Assessing the causal association between human blood metabolites and grip strength：a mendelian randomization analysis

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However, evidence about the causal role of metabolites in preventing sarcopenia is lacking. Systematic investigations of the causal relationships between blood metabolites and sarcopenia could help to identify novel targets for sarcopenia screening and prevention. Methods We conducted univariate and multivariable mendelian randomization (MR) analysis. The data for 486 human blood metabolites were obtained from a genome‑wide association study (GWAS) comprising 7824 participants. The GWAS data for grip strength were obtained from the UK Biobank consortium. GWAS data for type 2 diabetes and obesity from the FinnGen consortium. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Results Univariate MR analysis revealed four metabolites with causal effects on grip strength [phenylalanylserine: Beta = 1.04, 95% CI = 1.02–1.06, P = 0.0004; hyodeoxycholate: Beta = 1.03, 95% CI = 1.01–1.05, P = 0.01; 3-dehydrocarnitine: Beta = 0.89, 95% CI = 0.83 − 0.6 = 96, P = 0.003; X-11440: Beta = 1.05, 95% CI = 1.03–1.07, P = 0.00003]. However, after the multivariable MR analysis, only phenylalanylserine remained significantly associated with grip strength. Conclusions The phenylalanylserine is causatively associated with grip strength. The results provide novel insight into the underlying mechanisms of sarcopenia. Metabolites Grip strength Sarcopenia Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia is a syndrome characterized by muscle mass reduction, muscle strength decline, and/or impairment of physical function and often occurs during the process of aging. It increases the risk of mobility impairment, falls, and fractures; compromises people’s ability to perform daily activities; leads to disability, poor quality of life, and loss of independence; increases the risk of death; and leads to serious burdens on family healthcare and social public health expenditures[ 1 , 2 ]. As the aging process intensifies, approximately 50 million people worldwide are currently suffering from sarcopenia, and it is estimated that the number of sarcopenia patients will reach 500 million by 2050. Therefore, sarcopenia has received increased attention. The onset of sarcopenia is occult, and early diagnosis and intervention of sarcopenia are urgently needed. Recently, the European Working Group on Sarcopenia in Older People (EWGSOP) released a revised version of the previous criteria to guide the screening and diagnosis of sarcopenia[ 2 ]. The main parameters available for assessing and diagnosing sarcopenia include muscle mass, muscle strength, and physical function[ 3 , 4 ]. Muscle mass can be assessed by dual-energy X-ray absorption, CT, and MRI, but these devices are large, immobile, expensive, and lack a low mass measurement threshold, thus limiting their practical application[ 5 , 6 ]. Bioelectrical impedance analysis is a noninvasive and low-cost assessment method. It is suitable for large-scale population screening; however, the accuracy of its results depends on the algorithm used[ 7 , 8 ]. There are few accurate and effective methods for assessing muscle strength, among which upper limb grip strength is widely recognized as a common indicator. However, it is difficult to measure in patients with comorbidities, such as hand trauma, disability, and arthritis of the fingers. Although some guidelines recommend the use of the 5-time sit-to-stand test as an alternative method to measure muscle strength in patients with complications, this test is still difficult to perform in patients with disabilities and lower limb injuries. In fact, there are often multiple comorbidities in patients with sarcopenia. Therefore, in clinical practice, effective and feasible biomarkers are highly important for the early detection and diagnosis of sarcopenia. The occurrence of sarcopenia involves complex and diverse pathophysiological mechanisms[ 9 , 10 ]. There have been several studies on the potential relationships between metabolites and sarcopenia, indicating that certain metabolites are involved in the development of sarcopenia[ 11 , 12 ]. Previous studies have shown that testosterone[ 11 ], dehydroepiandrosterone sulfate (DHEAS)[ 12 ], growth hormone[ 13 ], and insulin-like growth factors[ 14 ]are associated with sarcopenia. However, most of these studies are observational studies. To the best of our knowledge, there is a lack of comprehensive and systematic research evaluating the causal relationship between blood metabolites and sarcopenia. Therefore, due to the limitations of traditional observational studies, it is impossible to determine the metabolite profile that promotes the development of sarcopenia based on existing evidence. This study used a Mendelian randomization (MR) approach, including univariable and multivariable MR analysis, to evaluate the causal relationship between blood metabolites and grip strength. Methods Study design We used a two-sample MR study to assess the causal relationship between blood metabolites and grip strength. The instrumental variables (IVs) must meet the following three conditions: 1) IVs are associated with outcome; 2) IVs are not associated with confounders; and 3) IVs influence outcomes only though exposures[ 15 ]. According to the above principle, horizontal pleiotropy should be avoided in MR studies. The independence of horizontal pleiotropy is an important assumption, and several statistical methods can be used to evaluate this assumption, such as MR‒Egger regression and the weighted median method. Multivariate MR analyses were conducted to determine the common risk factors for decreases in grip strength. The study overview can be found in Fig. 1 . Blood metabolite IVs We extracted IVs from the IEU GWAS database ( https://gwas.mrcieu.ac.uk/ ). Shin et al evaluated 486 metabolites from 7,824 European adult individuals. The 486 known metabolites were divided into eight groups (amino acids, carbohydrates, cofactors and vitamins, energy, lipids, nucleotides, peptides, and xenobiotic metabolism). Another 177 metabolites have not been conclusively identified[ 16 ]. Grip strength GWAS data The GWAS data for grip strength were obtained from the IEU GWAS database ( https://gwas.mrcieu.ac.uk/ ). We used genomic data from Ben Elsworth’s UK Biobank (ID: UKB-b-10215), which included 461,089 European individuals[ 17 ]. The details can be found in the MRC IEU UK Biobank GWAS pipeline version. ( https://doi.org/10.5523/bris.2fahpksont1zi26xosyamqo8rr ). The grip strength was measured by a Jamar J00105 hydraulic hand dynamometer. During the measurement, participants were asked to hold the device firmly, and the maximum grip strength was recorded. The grip strength measurement was repeated three times, and the average value was taken[ 18 ]. IV selection We selected the IVs according to the fundamental assumptions of MR for serum metabolites. First, we selected SNPs with P < 5×10 − 6 for each metabolite. When IVs were identified for each exposure, we used the IEU GWAS database to perform clumping and set a linkage disequilibrium threshold of r 2 10 was considered a valid IV. Then, based on the assumption that IVs influence outcomes only though exposures, we extracted exposure SNPs from outcome data. Univariable MR analysis Random-effect inverse variance weighted (IVW) was used as the major method to analyze the causal relationships between metabolites and grip strength. This method is a fixed-effect meta-analysis model that is most powerful when all IVs are valid(18). In this study, IVW was preliminarily conducted to screen for causal associations between metabolites and grip strength. The robust adjusted profile scores (RAPS), weighted median (WM), and MR‒Egger regression were used as supplementary methods. The RAPS is more robust to pleiotropy than the IVW. It has been shown to be more powerful than other MR methods when there are many weak IVs[ 15 ]. WM is a robust method that can provide consistent causal estimates even when up to 50% of the information contributing to the analysis comes from invalid IVs[ 19 ]. MR‒Egger regression is a method for MR analysis that can provide consistent causal estimates even when all genetic variants are invalid[ 20 ]. 