
==== Front
Diabetol Metab Syndr
Diabetol Metab Syndr
Diabetology & Metabolic Syndrome
1758-5996
BioMed Central London

39285470
1465
10.1186/s13098-024-01465-y
Research
Lipid metabolites and sarcopenia-related traits: a Mendelian randomization study
Liu Jianping
Wang Sufang
Shen Yuan
Shi Haicun
Han Lijian hljdyx183@163.com

grid.459351.f Department of Neurology, Yancheng Third People’s Hospital (The Sixth Affiliated Hospital of Nantong University, The Yancheng School of Clinical Medicine of Nanjing Medical University, The affiliated hospital of Jiangsu Vocational College of Medicine), Yancheng, Jiangsu China
16 9 2024
16 9 2024
2024
16 23123 4 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objective

To explore the influence of lipid metabolism on the risk of sarcopenia.

Methods

Two-sample Mendelian randomization (MR) analysis was used to determine causality. A total of 179 lipid metabolism data points were used for exposure, and the data were obtained from a plasma lipid metabolite study of 7174 participants. The total muscle mass and total muscle strength, as well as the muscle strength and muscle mass of different sex groups, were selected as the relevant traits of sarcopenia. Data for outcomes were obtained from the UK Biobank, and sample sizes ranged from 135 468 to 450 243. Inverse-variance weighted (IVW), as the main method for evaluating the causal relationship between lipid metabolites and sarcopenia, uses the false discovery rate (FDR) for multiple comparisons and conducts heterogeneity, pleiotropy, and reverse causality tests.

Results

Twenty-seven lipid metabolites, mainly phosphatidylcholine, phosphatidylethanolamine, ceramide, triacylglycerol, sphingomyelin, and sterol ester, were found to be associated with the risk of sarcopenia. Ceramide (d40:1), ceramide (d40:2), and sterol ester are risk factors for decreased muscle mass and strength. There is a positive causal relationship between various phosphatidylcholine lipids and muscle mass and strength. Sphingomyelin (d42:2) is a protective factor for total muscle strength and female muscle strength. There are inconsistent effects between different lipid metabolites, triacylglycerol, and muscle strength and muscle mass.

Conclusions

There was a causal relationship between 27 lipid metabolites and sarcopenia traits, and targeting specific lipid metabolites may benefit sarcopenia diagnosis, disease assessment, and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13098-024-01465-y.

Keywords

Sarcopenia
Muscle mass
Muscle strength
Mendelian randomization, lipid metabolites
Special Funds for Science Development of the Clinical Teaching Hospitals of Jiangsu Vocational College of MedicineNo. 20229107 Clinical Medicine Project of Nantong UniversityNo. 2023JZ026 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Sarcopenia is a debilitating disease associated with a loss of muscle mass and strength, affecting 10%~16% of elderly people worldwide [1] and significantly increasing the risk of swallowing difficulties, cognitive impairment, fractures, falls, hospitalization, and all-cause mortality in the elderly population [2]. With global population growth and extended life expectancy, sarcopenia has become an important public health issue.

The etiology of sarcopenia is complex, mainly involving an imbalance between synthetic and catabolic metabolic factors, and factors such as aging, malnutrition, and insufficient physical activity may further exacerbate this imbalance [3]. Sarcopenia is characterized by a decrease in stem cells and terminal differentiated muscle fibers, which are replaced by fat and fibrous tissue [4]. Therefore, fat infiltration in skeletal muscle is one of the signs of muscle atrophy and aging. In recent years, increasing evidence has shown that lipid metabolism factors play important roles in regulating skeletal muscle mass and function [5]. Gender differences are also an important factor in sarcopenia. Recent studies in mice have observed that the mechanism of sarcopenia occurs differently in males and females [6], which may be related to differences in metabolic levels between sexes. Multiple clinical studies have shown a correlation between lipid metabolites and sarcopenia, but there is still a lack of exploration of the causal relationship between lipid metabolites and sarcopenia.

