
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312380
MD-D-24-03077
00081
10.1097/MD.0000000000039688
3
5300
Research Article
Observational Study
Causal association between blood metabolites and vertigo: A Mendelian randomization study
Zhang Bin MD a
Chen Sulan MD a
Teng Xin MD a
Han Qi Doctor dr_hanqi@163.com
a
Wu Tong MD 21102570126@stu.ccucm.edu.cn
a
Liu Yin MD liuyin861206@163.com
b
Xiang Ke BS xiangke1961@163.com
b
https://orcid.org/0009-0003-6738-6147
Sun Li MD b*
a Changchun University of Chinese Medicine, Jilin, China
b Jilin Provincial Academy of Traditional Chinese Medicine, Jilin, China.
* Correspondence: Li Sun, Jilin Provincial Academy of Traditional Chinese Medicine, Jilin 130021, China (e-mail: 1085639208@qq.com).
20 9 2024
20 9 2024
103 38 e3968825 3 2024
06 8 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Metabolic disorders have been identified as an important factor causing nervous system diseases. However, due to the interference of confounding factors, the causal relationship between them has not been clearly elucidated, so it is necessary to study the causal relationship between them. To explore the causal relationship between blood metabolites and vertigo by Mendelian randomization. To assess causality, the inverse variance weighting method was employed as the primary analytical approach, complemented by additional sensitivity analyses. Metabolic pathway enrichment analysis and genetic correlation analysis were employed to further assess the metabolites. All statistical analyses were conducted using the R software. The study employed metabolite Genome Wide Association Study and vertigo diseases summary data sets to examine the causal relationship between 486 blood metabolites and 3 types of vertigo. A total of 55 potential metabolites associated with the 3 types of vertigo were identified, with 22, 16, and 13 candidate metabolites showing relatively reliable MR Evidence for Vestibular Dysfunction, Peripheral Vertigo, and Central Vertigo, respectively. Enrichment analysis was conducted to investigate the biological significance of these candidate metabolites, resulting in the identification of 7 key metabolic pathways across the 3 diseases, the metabolic pathway known as “Valine, leucine, and isoleucine biosynthesis” was found to be associated with all 3 types of vertigo, suggesting its potential influence on the vestibular system. Genetic correlation analysis revealed a genetic correlation between X-10510 and dodecanedioate with Vestibular Dysfunction. This study offers novel perspectives on the causal impact of blood metabolites on vertigo through the integration of genomics and metabolomics. Identifying metabolites that contribute to vertigo could serve as potential biomarkers and contribute to a better understanding of the underlying biological mechanisms associated with vertigo.

blood metabolites
causality
mendelian randomization
vertigo
vestibular system
OPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

Dizziness and vertigo are frequently reported symptoms among outpatients, representing a motor illusion rather than an independent ailment. This syndrome is characterized by rotational or non-rotational vertigo, nystagmus, ataxia, nausea, and vomiting. The underlying causes of this syndrome may originate from the inner ear, brain stem, or cerebellum.[1] It is noteworthy that approximately one fifth of adults will experience episodes of vertigo.[2] The human brain integrates 3 types of sensory input: vestibular sensation, visual sensation and somatic sensation in the vestibular system, so as to form the head’s perception of the direction and movement of the body.[1] Vertigo is caused by abnormal or asymmetric signals in the vestibular system. About 30% of people will have symptoms of vertigo at some stage of life.[3] Among all vertigo patients, benign paroxysmal vertigo was the most common, accounting for about 17.1%, followed by phobic vestibular vertigo (later classified as persistent posture-perception dizziness by Barany Society). Central vestibular syndrome was found in 12.3% of the patients, followed by vestibular migraine (11.4%), Meniere’s disease (10.1%) and vestibular neuritis/acute unilateral vestibulopathy (8.3%).[4] As diseases such as vestibular migraine, Meniere’s disease, Bilateral vestibular hypofunction and some benign recurrent vertigo have been shown to be hereditary in families,[5] clinicians should consider family history when understanding the patient’s vertigo history, although few diseases currently include family heredity in the diagnosis of the disease. However, it is necessary to understand the genetic associations of diseases and to find new diagnostic methods for these genetic associations. With the gradual maturation of OMIC Technology, genomics and metabolomics have been applied to the potential treatment mechanism and strategy of human diseases. Li[6] et al found that 19 metabolites were significantly increased and 57 metabolites were significantly decreased in acute vestibular function injury caused by Unilateral labyrinthectomy in mice. However, the causal relationship between metabolites and vestibular impairment remains uncertain, especially in human studies. In 2014, Shin et al[7] published a study on Genome Wide Association Study (GWAS) based on human blood metabolites. Our study will explore the causal relationship between blood metabolites and vestibular dysfunction, peripheral vertigo and central vertigo with the help of Mendelian randomization (MR) methods, to provide evidence support for clarifying the disease mechanism and identifying vertigo-related risk factors.

