The researchers summarized 21 cases treated at their center over three years. According to the abstract, functional hearing loss is a form of hearing impairment without identifiable organic damage to the auditory system, although the available publisher text provides no further details about how the diagnoses were established or the specific assessments and treatments used. The report therefore should be read as a descriptive clinical case series rather than evidence that a particular intervention caused recovery.

The reported factors associated with functional hearing loss included mental health issues such as anxiety and depression, reported by 23.81% of patients; academic stress, reported by 19.04%; violence by parents or teachers, reported by 14.29%; and interpersonal conflicts, reported by 4.76%. In addition, 47.62% of patients reported an unknown reason for their hearing loss. These percentages describe reported factors within the 21-case series and do not establish their prevalence among adolescents generally or demonstrate a causal relationship with functional hearing loss.

Psychological intervention was received by 55% of patients during the reported follow-up. The authors stated in the abstract that functional hearing loss should be considered in sudden-onset cases, particularly when psychological or behavioral abnormalities are present, and that early objective auditory assessment is important in unilateral hearing loss with inconsistent audiometric findings. They also emphasized accurate diagnosis and treatment and described timely, effective psychotherapy as favorable for children with dysthymic disorders. However, the abstract does not specify the intervention protocols, comparison groups, diagnostic criteria or analytic methods, and the findings are limited by the small, single-center sample and the absence of a control group.

Subject of Research: A descriptive case series of functional hearing loss and recovery in 21 adolescents treated at one center

Article Title: Unexplained sudden sensorineural hearing loss in adolescent: a warning for functional hearing loss

Article References: Zhang, K., Xin, X., Hou, B., Xu, X., Yan, T., Qiao, R., Han, X., & Zhang, H. (2026). Unexplained sudden sensorineural hearing loss in adolescent: a warning for functional hearing loss. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07632-6

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07632-6

Keywords: functional hearing loss, adolescent hearing, clinical case series, audiometry, mental health, psychological intervention, unilateral hearing loss, hearing recovery, Unexplained, sudden, sensorineural, hearing

The research drew on the Busselton Healthy Ageing Study, a population-based cohort conducted in the coastal town of Busselton in Western Australia, a region internationally renowned for the depth and continuity of its health data. Adults aged between 45 and 70 years at baseline underwent comprehensive audiometric testing and general health assessments between 2010 and 2015, then returned for a follow-up examination six years later. From the full cohort, 3,596 participants had reliable audiometry at both time points and formed the analytical sample, with a mean baseline age of 57.9 years and 55 percent of participants being women.

The researchers quantified hearing using two complementary measures derived from pure tone audiometry. The first, the four-frequency pure tone average, or 4FPTA, averaged hearing thresholds across the speech-critical frequencies of 0.5 to 4 kilohertz, capturing the range most relevant to everyday conversation. The second, the high-frequency pure tone average, or HFPTA, focused on 4 and 8 kilohertz, the frequencies that typically deteriorate earliest and most steeply with age. Hearing loss was classified according to World Health Organization criteria, allowing the team to track both the continuous shift in thresholds and the categorical movement of individuals across clinical severity grades.

The headline numbers are sobering. Over the six-year follow-up, the prevalence of mild-or-greater hearing loss in the better ear, as measured by the 4FPTA, rose from 14 percent to 26 percent of participants. When the high-frequency measure was applied, the picture was even more dramatic: prevalence climbed from 53 percent to 71 percent. In other words, by the end of the study period, more than seven in ten middle-aged adults showed high-frequency hearing loss of at least mild degree in their better ear, a figure that underscores how widespread subclinical auditory decline already is well before retirement age.

Thresholds worsened across every age category in the cohort, but the deterioration was not uniform. Men experienced greater hearing decline than women, and higher frequencies deteriorated faster than the speech frequencies, consistent with the classic pattern of noise-induced and age-related cochlear damage that begins at the basal end of the hearing organ. Because the study followed individuals longitudinally rather than comparing separate age groups at a single point in time, the results provide direct evidence of within-person change, eliminating many of the confounds that plague cross-sectional surveys of hearing.