2.6 Sensitivity analysis The Cochran-Q test was used to assess the heterogeneity of the causal effect of estimates obtained from multiple genetic variants. P < 0.05 indicated heterogeneity[ 21 ]. Horizontal pleiotropy was assessed by the Egger intercept test. A statistically significant intercept indicates that there is horizontal pleiotropy, which means that at least one genetic variant has a direct effect on the outcome other than through the exposure[ 20 ]. We further selected the leave-one-out (LOO) method to identify influential variants that may drive the overall causal effect estimate and to test the robustness of the causal effect estimate to different subsets of variants[ 22 ]. Direction validation Steiger’s test is a statistical test used to assess the directionality of the causal effect between exposure and outcome[ 23 ]. It is based on the assumption that a valid instrument should explain more variance in the exposure than the outcome, and it uses a statistical test to identify the strength of bidirectional effects[ 24 ]. It can help to avoid reverse causal instruments that may bias the MR estimate. In this study, Steiger’s test was used to examine the causal effect between blood metabolites and grip strength. If P < 0.05, there is evidence to support the assumption about the direction of causality. Multivariable MR analysis After the metabolites with causal relationships to grip strength were calculated by univariable MR, we used MVMR to adjust for common risk factors for decreased grip strength and sarcopenia, such as obesity and type 2 diabetes. In two-sample MR, it is important to ensure that the samples used for exposure and outcome data are independent and do not overlap. Since the outcome GWAS data were obtained from the UK Biobank, we used obesity and type 2 diabetes GWAS data from the Freeze 9 FinnGen consortium ( https://r9.finngen.fi ). The FinnGen consortium is a research project that aims to identify genotype-phenotype relationships in the Finnish population[ 25 ]. The obesity data included 21,375 obesity cases and 355,786 control cases. The type 2 diabetes data included 5,7698 cases and 30,8252 control cases. For the MVMR, we adjusted for multiple risk factors individually. The advantage of correcting for individual risk factors one by one is that confounding factors can be better controlled, thus reducing bias. In addition, individual corrections can help determine the contribution of each risk factor to the outcome. We conducted MVMR via the IVW method. All the analyses were conducted in R version 4.3.1, and “TwoSampleMR,” “mr. raps,” “MendelianRandomization” and “MVMR” R packages. We used publicly available GWAS summary data in this study, and no ethical approval was needed. Results Univariate MR analysis Following the IV selection steps, we selected SNPs with P < 5×10 − 6 for each metabolite, and 372 metabolites were retained for further analysis(Table S1 ). The harmonise data and the results of F-statistics were showed in Table S2. All of the F-statistics were greater than 10. According to the MR analysis, 28 metabolites were significantly associated with grip strength according to the IVW method. Among the 28 metabolites, 11 metabolites were unknown. The remaining metabolites belonged to distinct categories, including amino acids, carbohydrates, glycerophosphocholines, lipids, quaternary ammonium salts, short-chain keto acids and vitamins (Fig. 2 ). According to supplementary MR methods and sensitivity analyses, only four metabolites, namely, phenylalanylserine (beta = 1.04, 95% CI = 1.02–1.06, P = 0.0004), hyodeoxycholate (beta = 1.03, 95% CI = 1.01–1.05, P = 0.01), 3-dehydrocarnitine (beta = 0.89, 95% CI = 0.83 − 0.6 = 96, P = 0.003), and X-11440 (beta = 1.05, 95% CI = 1.03–1.07, P = 0.00003), had causal relationships with grip strength (Fig. 3 ). In short, as the screening criterion, the MR estimates obtained from MR‒Egger regression, WM, and the RAPS were consistent and had a significant causal relationship (Table 1 ). No heterogeneity was detected by the Cochran Q test, and low pleiotropy was detected by the intercept term of MR‒Egger (Table 1 ). According to the LOO analysis, there were no outliers that affected the results of the four metabolites after the gradual removal of SNPs (Table S3). Table 1 Sensitivity analysis for causal relationships between metabolites and hand grip strength Metabolites WM MR‒Egger RAPS Heterogeneity Pleiotropy P β (95%CI) P β (95%CI) P β (95%CI) P Q P Intercept Amino acid [3-(2-Oxopyrrolidin-1-yl) propyl] acetamide 0.07 0.05 (-0.004, 0.11) 0.03 0.10 (0.02, 0.18) 0.01 0.05 (0.01, 0.10) 0.11 21.75 0.19 -0.001 Phenylalanylserine 0.02 0.03 (0.01 0.06) 0.18 0.03 (0.01, 0.06) 0.001 0.04 (0.02, 0.06) 0.29 8.53 0.69 0.001 Leucylleucine 0.27 -0.04 (-0.12, 0.03) 0.6 0.08 (-0.21, 0.38) 0.047 -0.06 (-0.16, 0.05) 0.02 15.06 0.3 -0.004 Carbohydrates Glucose 0.56 -0.03 (-0.12, 0.07) 0.91 0.01 (-0.11, 0.13) 0.11 -0.08 (-0.18, 0.02) 0.82 16.86 0.11 -0.001 Glycerophosphocholines 2-Linoleoylglycerophosphocholine 0.37 0.03 (-0.04, 0.11) 0.56 0.05 (-0.12, 0.23) 0.02 0.07 (0.01, 0.13) 0.81 7.71 0.85 3.00E-04 Lipids 2-Hydroxystearate 0.03 -0.10 (-0.20, -0.01) 0.25 0-0.11 (-0.28, 0.07) 0.04 -0.08 (-0.16, -0.01) 0.13 27.22 0.75 3.00E-04 Hyodeoxycholate 0.02 0.03 (0.004, 0.06) 0.15 0.03 (0.01, 0.05) 0.001 0.03 (0.01, 0.05) 0.07 14.65 0.82 -4.00E-04 Propionylcarnitine 0.05 -0.07 (-0.14, 0.001) 0.15 -0.10 (-0.23, 0.03) 0.003 -0.10 (-0.16, -0.03) 0.22 37.22 0.59 5.00E-04 Docosapentaenoate 0.14 0.07 (-0.02, 0.16) 0.15 0.24 (0.03, 0.45) 0.07 0.09 (-0.01, 0.19) 0.26 2.68 0.28 -5.00E-03 1-Linoleoylglycerophosphoethanolamine 0.18 -0.04 (0.10, 0.02) 0.49 0.05 (-0.08, 0.18) 0.01 -0.06 (-0.11, -0.01) 0.53 5.15 0.13 -3.00E-03 2-Palmitoylglycerophosphocholine 0.04 0.10 (0.01, 0.20) 0.09 0.25 (-0.02, 0.52) 0.01 0.12 (0.03, 0.20) 0.04 23.3 0.29 -2.00E-03 Quaternary ammonium salts Choline 0.17 0.13 (-0.05, 0.30) 0.52 0.14 (-0.27, 0.55) 0.07 0.13 (-0.01, 0.27) 0.42 11.2 0.99 2.09E-07 Short-chain keto acids 3-Dehydrocarnitine 0.01 -0.12 (-0.21, -0.02) 0.77 -0.04 (-0.32, 0.23) 2E-04 -0.15 (-0.24, -0.07) 0.11 19.61 0.6 -1.00E-03 Vitamins and cofactors Pantothenate 0.3 -0.04 (-0.12, 0.04) 0.95 0.004 (-0.12, 0.13) 0.04 -0.07 (-0.13, -0.004) 0.1 19.76 0.16 -0.002 Unkown X-07765 0.25 -0.02 (-0.05, 0.01) 0.89 -0.004 (-0.06, 0.05) 0.04 -0.03 (-0.05, -0.001) 0.06 13.44 0.35 -0.002 X-10506 0.01 0.17 (0.04, 0.31) 0.19 0.20 (-0.05, 0.45) 0.03 0.13 (0.01, 0.25) 0.34 4.52 0.48 -0.002 X-11327 0.01 0.13 (0.03, 0.23) 0.4 0.11 (-0.14, 0.37) 0.08 0.08 (-0.01, 0.17) 5.09E-06 64.28 0.92 -0.0002 X-11440 8.00E-05 0.04 (0.02, 0.07) 0.05 0.04 (0.004, 0.07) 8.00E-05 0.05 (0.02, 0.07) 0.18 13.83 0.58 0.0005 X-11792 0.26 -0.01 (-0.04, 0.01) 0.04 -0.06 (-0.10, -0.01) 0.03 -0.02 (-0.05, -0.002) 0.42 8.18 0.22 0.002 X-11847 0.09 -0.02 (-0.05, 0.003) 0.65 -0.02 (-0.09, 0.05) 0.06 -0.02 (-0.05, 0.001) 0.79 2.43 0.89 -0.004 X-11905 0.17 -0.02 (-0.05, 0.01) 0.66 -0.01 (-0.07, 0.04) 0.03 -0.03 (-0.05, -0.004) 0.23 12.41 0.47 -0.001 X-12405 0.03 0.08 (0.01, 0.14) 0.63 0.08 (-0.20, 0.36) 0.02 0.08 (0.02, 0.14) 0.18 3.41 0.97 -0.0001 X-12556 0.01 -0.04 (-0.13, 0.04) 0.32 0.12 (-0.07, 0.32) 0.03 -0.07 (-0.14, -0.01) 0.57 9.53 0.05 -0.004 X-12704 0.13 0.03 (-0.01, 0.07) 0.89 -0.01 (-0.10, 0.08) 0.05 0.03 (-0.001, 0.06) 0.49 1.41 0.48 0.002 X-12786 0.03 -0.06 (-0.11, -0.01) 0.1 -0.08 (-0.15, -0.01) 0.01 -0.06 (-0.10, -0.02) 0.3 4.85 0.55 0.001 X-12798 0.02 -0.03 (-0.06, -0.01) 0.18 -0.03 (-0.07, 0.01) 0.02 -0.03 (-0.05, -0.003) 0.27 5.13 0.0004 0.75 X-13671 0.07 0.10 (-0.01, 0.22) 0.66 -0.09 (-0.49, 0.31) 0.2 0.09 (-0.05, 0.23) 0.03 16.48 0.31 0.003 X-14541 0.22 -0.02 (-0.05, 0.01) 0.99 -0.0004 (-0.09, 0.09) 0.04 -0.03 (-0.05, -0.001) 0.24 11.54 0.57 -0.001 CI, confidence interval; RAPS, robust adjusted profile scores; WM, weighted median In addition, we used Steiger’s test to validate the effect of metabolites on grip strength. The Steiger P values indicate that the identified causality is not biased by reverse causality (Table 2 ). Table 2 Steiger direction test from blood metabolites to grip strength. Exposure Hyodeoxycholate 3-Dehydrocarnitine Phenylalanylserine X-11440 Direction TRUE TRUE TRUE TRUE Steiger P 1.10E-96 1.43E-78 4.30E-64 2.10E-215 Multivariable MR analysis We used multivariable MR analysis adjusted for risk factors for sarcopenia, such as type 2 diabetes and obesity, and identified four metabolites with a causal relationship with grip strength in univariable MR. When adjusted for type 2 diabetes, phenylalanylserine (β = 0.04, 95% CI = 0.01–0.07, P = 0.01), 3-dehydrocarnitine (β = -0.12, 95% CI = -0.21- -0.03, P = 0.01), and X-11440 (β = 0.05, 95% CI = 0.02–0.08, P = 0.003) had causal relationships with grip strength. Phenylalanylserine (β = 0.04, 95% CI = 0.01–0.07, P = 0.02), hyodeoxycholate (β = 0.03, 95% CI = 0.003–0.06, P = 0.03), and X-11440 (β = 0.05, 95% CI = 0.02–0.08, P = 0.002) were used to adjust for obesity (Fig. 3 ). In summary, after correcting for type 2 diabetes and obesity, there was still a causal relationship between phenylalanylserine and grip strength. Discussion This study suggested that increased phenylalanylserine levels can increase grip strength. After adjusting for type 2 diabetes and obesity, the effects of 3-dehydrocarnitine and hyodeoxycholate on grip strength disappeared. This study is the first to evaluate the causal relationship between various blood metabolites and grip strength using both univariable and multivariable MR methods. The incidence of sarcopenia, which places a heavy burden on society, is increasing. Therefore, screening and prevention of sarcopenia have become extremely crucial. Due to the complex pathophysiology of sarcopenia, the pathogenesis of this disease is not fully understood. In addition to age-related sarcopenia, other factors, such as malnutrition, endocrine disorders, and the preservation of skeletal muscle, may also contribute to sarcopenia[ 26 – 28 ]. Furthermore, some studies have suggested that there may be potential biomarkers, such as glycosphingolipids, circulating C-terminal aggregation protein fragments, and N-terminal peptides of type III collagen, in the blood of sarcopenia patients[ 29 ]. Although existing data strongly suggest that