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Sudden sensorineural hearing loss (SSNHL) is an otologic emergency, and metabolic disturbance is involved in its pathogenesis. This study recruited 20 SSNHL patients and 20 healthy controls (HCs) and collected their serum samples. Serum metabolites were detected by liquid chromatography-mass spectrometry, and metabolic profiles were analyzed. All patients were followed up for 3 months and categorized into recovery and non-recovery groups. The distinctive metabolites were assessed between two groups, and their predictive values for hearing recovery were evaluated. Analysis results revealed that SSNHL patients exhibited significantly characteristic metabolite signatures compared to HCs. The top 10 differential metabolites were further analyzed, and most of them showed potential diagnostic values based on receiver operator characteristic (ROC) curves. Finally, 14 SSNHL patients were divided into the recovery group, and six patients were included in the non-recovery group. Twelve distinctive metabolites were observed between the two groups, and ROC curves demonstrated that N4-acetylcytidine, p-phenylenediamine, sphingosine, glycero-3-phosphocholine, and nonadecanoic acid presented good predictabilities in the hearing recovery. Multivariate analysis results demonstrated that serum N4-Acetylcytidine, sphingosine and nonadecanoic acid levels were associated with hearing recovery in SSNHL patients. Our results identified that SSNHL patients exhibited distinctive serum metabolomics signatures, and several serum biomarkers were proved to be potential in predicting hearing recovery. The discriminative metabolites might contribute to illustrating the mechanisms of SSNHL and provide possible clues for its treatments.
Sudden sensorineural hearing loss (SSNHL) is an otologic emergency which is defined as a sharp hearing loss of ≥30 dB in three sequential frequencies within 72 h (
Metabolomics is a burgeoning branch of omics science which is performed in a biological system to capture metabolic perturbations (
Therefore, we aimed to discover serum metabolic signatures of SSNHL patients and evaluate the capacities of distinctive metabolites in predicting the prognosis of SSNHL in the present study.
In this study, a total of 20 consecutive patients with SSNHL and 20 gender- and age-matched HCs were enrolled between June 2021 and October 2021 in our department. All included subjects signed informed consent, and the present study was approved by the ethical committee in our hospital (No.20191209-01, December 9th, 2019). SSNHL was diagnosed by pure tone audiometry (PTA), referring to the guidelines provided by the Chinese Medical Association of Otolaryngology (CMAO) (
All SSNHL patients received PTA before the onset of treatment, and they were treated with standard treatment protocol in our department, including oral or intravenous steroids, and adjuvant blood flowing promoting agents as our previous publication described (
Five mL of fasting peripheral blood from SSNHL patients before treatment and HCs were collected and stored for 1 h at room temperature. All collected samples were isolated and centrifuged at 3500 rpm at room temperature for 15 min, and supernatants were harvested and stored at −80°C until use. A 100 μL test specimen was mixed with 300 uL methanol containing internal standard for the preparation for LC-MS analysis. The mixture was further vortexed for 30 s and sonicated for 10 min, then centrifuged at 12,000 rpm at 4°C for 20 min. The liquid supernatant was collected and set to a second centrifugation at 13,000 rpm at 4°C for 20 min. The supernatants were transferred to a fresh glass vial for metabolomics analysis. A quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants derived from test specimens to evaluate the reproducibility and reliability of the metabolomics analytical system (
Metabonomic profiles of included samples were detected with 290 Infinity series ultrahigh-performance liquid chromatography (UHPLC) System (Waters Corporation, Milford, MA, United States), equipped with a UPLC BEH Amide column (2.1 mm×100 mm, 1.7 μm). Ultrahigh-performance liquid chromatography (UHPLC) (Waters, Milford, United States) system equipped with AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, United States). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (A) and acetonitrile (B). All analyzed samples were kept at 4°C, and the temperature of the column was kept at 25°C. The analysis was performed with an elution gradient as previously described (
The original data were converted to the mzXML format with Proteo Wizard and analyzed by the R package version. These processes include baseline removing, peak identification, peak alignment and integration, and retention time adjustment (
Numerical variables with normal distribution were shown as mean ± standard deviation (SD), and those without normal distribution were described as the median and interquartile range (IQR). Categorical variables are presented as numbers and percentages. Student’s t-test or Mann-Whitney U test was utilized to compare the differences between continuous variables, and the Chi-square test or Fisher’s exact test was used for categorical variables. Multivariate analysis was performed to assess the association between selected metabolite levels and auditory recovery. All statistical analyses were conducted with SPSS statistics software version23.0 (IBM, Chicago, IL, United States), and
A total of 20 SSNHL patients and 20 HCs were included in this study. The demographic and clinical characteristics of all subjects were displayed in
Demographic and clinical parameters of HC and SSNHL patients.
