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R E S E A R C H A R T I C L E Open Access
The association between reduced kidney
function and hearing loss: a cross-sectional
study
Wenwen Liu
1
, Qinqin Meng
2
, Yafeng Wang
2
, Chao Yang
3
, Lili Liu
3
, Huaiyu Wang
4
, Zaiming Su
4
, Guilan Kong
4,5*
,
Yaohui Zhao
6*
and Luxia Zhang
3,4,5*
Abstract
Background: The relationship between kidney function and hearing loss has long been recognized, but evidence
of this association mostly comes from small observational studies or other populations. The aim of this study is to
explore the association between reduced kidney function and hearing loss in a large population-based study
among the middle-aged and elderly Chinese.
Methods: Data collected from the Chinese Health and Retirement Longitudinal Study (CHARLS) in 2015 were used
for analysis. A cross-sectional study was conducted among 12,508 participants aged 45 years and older. Hearing
loss, the outcome of this study, was defined according to interviewees’responses to three survey questions related
to hearing in the CHARLS. Estimated glomerular filtration rate (eGFR) was employed to assess kidney function, and
participants were classified into three categories based on eGFR: ≥90, 60–89 and < 60 mL/min/1.73 m
2
. Multivariable
logistic regression was employed to adjust for potential confounders, including demographics, health-related
behaviors, and cardiovascular risk factors.
Results: The overall prevalence of self-reported hearing loss in the study population was 23.6%. Compared with
participants with eGFR ≥90 mL/min/1.73 m
2
, participants with eGFR of 60–89 mL/min/1.73 m
2
(odds ratio [OR]: 1.11,
95% confidence interval [CI]: 1.00–1.23) and eGFR < 60 mL/min/1.73 m
2
(OR: 1.25, 95% CI: 1.04–1.49) showed
increased risk of hearing loss after adjusting for potential confounders.
Conclusions: Reduced kidney function is independently associated with hearing loss. Testing for hearing should be
included in the integrated management of patients with chronic kidney disease.
Keywords: CHARLS, Reduced kidney function, eGFR, Hearing loss, Multivariable logistic regression
Background
The World Health Organization (WHO) reported that
approximately 432 million adults suffered from disabling
hearing loss in 2018 and estimated that over 900 million
people will have disabling hearing loss by 2050 [1]. Hear-
ing loss in adults not only brings about communication
difficulties in daily life but also has negative effects on an
individual’s cognitive and psychosocial function. Hearing
loss could lead to social isolation, financial strain, and a
low health-related quality of life [2–4]. Because most
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permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: guilan.kong@hsc.pku.edu.cn;yhzhao@nsd.pku.edu.cn;
zhanglx@bjmu.edu.cn
4
National Institute of Health Data Science, Peking University, 38 Xueyuan
Road, Haidian District, Beijing 100191, China
6
National School of Development, Peking University, 5 Yiheyuan Road,
Haidian District, Beijing 100871, China
3
Renal Division, Department of Medicine, Peking University First Hospital,
Peking University Institute of Nephrology, 8 Xishiku Street, Xicheng District,
Beijing 100034, China
Full list of author information is available at the end of the article
Liu et al. BMC Nephrology (2020) 21:145
https://doi.org/10.1186/s12882-020-01810-z
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cases of hearing loss are acquired and difficult to recover
from, but preventable, exploring the risk factors of hear-
ing loss is of great significance.
The effects of aging on the auditory system are consid-
ered the leading cause of adult-onset hearing loss [5].
The association between hearing loss and genetic muta-
tions [6], noise exposure [7], use of ototoxic drugs in
treatment [8,9], and chronic diseases such as hyperten-
sion and diabetes [10,11] has also been demonstrated in
existent studies.
Kidney disease has become a public health issue of
global concern in recent years. Since the first report on
Alport syndrome revealed that hereditary familial neph-
ritis is related to sensorineural deafness in 1927 [12],
several other congenital syndromes such as Fabry dis-
ease, branchio-oto-renal syndrome, Alstrom syndrome,
and Bartter syndrome, have been recognized to have
both hearing and kidney manifestations [13–15]. Some
observational studies have assessed the auditory function
of patients with end-stage kidney disease (ESKD) or re-
ceiving kidney replacement therapy [16–19]. For ex-
ample, Meena et al. [18] studied 50 cases of ESKD and
50 healthy volunteers and found that 28% of the former
but only 6% of the latter have sensorineural hearing loss.