Mendelian randomization (MR) is an epidemiological method that uses single nucleotide polymorphisms as instrumental variables (IVs) instead of target exposure variables to evaluate the causal association between exposure and specific outcomes [7]. The Mendelian randomization method is mainly based on the following three assumptions: [8] (1) instrumental variables and exposure are related; (2) instrumental variables are not associated with any confounding factors; and (3) the instrumental variables only affect outcomes through exposure. Currently, multiple studies have used Mendelian randomization to explore the causal associations between sarcopenia and osteoporosis [9], serum 25-hydroxyvitamin D levels [10] and other conditions. This study applied MR to explore the causal relationship between lipid metabolites and the risk of sarcopenia from the perspective of genetic variation, screened for potential lipid metabolite metabolites related to the onset of sarcopenia, and searched for disease activity evaluation, diagnosis, and treatment targets.

Object and methods

Data sources

The GWAS data related to lipid metabolites were obtained from a plasma lipid metabolomics study of 7174 participants, which used Lipotype GmbH (Dresden, Germany) to perform mass spectrometry-based lipid metabolite analysis and identification on a population from the GeneRISK cohort, retaining a high signal-to-noise ratio (> 5) of lipid metabolites that was at least 5 times greater than that of the corresponding blank samples. Genotyping was performed using the HumanCoreExome BeadChip from Illumina, Inc. (San Diego, CA, USA), and SNP heritability estimates for each lipid metabolite were calculated using biMM. In the end, the study detected 179 lipid metabolites belonging to 13 lipid categories, covering 4 main categories: glycerolipids, glycerophospholipids, sphingolipids, and sterols. The relevant GWAS summary statistical data were saved in the GWAS directory (number GCST90277238-GCST90277416) [11].

Muscle mass and strength are important indicators for measuring muscle function. According to previous related studies [12], this study used these parameters as relevant indicators for sarcopenia. The GWAS analysis of total muscle mass data included 450,243 participants from the UK biobank, mainly using the TanitaBC418ma body fat analyzer, and validated its measurement accuracy using the DEXA method [13]. The data on hand grip strength (i.e., muscle strength) came from a study related to muscle weakness, which included 256,523 European people aged 60 and above. A JamarJ00105 hydraulic hand force meter was used to measure the maximum hand grip strength, which was recorded in whole kilograms. Men with a grip strength < 30 kg and women with a grip strength < 20 kg were considered to have low grip strength [14]. Detailed GWAS data on muscle mass and strength for different sexes can be found in Supplementary Table S1. The genetic data used in this study met ethical requirements and passed local ethical review, providing publicly available research data.

Selection of instrumental variables

Instrumental variables were selected through the following steps:① screening for instrumental variables related to exposure, i.e., extracting SNPs with P < 5 × 10− 6 from GWAS data of lipid metabolites; ②removing SNPs with linkage effects from the above instrumental variables; and setting a threshold of r2 < 0.01 and kb < 5000. ③The corresponding instrumental variables were extracted from the outcome data, palindromic SNPs were removed, and instrumental variables related to the outcome were excluded (P < 5 × 10− 5) [15]. ④The F-statistic was calculated to test the strength of the SNPs obtained above, and weak instrumental variables with F < 10 were removed. The relevant calculation formula for F is as follows [16]:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{gathered}\:F = \frac{{{R^2} \times \:\left( {N - K - 1} \right)}}{{K\left( {1 - {R^2}} \right)}},\,\: \hfill \\{R^2} = \frac{{2 \times \:EAF \times \:\left( {1 - EAF} \right) \times \:{\beta ^2}}}{{2 \times \:EAF \times \:\left( {1 - EAF} \right) \times \:{\beta ^2} + 2 \times \:EAF \times \:\left( {1 - EAF} \right) \times \:N \times \:S{E^2}}}, \hfill \\ \end{gathered}$$\end{document}

Within this formula, N denotes the sample size for the exposure, K represents the number of instrumental variables, EAF is the effect allele frequency, b is the effect size, and SE is the standard error of the effect size.