Randomized controlled trials are widely regarded as the benchmark for establishing causal relationships in conventional research methodologies. Nevertheless, certain ethical constraints may impede the complete execution of randomized controlled trials in certain instances. In such scenarios, MR presents itself as a viable alternative, utilizing genetic variants as instrumental variables to evaluate the causal association between exposure and outcome. Based on the Mendelian law of inheritance, during the meiotic process, parental alleles are randomly allocated to the offspring, resulting in the emergence of random genetic variations. Furthermore, the selected genetic variants remain unaffected by intricate social and economic factors postnatally.[8] The MR Approach is conceptually similar to randomized controlled trials, except that MR Studies are assigned to patients according to their genotype. More and more GWAS data are being shared by researchers, and MR Studies have yielded a large number of reliable genetic variants.[9] As GWAS studies have expanded to the publication of genetically related metabolite profiles of metabolic diseases, we used a systematic Mendelian randomization approach to evaluate the causal role of serum metabolites in 3 vertigo diseases: vestibular dysfunction (VD), peripheral vertigo (PV), and central vertigo (CV). Multiple common metabolites with causal relationships were screened and metabolic pathways that may contribute to vertigo symptoms were identified.

2. Materials and methods

2.1. Study design

A two-sample MR Design was conducted to systematically assess the causal association between blood metabolites and the risk of vertigo. The MR Research must adhere to 3 fundamental assumptions: the instrumental variables (IVs) should exhibit a direct relationship with the exposure factors; the instrumental variables should not be correlated with the outcomes and should be independent of any known or positional confounders; and the instrumental variables should solely influence the outcomes through the exposure factors. The flow of this study is depicted in Figure 1.

Figure 1. Study design of this study. CV = central vertigo, GWAS = genome-wide association study, LDSC = linkage disequilibrium score, MR = Mendelian randomization, N = sample size, PV = peripheral vertigo, SNP = single nucleotide polymorphism, VD = vestibular dysfunction.

2.2. Data sources

2.2.1. Summary of metabolite GWAS

The GWAS data for metabolites were obtained from Shin et al’s[7] meta-analysis of 2 European cohorts, namely the KORA cohort and the Twins UK cohort. The study included a total of 7824 participants aged 17 to 85 years and identified 486 metabolites, consisting of 309 known metabolites and 177 unknown metabolites. The summary genome-wide association dataset comprised approximately 2.1 million single nucleotide polymorphisms (SNPs). The complete supplementary data of this article can be obtained through the Web version of PubMed Central.

2.2.2. Summary of vertigo GWAS

This study used the outcome data from FInnGen database (https://www.finngen.fi/en),[10] these data are available in download FInnGen database. We want to acknowledge the participants and investigators of the FinnGen study.

All data for this study were obtained from the publicly available GWAS study database and therefore did not require ethical approval.