Zeigelboim et al. [17] found that patients with ESKD
have significantly higher hearing thresholds for high fre-
quencies than the control group. Here the hearing
threshold refers to the minimum level of sound that
evokes an auditory sensation [20]. Renda et al. [19] also
found a significant association between the duration of
hemodialysis and hearing loss in children aged 6–18
years with dialytic chronic kidney disease (CKD).
The kidney and cochlea have common antigenicity
and similar physiologic mechanisms involving the trans-
port of fluid and electrolytes, which might explain the
hearing loss in patients with kidney disease [21]. Some
possible aetiological factors related to hearing loss in
kidney failure patients have also been reported, including
electrolyte disturbances, hypertension, the use of oto-
toxic drug and hemodialysis treatment [21–23].
Unfortunately, most of existing studies have small
sample sizes or enrolled patients with kidney failure;
moreover, few studies have focused on the association
between hearing loss and mildly-to-moderately reduced
kidney function in a large population [24,25]. Vilayur
et al. [24] presented the first community-based study on
an Australian population and demonstrated an associ-
ation between moderate CKD and hearing loss. Seo et al.
[25] used the Korean National Health and Nutritional
Examination Survey and found that individuals with
CKD are more likely to have hearing impairment than
those without. But the sample size of their study was
also relatively small and only the hearing status of two
eGFR groups, i.e., ≥60 and < 60 mL/min/1.73 m
2
were
compared. However, Gupta et al. [26] found that there is
no significant association between GFR estimated using
the serum creatinine-based equation and risk of incident
hearing loss.
The prevalence of hearing loss is consistently higher in
middle-aged and older populations than in younger
cases, but evidence of the association between reduced
kidney function and hearing loss in the Chinese middle-
aged and older population is limited. We conducted a
cross-sectional population-based study to explore the re-
lationship between reduced kidney function and hearing
loss by taking advantage of a representative sample of
the general population aged 45 years and older and the
strict quality control process of the Chinese Health and
Retirement Longitudinal Study (CHARLS) [27].
Methods
Study population
The CHARLS, a nationally representative survey of
China’s middle-aged and elderly population, provides a
high-quality public micro-database with social, eco-
nomic, and health information. Samples were selected
by using multistage probability sampling taking into
consideration regional and socioeconomic disparities.
The probability-proportional-to-size (PPS) sampling
technique was used to select 150 county-level units from
all counties of China except Tibet. PPS sampling was
also applied to select three primary sampling units
(PSUs) from each county-level unit. PSUs represent the
lowest level of government organization and consist of
administrative villages (cun) in rural areas and neighbor-
hoods (shequ or juweihui) in urban areas. Eighty or more
households with age-eligible members were selected
within each PSU, and one age-eligible member was ran-
domly selected from qualified households. If the chosen
person was willing to participate, this person and his or
her spouse were interviewed. All stages of sampling were
conducted by a computer to avoid potential biases aris-
ing from human manipulation.
CHARLS also has good cross-study comparability of
results because the survey instrument was developed on
the basis of the best international practices and harmo-
nized with over 25 leading international research studies
in the Health and Retirement Study model [27].
The data used in our study were obtained from the
CHARLS dataset collected in 2015, which included a
total of 20,967 individuals. Information on demographic
characteristics, health-related behaviors and lifestyles,
and health status were collected through face-to-face in-
terviews using the questionnaire. Most of the inter-
viewers were recruited from local colleges and
universities and had received 9 days of rigorous training
on the questionnaire content, interview techniques, se-
curity and quality control, and face-to-face interview
Liu et al. BMC Nephrology (2020) 21:145 Page 2 of 9
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practices. The surveyed data were recorded by using a
computer-assisted personal interview (CAPI) system,
which could help substantially improve the quality of the
surveyed dataset by detecting errors in data inputs.
When the interviewer enters an input item with a logical
error or abnormal value, the system will pop out a mes-
sage to alert the interviewer to this dubious entry. In this
way, the participants no longer need to read the ques-
tionnaire, and the interviewers would ask them face to
face and help them understand the questions and corre-
sponding options. Anthropometric and physical mea-
surements were provided, and blood samples were
collected by trained nurses from township hospitals or
China’s Center for Disease Prevention and Control
(CDC) according to the standard protocol.
CHARLS was approved by the Biomedical Ethics Re-
view Committee of Peking University (IRB00001052–
11015), and all participants signed informed consent be-
fore participation.