Two-sample Mendelian randomization

Methods such as inverse variance weighted (IVW), weighted media (WM), and MR‒Egger were used for analysis. The IVW method weights the causal effects of different genetic variations on traits and then combines the weighted estimated effects to estimate the causal effects of genetic variations on traits. When each genetic variation conforms to the assumption of instrumental variables, IVW will provide the most accurate results [17]. The IVW serves as the main indicator for evaluating causal effects, with a PIVW<0.05 indicating a potential causal relationship between related lipid metabolites and sarcopenia. Other methods are used to assist in evaluating MR effects, with different methods having varying effects. The direction of the β value is consistent with that of IVW, indicating the robustness of the results. Using FDR correction to perform multiple corrections on the P value of IVW, PFDR<0.05 indicated a causal relationship between related lipid metabolites and sarcopenia. Heterogeneity was evaluated through Cochran’s Q test [18], while pleiotropy was evaluated through the MR-PRESSO test and MR‒Egger intercept. P > 0.05 indicated that there was no heterogeneity or pleiotropy in the results [19]. The Steiger test can test the directionality of instrumental variables affecting outcomes through exposure, avoiding reverse causality [20], and P < 0.05 indicates reverse causality. The research was mainly conducted in the Two Sample MR software package of R software (version 4.2.1) to verify the level of testing (α = 0.05).

Results

The causal relationship between lipid metabolites and muscle mass

A total of 179 instrumental variables that met the conditions for lipid metabolites were screened. The number of instrumental variables obtained for each metabolite ranged from 2 to 33, and specific information on the instrumental variables can be found in Supplementary Table S2. The IVW method was used to evaluate the causal relationships between 179 lipid metabolites and total muscle mass, male muscle mass, and female muscle mass. MR analysis revealed potential causal relationships between 12 lipid metabolites and male muscle mass (136 instrumental variables, F: 20.91 ~ 79.21), 11 lipid metabolites and female muscle mass (168 instrumental variables, F: 20.87 ~ 441.73), and 9 lipid metabolites and total muscle mass (117 instrumental variables, F: 20.91 ~ 227.85).

The lipid metabolites that are causally associated with muscle mass are mainly phosphatidylcholine, phosphatidylethanolamine, ceramide, and triacylglycerol. Ceramide (d40:1) and ceramide (d40:2) are risk factors for decreased total muscle mass, and elevated ceramide and ceramide have similar risk effects on decreased total muscle mass. Most phosphatidylcholine lipids exhibit a positive causal relationship with muscle mass. For example, phosphatidylcholine (O-16:1:20:3) is a protective factor for total muscle mass and female muscle mass. Elevated levels of phosphatidylcholine (18:0_18:2), phosphatidylcholine (18:2_18:2), and phosphatidylcholine (O-18:0_16:1) are beneficial for the growth of male muscle mass. In addition, there were inconsistent causal effects between different triacylglycerol lipids and male muscle mass, with triacylglycerol (54:6) levels being a protective factor for male muscle mass (OR = 0.968, 95% CI = 0.950 ~ 0.987, PIVW=0.041, PFDR=0.111) and triacylglycerol (49:2) levels being a risk factor for male muscle mass (OR = 1.02, 95% CI = 1.000 ~ 1.046, PIVW=0.001, PFDR=0.662). Detailed MR analysis results for different methods are presented in Fig. 1 and Supplementary Table S3.

Fig. 1 Heat map show the results of five Mendelian randomization methods

The causal relationship between lipid metabolites and muscle strength

The inverse variance weighting method was used to evaluate the causal relationships between 179 lipid metabolites and total muscle strength, male muscle strength, and female muscle strength. There was a potential causal relationship between the 3 lipid metabolites and male muscle strength (39 instrumental variables, F: 20.87-491.37), between the 10 lipid metabolites and female muscle strength (173 instrumental variables, F: 20.89-535.11), and between the 7 lipid metabolites and total muscle strength (110 instrumental variables, F: 20.94–127.10).

