Age-related hearing loss accelerates cerebrospinal fluid tau levels and brain atrophy: a longitudinal study

Results

ARHL and CSF biomarkers

We first investigated the association of ARHL with the baseline levels of the CSF Aβ and tau proteins in total group. CSF measurements of Aβ₄₂, tau, and ptau₁₈₁ were available for 479 participants (67 HC, 209 preclinical AD, 93 MCI due to AD, and 110 dementia due to AD), of whom 60 had ARHL (Table 1). No association was found between ARHL and CSF Aβ₄₂ levels (β = 0.002, p = 0.28). (Figure 1A) ARHL was associated with higher levels of CSF total tau (β = 0.23, p = 0.002) and ptau₁₈₁ (β = 0.17, p = 0.017) after adjustment for age, gender, education, APOE4 status, pathological diagnosis, DM2, hypertension, hyperlipidemia, BMI, and extracted CSF volume. (Figure 1B–1C) CSF levels of tau (111 ± 54 pg/ml, 95% confidence interval (CI) = 95.6 to 124.6 pg/ml) and ptau₁₈₁ (49 ± 26 pg/ml, 95% CI = 42 to 55 pg/ml) were significantly higher in individuals with ARHL than those in HN group (100 ± 61 pg/ml, 95% CI = 94 to 106 pg/ml for tau; 45 ± 26pg/ml, 95% CI = 42 to 47 pg/ml for ptau₁₈₁). As for the biomarker ratios, ARHL was associated with higher CSF total tau/Aβ₄₂ ratio (β = 7.96, p = 0.01), but not with ptau₁₈₁ /Aβ₄₂ ratio (β = 0.15, p = 0.07). (Supplementary Figure 1)

Table 1. Participant characteristics at baseline.

CSF Aβ42, tau, and ptau

Amyloid PET

Hippocampus

Other ROIs#

HL

HN

p value

HL

HN

p value

HL

HN

p value

HL

HN

p value

No.

60

419

106

560

131

746

74

440

Age (Mean ± SD, year)

77.4 ± 5.0

72.7 ± 7.1

<0.0001

77.3 ± 5.2

72.4 ± 6.9

<0.0001

77.5 ± 5.1

73.0 ± 6.9

< 0.0001

77.2 ± 5.6

72.5 ± 6.9

<0.0001

Gender (M/F)

50/15

220/199

0.0002

70/36

265/295

0.0004

90/41

362/384

< 0.0001

53/21

201/239

<0.0001

Education (Mean ± SD, year)

16.2 ± 3.0

15.9± 2.7

0.46

16.5 ± 2.8

16.3 ± 2.6

0.38

16.3± 2.9

16.0± 2.7

0.27

16.3 ± 2.9

15.9 ± 2.8

0.25

APOE Ɛ4 carrier status (0/1/2)

37/19/4

180/180/61

0.0174

65/33/8

261/230/68

0.02

79/42/10

355/301/90

0.02

41/27/6

198/188/54

0.22

Hypertension (yes/no)

26/34

194/225

0.67

49/57

257/303

0.95

66/65

343/403

0.35

39/35

188/252

0.11

Hyperlipidemia (yes/no)

29/31

217/202

0.62

60/46

286/274

0.30

68/63

379/367

0.82

34/40

208/232

0.83

BMI (Mean ± SD, kg/m2)

26.2 ± 4.1

26.8 ± 5.1

0.43

26.4 ± 4.4

27.4± 5.4

0.06

26.5 ± 4.6

27.0± 5.0

0.31

26.2 ± 4.2

26.6 ± 4.9

0.46

DM2 (yes/no)

4/56

29/390

0.94

10/96

54/506

0.95

15/126

63/683

0.40

7/67

36/404

0.71

CSF volume (ml)

19.0 ± 4.7

18.5 ± 5.1

0.44

…

…

…

…

…

…

…

…

…

Disease continuum

Healthy control

11

56

0.13&

17

76

0.13&

30

164

0.15&

17

94

0.21&

Preclinical AD

27

182

33

147

27

123

15

70

MCI due to AD

13

80

41

229

56

295

30

163

Dementia due to AD

9

101

15

108

18

164

12

113

Abbreviations: HL = Hearing Loss; HN = Hearing Normal; AD = Alzheimer’s Disease; BMI = Body Mass Index; DM2 = Diabetes Mellitus Type 2; ROI = Region of Interest

#Other five ROIs include middle temporal cortex, entorhinal cortex, parahippocampal area, posterior cingulate, and precuneus

&Chi-square test for trend of proportion of HL across disease continuum groups

Figure 1. The cross-sectional associations between ARHL and CSF biomarkers. No association was found between ARHL and CSF Aβ₄₂ levels (A). ARHL was associated with higher levels of CSF total tau (B) and ptau₁₈₁ (C). All the above analyses were adjusted for age, gender, education, APOE4 status, pathological diagnosis of AD, DM2, hypertension, hyperlipidemia, BMI, and extracted CSF volume. *p value was calculated for the total sample.