The exclusion criteria of the study were as follows: (1)
demographic data were not recorded; (2) aged < 45 years
old; and (3) creatinine or hearing status data were not
recorded. A total of 12,508 participants were included in
our final analysis. The participant selection process is
shown in Fig. 1.
Hearing loss
In our study, hearing loss was identified through self-
reporting. Objective measurement, such as audiometry,
was not provided in CHARLS, but several studies have
demonstrated the reliability of self-reported hearing loss
[28,29]. We used hearing-related questions in the
CHARLS survey to determine whether a patient had
hearing loss. The hearing-related CHARLS survey ques-
tions are listed in Table 1. Participants were asked these
questions by trained investigators through face-to-face
interviews. A participant was defined as having hearing
loss if he or she met one of the following three criteria:
1) had a hearing problem; 2) wore a hearing aid; and 3)
had a poor hearing status.
Reduced kidney function
The estimated glomerular filtration rate (eGFR) is con-
sidered the best overall index of kidney function in
health and disease [30]. In this study, we determined
eGFR using the Chronic Kidney Disease Epidemiology
Collaboration (CKD-EPI) equation [31] as follows:
GFR mL=min=1:73 m2
¼141 min Scr=κ;1ðÞ
α
max Scr=κ;1ðÞ
−1:209 0:993Age
1:018 if female½1:159 if black½ð1Þ
where Scr represents serum creatinine measured in units
of mg/dL. κis 0.7 for females and 0.9 for males, αis −
0.329 for females and −0.411 for males, min refers to
the minimum value of Scr/κand 1, and max refers to
the maximum value of Scr/κand 1.
At the beginning of this study, we attempted to classify
the eGFR into five groups: ≥90, 60–89, 30–59, 15–29,
and < 15 mL/min/1.73 m
2
, according to the different
n=19566
Excluding participants aged
<45 years, n=718
Excluding participants
without creatinine data or
hearing status data, n=7058
Final dataset, n=12508
CHARLS 2015 dataset,
n=20967
n=20284
Excluding participants
without demographic data,
n=683
Fig. 1 Flow chart of the participant selection
Table 1 Hearing related questions in the CHARLS survey
(1) Do you have a hearing problem?
1. Yes
2. No
(2) Do you ever wear a hearing aid?
1. Yes
2. No
(3) Would you say your hearing is excellent, very good, good, fair,
or poor? (How is your hearing with a hearing aid if you normally use it?
How is your hearing without a hearing aid if you normally don’t use it?)
1. Excellent
2. Very good
3. Good
4. Fair
5. Poor
Liu et al. BMC Nephrology (2020) 21:145 Page 3 of 9
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stages of CKD defined by the Kidney Disease: Improving
Global Outcomes (KDIGO) guideline [32]. We calcu-
lated the numbers and percentages of participants in dif-
ferent eGFR groups, and found that only 74 (0.6%)
participants with eGFR of 15–29 mL/min/1.73 m
2
and 8
(0.1%) participants with eGFR < 15 mL/min/1.73 m
2
,
which may lead to limited statistical power to examine
the association between eGFR and hearing loss. This is a
study based on general population, and thus there are
less people in lower eGFR groups. Therefore, we finally
used only three eGFR groups of ≥90, 60–89, and < 60
mL/min/1.73 m
2
for analysis, which represented normal
kidney function, mildly reduced kidney function, and
moderately to severely reduced kidney function,
respectively.
Other predictor variables
We chose predictor variables by referring to hearing loss
risk factors reported in the existent literatures [5,33]
and their availabilities in the CHARLS dataset. Besides
eGFR, predictor variables used in the regression models
included demographic characteristics (i.e., age, gender,
education, area of residence), health-related behaviors
(i.e., smoking and drinking status) and cardiovascular
risk factors (i.e., body mass index [BMI], central obesity,
hypertension, diabetes, stroke, high-density lipoprotein
[HDL] cholesterol, and low-density lipoprotein [LDL]
cholesterol). Demographics and health-related behaviors
data were obtained from the questionnaire. BMI was de-
fined as weight in kilograms divided by the square of
height in meters, and acquired through physical mea-
surements. Participants were categorized as underweight
(< 18.5 kg/m
2
), normal weight (18.5 to 24.9 kg/m
2
), over-
weight (25.0 to 29.9 kg/m
2
), and obese (≥30 kg/m
2
) ac-
cording to their BMI. In our study, central obesity was
defined as a waist circumference of ≥80 cm for females
and ≥102 cm for males. Hypertension was defined as
mean systolic blood pressure (SBP) ≥140 mmHg, mean
diastolic blood pressure (DBP) ≥90 mmHg, or a self-
report of hypertension. Diabetes was defined as fasting
plasma glucose ≥126 mg/dL, HbA1c concentration ≥
6.5%, or a self-reported doctor diagnosis. Stroke was de-
fined as a self-reported history of doctor diagnosis. HDL
and LDL cholesterol levels were directly obtained from
the results of laboratory tests.