2.3. Selection of instrumental variables

In order for single SNPs to serve as IVs, it is imperative that they adhere to the 3 assumptions necessary for MR to be satisfied[11,12]: correlation, independence, and exclusion restrictions. In our study, the selection of optimal instrumental variables is conducted through the following steps: firstly, we employ a genome-wide statistical significance threshold (P < 1.0 × 10−5) to identify SNPs that exhibit associations with each metabolite. These identified SNPs are then considered as potential instrumental variables. Linkage Disequilibrium (LD) parameters r2 < 0.01, the genetic distance was 500 kb, and the SNPs with the smallest P value were selected to ensure the independence between instrumental variables and exclude the influence of LD on the results. Many studies have proved the reliability of this method. The data underwent preprocessing in order to retain the effect allele and effect size, while excluding palindromic SNPs, specifically alleles of A/T and A/C. The F-statistics of the selected SNPs were evaluated using the subsequent equations, and weak instrumental variables with F-statistics below 10 were excluded.[13] In order to ensure the consistency and reliability of the findings, the subsequent MR analysis only incorporated metabolites that possessed 3 or more instrumental SNPs.

F=R2(n−1−k)(1−R2)k

In this equation, where n, k, and R2 are sample size, number of IVs, and the variance explained by IVs.

2.4. Mendelian randomization analysis

The MR Analysis employed the inverse variance weighting (IVW) method for calculating effect estimates, and metabolites with a significance threshold below 0.05 were designated as potential metabolites.[14] Additionally, the stability of candidate metabolites was assessed using the MR-Eagger model,[15] Weighted Median estimator,[16] Simple Mode estimation, and Weighted model.[17] If the 5 models exhibited disparate estimates for a metabolite in terms of their directions, they were deemed to lack stability. Additionally, Cochran’s Q test was employed to assess the presence of heterogeneity resulting from horizontal pleiotropy and other biases, thereby verifying the credibility of the instrumental variables.[18] In order to examine the hypothesis of independence, various techniques were employed to eliminate the possibility of horizontal pleiotropy, including the utilization of the MR-Egger intercept,[19] the MR-PRESSO method, and the funnel plot.[20] Subsequently, leave-one-out analyses were conducted to evaluate the potential influence of individual SNPs on the MR Estimates. Additionally, reverse MR Analyses were carried out to investigate the causal effects of each vertigo diseases on candidate metabolites, thereby ruling out the presence of reverse causality.[9]

2.5. Genetic correlation analysis

Certain genetic correlations between different traits may lead to false positive MR Results, causing MR Estimation to violate causal effects. Although significant SNPs associated with vertigo diseases have been excluded in the previous IVs selection process, SNPs with no significant association may also be inherited from vertigo. Linkage disequilibrium regression can determine whether MR Estimates are confounded by shared genetic architecture by applying chi square statistics to different shapes by SNPS, so to ensure that causal effects are not confounded by co-heritability of exposure and outcome, Linkage Disequilibrium Score (LDSC) was employed to assess genetic associations between candidate metabolites and vertigo.[21]

2.6. Metabolic pathway analysis

To gain a deeper understanding of the metabolic pathways and potential pathogenesis of vertigo, we employed MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) to analyze blood metabolites and investigate their causal influence on biological mechanisms.[22]

2.7. Statistical analysis

The analyses were conducted using R (version 4.3.1) for all analyses, while MR Analyses were conducted using the “TwoSampleMR” software package (version 0.5.6). LDSC analysis was conducted using the “ldscr” software package (version 0.1.0).

3. Results

3.1. Selection of IVs

The 486 metabolites were analyzed according to strict instrumental variable selection steps. The number of selected instrumental variables ranged from 4 to 207 SNPs. All the SNPs associated with metabolites had F statistics greater than 10 (Fmin = 17.64, Fmax = 1151.83), indicating that all the SNPS were of sufficient strength. F-statistics for metabolites after screening are shown in Table S1, Supplemental Digital Content (Table S1 presents the IVs selected for the MR Analysis, http://links.lww.com/MD/N556).