The lipid metabolites that are causally associated with muscle strength are sterol ester, sphingomyelin, phosphatidylcholine, phosphatidylethanolamine, and triacylglycerol. The lipid metabolite Sterol ester is a risk factor for overall muscle strength (total muscle strength, male/female muscle strength) decline. Compared to females (OR = 1.072, 95% CI: 1.006 ~ 1.142, PIVW=0.031, PFDR=0.617), males had a greater decrease in muscle strength (OR = 1.111, 95% CI: 1.012 ~ 1.221, PIVW=0.026, PFDR=0.986). The result showed that the high level of triacylglycerol (46:1) is detrimental to total muscle strength as well as female muscle strength, it means the increase in its level can reduce muscle strength levels. In contrast to the MR results for triacylglycerol, the MR results showed that sphingomyelin (d42:2) is a protective factor for total muscle strength and female muscle strength (OR = 0.949, 95% CI = 0.912 ~ 0.988, PIVW=0.011, PFDR=0.617), and an increase in its level is beneficial for improving muscle strength. In addition, there are inconsistent effects between different types of phosphatidylcholine and muscle strength. An increase in phosphatidylcholine (16:1_18:2) levels can reduce overall muscle strength (total muscle strength, male/female muscle strength), while an increase in phosphatidylinositol (16:0_18:2) levels is beneficial for female muscle strength.

Sensitivity analysis

The Cochran’s Q test revealed heterogeneity in the results for multiple lipid metabolites and muscle mass/strength (P < 0.05); therefore, a random effects model was used for analysis. The MR‒Egger regression test did not reveal any horizontal pleiotropy (P > 0.05) between lipid metabolites and muscle mass or muscle strength, but the MR-PRESSO results showed that sphingomyelin (d42:2) had horizontal pleiotropy with total muscle strength, and 13 lipid metabolites had horizontal pleiotropy (P < 0.05) with muscle mass (total muscle strength, male/female muscle strength), indicating that these causal relationships lacked stability and were excluded. The specific results are shown in Supplementary Table S3. The accuracy of the instrumental variable direction was determined via the MR steiger directionality test (P < 0.05), and there was no potential reverse causal relationship. Figure 2 visualizes the detailed sensitivity analysis results.

Fig. 2 displays the heterogeneity and pleiotropy analysis results of lipid metabolites which have casual effects with muscle strength (A) and muscle mass (B)

Discussion

To reveal the potential impact of lipid metabolites on the risk of sarcopenia, this study used MR to explore lipid metabolites associated with the risk of sarcopenia. The results revealed a causal relationship between 19/18 lipid metabolites and muscle mass/strength and between lipid metabolism abnormalities such as phosphatidylcholine, phosphatidylethanolamine, ceramide, triacylglycerol, and sterol ester and the onset of sarcopenia.

