Hearing loss and cognitive decline in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) during eight years of follow-up

Abstract

Background

Hearing loss (HL) of moderate or higher grades is common in older adults with increasing prevalence as people age, rising from 12% at the age of 60 years to over 58% at 90 years. HL in midlife is one of the main potentially modifiable risk factors for dementia. It is estimated that 7% of dementia cases globally could be avoided if this risk factor was eliminated. However, much of the research conducted has been in high-income countries even though low- and middle-income countries have the highest prevalence of dementia.

Objective

To study the association between HL and cognitive decline during eight years of follow-up in a Brazilian sample.

Methods

Participants from the São Paulo center of the Brazilian Longitudinal Study of Adult Health were evaluated in three study waves (2008–10, 2012–14, and 2017–19). HL was defined as pure-tone audiometry above 25 dB in the better ear. Cognitive performance was evaluated with six tests related to memory, verbal fluency, and trail-making tests. A global cognitive z-score was derived from these tests. The association between HL and cognitive decline was evaluated with linear mixed-effects models adjusted for sociodemographic, lifestyle, and clinical factors.

Results

Of 805 participants (mean age 51 ± 9 years, 52% women, 60% White), 62 had HL. During follow-up, HL was associated with faster global cognitive decline (β = −0.012, 95% CI = −0.023; 0.000, p = 0.039).

Conclusions

HL was significantly associated with a faster rate of global cognitive decline after a median follow-up of eight years in a sample of middle-income country.

Keywords

Alzheimer's disease cognition cognitive aging hearing loss

Introduction

It is estimated that between 2015 and 2050, the number of people aged over 60 years old worldwide will double (from 900 million to more than 2 billion) and the number of people over 80 years old will triple (from 125 million to 434 million).¹ There is a strong association between aging and hearing loss (HL) as demonstrated by the prevalence of HL of moderate (pure-tone average of audiometric thresholds of 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz greater than 35 dB in the better ear) or higher grades increasing from 12% among people aged 60 and older to over of 58% among those aged 90 and older.² Given the increasing number of people aging into HL risk, the World Health Organization (WHO) estimates that people with disabling HL globally could be more than 900 million in 2050, with a prevalence of 10%.³

It is estimated that 7% of dementia cases globally⁴ as well as in Brazil⁵ could be avoided if the HL was eliminated as a risk factor. HL in midlife is one of the main potentially modifiable risk factors for dementia. Additionally, HL has been associated with faster cognitive decline^6–9 in a growing body of literature.

Although most of the studies on the topic have been conducted in high-income countries (HICs) the incidence is expected to increase most in low- and middle-income countries (LMICs) (around two-thirds of dementia cases will be in LMICs).¹⁰ Currently, 58% of dementia cases occur in LMIC, and this number is expected to reach 68% by 2050.¹¹ Furthermore, the socioeconomic status of a country appears to influence dementia incidence patterns,¹² and the availability of dementia care resources likely differs between LMICs and HICs. Given that healthcare systems in LMICs are less developed than in HICs, it is crucial to examine dementia care in LMICs to identify specific needs.¹³ In Latin America, the economic, social, and cultural contexts vary considerably, suggesting that the distribution of potentially preventable dementia cases through risk factor management may not be universally applicable across countries.¹⁴

Although pure-tone audiometry is the gold standard for hearing assessment, several studies investigating the association between HL and cognitive decline relied on hearing screenings, administrative notifications, or self-reported data.^7,15–18 However, self-report measures of hearing had limited accuracy because they indicated participants’ self-perception of auditory function. For this reason, they are not sufficiently sensitive to identify HL and can lead to information biases, especially for older individuals with some degree of cognitive impairment.^19–21 In addition, many studies investigating the association between HL and cognitive function were cross-sectional.