metabolic disorders are associated with sarcopenia, the current evidence is insufficient to establish a causal role for circulating metabolites in the development of sarcopenia. Moreover, no biomarker can accurately describe the full characteristics of sarcopenia. Therefore, we designed this MR study to systematically evaluate the causal relationship between blood metabolites and sarcopenia, explore the metabolic factors underlying the pathogenesis of sarcopenia, establish complementary biomarkers, address the limitations of existing indicators, and provide new targets for the early identification and prevention of sarcopenia. This study suggested that phenylalanylserine has a protective effect on grip strength. Phenylalanylserine is a dipeptide composed of phenylalanine and serine, and few studies have focused on its role in sarcopenia. A study on amino acid metabolism in sarcopenic patients revealed that the levels of phenylalanine and serine were significantly reduced in elderly sarcopenic patients, and the level of serine remained significantly decreased after correction for various influencing factors[ 30 ]. Therefore, this study suggested that serine is a potential biomarker for sarcopenia. In a mouse model, serine deficiency can impair the function of skeletal muscle stem cells and progenitor cells. The level of serine in skeletal muscle decreases with age. Moreover, aging reduces the level of serine in the microenvironment of skeletal muscle progenitor cells and limits the biosynthetic capacity of serine in these cells[ 31 ]. However, the impact of serine on sarcopenia is still controversial. In another observational study, it was found that high baseline levels of branched-chain amino acids and nonessential amino acids (arginine, taurine, and serine) may increase the risk of sarcopenia in women but not in men or elderly individuals [ 32 ]. Most previous studies were observational. Due to the limitations of observational studies, they may only reflect a specific state at a certain time, and it is difficult to clarify the causal relationship between exposure factors and outcomes, which affects the clinical application of research results. Based on the results of this study, which suggest that phenylalanylserine has a protective effect on grip strength, further basic research and randomized controlled trials (RCTs) should be conducted to clarify the impact of phenylalanylserine on sarcopenia. Our study revealed that the influence of 3-dehydrocarnitine and hyodeoxycholate on grip strength disappeared after correcting for obesity and type 2 diabetes. A recent MR study also analyzed the causal relationship between blood metabolites and grip strength, and the results were similar to our study[ 33 ]. Univariate MR analysis revealed that 3-dehydrocarnitine and hyodeoxycholate were causally related to grip strength, but a previous study did not perform multivariate MR analysis to correct for the impact of obesity and diabetes. After correcting for obesity, there was no causal relationship between 3-dehydrocarnitine and handgrip strength. 3-Dehydrocarnitine is a metabolic derivative of carnitine. Previous studies have reported that 3-dehydrocarnitine levels are significantly associated with type 2 diabetes [ 34 ] and polycystic ovary syndrome [ 35 ]. We hypothesize that as a metabolic product of the lipid family, 3-dehydrocarnitine may be related to lipid metabolism-related diseases. Studies have shown that disorders of lipid metabolism are risk factors for sarcopenia[ 36 ]. Another metabolic product, hyodeoxycholate, had no causal relationship with grip strength after correcting for type 2 diabetes. Hyodeoxycholate is a bile acid, and studies suggest that it plays a key role in regulating glucose homeostasis and can reduce the risk of developing type 2 diabetes[ 37 ]. Type 2 diabetes is a risk factor for sarcopenia; diabetic patients experience accelerated muscle mass loss, and skeletal muscle damage is exacerbated[ 38 ]. Insulin resistance and oxidative stress may be the pathophysiological mechanisms that accelerate the occurrence of sarcopenia[ 39 ]. Therefore, we believe that 3-dehydrocarnitine and hyodeoxycholate have no direct causal relationship with sarcopenia, and we should be careful in drawing conclusions. In summary, our study revealed the protective effect of phenylalanylserine on handgrip strength through the use of GWAS data and MR analysis. This study has several advantages. First, in this MR study, we included a total of 482 metabolites for univariate and multivariate MR analysis. Because of the wide range of risk factors involved in sarcopenia, multivariate MR analysis helped to exclude potential confounders and was more conducive to explaining the causal relationship. Second, our study largely avoided reverse causation through the MR design. Specifically, a series of methods were employed to validate any violations of the MR assumptions, ensuring the reliability of the MR estimates. The consistent direction and similar magnitude across different MR models confirmed the robustness of the MR estimates. There are several limitations in our study. First, due to the limited number of SNPs reaching genome-wide significance, we relaxed the P threshold, which is a commonly used approach. The calculated F-statistic values all exceed 10, indicating that there is no weak instrumental variable. Moreover, the Steiger test was employed to validate the effective causal direction from exposure to outcome, supporting the validity of the relaxed P value. Second, the majority of participants in this study were Europeans, and the results of this study need to be further validated in other populations. Third, we only evaluated the causal relationship between metabolic products and handgrip strength. Handgrip strength is known to be an important indicator for assessing sarcopenia, and due to its simple implementation, handgrip strength has been recommended as an important measure for the diagnosis of malnutrition and the assessment of muscle mass in the Global Leadership Initiative on Malnutrition (GLIM) standards and the European Working Group on Sarcopenia in Older People (EWGSOP) guidelines[ 40 ]. However, handgrip strength can only reflect muscle mass and cannot completely replace sarcopenia, and further assessment of other related indicators of sarcopenia is needed. Finally, although the MR method performs well in causal inference, the results of MR studies should be further validated in RCTs to firmly establish the presence of a causal relationship. Conclusion This study demonstrated the causal relationship between blood metabolites and grip strength through MR analysis, providing preliminary evidence of the impact of circulating metabolic disorders on the risk of poor grip strength. Specifically, we found that phenylalanylserine might be a useful circulating metabolic biomarker associated with sarcopenia in clinical practice and could serve as a candidate molecule for the study of sarcopenia mechanisms. Declarations Ethics approval and consent to participate Only publicly available GWAS data were used in this study, and the Ethics approval and consent to participate could be available in the original GWAS study. Consent for publication We agree to the publication of our research paper by the publisher. Availability of data and material The data available statement is available upon request. The GWAS summary statistics for human blood metabolites are publicly available at https://gwas.mrcieu.ac.uk/. The GWAS summary statistics for grip strength were obtained from the IEU GWAS database (https://gwas.mrcieu.ac.uk/). The obesity and type 2 diabetes GWAS data were obtained from the Freeze 9 FinnGen consortium (https://r9.finngen.fi). Conpeting interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Funding This study was supported by the Foundation of Sichuan Provincial People’s Hospital (No. 2022QN14; granted to LZ) and the Cadre Health Care Project (No. 2021-208; granted to QX). Author contributions LZ, MZ, and YL designed the study. LZ, QX, SSL and CJW performed the statistical analyses. LZ performed the visualization. LZ wrote the first version of the draft. All the authors have read and approved the final manuscript. Acknowledgments We thank all the participants and investigators of the UK Biobank consortium, FinnGen consortium and Metabolomics GWAS. 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Dehydroepiandrosterone and age-related musculoskeletal diseases: Connections and therapeutic implications. Ageing Res Rev. 2020;62:101132. Bian A, Ma Y, Zhou X, Guo Y, Wang W, Zhang Y, Wang X. Association between sarcopenia and levels of growth hormone and insulin-like growth factor-1 in the elderly. BMC Musculoskelet Disord. 2020;21(1):214. Widajanti N, Soelistijo S, Hadi U, Thaha M, Aditiawardana, Widodo, et al. Association between Sarcopenia and Insulin-Like Growth Factor-1, Myostatin, and Insulin Resistance in Elderly Patients Undergoing Hemodialysis. J Aging Res. 2022;2022:1327332. Richmond RC, Davey Smith G. Mendelian Randomization: Concepts and Scope. Cold Spring Harb Perspect Med. 2022;12(1). Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50. Yuan S, Carter P, Vithayathil M, Kar S, Giovannucci E, Mason AM et al. Iron Status and Cancer Risk in UK Biobank: A Two-Sample Mendelian Randomization Study. Nutrients. 