Ceramide is a bioactive sphingolipid and a core molecule in the sphingolipid metabolism pathway. Intracellular ceramides are produced mainly through sphingomyelin hydrolysis, the de novo synthesis pathway, and the remedial synthesis pathway [21]. Six mammalian ceramide synthetase (CerS) families synthesize ceramides with different acyl chains (C-14:0 to C-30:0) based on the degree of fatty acid saturation, the length of the fatty acid chain, and the location of the chain [22]. In skeletal muscle, CerS1, 4, 5, and 6 are expressed at the highest levels, and consequently, the levels of their ceramide products C-16:0 and C-18:0 are also highest in the tissue. Current research on the relationship between ceramide and skeletal muscle indicates that excessive accumulation of ceramide can lead to a decrease in muscle fibers, ultimately leading to muscle malnutrition. Inhibiting the synthesis of ceramides can alleviate this process [23]. In addition, ceramide is also associated with age-related muscle degeneration. As a signaling molecule involved in muscle aging, it can promote muscle aging by affecting mitochondrial and protein balance [24]. Ceramide accumulation may also occur by blocking the phosphatidylinositol 3-kinase/protein kinase B pathway or the IGF-1/Akt/mTOR signaling pathway, thereby inducing an inflammatory response in skeletal muscle cells and impaired regeneration of skeletal muscle, which ultimately leads to the development of sarcopenia [25]. Furthermore, lipidomic studies have confirmed that ceramide is significantly upregulated in patients with amyotrophic lateral sclerosis characterized by muscle atrophy and weakness [26]. Previous studies have revealed an association between aging and ceramide levels but have not been able to prove a causal link. The results of this study suggest that high levels of ceramide are a risk factor for muscle mass, which is consistent with previous studies. Therefore, inhibiting neoceramide synthesis may protect muscle mass, which is particularly beneficial for age-related muscle loss.

According to the number of carbon atoms in the fatty acid molecule, triacylglycerol can be categorized into three types: long-chain triglycerides, medium-chain triglycerides (MCTs), and short-chain triglycerides. There is a correlation between nutritional status and frailty, but available research suggests that the relationship between TG and muscle quality is inconclusive. Studies targeting MCT suggest that increasing dietary MCT intake may inhibit wasting muscle atrophy in the soleus muscle of rats [27]. Similarly, a clinical trial in elderly Japanese individuals showed that supplementation with low doses of MCT increased muscle mass in frail elderly individuals [28]. Since improvements were also achieved with MCT alone, MCT is considered a promising nutrient for the treatment of sarcopenia [29]. Contrary to these findings, studies by other scholars have shown that sarcopenia is associated with high triglyceride levels and that there is consistency across races. The results from a large cross-sectional study in Asia confirmed that high triglyceride levels are associated with low skeletal muscle mass [30]. Similarly, high triglyceride levels were shown to be a risk factor for age-related loss of skeletal muscle mass in a study of middle-aged Korean women [31]. Buchmann et al. analyzed hematological data from an elderly Berlin population and reported elevated triglyceride levels in a population with sarcopenia [32]. In line with existing studies, the results of the present study suggest an inconsistent effect of triacylglycerol in relation to muscle; there is a positive causal relationship between triacylglycerol (46:1) levels and total muscle strength and female muscle strength, with elevated levels decreasing the level of muscle strength. Triacylglycerol (54:6) levels were a protective factor for muscle mass in men, while triacylglycerol (49:2) levels were a risk factor for muscle mass in men; therefore, the relationship between triacylglycerol and muscle strength and muscle mass warrants in-depth mechanistic study in the future.

Phosphatidylcholine (PC) is the main lipid component of muscle tissue biofilms and plays an important role in muscles. One of the common characteristics of muscle metabolic disorders is a change in lipid content within muscle cells. The relationship between PC and muscle mass is currently controversial. Moaddel et al. utilized targeted metabolomics and reported that PC levels were also reduced in patients with low muscle mass. The decrease in PC may be related to muscle mitochondrial dysfunction in patients with low muscle mass, suggesting that increased PC may be a protective factor for muscle mass [33]. In contrast, studies have shown that the total PC level in the skeletal muscle of elderly people with sarcopenia is increased [34], and lipidomic data from mice have confirmed that there are changes in the membrane phospholipids of skeletal muscle cells during sarcopenia and that the PC content in elderly mice is significantly greater than that in young mice [35]. These studies indicate that phospholipid levels in skeletal muscle are associated with age-related muscle atrophy, and high levels of PC may be a risk factor for muscle atrophy [36]. Consistent with existing research, this investigation there are inconsistent effects between lipid metabolites of different phosphatidylcholines and muscle strength. Increasing the level of phosphatidylcholine (16:1_18:2) can reduce overall muscle strength (total muscle strength, male/female muscle strength), while increasing the levels of phosphatidylinositol (16:0_18:2), phosphatidylcholine (O-16:1_20:3), and phosphatidylcholine (18:0_18:2) are beneficial for increasing muscle mass and strength. Therefore, further mechanistic research is needed regarding the role of phosphatidylcholine in muscles.

