Hearing loss associated with mobility and cognitive impairment: comorbid condition, an intrinsic capacity-aligned vulnerability cluster or a syndromic clustering?

Hearing impairment is highly prevalent among older adults and, although often treated as a discrete sensory deficit, growing evidence suggests it is embedded in a broader network of age-related vulnerabilities that can shape functional trajectories. This perspective aligns with the World Health Organization’s concept of intrinsic capacity (IC) - a consolidated model of aging reserves that conceptualizes functional ability as emerging from interacting domains (cognition, mobility, sensory capacity, psychological well-being, and vitality), rather than isolated impairments [1]. The Integrated Care for Older People (ICOPE) approach operationalizes this construct for clinical practice by recommending structured screening and person-centered care pathways to detect and address early losses across domains [2]. Within this framework, the study by Zhang and colleagues [3] provides an important signal: hearing impairment detected by the whisper test is associated with physio-cognitive decline (PCDS) among community-dwelling older adults, supporting the relevance of sensory screening when assessing integrated vulnerability phenotypes.

Zhang et al. examined community-dwelling older adults and defined PCDS as the co- occurrence of cognitive impairment without dementia (CIND) and mobility impairment without disability (MIND). Their findings indicate that whisper-test hearing impairment is associated with PCDS in adjusted models, alongside older age, educational attainment, and polypharmacy. The clinical appeal of this work lies in its pragmatic orientation: a low-cost bedside test may help identify older adults who already display a convergent pattern of cognitive and motor vulnerability, a phenotype consistently associated with adverse outcomes [4].

1. Biological plausibility and clinical relevance within an intrinsic capacity perspective

Several mechanisms can be raised as plausible in connecting hearing impairment to a combined physio-cognitive phenotype. One potential pathway is “cognitive load” or the listening effort hypothesis, in which degraded auditory input increases the need to compensate by recruiting attentional and executive resources (often involving prefrontal networks) to decode speech, potentially reducing reserve for parallel cognitive operations and dual-task gait control as demonstrated by Hunter et al. [5]. Additionally, hearing loss can lead to cognitive and mobility impairments due to social isolation [6]. Hearing loss can contribute to reduced social participation and cognitive stimulation, with downstream effects on mood and physical activity that may amplify vulnerability across intrinsic capacity domains [7]. However, explaining the relationships observed between hearing loss, cognitive, and mobility impairments through a single or partial-mediation pathway is unlikely [8]. In this scenario, the intrinsic capacity framework is particularly informative because it supports an interpretation beyond single-pathway causality. In IC, functional ability reflects an interconnected system of reserves, and co-occurring losses across domains may cluster into patterns that behave as latent constructs of reduced resilience rather than discrete causal chains [9]. From this perspective, the concurrent presence of sensory impairment, cognitive vulnerability, and motor slowing may represent one phenotypic presentation of multisystem decline, in which heterogeneous biological axes (eg, vascular burden, inflammaging, oxidative stress, neurodegenerative processes, energetic dysregulation) converge on parallel functional manifestations, even when no single unifying mechanism outstands [10].

This clustering model has two practical implications. First, it frames hearing impairment not merely as a comorbidity but as a potential marker within an IC-aligned vulnerability cluster, which may better stratify risk than any single domain assessed in isolation, as demonstrated by Gonzalez-Bautista et al. [11]. Second, it fosters analytic approaches to model domains’ co-occurrence and latent structure — for example, cluster analyses, latent class/latent profile models, and network approaches — to clarify whether hearing impairment is acting as an upstream driver, a parallel manifestation of shared biology, a contributor to measurement artifacts in cognition, or a co-existent factor related to aging itself. In this context, the association reported by Zhang et al. is consistent with a clustered vulnerability phenotype and warrants further exploration through longitudinal validation and interventional testing, regardless of whether statistical interactions between domains are demonstrated.

Even if this three-factor cluster represents only one of the ten possible three-factor combinations across the five intrinsic capacity domains, this situation differs from the context that underpins criticisms of metabolic syndrome in older adults. The Rotterdam Study [12] showed that combining factors did not yield effects greater than the simple sum of the individual components. However, from the ICOPE framework perspective, a domain-based approach remains relevant even in the absence of a clear interaction. A “syndrome-pattern” is not necessarily required to establish the need for intervention by the clinical approach, thereby reinforcing the applicability of Zhang et al.’s findings.

2. Methodological considerations and interpretation boundaries

As noted by the authors, the study's cross-sectional design limits causal inference. Therefore, it is not possible to conclude directly that hearing loss contributes to physio- cognitive decline or, conversely, that neurodegenerative processes related to cognitive deterioration lead to hearing impairment. Moreover, residual confounding can persist. Although the models adjust for age, sex, and education, hearing impairment is closely intertwined with factors such as depression, social isolation, vascular risk, and multimorbidity, each of which can independently influence cognition and mobility.

The whisper test is practical, but it is subject to variability related to examiner technique and environmental conditions, which can raise measurement concerns [13]. Misclassification could bias associations toward or away from the null depending on its structure. Additionally, hearing impairment can inflate false-positive cognitive impairment when standard cognitive tools rely on auditory comprehension. This is not a minor technical detail: it highlights that sensory impairment can distort cognitive measurement, potentially leading to apparent clustering unless hearing-adapted cognitive assessments are used [14].

3. Clinical and public health implications

Despite some limitations, the study has immediate practical relevance in one central spot: it underscores the need for systematic sensory screening as part of integrated aging care. Embedding hearing assessment within IC/ICOPE-aligned workflows is a pragmatic strategy for identifying older adults with compound vulnerability, particularly in resource-constrained settings where specialized audiology services are limited.

Nevertheless, translating detection into benefit requires addressing persistent barriers to hearing rehabilitation, including cost, stigma, limited availability, and adherence challenges [15]. These barriers are modifiable but structurally complex, and they often intersect with broader inequities in access to geriatric care.

4. Call to action

Zhang et al. provide a well-executed “spotlight study” supporting hearing impairment as a marker of physio-cognitive vulnerability. The next steps involve longitudinal and interventional research to clarify whether hearing rehabilitation (including hearing aids, implants, and structured communication interventions) can prevent or mitigate the progression of cognitive/mobility-declining trajectories, ideally as part of multicomponent intervention models aligned with IC preservation.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used an AI-assisted language model (ChatGPT, OpenAI) to support English-language editing and improve readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.