2020;12(2). Chen L, Fan Z, Lv G. Associations of muscle mass and grip strength with severe NAFLD: A prospective study of 333,295 UK Biobank participants. J Hepatol. 2022;77(5):1453–4. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. Kulinskaya E, Hoaglin DC, Bakbergenuly I, Newman J. A Q statistic with constant weights for assessing heterogeneity in meta-analysis. Res Synth Methods. 2021;12(6):711–30. Cai J, Li X, Wu S, Tian Y, Zhang Y, Wei Z, et al. Assessing the causal association between human blood metabolites and the risk of epilepsy. J Transl Med. 2022;20(1):437. Xue H, Pan W. Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data. PLoS Genet. 2020;16(11):e1009105. Liu B, Liu Z, Jiang T, Gu X, Yin X, Cai Z, et al. Univariable and multivariable Mendelian randomization study identified the key role of gut microbiota in immunotherapeutic toxicity. Eur J Med Res. 2024;29(1):161. Kurki MI, Karjalainen J, Palta P, Sipila TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18. Picca A, Calvani R, Cesari M, Landi F, Bernabei R, Coelho-Junior HJ, Marzetti E. Biomarkers of Physical Frailty and Sarcopenia: Coming up to the Place? Int J Mol Sci. 2020;21(16). Gupta P, Kumar S. Sarcopenia and Endocrine Ageing. Are They Related? Cureus. 2022;14(9):e28787. Delbono O, Rodrigues ACZ, Bonilla HJ, Messi ML. The emerging role of the sympathetic nervous system in skeletal muscle motor innervation and sarcopenia. Ageing Res Rev. 2021;67:101305. Lian R, Liu Q, Jiang G, Zhang X, Tang H, Lu J, Yang M. Blood biomarkers for sarcopenia: A systematic review and meta-analysis of diagnostic test accuracy studies. Ageing Res Rev. 2024;93:102148. Spitali P, Hettne K, Tsonaka R, Sabir E, Seyer A, Hemerik JBA, et al. Cross-sectional serum metabolomic study of multiple forms of muscular dystrophy. J Cell Mol Med. 2018;22(4):2442–8. Gheller BJ, Blum JE, Lim EW, Handzlik MK, Hannah Fong EH, Ko AC, et al. Extracellular serine and glycine are required for mouse and human skeletal muscle stem and progenitor cell function. Mol Metab. 2021;43:101106. Duan Y, Tao K, Fang Z, Lu Y. Possible-sarcopenic screening with disturbed plasma amino acid profile in the elderly. BMC Geriatr. 2023;23(1):427. Sha T, Wang N, Wei J, He H, Wang Y, Zeng C, Lei G. Genetically Predicted Levels of Serum Metabolites and Risk of Sarcopenia: A Mendelian Randomization Study. Nutrients. 2023;15(18). Sun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, et al. Early Prediction of Developing Type 2 Diabetes by Plasma Acylcarnitines: A Population-Based Study. Diabetes Care. 2016;39(9):1563–70. Zhu T, Goodarzi MO. Causes and Consequences of Polycystic Ovary Syndrome: Insights From Mendelian Randomization. J Clin Endocrinol Metab. 2022;107(3):e899–911. Al Saedi A, Debruin DA, Hayes A, Hamrick M. Lipid metabolism in sarcopenia. Bone. 2022;164:116539. Zheng X, Chen T, Zhao A, Ning Z, Kuang J, Wang S, et al. Hyocholic acid species as novel biomarkers for metabolic disorders. Nat Commun. 2021;12(1):1487. Cheng L, Sit JWH, Chan HYL, Choi KC, Cheung RKY, Wong MMH, et al. Sarcopenia risk and associated factors among Chinese community-dwelling older adults living alone. Sci Rep. 2021;11(1):22219. Spexoto MCB, Ramirez PC, de Oliveira Maximo R, Steptoe A, de Oliveira C, Alexandre TDS. European Working Group on Sarcopenia in Older People 2010 (EWGSOP1) and 2019 (EWGSOP2) criteria or slowness: which is the best predictor of mortality risk in older adults? Age Ageing. 2022;51(7). Sutil DV, Parentoni AN, Da Costa Teixeira LA, de Souza Moreira B, Leopoldino AAO, Mendonca VA, et al. Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). BMC Musculoskelet Disord. 2023;24(1):182. Additional Declarations No competing interests reported. Supplementary Files TableS.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4099640","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280464064,"identity":"58909e06-f9d1-4346-9914-0b8a9745dd05","order_by":0,"name":"Li Zeng","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zeng","suffix":""},{"id":280464065,"identity":"b8162162-8228-4e72-833b-7ec302cde82b","order_by":1,"name":"Qin Xie","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Xie","suffix":""},{"id":280464066,"identity":"71f3c362-6642-4506-b0ce-d45121c10ef7","order_by":2,"name":"Shasha Liu","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Liu","suffix":""},{"id":280464067,"identity":"f06c78ca-e8d7-49f4-b068-88ae0be94dd8","order_by":3,"name":"Caojie Wu","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Caojie","middleName":"","lastName":"Wu","suffix":""},{"id":280464068,"identity":"67396bad-0075-41a0-9549-9ba2c1aa60dc","order_by":4,"name":"Ying Li","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":280464069,"identity":"8c5716a9-9bcb-4845-ab85-d420d19331ae","order_by":5,"name":"Min Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-03-14 10:12:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4099640/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4099640/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53033348,"identity":"f3216092-f5aa-4231-ad72-deda6f77d1b5","added_by":"auto","created_at":"2024-03-19 20:21:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":628050,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the current Mendelian randomization (MR) study. Assumption 1, the instrumental variables related to the exposure; Assumption 2, instrumental variables must be independent of any confounding variables; Assumption 3, instrumental variables affect grip strength only through the exposure; SNP, single nucleotide polymorphisms; LD, linkage disequilibrium; IVW, inverse variance weighted; RAPS, robust adjusted profile scores; WM, weighted median; LOO, leave‑one‑out.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/c782fd3fbf2b17233848cecf.png"},{"id":53033344,"identity":"3fc6df41-6ae3-46f3-89ce-566879efeb77","added_by":"auto","created_at":"2024-03-19 20:21:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172065,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for the causal effect between metabolites and grip strength derived from inverse variance weighted (IVW) via the univariable Mendelian randomization (MR) method. CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/44da02e780f1a52f85d3320a.png"},{"id":53033346,"identity":"460ab76f-6e65-446e-acd3-ad7ab027abe3","added_by":"auto","created_at":"2024-03-19 20:21:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139856,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of the significant Mendelian randomization (MR) associations (P \u0026lt; 0.05) between metabolites and grip strength. SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/722f19dbeddff7304900d070.png"},{"id":53033345,"identity":"89f7d3dc-a903-4564-9f10-c158a812fbc4","added_by":"auto","created_at":"2024-03-19 20:21:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94244,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for the causal effect between metabolites and grip strength derived from inverse variance weighted (IVW) via the Mendelian randomization (MR) method. CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/f8ef260adea85ae3c765e215.png"},{"id":71851228,"identity":"933d0f0b-4971-46dd-bc42-14f92f92cd29","added_by":"auto","created_at":"2024-12-19 07:25:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1724925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/f6ca1024-b983-49fe-a5d9-76c0f821f985.pdf"},{"id":53033349,"identity":"f911acfd-d1ef-4849-8436-b0fd2d27e36c","added_by":"auto","created_at":"2024-03-19 20:21:48","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3851701,"visible":true,"origin":"","legend":"","description":"","filename":"TableS.zip","url":"https://assets-eu.researchsquare.com/files/rs-4099640/v1/991ea2b2e393214cf8894b22.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the causal association between human blood metabolites and grip strength：a mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is a syndrome characterized by muscle mass reduction, muscle strength decline, and/or impairment of physical function and often occurs during the process of aging. It increases the risk of mobility impairment, falls, and fractures; compromises people\u0026rsquo;s ability to perform daily activities; leads to disability, poor quality of life, and loss of independence; increases the risk of death; and leads to serious burdens on family healthcare and social public health expenditures[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the aging process intensifies, approximately 50\u0026nbsp;million people worldwide are currently suffering from sarcopenia, and it is estimated that the number of sarcopenia patients will reach 500\u0026nbsp;million by 2050. Therefore, sarcopenia has received increased attention.