Cardiovascular Risk Profile in Ménière’s Disease and Posterior Circulation Infarction: A Comparative Study

Abstract

Ménière’s disease (MD) has an unclear cause. The microvascular dysregulation of the inner ear has been increasingly pointed out as a potential contributor. This study investigates the prevalence of cardiovascular risk factors (CVRFs) in MD patients compared to those with posterior circulation cerebral infarction (POCI). CVRFs like hypertension, diabetes, dyslipidemia, obesity, coronary heart disease, and smoking were assessed in both MD and POCI patients. Brain MRI identified POCI etiology as “small vessel occlusion” (SVO) or “other etiology” (OE). This study included 64 MD and 84 POCI patients. Compared to MD, POCI OE showed a higher prevalence of CVRFs across various age groups, including hypertension, diabetes, dyslipidemia, and smoking. Notably, the odds of having POCI OE were significantly higher for individuals with hypertension and smoking. On the other hand, POCI SVO showed a similar prevalence of CVRFs compared to MD. This study revealed no significant differences in CVRF prevalence between MD and smaller vessel POCI. However, a clear distinction emerged when comparing MD to POCI with the involvement of larger blood vessels. Further research is needed to confirm these findings and explore potential shared risk factors between POCI (SVO) and MD.

1. Introduction

Ménière’s disease (MD) is a multifactorial inner ear disorder characterized by episodic vestibular symptoms, sensorineural hearing loss, tinnitus, and aural pressure [1]. Despite the increasing use of magnetic resonance imaging (MRI) and computed tomography (CT) scans, MD is diagnosed clinically [2].

Endolymphatic hydrops (EH) is a hallmark pathologic characteristic of MD, as described by Hallpike and Cairns [3,4]. While EH is observed in all MD patients, not all EH patients show MD symptoms (the so-called “asymptomatic hydrops”) [3,5,6]. The observation of EH in the temporal bones of asymptomatic individuals prompted a discussion on the true role of EH as a mere ubiquitous finding of MD instead of a causal mechanism for MD symptoms [7]. EH is often evident in the cochlea as a distension of Reissner’s membrane into the Scala vestibuli [8]. Other membrane structures in the ear, such as those enclosing the saccule, utricle, and semicircular canal ampullae, may also be displaced to variable degrees [9]. Membrane ruptures, herniations, and scarring have been found in certain specimens [10]. Those who defend a causal relationship between EH and MD point to membrane rupture as an important contributor to MD periodic attacks and functional alterations [3].

To date, it is unknown why certain individuals are more prone to developing EH. Similarly, it is uncertain which factors make EH more likely to translate into clinical MD [3]. An interaction between genetic and environmental factors has been proposed [11]. However, the findings of genetic research on MD are debated due to the complexity of MD´s pathophysiology. MD has been linked to a plethora of different disorders such as inflammation, immunology, water and ion balance in endolymphatic fluid, viral infections, metabolism, and aberrant nerve conduction function [12].

It is clear that no unanimously accepted model explains the pathogenesis of MD [6,13]. However, a recent hypothesis of microvascular dysregulation of the inner ear has been explored [6,13]. Some speculate that impaired endolymphatic sac fluid balance as a result of vascular dysfunction may lead to endolymph buildup, resulting in vertigo bouts [14]. A decrease in blood flow to the inner ear caused by microvascular damage, oxidative stress, atherosclerotic plaque development, or microthrombosis might disturb the balance of endolymphatic fluid production and absorption, raising the risk of EH and, eventually, MD [15,16,17]. On the other hand, EH constitutes itself as a resistor to inner ear vascular perfusion [6]. In cases where EH is present, chronic vascular impairment of the inner ear may impose an additional irrigation challenge, resulting in lower ear perfusion pressures. This scenario of chronic ear ischemia may alter ion and fluid balance within the inner ear, favoring MD attacks [6]. An important study revealed important degenerative changes in the capillaries of the blood–labyrinthine barrier (BLB) in MD [18].