Statistical analysis
Descriptive statistics for continuous variables are pre-
sented by using means and standard deviations (SD),
while frequencies and percentages are used to describe
categorical variable characteristics. Student’st-test was
used to compare the mean values of continuous vari-
ables between participants with and without hearing
loss, and differences in hearing loss prevalence among
different categorical variable groups were tested by using
Pearson’sχ
2
test. The association between reduced eGFR
and hearing loss was modeled by using a logistic regres-
sion function, and odds ratios (ORs) with 95% confi-
dence intervals (CIs) of hearing loss for different eGFR
categories were calculated. Multivariable logistic regres-
sion models were constructed to adjust for potential
confounding variables (i.e., age [45–54 years, 55–64
years, ≥65 years], gender [male, female], education
[illiterate, literate, primary school, middle school, high
school and above], area of residence [urban or rural],
smoking [never, current, past], drinking [never, current,
past], BMI [underweight, normal weight, overweight,
obese], central obesity [yes or no], hypertension [yes or
no], diabetes [yes or no], stroke [yes or no], HDL choles-
terol [continuous value], and LDL cholesterol [continu-
ous value]). To build the multivariable logistic regression
models, we used a separate unknown category to repre-
sent missing data for BMI, central obesity, and stroke,
which had missing rates greater than 1%. Other variables
included in the models that had missing rates lower than
1% were not preprocessed as cases containing missing
data were automatically deleted during logistic regres-
sion analysis.
All analyses were performed by using STATA software
(version 14.0). All pvalues were based on two-sided tests
with a significance level of 0.05.
Results
A total of 12,508 participants were included in the final
dataset for analysis. The characteristics of participants
are described in Table 2. The mean age was 60.5 years,
and 52.9% of the participants were females. Significant
differences in hearing loss prevalence were observed
among participants of different age, educational back-
ground, area of residence, smoking status, drinking sta-
tus, BMI, chronic health status (i.e., with/without
hypertension, with/without diabetes, with/without
stroke), SBP, DBP, and eGFR (all pvalues < 0.001). The
distribution of self-reported hearing loss prevalence is
presented in Table 3. Overall, the prevalence of self-
reported hearing loss in our study was 23.6, and 35.8%
of the participants with eGFR < 60 mL/min/1.73 m
2
re-
ported hearing loss. The prevalence of hearing loss in
this group was nearly twice that in the group with eGFR
≥90 mL/min/1.73 m
2
(19.4%).
The results of logistic regression analysis of the associ-
ation between eGFR and hearing loss after adjusting for
potential confounders are listed in Table 4. Compared
with participants with eGFR ≥90 mL/min/1.73 m
2
, the
ORs of participants with eGFR of 60–89 mL/min/1.73
m
2
and eGFR < 60 mL/min/1.73 m
2
were 1.11 (95%CI,
1.00–1.23; p= 0.043) and 1.25 (95%CI, 1.04–1.49; p=
0.017), respectively. The detailed multivariable logistic
Liu et al. BMC Nephrology (2020) 21:145 Page 4 of 9
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regression analysis results are shown in Table S1of the
Additional file.
Discussion
The results of our study indicated that 23.6% of middle-
aged and older Chinese have hearing loss and that this
prevalence was higher at older ages. In the process of
exploring the association between eGFR and hearing
loss, we observed that the odds of hearing loss are higher
among participants with lower eGFR.