Considering the higher relative risk and increasing prevalence of HL in older adults, the management of HL is a plausible and important target that may contribute to the prevention of dementia.²² Moreover, given the gaps that still exist in the literature, especially concerning LMIC and the different characteristics between LMIC and HIC that do not allow generalization regarding risk factors between different countries, our study aimed to investigate the association between objectively measured HL with pure-tone audiometry and cognitive decline up to 10 years among a subsample of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods

Study population

The ELSA-Brasil is an ongoing multicenter cohort of 15,105 civil servants (active and retired) from six Brazilian states. Data were collected in three waves: 2008–2010, 2012–2014, and 2017–2019. At baseline, participants ranged in age from 35 to 74 years. Each assessment encompassed data regarding cognitive performance, mental health status, imaging and laboratory evaluations, as well as sociodemographic variables and clinical conditions. A comprehensive description of the study design and participant characteristics is provided in previous publications.^23,24 The research received approval from the local institutional review board, and all participants granted informed consent.

A subset of 901 participants in the São Paulo center, who agreed to perform the hearing assessment, were evaluated.²⁵ Participants who, at baseline, had a history of stroke (n = 14), had incomplete cognitive test data (n = 29), or who reported taking drugs (i.e., antipsychotics, antiseizure, and antiparkinsonian drugs) that could interfere with cognition (n = 53) were excluded. Missing covariates were imputed with Multivariate Imputation by Chained Equations.²⁶ Our final analytic sample included baseline data from 805 participants (Figure 1). In Wave 2, 8 participants had died, 29 did not attend the hearing assessment, 437 were younger than 55 and did not complete the cognitive assessment, and 11 had incomplete cognitive data; in Wave 3, all participants underwent cognitive assessment, independently of age, 17 participants had died, 56 were lost to follow-up, and 30 had missing cognitive data (Figure 1).

Hearing assessment

Hearing was evaluated utilizing pure-tone audiometry. The audiometry was performed via air conduction at octave frequencies ranging from 250 to 8000 Hz within a soundproof environment, employing a Madsen Itera II audiometer. PTA thresholds were determined for each ear at 500, 1000, 2000, and 4000 Hz.²⁷ Consistent with prior work in this cohort,²⁸ HL was defined as a PTA >25 dB in the better ear (i.e., the ear with the best hearing in cases of bilateral HL).²⁷

Cognitive assessment

While all participants underwent cognitive assessments in Wave 1 and 3, only those 55 and older received a cognitive assessment in Wave 2. Cognitive performance was assessed by trained interviewers in a quiet environment characterized by adequate lighting and minimal background noise. In the case of using glasses or hearing aids, the participant used them during the cognitive assessment. The Consortium to Establish a Registry for Alzheimer's Disease word list, which has been validated for the Brazilian population²⁹ was used to assess episodic memory. For the immediate recall assessment, participants were instructed to retrieve ten words from a provided list. Participants read the list in up to three trials. In each trial, the words were presented in a different sequence, and participants were instructed to repeat the words immediately following their presentation. The score was calculated as the sum of the correctly recalled words from the three trials (0–30). Following a 5-min distraction interval, participants were asked to recall words from the same list without any additional presentation. The delayed memory score was assessed based on the number of correctly recalled words (0–10). Subsequently, participants were asked to recognize previously presented words from a list of 20 words, which included the original ones and ten distractor words. The recognition score was calculated from the count of correctly recognized words (0–10). The final memory score was calculated by summing all partial scores from the word list assessment (0–50 words).

To assess language and executive function, we used semantic and phonemic verbal fluency tests.³⁰ For the semantic fluency test, participants were instructed to say as many names as they could in one minute within a certain semantic category (in Waves 1 and 3, the animal category was used, as well as the vegetable category in Wave 2). In the phonemic fluency test, participants were instructed to name as many words as possible that began with a specified letter (the letter F was employed in Waves 1 and 3, while the letter A was used in Wave 2). The score for each test corresponded to the number of correct words generated. To mitigate learning effects, the verbal fluency categories were altered in Wave 2. Consequently, the test scores from Wave 2 were harmonized to enable comparison with scores from Wave 1 and 3.³¹ Additionally, the Trail-Making Test (TMT) version B was administered to assess processing speed and executive function.³² Participants were asked to draw a line connecting numbers and letters placed randomly on a sheet of paper in ascending and alternating order as quickly as possible (e.g., 1-A-2-B-3-C, with numbers ranging from 1 to 13 and letters from A to L). The test score was calculated according the time, in seconds, taken by participants to complete the task. Z-scores for each wave was calculated using Wave 1 mean scores and standard deviations to ensure the results from the cognitive assessments are comparable. In contrast to the other tests, higher scores in the TMT indicate poorer performance. To facilitate comparison with the other tests, the z-scores were multiplied by −1. Composite z-scores for memory and verbal fluency were generated at each wave by calculating the average of the z-scores from their corresponding tests. A global cognitive score was obtained by averaging the z-scores from all the cognitive tests at each wave.

