Causality of genetically determined metabolites and metabolic pathways on asthenia: a two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-4619656/v1

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Causality of genetically determined metabolites and metabolic pathways on asthenia: a two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4619656/v1

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Objectives

Previous studies attempted to relieve drug-related asthenia by analyzing the blood metabolites. Metabolism as a potential mechanism of asthenia may be a clue for developing effective prevention and treatment methods. This study aimed to explore the causal relationship of metabolites or metabolic pathways to asthenia.

Methods

We adopted single nucleotide polymorphisms (SNPs) as genetic instruments to evaluate the levels of 486 circulating metabolites. These SNPs were obtained from large-scale genome-wide association studies originating from Europe. We investigated the causal impact of genetically predicted circulating metabolites on asthenia by a two-sample Mendelian randomization (MR) analysis. The sensitivity analysis estimated the reliability of the results, containing MR-Egger, weighted median, and weighted mode. Every one standard deviation (SD) elevation in exposure was revealed as odds ratios (OR) with 95% confidence intervals (CIs).

Results

After screening 486 circulating metabolites in this MR, we identified a causal relationship between 17 metabolites and asthenia. Among which bilirubin Z (IVW OR = 0.956; 95% CI: 0.934–0.979; P < 0.01), myristoleate (OR = 0.903; 95% CI: 0.822–0.993; P = 0.035), N-acetylalanine (OR = 0.961; 95% CI: 0.938–0.985; P = 0.001), threonate (OR = 0.922; 95% CI: 0.857–0.991; P = 0.028), 1-methylxanthine (OR = 0.917; 95% CI: 0.859–0.978; P = 0.009) were causally associated with reduced risk of asthenia. Arachidonate (OR = 1.305; 95% CI: 1.165–1.462; P < 0.01), kynurenine (OR = 1.204; 95% CI: 1.067–1.359; P = 0.003), lysine (OR = 1.335; 95% CI: 1.116–1.597; P = 0.002), octadecanedioate (OR = 1.121; 95% CI: 1.041–1.208; P = 0.003), oleoylcarnitine (OR = 1.161; 95% CI: 1.015–1.329; P = 0.030), palmitoleate (OR = 1.231; 95% CI: 1.023–1.480; P = 0.028), stearoylcarnitine (OR = 1.194; 95% CI: 1.042–1.369; P = 0.011), thymol sulfate (OR = 1.035; 95% CI: 1.011–1.059; P = 0.004), valine (OR = 2.042; 95% CI: 1.400-2.979; P < 0.01), 3-dehydrocarnitine (OR = 1.182; 95% CI: 1.076-1.300; P < 0.01), 4-methyl-2-oxopentanoate (OR = 1.235; 95% CI: 1.009–1.511; P = 0.040), 4-vinylphenol sulfate (OR = 1.098; 95% CI: 1.046–1.153; P < 0.01) were related to a higher risk of asthenia. This result is not affected by heterogeneity and horizontal pleiotropy. Metabolic pathway analysis suggested three pathways that probably correlated to asthenia, they were “Glycine, serine and threonine metabolism (P = 0.00174)”, “Glycerophospholipid metabolism (P = 0.0304)”, “Phenylalanine, tyrosine and tryptophan biosynthesis (P = 0.0309)”.

Conclusions

This MR study discovered a causal effect of blood metabolites on the asthenia risk. These metabolites and metabolic pathways can be applied in asthenia prevention and treatment strategies.

Mendelian randomization

metabolomics

metabolic pathway

asthenia

Asthenia presents a consistent, personal consciousness of tiredness, exhaustion, weakness, and burden¹. Apart from social-related factors, it extensively occurs in all kinds of diseases or drug treatment, such as cardiovascular disease, psychiatric disorders, multiple sclerosis, adverse drug reactions, and so on, especially anti-cancer drugs. Asthenia is a multifactorial syndrome that has an extreme impact on the quality of life in patients with disease issues. For instance, almost all patients with advanced cancer probably experience asthenia. Compared with cancer-induced pain, asthenia leads to a more severe influence on daily living and health status². Of course, Asthenia also appears in other diseases. Among them, asthenia accounts for 76–93% of patients with multiple sclerosis and is also a feature of chronic diseases, including heart failure, inflammatory bowel disease, systemic lupus erythematosus, and the like^3–5. The other phenomenon of asthenia is frequently illustrated in adverse drug reactions. Anti-cancer drug-related asthenia is the most common appearance. Asthenia is frequently a major reason for the dose decrease of anti-cancer drugs or the poor efficacy of drug treatment⁶. The development of asthenia burdens not only the disease severity but also obstacles to human activities, even limiting them to bed^7–9. Asthenia cannot be relieved only by rest¹⁰. We need to understand the generative mechanisms and discover effective methods of prevention and alleviation.