Small artery disease has been explicitly hypothesized to contribute to inner ear homeostasis instability and result in EH [6,13]. Clinical signs of inner ear chronic small vessel disease are hard to describe either clinically or with imaging [19,20]. It is known that the inner ear is irrigated by the labyrinthine artery coming from the vertebrobasilar arterial system [20]. In parallel, small-vessel strokes are a well-described clinical and imagological subtype of vertebrobasilar (posterior circulation) vascular events. Small-vessel irrigation of the inner ear could relate to MD physiopathology, and MD patients could then share similar cardiovascular risk factors to posterior circulation infarction patients (POCI), namely, the ones caused by small vessel disease.

The inner ear and brainstem/posterior cerebral regions rely on shared arterial irrigation. Nevertheless, to date, no studies have compared cardiovascular risk factors (CVRFs) between MD and POCI patients. With this in mind, this study compared the prevalence of CVRFs in MD to a group of individuals with POCI.

2. Materials and Methods

In order to perform a retrospective study, a sample of patients with a definitive diagnosis of MD from an otorhinolaryngology consultation was compared with a sample of patients with POCI from a neurology consultation. Brain MRIs were assessed by a member of the Neuroradiology Department (JT) and a Neurologist (RR). MD patients who had undergone brain MRI were specifically selected to ensure that no other structural pathologies, such as tumors or other neurological conditions, were contributing to the clinical presentation. Data acquisition was conducted between 2019 and 2023 using non-probability sampling. Figure 1 depicts the methodological approach.

Inclusion criteria for MD patients were definite MD according to Bárany Society, EAONO, the AAO-HNS, the Japan Society for Equilibrium Research, and the Korean Balance Society [21]; age ≥ 18 years; available brain MRI; absence of other cochleovestibular lesions on MRI (namely, facial or vestibular schwannoma and cerebellopontine meningioma); and adequate clinical records. The exclusion criterion was evidence of any former stroke sequelae on MRI.

For POCI, inclusion criteria were age > 18 years, brain MRI with anatomical localization of the lesion, absence of reported hearing or vestibular impairment prior to the event, and adequate clinical records.

The following CVRFs were assessed based on existing hospital and primary practice records: hypertension, diabetes mellitus, dyslipidemia, obesity, heart disease, and smoking. Hypertension was considered in any patient receiving pharmacological treatment specifically aimed at addressing elevated blood pressure. Diabetes mellitus was ascertained based on documented medical history or the use of antidiabetic medications. Dyslipidemia was defined by abnormal lipid panel results or the documented use of lipid-lowering agents. Obesity was determined by body mass index (BMI) measurements, with values ≥30 indicating obesity, as per established criteria. Heart disease encompassed a history of myocardial infarction, coronary artery disease, arrhythmias, or other clinically documented cardiac conditions. Smoking status was determined by self-reporting or the documentation of current or recent (within the last 12 months) smoking habits in medical records.

The etiology of POCI was classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST). For analysis purposes, the sample was further divided into “small vessel occlusion” (SVO)—corresponding to the type 3 classification of TOAST—and “other etiologies” (OE)—corresponding to types 1, 2, 4, and 5 of the TOAST classification (see Figure 1 and Figure 2). Age categories were created to compare groups in order to minimize age bias on risk factors’ prevalence.

SPSS (IBM SPSS Statistics 29) was used for statistical analysis. Specific risk factors were coded as categorical variables and are reported as percentages in the descriptive analysis. The number of comorbidities in each subject was measured as an ordinal variable. Continuous variables such as age are shown as means and standard deviations. Skewness, kurtosis, and Kolmogorov–Smirnov tests were used to ensure that the distribution was normal. The bivariate correlations were analyzed using Pearson’s chi-square test in the descriptive analysis and then inside each age category to compare the prevalence of each risk factor. A multinomial logistic regression (MLR) was employed to produce a predictive model adjusted for age, taking group (Ménière, POCI SVO, or POCI OE) as the dependent variable and CVRFs as covariates. MLR was preferred since the dependent outcome variable defined as “group” had 3 categories and was unordered. Additionally, the proportional odds assumption was violated in the test of parallel lines. In the regression analysis, the decision not to include sex as an independent variable was based on the consideration of potential collider bias. All presented p-values are two-tailed, with a p-value ≤ 0.05 indicating statistical significance.