Parameters | HC ( |
SSNHL ( |
|
---|---|---|---|
Age, year | 37.0 (32.3, 42.8) | 41.5 (30.0, 44.8) | 0.605 |
Male/female | 12/8 | 10/10 | 0.751 |
BMI, kg/m2 | 23.1 (21.1, 24.3) | 22.0 (21.1, 25.0) | 0.284 |
Side, left/right | 11/9 | ||
Tinnitus, n (%) | 15 (75.0) | ||
Vertigo, n (%) | 9 (45.0) | ||
Initial hearing thresholds, dB | 62.5 (35.0, 72.5) | ||
Audiogram type, n (%) | |||
Ascending | 6 (30.0) | ||
Descending | 4 (20.0) | ||
Flat | 5 (25.0) | ||
Profound | 5 (25.0) | ||
Recovery, n (%) | |||
Complete recovery | 6 (30.0) | ||
Marked recovery | 8 (40.0) | ||
Slight recovery | 4 (20.0) | ||
No recovery | 2 (10.0) |
HC, healthy control; SSNHL, sudden sensorineural hearing loss; BMI, body mass index.
A total of 1943 metabolites were obtained in tested samples, and 1595 of them were identified as known substances. Based on these metabolites, PCA and OPLS-DA models were constructed, and the results presented clear and distinctive clustering between the serum of SSNHL patients and HCs (
Serum metabolomics analysis of samples from SSNHL and HC groups.
Heat map of the 98 discriminative metabolites between SSNHL group and HC group. SSNHL, sudden sensorineural hearing loss; HC, healthy control.
Top ten distinctive metabolites between SSNHL patients and HCs.
Metabolites | VIP | FC | Regulation |
|
Classification of metabolites |
---|---|---|---|---|---|
Guanosine | 2.56 | 0.355 | down | <0.001 | Nucleotide and its metabolomics |
Ribothymidine | 2.44 | 2.523 | up | <0.001 | Pyrimidine nucleosides |
Octanoylcarnitine | 2.387 | 1.987 | Up | 0.002 | Fatty Acyls |
Moxisylyte | 2.381 | 2.265 | Up | 0.001 | Prenol lipids |
3-Hydroxycapric acid | 2.322 | 0.341 | down | 0.005 | Hydroxy acids and derivatives |
Tumonoic acid A | 2.266 | 0.355 | down | <0.001 | Carboxylic acids and derivatives |
Prolyl-tyrosine | 1.978 | 0.410 | down | <0.001 | Carboxylic acids and derivatives |
Nylidrin | 1.913 | 0.439 | down | 0.004 | Benzene and its derivatives |
Chaetoglobosin N | 1.782 | 1.672 | Up | 0.001 | Cytochalasans |
Sphingosine | 1.71 | 2.756 | Up | 0.006 | Sphingolipids |
SSNHL, sudden sensorineural hearing loss; HC, healthy control; VIP, variable importance for project; FC, fold change.
Top 10 most discriminant metabolites
ROC analysis of top 10 most discriminant metabolites
After 3 months of follow-ups, 14 patients were included in the recovery, and six patients were categorized into the non-recovery group.