The probable reasons for asthenia vary, including dehydration, anemia, infection, metabolic, etc. The unknown etiology always confronts clinicians with difficulty in exploring the cause¹¹. Moreover, the absence of effective pharmaceuticals is a major issue hindering the treatment of asthenia. Under these circumstances, symptomatic treatment or intervention of risk factors may be a promising and feasible approach to dealing with asthenia. In recent years, omics study has largely contributed to precision medicine development. It is extensively applied to several fields, such as causal association verification, pathology inquiry, and alleviation of adverse drug reactions. Previous researchers tend to conduct metabolomics studies to prevent asthenia caused by the side effects of drugs. For example, some authors screened the significantly related metabolite to suggest vitamin A and D supplements prevent the asthenia induced by tamsulosin¹². According to the fact that metabolism is one of the underlying factors associated with asthenia, Metabolomics analysis is a possible breach for preventing and healing asthenia. Confirming the causal association between metabolites and asthenia will provide valuable recommendations for seeking effective treatment methods.

Mendelian randomization (MR) analysis is a popular approach for estimating the causality of factors with disease from the genetic perspective. Single nucleotide polymorphisms (SNPs) are recognized as instrumental variables in MR analysis. Due to the random assignation of the genetic variants during gametogenesis and freedom from external instances, MR prioritizes immunizing the results from the interference of confounding factors and reverse causality. It has been widely used for assessing the disease risk with metabolites^13–17. There is a lack of MR studies utilized to trace the causal association between metabolites and asthenia. Considering the poor understanding of the causal impact of serum metabolites on asthenia, we applied a two-sample MR study to screen a range of serum metabolites and latent metabolic pathways related to asthenia. This study will help realize asthenia's pathophysiological mechanisms and supply a reliable treatment target and strategies.

Study design

We adopted the two-sample MR method to investigate the causal role of serum metabolites on the asthenia risk. We designed a convincing MR analysis according to the following demand: genetic instruments should be significantly correlated to exposure rather than confounders. The genetic instruments have no independent association with the outcome. The influence of genetic instruments on the outcome can only be created by exposure. The latter two assumptions are viewed as the independent character of horizontal pleiotropy. The genetic data of asthenia is derived from OpenGWAS consortia (https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90018598/). Figure 1 manifested the research overview. All statistics were conducted using the "TwoSampleMR" package (Version 0.5.7) in the R program (Version 4.2.0) and the Reviewer Manager software (Version 5.4.1).

GWAS information for human blood metabolites

The Metabolomics GWAS server obtained genetic information for each blood metabolite (http://metabolomics.helmholtz-muenchen.de/gwas/). This study’s genetic variants came from the genome-wide association scans with high-throughput metabolic profiling accomplished by Shin et al.¹⁸. This GWAS comprises 7824 European descents and 486 metabolites with nearly 2.1 million SNPs, of which 278 are divided into eight major metabolomics based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. They were amino acids, energy, vitamins, lipids, carbohydrates, peptides, cofactors, and xenobiotic metabolism.

GWAS data for asthenia

The published study provided the GWAS statistics of asthenia available on the OpenGWAS database (GWASID: ebi-a-GCST90018598; website: https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90018598/), consisting of 1,860 asthenia cases and 168,253 control subjects¹⁹.

