reduction (sarcopenia) from 2965 elderly (≥ 60 years old) patients in outpatient clinic and randomly selected 100 elderly patients for a survey study, which we categorized into sarcopenic and non-sarcopenic groups according to the diagnostic criteria of Asian Working Group on Sarcopenia (AWGS). Each group had 25 patients each and we collected their general information and retained their serum samples for testing. RESULTS The results of the study showed that there was a significant difference in body mass index (BMI), grip strength, and albumin levels between these two groups of samples (all p-values were less than 0.05). This suggests that these physiological indicators are associated with the development of sarcopenia. In addition, we found no significant differences in total cholesteroll (TC), triglycerides (TG), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C),levels between these two groups of samples. Upon further analysis of human serum metabolites, we found that arginine, histidine, leucine, palmitic acid, and carnitine levels were significantly different between the sarcopenia group and the non-sarcopenia group (all P-values were less than 0.05). These results reveal differences in amino acid and fatty acid metabolism between sarcopenia patients and non-sarcopenia patients. CONCLUSION There are differences in amino acid and fatty acid metabolism between sarcopenia and non-sarcopenia patients. By supplementing protein and essential amino acids, and reducing palmitic acid and carnitine levels, we can improve skeletal muscle mass and function, and enhance the quality of life in older adults. This finding provides new ideas and approaches for the prevention and treatment of age-related skeletal sarcopenia. sarcopenia elderly amino acid metabolism fatty acid metabolism metabolomic assays Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Age-related skeletal sarcopenia is a serious global health problem for older people and communities, as it leads to disability and a significant socio-economic burden for older people. Muscle mass and strength change with age, reaching a maximum level in midlife at about age 40, with a subsequent decline in skeletal muscle mass and strength with age[ 1 ], and loss of skeletal muscle fibers beginning at age 50, with about 50% of fibers lost by age 80[ 2 ]. In addition to age-related factors, low-grade inflammation, endocrine factors, oxidative stress and malnutrition can cause loss of skeletal muscle[ 3 ]. Skeletal muscle is primarily composed of protein, and an imbalance between protein synthesis and catabolism is thought to be the most direct cause of muscle loss. Studies have shown that protein intake is strongly correlated with muscle mass, and when protein intake is insufficient to meet daily requirements, a negative protein balance occurs and leads to skeletal muscle atrophy, impaired muscle growth, and functional decline[ 4 ]. Protein is also an independent predictor of muscle mass[ 5 ], and higher doses of protein are required to promote muscle protein synthesis in aging, inflammation, and disease[ 6 ]. A cohort study found that protein intake was positively associated with the maintenance of lean body mass in older adults, while lower protein intake was associated with greater loss of lean body mass. Although skeletal muscle protein synthesis and its quality are regulated by a variety of factors, the basic prerequisite for its synthesis is dietary supplementation with amino acids[ 7 ]. The concentration of essential amino acids in the peripheral blood determines protein synthesis at the muscle and systemic level[ 8 ]; therefore, protein and essential amino acid supplementation is necessary to prevent the development of sarcopenia. A new term obesity sarcopenia refers to the coexistence of obesity and sarcopenia, and obesity, as an independent risk factor for sarcopenia, may be associated with insulin resistance as well as various metabolic disorders such as cardiovascular disease, type 2 diabetes mellitus, and nonalcoholic fatty liver disease[ 9 ]. In people suffering from obesity, cytokines differentiate muscle progenitor cells into an adipocyte-like phenotype via a paracrine pathway, leading to a vicious cycle of sarcopenia, and increased fat infiltration in the muscle, which further affects skeletal muscle function[ 10 ]. White adipose tissue serves as the primary postprandial nutrient storage site for a variety of nutrients in the form of triacylglycerol (TG)[ 11 ]. Insulin can reduce fatty acid release from adipocytes by inhibiting enzymes involved in triglyceride hydrolysis and promoting triglyceride synthesis, but in insulin-resistant adipose tissue these functions are compromised[ 12 ]. Whereas insulin resistance is often present in patients with sarcopenia, serum levels of fatty acids may be elevated in these patients. In addition, white adipose tissue does not grow efficiently in the presence of excess nutrients, which may promote lipid accumulation in skeletal muscle tissue[ 13 ]. The number of patients with obesity-related sarcopenia is increasing, and in obese individuals, excess lipid supply raises circulating levels of free fatty acids, and high plasma free fatty acid levels can induce insulin resistance and tissue (e.g., pancreas and skeletal muscle) damage. In patients with sarcopenia, there may be multiple abnormalities in their amino acid metabolism and fatty acid metabolism, but the use of a particular metabolite as a single biomarker does not explain the pathogenesis of this disease. However, the molecular events related to metabolic dysfunction in skeletal muscle cells during aging are not fully understood. Metabolomics provides us with a link between clinical disease and basic biology and offers a research approach to identify the metabolic factors associated with sarcopenia by determining the pathogenesis of the metabolic properties involved in sarcopenia[ 14 ]. The overall aim of this study was to explore the different metabolites associated with muscle mass, strength and function in humans and to provide valuable information related to the pathogenesis and management of sarcopenia. We hope to use metabolomic identification to differentiate the metabolites of sarcopenia patients from those of healthy subjects; to look for detailed metabolic changes and associated metabolic pathways in sarcopenia patients, and to provide a basis for scientific diagnosis and treatment of sarcopenia patients. Methods This cross-sectional study was conducted from December 2018 to August 2021 at Tianjin First Central Hospital. The study was approved by the local ethics committee. Written informed consent was obtained from all participants. A total of 650 patients with sarcopenia were screened from 2965 elderly patients (≥ 60 years old) in the outpatient clinic, and 100 elderly patients (≥ 60 years old) were randomly selected for the survey study, which were divided into the sarcopenia group and the non-sarcopenia group according to the diagnostic criteria of the Asian Working Group on Sarcopenia (AWGS), with 25 cases in each of the two groups, and their general information was collected (gender/age/biochemistry/blood pressure/height General information (gender/age/biochemistry/blood pressure/height/weight/smoking/alcohol consumption) was collected, serum samples were taken, and frozen blood samples were sent to Shanghai Huipu Biotechnology Co. 720, Korea). Participants wore light clothing and stood barefoot on the instrument. Weight and height were measured to an accuracy of 0.1 kg and 0.1 cm, respectively. limb skeletal muscle mass (ASMI) was limb mass (kg)/height2 (m2)). Grip strength and stride speed (measurements of muscle strength and muscle function: using a grip strength meter (Model WCS-II, Beijing Zhuochuan Electronic Science and Technology Co. Each hand was measured twice and the highest value was taken. Muscle function was measured using a 4-meter stride. The stride speed test is the time required for a participant to walk 4 m from a standing position at the usual speed. It was measured using an electronic timer. (Calculated speed, critical level ≤ 0.8 m/s.) Investigations and measurements were performed. Biochemical indices were compared between sarcopenic and non-sarcopenic patients (subjects fasted for 8 h. Blood was collected in vacuum blood collection tubes by venipuncture between 7:00 and 9:00 a.m. and left to stand for 30 min. Serum was separated at 4°C. The test indexes included total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and serum albumin (ALB). All indices were measured on the same day. Aliquots of 400 µl of serum were prepared in two Eppendorf tubes and frozen at − 70°C. These indices are routine markers for determining the health status of the elderly.ALB, TG were measured by colorimetric method. TC, HDL-C and LDL-C) were determined by enzyme colorimetric assay to identify relevant factors. Diagnostic Criteria: According to the AWGS diagnostic criteria for sarcopenia, patients were diagnosed with sarcopenia if they met at least one of criteria 1 and 2 and 3, including: 1) muscle mass < 7.0 kg/m2 (males) or < 5.7 kg/m2 (females); 2) grip strength < 26 kg (males) or 18 kg (females); and 3) 4-meter stride speed < 0.8 m/s. INCLUSION CRITERIA: Elderly subjects (≥ 60 years old) enrolled for counseling or health screening at the outpatient clinic. EXCLUSION CRITERIA: 1) implanted metal objects, such as pacemakers and fixed steel nails; 2) Complete bed rest; 3) Significant physical disability. 4) Permanent loss of ability to perform activities of daily living (ADL); 5) Extracellular water (ECW)/Total body water (TBW) value ≥ 0.40. Instruments: The instrumental platform for this LC-MS analysis was an ultra high performance liquid chromatograph from Thermo. We used two modes i.e., separate assays, Hilic and reversed phase modes. Ultrasonic cleaner (KQ2200DE CNC ultrasonic cleaner, Kunshan Ultrasonic Instrument Co., Ltd.); Vacuum concentrator (eppendorf TD-10-017, Shanghai Tongda Kexin Biological Co., Ltd.); Constant temperature incubation shaker (THZ-100B, Shanghai Yihang Scientific Instrument Co., Ltd.); Benchtop high-speed centrifuge (SiGMA, Bohli); Vortex Mixer (Scientific Industries Vortex-genie2); ultrapure water purifier (Milli-Q, Merck); electronic balance (METTLER TOLEDO AL204); high-performance liquid chromatograph (Thermo DIONEX UltiMate3000); ACQUITY UPLC BEH Amide C18 column (100 mm × 2.1 mm i.d., 1.7 µm; Waters, Milford, USA) Reagents: Formic acid, sodium formate (chromatographic purity, CNW, Germany); methanol, acetonitrile, isopropanol (chromatographic purity, Merck, Germany); methyl tert-butyl ether (chromatographic purity, ACROS); and purified water was prepared by Nanopure purification system (Barnstead, USA). Internal standards were hemolysed phosphatidylcholine LPC 12:0, phosphatidylcholine PC 22:0, and fatty acid FA 19:0 (Avanti). Pre-treatment methods: Methanol/acetonitrile (1:1, v/v) solvent was prepared; acetonitrile water (1:1, v/v) containing the internal standard lysophosphatidylcholine LPC 12:0, phosphatidylcholine PC 22:0, and fatty acid FA 19:0, at a concentration of 2 µg/ml was prepared. Take 100 µl of serum sample, add 400 µl of pre-cooled methanol/acetonitrile (1:1, v/v) solvent; vortex for 30 s; ultrasonic 10 min (4 ℃ water bath); 20 ℃ static for 30 min; 4 ℃, 12000 rpm centrifugation for 15 min; 200 µL of the supernatant, concentrated by vacuum centrifugation and evaporation; add 100 µL of ACN: H2O (1:1, v/v) Add 100 µL of ACN:H2O (1:1, v/v); vortex for 30 s, sonicate for 10 min (4 ℃ water bath); centrifuge at 4 ℃ and 12000 rpm for 15 min; take 80 µl of the supernatant into the injection bottle and store at 4 ℃ for measurement. Quality Control (QC): After sample reconstitution and centrifugation, take 20 µl of each sample to obtain a mixed sample, mix and centrifuge, transfer to the injection bottle, and store at 4℃ for measurement. Chromatographic conditions : (1) Hilic chromatographic conditions: the column was an ACQUITY UPLC BEH Amide C18 column (100 mm × 2.1 mm i.d., 1.7 µm; Waters, Milford, USA), the mobile phase A was water (containing 0.1% formic acid, 5% acetonitrile), and the mobile phase B was acetonitrile (containing 0.1% formic acid); the gradient elution program was 0–1 min: 85% B-85% B, 1–12 min: 85%-65% B, 12-12.1 min: 65%-40% B, 40% B maintained for 3 min, 15–20 min. 1 min: 85% B-85% B, 1–12 min: 85%-65% B, 12-12.1 min: 65%-40% B, 40% B for 3 min, 15–20 The flow rate was 0.40 mL/min, the injection volume was 3 µL, and the column temperature was 45 ℃. (2) Reversed-phase chromatographic conditions: Thermo C18 column; mobile phase A was acetonitrile (containing 0.1% formic acid), and mobile phase B was water (containing 0.1% formic acid and 5% acetonitrile); the gradient elution program was 0-1.5 min: 0%-20% A, 1.5–10 min. : 20%-100% A, 100% A maintained for 3 min, 13-13.5 min: 100%-0% A. The flow rate was 0.40 mL/min, the injection volume was 3 µL, and the column temperature was 45 ℃. Statistical analysis: Statistical analysis was performed using SPSS 20.0 (IBM, Almonk, NY, USA). Continuous data were expressed as mean ± standard deviation and analyzed using the independent samples t-test. Categorical data were expressed as frequencies and analyzed using the chi-square test. Pearson correlation analysis was used for continuous variables. Spearman's correlation analysis was performed for ordinal variables. Interactions between multiple factors were analyzed using binary logistic regression and multiple linear regression. p-value < 0.05 was considered statistically significant. 1) Integration of the data matrix Data preprocessing Before pattern recognition, the raw data were subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization by the metabolomics processing software that comes with the instrument, resulting in a data matrix of retention times, mass-to-charge ratios and peak intensities, which yielded a total of 1,863 m/z in the Hilic mode, and 3,328 m/z in the positive-negative ion mode. 2) Multivariate statistical analysis (Hilic) The normalized data matrix was imported into the SIMCA-P + 13.0 software package (Umetrics, Umeå, Sweden), and unsupervised principal component analysis (PCA) was used first to observe the overall distribution between samples and the stability of the whole analytical process, and then supervised (orthogonal) partial least squares analysis (O)PLS-DA was used to differentiate the metabolic profiles between groups of overall differences between groups and find differential metabolites between groups. (Variables with variable weight values (Variable important in projection, VIP) greater than 1 were considered differential in the (O)PLS-DA analysis. To prevent overfitting of the model, seven cycles of interactive validation and 200 response ordering tests were used to examine the quality of the model. 3) The evaluation indicators of the model are as follows: Internal validation: R2X and R2Y represent the explanation rate of the model on the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model, theoretically, the closer the value of R2 and Q2 is to 1, the better the model is, and the lower it is, the worse the model's fitting accuracy is, usually, R2 and Q2 are better than 0.5 (50%), and the difference between the two values should not be too large. External validation: Response ordering test is used, the closer the slopes of the R2Y and Q2Y straight lines are to a horizontal straight line, the more likely the model is to be overfitted, and Q2 is generally required to be less than zero. Results (1) Grouping: 100 cases of human serological samples were divided into four groups (see Table 1-1), namely, male control group (CtrlM), male experimental group (SarM), female control group (CtrlF), female experimental group (SarF), 25 cases in each group. The samples were renumbered to correspond to groups A, B, C, and D for the convenience of the machine. Table 1-1 Grouping of 100 serum samples original number new number original number new number original number new number original number new number Control group women A Control group men B Experimental group women C Experimental group men D 6 A1 6 B1 1 C1 la D1 7 A2 7a B2 2 C2 1b D2 8 A3 7b B3 3 C3 2a D3 9a A4 8 B4 4a C4 2b D4 9b A5 9a B5 4b C5 3a D5 9c A6 9b B6 4c C6 3b D6 10a A7 10a B7 5a C7 3c D7 10b A8 10b B8 5b C8 4a D8 10c A9 12a B9 5c C9 4b D9 1la A10 12b B10 6a C10 5a D10 11b A11 13a B11 6b C11 5b D11 11c A12 13b B12 7 C12 7 D12 12a A13 14 B13 8 C13 8 D13 12b A14 15a B14 9 C14 11 D14 13 A15 15b B15 16a C15 12 D15 14a A16 16a B16 16b C16 13 D16 14b A17 16b B17 17 C17 14 D17 15 A18 20 B18 18 C18 15 D18 16 A19 21 B19 19 C19 17 D19 22 A20 23 B20 20 C20 18 D20 32 A21 30 B21 22 C21 21 D21 33 A22 31 B22 28 C22 24 D22 34 A23 35 B23 29 C23 25 D23 36 A24 37 B24 40 C24 26 D24 41 A25 38 B25 42 C25 27 D25 2) General: (See Table 1-2) A total of 100 elderly participants, 50 male and 50 female, were included in this study, and the subjects were aged 60-87 years (mean 74 ± 18 years). All participants were categorized into myasthenia gravis and non-myasthenia gravis groups. Table 1-2 Comparison of basic clinical characteristics between sarcopenia and non-sarcopenia groups variant Sar (n=50) Contr (n=50) t p BMI 20.94±2.29 24.19±2.46 -6.25 <0.01 Grip strength (kg) 23.91±8.34 26.51±8.36 -1.2 <0.05 ALB (g/L) 42.58±2.12 43.54±1.55 -2.31 <0.05 TC (mmol/L) 4.85±0.87 4.89±0.94 0.23 0.81 TG (mmol/L) 1.23±0.55 1.23±0.48 -0.36 0.972 HDL (mmol/L) 1.38±0.30 1.28±0.30 1.40 0.16 LDL (mmol/L) 3.10±0.77 3.17±0.18 -3.90 0.69 Abbreviations: Body Mass Index (BMI), Total Cholesterol (TC), Triglycerides (TG), High Density Lipoprotein (HDL-C), Low Density Lipoprotein (LDL-C), Albumin (ALB). 3) Metabolomics analysis showed: ASSAYS : This study examined differences in metabolite profiles between serum samples from patients with sarcopenia and healthy controls with the aim of identifying candidate biomarkers and pathogenic pathways for sarcopenia. Serum samples were collected from patients with sarcopenia (n = 50) and healthy controls (n = 50). Using Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) combined with unidimensional statistical analysis, as well as repetitive feature extraction analysis and filtration, the analysis showed that there were differences in serum metabolic profiles between myasthenia gravis and control groups. (O) Variables with variable weight values (Variable important in projection, VIP) greater than 1 were considered as difference variables in the PLS-DA analysis. To prevent model overfitting, the quality of the model was examined using seven cycles of interactive validation and 200 response ordering tests. A combination of multidimensional analysis OPLS-DA and unidimensional analysis (student t-test) was used to screen for differential metabolites between groups (VIP>1, p<0.05),and further line component analysis. In positive ion mode, principal component analysis (PCA) showed no significant difference between control and experimental groups [R 2 X (cum) = 0.537, Q 2 (cum) = 0.307)] as shown in Fig 1-1. In the negative ion mode, the situation was similar and the principal component analysis (PCA) showed no significant difference between the control and experimental groups [R 2 X (cum) = 0.539, Q 2 (cum) = 0.314)],as shown in Fig 1-2. Also, we found signs of separation between the control and the The lake-blue diamonds, red triangles, ink-blue squares, and yellow triangles in the figure represent the control group male (CtrlM), the experimental group male (SarM), the control group female (CtrlF), and the experimental group female (SarF), respectively (Fig 1-1, 1-2, 1-3, and 1- 4). Metabolite profiles of serum samples were analyzed using the PLS-DA method. As shown in Fig 1-3, the metabolomics data of serum samples analyzed by PLS-DA in positive ion mode indicated that there was a significant difference between the female sarcopenia group, the female control group, the male sarcopenia group, and the male control group [R 2 X (cum) = 0.077, R 2 Y (cum) = 0.432, and Q 2 (cum) = 0.178); and in Fig 1-4, there was a significant difference between the female sarcopenia group and the significant difference between the female sarcopenia group and the control group in Fig 1-4 [R 2 X (cum) = 0.0393, R 2 Y (cum) = 0.768, Q 2 (cum) = 0.27)]; and between the male sarcopenia group and the control group in Fig 1-5 [R 2 X (cum) = 0.0412, R 2 Y (cum) = 0.759, Q 2 (cum) = 0.317)]. Table 1-3 Model quality for models under HILIC and ESI+ mode N0. Model Type Groups A N R X 2 R Y 2 Q 2 1 M4 PCA-X ALL 13 100 0.537 0.307 2 M5 PLS-DA ALL 2 100 0.077 0.432 0.178 3 M6 PLS-DA CtrlF,SarF 1 50 0.0393 0.768 0.27 4 M8 PLS-DA CtrlM, SarM 1 50 0.0412 0.759 0.317 5 M10 PLS-DA Ctrl, Sar 1 100 0.031 0.612 0.243 As shown in Fig 1-6: In the negative ion mode, there were also significant differences between the female sarcopenia group, the female control group, the male sarcopenia group, and the male control group [R 2 X (cum) = 0.0757, R 2 Y (cum) = 0.423, and Q 2 (cum) = 0.203]; in Fig 1-7 there was a significant difference between the female sarcopenia group and the control group [R 2 X (cum) = 0.0484, R 2 Y (cum) = 0.705, Q 2 (cum) = 0.285)]; Fig 1-8 Significant difference between male sarcopenia group and control group [R 2 X (cum) = 0.0348, R 2 Y (cum) = 0.796, Q 2 (cum) = 0.288)]. These results suggest that the PLS-DA model can be used to differentiate between male and female sarcopenia patients and non-sarcopenia patients; the parameters included in the model in the two ionic models shown in Supplementary Tables 1-3 and 1-4. Table 1-4 Model quality for models under HILIC and ESI- mode N0. Model Type Groups A N R X 2 R Y 2 Q 2 1 M4 PCA-X ALL 13 100 0.539 0.314 2 M5 PLS-DA ALL 2 100 0.0757 0.423 0.203 3 M6 PLS-DA CtrlF,SarF 1 50 0.0484 0.705 0.285 4 M8 PLS-DA CtrlM, SarM 1 50 0.0348 0.796 0.282 5 M10 PLS-DA Ctrl, Sar 1 100 0.1 0.734 0.271 There was a clear separation between groups in the positive and negative ion modes; at the same time, serum metabolic profiles showed significant intergroup differences. Samples from both groups were similarly clearly separated, exhibiting significant within-group differences. To distinguish the most important metabolites between groups, differential metabolites were screened using p-values and VIP scores. PCA-, PLS-DA-based models for distinguishing the groups were constructed in positive and negative ion mode, and metabolic differences between the