When stratified by AD continuum group, as expected, CSF Aβ₄₂ levels decreased and tau (total tau or ptau₁₈₁) levels increased across groups from HC through preclinical AD and MCI to dementia. ARHL was associated with a higher level of CSF total tau only among those at preclinical and MCI stages (Figure 1A), but was not associated with CSF ptau₁₈₁ or Aβ₄₂ in any group (Figure 1B–1C). Potential interactions with APOE4 status and gender were found for CSF Aβ₄₂ and total tau (p=0.07), respectively. Subgroup analyses showed that ARHL was associated with higher levels of CSF tau or ptau₁₈₁ only among APOE4 carrier (β = 0.24, p = 0.03 for tau; β = 0.25, p = 0.03 for ptau₁₈₁) and male (β = 0.30, p = 0.001 for tau; β = 0.19, p = 0.03 for ptau₁₈₁) groups (Supplementary Figure 2).

We next studied whether ARHL was associated with the changes of Aβ and tau levels through longitudinal analyses of the following 24 month widow from the baseline. ARHL was not associated with the change rate of Aβ₄₂ (Figure 2A), but with the faster elevation rates of CSF tau (Figure 2B) and ptau₁₈₁ (Figure 2C) in HC or preclinical AD, after adjustment for age, gender, education, APOE4 status, DM2, hypertension, hyperlipidemia, BMI, and extracted CSF volume. Similar results were concluded when the follow-up window was extended to 5 years.

Figure 2. The longitudinal influences of ARHL on CSF biomarkers. ARHL at baseline was not associated with the change rate of Aβ₄₂ (A), but with the faster elevation rates of CSF tau (B) and ptau₁₈₁ (C). All the above analyses were adjusted for age, gender, education, APOE4 status, pathological diagnosis of AD, DM2, hypertension, hyperlipidemia, BMI, and extracted CSF volume. *p value was calculated for the total sample.

ARHL and amyloid PET

We then investigated the association of ARHL with the molecular imaging results. Florbetapir PET scans were available for 666 participants (93 HC, 180 preclinical AD, 270 MCI, and 123 AD), of whom 106 had ARHL (Table 1). As expected, a florbetapir cortical summary measurement (SUVR) increased across groups through the AD continuum. We did not identify a statistically significant association between ARHL and (summary or regional) SUVR when adjusting for all variables mentioned above. Similarly, no associations were detected between ARHL and SUVR when the analyses were stratified by AD continuum group. (Supplementary Figure 3)

ARHL and MRI biomarkers

Cortical thickness or volume measures were available in 877 participants for hippocampus and 514 participants for another 20 ROIs. As expected, the cortical measures were greatest in HC, followed by those at the preclinical stage, and then those with MCI and dementia due to AD. In the fully-adjusted model, ARHL was associated with higher volume of hippocampus (β = 188.8, p = 0.04) (Figure 3A) and thickness/volume of parahippocampus (β = 8.99, p = 0.03 for volume of left side; β = 7.18, p = 0.07 for volume of right side; β = 0.33, p = 4.86×10^-3 for thickness of left side; β = 0.27, p = 0.01 for thickness of right side) and entorhinal cortex (β = 5.41, p = 1.44×10^-5 for volume of left side; β = 2.08, p = 0.01 for volume of right side; β = 0.76, p = 1.55×10^-4for thickness of left side; β = 0.80, p = 3.71×10^-4for thickness of right side) (Figure 4A–4D). Only the associations of ARHL with entorhinal cortex survived the Bonferrroni adjustment. When stratified by AD continuum stage, no association was found with hippocampus volume in any group. (Figure 3A) ARHL showed significant association with higher entorhinal cortex at baseline only in the MCI and dementia stage. (Figure 4A–4D) There were no significant associations of ARHL with cortical structural measures of middle temporal region, posterior cingulate, and precuneus.

Figure 3. The relationship between ARHL and hippocampus volume. ARHL was associated with larger volume of hippocampus. Nonetheless, when stratified by AD continuum stage, no association was found in any group. (A) ARHL was associated with more rapid cortical thinning, especially in the preclinical or prodromal AD stage (B). All analyses were adjusted for age, gender, education, APOE4 status, pathological diagnosis of AD (for total sample), DM2, hypertension, hyperlipidemia, BMI, and ICV. *p value was calculated for the total sample.