3. Results

3.1. Study Population

A total of 81 patients with MD were initially recruited. Eleven exclusions were due to insufficient clinical data, namely, a lack of complete information regarding concomitant comorbidities. Six additional exclusions consisted of MD patients with incidental cerebrovascular disease on MRI, despite fulfilling the diagnostic criteria for MD and lacking a history of stroke symptoms. The final sample comprised a total of 64 MD patients without signs of prior stroke on imaging. In parallel, a total of 84 POCI patients were included, accounting for the final 148 patients. Of those, 88 were men (56.1%) and 60 were women (38.2%), with a mean age of 59 ± 14 years.

Table 1. Descriptive and bivariate analysis of Ménière’s and posterior circulation infarction (POCI) groups concerning age and sex.

Continuous Variables	Mean (±Standard Deviation)		p-Value	Categorical Variables	Frequency (%)		p-Value
	Ménière	POCI			Ménière	POCI
Age (years) 1	56.2 ± 12.9	61.7 ± 13.8	0.014	Age (categories)
				<45 years	18.8	14.3	0.465
				45–55 years	34.4	10.7	<0.001
				55–65 years	20.3	35.7	0.041
				65–75 years	17.2	20.2	0.639
				>75 years	9.4	19	0.101
				Sex (male)	40.6	73.8	<0.001

¹ Age was considered at date of MRI. POCI: posterior circulation infarction. p-value refers to results from bivariate analysis comparison between case and control groups, utilizing the independent t-test for continuous data and the Pearson chi-square test for categorical variables. Bold for p-values indicates statistical significance.

describes the sample’s characterization within and between groups.

Table 2. Distribution of the sample by age category and type of pathology.

			Group			Total
			Ménière	POCI SVO	POCI OE
Number of patients	Age category (years)	<45	12	2	10	24
		45–55	22	3	6	31
		55–65	13	14	16	43
		65–75	11	2	15	28
		>75	6	3	13	22
	Total		64	24	60	148

POCI: posterior circulation infarction; SVO: small vessel occlusion; OE: other etiologies.

and Figure 3 are representations of the sample distribution within age and sex categories, respectively. Figure 4 depicts in detail the group description within the sample.

3.2. Ménière’s versus POCI: Risk Factors

The group comparison revealed a higher prevalence of various CVRFs in POCI OE patients, namely, hypertension, dyslipidemia, diabetes mellitus, and smoking (see

Table 3. Bivariate analysis of Ménière’s and posterior circulation infarction (POCI) groups concerning comorbidity prevalence within age categories.

Comorbidity	Age in Years (Category)	Prevalence			p-Value
		Ménière (1)	POCI SVO (2)	POCI OE (3)	1 vs. 2	2 vs. 3	1 vs. 3
Hypertension	<45	25%	40%	50%	0.119	0.882	0.029
	45–55	13.6%	40%	83.3%	0.091	0.571	<0.001
	55–65	46.2%	71.4%	68.75%	0.182	0.873	0.219
	65–75	54.5%	66.7%	86.7%	0.224	0.582	0.068
	>75	66.7%	100%	92.3%	0.257	0.620	0.154
Diabetes Mellitus	<45	8.3%	5.2%	10%	0.672	0.593	0.350
	45–55	4.5%	0%	33.3%	0.706	0.257	0.043
	55–65	50%	37.5%	23.1%	0.148	0.491	0.404
	65–75	18.2%	50%	33.3%	0.326	0.643	0.390
	>75	33.3%	0%	23.1%	0.257	0.356	0.637
Dyslipidemia	<45	20%	40%	30%	0.119	0.584	0.190
	45–55	31.8%	66.7%	50%	0.239	0.687	0.410
	55–65	38.5%	92.9%	75%	0.003	0.190	0.047
	65–75	45.5%	50%	86.7%	0.906	0.201	0.024
	>75	50%	66.7%	53.8%	0.635	0.687	0.876
Obesity	<45	16.7%	0%	30%	0.533	0.371	0.457
	45–55	9.1%	33%	16.7%	0.225	0.571	0.595
	55–65	7.7%	28.6%	37.5%	0.163	0.605	0.062
	65–75	18.2%	50%	6.7%	0.326	0.074	0.364
	>75	0%	0%	23.1%	NC	0.356	0.200
Cardiac Disease	<45	8.3%	0%	0%	0.350	0.462	0.350
	45–55	0%	0%	0%	NC	NC	NC
	55–65	7.7%	0%	18.8%	0.290	0.088	0.390
	65–75	0%	0%	20%	NC	0.486	0.115
	>75	33.3%	33.3%	38.5%	1	0.869	0.829
Smoking	<45	16.7%	40%	70%	0.119	0.371	0.011
	45–55	0%	16.7%	66.7%	0.529	0.134	0.048
	55–65	0%	28.6%	37.5%	0.057	0.605	0.013
	65–75	0%	0%	20%	NC	0.486	0.115
	>75	16.7%	33.3%	15.4%	0.571	0.473	0.943