\u003c/p\u003e \u003cp\u003eThe onset of sarcopenia is occult, and early diagnosis and intervention of sarcopenia are urgently needed. Recently, the European Working Group on Sarcopenia in Older People (EWGSOP) released a revised version of the previous criteria to guide the screening and diagnosis of sarcopenia[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The main parameters available for assessing and diagnosing sarcopenia include muscle mass, muscle strength, and physical function[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Muscle mass can be assessed by dual-energy X-ray absorption, CT, and MRI, but these devices are large, immobile, expensive, and lack a low mass measurement threshold, thus limiting their practical application[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Bioelectrical impedance analysis is a noninvasive and low-cost assessment method. It is suitable for large-scale population screening; however, the accuracy of its results depends on the algorithm used[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There are few accurate and effective methods for assessing muscle strength, among which upper limb grip strength is widely recognized as a common indicator. However, it is difficult to measure in patients with comorbidities, such as hand trauma, disability, and arthritis of the fingers. Although some guidelines recommend the use of the 5-time sit-to-stand test as an alternative method to measure muscle strength in patients with complications, this test is still difficult to perform in patients with disabilities and lower limb injuries. In fact, there are often multiple comorbidities in patients with sarcopenia. Therefore, in clinical practice, effective and feasible biomarkers are highly important for the early detection and diagnosis of sarcopenia.\u003c/p\u003e \u003cp\u003eThe occurrence of sarcopenia involves complex and diverse pathophysiological mechanisms[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There have been several studies on the potential relationships between metabolites and sarcopenia, indicating that certain metabolites are involved in the development of sarcopenia[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have shown that testosterone[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], dehydroepiandrosterone sulfate (DHEAS)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], growth hormone[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and insulin-like growth factors[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]are associated with sarcopenia. However, most of these studies are observational studies. To the best of our knowledge, there is a lack of comprehensive and systematic research evaluating the causal relationship between blood metabolites and sarcopenia. Therefore, due to the limitations of traditional observational studies, it is impossible to determine the metabolite profile that promotes the development of sarcopenia based on existing evidence. This study used a Mendelian randomization (MR) approach, including univariable and multivariable MR analysis, to evaluate the causal relationship between blood metabolites and grip strength.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe used a two-sample MR study to assess the causal relationship between blood metabolites and grip strength. The instrumental variables (IVs) must meet the following three conditions: 1) IVs are associated with outcome; 2) IVs are not associated with confounders; and 3) IVs influence outcomes only though exposures[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to the above principle, horizontal pleiotropy should be avoided in MR studies. The independence of horizontal pleiotropy is an important assumption, and several statistical methods can be used to evaluate this assumption, such as MR‒Egger regression and the weighted median method. Multivariate MR analyses were conducted to determine the common risk factors for decreases in grip strength. The study overview can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood metabolite IVs\u003c/h2\u003e \u003cp\u003eWe extracted IVs from the IEU GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Shin et al evaluated 486 metabolites from 7,824 European adult individuals. The 486 known metabolites were divided into eight groups (amino acids, carbohydrates, cofactors and vitamins, energy, lipids, nucleotides, peptides, and xenobiotic metabolism). Another 177 metabolites have not been conclusively identified[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGrip strength GWAS data\u003c/h2\u003e \u003cp\u003eThe GWAS data for grip strength were obtained from the IEU GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used genomic data from Ben Elsworth\u0026rsquo;s UK Biobank (ID: UKB-b-10215), which included 461,089 European individuals[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The details can be found in the MRC IEU UK Biobank GWAS pipeline version. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5523/bris.2fahpksont1zi26xosyamqo8rr\u003c/span\u003e\u003cspan address=\"10.5523/bris.2fahpksont1zi26xosyamqo8rr\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe grip strength was measured by a Jamar J00105 hydraulic hand dynamometer. During the measurement, participants were asked to hold the device firmly, and the maximum grip strength was recorded. The grip strength measurement was repeated three times, and the average value was taken[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIV selection\u003c/h2\u003e \u003cp\u003eWe selected the IVs according to the fundamental assumptions of MR for serum metabolites. First, we selected SNPs with P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e for each metabolite. When IVs were identified for each exposure, we used the IEU GWAS database to perform clumping and set a linkage disequilibrium threshold of \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 at a 500-kb distance. Moreover, to avoid the presence of weak IVs, the strength of each IV was calculated by the F-statistic. F\u0026thinsp;\u0026gt;\u0026thinsp;10 was considered a valid IV. Then, based on the assumption that IVs influence outcomes only though exposures, we extracted exposure SNPs from outcome data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eUnivariable MR analysis\u003c/h2\u003e \u003cp\u003eRandom-effect inverse variance weighted (IVW) was used as the major method to analyze the causal relationships between metabolites and grip strength. This method is a fixed-effect meta-analysis model that is most powerful when all IVs are valid(18). In this study, IVW was preliminarily conducted to screen for causal associations between metabolites and grip strength. The robust adjusted profile scores (RAPS), weighted median (WM), and MR‒Egger regression were used as supplementary methods. The RAPS is more robust to pleiotropy than the IVW. It has been shown to be more powerful than other MR methods when there are many weak IVs[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. WM is a robust method that can provide consistent causal estimates even when up to 50% of the information contributing to the analysis comes from invalid IVs[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. MR‒Egger regression is a method for MR analysis that can provide consistent causal estimates even when all genetic variants are invalid[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe Cochran-Q test was used to assess the heterogeneity of the causal effect of estimates obtained from multiple genetic variants. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated heterogeneity[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Horizontal pleiotropy was assessed by the Egger intercept test. A statistically significant intercept indicates that there is horizontal pleiotropy, which means that at least one genetic variant has a direct effect on the outcome other than through the exposure[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We further selected the leave-one-out (LOO) method to identify influential variants that may drive the overall causal effect estimate and to test the robustness of the causal effect estimate to different subsets of variants[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDirection validation\u003c/h2\u003e \u003cp\u003eSteiger\u0026rsquo;s test is a statistical test used to assess the directionality of the causal effect between exposure and outcome[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It is based on the assumption that a valid instrument should explain more variance in the exposure than the outcome, and it uses a statistical test to identify the strength of bidirectional effects[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It can help to avoid reverse causal instruments that may bias the MR estimate. In this study, Steiger\u0026rsquo;s test was used to examine the causal effect between blood metabolites and grip strength. If \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, there is evidence to support the assumption about the direction of causality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable MR analysis\u003c/h2\u003e \u003cp\u003eAfter the metabolites with causal relationships to grip strength were calculated by univariable MR, we used MVMR to adjust for common risk factors for decreased grip strength and sarcopenia, such as obesity and type 2 diabetes. In two-sample MR, it is important to ensure that the samples used for exposure and outcome data are independent and do not overlap. Since the outcome GWAS data were obtained from the UK Biobank, we used obesity and type 2 diabetes GWAS data from the Freeze 9 FinnGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.finngen.fi\u003c/span\u003e\u003cspan address=\"https://r9.finngen.