Baseline sociodemographic characteristics, lifestyle, and clinical conditions

The sociodemographic variables were self-reported and included: age, sex, race (White, Black, and other), and education (less than college and college degree or more). Clinical variables included body mass index (BMI), hypertension, cardiovascular disease, diabetes, and depression. BMI was calculated by dividing the measured weight (in kilograms) by the square of the measured height (in meters). Hypertension was self-reported or determined by a systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure of ≥ 90 mmHg, or self-reported use of antihypertensive medication. Cardiovascular disease was defined based on self-reported myocardial infarction, myocardial revascularization, or heart failure. Diabetes was obtained through self-reported or determined by fasting glucose ≥ 7.0 mmol/L, 2-h-postprandial 75 g glucose ≥ 11.1 mmol/L, glycated hemoglobin ≥ 6.5%, or self-reported use of insulin or hypoglycemic medication. Depression was assessed with the Clinical Interview Scheduled Revised (the Brazilian version).³³ Self-reported occupational exposure to noise was classified into three categories: past or current exposure, and no history of exposure to noise in the workplace.

Lifestyle factors included leisure-time physical activity, smoking, and binge drinking. The long form of the International Physical Activity Questionnaire was utilized to assess the leisure physical activity, which was categorized based on the duration of engagement in light (<600 MET-min/week), moderate (600–2999 MET-min/week), or vigorous physical activity (≥3000 MET-min/week).³⁴ Smoking status was self-reported and classified into three categories: never, former, or current smoker. Binge drinking was defined as self-reported sporadic excessive drinking using a threshold of ≥ 210 g of alcohol for men and ≥ 140 g of alcohol for women.

Statistical analysis

Descriptive analyses were presented as mean and standard deviation (SD) for continuous variables and as percentages for categorical variables. To compare continuous variables the unpaired t-test was used, and to compare categorical variables according to HL status, we used the chi-square test.

Linear mixed-effects models with random intercepts, slopes and unstructured covariance was used to investigate the association between HL and global cognitive decline per cognitive domain. The timescale was defined by the age of the participant at each wave. The longitudinal association between baseline HL and cognitive decline was assessed by the interaction between HL status and the timescale. Model 1 was adjusted for age, sex, race/ethnicity, and education, while Model 2 was additionally adjusted for baseline physical activity, BMI, hypertension, diabetes, cardiovascular disease, depressive symptoms, binge drinking, smoking, and occupational noise exposure.

We implemented person-and time-specific stabilized inverse probability weighting (IPW_ij) to account for attrition during follow-up.³⁵ These person- and time-specific weights were the product of the inverse of the probability of survival (IPSW_ij) and the inverse of the probability of participating (IPPW_ij). For each follow-up wave j, separate logistic regressions were used to calculate the probability of survival up to wave j and participation at wave j conditional on survival up to wave j. Models estimating the numerator included baseline age, sex, education, and race. Models weight denominator additionally included time-varying BMI, binge drinking, smoking, diabetes, hypertension, cardiovascular disease, physical activity, depression, occupational noise exposure, the global cognitive score, and baseline HL. Weights were truncated at p1 and p99 to decrease the influence of outliers.

