Benign paroxysmal positional vertigo (BPPV) is the most common peripheral vestibular disorder and exhibits marked heterogeneity in symptom burden and clinical course. Objective biomarkers reflecting inner-ear structural status and their relationship with clinical manifestations remain limited. Emerging evidence suggests an association between sleep quality and vertigo symptoms; however, the biological basis underlying this relationship is poorly understood.
In this case–control study, 268 patients with BPPV and 268 age- and sex-matched healthy controls were enrolled. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and vertigo severity was evaluated using the Dizziness Handicap Inventory (DHI). Serum levels of the inner-ear–specific structural biomarkers Otolin-1 and otoconin-90 (OC90) were measured using enzyme-linked immunosorbent assays. Logistic regression was used to examine associations with BPPV presence. Among patients with BPPV, multivariable linear regression, joint models, and exploratory mediation analyses were conducted to evaluate relationships among sleep quality, biomarkers, and symptom severity.
Compared with healthy controls, patients with BPPV exhibited significantly higher serum levels of the inner-ear structural biomarkers Otolin-1 (median 6.38 vs. 3.94 ng/mL) and otoconin-90 (median 12.64 vs. 7.58 ng/mL), both of which were independently associated with the odds of BPPV (adjusted OR for Otolin-1: 2.08, 95% CI: 1.49–2.91; adjusted OR for otoconin-90: 1.71, 95% CI: 1.21–2.42). Among patients with BPPV, poorer sleep quality was associated with greater vertigo severity (β = 2.14 per PSQI point, 95% CI: 1.56–2.72). Higher PSQI scores were also associated with increased levels of Otolin-1 and otoconin-90, both of which were independently related to vertigo severity. Inclusion of these biomarkers in joint models attenuated the PSQI–DHI association (β from 2.14 to 1.28) and improved model explanatory power (R2 from 0.26 to 0.38). Exploratory mediation analyses suggested that Otolin-1 and otoconin-90 statistically accounted for approximately 40 and 29% of the sleep–symptom association, respectively, with consistent findings across sensitivity and subgroup analyses.
These findings indicate that otolith-related inner-ear structural biomarkers are associated with both the presence and severity of BPPV and may partially explain the relationship between sleep quality and vertigo symptoms.
Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders and represents a substantial source of disability in clinical practice, with a pronounced impact on patients’ daily functioning and quality of life (
Biological indicators that reflect inner-ear structural status and correspond to symptom burden in BPPV remain limited (
An increasing body of evidence suggests that sleep quality is associated with vertigo-related symptoms and recurrence in patients with BPPV (
Despite growing interest in otolith-related mechanisms, important gaps remain in the current literature. Moreover, existing research has rarely integrated sleep quality, inner-ear structural biomarkers, and symptom severity within a unified analytical framework, limiting insight into how these factors may jointly contribute to the marked heterogeneity of clinical manifestations. To address these gaps, the present study aimed to comprehensively investigate the relationships among sleep quality, inner-ear–specific structural biomarkers (Otolin-1 and otoconin-90), and vertigo severity in a well-characterized cohort of patients with BPPV and clarify the clinical relevance of otolith-related biomarkers and to explore their potential role in linking sleep quality to symptom burden. Through this integrative analytical framework, we sought to provide clinical and biological insights into the heterogeneity of BPPV manifestations.
This study employed a case–control design, enrolling patients with BPPV as the case group and healthy individuals as the control group. A total of 268 consecutive patients with BPPV were recruited between January 2022 and June 2024 from the outpatient clinics, inpatient wards, and vertigo specialty center of The Fifth Affiliated Hospital of Zhengzhou University. All patients were evaluated by neurologists or otolaryngologists with expertise in vestibular disorders. Specifically, characteristic vertigo and positional nystagmus were elicited during the Dix–Hallpike maneuver or the supine roll test. Affected semicircular canals were classified as posterior canal, horizontal canal, or multiple canal involvement according to clinical and nystagmus features.
The control group consisted of 268 healthy volunteers recruited during the same period from the health examination center or community health screening programs. All control participants had no history of vertigo or balance disorders and no diagnosed vestibular or central nervous system diseases. Healthy controls were frequency-matched to patients with BPPV by age and sex, and comparability between groups in terms of body mass index (BMI) was assessed to minimize potential confounding.