groups were determined. The results showed significant differences between the two groups, combined with the variable importance in projection (VIP) > 1 and p < 0.05 finally screened 37 differential metabolites in positive ion mode and 20 differential metabolites in negative ion mode, the main metabolites were arginine, histidine, leucine, cysteine, aminobutyric acid and derivatives, and quantitatively suggested that the content of such metabolites was reduced in the experimental group compared with the control group. The quantification suggested that the content of these metabolites was reduced in the experimental group compared with the control group, which confirmed that the plasma metabolic profile of amino acids was indeed altered in sarcopenia patients relative to healthy subjects; meanwhile, the content of metabolites such as palmitic acid, phosphatidylcholine, glucosamine, and arachidonic acid was higher than that of the control group, and the analysis suggested that the sarcopenia patients had abnormalities in metabolic pathways such as fatty acid and phospholipid metabolism. Discussion A study by Wu LC et al. found that BMI was significantly higher in non-myasthenic patients compared to myasthenic patients, with a 0.45-fold reduction in the odds of myasthenia gravis for every 1 kg /㎡ increase in BMI, and that higher BMI resulted in a lower risk of developing myasthenia gravis[15]. However, BMI remains controversial in the assessment of sarcopenia in older adults because BMI and does not distinguish between adiposity and lean body mass, and increased lean body mass results in decreased mortality[16]. Aging is associated with an increase in visceral adiposity and progressive loss of muscle mass, which has an opposite effect on mortality[17].Santos et al. evidence suggests that sarcopenia with obesity may be associated with higher levels of metabolic disorders and an increased risk of death compared with obesity or sarcopenia alone[18]. In line with previous findings, higher levels of Alb were significantly associated with non-muscular hypomuscular disorders in the elderly[19]. Albumin, the most abundant plasma protein in the body, is recognized as a very important plasma protein in the assessment of the body's nutritional status, and its reduction affects wound healing, lowers immunity and reduces lean body mass[20]. In the Smith S. et al. study, albumin levels were associated with poorer physical function and lower muscle strength or muscle mass in older adults; however, this association has not been confirmed in other populations[21]. This contradicts previous findings that serum TC, TG, and LDL levels were significantly lower in the sarcopenia group versus the non-sarcopenia group, whereas HDL was not significantly different between groups. The reason for the difference is not clear, but may lie in environmental or age range differences between the included and our study populations. The decline in muscle mass, strength, and function associated with sarcopenia can lead to poor clinical outcomes and a loss of independence in older adults. A study by Francesco Landi et al. showed that by comparing patients with sarcopenia to non-sarcopenic patients during a 2-year follow-up period, it was found that sarcopenic patients were more than three times more likely to fall than their counterparts[23]. Therefore, the analysis of metabolites associated with reduced muscle mass and strength in the elderly is important for the identification of sarcopenia as well as for early prevention and treatment. The results of this paper show that differences in amino acid and fatty acid metabolic profiles do exist in the plasma of patients with sarcopenia, and by analyzing the conditions associated with possible abnormalities in amino acid and fatty acid metabolic pathways, the results suggest that arginine, leucine, histidine, palmitic acid, and carnitine play important roles in the development of sarcopenia, and can be used as potential biomarkers for muscle mass and sarcopenia prediction. Proteins consumed through the diet can be degraded in the body by lysosomes and proteasomes to amino acids, whose main function in the body is to synthesize peptides and proteins, but can also be converted to other compounds. Amino acid availability is a major regulator of mTOR signaling and muscle protein synthesis in human skeletal muscle, and leucine, in particular, is responsible for the anabolic effects of amino acids in skeletal muscle. Leucine is both an insulinotropic secretagogue and a trophic activator of rapamycin (mTOR) in skeletal muscle. Increased leucine promotes the phosphorylation and activation of downstream effectors of mTOR and may enhance the phosphorylation of Akt/ PKB (an upstream regulator of mTOR) by increasing the action of insulin, affecting translation initiation and muscle protein synthesis[24]. The primary cellular energy sensor in human muscle cells is AMPK[25], which catalyzes a modest decrease in the phosphorylation of the α-subunit following the ingestion of essential amino acids to abrogate the inhibition of mTOR by TSC 2 and/or to help augment protein synthesis by eliminating the negative regulation of eEF 2[24]. Multiple branched-chain amino acid (BCAA) levels have been found to correlate with thigh muscle cross-sectional area (CSA) in older adults[26], and increasing the amount of leucine in a given diet may be able to promote muscle protein synthesis in older adults[27].Consistent with previous studies, sarcopenia is associated with reduced non-fasting plasma concentrations of the BCAAs leucine and isoleucine, as well as with reduced absolute protein intake[28]. Malnutrition is considered to be a strong predictor of sarcopenia[29], and increasing levels of amino acids in the body can help stimulate muscle protein synthesis[30].Smith.L.W. found that arginine-mediated NO release can improve tissue perfusion through mechanisms such as vasodilation and angiogenesis, and that endogenous NO is associated with the induction of skeletal muscle fiber hypertrophy by reducing protein degradation and increasing protein synthesis closely associated with the induction of skeletal muscle fiber hypertrophy by decreasing protein degradation and increasing protein synthesis, and through these actions can lead to better muscle tissue utilization of nutrients (glucose, fatty acids, and amino acids). In this case, the cells can produce more ATP[31], and it has been shown that arginine protects myocytes from depletion by stimulating protein synthesis during catabolic conditions in C2 C12 cells[32], possibly related to the stimulation of protein synthesis by L-Arg in a NO-dependent manner through activation of the mTOR pathway[32][33].It has been demonstrated in animal experiments by K. Yao that L-Arg enhances protein synthesis and metabolism in skeletal muscle cells and L-Arg supplementation is beneficial in helping burn patients maintain muscle mass[34], and that increased nutritional support for skeletal muscle cells also contributes to glycolipid metabolism, thereby preventing muscle fat infiltration. Another animal experiment suggests that the concentration of the histidine metabolite N-methylhistidine is a sensitive indicator of myofibrillar protein degradation in starved rats. During proteolysis, 3-MH (3-Methylhistidine is released into the blood but cannot be reused. Therefore, plasma concentration and urinary excretion of 3-MH are sensitive markers of myofibrillar protein degradation and may be used as biomarkers for the diagnosis of sarcopenia[35]. β-Alanyl-histidine is the only myopeptide present in human muscle and most of it is found in skeletal muscle[36], and it has been shown that histidyl-containing dipeptides act as intracellular buffers, metal ion chelators, antioxidants, and/or free radical scavengers, and have some significance for the protection of myocytes[37]. Creatine phosphate is more abundant in skeletal muscle as a form of energy storage. Creatine is synthesized using glycine as the backbone, arginine to provide the amidine group, and S-adenosylmethionine to provide the methyl group, and catalyzed by creatine kinase, creatine receives the high-energy phosphoryl bonding group of ATP to form phosphocreatine, which is particularly important for exercise-type skeletal muscle function. Decreased skeletal muscle mass has also been found to correlate with reduced serum levels of phosphocreatine in the elderly[38]. Most of the amino acids in the body can undergo transamination under the action of aminotransferase, reversibly transferring the amino group of a-amino acid to a-keto acid, as a result of which the amino acid is deaminated to generate the corresponding a-keto acid, and the original a-keto acid is transformed into another amino acid, such as leucine and isoleucine in the body can be transformed into ketone bodies and enter into lipid metabolism pathway, which can suggest that the amino acid metabolism is closely related to the lipid metabolism. It can be suggested that amino acid metabolism is closely related to lipid metabolism. Fat in white adipocytes, under the action of hormone-sensitive triglyceride lipase (HSL) and adipose tissue triglyceride lipase (ATGL), is broken down to produce fatty acids and glycerol, and subsequently, fatty acids pass through the B oxidation pathway to produce lipoyl CoA catalyzed by lipoyl CoA synthetase, and lipoyl CoA crosses through the inner mitochondrial membrane under the action of carnitine and then is catalyzed by carnitine-lipoyltransferase I After crossing the inner mitochondrial membrane in the presence of carnitine and catalyzed by carnitine-lipoyltransferase I, lipoyl CoA combines with carnitine to form lipoyl carnitine, which is then converted to lipoyl CoA and released from carnitine by carnitine-lipoyltransferase II after crossing the inner mitochondrial membrane in the presence of carnitine. Previous studies have explored the role of fatty acids in sarcopenia. Palmitic acid, the most abundant circulating saturated fatty acid, may have an effect on muscle tissue, and it has been suggested that palmitic acid induces lipid droplet accumulation and insulin resistance in skeletal muscle by inhibiting the expression of IRS-α 1 (a key molecule in the insulin signaling pathway) and GLUT-α 4 (an important glucose transporter protein), which play an important role in the maintenance of glucose homeostasis and insulin sensitivity play an important role in the maintenance of glucose homeostasis and insulin sensitivity[39]. In addition, it has been shown that MOTS-c is associated with palmitic acid-induced sarcopenia[40], and that the fibroblast factor FGF19 can ameliorate palmitic acid-induced muscle atrophy, glucose and lipid metabolism disorders[39]. palmitic acid, as a type of fatty acid, interacts with carnitine in metabolism, and carnitine levels correlate with insulin resistance. It has also been shown that carnitine levels correlate with grip strength and gait speed in older men with sarcopenia [41], and these results could aid in the prevention and treatment of sarcopenia, which brings important implications for patients and the healthcare system. Palmitate was found to cause lipotoxicity-mediated loss of myofibers, and