3.2. Causal effect of metabolites on vertigo

In the analysis conducted using the IVW method, a total of 88 vertigo diseases were established, with a statistical significance threshold of PIVW < 0.05. The MR-PRESSO technique was employed to remove associations that included outliers but did not exhibit statistical significance. Through examination of scatter plots, 5 models displaying inconsistent directions were excluded, resulting in a final set of 55 metabolite associations. Following the application of the Bonferroni correction, no statistically significant P values were observed, potentially attributable to the overly stringent nature of the Bonferroni correction method. Consequently, we opted for the comparatively more lenient False Discovery Rate (FDR) correction. After conducting calculations to determine the odds ratio (OR) values for each candidate metabolite, it was observed that certain metabolites exhibited excessively high OR values. To enhance the accuracy of detecting the actual effect, we utilized the Mendelian randomization efficacy levels (Power) website (https://shiny.cnsgenomics.com/mRnd) to calculate the efficiency of each metabolite level. Considering that the study design had an 80% probability of detecting a true effect, we aimed to identify metabolites with an effect level of 0.8 or higher. After calculating the Power of each candidate metabolite, it was found that we may not have detected a true effect for some causal effects, such as lysine (OR: 34422.35, 95% CI: 98.66–12009850.38), phenyllactate (PLA) (OR: 38.11, 95% CI: 1.47–989.53), which were excluded in the subsequent analyses. Complete content can be viewed in Table S2, Supplemental Digital Content, http://links.lww.com/MD/N556. (Table S2 shows the causality metabolites with vertigo mainly IVW method), Cochran ‘s Q test, FDR correction after the information such as the P value and Power). Tables S3–S5, Supplemental Digital Content, http://links.lww.com/MD/N556 shows the causal relationship between metabolites and vertigo (5 analysis methods). Simultaneously, our investigation also revealed that certain metabolites exhibit causal influences on multiple diseases. For instance, phosphate demonstrates contrasting causal effects on VD and PV, as indicated in Table S6, Supplemental Digital Content, http://links.lww.com/MD/N556. In conclusion, the IVW method produced statistically significant estimates. Furthermore, the absence of significant outliers in the MR-PRESSO test, Cochran’s Q test (P > .05), and MR-Egger intercept test (P > .05) indicated the absence of heterogeneity or multiplicity in the study’s findings. Leave-one-out analysis demonstrated that individual SNPs did not contribute to bias in the MR Effect. Additionally, we identified and analyzed 69 metabolic associations related to vertigo, with specific results presented in Table S3. Causal relationships between metabolites and the 3 outcomes are depicted in Figure 2A to C. At the same time, we conducted sensitivity analysis and heterogeneity test on the results of the study, and the results were presented by funnel plot, scatter plot, forest plot and leave-one-out plot. In the forest plot, the vertical axis represents the SNPs of each metabolite, the horizontal axis represents the OR value, and the red line segment represents the effect of this exposure factor on the outcome under different analysis methods. Funnel plots were mainly used to observe the heterogeneity of each SNP and used to remove outliers, each black dot represents a single SNP. The leave-one-out method is mainly to check whether each instrumental variable has a serious biased effect on the results of the overall MR. In the leave-one-out plot, the ordinate corresponds to each SNPs, the abscordinate corresponds to the specific value of the analysis result by the IVW method, and the line segments and points represent the effect size and confidence intervals. Scatter plot shows the results of MR Analysis between the exposure factor and the outcome factor. Each point represents the line on each point of an instrumental variable SNP, and the abscismal is the effect of SNP on the exposure factor. The ordinate is the effect of SNP on the outcome factor, and the ratio of the 2 effects is the effect of exposure on the outcome. The detailed results are shown in Figure Materials S1–12, Supplemental Digital Content, http://links.lww.com/MD/N557, http://links.lww.com/MD/N558, http://links.lww.com/MD/N559, http://links.lww.com/MD/N560, http://links.lww.com/MD/N561, http://links.lww.com/MD/N562, http://links.lww.com/MD/N563, http://links.lww.com/MD/N564, http://links.lww.com/MD/N565, http://links.lww.com/MD/N566, http://links.lww.com/MD/N567, http://links.lww.com/MD/N568.

Figure 2. (A–C) Forest plots illustrate the perfect estimation of the identified candidate metabolites, known and unknown, associated with vertigo. The dashed red and solid blue lines indicate MR Odds ratios and 95% confidence intervals. The blue dots represent MR. CI = confidence interval, IVW = inverse variance weighting, MR = Mendelian randomization, OR = odds ratio.