In sensitivity analyses, we compared the baseline characteristics of the subset participants that underwent hearing assessment to the overall ELSA-Brasil cohort. We also verified the robustness of our analysis by imputing missing cognitive data for individuals who were less than 55 years old in Wave 2 by using the next observation carried backward (i.e., using scores from Wave 3 in Wave 2) since they were ineligible for cognitive performance in Wave 2. In addition, we analyzed HL as a continuous variable (10 dB increase in PTA) and included a sensitivity analysis using PTA > 20 dB as cut-off point. Regarding hearing assessment, since the mean age of participants with normal hearing (49.9 years) and participants with hearing loss (59.4 years) were different, we performed a sensitivity analysis including only the participants with overlapping age ranges. The alpha level was set at 5%. All statistical analyses were performed using R version 4.1.2, using the lme4 package.

Results

Sample characteristics

The median (range) duration of follow-up was 8 (7–10) years. At baseline, the mean (standard deviation [SD]) age of the participants (n = 805) was 50.6 (9.4) years old, 51.9% were women, 59.9% were White, and 43.2% had at least a college degree (Table 1). Compared to those who did not have HL, those who had HL were more likely to be older, men, and had lower education. The frequency of hypertension, diabetes, cardiovascular disease, depressive symptoms, binge drinking, smoking, and exposure to occupational noise did not differ between those with and without HL (Table 1).

Table 1.

Sample characteristics at the study baseline by hearing loss^f at the better ear status (n = 805).

	Total(n = 805)	Without hearing loss(n = 743)	With hearing loss(n = 62)	p ^a
Age, mean (SD)^b	50.6 (9.4)	49.9 (9.1)	59.4 (9.6)	<0.001*
Women, %^c	51.9	53.4	33.9	0.004*
Race, %^c				0.747
Black or mixed	33.5	34.2	25.8
White	59.9	59.3	66.1
Other	6.6	6.5	8.1
Education (less than college degree), %^c	56.8	56.1	64.5	<0.001*
BMI, mean (SD)^b	27.3 (4.9)	27.3 (4.9)	27.4 (4.9)	0.946
Physical activity, %^c,d				0.348
None or light	78.0	78.1	77.4
Moderate	14.8	15.1	11.3
Vigorous	7.2	6.8	11.3
Hypertension, %^c	32.4	31.8	40.3	0.214
Diabetes, %^c	15.1	14.4	24.2	0.060
Cardiovascular disease, %^c,e	3.1	2.8	6.5	0.230
Depressive symptoms, %^c	11.5	12.0	6.4	0.271
Binge drinking, %^c	9.3	9.5	6.4	0.562
Smoking, %^c				0.236
Never	56.6	57.5	46.8
Former	29.6	28.8	38.7
Current	13.8	13.7	14.5
Noise exposure, %^c	38.7	37.8	50.0	0.079

SD: standard deviation; BMI: body mass index.

p value for the comparison between each variable and HL status.

Chi-square test.

T-test.

Light physical activity (PA): < 600 METs/week; Moderate PA: ≥ 600 and <3000 METs/week; High PA: ≥ 3000 METs/week.

Myocardial infarction, myocardial revascularization, or heart failure.

Hearing loss was defined as pure tone average (PTA) above 25 dB in the better ear.

*p < 0.05.

HL and cognitive decline

During a median follow-up of eight years, participants who had HL at baseline had a faster rate of global cognitive decline compared with those who did not have HL (β = −0.012, 95%CI = −0.023, 0.000, p = 0.039) (Figure 2, Table 2) adjusting for baseline sociodemographic characteristics, lifestyle, clinical conditions, and occupational noise exposure. Point estimates were similar but non-significant for the association between baseline HL and decline in memory (β = −0.012, 95% CI = −0.029, 0.004; Figure 2, Table 2), verbal fluency (β = −0.009, 95% CI = −0.024, 0.006), and executive function (β = −0.012, 95%CI = −0.030, 0.006).

Table 2.

Association between baseline hearing loss (HL)^a in the better ear and annual cognitive change during the study period (n = 805).