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The FinnGen consortium is a research project that aims to identify genotype-phenotype relationships in the Finnish population[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The obesity data included 21,375 obesity cases and 355,786 control cases. The type 2 diabetes data included 5,7698 cases and 30,8252 control cases.\u003c/p\u003e \u003cp\u003eFor the MVMR, we adjusted for multiple risk factors individually. The advantage of correcting for individual risk factors one by one is that confounding factors can be better controlled, thus reducing bias. In addition, individual corrections can help determine the contribution of each risk factor to the outcome. We conducted MVMR via the IVW method.\u003c/p\u003e \u003cp\u003eAll the analyses were conducted in R version 4.3.1, and \u0026ldquo;TwoSampleMR,\u0026rdquo; \u0026ldquo;mr. raps,\u0026rdquo; \u0026ldquo;MendelianRandomization\u0026rdquo; and \u0026ldquo;MVMR\u0026rdquo; R packages. We used publicly available GWAS summary data in this study, and no ethical approval was needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate MR analysis\u003c/h2\u003e \u003cp\u003eFollowing the IV selection steps, we selected SNPs with P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e for each metabolite, and 372 metabolites were retained for further analysis(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The harmonise data and the results of F-statistics were showed in Table S2. All of the F-statistics were greater than 10. According to the MR analysis, 28 metabolites were significantly associated with grip strength according to the IVW method. Among the 28 metabolites, 11 metabolites were unknown. The remaining metabolites belonged to distinct categories, including amino acids, carbohydrates, glycerophosphocholines, lipids, quaternary ammonium salts, short-chain keto acids and vitamins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to supplementary MR methods and sensitivity analyses, only four metabolites, namely, phenylalanylserine (beta\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004), hyodeoxycholate (beta\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), 3-dehydrocarnitine (beta\u0026thinsp;=\u0026thinsp;0.89, 95% CI\u0026thinsp;=\u0026thinsp;0.83\u0026thinsp;\u0026minus;\u0026thinsp;0.6\u0026thinsp;=\u0026thinsp;96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and X-11440 (beta\u0026thinsp;=\u0026thinsp;1.05, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00003), had causal relationships with grip strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In short, as the screening criterion, the MR estimates obtained from MR‒Egger regression, WM, and the RAPS were consistent and had a significant causal relationship (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No heterogeneity was detected by the Cochran Q test, and low pleiotropy was detected by the intercept term of MR‒Egger (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the LOO analysis, there were no outliers that affected the results of the four metabolites after the gradual removal of SNPs (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis for causal relationships between metabolites and hand grip strength\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMR‒Egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRAPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePleiotropy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[3-(2-Oxopyrrolidin-1-yl) propyl] acetamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (-0.004, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10 (0.02, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.01, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanylserine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.01 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd 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\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03 (-0.12, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01 (-0.11, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08 (-0.18, 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycerophosphocholines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Linoleoylglycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (-0.04, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05 (-0.12, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07 (0.01, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Hydroxystearate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10 (-0.20, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0-0.11 (-0.28, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08 (-0.16, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyodeoxycholate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.004, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03 (0.01, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03 (0.01, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropionylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07 (-0.14, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (-0.23, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.10 (-0.16, -0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocosapentaenoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (-0.02, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24 (0.03, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09 (-0.01, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-5.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-Linoleoylglycerophosphoethanolamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (0.10, 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05 (-0.08, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (-0.11, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Palmitoylglycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.01, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25 (-0.02, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12 (0.03, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuaternary ammonium salts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (-0.05, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14 (-0.27, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13 (-0.01, 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.09E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShort-chain keto acids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Dehydrocarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.12 (-0.21, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (-0.32, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.15 (-0.24, -0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVitamins and cofactors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePantothenate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (-0.12, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004 (-0.12, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07 (-0.13, -0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnkown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-07765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.05, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.004 (-0.06, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (-0.05, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-10506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (0.04, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20 (-0.05, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13 (0.01, 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-11327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.03, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11 (-0.14, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08 (-0.01, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.09E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e64.