Instruments selection

We carried out several processes to screen workable genetic variants related to metabolites. First, due to the limitation of SNPs with genome-wide significance, we widen the association threshold to P < 10 × 10^–6 and pairwise linkage disequilibrium (LD) r2 < 0.01 within a 1000 kb distance to acquire top independent SNPs. This method is prevalently applied in other MR analyses. We calculated the F value of every SNP and included the SNPs with F > 10 to avoid the interference of weak instruments. We excluded absent and inappropriate proxies. We aligned the exposure- and outcome-SNPs alleles and discarded palindromic SNPs with intermediate effect allele frequencies or SNPs with incompatible alleles (e.g., A/G vs. A/C). Eventually, we reserved the metabolites with more than two SNPs for this MR analysis.

Mendelian randomization analysis

We considered the inverse-variance weighted (IVW) as the primary approach for causal assessment because it provides a reliable causality despite heterogeneity. We integrated the Wald ratios, the effect of individual SNP on the outcome, to a fixed-effect IVW if IV ≤ 3. Otherwise, we included a random-effect IVW. Three methods tested the IVW analyses’ heterogeneities, including Cochran’s Q values, I², and the H-statistics²⁰. P < 0.05 in the Cochran Q test suggests heterogeneity in the results²⁰. Sensitivity analyses contain weighted median, weighted mode, and MR-Egger^21–23. MR-Egger can detect and redress the underlying horizontal pleiotropy. Therefore, this method still supplies a valid causal association even with the pleiotropy existence (P for intercept < 0.05)²³. When invalid instrument variables account for over fifty percent of the weight in this MR study, the weighted median can strengthen the causal association²¹. The weighted mode method manifests the effect evaluation supported by the largest number of genetic instruments²². We accomplished all statistics by R version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria) with the “TwoSampleMR”²⁴ and “Mendelian randomization”²⁵ packages. A two-tailed p-value of 0.05 indicated statistically significant.

Result visualization and metabolic pathway analysis

The volcanic plot showed the final results. The forest plots revealed significantly related circulating metabolites. We performed KEGG pathway enrichment analyses for circulating metabolites with statistically significant differences and analyzed the metabolic pathway of those on the MetaboAnalyst 5.0 website (http://www.metaboanalyst.ca/). This database systematically annotates biological functions for vast lists of genes or proteins by biological data and analytical methods. P < 0.05 demonstrated the findings as statistically significant. 0.05 < P < 0.10 indicated potentially statistically significant.

Table 1 shows the origin of Mendelian randomization data in this study. According to the criteria of instrument selection, 278 of 486 metabolites were retained in this MR study (9 metabolites with unavailable data) (Fig. 2). (Additional file 1: Table S1: Summary of calculation results of detailed causal effect value estimates by various MR Methods; Table S2: Summary of the results of detailed heterogeneity analysis by various MR Methods; Table S3: Summary of the results of detailed level pleiotropy calculations for various MR Methods; Table S4: Raw data summary). Every metabolite’s SNP number ranges from 2 to 202. All SNPs’ F values of more than 10 mean no weak instruments.

Table 1

Summary of genome-wide association studies included in this study.
Phenotype	GWAS data source	Sample size	website
human blood metabolites	Shin SY et al., 2014	7,824 participants	https://gwas.mrcieu.ac.uk/datasets/
Frailty index	ebi-a-GCST90020053	175,226 participants	https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90020053/