This study was approved by the Medical Ethics Committee of The Fifth Affiliated Hospital of Zhengzhou University and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrollment.
Patients were eligible for inclusion if they met all of the following criteria: (1) aged 18 years or older; (2) a confirmed diagnosis of BPPV according to the International Classification of Vestibular Disorders (ICVD) and relevant clinical guidelines, supported by typical clinical history and positive positional test findings; (3) ability to complete standardized questionnaires assessing sleep quality and vertigo severity; (4) willingness to provide peripheral blood samples for biomarker analysis; (5) provision of written informed consent.
Patients were excluded if any of the following conditions were present: (1) evidence of central causes of vertigo, including but not limited to cerebrovascular disease, intracranial tumors, or demyelinating disorders; (2) a history of major cerebrovascular events or other severe neurological diseases; (3) severe psychiatric or neurological disorders that could interfere with reliable assessment of sleep quality or vertigo-related symptoms; (4) severe hepatic or renal dysfunction, which might influence circulating protein levels; (5) use of medications within the preceding 4 weeks that could significantly affect vestibular function or sleep patterns, including vestibular suppressants, sedative-hypnotics, or psychoactive drugs; (6) incomplete clinical data or refusal to participate in questionnaire assessment or blood sampling.
Baseline demographic and anthropometric data were collected for all participants at the time of enrollment. Age and sex were recorded based on self-report and medical records. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). Body weight and height were measured using standardized equipment during the clinical visit on the day of study inclusion.
For patients with BPPV, disease-related clinical information was obtained through structured interviews and medical record review. Disease duration was defined as the time interval, in months, from the first documented episode of vertigo consistent with BPPV to the date of enrollment. Affected semicircular canal involvement was determined based on clinical presentation and nystagmus characteristics during positional testing and categorized as posterior canal, horizontal canal, or multiple canal involvement.
Recurrent BPPV was defined as the occurrence of one or more new episodes of positional vertigo consistent with BPPV following complete resolution of symptoms after appropriate canalith repositioning therapy, within the observation period prior to enrollment. This definition was applied consistently across all patients for descriptive and subgroup analyses.
Vertigo severity was assessed using the Dizziness Handicap Inventory (DHI), a validated self-report questionnaire designed to evaluate the impact of dizziness on daily functioning. The DHI consists of 25 items covering three domains: physical (P), emotional (E), and functional (F), with a total score ranging from 0 to 100, where higher scores indicate greater vertigo-related handicap. According to established criteria, DHI scores can be categorized as mild (0–30), moderate (31–60), or severe (61–100) impairment. In the present study, DHI was primarily analyzed as a continuous variable to preserve statistical power and capture the full spectrum of symptom severity. All participants completed the validated language version of the DHI at the time of enrollment, and the instrument has demonstrated good reliability in the study population.
Sleep quality was evaluated using the PSQI, a widely used questionnaire that assesses sleep quality over the preceding month. The PSQI comprises 19 self-rated items, which are aggregated into seven component scores—subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction—and summed to yield a global score ranging from 0 to 21, with higher scores indicating poorer sleep quality. A global PSQI score greater than 5 is commonly used to define poor sleep quality.
Peripheral venous blood samples were collected from all participants on the day of enrollment, under standardized conditions. Blood was drawn in the morning after an overnight fast whenever feasible. Samples were allowed to clot at room temperature and were then centrifuged at 3,000 rpm for 10 min to separate serum. The resulting serum was aliquoted into pre-labeled tubes and stored at -80°C until analysis. To minimize degradation and variability, all samples were subjected to no more than one freeze–thaw cycle prior to biomarker measurement.
Serum concentrations of Otolin-1 and otoconin-90 (OC90) were quantified using commercially available enzyme-linked immunosorbent assay (ELISA) kits, according to the manufacturers’ instructions. For each assay, standard curves were generated using serial dilutions of recombinant standards. All samples were measured in duplicate, and the mean value was used for statistical analysis.
Assay performance was monitored by calculating intra-assay and inter-assay coefficients of variation (CVs), which were maintained within acceptable ranges specified by the manufacturers. Laboratory personnel performing the assays were blinded to clinical information, and samples were analyzed in a randomized order to reduce potential batch effects. Quality control samples were included across assay plates to ensure consistency and reliability of measurements.