NC: not computable; POCI: posterior circulation infarction; SVO: small vessel occlusion; OE: other etiologies. Bold for p-values indicates statistical significance.

). The analysis was divided by age. Age < 45 years: MD vs. patients with POCI SVO—p > 0.05 for all CVRFs; MD vs. patients with POCI OE—p = 0.029 for hypertension and p = 0.011 for smoking (both higher in POCI OE). Age 45–55: MD vs. POCI SVO patients—p > 0.05 for all CVRFs; MD vs. POCI OE patients—p = 0.043 for diabetes mellitus, p < 0.001 for hypertension, and p = 0.048 for smoking (all higher in POCI OE). Age 55–65: MD vs. POCI SVO patients—p > 0.05 for all CVRFs, with the exception of dyslipidemia (higher prevalence POCI SVO, p = 0.003); MD vs. patients with POCI OE—p = 0.047 for dyslipidemia and p = 0.013 for smoking (both higher in POCI OE). Age 65–75: MD vs. patients with POCI SVO—p > 0.05 for all CVRFs; MD vs. patients with POCI OE—p = 0.024 for dyslipidemia (higher in POCI OE). Age > 75 years: MD vs patients with POCI SVO and MD vs. patients with POCI OE—p > 0.05 for all CVRFs.

3.3. Cardiovascular Risk Factors and Stroke Risk: A Model

A multinomial logistic regression was performed to model the relationship between predictor variables (various CVRFs and age) and membership in the three groups (Ménière, POCI SVO, POCI OE). With the addition of the predictor variables, the fit between the model including only the intercept and data improved: χ² (14, N = 148) = 76.53, Nagelkerke R² = 0.464, and p < 0.001. As seen in

Table 4. Predictors’ unique contributions in the multinomial logistic regression (N = 148).

Predictor	χ2	df	p-Value
Hypertension	16.146	2	<0.001
Diabetes Mellitus	1.140	2	0.566
Dyslipidemia	8.766	2	0.012
Obesity	0.589	2	0.745
Cardiac Disease	4.625	2	0.099
Smoking	29.522	2	<0.001
Age (years)	1.049	2	0.592

χ²: amount by which −2 log-likelihood increased when a predictor was removed from the full model. Bold for p values indicates statistical significance.

, hypertension, dyslipidemia, and smoking each provided substantial, distinct contributions. POCI OE was the reference group. As a result, each predictor contained two parameters: one for predicting Ménière group membership and one for predicting POCI SVO group membership.

Table 5. Estimated parameter values when comparing the POCI OE group to the other groups (N = 148).

Predictor	POCI OE vs.	B	OR	p-Value
Hypertension	Méniére’s disease	−2.070	0.126	<0.001
	POCI SVO	−0.406	0.666	0.556
Diabetes Mellitus	Ménière’s disease	−0.173	0.841	0.750
	POCI SVO	0.507	1.660	0.371
Dyslipidemia	Ménière’s disease	−0.398	0.672	0.413
	POCI SVO	1.506	4.509	0.024
Obesity	Ménière’s disease	−0.455	0.635	0.446
	POCI SVO	−0.145	0.865	0.815
Cardiac Disease	Ménière’s disease	−0.345	0.708	0.645
	POCI SVO	−2.008	0.134	0.074
Smoking	Ménière’s disease	−3.198	0.041	<0.001
	POCI SVO	0.285	1.330	0.630
Age	Ménière’s disease	−0.020	0.980	0.315
	POCI SVO	−0.003	0.997	0.913

POCI OE: posterior circulation infarction of other etiologies (not small vessel); POCI SVO: posterior circulation infarction due to small vessel occlusion; OR: odds ratio associated with the effect of a one-standard-deviation increase in the predictor. Bold for p-values indicates statistical significance.

displays the parameter estimates.