SM is a lipid molecule mainly located in the outer layer of the cytoplasmic membrane and organelle membrane. In mammalian cells, sphingolipids are found predominantly in the plasma membrane, lysosomes, and Golgi membranes, where they colocalize with cholesterol. Currently, there is limited research on the relationship between SM and sarcopenia. Through lipid metabolomics, some clinical studies have shown that sphingomylin is downregulated in the plasma of sarcopenia patients [37]. Xuan et al.‘s study also showed that downregulated SM (26:0) can be considered a prospective biomarker for the onset of mild cognitive infection and sarcopenia [38]. Ishida et al. explored the therapeutic potential of sphingolipids in sarcopenia. For this purpose, oral phospholipid-based liposomes (SM-lips), therapeutic agents consisting of SM and cholesterol, were first prepared, and the experimental results demonstrated that SM-lips have good stability both in vivo and in vitro. After 110 weeks of feeding an accelerated model of aging mice, quadriceps muscle mass was significantly increased in mice that ingested SM-lips, with no differences observed in the weights of the flounder and gastrocnemius muscles. The intermuscle incremental differences induced by SM-lipo treatment may be related to the higher levels of myosin heavy chain IIB and myosin heavy chain X expression in the quadriceps muscle [39]. This study also provides new ideas for the prevention of sarcopenia by considering increased intake of nutritious foods in addition to exercise, special resistance training or strength training. Our results revealed that sphingomyelin (d42:2) is a protective factor for total muscle strength and female muscle strength, and an increase in its level is beneficial for improving muscle strength. This may be due to the important role of SM in the construction and maintenance of cell membranes, as improving the membrane proliferation and stability of muscle cells is beneficial for increasing muscle mass and strength.

Lipids are distributed in both the inner and outer mitochondrial membranes, and phosphatidylethanolamine (PE) is highly abundant in the inner mitochondrial membrane, accounting for approximately 1/3 of mitochondrial phospholipids, which is much greater than in other organelles [40]. The mitochondrial lipidome is particularly susceptible to oxidative damage, which can lead to mitochondrial dysfunction as we age, thereby affecting lipid homeostasis and decreasing the level of lipid abundance within the mitochondria, a process also associated with aging [41]. The role of PE in aging-associated diseases is controversial. Amelia et al. analyzed the mitochondrial lipidome of skeletal muscle from young and middle-aged mice and showed reduced PE abundance in the mitochondria of skeletal muscle in middle-aged mice, which may be related to reduced mitochondrial membrane fluidity and metabolic transfer, demonstrating the important role of PEs in maintaining skeletal muscle health [42]. However, in older adults suffering from sarcopenia, the results of resting phosphorus metabolite identification and lipidomics showed increased levels of PE, suggesting a negative correlation between phospholipids and muscle volume [34]. Similarly, Uchitomi et al. reported significant differences in PE levels between older and younger mice, with PE increasing in skeletal muscle phospholipids in older mice and the most significant differences in PE (16:0/18:1), PE (16:0/18:2), and PE (18:1/18:2) in particular, with the greatest increase occurring by a factor of nearly two [35]. Our findings are consistent with existing studies showing that PE has different effects on muscle volume and muscle strength. PE (18:1) and PE (18:0–18:2) were protective factors for both female muscle mass and muscle strength, and PE (O-18:2–18:1) was also protective for male muscle mass, but PE (O-16:1_18:2) was a risk factor for male muscle mass. Therefore, more studies are needed in the future to further explore the intrinsic biological mechanisms of PE and aging-related muscle diseases.