Beyond age and sex, the analysis uncovered a set of modifiable and clinical factors associated with faster decline. Using linear mixed models adjusted for age and sex, the researchers found that higher body mass index, smoking, tinnitus, chronic ear infection, and noisy workplace exposure were each significantly linked to steeper hearing deterioration. Notably, the effect of occupational noise was most pronounced among workers with inconsistent or absent hearing protection, a finding with obvious implications for workplace safety programs. Hypertension and pre-diabetes also emerged as significant predictors, suggesting that the vascular and metabolic changes that precede overt disease may already be compromising the delicate blood supply of the inner ear.

The involvement of metabolic factors fits with a growing body of research implicating microvascular health in hearing. The cochlea depends on an extraordinarily dense capillary network and high metabolic activity to transduce sound into neural signals, and it has minimal capacity for repair once the sensory hair cells or their supporting structures are lost. Conditions such as hypertension and impaired glucose regulation are known to damage small blood vessels throughout the body, and the inner ear may be among the first organs to show the consequences. Similarly, smoking delivers oxidative stress and vasoconstrictive compounds that can starve cochlear tissue, while elevated body mass index is associated with chronic inflammation and metabolic dysregulation that may accelerate cellular aging in the auditory system.

The study’s authors emphasize that hearing loss progression in midlife is shaped not only by age and sex but also by broader metabolic and environmental health profiles. This reframing matters clinically because it suggests that hearing checks should not be reserved for older adults. If a substantial fraction of people in their fifties are crossing the threshold of measurable hearing loss within six years, and if factors such as blood pressure, blood glucose, smoking status, and noise protection are influencing the pace of that decline, then midlife represents a critical intervention window. Managing cardiovascular risk factors, enforcing consistent hearing protection in noisy occupations, and lowering the threshold for audiometric screening could potentially delay the onset of disabling hearing loss by years.

The public health stakes are considerable. Disabling hearing loss affects hundreds of millions of people worldwide, and as the global population ages, its burden is projected to grow substantially. Untreated hearing loss in adults is associated with social isolation, depression, cognitive decline, and dementia, making it one of the largest modifiable risk factors for dementia identified in recent Lancet Commission analyses. A decline that begins in midlife, decades before most people seek help, means that the cumulative exposure to auditory deprivation and its downstream consequences may be far greater than current clinical practice assumes.

The researchers caution that their findings highlight the need for future research to further clarify the complex interplay between metabolic health, environmental exposures, and hearing outcomes. Longitudinal data from the Busselton cohort will continue to support this effort, and the study’s community-based design, with its high participation rates and standardized audiometric protocols, lends credibility to the estimates. For now, the message for middle-aged adults is clear: hearing is not a fixed asset that can be taken for granted until retirement. It is a dynamic health parameter, responsive to the same lifestyle and metabolic factors that shape heart disease and diabetes, and it deserves the same early and proactive attention.

Subject of Research: Hearing loss progression and general health in middle-aged adults

Article Title: Change in hearing loss and its association with general health in a community-based longitudinal study of middle-aged individuals: The Busselton healthy ageing study

Article References: Smit, A. L., Eikelboom, R. H., Mao, Y., Hunter, M., Atlas, M. D., Swanepoel, D. W., & Stegeman, I. (2026). Change in hearing loss and its association with general health in a community-based longitudinal study of middle-aged individuals: The Busselton healthy ageing study. PLOS One, 21(10), e0360651. https://doi.org/10.1371/journal.pone.0360651

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360651

Keywords: hearing loss, audiometry, ageing, Busselton Healthy Ageing Study, public health, hypertension, pre-diabetes, occupational noise, tinnitus, longitudinal study, Change, hearing

The stakes are enormous. According to the World Health Organization, nearly 2.5 billion people, roughly one in four worldwide, are projected to have some degree of hearing loss by 2050, with an estimated 700 million requiring rehabilitation services. But the problem extends far beyond those with diagnosed hearing loss. Many people whose audiograms look perfectly normal still report substantial difficulty following conversations in noisy environments. This gap between what the audiogram says and what the listener experiences has long frustrated clinicians, because hearing thresholds alone provide an incomplete and often misleading picture of real-world functional hearing.