Comparing the prevalence of hearing loss observed in
this study with previous studies is difficult because dif-
ferent studies included participants belonging to differ-
ent age groups and defined and measured hearing loss
Table 2 Characteristics of the participants
Variables Overall (n= 12,508) Hearing loss (n= 2946) No hearing loss (n= 9562) Pvalue
Age (years) 60.5 ± 9.6 64.8 ± 10.1 59.1 ± 9.1 < 0.001
Gender 0.832
Male 5889 (47.1) 1382 (46.9) 4507 (47.1)
Female 6619 (52.9) 1564 (53.1) 5055 (52.9)
Education < 0.001
Illiterate 3851 (30.8) 1249 (42.4) 2602 (27.3)
Literate 947 (7.6) 237 (8.0) 710 (7.4)
Primary 3232 (25.9) 788 (26.8) 2444 (25.6)
Middle 3016 (24.1) 500 (17.0) 2516 (26.4)
High and above 1449 (11.6) 172 (5.8) 1277 (13.4)
Rural 10,191 (81.7) 2523 (85.7) 7668 (80.4) < 0.001
Smoking < 0.001
Never 7388 (59.1) 1702 (57.8) 5686 (59.5)
Current 3475 (27.8) 768 (26.1) 2707 (28.3)
Past 1638 (13.1) 474 (16.1) 1164 (12.2)
Drinking < 0.001
Never 6717 (53.8) 1611 (54.8) 5106 (53.5)
Current 4381 (35.1) 912 (31.0) 3469 (36.3)
Past 1392 (11.1) 419 (14.2) 973 (10.2)
BMI (kg/m
2
)< 0.001
< 18.5 702 (5.6) 233 (7.9) 469 (4.9)
18.5–24.9 7137 (57.1) 1730 (58.7) 5407 (56.6)
25.0–29.9 3717 (29.7) 758 (25.7) 2959 (31.0)
≥30.0 688 (5.5) 155 (5.3) 533 (5.6)
Central Obesity 3307 (26.4) 753 (25.6) 2554 (26.7) 0.383
Hypertension 5584 (44.7) 1543 (52.5) 4041 (42.3) < 0.001
Diabetes 2286 (18.3) 638 (21.7) 1648 (17.3) < 0.001
Stroke 329 (2.6) 116 (3.9) 213 (2.2) < 0.001
HDL Cholesterol (mg/dL) 51.2 ± 11.6 51.4 ± 12.1 51.2 ± 11.5 0.424
LDL Cholesterol (mg/dL) 102.5 ± 29.0 101.8 ± 29.2 102.7 ± 29.0 0.152
SBP (mmHg) 128.7 ± 20.0 131.2 ± 21.1 128.0 ± 19.6 < 0.001
DBP (mmHg) 75.8 ± 11.7 75.2 ± 11.5 75.9 ± 11.7 0.006
eGFR (mL/min/1.73 m
2
)90.1 ± 16.4 86.0 ± 17.1 91.3 ± 16.0 < 0.001
eGFR groups (mL/min/1.73 m
2
)< 0.001
≥90 7498 (60.0) 1456 (49.4) 6042 (63.2)
60–89 4315 (34.5) 1241 (42.1) 3074 (32.2)
< 60 695 (5.6) 249 (8.5) 446 (4.7)
Note: Data are expressed as number (%) or mean ± SD, unless otherwise indicated
†Number (%) of missing data points: 13 (0.10%) in education, 30 (0.24%) in area of residence, 7 (0.06%) in smoking, 18 (0.14%) in drinking, 264 (2.11%) in BMI, 443
(3.54%) in central obesity, 25 (0.20%) in hypertension, 38 (0.30%) in diabetes, 146 (1.17%) in stroke, 1 (0.00%) in LDL cholesterol, 259 (2.07%) in SBP, 259 (2.07%)
in DBP
Abbreviations: BMI body mass index, HDL high-density lipoprotein, LDL low-density lipoprotein, SBP systolic blood pressure, DBP diastolic blood pressure, eGFR
estimated glomerular filtration rate
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using different instruments. Pure-tone audiometry and
self-reporting are two common ways to assess hearing
status in hearing-related studies [24,25,33–38]. Pure
tone audiometry detects the hearing threshold at a spe-
cific frequency [20]. Most studies [24,33] performed
pure-tone audiometry at 0.5, 1, 2, and 4 kHz. According
to the WHO’s recommendations [39], an average thresh-
old of > 25 dB hearing level (HL) in the better-hearing
ear is defined as hearing loss and an average threshold
of > 40 dB HL in the better-hearing ear is defined as dis-
abling hearing loss. Two of the most recent large-scale
hearing loss studies [33,40] based on the Chinese popu-
lation were published nearly a decade apart. In 2006,
Sun et al. [40] reported that 11.0% of older adults (≥60
years) could be diagnosed as hearing disabled (> 40 dB
HL); this study was based on the data of the Second
China National Sample Survey on Disability. In 2015,
Gong et al. [33] conducted a survey including 6984 older
adults (≥60 years) in the four provinces of Jilin, Guang-
dong, Gansu, and Shaanxi of China and reported preva-
lences of hearing loss (> 25 dB HL) and disabling hearing
loss (> 40 dB HL) of 58.9 and 24.1% respectively. A study
[34] based on the Health Survey for England 2014, a na-
tionally representative cross-sectional survey, reported
that 26% of men and 20% of women aged 45 years and
older have hearing loss (> 35 dB HL at 3.0 kHz of the
better-hearing ear). Amieva et al. [36] used self-
perceived hearing loss in their study and conducted a
short questionnaire survey to assess hearing loss in eld-
erly participants (≥65 years) randomly selected from the
general French population. In this study, 4% of the par-
ticipants reported major hearing loss and 31% reported
moderate hearing loss. A WHO report [1] showed that
approximately one-third of the population aged over 65
years worldwide suffer from disabling hearing loss. In
the present study, the prevalences of hearing loss among
participants aged 45–54, 55–64, and ≥65 years were
13.6, 20.1, 37.1%, respectively, which is similar to reports
from England, France and the WHO.