3.3. Metabolic pathway analysis

Following the completion of metabolic pathway analysis, it was determined that the 3 vertigo diseases examined exhibited 7 significant metabolic pathways, as indicated in Table S7, Supplemental Digital Content, http://links.lww.com/MD/N556 (Table S7 shows the Results of enrichment analysis for known potential metabolites). Notably, the “Valine, leucine and isoleucine biosynthesis” pathway demonstrated an association with all 3 vertigo diseases (PVD = 0.021, PPV = 0.015, PCV = 0.026). The association between the metabolism pathways of ascorbate and aldarate and VD was found to be statistically significant (P = .021). Similarly, the “Taurine and hypotaurine metabolism” pathway showed a significant association with PV (P = .015). Furthermore, the pathways of “Aminoacyl-tRNA biosynthesis” and “Butanoate metabolism” were found to be associated with CV.

3.4. Reverse causation between metabolites and vertigo

The selection criteria utilized for the identification of IVs in reverse MR Analysis were defined as “P < 5 × 10−5, r2 = 0.01, Kb = 10,000.” Subsequently, MR Analysis was conducted on candidate metabolites associated with 3 distinct vertigo diseases, aiming to yield robust evidence through the application of MR Analysis. The outcomes of this investigation revealed no reverse causal effects between the 3 vertigo diseases and the candidate metabolites, as observed in the IVW model (The details of the results of this part are presented in Table S8, Supplemental Digital Content, http://links.lww.com/MD/N556).

3.5. Genetic correlation analysis

LDSC analysis showed that X-10510 (rg = −1.429, P = .019) and dodecanedioate (rg = −3.298, P = .001) were metabolites that were genetically related to VD. At the same time, in the process of analysis, we found some metabolites of heritability for negative, this may be due to the problem of heritability and the sample size. The interpretation of LDSC results also needs to be combined with the specific research background, sample characteristics and the results of other genetic analyses, and the results need to be interpreted with caution. (Table S9, Supplemental Digital Content, http://links.lww.com/MD/N556 shows the specific results of Genetic Correlation Analysis.)

4. Discussion

In this study, we employed the 2 sample MR method to examine the causal association between 486 blood metabolites and 3 distinct types of vertigo. This investigation utilized metabolite GWAS and pooled datasets of vertigo diseases. By utilizing genetic data pertaining to metabolites, we have successfully identified a grand total of 88 potential metabolites that are linked to the 3 distinct types of vertigo. Following the application of FDR correction to these potential metabolites, as well as conducting stability and sensitivity analyses, we have determined that 22, 16, and 13 candidate metabolites exhibit more robust MR evidence for VD, BPV, PV, and CV, respectively. To explore the biological roles of the candidate metabolites, we used enrichment analysis to obtain 7 key metabolic pathways for the 3 disorders, among which the Valine, leucine and isoleucine biosynthesis metabolic pathway was associated with all 3 types of vertigo, suggesting that this pathway has some influence on the vestibular system. Subsequently, we conducted reverse MR, MR-PRESSO pleiotropy test, and LDSC analysis on the potential metabolites under investigation. Our findings revealed no evidence of reverse causality between the selected candidate metabolites and the vertigo diseases. Furthermore, LDSC analysis revealed a genetic association between X-10510 and dodecanedioate with VD.