Serum concentrations of Otolin-1 and OC90 were expressed in nanograms per milliliter (ng/mL). Distributional assessment revealed that both biomarkers exhibited right-skewed distributions. Therefore, logarithmic transformation was applied prior to regression analyses to improve normality and stabilize variance.
All statistical analyses were performed using R software and SPSS Statistics. All tests were two-sided, and a
Normality of continuous variables was evaluated using the Shapiro–Wilk test and visual inspection of distributions. Continuous variables with approximately normal distributions were summarized as mean ± standard deviation (SD), whereas non-normally distributed variables were presented as median (interquartile range, IQR). Categorical variables were expressed as counts and percentages. Between-group comparisons were performed using the independent-samples
Logistic regression models were used to evaluate the associations between inner-ear structural biomarkers and the presence of BPPV. BPPV status was treated as the dependent variable (1 = BPPV, 0 = healthy control). Independent variables included log-transformed serum Otolin-1 and log-transformed otoconin-90 (OC90), which were analyzed in separate models. Two models were constructed: Model 1, an unadjusted model, and Model 2, adjusted for age, sex, and body mass index (BMI). Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding
Among patients with BPPV, linear regression analyses were conducted to examine the association between sleep quality and vertigo severity. The Dizziness Handicap Inventory (DHI) total score was modeled as a continuous dependent variable, with the PSQI global score as the primary independent variable. Models were adjusted for age, sex, BMI, disease duration, and affected semicircular canal type. Results were expressed as regression coefficients (β) with 95% CIs and
Linear regression models were used to assess the associations between sleep quality and inner-ear structural biomarkers (
To examine the relationships between inner-ear biomarkers and vertigo severity, linear regression models were constructed with DHI total score as the dependent variable. Otolin-1 and OC90 were first analyzed in separate models, followed by a joint model including both biomarkers simultaneously. All models were adjusted for the same covariates as the primary analyses. Multicollinearity was assessed using the variance inflation factor (VIF), with values < 5 indicating no substantial collinearity.
Nested multivariable linear regression models were constructed to evaluate the combined contributions of sleep quality and inner-ear biomarkers to vertigo severity. Four models were specified: Model A included PSQI only; Model B included PSQI and Otolin-1; Model C included PSQI and OC90; and Model D included PSQI, Otolin-1, and OC90 simultaneously. All models were adjusted for the same set of covariates. Changes in regression coefficients for PSQI and model explanatory power (R2) were examined to assess the extent to which biomarker inclusion attenuated the association between sleep quality and vertigo severity.
Exploratory mediation analyses were conducted to examine whether inner-ear structural biomarkers statistically account for part of the association between sleep quality and vertigo severity. Sleep quality (PSQI score) was specified as the exposure, log-transformed Otolin-1 or OC90 as the mediator, and DHI total score as the outcome. Covariates included age, sex, BMI, and the same clinical variables used in the primary regression models. Mediation effects were estimated using regression-based mediation analysis with bootstrap resampling (5,000 iterations) to derive 95% confidence intervals for indirect effects. Total, direct, and indirect effects were calculated, along with the proportion mediated. These analyses were exploratory in nature and intended to provide statistical interpretation rather than causal inference.
Sensitivity analyses were performed to evaluate the robustness of the primary findings. First, analyses were repeated after excluding patients with recurrent BPPV, restricting the sample to first-episode cases. Second, subgroup analyses were conducted according to affected semicircular canal type, comparing posterior canal BPPV with non-posterior canal involvement. Key regression models were re-estimated within each subgroup to assess consistency of associations.