treatment with palmitate resulted in a reduction in the number, width, and length of myotubes in a dose-dependent manner [42]. Oleate protects skeletal myotube atrophy from the negative effects of palmitate, and one of the important factors in the regulation of myotube atrophy is the fatty acid-mediated mitochondrial redox state. One of the important factors in the regulation of myotube atrophy is the mitochondrial redox state mediated by fatty acids, and the key to mitochondrial fragmentation in skeletal muscle is the increase in mitochondrial ROS, which cause cellular damage through nonspecific modification and destruction of proteins, phospholipids, and DNA [43]. Park JM et al. demonstrated that hispidin protects the C2 C12 myotubes from oxidative stress induced by palmitate [44]. Myotubes were significantly atrophied, MuRf1 expression was increased, myosin heavy chain protein content was decreased, and SGLT 2 i resulted in a reduction in visceral fat accumulation and also led to an increase in muscle mass and grip strength, as well as a decrease in muscle and serum saturated fatty acid levels, especially palmitic acid, after SGLT 2 i administration [45]. Kenneth D' Souza et al. demonstrated that whey peptides promote adipocyte differentiation and lipid accumulation, promote mitochondrial fatty acid oxidation in 3 T3-L1 adipocytes, as well as ameliorate palmitic acid-induced insulin resistance, which was associated with a reduction in endoplasmic reticulum stress, inflammation, and accumulation of diglycerides by whey peptides [46].Consistent with previous studies, high and low levels of carnitine are associated with lower limb dysfunction in the elderly, and the correlation is especially pronounced with levels of medium- and long-chain acylcarnitines[41].A clinical trial by Malaguarnera, M., et al. found improved physical and cognitive function in 70 centenarians treated with L-carnitine for a period of 6 months[47]. On the other hand, several studies have found that elevated levels of carnitine can predict the development of diabetes[48]. This may be due to the fact that medium- and long-chain acylcarnitines are elevated in the presence of vascular inflammation and insulin resistance[49]. Diabetes is associated with weakness and loss of mobility through low-grade inflammation, metabolic acidosis and insulin resistance, altering intracellular energy production and muscle contraction[50]. Thus, these mechanisms may help clarify the association between high levels of acylcarnitines and impaired physical function. Consistent with previous findings, the results of the present study reveal significant differences in the metabolism of amino acids and fatty acids between sarcopenic and non-sarcopenic patients. This study has both limitations and unique strengths. First, the limitations are that our study population consisted mainly of outpatients in a general hospital, with a relatively small sample size, and that they were taking a wide range of medications, were generally older, and were often accompanied by the coexistence of multiple diseases. Such conditions may have an effect on serologic metabolites, but we cannot completely rule out the influence of other diseases or medications taken on metabolites. In addition, the large number of influencing factors may have biased the results somewhat, and future studies should control for these confounding factors as much as possible. In addition, due to the lack of long-term follow-up and follow-up, we were unable to obtain useful information about the long-term effects of these metabolites on patients with sarcopenia. However, the main strength of this study is that we can provide new perspectives for understanding the mechanisms and potential causes of myasthenia gravis by identifying pointers to markers and metabolic pathways. Conclusion We performed a metabolomic analysis of 50 patients with sarcopenia and 50 patients without sarcopenia. The results showed that significant differences in amino acid and fatty acid metabolites did exist between sarcopenia patients and non-sarcopenia patients. Among the important differential metabolites were arginine, histidine, leucine, palmitic acid, and carnitine. We found that normal levels of amino acid and fatty acid metabolism play an important role in maintaining the integrity of skeletal muscle cells, muscle mass and strength. In clinical practice , supplementation of protein and essential amino acids and reduction of palmitic acid and carnitine levels can improve skeletal muscle mass and function and enhance quality of life in older adults. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Tianjin First Central Hospital. We explained to all participants the purpose of the study and how the data collected in this research study would be used. Written, informed consent was obtained from all participants before inclusion in the study. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to the need to protect patient privacy and the Fund provider requires data secrecy. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Tianjin Municipal Health Commission Science and Technology Program (ZC20220). The funding was used for the collection of data. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3863000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268020816,"identity":"ed53a52b-2a65-4b30-829e-572a24a9cad3","order_by":0,"name":"Xinbo Ma","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinbo","middleName":"","lastName":"Ma","suffix":""},{"id":268020817,"identity":"9fe6b667-4170-4203-b388-5e69ec7be936","order_by":1,"name":"Ailin 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19:19:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":45718,"visible":true,"origin":"","legend":"\u003cp\u003e1-6 The PLS-DA scores plot of all groups under HILIC and ESI- mode\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3863000/v1/5a4aab4255c9446e15657a42.png"},{"id":50059478,"identity":"cbd49f49-516c-4263-9ef7-10ffdf789e64","added_by":"auto","created_at":"2024-01-23 19:11:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24078,"visible":true,"origin":"","legend":"\u003cp\u003e1-7 The PLS-DA scores plot of groups of CtrlF and SarF under HILIC and ESI- mode\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3863000/v1/70f7ce3a86241f736720ff7d.png"},{"id":50059482,"identity":"9da65e1d-1a3e-4a37-b0ba-13d29deec846","added_by":"auto","created_at":"2024-01-23 19:11:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":25265,"visible":true,"origin":"","legend":"\u003cp\u003e1-8 The PLS-DA scores plot of groups of CtrlM and SarM under HILIC and ESI- mode\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3863000/v1/f205de9d496829268e327a05.png"},{"id":66189711,"identity":"4803d4c1-e955-4cb3-a0ba-399e980519be","added_by":"auto","created_at":"2024-10-08 14:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":865225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3863000/v1/624cdb75-8df5-43f8-8c97-64e8934a1aba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal fatty acid and amino acid metabolism in patients with sarcopenia","fulltext":[{"header":"Background","content":"\u003cp\u003eAge-related skeletal sarcopenia is a serious global health problem for older people and communities, as it leads to disability and a significant socio-economic burden for older people. Muscle mass and strength change with age, reaching a maximum level in midlife at about age 40, with a subsequent decline in skeletal muscle mass and strength with age[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and loss of skeletal muscle fibers beginning at age 50, with about 50% of fibers lost by age 80[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to age-related factors, low-grade inflammation, endocrine factors, oxidative stress and malnutrition can cause loss of skeletal muscle[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSkeletal muscle is primarily composed of protein, and an imbalance between protein synthesis and catabolism is thought to be the most direct cause of muscle loss. Studies have shown that protein intake is strongly correlated with muscle mass, and when protein intake is insufficient to meet daily requirements, a negative protein balance occurs and leads to skeletal muscle atrophy, impaired muscle growth, and functional decline[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Protein is also an independent predictor of muscle mass[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and higher doses of protein are required to promote muscle protein synthesis in aging, inflammation, and disease[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A cohort study found that protein intake was positively associated with the maintenance of lean body mass in older adults, while lower protein intake was associated with greater loss of lean body mass. Although skeletal muscle protein synthesis and its quality are regulated by a variety of factors, the basic prerequisite for its synthesis is dietary supplementation with amino acids[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The concentration of essential amino acids in the peripheral blood determines protein synthesis at the muscle and systemic level[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; therefore, protein and essential amino acid supplementation is necessary to prevent the development of sarcopenia.\u003c/p\u003e \u003cp\u003eA new term obesity sarcopenia refers to the coexistence of obesity and sarcopenia, and obesity, as an independent risk factor for sarcopenia, may be associated with insulin resistance as well as various metabolic disorders such as cardiovascular disease, type 2 diabetes mellitus, and nonalcoholic fatty liver disease[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In people suffering from obesity, cytokines differentiate muscle progenitor cells into an adipocyte-like phenotype via a paracrine pathway, leading to a vicious cycle of sarcopenia, and increased fat infiltration in the muscle, which further affects skeletal muscle function[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. White adipose tissue serves as the primary postprandial nutrient storage site for a variety of nutrients in the form of triacylglycerol (TG)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Insulin can reduce fatty acid release from adipocytes by inhibiting enzymes involved in triglyceride hydrolysis and promoting triglyceride synthesis, but in insulin-resistant adipose tissue these functions are compromised[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whereas insulin resistance is often present in patients with sarcopenia, serum levels of fatty acids may be elevated in these patients. In addition, white adipose tissue does not grow efficiently in the presence of excess nutrients, which may promote lipid accumulation in skeletal muscle tissue[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The number of patients with obesity-related sarcopenia is increasing, and in obese individuals, excess lipid supply raises circulating levels of free fatty acids, and high plasma free fatty acid levels can induce insulin resistance and tissue (e.g., pancreas and skeletal muscle) damage.