Figure 4. The cross-sectional associations between ARHL and structural measurements of entorhinal cortex. ARHL was associated with higher thickness/volume of entorhinal cortex. The significant associations with higher entorihnal cortex at baseline were observed only in MCI and/or dementia stage. (A–D) All analyses were adjusted for age, gender, education, APOE4 status, pathological diagnosis of AD, DM2, hypertension, hyperlipidemia, BMI, and intracranial volume. *p value was calculated for the total sample.

Moreover, the longitudinal analyses showed that ARHL at baseline was associated with more rapid cortical thinning in hippocampus (Figure 3B) and entorhinal cortex (Figure 5A–5D) in preclinical or prodromal AD, after adjusting for age, gender, education, APOE4 status, DM2, hypertension, hyperlipidemia, BMI, and ICV. Similar trends were observed in HC and preclinical stages.

Figure 5. The longitudinal influences of ARHL on structural measurements of entorhinal cortex. ARHL at baseline was associated with more rapid cortical thinning in bilateral entorhinal cortex in prodromal AD stage (A–D). All analyses were adjusted for age, gender, education, APOE4 status, pathological diagnosis of AD, DM2, hypertension, hyperlipidemia, BMI, and intracranial volume. *p value was calculated for the total sample.

Among those who had both cortical measures and CSF measures available (n = 822 for hippocampus; n = 491 for other ROIs), the addition of Aβ₄₂, tau, and ptau₁₈₁ in the regression of ARHL with cortical measures barely influenced the effect size (β) of ARHL. We did not identify any interaction between APOE4 or gender and ARHL in predicting brain imaging or CSF biomarker measures.

Materials and Methods

ADNI

All data (including the baseline demographic characteristics, biomarkers, medical history (MH), and physical examination (PE)) were downloaded from the ADNI database (adni.loni.usc.edu). As a multicenter study, ADNI is designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of AD. The participants are adults aged 55–90 years with healthy control (HC), mild cognitive impairment (MCI), or mild Alzheimer’s disease (AD). Further information can be found at http://www.adni-info.org/ and in previous reports [24–29].

Participants

The search terms used for the screening for HL include “hear”, “auditory”, “ear”, “deaf”, “presbycusis”, and “HOH (hard of hearing)”. Based on the records in MH and PE datasets, 559 participants were found to be with HL at baseline, among which 340 had information of covariates available. Furthermore, another 96 participants with HL were excluded, including 75 who had HL before 60 years and 21 who had no information about age of onset for HL, leaving 244 who had HL after 60 years old (69.7 ± 5.9, defined as age-related HL (ARHL) herein). In the subsequent analyses, we further excluded individuals with a history of psychiatric conditions (including depression), neurologic diseases other than AD, malignancy, or stroke. To restrict the study population to the AD continuum, we excluded participants with clinical diagnosis of MCI or dementia but not with evidence of cerebral Aβ deposition detected by PET (AV45 > 1.11) or CSF (Aβ < 192 pg/ml) [30]. (Figure 6) Finally, samples with data of CSF (n = 479 for Aβ42, tau, and ptau₁₈₁), amyloid PET (n = 666), and brain structural measures (n = 877 for hippocampus; n = 514 for other regions) were included in the cross-sectional analyses. (Table 1).

Figure 6. Flow chart of searching for ARHL. Abbreviations: HL = hearing loss; ARHL = age-related HL; CSF = cerebrospinal fluid, ROI = regions of interest. Searching terms for covariates: Hypertension: “hypert”, “HTN”, and “blood pressure”; Hyperlipemia: “lipid”, “cholesterol”, “hyperlipemia”, and “HLP”; DM2: “diabete”, “diabetic”, and “insulin”; Depression: “depress”. Other covariates (including age, gender, apoe4 status, education, diagnosis, and BMI) are accessible without searching the relevant database. ^*Other ROIs include middle temporal cortex, entorhinal cortex, parahippocampal area, posterior cingulate, and precuneus.

CSF measurements

CSF collection and procedural protocols have been described previously [30]. Baseline CSF Aβ_1-42, tau, and p-tau₁₈₁ (pg/ml) were measured using the INNOBIA AlzBio3 immunoassay (Fujirebio, Belgium). The within-batch precision values were <10% (5.1–7.8% for Aβ_1-42, 4.4–9.8% for tau and 5.1–8.8% for ptau₁₈₁, respectively).