The study of sarcopenia and sex differences has also been a major focus of research. A cross-sectional study conducted in eastern China noted a significant difference in the prevalence of sarcopenia between men and women. The prevalence of sarcopenia in elderly men was 19.2%, which was twice the prevalence in elderly women [43]. Similarly, an Indian study using the Asian classification criteria for sarcopenia and including 1057 middle-aged and older adults for evaluation showed that the prevalence of sarcopenia was greater in men (37%) than in women (17%) and that sarcopenia was a risk factor for poor skeletal health in all sexes [44]. According to the Asian Sarcopenia Working Group criteria, a cross-sectional study of community-dwelling middle-aged and older adults revealed that the prevalence of sarcopenia due to malnutrition was greater in women than in men and that sarcopenia in men was not associated with nutritional status. Although lower hemoglobin and lymphocyte counts were observed in men with sarcopenia, there were no significant differences in women with sarcopenia. This study demonstrated that there is a sex-specific physiopathological mechanism for sarcopenia [45]. Notably, it has also been reported that sex is not a differential factor influencing the occurrence of sarcopenia [46]. Consistent with the findings of existing studies, our findings revealed inconsistent effects of liposomes on sarcopenia across sexes. Further studies on the mechanisms of sarcopenia under the influence of sex differences are still needed in the future to provide clinical evidence to guide sex-targeted interventions.

Currently, there are problems in the clinical diagnostic process and criteria for sarcopenia, and the etiology and pathogenesis of the disease are not clearly defined. In age-related musculoskeletal disorders, significant progress has been made in applying common and targeted lipid metabolites for lipidomic analysis, which has identified alterations in the lipid profiles of bone and skeletal muscle due to aging and identified potential biomarkers for detection and diagnosis [47]. This also suggests that by analyzing metabolic pathways of metabolites with potential causal relationships, complex metabolic networks associated with sarcopenia can be revealed. These findings not only contribute to understanding the pathophysiological processes of diseases, but may also provide clues for developing new therapeutic targets. In addition, metabolites identified with potential causal relationships can serve as effective circulating metabolic biomarkers for screening and preventing sarcopenia in clinical practice. These biomarkers help intervene in the early stages of the disease and prevent further progression of the condition. Moreover, incorporating metabolite detection into the diagnostic process of sarcopenia can improve the accuracy and timeliness of diagnosis. By combining with traditional clinical assessment methods, the muscle health status of patients can be more comprehensively evaluated. For clinical treatment, based on the causal relationship between metabolites and sarcopenia, more personalized treatment plans can be developed. Interventions targeting specific metabolic pathways may help improve muscle mass and function in patients. Also, the metabolic pathways and biomarkers discovered in the study provide new targets for drug development. In the future, new drugs or treatment methods can be developed targeting these targets to more effectively treat sarcopenia.

Using Mendelian randomization, this study identified 27 metabolites associated with the risk of sarcopenia, providing new evidence for screening, evaluating, and treating target metabolites related to sarcopenia. This study also has the following limitations: (1) because the study population data come from individuals of European ancestry, the research results may not be generalizable to individuals of other races; (2) the causal relationship between multiple metabolites and sarcopenia is excluded due to the existence of horizontal pleiotropy; and (3) the authenticity of MR analysis largely depends on the explanation of the instrumental variables exposed, and many metabolites have not reached significance after FDR correction. In the future, it is still necessary to expand the sample size or perform a combined meta-analysis to more accurately evaluate the impact of lipid metabolites on sarcopenia.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The authors thank all investigators for sharing these data.

Author contributions

Conception and design: LJH，JPL，HCS; collection and assembly of data: JPL，SFW; data analysis and interpretation: JPL，YS，LJH; manuscript writing: all authors; final approval of manuscript: all authors.

Funding

This work was supported by the Special Funds for Science Development of the Clinical Teaching Hospitals of Jiangsu Vocational College of Medicine (No. 20229107) and Clinical Medicine Project of Nantong University (No. 2023JZ026).

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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