The association between reduced kidney function and
hearing loss found in our study is consistent with the re-
sults of some previously published studies [24,25]. Seo
et al. [25] found that eGFR < 60 mL/min/1.73 m
2
has a
significant independent influence on the hearing status
of adults. The authors also reported that the OR of hear-
ing impairment in participants with eGFR < 60 mL/min/
1.73 m
2
compared with those having eGFR ≥60 mL/min/
1.73 m
2
is 1.25 (95%CI, 1.12–1.64), after adjusting for
age, sex, smoking, alcohol, BMI, diabetes mellitus, hyper-
tension, dyslipidemia and microalbuminuria. Similarly,
Vilayur et al. [24] defined moderate CKD as eGFR < 60
mL/min/1.73 m
2
and found an independent association
between moderate CKD and hearing loss with an OR of
1.43 (95%CI, 1.10–1.84). In a prospective study of 1843
individuals, Gupta et al. [26] estimated GFR by using an
equation that considers SCr and cystatin C and found
that lower eGFR is significantly associated with incident
hearing loss at speech frequencies but not at high fre-
quencies. However, when they estimated GFR by using
an SCr-based equation, no significant association could
be found between either lower baseline eGFR or decline
in eGFR and incident hearing loss. This result is incon-
sistent with our finding, and the difference observed
may be due to differences in study populations, defini-
tions of hearing loss, and SCr measurement. Our study
Table 3 Prevalence of hearing loss in different age and eGFR
groups
Individuals Hearing loss Prevalence (%)
Total 12,508 2946 23.6
Age groups (years)
45–54 3935 534 13.6
55–64 4498 902 20.1
≥65 4075 1510 37.1
eGFR groups (mL/min/1.73 m
2
)
≥90 7498 1456 19.4
60–89 4315 1241 28.8
< 60 695 249 35.8
Table 4 Odds ratios (ORs) and 95% confidence intervals (CIs) for hearing loss in relation to the eGFR categories
Variable Model 1 Model 2 Model 3 Model 4
OR (95%CI) p-value OR (95%CI) p-value OR (95%CI) p-value OR (95%CI) p-value
eGFR (mL/min/1.73 m
2
)
≥90 Reference Reference Reference Reference
60–89 1.68 (1.54–1.83) < 0.001 1.08 (0.98–1.19) 0.122 1.11 (1.00–1.22) 0.043 1.11 (1.00–1.23) 0.043
< 60 2.32 (1.96–2.73) < 0.001 1.30 (1.09–1.55) 0.004 1.29 (1.08–1.54) 0.006 1.25 (1.04–1.49) 0.017
Model 1: Unadjusted;
Model 2: Adjusted for age;
Model 3: Adjusted for age, gender, education, area of residence, smoking, and drinking;
Model 4: Adjusted for age, gender, education, area of residence, smoking, drinking, BMI, central obesity, hypertension, diabetes, stroke, HDL cholesterol, and
LDL cholesterol
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provided valid epidemiological evidence of the associ-
ation between reduced kidney function and hearing loss
in middle-aged and older Chinese population, and is
based on a nationally representative large-scale dataset
of high quality. We classified eGFR into three groups
and found that participants in two eGFR groups, specif-
ically, those with eGFR of 60–89 and < 60 mL/min/1.73
m
2
have higher odds of hearing loss, compared with par-
ticipants with eGFR ≥90 mL/min/1.73 m
2
. These results
suggest that, not only the CKD patients at stage 3–5, but
also those at stage 2 have an increased risk of hearing
loss. More attention should be paid to the hearing status
of CKD patients from early stage to enable the imple-
mentation of suitable interventions at an appropriate
time and reduce the burden of the disease.