\u003c/p\u003e \u003cp\u003eIn patients with sarcopenia, there may be multiple abnormalities in their amino acid metabolism and fatty acid metabolism, but the use of a particular metabolite as a single biomarker does not explain the pathogenesis of this disease. However, the molecular events related to metabolic dysfunction in skeletal muscle cells during aging are not fully understood. Metabolomics provides us with a link between clinical disease and basic biology and offers a research approach to identify the metabolic factors associated with sarcopenia by determining the pathogenesis of the metabolic properties involved in sarcopenia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe overall aim of this study was to explore the different metabolites associated with muscle mass, strength and function in humans and to provide valuable information related to the pathogenesis and management of sarcopenia. We hope to use metabolomic identification to differentiate the metabolites of sarcopenia patients from those of healthy subjects; to look for detailed metabolic changes and associated metabolic pathways in sarcopenia patients, and to provide a basis for scientific diagnosis and treatment of sarcopenia patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis cross-sectional study was conducted from December 2018 to August 2021 at Tianjin First Central Hospital. The study was approved by the local ethics committee. Written informed consent was obtained from all participants. A total of 650 patients with sarcopenia were screened from 2965 elderly patients (\u0026ge;\u0026thinsp;60 years old) in the outpatient clinic, and 100 elderly patients (\u0026ge;\u0026thinsp;60 years old) were randomly selected for the survey study, which were divided into the sarcopenia group and the non-sarcopenia group according to the diagnostic criteria of the Asian Working Group on Sarcopenia (AWGS), with 25 cases in each of the two groups, and their general information was collected (gender/age/biochemistry/blood pressure/height General information (gender/age/biochemistry/blood pressure/height/weight/smoking/alcohol consumption) was collected, serum samples were taken, and frozen blood samples were sent to Shanghai Huipu Biotechnology Co. 720, Korea). Participants wore light clothing and stood barefoot on the instrument. Weight and height were measured to an accuracy of 0.1 kg and 0.1 cm, respectively. limb skeletal muscle mass (ASMI) was limb mass (kg)/height2 (m2)). Grip strength and stride speed (measurements of muscle strength and muscle function: using a grip strength meter (Model WCS-II, Beijing Zhuochuan Electronic Science and Technology Co. Each hand was measured twice and the highest value was taken. Muscle function was measured using a 4-meter stride. The stride speed test is the time required for a participant to walk 4 m from a standing position at the usual speed. It was measured using an electronic timer. (Calculated speed, critical level\u0026thinsp;\u0026le;\u0026thinsp;0.8 m/s.) Investigations and measurements were performed. Biochemical indices were compared between sarcopenic and non-sarcopenic patients (subjects fasted for 8 h. Blood was collected in vacuum blood collection tubes by venipuncture between 7:00 and 9:00 a.m. and left to stand for 30 min. Serum was separated at 4\u0026deg;C. The test indexes included total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and serum albumin (ALB). All indices were measured on the same day. Aliquots of 400 \u0026micro;l of serum were prepared in two Eppendorf tubes and frozen at \u0026minus;\u0026thinsp;70\u0026deg;C. These indices are routine markers for determining the health status of the elderly.ALB, TG were measured by colorimetric method. TC, HDL-C and LDL-C) were determined by enzyme colorimetric assay to identify relevant factors.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Criteria:\u003c/h2\u003e \u003cp\u003eAccording to the AWGS diagnostic criteria for sarcopenia, patients were diagnosed with sarcopenia if they met at least one of criteria 1 and 2 and 3, including: 1) muscle mass\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m2 (males) or \u0026lt;\u0026thinsp;5.7 kg/m2 (females); 2) grip strength\u0026thinsp;\u0026lt;\u0026thinsp;26 kg (males) or 18 kg (females); and 3) 4-meter stride speed\u0026thinsp;\u0026lt;\u0026thinsp;0.8 m/s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eINCLUSION CRITERIA:\u003c/h2\u003e \u003cp\u003eElderly subjects (\u0026ge;\u0026thinsp;60 years old) enrolled for counseling or health screening at the outpatient clinic.\u003c/p\u003e \u003cp\u003eEXCLUSION CRITERIA:\u003c/p\u003e \u003cp\u003e1) implanted metal objects, such as pacemakers and fixed steel nails;\u003c/p\u003e \u003cp\u003e2) Complete bed rest;\u003c/p\u003e \u003cp\u003e3) Significant physical disability.\u003c/p\u003e \u003cp\u003e4) Permanent loss of ability to perform activities of daily living (ADL);\u003c/p\u003e \u003cp\u003e5) Extracellular water (ECW)/Total body water (TBW) value\u0026thinsp;\u0026ge;\u0026thinsp;0.40.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInstruments:\u003c/h2\u003e \u003cp\u003eThe instrumental platform for this LC-MS analysis was an ultra high performance liquid chromatograph from Thermo. We used two modes i.e., separate assays, Hilic and reversed phase modes.\u003c/p\u003e \u003cp\u003eUltrasonic cleaner (KQ2200DE CNC ultrasonic cleaner, Kunshan Ultrasonic Instrument Co., Ltd.); Vacuum concentrator (eppendorf TD-10-017, Shanghai Tongda Kexin Biological Co., Ltd.); Constant temperature incubation shaker (THZ-100B, Shanghai Yihang Scientific Instrument Co., Ltd.); Benchtop high-speed centrifuge (SiGMA, Bohli); Vortex Mixer (Scientific Industries Vortex-genie2); ultrapure water purifier (Milli-Q, Merck); electronic balance (METTLER TOLEDO AL204); high-performance liquid chromatograph (Thermo DIONEX UltiMate3000); ACQUITY UPLC BEH Amide C18 column (100 mm \u0026times; 2.1 mm i.d., 1.7 \u0026micro;m; Waters, Milford, USA)\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eReagents:\u003c/h2\u003e \u003cp\u003eFormic acid, sodium formate (chromatographic purity, CNW, Germany); methanol, acetonitrile, isopropanol (chromatographic purity, Merck, Germany); methyl tert-butyl ether (chromatographic purity, ACROS); and purified water was prepared by Nanopure purification system (Barnstead, USA). Internal standards were hemolysed phosphatidylcholine LPC 12:0, phosphatidylcholine PC 22:0, and fatty acid FA 19:0 (Avanti).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePre-treatment methods:\u003c/h2\u003e \u003cp\u003eMethanol/acetonitrile (1:1, v/v) solvent was prepared; acetonitrile water (1:1, v/v) containing the internal standard lysophosphatidylcholine LPC 12:0, phosphatidylcholine PC 22:0, and fatty acid FA 19:0, at a concentration of 2 \u0026micro;g/ml was prepared.\u003c/p\u003e \u003cp\u003eTake 100 \u0026micro;l of serum sample, add 400 \u0026micro;l of pre-cooled methanol/acetonitrile (1:1, v/v) solvent; vortex for 30 s; ultrasonic 10 min (4 ℃ water bath); 20 ℃ static for 30 min; 4 ℃, 12000 rpm centrifugation for 15 min; 200 \u0026micro;L of the supernatant, concentrated by vacuum centrifugation and evaporation; add 100 \u0026micro;L of ACN: H2O (1:1, v/v) Add 100 \u0026micro;L of ACN:H2O (1:1, v/v); vortex for 30 s, sonicate for 10 min (4 ℃ water bath); centrifuge at 4 ℃ and 12000 rpm for 15 min; take 80 \u0026micro;l of the supernatant into the injection bottle and store at 4 ℃ for measurement.\u003c/p\u003e \u003cp\u003eQuality Control (QC): After sample reconstitution and centrifugation, take 20 \u0026micro;l of each sample to obtain a mixed sample, mix and centrifuge, transfer to the injection bottle, and store at 4℃ for measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eChromatographic conditions :\u003c/h2\u003e \u003cp\u003e(1) Hilic chromatographic conditions: the column was an ACQUITY UPLC BEH Amide C18 column (100 mm \u0026times; 2.1 mm i.d., 1.7 \u0026micro;m; Waters, Milford, USA), the mobile phase A was water (containing 0.1% formic acid, 5% acetonitrile), and the mobile phase B was acetonitrile (containing 0.1% formic acid); the gradient elution program was 0\u0026ndash;1 min: 85% B-85% B, 1\u0026ndash;12 min: 85%-65% B, 12-12.1 min: 65%-40% B, 40% B maintained for 3 min, 15\u0026ndash;20 min. 1 min: 85% B-85% B, 1\u0026ndash;12 min: 85%-65% B, 12-12.1 min: 65%-40% B, 40% B for 3 min, 15\u0026ndash;20 The flow rate was 0.40 mL/min, the injection volume was 3 \u0026micro;L, and the column temperature was 45 ℃.\u003c/p\u003e \u003cp\u003e(2) Reversed-phase chromatographic conditions: Thermo C18 column; mobile phase A was acetonitrile (containing 0.1% formic acid), and mobile phase B was water (containing 0.1% formic acid and 5% acetonitrile); the gradient elution program was 0-1.5 min: 0%-20% A, 1.5\u0026ndash;10 min. : 20%-100% A, 100% A maintained for 3 min, 13-13.5 min: 100%-0% A. The flow rate was 0.40 mL/min, the injection volume was 3 \u0026micro;L, and the column temperature was 45 ℃.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 20.0 (IBM, Almonk, NY, USA). Continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed using the independent samples t-test. Categorical data were expressed as frequencies and analyzed using the chi-square test. Pearson correlation analysis was used for continuous variables. Spearman's correlation analysis was performed for ordinal variables. Interactions between multiple factors were analyzed using binary logistic regression and multiple linear regression. p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e1) Integration of the data matrix\u003c/p\u003e \u003cp\u003eData preprocessing Before pattern recognition, the raw data were subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization by the metabolomics processing software that comes with the instrument, resulting in a data matrix of retention times, mass-to-charge ratios and peak intensities, which yielded a total of 1,863 m/z in the Hilic mode, and 3,328 m/z in the positive-negative ion mode.\u003c/p\u003e \u003cp\u003e2) Multivariate statistical analysis (Hilic)\u003c/p\u003e \u003cp\u003eThe normalized data matrix was imported into the SIMCA-P\u0026thinsp;+\u0026thinsp;13.0 software package (Umetrics, Ume\u0026aring;, Sweden), and unsupervised principal component analysis (PCA) was used first to observe the overall distribution between samples and the stability of the whole analytical process, and then supervised (orthogonal) partial least squares analysis (O)PLS-DA was used to differentiate the metabolic profiles between groups of overall differences between groups and find differential metabolites between groups. (Variables with variable weight values (Variable important in projection, VIP) greater than 1 were considered differential in the (O)PLS-DA analysis. To prevent overfitting of the model, seven cycles of interactive validation and 200 response ordering tests were used to examine the quality of the model.