	Unadjusted		Model 1		Model 2
	β (95% CI)	p	β (95% CI)	p	β (95% CI)	p
Memory
HL^aTime	−0.010(−0.027; 0.007)	0.261	−0.012(−0.028; 0.004)	0.155	−0.012(−0.029; 0.004)	0.144
Verbal fluency
HL^aTime	−0.004(−0.020; 0.011)	0.613	−0.009(−0.024; 0.005)	0.216	−0.009(−0.024; 0.006)	0.232
Executive function
HL^aTime	−0.004(−0.024; 0.017)	0.724	−0.007(−0.026; 0.012)	0.472	−0.012(−0.030; 0.006)	0.191
Global cognition
HL^aTime	−0.010(−0.021; 0.002)	0.119	−0.013(−0.024; −0.001)	0.028*	−0.012(−0.023; 0.000)	0.039*

HL: hearing loss.

Hearing loss was defined as pure tone average (PTA) above 25 dB in the better ear.

Reference: without hearing loss.

Model 1: Weighted linear mixed models adjusted for age, sex, race/ ethnicity, and education.

Model 2: Weighted linear mixed models adjusted for age, sex, race/ ethnicity, education, physical activity, body mass index, hypertension, diabetes, cardiovascular disease, depressive symptoms, alcohol consumption, smoking, and noise exposure.

*p < 0.05.

Sensitivity analyses

We performed a sensitivity analysis of the subset participants that underwent hearing assessment and the overall ELSA-Brasil cohort. The results of this analysis showed some differences between the samples regarding sociodemographic and health characteristics (Supplemental Table 1). To assess the robustness of our findings, we performed sensitivity analysis by imputing cognitive outcomes for participants who were younger than 55 years in Wave 2 and therefore did not receive a cognitive assessment. The results of the sensitivity analysis were similar to those found in the main analysis (Supplemental Table 2). Moreover, we analyzed HL as a continuous variable (10 dB increase in PTA) (Supplemental Table 3), as well as we performed a sensitivity analysis using PTA >20 dB as cut-off point (Supplemental Table 4) and the results were similar to the main analysis. Finally, we performed a sensitivity analysis including only the participants with overlapping age ranges and the results remained mostly unchanged with the exception of the association with global cognitive score that was not significant in Model 2 (Supplemental Table 5).

Discussion

We found that HL assessed by pure-tone audiometry was associated with a faster rate of global cognitive decline after a median follow-up of eight years in a large Brazilian community-dwelling sample. Point estimates were similar but less precise and did not reach statistical significance when examining declines in memory, verbal fluency, and executive function.

The prevalence of HL is low among this population (∼8%), likely due to the relatively young age of the sample (mean age of our sample ∼51 years), which is lower than the mean age reported in other studies,^6–9,36 and may have resulted in underpowered analyses, since age is strongly associated with the hearing thresholds.^37,38

The cross-sectional findings build on our prior work in this population showing an association between HL in either ear (ear with better or worse hearing) and worse performance for verbal fluency, but no association between HL in the better ear and other cognitive domains (memory, verbal fluency, and executive function).²⁸ On the other hand, our findings in the present study showed an association between HL in the better ear with a faster rate of global cognitive decline after a median follow-up of eight years. In addition, cognitive domain-specific tests showed similar but less precise declines in memory, verbal fluency, and executive function.

Our finding of an association between HL and decline in cognition is consistent with some prior longitudinal studies,^7,9 though not all.^6,36 For example, in a cohort of 1164 participants (31 to 92 years old) followed for up to 24 years and including up to six cognitive evaluations, hearing impairment (PTA in the better ear >25 dB) was associated with accelerated decline in measures of global cognitive function and executive function, but not verbal fluency.⁷ Similarly, a study of 313 community-dwelling older adults (ages 61 to 98 years old) found that HL (defined as PTA in the better ear >25 dB) was associated with a faster decline during a two-year follow-up period in immediate verbal learning, short-delayed, and long-delayed recall tests.⁹ On the other hand, a study by Croll et al. found no association between HL and faster domain-specific cognitive decline in a mean follow-up of 4.4 years.⁶ In addition, HL (PTA in the better ear >25 dB) was not associated with domain-specific cognitive decline during seven years of follow-up in a sample of 387 adults aged 70 to 79 years old at baseline.³⁶