Scientists have proposed several mechanisms to explain these hidden deficits. Emerging evidence points to conditions such as cochlear synaptopathy, a loss of connections between sensory cells and auditory nerve fibers, and impairments in extended high-frequency hearing, both of which can degrade the neural coding of sound without shifting standard threshold measures. The result is a central challenge for hearing healthcare: current clinical tools do not accurately predict how well an individual will understand speech in the acoustically messy conditions of everyday life. Conventional linear statistical models built on audiometric and demographic variables capture some group-level trends, but they miss the nonlinear interplay among auditory, acoustic, and cognitive factors, such as attention and working memory, that shape each listener’s performance.

Enter machine learning. The new review, conducted by Prabhani Athukorala, Anu Nair, and Srikanta K. Mishra of The University of Texas at Austin and the University of South Dakota, followed PRISMA 2020 guidelines and prospectively registered its protocol. The team searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar, screening 6,468 records after duplicate removal. They included studies using traditional machine learning models such as support vector machines and random forests, neural network architectures ranging from early artificial networks to deep and convolutional models, automatic speech recognition systems, and mechanistic auditory models that simulate the physiology of hearing itself.

The quantitative synthesis focused on the model-metric combinations with enough comparable studies to pool: support vector machine accuracy, support vector machine area under the curve, random forest accuracy, and random forest recall. Using random-effects meta-analysis with the DerSimonian-Laird method, the researchers found that support vector machines pooled at an accuracy of 0.71 and an area under the curve of 0.84, the latter indicating good discrimination across the included classification tasks. Random forests fared better on the available evidence, pooling at an accuracy of 0.86 and a recall of 0.81. Individual study estimates varied widely, however, with accuracies ranging from 0.53 to 0.96 depending on the task and dataset.

Those wide ranges point to the review’s most important caveat: heterogeneity. Between-study variability ranged from moderate to very high across analyses, reflecting genuine differences in participant populations, prediction targets, feature sets, outcome definitions, and validation procedures. Because each pooled analysis included only three or four studies, the authors urge cautious interpretation of the heterogeneity statistics and pooled estimates. Bootstrap resampling with 10,000 study-level draws produced estimates broadly consistent with the primary analyses, and sensitivity analyses, including exclusion of a large dataset of 12,697 subjects, shifted pooled values only modestly. But the evidence base remains too small and too varied to declare any single algorithm the winner.

Indeed, the review explicitly rejects the idea that one model class is universally superior. Random forests may excel at modeling nonlinear interactions among correlated clinical predictors, while support vector machine performance appears more sensitive to feature scaling and kernel specification. Neural networks showed real promise for problems involving complex spectrotemporal representations, with deep architectures outperforming conventional objective measures in speech intelligibility estimation. Yet greater complexity did not consistently help: in several studies, deep neural networks and multilayer perceptrons underperformed simpler algorithms when datasets were small relative to model capacity. The practical lesson is that neural networks suit high-dimensional acoustic or signal-level data, whereas traditional machine learning remains highly effective for smaller, structured clinical datasets.

Two other model families offered complementary perspectives. Automatic speech recognition systems, including hybrid deep neural network and hidden Markov model architectures, reproduced key patterns of human performance, such as declining recognition scores as signal-to-noise ratios worsen, and incorporating listener-specific auditory parameters improved predictions for hearing-impaired profiles. These systems also proved useful for automating the design of speech-in-noise tests themselves. Mechanistic auditory models, represented by a single study predicting consonant recognition, embedded cochlear filtering, compression, and hearing loss simulations directly into the prediction pipeline, demonstrating that reduced audibility and suprathreshold distortions jointly degrade speech perception. Such physiologically grounded models offer interpretability that purely data-driven approaches lack, and the authors suggest that future progress may lie in hybrid frameworks combining both.

Perhaps the most clinically intriguing finding concerns what the models use as inputs. Several studies showed that speech-in-noise outcomes can be predicted more effectively when models incorporate information beyond conventional audiometric thresholds. Extended high-frequency thresholds, acoustic features, behavioral measures, and even cortical speech-evoked neural responses all contributed predictive information in different analyses. Extended high-frequency hearing, in particular, was implicated in speech-in-noise variability among listeners with clinically normal audiograms, hinting that deficits invisible to the standard audiogram may underlie real-world listening difficulty. This multidimensional picture supports the hope that machine learning could eventually identify people whose communication struggles are disproportionate to their measured hearing loss.