A total of 268 patients with benign paroxysmal positional vertigo were included in the analysis (
Baseline characteristics of patients with benign paroxysmal positional vertigo and healthy controls.
| Characteristic | BPPV |
Healthy controls |
|
|---|---|---|---|
| Age, years | 53.47 ± 11.26 | 52.88 ± 10.94 | 0.534 |
| Sex, n (%) | 0.447 | ||
| Male | 78 (29.10) | 84 (31.34) | |
| Female | 190 (70.90) | 184 (68.66) | |
| Body mass index, kg/m2 | 22.84 ± 3.12 | 23.06 ± 3.25 | 0.412 |
| Disease duration, months | 3.60 (1.20–9.80) | / | — |
| Affected semicircular canal, n (%) | / | — | |
| Posterior canal | 189 (70.52) | / | |
| Horizontal canal | 59 (22.01) | / | |
| Multiple canals | 20 (7.46) | / | |
| Recurrent BPPV, n (%) | 92 (34.33) | / | — |
| Sleep quality (PSQI score) | 8.42 ± 3.11 | 5.36 ± 2.42 | < 0.001 |
| Vertigo severity (DHI score) | 46.75 ± 18.92 | / | — |
| Serum Otolin-1, ng/mL | 6.38 (4.92–8.71) | 3.94 (3.12–5.06) | < 0.001 |
| Serum otoconin-90, ng/mL | 12.64 (9.35–17.18) | 7.58 (5.94–9.66) | < 0.001 |
Values are presented as mean ± standard deviation for normally distributed continuous variables, median (interquartile range) for non-normally distributed variables, and number (percentage) for categorical variables. Comparisons between patients with BPPV and healthy controls were performed using the independent-samples
Regarding the affected semicircular canals, posterior canal involvement was the most common presentation, observed in 189 patients (70.52%), followed by horizontal canal involvement in 59 patients (22.01%), and multiple canal involvement in 20 patients (7.46%). Recurrent BPPV was identified in 92 patients (34.33%).
The mean Pittsburgh Sleep Quality Index (PSQI) score was 8.42 ± 3.11, indicating a high prevalence of impaired sleep quality in the cohort. The mean Dizziness Handicap Inventory (DHI) score was 46.75 ± 18.92, reflecting a moderate overall burden of vertigo-related disability. Median serum levels of Otolin-1 and Otoconin-90 were 6.38 ng/mL (interquartile range, 4.92–8.71 ng/mL) and 12.64 ng/mL (interquartile range, 9.35–17.18 ng/mL), respectively. Serum levels of the inner-ear–specific biomarkers Otolin-1 and Otoconin-90 were both significantly higher in patients with BPPV than in controls (
As shown in
Associations of inner-ear structural biomarkers with the odds of benign paroxysmal positional vertigo.
| Variable | Model 1 OR (95% CI) | Model 2 OR (95% CI) | ||
|---|---|---|---|---|
| Serum Otolin-1 (log-transformed) | 2.41 (1.78–3.26) | < 0.001 | 2.08 (1.49–2.91) | < 0.001 |
| Serum otoconin-90 (log-transformed) | 1.96 (1.44–2.68) | < 0.001 | 1.71 (1.21–2.42) | 0.002 |
Logistic regression analyses were performed with BPPV status (yes/no) as the dependent variable. Model 1 represents the unadjusted model. Model 2 was adjusted for age, sex, and body mass index. Serum Otolin-1 and Otoconin-90 levels were log-transformed prior to analysis due to right-skewed distributions. Results are presented as odds ratios (ORs) with 95% confidence intervals (CI). All statistical tests were two-sided, and
After adjustment for age, sex, and body mass index, these associations remained statistically significant. Specifically, the adjusted odds ratio for Otolin-1 was 2.08 (95% CI: 1.49–2.91,
As shown in
Association between sleep quality (PSQI) and Vertigo severity (DHI).
| Model | Exposure variable | Outcome variable | β (95% confidence interval) | |
|---|---|---|---|---|
| Unadjusted | PSQI score (per 1-point increase) | Total DHI score | 2.38 (1.81–2.95) | < 0.001 |
| Adjusted | PSQI score (per 1-point increase) | Total DHI score | 2.14 (1.56–2.72) | < 0.001 |
Regression coefficients are presented as β values with 95% confidence intervals (CI), indicating the estimated change in DHI score associated with each 1-point increase in PSQI score. The adjusted model was controlled for age, sex, body mass index, disease duration, and affected semicircular canal type. All statistical tests were two-sided. P values are reported to three decimal places. PSQI indicates Pittsburgh Sleep Quality Index; DHI, Dizziness Handicap Inventory.