The human vestibular system is referred to as the inner ear’s balancing apparatus. Clinicians categorize the vestibular system into the peripheral vestibular system and the central vestibular system, employing the brainstem vestibular nerve nucleus as the demarcation point. The peripheral vestibular system encompasses the otolithic apparatus, semicircular canals, balloon, and elliptic sac, which are symmetrically located within the inner ear. This system serves as the human body’s sensory organ for various forms of acceleration, maintaining continuous communication with the brain. Operating within the subconscious realm of the human mind, it undertakes the intricate task of perceiving both self and environmental movements, while also furnishing a comprehensive three-dimensional representation of head motion and orientation,[1] however, the complex three-dimensional architecture of the vestibular system presents a significant challenge in the neuropathological investigation of both the vestibular and inner ear structures, due to their distinct spatial orientations. In prior research,[23] compelling evidence has indicated a strong correlation between the deterioration of the vestibular system and factors such as aging, oxidative stress, mitochondrial dysfunction, apoptosis, among others. However, the current studies face challenges in replicating the diverse range of vestibular disorders accurately in animal models due to their complex nature. The commonly employed modeling techniques, such as unilateral vestibular neurectomy,[24] unilateral labyrinthine disruption,[25] and the tympanic chamber asanic acid injection method,[26] primarily mimic acute vestibular injury or vestibular compensatory stage, thus lacking comprehensive representation of all vestibular disorders. However, in these models, a metabolic disorder was observed, which may suggest that the impairment of vestibular function may cause metabolic changes. In clinical practice, the majority of vertigo symptoms start with functional vestibular damage and gradually evolve into peripheral or central vertigo. The role of metabolic system in this process is still unclear. Therefore, our study may provide some theoretical support for such research.

In the past, healthcare professionals employed non-interactive modes of interviews and questionnaires to carry out epidemiologic surveys on patients experiencing vertigo. However, due to the subjective nature of dizziness and vertigo, which hinders a comprehensive and unambiguous description, this investigative approach may result in significant misclassification. With newer iterations of diagnostic techniques, there has been a change from subjective identification of vertigo to objective evaluation of vertigo, and the horizontal head impulse test of the vestibular oculomotor reflex was first used as a bedside test to characterize peripheral vestibular disorders in 1988 by Halmagyi and Curthoys.[27] Another bedside predictor of vertigo for acute vestibular lesions is through eccentric gaze Nystagmus that changes direction.[28] For central lesions the predictors are bias, and left-right imbalance due to vestibular nerve discharges, especially vertical ocular dissonance due to otolith dislodgement.[29] The head impulse, nystagmus type, test of skew, a bedside test commonly used by vertigo physicians, was introduced in 2009 by Kattah et al.[30] In addition, audiometry, psychiatric scale screening, and MRI for patients with acute onset of the disease are necessary. Despite significant advancements in the identification of vestibular system disorders, certain limitations persist. These include the specialized nature of screening patients with vertigo, the absence of specific diagnostic markers such as bilirubin and platelet counts, and the lack of disease-specific metabolic profiling for vestibular system disorders. Consequently, conducting extensive epidemiologic surveys and rapid screening in outpatient clinics becomes challenging, thereby necessitating further exploration in this direction.

According to the existing research evidence, it has been firmly established that the vestibular pathway is influenced by at least 4 neurotransmitters, namely Glutamate, GABA, Acetylcholine, and Noradrenaline (NE). Among these neurotransmitters, Glutamate, GABA, and Acetylcholine have been identified as significant contributors to vestibular neurotransmission. Although the precise role of GABA in this process remains incompletely understood, it is undeniable that both GABAA and GABAB receptors exert comparable inhibitory effects on the vestibular pathway.[1] In our study, phosphate can reduce the risk of VD, which we believe may be due to the fact that during the early phase of VD, the level of NE rises rapidly and is transmitted to the vestibular nerves via the blue patch, which excites the vestibulospinal tracts, which show alertness and postural sway, increased vestibular eye movements, and the occurrence of equilibrium instability and anxiety,[31] whereas in the study by W. Rascher,[32] phosphate depletion leads to an increase in the volume concentration of NE in rats, resulting in elevated sympathetic activity, and an increase in phosphate may inhibit sympathetic excitation, thereby alleviating the symptoms associated with VD. As for androsterone sulfate, our results indicate that it is negatively correlated with VD and CV. The findings of Elizabeth Altmaier’s research[33] indicate a positive association between heightened levels of androsterone sulfate in individuals and the manifestation of Type D personality, characterized by a propensity for psychological distress or negative emotions. Furthermore, individuals with Type D personality are more prone to experiencing anxiety or depression. Considering the close relationship between the anxiety pathway in the brain and the vestibular pathway, it is imperative to exercise caution when interpreting the results obtained. Meanwhile, in our findings, there are still some areas that need to be noted, for example, when observing the effect size of the unknown metabolite X-11315 (M32632) on CV, the P value was less than .05 in the 4 models, and it is hypothesized that it may be a strong protective factor for CV.