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-11440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.02, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04 (0.004, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.02, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-11792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (-0.04, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06 (-0.10, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (-0.05, -0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-11847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.05, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (-0.09, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (-0.05, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-11905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.05, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (-0.07, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (-0.05, -0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-12405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.01, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08 (-0.20, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08 (0.02, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-12556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (-0.13, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12 (-0.07, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07 (-0.14, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-12704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (-0.01, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (-0.10, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03 (-0.001, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-12786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (-0.11, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08 (-0.15, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (-0.10, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-12798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03 (-0.06, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (-0.07, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (-0.05, -0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-13671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (-0.01, 0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09 (-0.49, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09 (-0.05, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-14541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.05, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0004 (-0.09, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (-0.05, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCI, confidence interval; RAPS, robust adjusted profile scores; WM, weighted median\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, we used Steiger\u0026rsquo;s test to validate the effect of metabolites on grip strength. The Steiger P values indicate that the identified causality is not biased by reverse causality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSteiger direction test from blood metabolites to grip strength.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHyodeoxycholate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3-Dehydrocarnitine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhenylalanylserine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX-11440\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSteiger P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10E-96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43E-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.30E-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10E-215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable MR analysis\u003c/h2\u003e \u003cp\u003eWe used multivariable MR analysis adjusted for risk factors for sarcopenia, such as type 2 diabetes and obesity, and identified four metabolites with a causal relationship with grip strength in univariable MR. When adjusted for type 2 diabetes, phenylalanylserine (β\u0026thinsp;=\u0026thinsp;0.04, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), 3-dehydrocarnitine (β = -0.12, 95% CI = -0.21- -0.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and X-11440 (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI\u0026thinsp;=\u0026thinsp;0.02\u0026ndash;0.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) had causal relationships with grip strength. Phenylalanylserine (β\u0026thinsp;=\u0026thinsp;0.04, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), hyodeoxycholate (β\u0026thinsp;=\u0026thinsp;0.03, 95% CI\u0026thinsp;=\u0026thinsp;0.003\u0026ndash;0.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and X-11440 (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI\u0026thinsp;=\u0026thinsp;0.02\u0026ndash;0.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were used to adjust for obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, after correcting for type 2 diabetes and obesity, there was still a causal relationship between phenylalanylserine and grip strength.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study suggested that increased phenylalanylserine levels can increase grip strength. After adjusting for type 2 diabetes and obesity, the effects of 3-dehydrocarnitine and hyodeoxycholate on grip strength disappeared. This study is the first to evaluate the causal relationship between various blood metabolites and grip strength using both univariable and multivariable MR methods.\u003c/p\u003e \u003cp\u003eThe incidence of sarcopenia, which places a heavy burden on society, is increasing. Therefore, screening and prevention of sarcopenia have become extremely crucial. Due to the complex pathophysiology of sarcopenia, the pathogenesis of this disease is not fully understood. In addition to age-related sarcopenia, other factors, such as malnutrition, endocrine disorders, and the preservation of skeletal muscle, may also contribute to sarcopenia[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, some studies have suggested that there may be potential biomarkers, such as glycosphingolipids, circulating C-terminal aggregation protein fragments, and N-terminal peptides of type III collagen, in the blood of sarcopenia patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although existing data strongly suggest that metabolic disorders are associated with sarcopenia, the current evidence is insufficient to establish a causal role for circulating metabolites in the development of sarcopenia. Moreover, no biomarker can accurately describe the full characteristics of sarcopenia. Therefore, we designed this MR study to systematically evaluate the causal relationship between blood metabolites and sarcopenia, explore the metabolic factors underlying the pathogenesis of sarcopenia, establish complementary biomarkers, address the limitations of existing indicators, and provide new targets for the early identification and prevention of sarcopenia.\u003c/p\u003e \u003cp\u003eThis study suggested that phenylalanylserine has a protective effect on grip strength. Phenylalanylserine is a dipeptide composed of phenylalanine and serine, and few studies have focused on its role in sarcopenia. A study on amino acid metabolism in sarcopenic patients revealed that the levels of phenylalanine and serine were significantly reduced in elderly sarcopenic patients, and the level of serine remained significantly decreased after correction for various influencing factors[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, this study suggested that serine is a potential biomarker for sarcopenia. In a mouse model, serine deficiency can impair the function of skeletal muscle stem cells and progenitor cells. The level of serine in skeletal muscle decreases with age. Moreover, aging reduces the level of serine in the microenvironment of skeletal muscle progenitor cells and limits the biosynthetic capacity of serine in these cells[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the impact of serine on sarcopenia is still controversial. In another observational study, it was found that high baseline levels of branched-chain amino acids and nonessential amino acids (arginine, taurine, and serine) may increase the risk of sarcopenia in women but not in men or elderly individuals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Most previous studies were observational. Due to the limitations of observational studies, they may only reflect a specific state at a certain time, and it is difficult to clarify the causal relationship between exposure factors and outcomes, which affects the clinical application of research results. Based on the results of this study, which suggest that phenylalanylserine has a protective effect on grip strength, further basic research and randomized controlled trials (RCTs) should be conducted to clarify the impact of phenylalanylserine on sarcopenia.\u003c/p\u003e \u003cp\u003eOur study revealed that the influence of 3-dehydrocarnitine and hyodeoxycholate on grip strength disappeared after correcting for obesity and type 2 diabetes. A recent MR study also analyzed the causal relationship between blood metabolites and grip strength, and the results were similar to our study[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Univariate MR analysis revealed that 3-dehydrocarnitine and hyodeoxycholate were causally related to grip strength, but a previous study did not perform multivariate MR analysis to correct for the impact of obesity and diabetes. After correcting for obesity, there was no causal relationship between 3-dehydrocarnitine and handgrip strength. 3-Dehydrocarnitine is a metabolic derivative of carnitine. Previous studies have reported that 3-dehydrocarnitine levels are significantly associated with type 2 diabetes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and polycystic ovary syndrome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. We hypothesize that as a metabolic product of the lipid family, 3-dehydrocarnitine may be related to lipid metabolism-related diseases. Studies have shown that disorders of lipid metabolism are risk factors for sarcopenia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Another metabolic product, hyodeoxycholate, had no causal relationship with grip strength after correcting for type 2 diabetes. Hyodeoxycholate is a bile acid, and studies suggest that it plays a key role in regulating glucose homeostasis and can reduce the risk of developing type 2 diabetes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Type 2 diabetes is a risk factor for sarcopenia; diabetic patients experience accelerated muscle mass loss, and skeletal muscle damage is exacerbated[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Insulin resistance and oxidative stress may be the pathophysiological mechanisms that accelerate the occurrence of sarcopenia[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, we believe that 3-dehydrocarnitine and hyodeoxycholate have no direct causal relationship with sarcopenia, and we should be careful in drawing conclusions.