¹⁸F florbetapir AV45 PET imaging

Florbetapirdata in the most fully pre-processed format was downloaded from LONI (http://adni.loni.usc.edu). The data preprocessing is accessible online (adni.loni.ucla.edu/about-data-samples/image-data/). The mean florbetapir AV45 uptake (representing the Aβ retention) within each region was calculated by co-registering the florbetapir scan to the corresponding MRI. Florbetapir SUVRs were created by averaging across four cortical grey matter regions (frontal, cingulate, lateral parietal, lateral temporal) and dividing the summary by reference region. Further details of PET acquisition and the region-of-interest protocol have been summarized previously [31].

MRI structure

The process of MRI acquisition in ADNI has been described elsewhere [32, 33]. In brief, ADNI MRIs were acquired at multiple sites with1.5T GE, Philips, and Siemens MRI scanners using a magnetization prepared rapid acquisition gradient echo (MP-RAGE) sequence. Two high-resolution T1-weighted MRI scans were collected for each participant using a sagittal 3D MP-RAGE sequence with an approximate TR=2400ms, minimum full TE, approximate TI=1000ms, and approximate flip angle of 8 degrees. Scans were collected with a 24cm field of view and an acquisition matrix of 192 x 192 x 166 (x, y, z dimensions), to yield a standard voxel size of 1.25 x 1.25 x 1.2 mm. Images were then reconstructed to give a 256 x 256 x 166 matrix and voxel size of approximately 1 x 1 x 1.2 mm. Herein, six brain regions were defined as brain regions of interest (ROI) in the present study, including hippocampus, parahippocampal area, middle temporal cortex, entorhinal cortex, posteriorcingulate, and precuneus. The atrophy of the above regions in AD has been validated in previous MRI studies [33–38].

Statistical analyses

Chi-square tests (for categorical variables), Student t test (for continuous variables with normal distributions), and Mann-Whitney U test (for variables with skewed distributions) were used to compare demographic, clinical, and diagnostic variables between HL and hearing normal (HN) groups. In case of skewed distribution (Shapiro-Wilk test > 0.05) of biomarker data, transformation was performed to approximate a normal distribution via “car” package of R software.

In the cross-sectional analyses, we firstly studied the associations of ARHL with CSF biomarkers (Aβ42, tau, ptau₁₈₁, tau/Aβ42, and ptau₁₈₁/Aβ42) after adjusting for age, gender, education, APOE4 status, pathological diagnosis of AD, diabetes mellitus type-2 (DM2), hypertension, hyperlipidemia, body mass index (BMI), and extracted CSF volume. Next, differences in amyloid PET and cortical structural measures (volume or thickness) between ARHL and HN were tested in 21 ROIs using a regression model adjusting for age, gender, education, APOE4 status, pathological diagnosis of AD, DM2, hypertension, hyperlipidemia, BMI, and intracranial volume (ICV). Significance of the HL status was examined after correction for multiple comparisons using Bonferroni method (p < 0.05). A two-way analysis of covariance model was utilized to estimate whether the association differed across the diagnostic groups. Multiple interaction terms for gender, APOE4 status, and pathological diagnosis of AD were used to explore whether strata effect existed. In case of any potential interaction (p < 0.1), subgroup analysis was further performed. All the above analyses were repeated after stratifying by the AD continuum (preclinical AD, MCI, and dementia). Finally, to explore the influences of ARHL on the longitudinal change in the above biomarkers, we fitted linear mixed effects models to characterize individual paths of change. These models had random intercepts and slopes for time and an unstructured covariance matrix for the random effects and included the interaction between (continuous) time and ARHL as predictor. All outcome variables in linear mixed-effects models were standardized to z scores (healthy control as reference) to facilitate comparisons between modalities. Age, gender, education, APOE4 status, pathological diagnosis, DM2, hypertension, hyperlipidemia, BMI, extracted CSF volume or ICV were covariates.

P < 0.05 was considered significant in all analyses except where specifically noted. R version 3.5.1 and GraphPad Prism 7.00 software were used for statistical analyses and figure preparation.

Ethics approval

The study was approved by institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their guardians according to the Declaration of Helsinki (consent for research).

Author Contributions

Prof. Yu: conceptualization and design of the study, analysis and interpretation of data and revision of the manuscript. Dr. Xu: collection and analysis of the data, drafting and revision of the manuscript, and prepared all the figures. Prof. Zhang: drafting and revision of the manuscript. Dr.Li and Dr. Chen-Chen Tan: interpretation of the data and revision of the manuscript. MS. Cao: revision of the manuscript. Prof. Lan Tan: design and conceptualization of the study, revision of the manuscript. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Acknowledgments

The authors thank contributors, including the staff at Alzheimer’s Disease Centers who collected samples used in this study, patients, and their families whose help and participation made this work possible.

Conflicts of Interest

We declare that none authors have financial disclosures and conflicts of interest.

Funding

This study was supported by grants from the National Natural Science Foundation of China (91849126). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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