The causal impact of circulating metabolites on asthenia

Among these 278 metabolites, IVW screened 27 metabolites statistically associated with asthenia. However, of these 27 metabolites, there were 17 whose other models’ direction was consistent with IVW. Therefore, 17 metabolites were eventually verified as the causal impact of factors on asthenia in which bilirubin Z (IVW OR = 0.956; 95% CI: 0.934–0.979; P < 0.01), myristoleate (OR = 0.903; 95% CI: 0.822–0.993; P = 0.035), N-acetylalanine (OR = 0.961; 95% CI: 0.938–0.985; P = 0.001), threonate (OR = 0.922; 95% CI: 0.857–0.991; P = 0.028), 1-methylxanthine (OR = 0.917; 95% CI: 0.859–0.978; P = 0.009) has a causal role on reduced risk of asthenia. Arachidonate (OR = 1.305; 95% CI: 1.165–1.462; P < 0.01), kynurenine (OR = 1.204; 95% CI: 1.067–1.359; P = 0.003), lysine (OR = 1.335; 95% CI: 1.116–1.597; P = 0.002), octadecanedioate (OR = 1.121; 95% CI: 1.041–1.208; P = 0.003), oleoylcarnitine (OR = 1.161; 95% CI: 1.015–1.329; P = 0.030), palmitoleate (OR = 1.231; 95% CI: 1.023–1.480; P = 0.028), stearoylcarnitine (OR = 1.194; 95% CI: 1.042–1.369; P = 0.011), thymol sulfate (OR = 1.035; 95% CI: 1.011–1.059; P = 0.004), valine (OR = 2.042; 95% CI: 1.400-2.979; P < 0.01), 3-dehydrocarnitine (OR = 1.182; 95% CI: 1.076-1.300; P < 0.01), 4-methyl-2-oxopentanoate (OR = 1.235; 95% CI: 1.009–1.511; P = 0.040), 4-vinylphenol sulfate (OR = 1.098; 95% CI: 1.046–1.153; P < 0.01) were causally related to a higher risk of asthenia. The detailed information and causal connection are seen in Fig. 3 and Table 2.

Table 2

Summary of 278 metabolites have significant causal effect on frailty.
Phenotype	N SNP	OR	95% CI	P
arachidonate	27	1.305	1.165–1.462	4.250E-06
heme	13	1.082	1.042–1.124	5.230E-05
4-vinylphenol sulfate	10	1.098	1.046–1.153	1.829E-04
valine	4	2.042	1.400-2.979	2.102E-04
bilirubin (Z	18	0.956	0.934–0.979	2.307E-04
3-dehydrocarnitine	29	1.182	1.076-1.300	5.159E-04
N-acetylalanine	25	0.961	0.938–0.985	0.001
lysine	24	1.335	1.116–1.597	0.002
kynurenine	44	1.204	1.067–1.359	0.003
octadecanedioate	12	1.121	1.041–1.208	0.003
thymol sulfate	5	1.035	1.011–1.059	0.004
1-methylxanthine	16	0.917	0.859–0.978	0.009
1-palmitoylglycerol (1-monopalmitin)	12	1.154	1.035–1.288	0.010
stearoylcarnitine	6	1.194	1.042–1.369	0.011
taurolithocholate 3-sulfate	12	1.051	1.007–1.097	0.024
caprylate	37	0.861	0.756–0.981	0.025
1-palmitoleoylglycerophosphocholine	14	0.836	0.715–0.979	0.026
palmitoleate	28	1.231	1.023–1.480	0.028
threonate	17	0.922	0.857–0.991	0.028
1-stearoylglycerophosphocholine	10	1.170	1.017–1.347	0.029
caprate	13	0.966	0.936–0.997	0.030
oleoylcarnitine	11	1.161	1.015–1.329	0.030
theobromine	5	0.872	0.770–0.987	0.030
biliverdin	29	0.954	0.914–0.996	0.032
myristoleate	16	0.903	0.822–0.993	0.035
4-methyl-2-oxopentanoate	13	1.235	1.009–1.511	0.040
1-stearoylglycerophosphoethanolamine	14	1.090	1.003–1.184	0.041

Analysis of heterogeneity and horizontal pleiotropy test for circulating metabolites on cholecystitis

Supplementary Table 2 presents the heterogeneity analysis results. Other circulating metabolites have no heterogeneity except for arachidonate, kynurenine, and palmitoleate (P < 0.05). The causal effect of metabolites on the asthenia was not biased by SNP horizontal pleiotropy (Supplementary Table 3).

Metabolic pathway analysis

We analyzed the metabolic pathways of 41 circulating metabolites using the web-based MetaboAnalyst 5.0. ( https://www.metaboanalyst.ca/) and detected 8 potential metabolic pathways associated with asthenia. The most correlated four metabolic pathways were “Porphyrin and chlorophyll metabolism (P = 0.00131)”, “Valine, leucine and isoleucine biosynthesis (P = 0.00153)”, “Caffeine metabolism (P = 0.00243)”, “Ether lipid metabolism (P = 0.00984)”, “Valine, leucine and isoleucine degradation (P = 0.037)” (all P < 0.05), as showed in Fig. 4 and Fig. 5.