As shown in
Association between sleep quality and inner-ear structural biomarkers.
| Outcome variable | Model | Exposure variable | β (95% confidence interval) | |
|---|---|---|---|---|
| Serum Otolin-1 (log-transformed) | Unadjusted | PSQI score (per 1-point increase) | 0.08 (0.05–0.11) | < 0.001 |
| Adjusted | PSQI score (per 1-point increase) | 0.07 (0.04 to 0.10) | < 0.001 | |
| Serum otoconin-90 (log-transformed) | Unadjusted | PSQI score (per 1-point increase) | 0.06 (0.03–0.09) | < 0.001 |
| Adjusted | PSQI score (per 1-point increase) | 0.05 (0.02–0.08) | 0.001 |
Regression coefficients are presented as β values with 95% confidence intervals (CI), representing the estimated change in log-transformed biomarker levels per 1-point increase in PSQI score. The adjusted model was controlled for age, sex, body mass index, disease duration, and affected semicircular canal type. All statistical tests were two-sided.
As presented in
Associations of inner-ear structural biomarkers with vertigo severity.
| Model | Exposure variable | Outcome variable | β (95% confidence interval) | |
|---|---|---|---|---|
| Unadjusted | Serum Otolin-1 (log-transformed) | Total DHI score | 9.42 (6.21–12.63) | < 0.001 |
| Unadjusted | Serum otoconin-90 (log-transformed) | Total DHI score | 7.15 (4.02–10.28) | < 0.001 |
| Adjusted | Serum Otolin-1 (log-transformed) | Total DHI score | 8.36 (5.14–11.58) | < 0.001 |
| Adjusted | Serum otoconin-90 (log-transformed) | Total DHI score | 6.02 (2.91–9.13) | < 0.001 |
| Joint model | Serum Otolin-1 (log-transformed) | Total DHI score | 6.84 (3.58–10.10) | < 0.001 |
| Joint model | Serum otoconin-90 (log-transformed) | Total DHI score | 4.21 (1.18–7.24) | 0.007 |
Multiple linear regression analyses were performed with Dizziness Handicap Inventory (DHI) total score as a continuous dependent variable. Regression coefficients are presented as β values with 95% confidence intervals (CI), representing the estimated change in DHI score associated with a one-unit increase in log-transformed biomarker levels. Adjusted and joint models were controlled for age, sex, body mass index, disease duration, and affected semicircular canal type. All statistical tests were two-sided.
When both biomarkers were included simultaneously in the joint model, Otolin-1 and Otoconin-90 remained independently associated with vertigo severity, although the magnitude of the associations was moderately attenuated (Otolin-1: β = 6.84, 95% CI: 3.58–10.10;
Results of the joint regression models are summarized in
Joint models of sleep quality and inner-ear biomarkers for vertigo severity.
| Model | Predictors | β (95% confidence interval) | ||
|---|---|---|---|---|
| Model A | PSQI score (per 1-point increase) | 2.14 (1.56–2.72) | < 0.001 | 0.26 |
| Model B | PSQI score | 1.62 (1.05–2.19) | < 0.001 | 0.33 |
| Otolin-1 (log-transformed) | 6.11 (3.12–9.10) | < 0.001 | ||
| Model C | PSQI score | 1.74 (1.17–2.31) | < 0.001 | 0.31 |
| Otoconin-90 (log-transformed) | 4.02 (1.08–6.96) | 0.007 | ||
| Model D | PSQI score | 1.28 (0.74–1.82) | < 0.001 | 0.38 |
| Otolin-1 (log-transformed) | 4.58 (1.62–7.54) | 0.002 | ||
| Otoconin-90 (log-transformed) | 2.93 (0.24–5.62) | 0.033 |
Multiple linear regression analyses were conducted with Dizziness Handicap Inventory (DHI) total score as a continuous dependent variable. Model A included sleep quality only, with the Pittsburgh Sleep Quality Index (PSQI) score entered as the primary independent variable. Model B included PSQI score and serum Otolin-1 (log-transformed). Model C included PSQI score and serum Otoconin-90 (log-transformed). Model D included PSQI score, serum Otolin-1, and serum Otoconin-90 simultaneously (both biomarkers log-transformed). All models were adjusted for age, sex, body mass index, disease duration, and affected semicircular canal type. Regression coefficients are presented as β values with 95% confidence intervals (CI), representing the estimated change in DHI score per unit increase in each predictor. Model performance was evaluated using the coefficient of determination (R2). All statistical tests were two-sided, and
After inclusion of Otolin-1 in Model B, the association between PSQI and DHI was attenuated but remained statistically significant (β = 1.62, 95% CI: 1.05–2.19;
In the fully adjusted Model D, which included PSQI, Otolin-1, and Otoconin-90 simultaneously, all three variables remained independently associated with vertigo severity. Compared with Model A, the regression coefficient for PSQI was further reduced (β = 1.28, 95% CI: 0.74–1.82;
Exploratory mediation analyses were conducted to examine whether inner-ear–specific structural biomarkers statistically accounted for part of the association between sleep quality and vertigo severity (
Exploratory mediation analysis of inner-ear structural biomarkers in the association between sleep quality and vertigo severity.