Prior work has also demonstrated an association between HL and incident cognitive outcomes of mild cognitive impairment and dementia. A meta-analysis of 11 studies (representing a total of 15,521 individuals) found that hearing impairment was associated with a higher risk of mild cognitive impairment (RR = 1.30, 95% CI: 1.12, 1.51) and greater risk of dementia (RR = 2.39, 95% CI: 1.58, 3.61) among older adults.³⁹ A separate meta-analysis including 37 studies with at least 2 years of follow-up estimated that risk of dementia increases by 16% for each 10-dB worsening of hearing.⁴⁰

Studies with shorter follow-up times or capturing less severe HL may find associations among overall cognitive scores, as opposed to domain-specific associations,^6,7,36 probably due to the compensatory changes in the allocation of cortical resources experienced by individuals with the early stages of HL.⁴¹ According to the sensory deprivation hypothesis underlying the link between HL and cognitive decline, peripheral deprivation caused by HL may induce the reallocation of “high-level” cognitive resources that would take longer to observe. Therefore, as HL progresses, more cognitive domains may become affected.⁸

A previous study with 1445 participants modeled the participants’ trajectory, based on HL and cognitive function. Individuals without HL showed a higher conditional probability (∼90%) of having “high-normal” cognitive function, while individuals with moderate to severe HL showed a conditional probability of 65% (55–69 age group) to 79% (70 + age group),⁴² demonstrating the co-occurrence of trajectories in the development of these two common changes in older adults³⁸ and the interference of more severe HL on cognitive function.⁴³

Alternatively, differences in the association between HL and domain-specific cognition may reflect the interaction between auditory information and cognitive abilities in that domain. Therefore, HL may interfere with performance more strongly, depending on the domains.²⁸ Tasks that depend on phonological cues (such as verbal fluency tests) are highly dependent on auditory cues resulting in worse performance among those with HL, while performance on tasks relying on visual cues, such as the trailing-making test used to assess executive function, which is less impacted by HL and more affected by aging.⁴⁴ Details about the oral presentation or the use of larger fonts in words during the tests of that study were not presented, although individuals with severe visual impairment were excluded.

The findings of a previous study demonstrated the effect of the auditory cortical reorganization, through cortical auditory evoked potentials in adults with HL.⁴⁵ The study verified a reduction in activation of the temporal cortex, as well as an increased activation of the frontal cortex in the early stages of mild-moderate HL, which is in line with the resource reallocation hypothesis and would lead to an increase in cognitive load.⁴⁵ Meta-analyses demonstrated that cochlear implants or hearing aids could induce positive effects on the maintenance and improvement of cognitive performance over time in adults with HL, as they would contribute to the reduction of sensory deprivation and cognitive load due to reduced effort hearing loss, as well as the social isolation that can accompany HL. However, such benefits should be further investigated in randomized trials.^46,47 Based on our results and the promising findings of these previous meta-analyses, rehabilitation could contribute to slowing cognitive decline in older adults.

The 2024 update of the Lancet Commission on dementia⁴ brought new and stronger evidence about dementia prevention. It highlighted that there is greater potential for risk reduction in LMIC, especially among minorities and lower socioeconomic groups compared to HIC. In addition, more robust evidence was presented that treating HL decreases the risk of dementia, emphasizing the importance of intervention, especially in people with HL and additional risk factors for dementia.⁴⁸ Future studies need to confirm the role of using cochlear implants or hearing aids in preventing or slowing cognitive decline.

It is important to highlight that in adults, HL usually starts at the higher frequencies and these frequencies contribute to the clarity of speech. Most studies with adults use the criteria based on mid-frequencies,⁸ and may underestimate the impairment linked to HL at high frequencies, which may affect central auditory processing, and even limit social skills and cognition.²⁸

Some methodological differences among studies may justify some heterogeneous results: the age of the participants,^6–9 that may interfere with the cognition, severity, and prevalence of HL; the procedures adopted to assess HL (self-report, screening, and audiometry), to evaluate cognitive function, criteria of HL, covariates included in the analyses, ways of accessing these covariates (objective versus subjective measures), and different characteristics between the populations studied.^{6,28,37,42,49}

It is worth mentioning that the São Paulo center is the only one that has provided audiological evaluation since the beginning of the study for those individuals who agreed to participate. When we compared the sample that underwent hearing assessment to the overall ELSA-Brasil cohort, we noted some differences regarding socioeconomic and health factors. It is known that factors, such as income, education, access to health services, nutrition, and physical activity, can affect the people's health.⁵⁰ Furthermore, inequalities related to race, gender, socioeconomic and geographic conditions can be related to health, which may explain the differences observed within the country and among the ELSA-Brasil centers.