In this MR study, we conducted a large-scale human genetic analysis to explore the causal relationship between serum metabolites and asthenia. There were four metabolite pathways associated with asthenia: “Porphyrin and chlorophyll metabolism,” “Valine, leucine and isoleucine biosynthesis, Caffeine metabolism,” “Ether lipid metabolism,” “Valine, leucine and isoleucine degradation.” Seventeen blood metabolites were observed to have a causal role in the asthenia risk. Genetically determined lower serum plasma levels of five metabolites were associated with higher asthenia risk: bilirubin Z, myristoleate, N-acetylalanine, threonate, and 1-methylxanthine. Additionally, the other twelve blood metabolites were detected to present a significant relationship with an increased risk of asthenia development, including arachidonate, kynurenine, lysine, octadecanedioate, oleoylcarnitine, palmitoleate, stearoylcarnitine, thymol sulfate, valine, 3-dehydrocarnitine, 4-methyl-2-oxopentanoate, 4-vinylphenol sulfate. These findings selected the underlying causal effects of metabolites on asthenia. As far as we know, this study is the first MR analysis merging metabolomics with genomics to uncover the pathophysiology of asthenia. This study supplies innovative conceptions of the prevention and treatment of asthenia.

The reason for asthenia is correlated with nervous systemic, endocrine, immune disorders, and abnormal metabolisms^26,27. From the point of central mechanism, the monoaminergic systems and abnormal 5-hydroxytryptamine metabolism were widely studied and considered as the potential mechanism of asthenia syndrome²⁸. The morphological perspective of asthenia syndrome manifests as dysfunctions of the ascending reticular system, leading to the separation of cortical and subcortical structures. On the peripheral mechanisms aspect, the energy metabolism of muscle cells has an important influence on skeletal muscle activity. The impairment of carnitine transfer into mitochondria may result in a disorder of oxidative ability and a deficiency of phosphocreatine resynthesis. This phenomenon has been verified in asthenia syndrome^29,30. Nowadays, several studies have advocated conducting suitable individualized therapy to correct the pathogenesis of asthenia syndrome. Substances that improve brain function and muscle energy may be feasible approaches for asthenia syndrome.

More and more researchers support agents influencing the condition of acety1-L-carnitine as a metabolic treatment that plays an important role in asthenia^31,32. Carnitine was regarded as a supplement effectively addressing the issue of asthenia³³. Carnitine is a kind of similar vitamin that stimulates fat into energy. As a carrier for transporting fatty acids, carnitine transfers the medium and long-chain fatty acids from the extracellular mitochondrial membrane to the intramembrane, which causes oxidation to create energy. Patients with carnitine administration will experience alleviation of asthenia in the mind and muscles. Carnitine also crosses the blood-brain barrier and addresses neurohypoplasia by protecting mitochondrion and nerve stimulation, contributing to treating asthenia syndrome through a central mechanism. This study suggests 3-dehydrocarnitine as a risk factor for asthenia. 3- dehydrocarnitine, the precursor of L-carnitine productions, performs an intermediate role in carnitine degradation. A previous study reported that 3-dehydrocarnitine administration elevated fatty acid metabolism in a mouse model with a deficiency of L-carnitine transporter. This caritine precursor plays a potential advantage in sustaining energy³⁴. The positive effect of 3-dehydrocarnitine on asthenia seems contrary to our study result. However, this study does not deny the protective role of 3-dehydrocarnitine on asthenia. This study supported the causal association of 3-dehydrocarnitine with asthenia. At the same time, we also indicated that excessive 3-dehydrocarnitine may lead to adverse effects on asthenia. 3-dehydrocarnitine has a role in abnormal glucose metabolism, manifesting as fatigue, lack of concentration and energy, etc. Moreover, excessive carnitine likely produces cardiovascular, nervous, and gastrointestinal tract-related adverse effects, such as insomnia, excessive dreaming, anxiety, chest tightness, shortness of breath, increased heart rate, etc. The causality between carnitine and asthenia provides an insight that carnitine deficiency may be a potential cause of asthenia. As long as excessive supplementation is avoided, carnitine is a promising method for patients with asthenia due to a lack of carnitine or abnormal muscle energy and nervous function.