Demographic and clinical parameters between recovery and non-recovery groups.
Parameters | Recovery ( |
Non-recovery ( |
|
---|---|---|---|
Age, year | 38.5 (38.0, 44.3) | 42.0 (34.0, 48.0) | 0.438 |
Male/female | 6/8 | 4/2 | 0.628 |
BMI, kg/m2 | 21.7 (21.1, 25.1) | 21.1 (21.4, 23.6) | 0.340 |
Side, left/right | 8/6 | 3/3 | 1.000 |
Tinnitus, n (%) | 12 (85.7) | 3 (50.0) | 0.131 |
Vertigo, n (%) | 7 (50.0) | 2 (33.3) | 0.642 |
Initial hearing thresholds, dB | 61.3 (35.0, 70.0) | 65.0 (42.5, 75.0) | 0.867 |
Post-treatment hearing thresholds, dB | 32.5 (27.5, 40.0) | 57.5 (41.3, 67.5) | 0.006 |
Hearing improvement, dB | 30.0 (22.5, 37.5) | 6.3 (5.0, 10.0) | <0.001 |
Audiogram type, n (%) | 0.172 | ||
Ascending | 6 (42.9) | 0 (0) | |
Descending | 3 (21.4) | 1 (16.7) | |
Flat | 3 (21.4) | 2 (33.3) | |
Profound | 2 (14.3) | 3 (50.0) |
BMI, body mass index.
Serum metabolomics analysis of samples from recovery group and non-recovery group.
Heat map of the 12 discriminative metabolites between recovery and non-recovery groups.
Top ten distinctive metabolites between recovery and non-recovery groups.
Metabolites | VIP | FC | Regulation |
|
Classification of metabolites |
---|---|---|---|---|---|
N4-Acetylcytidine | 2.823 | 2.19 | Up | 0.013 | Pyrimidine nucleosides |
p-Phenylenediamine | 2.651 | 0.552 | down | 0.005 | Benzene and its derivatives |
Sphingosine | 2.613 | 2.024 | up | 0.021 | Sphingolipids |
Prostaglandin A1 | 2.514 | 2.408 | up | 0.019 | Fatty acyls |
Glycero-3-phosphocholine | 2.419 | 0.649 | down | 0.014 | Glycerophospholipids |
Propenoic acid | 2.401 | 2.076 | up | 0.002 | Fatty Acyls |
1,3-Dimethylbenzene | 2.187 | 0.423 | down | 0.040 | Benzene and its derivatives |
Nonadecanoic acid | 2.023 | 2.521 | up | 0.036 | Fatty acyls |
2,3,4-Trichlorobiphenyl | 1.923 | 1.969 | up | 0.044 | Benzene and its derivatives |
Tumonoic acid A | 1.873 | 2.6538 | up | 0.008 | Carboxylic acids and derivatives |
VIP, variable importance for project; FC, fold change.
Top 10 most discriminant metabolites
ROC analysis of top 10 most discriminant metabolites
Unadjusted and adjusted multivariate logistic regression analysis of metabolites associated with hearing recovery.