When comparing the POCI OE group to the Ménière group, two of the predictors exhibited significant parameters: hypertension and smoking. If the patient had hypertension, the odds of being in the POCI OE group rather than the Ménière group were seven times greater (OR: 0.126, p < 0.001, see Table 5). Likewise, if the patient was a smoker, the odds of being in the POCI OE group rather than the Ménière group were more than 20 times higher (OR: 0.041, p < 0.001, see Table 5). When comparing the POCI OE group to the POCI SVO group, only one predictor showed a significant parameter: dyslipidemia (see Table 5). In a patient with dyslipidemia, the odds of being in the POCI SVO group rather than the POCI OE group were four times higher (OR: 4.509, p = 0.024, see Table 5).

4. Discussion

Cardiovascular risk factors such as hypertension, diabetes, dyslipidemia, smoking, and obesity are well-known causes of microvascular impairment, oxidative stress, and compromise of the brain–blood and blood–labyrinth barriers [22]. Although there are some important studies linking MD to CVRF [6,13], this association is still relatively unexplored. On the other hand, it remains unclear whether there are shared risk factors between Ménière’s disease (MD) and vertebrobasilar ischemic events. Given recent evidence suggesting a role of microvascular dysregulation in MD, this study hypothesized that common CVRFs might be shared with POCI due to their common vascular supply from the vertebrobasilar system. The need for a better understanding of the etiopathogenesis of MD along with the pertinency to unravel new lines of investigation on MD motivated the present work.

The primary research question was whether CVRF prevalence would differ between MD and POCI patients. The primary objective of the work was met. A higher prevalence of hypertension, diabetes mellitus, dyslipidemia, and smoking was found in POCI OE patients compared to MD in various age categories.

Conversely, no significant differences were found regarding the prevalence of CVRF between MD and POCI SVO, with the exception of dyslipidemia within the 45–55 age category. The multivariate model further reinforced such findings, while supporting the role of dyslipidemia in POCI SVO. These results suggest a partial overlap in CVRF prevalence between MD and POCI SVO, as opposed to non-small vessel disease (POCI OE), where CVRF prevalence was more pronounced. Importantly, the similarity in CVRF prevalence between MD and POCI SVO frames the possibility of microvascular dysregulation as a common contributor in these two entities.

Well-known target end-organs of cardiovascular disease such as the brain, heart, kidney, or eye have already been described [22,23,24]. Nevertheless, a lot less is known concerning microvascular affection of the inner ear. Since microvascular dysfunction is pointed out as a systemic disorder [23], it seems licit to consider that the inner ear may not be an exception [22]. There are two main potential pathways in which microvascular disease could relate to MD. One would be by increasing the risk of EH (etiopathogenic theory), the other by enhancing attacks in an already hydropic ear (attack-triggering theory) [25].

There are some arguments in favor of the etiopathogenic pathway. Microvascular dysfunction has been associated with various markers common to both typical end-target organs (brain, heart, kidney) and the inner ear. Aquaporins, adducins, dermatopontin, and potassium voltage-gated channel subunits are some examples [1,11,23,26]. Many endolymph-bound ion transport channels have been shown to be controlled by hormonal processes such as β-adrenergic, muscarinic, and purinergic receptors [27,28]. However, the relationship between ion transport and endolymph ion concentration and volume remains unknown. Only an osmotic inflow of water may cause a volume change. Aquaporins, which are expressed in the inner ear, have a function in water equilibration across endolymphatic borders and are regulated by hormones [3]. In fact, vasopressin (V2) receptors have been found in the inner ear and may balance water flow by the control of aquaporin expression, in a mechanism similar to that of the kidney [3]. Nevertheless, it is still unclear how this system is regulated and what are the exact roles of the various inner ear structures [3]. MD etiology has already been linked to a variety of genes involved in ionic composition, water transport, and cardiovascular development [1,11,26].