\u003c/p\u003e \u003cp\u003e3) The evaluation indicators of the model are as follows:\u003c/p\u003e \u003cp\u003eInternal validation: R2X and R2Y represent the explanation rate of the model on the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model, theoretically, the closer the value of R2 and Q2 is to 1, the better the model is, and the lower it is, the worse the model's fitting accuracy is, usually, R2 and Q2 are better than 0.5 (50%), and the difference between the two values should not be too large.\u003c/p\u003e \u003cp\u003eExternal validation: Response ordering test is used, the closer the slopes of the R2Y and Q2Y straight lines are to a horizontal straight line, the more likely the model is to be overfitted, and Q2 is generally required to be less than zero.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e(1)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGrouping:\u0026nbsp;\u003c/strong\u003e100 cases of\u0026nbsp;human\u0026nbsp;serological\u0026nbsp;samples were divided into four groups\u0026nbsp;(see Table 1-1), namely, male control group\u0026nbsp;(CtrlM), male experimental group\u0026nbsp;(SarM), female control group\u0026nbsp;(CtrlF), female experimental group\u0026nbsp;(SarF), 25 cases in each group. The samples were renumbered to correspond to groups A, B, C, and D for the convenience of the machine.\u003c/p\u003e\n\u003cp\u003eTable 1-1 Grouping of 100 serum samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eoriginal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003enew number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eoriginal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003enew number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eoriginal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003enew number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eoriginal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003enew number\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eControl group women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eControl group men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eExperimental group women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eExperimental group men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n 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width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e7a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e7b\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e5a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e13a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e6b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e5b\u003c/p\u003e\n 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width=\"15.306122448979592%\"\u003e\n \u003cp\u003e12a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA14\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eA25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eB25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eC25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eD25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2) General: (See Table 1-2) A total of 100 elderly participants,\u0026nbsp;50\u0026nbsp;male\u0026nbsp;and 50 female,\u0026nbsp;were included in this study, and the subjects were aged 60-87 years (mean 74 \u0026plusmn; 18 years).\u0026nbsp;All participants were categorized into myasthenia gravis and non-myasthenia gravis groups.\u003c/p\u003e\n\u003cp\u003eTable 1-2 Comparison of basic clinical characteristics between sarcopenia and non-sarcopenia groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003evariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003eSar (n=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003eContr (n=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e20.94\u0026plusmn;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e24.19\u0026plusmn;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e-6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eGrip strength (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e23.91\u0026plusmn;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e26.51\u0026plusmn;8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e42.58\u0026plusmn;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e43.54\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e-2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e4.85\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e4.89\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e1.38\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e1.28\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.830985915492956%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.007042253521128%\"\u003e\n \u003cp\u003e3.10\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.295774647887324%\"\u003e\n \u003cp\u003e3.17\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\"\u003e\n \u003cp\u003e-3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: Body Mass Index (BMI), Total Cholesterol (TC), Triglycerides (TG), High Density Lipoprotein (HDL-C), Low Density Lipoprotein (LDL-C), Albumin (ALB).\u003c/p\u003e\n\u003cp\u003e3)\u0026nbsp;Metabolomics\u0026nbsp;analysis showed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASSAYS\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis study examined differences in metabolite profiles between serum samples from patients with sarcopenia and healthy controls with the aim of identifying candidate biomarkers and pathogenic pathways for\u0026nbsp;sarcopenia.\u0026nbsp;Serum samples were collected from patients with sarcopenia (n = 50) and healthy controls (n = 50). Using Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) combined with unidimensional statistical analysis, as well as repetitive feature extraction analysis and filtration, the analysis showed that there were differences in serum metabolic profiles between myasthenia gravis and control groups. (O) Variables with variable weight values (Variable important in projection, VIP) greater than 1 were considered as difference variables in the PLS-DA analysis. To prevent model overfitting, the quality of the model was examined using seven cycles of interactive validation and 200 response ordering tests. A combination of multidimensional analysis OPLS-DA and unidimensional analysis (student t-test) was used to screen for differential metabolites between groups (VIP\u0026gt;1, p\u0026lt;0.05),and further line component analysis.\u003c/p\u003e\n\u003cp\u003eIn positive ion mode,\u0026nbsp;principal component analysis (PCA) showed no significant difference between\u0026nbsp;control\u0026nbsp;and\u0026nbsp;experimental\u0026nbsp;groups [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.537, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.307)] as shown in Fig 1-1. In the negative ion mode, the situation was similar and the principal component analysis (PCA) showed no significant difference between the control and experimental groups [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.539, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.314)],as shown in Fig 1-2. Also, we found signs of separation between the control and the The lake-blue diamonds, red triangles, ink-blue squares, and yellow triangles in the figure represent the control group male (CtrlM), the experimental group male (SarM), the control group female (CtrlF), and the experimental group female (SarF), respectively (Fig 1-1, 1-2, 1-3, and 1- 4).\u003c/p\u003e\n\u003cp\u003eMetabolite profiles of serum samples were analyzed using the PLS-DA method. As\u0026nbsp;shown in\u0026nbsp;Fig 1-3, the\u0026nbsp;metabolomics data of serum samples analyzed by PLS-DA in positive ion mode indicated that there was a significant difference between the\u0026nbsp;female sarcopenia\u0026nbsp;group, the\u0026nbsp;female\u0026nbsp;control group, the\u0026nbsp;male sarcopenia\u0026nbsp;group, and the\u0026nbsp;male\u0026nbsp;control group [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.077, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.432, and Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.178); and in Fig 1-4, there was a significant difference between the female sarcopenia group and the significant difference between the female sarcopenia group and the control group in Fig 1-4 [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.0393, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.768, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.27)]; and between the male sarcopenia group and the control group in Fig 1-5 [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.0412, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.759, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.317)].\u003c/p\u003e\n\u003cp\u003eTable 1-3 Model quality for models under HILIC and ESI+ mode\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003eN0.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eR X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eR Y\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePCA-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrlF,SarF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.0393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrlM, SarM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.0412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrl, Sar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Fig 1-6: In the negative ion mode, there were also significant differences between the\u0026nbsp;female sarcopenia\u0026nbsp;group, the\u0026nbsp;female\u0026nbsp;control group, the\u0026nbsp;male sarcopenia\u0026nbsp;group, and the\u0026nbsp;male\u0026nbsp;control group [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.0757, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.423, and Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.203]; in Fig 1-7 there was a significant difference between the female sarcopenia group and the control group [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.0484, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.705, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.285)]; Fig 1-8 Significant difference between male sarcopenia group and control group [R\u003csup\u003e2\u003c/sup\u003e X (cum) = 0.0348, R\u003csup\u003e2\u003c/sup\u003e Y (cum) = 0.796, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.288)]. These results suggest that the PLS-DA model can be used to differentiate between male and female sarcopenia patients and non-sarcopenia patients; the parameters included in the model in the two ionic models shown in Supplementary Tables 1-3 and 1-4.\u003c/p\u003e\n\u003cp\u003eTable 1-4 Model quality for models under HILIC and ESI- mode\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003eN0.