As strong points of this study, we can highlight the prospective and longitudinal design, being a study carried out in an LMIC, use of standardized assessments (pure-tone audiometry with different measures over time and detailed cognitive tests), as well as comprehensive clinical and laboratory evaluations. As a limitation, we can point out that neuroimaging evaluation was not available to identify possible brain structures implicated in the association between HL and cognition. Moreover, detailed information about the use of hearing aids began to be collected in the ELSA-Brasil study from the 4th wave onwards (approximately 2.5% of individuals use them). Therefore, this variable was not considered in the present analysis. Furthermore, the ELSA-Brasil is composed of civil servants, active or retired, in universities, with a higher income and education compared to the general Brazilian population.²⁴

Conclusion

In conclusion, HL was significantly associated with a faster rate of global cognitive decline after a median follow-up of eight years in a diverse sample.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251315043 - Supplemental material for Hearing loss and cognitive decline in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) during eight years of follow-up

Supplemental material, sj-docx-1-alz-10.1177_13872877251315043 for Hearing loss and cognitive decline in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) during eight years of follow-up by Alessandra Giannella Samelli, Natalia Gomes Gonçalves, Fernanda Yasmin Odila Maestri Miguel Padilha, Vitor Martins Guesser, Carla Gentile Matas, Camila Maia Rabelo, Renata Rodrigues Moreira, Itamar S Santos, Paulo Andrade Lotufo, Isabela J Bensenõr, Paola Gilsanz and Claudia Kimie Suemoto in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

The authors have no acknowledgments to report.

ORCID iDs

Alessandra Giannella Samelli

Natalia Gomes Gonçalves

Fernanda Yasmin Odila Maestri Miguel Padilha

Vitor Martins Guesser

Carla Gentile Matas

Camila Maia Rabelo

Renata Rodrigues Moreira

Itamar S Santos

Paulo Andrade Lotufo

Isabela J Bensenõr

Paola Gilsanz

Claudia Kimie Suemoto

Author contributions

Alessandra Giannella Samelli (Conceptualization; Data curation; Funding acquisition; Investigation; Resources; Supervision; Writing – original draft; Writing – review & editing); Natalia Gomes Gonçalves, PhD (Data curation; Formal analysis; Validation; Visualization; Writing – original draft; Writing – review & editing); Fernanda Yasmin Odila Maestri Miguel Padilha, Ms (Writing – original draft; Writing – review & editing); Vitor Martins Guesser (Writing – original draft; Writing – review & editing); Carla Gentile Matas, PhD (Writing – original draft; Writing – review & editing); Camila Maia Rabelo (Writing – original draft; Writing – review & editing); Renata Rodrigues Moreira (Writing – original draft; Writing – review & editing); Itamar S Santos (Data curation; Writing – original draft; Writing – review & editing); Paulo Andrade Lotufo (Funding acquisition; Writing – original draft; Writing – review & editing); Isabela J Bensenõr (Writing – original draft; Writing – review & editing); Paola Gilsanz (Writing – original draft; Writing – review & editing); Claudia Kimie Suemoto (Conceptualization; Data curation; Formal analysis; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing).

Funding

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The ELSA-Brasil study was supported by the Brazilian Ministry of Health, the Ministry of Science Technology and Innovation, and the National Council for Scientific and Technological Development (CNPq). Drs Samelli, Bensenor, and Suemoto receive support from CNPq, Brazil (research productivity fellowship). This research was supported by a grant from FAPESP (Foundation for Research Support of the State of São Paulo) (n. 2011/10186-9).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available within the article and/or its supplementary material.

Supplemental material

Supplemental material for this article is available online.

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