Endothelial dysfunction, inflammation (including reactive oxidation), immunological activation, and coagulation are all possible mechanisms driving systemic microvascular dysfunction [23]. In fact, a recent study of the human utricle’s macula microvasculature demonstrated that vascular endothelial cells and pericytes are damaged in MD [18,29]. Two oxidative stress markers have been implicated in such damage: inducible nitric oxide synthase (iNOS) and nitrotyrosine. These markers have been found in vascular endothelial cells of BLB from MD patients, suggesting that oxidative stress is involved in BLB disruption [30]. Recent research on BLB pathophysiology emphasizes the relevance of BLB integrity for ion and water homeostasis [31,32,33], suggesting that BLB dysfunction is important in understanding the pathophysiology of EH and possibly MD.

The attack-triggering theory, on the other hand, proposes that hydrops operates as a variable starling resistor on the inner ear vasculature, capable of generating ischemic episodes in those with low ear perfusion pressure [6]. An animal experiment showed that MD attacks are not caused by EH itself, but they can be provoked by decreased vascular flow in the inner ear [34]. The BLB is essential for maintaining inner ear fluid ionic equilibrium [18]. In the case of hydropic ear transient ischemia, resulting BLB disruption could activate deleterious mechanisms, culminating in MD attack. Moreover, ischemia and hypoxia cause fast calcium translocation from extracellular to intracellular regions in brain tissues [3]. Since endolymph calcium has been shown to influence transduction in hair cells, it is possible that calcium influx in the setting of inner ear hypoxia contributes to the functional losses observed in MD attacks [3].

This study has its strengths. It is the first to compare an MD with a POCI population from a cardiovascular point of view. It included a relatively large sample of patients (both MD and POCI) formerly submitted to brain imaging. Also, it underscores microvascular dysregulation as a potential novel mechanism for MD development and/or progression.

It is essential to acknowledge some limitations of our study. Firstly, the retrospective nature of this research may have incurred selection bias. Secondly, it would be pertinent to include an age-matched “control” group without MD or POCI to further validate our results and check for asymmetries in the prevalence of CVRF between MD and the general population. Considering broader demographic variations in future studies could offer more comprehensive insights into the applicability of the findings across different populations. Additionally, since the POCI SVO group included a relatively small number of patients, comparison with the MD population may have been statistically affected. Also, since clinical data were derived from pre-existing records; it is possible that certain comorbidities were overlooked. The retrospective assessment of CVRFs relied on existing medical records and current medication use, potentially underestimating the true prevalence of undiagnosed or subclinical conditions. It is important to note that the duration and severity of risk factors such as hypertension, diabetes mellitus, smoking, and related indices, including the smoking index, could not be precisely ascertained due to limitations in the available database. The absence of this information represents a constraint in our analysis and necessitates a cautious interpretation of the results with regard to the temporal aspects of risk factor exposure. For instance, the design limited the assessment of tobacco consumption to recent smoking status (within the past year) due to inconsistencies in documenting lifetime smoking histories. This approach may have underestimated the true impact of tobacco exposure on MD but remained valid for comparing the prevalence of CVRFs across groups. Future prospective studies should aim not only to qualify but also to quantify CVRF assessments. The significant difference in sex distribution between the study groups may have influenced our results, as the prevalence of CVRFs can vary between men and women. Though reflective of the known epidemiology of MD and stroke, adjusting for sex could have introduced collider bias, masking true associations. Therefore, the authors consider that the results should be regarded thoughtfully and validated by further larger, prospective studies.

5. Conclusions

In conclusion, this study compared cardiovascular risk factors in Ménière’s disease (MD) and posterior circulation infarction (POCI) patients. Similar cardiovascular risk factor (CVRF) prevalence was observed between MD and small vessel POCI (POCI-SVO), with the exception of dyslipidemia in the 55–65 age group. More notable disparities were evident in larger vessel POCI cases (POCI-OE), with the odds of POCI OE more significantly associated with hypertension and smoking in the multivariate analysis. This work points to the potential to explore determinants of microvascular dysregulation in Ménière’s disease. While the exact MD pathophysiologic mechanisms remain unknown, it is possible that MD patients share common ground with thrombotic microangiopathy entities such as POCI-SVO. Finally, further research is needed on this topic. New future findings could have more substantial impacts. Early preventive action on CVRFs or their adequate treatment should be investigated in prospective studies with more comprehensive assessments of CVRFs and the inclusion of a control group to confirm and expand upon our findings. Considering the complex interplay of factors involved, exploring collaborations across disciplines like otolaryngology, neurology, and primary care could offer valuable insights.