The present study conducted a metabolic pathway analysis, which demonstrated that the pathway of “valine, leucine, and isoleucine biosynthesis” is predominantly linked to the occurrence of vertigo. It is noteworthy that valine, leucine, and isoleucine exhibit structural similarities to branched-chain fatty acids, rendering them significant constituents of the essential amino acid group. Several studies have indicated a potential correlation between the biosynthesis of valine, leucine, and isoleucine and the underlying biological mechanisms of anxiety disorders. In clinical practice, a considerable number of patients experiencing vertigo also present varying degrees of anxiety. Consequently, exploring this association may offer a novel therapeutic approach, particularly for individuals afflicted with vertigo accompanied by anxiety.

Our study possesses several notable strengths. Primarily, we employed a substantial number of genetic variables to investigate the relationship between blood metabolites and various vertigo disorders. Additionally, the genetic variables for vertigo, derived from GWAS, encompassed data from over 200,000 individuals across 3 distinct vertigo diseases. Utilizing these extensive datasets, we conducted a relatively comprehensive and systematic analysis of metabolic profiles associated with the development of vertigo. Finally, we utilized an MR design to exclude reverse causality and residual confounders to some extent, and excluded the possibility of variable polymorphisms with extensive sensitivity analyses, and inferences about causality between metabolites and vertigo risk in our study were considered robust.

At the same time, there are some limitations in this study. Firstly, the limitations of the database resulted in the original data only containing vestibular dysfunction, benign recurrent vertigo, peripheral vertigo, and central vertigo, and we were unable to further subdivided vertigo into more types of disorders in conjunction with the International Classification Standards for Vestibular Diseases, such as vestibular migraines and persistent postural perceptual vertigo, and this should be supplemented in the future. Second, the strength of the instrumental variables depends heavily on the sample size of the GWAS dataset, and we need more data to improve the accuracy of the generated results. Third, although MR methods have been shown to be effective in assessing the causal relationship between exposure factors and outcomes, there is a need to expand the sample size in order to more accurately assess the genetic influence on metabolites, and, our results still need to be further investigated in conjunction with experimental data. Fourth, we failed to correct the P values using the rigorous Bonferroni method, which may be due to insufficient data. Finally, although our study identified multiple metabolic and metabolic pathways that contribute to the increased risk of vertigo, further studies are needed to reveal their roles in the link.

5. Conclusion

In summary, our study has identified a total of 55 metabolites that exhibit potential causal relationships with the pathogenesis of vertigo. Furthermore, our investigation has revealed that the “valine, leucine, and isoleucine biosynthesis” pathway may play a significant role as a pathologically relevant metabolic pathway in vertigo. Consequently, our study holds promise in serving as a valuable reference for the identification of specific metabolites that could potentially serve as biomarkers for the development of targeted drugs or diagnostic techniques for the treatment of vertigo-related diseases.

Author contributions

Data curation: Sulan Chen, Xin Teng.

Formal analysis: Bin Zhang.

Methodology: Bin Zhang, Yin Liu.

Software: Qi Han, Tong Wu.

Visualization: Sulan Chen, Xin Teng.

Writing – original draft: Bin Zhang.

Writing – review & editing: Ke Xiang, Li Sun.

Supplementary Material

Abbreviations:

CV central vertigo

FDR False Discovery Rate

GWAS Genome Wide Association Study

IVs the instrumental variables

IVW inverse variance weighting

LD linkage disequilibrium

LDSC Linkage Disequilibrium Score

MR Mendelian randomization

NE noradrenaline

OR odds ratio

PV peripheral vertigo

VD vestibular dysfunction

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Zhang B, Chen S, Teng X, Han Q, Wu T, Liu Y, Xiang K, Sun L. Causal association between blood metabolites and vertigo: A Mendelian randomization study. Medicine 2024;103:38(e39688).
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