The authors are careful, however, to distinguish proof of concept from clinical utility. Only one included study reported independent external validation on an additional dataset, and most relied on retrospective data with internal validation alone. Metrics such as accuracy, area under the curve, recall, and correlation are not directly comparable across tasks, and the small number of studies precluded publication-bias assessment and subgroup analyses. The path forward, the review concludes, requires larger, standardized, externally validated, and prospectively evaluated studies spanning multiple clinics, populations, languages, and listening environments. Until then, the models remain promising prototypes rather than diagnostic tools. Still, the trajectory is clear: as datasets grow and validation standards mature, data-driven prediction could transform how clinicians detect hidden hearing deficits, personalize hearing aids and cochlear implants, and design smarter listening technology for a world that is only getting noisier.

Subject of Research: Machine learning prediction of speech-in-noise perception in listeners with normal and impaired hearing

Article Title: Data-Driven Prediction of Speech-in-Noise Performance: A Systematic Review and Meta-analysis of Machine Learning Models

Article References: Athukorala, P., Nair, A., & Mishra, S. K. (2026). Data-Driven Prediction of Speech-in-Noise Performance: A Systematic Review and Meta-analysis of Machine Learning Models. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04407-z

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04407-z

Keywords: machine learning, speech-in-noise, hearing loss, support vector machine, random forest, neural networks, automatic speech recognition, auditory models, meta-analysis, audiometry, cochlear synaptopathy, hearing rehabilitation

The research, published in GeroScience by Ho Yun Lee, Seung-Ho Shin, and Sung Wan Byun of Ewha Womans University College of Medicine, analyzed 5,436 adults aged 40 or older from the 2022–2023 Korea National Health and Nutrition Examination Survey (KNHANES). Phase angle is calculated from resistance and reactance recorded when a weak alternating current at 50 kilohertz passes through the body. Resistance reflects how strongly the body’s fluids impede the current, while reactance reflects the capacitive behavior of cell membranes—essentially, how intact and functional those membranes are. A higher phase angle therefore signals healthier cell membranes, a better balance of intracellular to extracellular fluid, and greater nutritional and functional reserve. A lower value has previously been linked to frailty, sarcopenia, malnutrition, disability, and mortality.

The technical details of the hearing assessment matter here. Audiologists computed the speech-frequency pure-tone average from thresholds at 0.5, 1, 2, and 4 kilohertz in each ear, taking the better ear as the primary outcome. They also calculated a high-frequency threshold from 4 and 8 kilohertz, the range where age-related hearing loss typically first appears. Hearing loss was defined as a better-ear pure-tone average of 25 decibels hearing level or worse, the conventional clinical cutoff. All analyses applied complex survey weights to make the sample representative of the entire noninstitutionalized Korean population, and the models progressed through stages of adjustment, from age and sex alone to the full panel of metabolic and lifestyle covariates.

The results were strikingly graded. Participants in the lowest sex-specific phase angle quartile had an average better-ear pure-tone average of 25.9 decibels, while those in the highest quartile averaged just 14.5 decibels. High-frequency thresholds fell from 43.6 to 24.8 decibels across the same gradient. Most dramatically, the prevalence of audiometric hearing loss dropped from 48.3 percent in the lowest quartile to 11.8 percent in the highest. In the fully adjusted regression models, each one-degree increase in phase angle was associated with a 1.50-decibel lower better-ear pure-tone average and a 1.51-decibel lower better-ear high-frequency threshold, both highly statistically significant. Each additional degree also corresponded to a 21 percent reduction in the odds of audiometric hearing loss, with an odds ratio of 0.79.