Among all organ systems, the liver occupies the central position for pathogens. Some metabolism factors induce asthenia by releasing energy from the liver, muscles, and brain³⁵. Bilirubin is a heme decomposition from hemoglobin in aging red blood cells, which exists and transports in the form of the bilirubin albumin complex. The liver has the function of absorbing, transferring, and excreting bilirubin. This study discovered that lower levels of bilirubin Z are causally associated with increased asthenia risk. Bilirubin Z, located in the liver, performs the function of combining with bilirubin. There are two kinds of albumen in the liver: Z and Y. Bilirubin entering the liver needs to combine with Z and Y albumens to excrete to the intestine by bile. Z and Y albumens are responsible for binding and transporting bilirubin. The reduced levels of bilirubin Z downgrade the content of the bilirubin albumin complex to result in increased levels of bilirubin in serum. This phenomenon probably allows for hyperbilirubinemia occurrence. Due to the toxicity characteristic, excessive bilirubin in the serum tends to impair the brain and nervous system, inhibit oxidation of linoleic acid and phospholipids, and frequently create jaundice and liver injury. Excessive bilirubin-related adverse effects often manifest a common symptom as asthenia. Therefore, this study indicated the role of bilirubin metabolism in asthenia by the potential causal association of bilirubin Z with asthenia risk.

This study concluded a causal impact of higher arachidonate levels on increased asthenia risk. Arachidonic acid (AA), a polyunsaturated fatty acid, is a precursor of prostaglandins and thromboxanes. AA maintains normal growth, cellular function, and immune response. AA-induced inflammation can activate the immune system to protect against infectious events. However, excessive levels of AA will boost the inflammatory response easily, leading to various adverse outcomes, such as tissue injury, angiogenesis, oxidative stress, and so on. Therefore, the imbalance of AA levels could contribute to the damage that influences the pathogenesis of many chronic diseases, including cardiovascular, liver, cancer, and so on^36,37. Previous researchers have reported that higher AA intake elevates the inflammatory response by improving the production of pro-inflammatory eicosanoids. The levels of AA always is positive for myocardial infarction risk^38,39. Asthenia syndrome has several characteristics, including a chronic inflammatory condition⁴⁰. Consequently, this finding indicated that inflammatory-related metabolites deserve to be given more emphasis by patients with asthenia. It can be a signal for asthenia in the early detection.

In conclusion, we first identified the casual association of blood metabolites with asthenia risk and screened 17 kinds of metabolites and four metabolic pathways. The findings recommend creative insights into the potential mechanisms of asthenia from a metabolism perspective.

MR Mendelian randomization

SNPs Single nucleotide polymorphisms

GWAS Genome-wide association studies

CIs Confidence intervals

OR Odds ratio

SD Standard deviation

RCTs Randomized controlled trials

KEGG Kyoto Encyclopedia of Genes and Genomes

IVW Inverse variance weighted

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

Not applicable.

Availability of data and materials: All data generated or analyzed during this study are included in this published article. The metabolites data was derived from https://gwas.mrcieu.ac.uk/.

Competing interests

The authors declare that they have no competing interests.

Funding

None.

Author contributions

WJ, QJ, XW, and JH made substantial contributions to conception and design, and revised the manuscript critically for important intellectual content. JH and CJ revised the manuscript and gave final approval of the version to be published. All authors read and approved the final manuscript.

Acknowledgments: The authors thank all participants for their support for UK-Biobank.

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No competing interests reported.

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Causality of genetically determined metabolites and metabolic pathways on asthenia: a two-sample Mendelian randomization study

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Abstract

Objectives

Methods

Results

Conclusions

Figures

Introduction

Methods and materials

Study design

GWAS information for human blood metabolites

GWAS data for asthenia

Instruments selection

Mendelian randomization analysis

Result visualization and metabolic pathway analysis

Result

The causal impact of circulating metabolites on asthenia

Analysis of heterogeneity and horizontal pleiotropy test for circulating metabolites on cholecystitis

Metabolic pathway analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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