Metabolites | Unadjusted | Adjusted | ||
---|---|---|---|---|
Or (95% CI) |
|
Or (95% CI) |
|
|
N4-Acetylcytidine | 3.025 (1.492–7.098) | 0.021 | 3.874 (1.375–8.514) | 0.030 |
p-Phenylenediamine | 1.834 (1.073–3.109) | 0.042 | 1.643 (0.854–2.853) | 0.118 |
Sphingosine | 3.652 (1.486–8.951) | 0.003 | 2.977 (1.512–7.755) | 0.009 |
Prostaglandin A1 | 1.279 (0.699–2.708) | 0.813 | 1.314 (0.707–2.892) | 0.684 |
Glycero-3-phosphocholine | 2.094 (0.873–4.916) | 0.308 | 1.836 (0.903–5.045) | 0.145 |
Propenoic acid | 1.596 (1.128–5.217) | 0.038 | 1.476 (0.915–5.735) | 0.208 |
1,3-Dimethylbenzene | 1.902 (0.592–4.083) | 0.580 | 1.767 (0.613–4.982) | 0.580 |
Nonadecanoic acid | 2.087 (1.312–7.659) | 0.026 | 1.894 (1.276–6.942) | 0.033 |
2,3,4-Trichlorobiphenyl | 1.093 (0.871–3.129) | 0.742 | 1.387 (0.901–2.896) | 0.589 |
Tumonoic acid A | 1.472 (0.762–3.238) | 0.519 | 1.089 (0.884–2.676) | 0.479 |
OR, odds rate; CI, confidence interval. Adjusted for age, gender, BMI, atopy, side, tinnitus and vertigo.
To the best of our knowledge, this study was the first one to conduct a novel application of metabolomics to explore serum metabolic profiles of SSNHL patients and identify potential biomarkers for predicting hearing recovery based on distinctive metabolites. Our results demonstrated that SSNHL patients presented discriminative serum metabolites and metabolic pathways compared to HCs, and several metabolites exhibited potential diagnostic values for SSNHL. We also found that SSNHL patients with good hearing recovery manifested significantly different metabolic profiles in comparison with those with poor hearing recovery, and several metabolites were proved to be associated with the auditory recovery and exhibit powerful abilities for predicting the hearing prognosis. Collectively, these results suggested that serum metabolomics was a useful and easily performed method for diagnosing SSNHL and predicting hearing recovery after treatment, which provided a new perspective to understand the underlying mechanism of SSNHL and better personalize its therapies.
Metabolomics is a promising and powerful method for describing the metabolic variety and discovering potential biomarkers for disease phenotype and prognosis prediction (
In the present study, we first observed that fatty acid metabolism was significantly perturbed in the serum of SSNHL patients and involved in the hearing prognosis. Substantial evidence suggested that the metabolism of the fatty acids played a crucial role in maintaining the inner ear and vestibule cellular mechanisms and were involved in hearing and balance system development (
Another interesting finding was that the serum concentrations of sphingosine were increased in the SSNHL patients, and serum sphingosine exhibited prognostic value as a reliable biomarker for predicting hearing recovery before the onset of treatment. Sphingosine, an important member of sphingolipids, is a ubiquitous component of the cell membrane and serves a pivotal role in cell growth, metabolism regulation, signal transduction, and various physiological function (
Several limitations exist in the present study, which may affect the reliability of the reported results. First, this work is a preliminary and exploratory study with a relatively small sample size, and the sample size is not a detailed calculation. We conducted multiple data processing analyses and multivariate analyses to recheck the reliability of the results. Second, all subjects were recruited in a single medical centre, which may limit its generation. Third, we do not validate the diagnostic and predictive values of identified metabolites in another validation cohort. Lastly, and no consensus currently exists regarding the hearing recovery evaluation criterion in SSNHL, this may weaken the reliability of the conclusion. Further prospective multicenter studies with a large sample size to further explore SSNHL disease-specific metabolites in different subgroups based on audiogram types and assess their predictive values in hearing recovery in each subgroup.
In conclusion, our study performed the metabolic profiles and indicated that metabolomics could be successfully performed to detect SSNHL-specific metabolic shifts. Our study also identified several metabolites that exhibited potential diagnostic and predictive values in SSNHL, which might contribute to illustrating the mechanisms of SSNHL and provide new insights for its treatments.
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
The studies involving human participants were reviewed and approved by ethical committee of Urumqi Maternal and Child Health Care Hospital. The patients/participants provided their written informed consent to participate in this study.
XW wrote the manuscript. YG collected samples and performed data analysis. RJ provided statistical support and designed the research study. All authors reviewed the manuscript and approved the final version.
This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (NO. 2020D01A18).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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