| Mediator | Effect type | Effect estimate | 95% confidence interval | Proportion mediated (%) | |
|---|---|---|---|---|---|
| Otolin-1 | Total effect | 2.14 | 1.56–2.72 | < 0.001 | — |
| Direct effect | 1.28 | 0.74–1.82 | <0.001 | — | |
| Indirect effect | 0.86 | 0.42–1.30 | < 0.001 | 40.19 | |
| Otoconin-90 | Total effect | 2.14 | 1.56–2.72 | < 0.001 | — |
| Direct effect | 1.52 | 0.96–2.08 | <0.001 | — | |
| Indirect effect | 0.62 | 0.28–0.96 | 0.001 | 28.97 |
All models were adjusted for age, sex, body mass index, disease duration, and affected semicircular canal type, consistent with the main regression analyses. Effect estimates represent changes in DHI score per 1-point increase in PSQI. The proportion mediated was calculated as the ratio of the indirect effect to the total effect and is reported as a percentage. All statistical tests were two-sided.
Results of sensitivity and subgroup analyses are presented in
Subgroup analyses stratified by affected semicircular canal type demonstrated similar associations across posterior and non-posterior canal involvement, with no evidence that a specific canal subtype disproportionately influenced the results (
In this study, we employed a case–control design to examine the relationships among sleep quality, Otolin-1 and otoconin-90, and clinical manifestations of BPPV. Several key findings emerged. First, serum levels of Otolin-1 and otoconin-90 were elevated in patients with BPPV and were associated with disease presence. Second, poorer sleep quality was associated with greater vertigo-related symptom burden among patients with BPPV. Third, sleep quality was also related to levels of otolith-associated structural biomarkers, and the strength of the sleep–symptom association was attenuated after accounting for these biomarkers, accompanied by improved model explanatory power.
Otolin-1 and otoconin-90 are inner-ear–specific matrix proteins that play important roles in otolith formation and structural stability (
In this study, poorer sleep quality was significantly associated with greater vertigo-related symptom severity among patients with BPPV, as reflected by higher DHI scores. This finding indicates that sleep status is closely related to the subjective symptom burden experienced by patients. Such an association is in line with previous observations that sleep disturbances frequently coexist with vestibular symptoms (
Beyond its association with symptom severity, this study further demonstrated that sleep quality was significantly related to serum levels of the otolith-associated structural biomarkers Otolin-1 and otoconin-90. This observation suggests that sleep status may be linked to otolith-related structural alterations, indicating that these factors could operate within a shared biological framework rather than representing isolated clinical features (
In addition to their associations with disease presence, both Otolin-1 and otoconin-90 were independently associated with vertigo severity among patients with BPPV. When examined in separate models, higher levels of each biomarker were related to greater DHI scores. Importantly, these associations remained statistically significant in joint models including both biomarkers, although partial attenuation of effect estimates was observed. This pattern suggests that Otolin-1 and otoconin-90 capture overlapping yet non-identical aspects of otolith-related structural status. Together, these findings support the notion that variations in otolith structure may independently contribute to differences in symptom burden and further strengthen the rationale for considering these molecules as clinically relevant biological indicators in BPPV.
In the joint models, the association between sleep quality and vertigo severity was notably attenuated after the inclusion of Otolin-1 and otoconin-90, accompanied by a meaningful improvement in model explanatory power. This pattern indicates that otolith-related structural biomarkers statistically account for part of the observed relationship between sleep quality and symptom burden. Consistent with this observation, exploratory mediation analyses further suggested that these biomarkers explain a proportion of the sleep–symptom association at a statistical level. Importantly, these findings do not support a single-pathway interpretation but are more consistent with a multifactorial framework in which partially overlapping biological pathways jointly contribute to clinical heterogeneity. From a methodological perspective, the convergence of results across joint regression and mediation analyses strengthens the internal coherence and credibility of the study findings.