Perhaps the most intriguing finding emerged from an exploratory comparison with younger participants. When the researchers extended their analysis to people aged 12 to 39, the association between phase angle and hearing vanished entirely—coefficients hovered near zero and were nowhere near statistical significance. Among adults 40 and older, however, the association remained robust. This age-dependent pattern suggests that phase angle is not a general marker of hearing status across the lifespan but rather becomes informative specifically in the context of systemic aging, when physiological reserve begins to erode and the cochlea becomes vulnerable to cumulative metabolic and vascular stress.

The frequency-specific results added another layer of complexity. Inverse associations between phase angle and hearing thresholds appeared across the tested frequencies, but the estimates were largest and most consistent at low-to-mid frequencies, particularly around 2 kilohertz, and weaker at 8 kilohertz. That pattern diverges from the classical high-frequency-dominant configuration of presbycusis, which is typically attributed to degeneration of the basal turn of the cochlea. The authors are careful to frame this as hypothesis-generating rather than evidence of a distinct mechanism, but it hints that systemic reserve may influence cochlear structures differently than chronological aging alone does.

What biological pathways could connect a whole-body electrical measurement to the delicate sensory machinery of the inner ear? The cochlea is an extraordinarily metabolically active organ, and its energy supply depends on the stria vascularis, a vascularized structure that maintains the ionic gradients hair cells require to transduce sound. Chronic low-grade inflammation, oxidative stress, and vascular compromise—all conditions associated with lower phase angle in prior research—could degrade these systems over decades. Alterations in cellular membrane integrity and fluid balance, which phase angle directly reflects, may parallel changes in inner-ear homeostasis. The supplementary analyses bolster this systemic interpretation: higher phase angle was also associated with better quality-of-life scores on the EQ-5D, greater maximum handgrip strength, and lower odds of low grip strength among older adults.

Causality, however, remains firmly open. This was a cross-sectional study, capturing a single moment in time, so it cannot establish whether low phase angle precedes hearing decline or merely accompanies it. Genetic epidemiology complicates the picture further: a bidirectional Mendelian randomization study found no robust forward causal effect of body constitution on hearing loss, and a UK Biobank analysis observed associations between body composition and sensorineural hearing loss without clear genetic support for causation, pointing instead to shared genetic architecture or reverse pathways. Consistent with this caution, the associations in the present study survived adjustment for fat-free mass and percent body fat, indicating that phase angle carries information beyond body composition—but the authors explicitly state that the findings do not imply raising phase angle would improve hearing.

The study also has measurement caveats worth noting. Year-specific sensitivity analyses showed stronger associations in 2022, when a dedicated bioelectrical impedance survey weight was available, and attenuated, non-significant estimates in 2023, with formal heterogeneity between survey years. Residual confounding from lifetime noise exposure, ototoxic medications, socioeconomic factors, and prior otologic disease cannot be excluded, and phase angle itself can be influenced by hydration status and device characteristics, though KNHANES uses standardized examination protocols. Subgroup analyses by sex and age showed numerically larger associations in men and in adults 65 and older, but formal heterogeneity testing did not reach significance, so these patterns should be treated as exploratory.

The clinical message, for now, is one of integration rather than replacement. Phase angle is not a substitute for audiometry, and the authors caution that longitudinal validation is required before any screening application could be considered. But the findings reframe hearing loss as something more than an isolated ear problem: it appears to travel with the broader erosion of cellular integrity and physiological reserve that defines functional aging, alongside frailty, falls, depression, and dementia. If future cohort studies confirm that phase angle predicts hearing decline ahead of symptoms, a measurement already embedded in routine body-composition assessments could become an early, inexpensive flag for one of aging’s most common and most undertreated sensory impairments.

Subject of Research: Association between bioelectrical phase angle and audiometric hearing loss in middle-aged and older adults

Article Title: Lower bioelectrical phase angle is associated with audiometric hearing loss in middle-aged and older adults

Article References: Lee, H. Y., Shin, S.-H., & Byun, S. W. (2026). Lower bioelectrical phase angle is associated with audiometric hearing loss in middle-aged and older adults. GeroScience. https://doi.org/10.1007/s11357-026-02528-w

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02528-w

Keywords: bioelectrical phase angle, hearing loss, audiometry, aging, bioelectrical impedance analysis, KNHANES, physiological reserve, sensory aging, cochlea, frailty, body composition, GeroScience