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eR X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eR Y\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePCA-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.0757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrlF,SarF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.0484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrlM, SarM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.0348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.052631578947368%\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003eCtrl, Sar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87719298245614%\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;There was a clear separation between groups in the positive and negative ion modes; at the same time, serum metabolic profiles showed significant intergroup differences. Samples from both groups were similarly clearly separated, exhibiting significant within-group differences. To distinguish the most important metabolites between groups, differential metabolites were screened using p-values and VIP scores. PCA-, PLS-DA-based models for distinguishing the groups were constructed in positive and negative ion mode, and metabolic differences between the groups were determined. The results showed significant differences between the two groups, combined with the variable importance in projection (VIP) \u0026gt; 1 and p \u0026lt; 0.05 finally screened 37 differential metabolites in positive ion mode and 20 differential metabolites in negative ion mode, the main metabolites were arginine, histidine, leucine, cysteine, aminobutyric acid and derivatives, and quantitatively suggested that the content of such metabolites was reduced in the experimental group compared with the control group. The quantification suggested that the content of these metabolites was reduced in the experimental group compared with the control group, which confirmed that the plasma metabolic profile of amino acids was indeed altered in sarcopenia patients relative to healthy subjects; meanwhile, the content of metabolites such as palmitic acid, phosphatidylcholine, glucosamine, and arachidonic acid was higher than that of the control group, and the analysis suggested that the sarcopenia patients had abnormalities in metabolic pathways such as fatty acid and phospholipid metabolism.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA study by Wu LC et al. found that BMI was significantly higher in non-myasthenic patients compared to myasthenic patients, with a 0.45-fold reduction in the odds of myasthenia gravis for every 1 kg /㎡ increase in BMI, and that higher BMI resulted in a lower risk of developing myasthenia gravis[15]. However, BMI remains controversial in the assessment of sarcopenia in older adults because BMI and does not distinguish between adiposity and lean body mass, and increased lean body mass results in decreased mortality[16]. Aging is associated with an increase in visceral adiposity and progressive loss of muscle mass, which has an opposite effect on mortality[17].Santos et al. evidence suggests that sarcopenia with obesity may be associated with higher levels of metabolic disorders and an increased risk of death compared with obesity or sarcopenia alone[18].\u003c/p\u003e\n\u003cp\u003eIn line with previous findings, higher levels of Alb were significantly associated with non-muscular hypomuscular disorders in the elderly[19]. Albumin, the most abundant plasma protein in the body, is recognized as a very important plasma protein in the assessment of the body\u0026apos;s nutritional status, and its reduction affects wound healing, lowers immunity and reduces lean body mass[20]. In the Smith S. et al. study, albumin levels were associated with poorer physical function and lower muscle strength or muscle mass in older adults; however, this association has not been confirmed in other populations[21].\u003c/p\u003e\n\u003cp\u003eThis contradicts previous findings that serum TC, TG, and LDL levels were significantly lower in the sarcopenia group versus the non-sarcopenia group, whereas HDL was not significantly different between groups. The reason for the difference is not clear, but may lie in environmental or age range differences between the included and our study populations.\u003c/p\u003e\n\u003cp\u003eThe decline in muscle mass, strength, and function associated with sarcopenia can lead to poor clinical outcomes and a loss of independence in older adults. A study by Francesco Landi et al. showed that by comparing patients with sarcopenia to non-sarcopenic patients during a 2-year follow-up period, it was found that sarcopenic patients were more than three times more likely to fall than their counterparts[23]. Therefore, the analysis of metabolites associated with reduced muscle mass and strength in the elderly is important for the identification of sarcopenia as well as for early prevention and treatment.\u003c/p\u003e\n\u003cp\u003eThe results of\u0026nbsp;this paper\u0026nbsp;show that\u0026nbsp;differences\u0026nbsp;in\u0026nbsp;amino acid and fatty acid\u0026nbsp;metabolic profiles do exist in the plasma of patients with sarcopenia, and\u0026nbsp;by analyzing the conditions associated with possible abnormalities in amino acid and fatty acid metabolic pathways, the results suggest that arginine, leucine, histidine,\u0026nbsp;palmitic acid, and carnitine\u0026nbsp;play important roles in the development of sarcopenia, and can\u0026nbsp;be used as potential\u0026nbsp;biomarkers\u0026nbsp;for muscle mass and\u0026nbsp;sarcopenia\u0026nbsp;prediction.\u003c/p\u003e\n\u003cp\u003eProteins consumed through the diet can be degraded in the body by lysosomes and proteasomes to amino acids, whose main function in the body is to synthesize peptides and proteins, but can also be converted to other compounds. Amino acid availability is a major regulator of mTOR signaling and muscle protein synthesis in human skeletal muscle, and leucine, in particular, is responsible for the anabolic effects of amino acids in skeletal muscle. Leucine is both an insulinotropic secretagogue and a trophic activator of rapamycin (mTOR) in skeletal muscle. Increased leucine promotes the phosphorylation and activation of downstream effectors of mTOR and may enhance the phosphorylation of Akt/ PKB (an upstream regulator of mTOR) by increasing the action of insulin, affecting translation initiation and muscle protein synthesis[24]. The primary cellular energy sensor in human muscle cells is AMPK[25], which catalyzes a modest decrease in the phosphorylation of the \u0026alpha;-subunit following the ingestion of essential amino acids to abrogate the inhibition of mTOR by TSC 2 and/or to help augment protein synthesis by eliminating the negative regulation of eEF 2[24]. Multiple branched-chain amino acid (BCAA) levels have been found to correlate with thigh muscle cross-sectional area (CSA) in older adults[26], and increasing the amount of leucine in a given diet may be able to promote muscle protein synthesis in older adults[27].Consistent with previous studies, sarcopenia is associated with reduced non-fasting plasma concentrations of the BCAAs leucine and isoleucine, as well\u0026nbsp;as with reduced absolute protein intake[28].\u0026nbsp;Malnutrition is\u0026nbsp;considered to\u0026nbsp;be a strong predictor of sarcopenia[29], and\u0026nbsp;increasing levels of\u0026nbsp;amino acids\u0026nbsp;in the body can help\u0026nbsp;stimulate muscle protein synthesis[30].Smith.L.W.\u0026nbsp;found that\u0026nbsp;arginine-mediated NO release\u0026nbsp;can improve\u0026nbsp;tissue perfusion through mechanisms\u0026nbsp;such\u0026nbsp;as vasodilation and angiogenesis,\u0026nbsp;and that\u0026nbsp;endogenous NO is associated with the induction of skeletal muscle fiber hypertrophy by reducing protein degradation and increasing protein synthesis closely associated with the induction of skeletal muscle fiber hypertrophy by decreasing protein degradation and increasing protein synthesis, and\u0026nbsp;through\u0026nbsp;these\u0026nbsp;actions can\u0026nbsp;lead to better\u0026nbsp;muscle tissue\u0026nbsp;utilization of nutrients (glucose, fatty acids, and amino acids). In this case,\u0026nbsp;the cells can produce more ATP[31], and\u0026nbsp;it has been shown that\u0026nbsp;arginine protects myocytes from depletion by stimulating protein synthesis during catabolic conditions in C2 C12 cells[32], possibly\u0026nbsp;related to the\u0026nbsp;stimulation of protein synthesis by L-Arg in a NO-dependent manner through activation of the mTOR\u0026nbsp;pathway[32][33].It\u0026nbsp;has been\u0026nbsp;demonstrated in animal experiments by\u0026nbsp;K. Yao\u0026nbsp;that L-Arg enhances protein synthesis and metabolism in skeletal muscle\u0026nbsp;cells\u0026nbsp;and\u0026nbsp;L-Arg supplementation\u0026nbsp;is beneficial in helping\u0026nbsp;burn patients\u0026nbsp;maintain\u0026nbsp;muscle mass[34], and that\u0026nbsp;increased\u0026nbsp;nutritional\u0026nbsp;support for skeletal muscle cells also contributes to glycolipid metabolism, thereby preventing muscle fat infiltration. Another animal experiment suggests that the concentration of the histidine metabolite N-methylhistidine is a sensitive indicator of myofibrillar protein degradation in starved rats. During proteolysis, 3-MH (3-Methylhistidine is released into the blood but cannot be reused. Therefore, plasma concentration and urinary excretion of 3-MH are sensitive markers of myofibrillar protein degradation and may be used as biomarkers for the diagnosis of sarcopenia[35]. \u0026beta;-Alanyl-histidine is the only myopeptide present in human muscle and most of it is found in skeletal muscle[36], and it has been shown that histidyl-containing dipeptides act as intracellular buffers, metal ion chelators, antioxidants, and/or free radical scavengers, and have some significance for the protection of myocytes[37]. Creatine phosphate is more abundant in skeletal muscle as a form of energy storage. Creatine is synthesized using glycine as the backbone, arginine to provide the amidine group, and S-adenosylmethionine to provide the methyl group, and catalyzed by creatine kinase, creatine receives the high-energy phosphoryl bonding group of ATP to form phosphocreatine, which is particularly important for exercise-type skeletal muscle function. Decreased skeletal muscle mass has also been found to correlate with reduced serum levels of phosphocreatine in the elderly[38]. Most of the amino acids in the body can undergo transamination under the action of aminotransferase, reversibly transferring the amino group of a-amino acid to a-keto acid, as a result of which the amino acid is deaminated to generate the corresponding a-keto acid, and the original a-keto acid is transformed into another amino acid, such as leucine and isoleucine in the body can be transformed into ketone bodies and enter into lipid metabolism pathway, which can suggest that the amino acid metabolism is closely related to the lipid metabolism. It can be suggested that amino acid metabolism is closely related to lipid metabolism.\u003c/p\u003e\n\u003cp\u003eFat in white adipocytes, under the action of hormone-sensitive triglyceride lipase (HSL) and adipose tissue triglyceride lipase (ATGL), is broken down to produce fatty acids and glycerol, and subsequently, fatty acids pass through the B oxidation pathway to produce lipoyl CoA catalyzed by lipoyl CoA synthetase, and lipoyl CoA crosses through the inner mitochondrial membrane under the action of carnitine and then is catalyzed by carnitine-lipoyltransferase I After crossing the inner mitochondrial membrane in the presence of carnitine and catalyzed by carnitine-lipoyltransferase I, lipoyl CoA combines with carnitine to form lipoyl carnitine, which is then converted to lipoyl CoA and released from carnitine by carnitine-lipoyltransferase II after crossing the inner mitochondrial membrane in the presence of carnitine. Previous studies have explored the role of\u0026nbsp;fatty acids\u0026nbsp;in sarcopenia.