The robustness of the primary findings was supported by a series of sensitivity and subgroup analyses. After excluding patients with recurrent BPPV, the main associations among sleep quality, inner-ear structural biomarkers, and vertigo severity remained directionally consistent. Similar patterns were observed across subgroups stratified by affected semicircular canal type and sex. These results indicate that the observed associations are unlikely to be driven by a specific clinical subtype or demographic characteristic and enhance the generalizability of the findings across different BPPV subpopulations.
Clinically, our results support a more integrative view of BPPV in which symptom burden may be influenced by underlying otolith-related biological alterations in addition to perceptual and psychological factors. While immediate clinical application is premature, these findings may assist clinicians in interpreting symptom heterogeneity and highlight sleep quality as a potentially relevant, modifiable factor in patient assessment.
At present, our study has focused exclusively on BPPV; however, the biological roles of Otolin-1 and Otoconin-90 suggest that these biomarkers may also be relevant in other vestibular disorders associated with otolith dysfunction, such as Meniere’s disease, vestibular neuritis, or bilateral vestibular hypofunction. Future studies will be essential to explore the potential diagnostic and prognostic value of these biomarkers in a broader range of vertigo-related conditions.
Several limitations should be acknowledged. First, the cross-sectional nature of this study precludes establishing temporal ordering and does not allow causal inference regarding the observed associations among sleep quality, inner-ear biomarkers, and symptom severity. Second, Otolin-1 and otoconin-90 were measured at a single time point, and thus may not capture within-individual variability or dynamic changes across disease episodes or treatment responses. Third, the control group consisted of healthy individuals; the absence of disease controls with other causes of dizziness limits the specificity assessment of these biomarkers for BPPV. Fourth, although key demographic and clinical covariates were considered, residual confounding remains possible because factors such as vitamin D status, bone mineral density, and medication exposures were not comprehensively assessed. Based on their biological roles as otolith-related structural proteins, it is reasonable to hypothesize that Otolin-1 and Otoconin-90 concentrations may decrease after successful canalith repositioning, with potential changes occurring over a short-to-intermediate time course rather than immediately, and possibly lagging behind subjective symptom resolution. Finally, the mediation analyses were exploratory and intended to provide statistical interpretation; their results should be interpreted cautiously and warrant validation in well-designed prospective and longitudinal studies.
In conclusion, this study demonstrates that the inner-ear structural biomarkers Otolin-1 and otoconin-90 are elevated in patients with benign paroxysmal positional vertigo and are associated with both disease presence and vertigo severity. Sleep quality was significantly related to symptom burden and was also associated with levels of otolith-related structural biomarkers. Joint analyses further suggest that these biomarkers statistically account for part of the association between sleep quality and vertigo severity. Taken together, these findings provide new insights into the clinical heterogeneity of BPPV from a structural biological perspective and lay a foundation for future prospective and mechanistic studies.
The original contributions presented in the study are included in the article/
The studies involving humans were approved by the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
KL: Resources, Data curation, Visualization, Validation, Project administration, Formal analysis, Writing – review & editing, Methodology, Conceptualization, Investigation, Writing – original draft, Funding acquisition, Software, Supervision. Y-HH: Supervision, Methodology, Funding acquisition, Conceptualization, Software, Writing – review & editing, Formal analysis, Project administration, Writing – original draft, Data curation, Validation, Investigation, Resources, Visualization. S-JP: Conceptualization, Resources, Validation, Methodology, Data curation, Writing – review & editing, Visualization, Investigation, Writing – original draft, Formal analysis, Software, Supervision, Project administration, Funding acquisition. Y-ZZ: Writing – original draft, Supervision, Formal analysis, Writing – review & editing, Software, Data curation, Funding acquisition, Conceptualization, Resources, Investigation, Methodology, Visualization, Validation, Project administration.
The authors sincerely thank all study participants for their invaluable contributions.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that generative AI was not used in the creation of this manuscript.
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