\u003c/p\u003e \u003cp\u003eIn summary, our study revealed the protective effect of phenylalanylserine on handgrip strength through the use of GWAS data and MR analysis. This study has several advantages. First, in this MR study, we included a total of 482 metabolites for univariate and multivariate MR analysis. Because of the wide range of risk factors involved in sarcopenia, multivariate MR analysis helped to exclude potential confounders and was more conducive to explaining the causal relationship. Second, our study largely avoided reverse causation through the MR design. Specifically, a series of methods were employed to validate any violations of the MR assumptions, ensuring the reliability of the MR estimates. The consistent direction and similar magnitude across different MR models confirmed the robustness of the MR estimates.\u003c/p\u003e \u003cp\u003eThere are several limitations in our study. First, due to the limited number of SNPs reaching genome-wide significance, we relaxed the P threshold, which is a commonly used approach. The calculated F-statistic values all exceed 10, indicating that there is no weak instrumental variable. Moreover, the Steiger test was employed to validate the effective causal direction from exposure to outcome, supporting the validity of the relaxed P value. Second, the majority of participants in this study were Europeans, and the results of this study need to be further validated in other populations. Third, we only evaluated the causal relationship between metabolic products and handgrip strength. Handgrip strength is known to be an important indicator for assessing sarcopenia, and due to its simple implementation, handgrip strength has been recommended as an important measure for the diagnosis of malnutrition and the assessment of muscle mass in the Global Leadership Initiative on Malnutrition (GLIM) standards and the European Working Group on Sarcopenia in Older People (EWGSOP) guidelines[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, handgrip strength can only reflect muscle mass and cannot completely replace sarcopenia, and further assessment of other related indicators of sarcopenia is needed. Finally, although the MR method performs well in causal inference, the results of MR studies should be further validated in RCTs to firmly establish the presence of a causal relationship.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated the causal relationship between blood metabolites and grip strength through MR analysis, providing preliminary evidence of the impact of circulating metabolic disorders on the risk of poor grip strength. Specifically, we found that phenylalanylserine might be a useful circulating metabolic biomarker associated with sarcopenia in clinical practice and could serve as a candidate molecule for the study of sarcopenia mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eOnly publicly available GWAS data were used in this study, and the Ethics approval and consent to participate could be available in the original GWAS study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eWe agree to the publication of our research paper by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data available statement is available upon request. The GWAS summary statistics for human blood metabolites are publicly available at https://gwas.mrcieu.ac.uk/. The GWAS summary statistics for grip strength were obtained from the IEU GWAS database (https://gwas.mrcieu.ac.uk/). The obesity and type 2 diabetes GWAS data were obtained from the Freeze 9 FinnGen consortium (https://r9.finngen.fi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConpeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study was supported by the Foundation of Sichuan Provincial People\u0026rsquo;s Hospital (No. 2022QN14; granted to LZ) and the Cadre Health Care Project (No. 2021-208; granted to QX).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ, MZ, and YL designed the study. LZ, QX, SSL and CJW performed the statistical analyses. LZ performed the visualization.\u0026nbsp;LZ\u0026nbsp;wrote the first version of the draft. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e We thank all the participants and investigators of the UK Biobank consortium, FinnGen consortium and Metabolomics GWAS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, Celis-Morales C. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Sayer AA, Sarcopenia. Lancet. 2019;393(10191):2636\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, et al. 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Nutrients. 2023;15(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, et al. Early Prediction of Developing Type 2 Diabetes by Plasma Acylcarnitines: A Population-Based Study. Diabetes Care. 2016;39(9):1563\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu T, Goodarzi MO. Causes and Consequences of Polycystic Ovary Syndrome: Insights From Mendelian Randomization. J Clin Endocrinol Metab. 2022;107(3):e899\u0026ndash;911.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Saedi A, Debruin DA, Hayes A, Hamrick M. Lipid metabolism in sarcopenia. Bone. 2022;164:116539.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X, Chen T, Zhao A, Ning Z, Kuang J, Wang S, et al. Hyocholic acid species as novel biomarkers for metabolic disorders. Nat Commun. 2021;12(1):1487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng L, Sit JWH, Chan HYL, Choi KC, Cheung RKY, Wong MMH, et al. Sarcopenia risk and associated factors among Chinese community-dwelling older adults living alone. Sci Rep. 2021;11(1):22219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpexoto MCB, Ramirez PC, de Oliveira Maximo R, Steptoe A, de Oliveira C, Alexandre TDS. European Working Group on Sarcopenia in Older People 2010 (EWGSOP1) and 2019 (EWGSOP2) criteria or slowness: which is the best predictor of mortality risk in older adults? Age Ageing. 2022;51(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutil DV, Parentoni AN, Da Costa Teixeira LA, de Souza Moreira B, Leopoldino AAO, Mendonca VA, et al. Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). BMC Musculoskelet Disord. 2023;24(1):182.\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":"Metabolites, Grip strength, Sarcopenia, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4099640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4099640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic disturbance has been reported in patients with sarcopenia. However, evidence about the causal role of metabolites in preventing sarcopenia is lacking. Systematic investigations of the causal relationships between blood metabolites and sarcopenia could help to identify novel targets for sarcopenia screening and prevention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted univariate and multivariable mendelian randomization (MR) analysis. The data for 486 human blood metabolites were obtained from a genome‑wide association study (GWAS) comprising 7824 participants. The GWAS data for grip strength were obtained from the UK Biobank consortium. GWAS data for type 2 diabetes and obesity from the FinnGen consortium. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate MR analysis revealed four metabolites with causal effects on grip strength [phenylalanylserine: Beta\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004; hyodeoxycholate: Beta\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; 3-dehydrocarnitine: Beta\u0026thinsp;=\u0026thinsp;0.89, 95% CI\u0026thinsp;=\u0026thinsp;0.83\u0026thinsp;\u0026minus;\u0026thinsp;0.6\u0026thinsp;=\u0026thinsp;96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; X-11440: Beta\u0026thinsp;=\u0026thinsp;1.05, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00003]. However, after the multivariable MR analysis, only phenylalanylserine remained significantly associated with grip strength.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe phenylalanylserine is causatively associated with grip strength. The results provide novel insight into the underlying mechanisms of sarcopenia.\u003c/p\u003e","manuscriptTitle":"Assessing the causal association between human blood metabolites and grip strength：a mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 20:21:41","doi":"10.21203/rs.3.rs-4099640/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":"43473835-6c92-48ab-86d6-702cc7704597","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-19T07:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 20:21:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4099640","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4099640","identity":"rs-4099640","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}