\u0026nbsp;Palmitic acid,\u0026nbsp;the most abundant circulating saturated fatty acid, may have an effect on muscle tissue, and it has been\u0026nbsp;suggested\u0026nbsp;that palmitic acid induces\u0026nbsp;lipid droplet accumulation and insulin resistance in skeletal muscle by\u0026nbsp;inhibiting\u0026nbsp;the\u0026nbsp;expression of\u0026nbsp;IRS-\u0026alpha; 1 (a key molecule in the insulin signaling pathway) and GLUT-\u0026alpha; 4 (an important glucose transporter protein), which play an important role in the maintenance of glucose homeostasis and insulin sensitivity play an important role in the maintenance of glucose homeostasis and insulin sensitivity[39].\u0026nbsp;In addition, it has been shown that\u0026nbsp;MOTS-c\u0026nbsp;is\u0026nbsp;associated\u0026nbsp;with palmitic acid-induced\u0026nbsp;sarcopenia[40],\u0026nbsp;and\u0026nbsp;that the fibroblast factor FGF19 can ameliorate palmitic acid-induced\u0026nbsp;muscle atrophy, glucose and lipid metabolism disorders[39]. palmitic acid, as a type of fatty acid, interacts with carnitine in metabolism, and carnitine\u0026nbsp;levels\u0026nbsp;correlate\u0026nbsp;with insulin\u0026nbsp;resistance. It has also been shown that carnitine levels correlate with grip strength and gait speed in older men with sarcopenia\u0026nbsp;[41], and these results could aid in the prevention and treatment of sarcopenia, which brings important implications for patients and the healthcare system. Palmitate was found to cause lipotoxicity-mediated loss of myofibers, and treatment with palmitate resulted in a reduction in the number, width, and length of myotubes in a dose-dependent manner\u0026nbsp;[42]. Oleate protects skeletal myotube atrophy from the negative effects of palmitate, and one of the important factors in the regulation of myotube atrophy is the fatty acid-mediated mitochondrial redox state. One of the important factors in the regulation of myotube atrophy is the mitochondrial redox state mediated by fatty acids, and the key to mitochondrial fragmentation in skeletal muscle is the increase in mitochondrial ROS, which cause cellular damage through nonspecific modification and destruction of proteins, phospholipids, and DNA\u0026nbsp;[43]. Park JM et al. demonstrated that hispidin protects the C2 C12 myotubes from oxidative stress induced by palmitate\u0026nbsp;[44]. Myotubes were significantly atrophied, MuRf1 expression was increased, myosin heavy chain protein content was decreased, and SGLT 2 i resulted in a reduction in visceral fat accumulation and also led to an increase in muscle mass and grip strength, as well as a decrease in muscle and serum saturated fatty acid levels, especially palmitic acid, after SGLT 2 i administration\u0026nbsp;[45]. Kenneth D\u0026apos; Souza et al. demonstrated that whey peptides promote adipocyte differentiation and lipid accumulation, promote mitochondrial fatty acid oxidation in 3 T3-L1 adipocytes, as well as ameliorate palmitic acid-induced insulin resistance, which was associated with a reduction in endoplasmic reticulum stress, inflammation, and accumulation of diglycerides by whey peptides\u0026nbsp;[46].Consistent with previous studies, high and low levels of carnitine are associated with lower limb dysfunction in the elderly, and the correlation is especially pronounced with\u0026nbsp;levels of medium- and long-chain acylcarnitines[41].A\u0026nbsp;clinical trial by Malaguarnera, M., et al. found improved physical and cognitive function in 70 centenarians treated with L-carnitine for a period of 6 months[47]. On the other hand, several studies have found that elevated levels of carnitine can predict the development of diabetes[48]. This may be due to the fact that medium- and long-chain acylcarnitines are elevated in the presence of vascular inflammation and insulin resistance[49]. Diabetes is associated with weakness and loss of mobility through low-grade inflammation, metabolic acidosis and insulin resistance, altering intracellular energy production and muscle contraction[50]. Thus, these mechanisms may help\u0026nbsp;clarify\u0026nbsp;the association between high levels of acylcarnitines and impaired physical function.\u003c/p\u003e\n\u003cp\u003eConsistent with previous findings, the results of the present study reveal significant differences in the metabolism of amino acids and fatty acids between sarcopenic and non-sarcopenic patients. This study has both limitations and unique strengths. First, the limitations are that our study population consisted mainly of outpatients in a general hospital, with a relatively small sample size, and that they were taking a wide range of medications, were generally older, and were often accompanied by the coexistence of multiple diseases. Such conditions may have an effect on serologic metabolites, but we cannot completely rule out the influence of other diseases or medications taken on metabolites. In addition, the large number of influencing factors may have biased the results somewhat, and future studies should control for these confounding factors as much as possible. In addition, due to the lack of long-term follow-up and follow-up, we were unable to obtain useful information about the long-term effects of these metabolites on patients with sarcopenia. However, the main strength of this study is that we can provide new perspectives for understanding the mechanisms and potential causes of myasthenia gravis by identifying pointers to markers and metabolic pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe performed a metabolomic analysis of 50 patients with sarcopenia and 50 patients without sarcopenia. The results showed that significant differences in amino acid and fatty acid metabolites did exist between sarcopenia patients and non-sarcopenia patients. Among the important differential metabolites were arginine, histidine, leucine, palmitic acid, and carnitine. We found that normal levels of amino acid and fatty acid metabolism play an important role in maintaining the integrity of skeletal muscle cells, muscle mass and strength. In clinical practice , supplementation of protein and essential amino acids and reduction of palmitic acid and carnitine levels can improve skeletal muscle mass and function and enhance quality of life in older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Tianjin First Central Hospital. We explained to all participants the purpose of the study and how the data collected in this research study would be used. Written, informed consent was obtained from all participants before inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to the need to protect patient privacy and the Fund provider requires data secrecy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Tianjin Municipal Health Commission Science and Technology Program (ZC20220). The funding was used for the collection of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBAL made substantial contributions to conception and design; MXB were involved in acquisition of data, analysis, and interpretation of data; MXB and HSM drafted the manuscript and revised the version to be published. All authors read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMatsuba I, Fujita R, Iida K. Palmitic Acid Inhibits Myogenic Activity and Expression of Myosin Heavy Chain MHC IIb in Muscle Cells through Phosphorylation-Dependent MyoD Inactivation. 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Arch Intern Med. 2007;167(7):635\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/archinte.167.7.635\u003c/span\u003e\u003cspan address=\"10.1001/archinte.167.7.635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sarcopenia, elderly, amino acid metabolism, fatty acid metabolism, metabolomic assays","lastPublishedDoi":"10.21203/rs.3.rs-3863000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3863000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eAge-associated skeletal muscle loss, a serious global health problem, causes undeniable distress to older people and communities. It can lead to disability and significant economic burden, with serious implications for people's quality of life and physical health. Relevant metabolic studies have shown that loss of skeletal muscle is closely associated with abnormalities in amino acid and fatty acid metabolism. A comprehensive study was conducted to delve into the factors associated with sarcopenia and the role of amino acid and fatty acid metabolism in the development of sarcopenia.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eIn this study, we screened 650 patients with skeletal muscle reduction (sarcopenia) from 2965 elderly (\u0026ge;\u0026thinsp;60 years old) patients in outpatient clinic and randomly selected 100 elderly patients for a survey study, which we categorized into sarcopenic and non-sarcopenic groups according to the diagnostic criteria of Asian Working Group on Sarcopenia (AWGS). Each group had 25 patients each and we collected their general information and retained their serum samples for testing.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eThe results of the study showed that there was a significant difference in body mass index (BMI), grip strength, and albumin levels between these two groups of samples (all p-values were less than 0.05). This suggests that these physiological indicators are associated with the development of sarcopenia. In addition, we found no significant differences in total cholesteroll (TC), triglycerides (TG), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C),levels between these two groups of samples. Upon further analysis of human serum metabolites, we found that arginine, histidine, leucine, palmitic acid, and carnitine levels were significantly different between the sarcopenia group and the non-sarcopenia group (all P-values were less than 0.05). These results reveal differences in amino acid and fatty acid metabolism between sarcopenia patients and non-sarcopenia patients.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eThere are differences in amino acid and fatty acid metabolism between sarcopenia and non-sarcopenia patients. By supplementing protein and essential amino acids, and reducing palmitic acid and carnitine levels, we can improve skeletal muscle mass and function, and enhance the quality of life in older adults. This finding provides new ideas and approaches for the prevention and treatment of age-related skeletal sarcopenia.\u003c/p\u003e","manuscriptTitle":"Abnormal fatty acid and amino acid metabolism in patients with sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 19:11:04","doi":"10.21203/rs.3.rs-3863000/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06d4433f-6582-4d6a-b491-ee58b2637dea","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T13:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 19:11:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3863000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3863000","identity":"rs-3863000","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}