Possible mechanisms of the association between re-
duced kidney function and hearing loss have been re-
ported. Some evidence indicates that many physiological,
pathological and pharmacological similarities exist in the
cochlea and kidney [21] and that these similarities may
account for the similar effects of some medications and
immunological factors on the two organs. The stria vas-
cularis of the cochlea and the glomerulus of the kidney
are epithelial structures that are intimately associated
with the vascular system [21,41], and a number of ion
channels and transporters involved in K
+
cycling and en-
dolymphatic K
+
,Na
+
,Ca
2+
, and pH homeostasis are
expressed in both the inner ear and kidney [42]. Some
studies [43–45] also indicate that abnormalities of nerve
conduction in central and peripheral pathways in ESKD
patients probably influence the auditory response. Yassin
et al. [22] found that the degree of hearing loss is dir-
ectly related to the degree of hyponatremia, regardless of
the level of blood urea, and that cochlear affections are
greatly improved by correcting kidney failure and restor-
ing serum Na. Govender et al. [23] reported that coch-
lear function in patients with CKD could be affected by
elevated electrolyte, urea and creatinine levels, concomi-
tant conditions such as hypertension, and ototoxic drugs
such as furosemide.
Besides the reduced eGFR, our study also confirmed
that health condition factors, including older age, hyper-
tension, diabetes, and stroke, are significantly associated
with hearing loss. Hearing loss in older persons may be
caused by the death or damage of cochlear sensory hair
cells and decreased functioning of the stria vascularis
[5]. Explanations for the association between diabetes
and hearing loss include microvascular and neuropathic
complications affecting diabetics in multiple organ sys-
tems that may also affect the inner ear [46]. Duck et al.
[47] observed that hypertension and diabetes have a syn-
ergistic effect on high-frequency hearing loss. All levels
of the auditory pathway, especially the hearing end-
organ and auditory nerve, are very vulnerable to stroke
damage, which is related to an abnormal arterial blood
supply [48]. In contrast to previous studies [49], we did
not find an association between obesity, either BMI ≥30
kg/m
2
or central obesity, and hearing loss. However,
overweight (BMI in 25.0–29.9 kg/m
2
) appeared to be a
protective factor of hearing loss, which may be due to
the low rate of obesity in the middle-aged and elderly
Chinese population. Based on the logic of deriving clin-
ical risk scoring system in previous studies [50], the risk
of hearing loss would increase when multiple risk factors
with OR larger than 1.00 co-exist. This means that a pa-
tient with hypertension and diabetes may have higher
risk of hearing loss than a patient with only
hypertension.
The main strengths of our study include the nation-
ally representative large-scale dataset, high response
rate, and strict quality control process. However, cer-
tain limitations must also be noted. First, causal infer-
ences could not be drawn based on the current cross-
sectional study. Second, information bias may exist as
hearing loss was defined on the basis of self-reported
results. However, the reliability of self-reported hear-
ing loss has been previously confirmed [28,29]. For
example, Ferrite et al. [29] studied the validity of the
following two questions: “Do you feel you have a
hearing loss?”and “In general, would you say your
hearing is ‘excellent,’‘very good,’‘good,’‘fair,’‘poor’?”
and revealed that responses to each of the two ques-
tions are sufficiently accurate to be used as evidence
of hearing loss in epidemiological studies on adult
populations. These two questions are similar to the
survey questions used in our study. Third, the infor-
mation about albuminuria, congenital hearing loss, ex-
ternal or middle-ear pathology, use of ototoxic drugs,
and exposure to noise, which may be potential con-
founders in the association between reduced kidney
function and hearing loss or causes of hearing loss,
was not available in our study. Fourth, we excluded
7058 participants without creatinine data or hearing
status, which constituted 36.1% of the total number
of participants aged 45 and over in the CHARLS
2015 dataset, and such exclusion may cause selection
bias to some extent. The highest data missing rate for
some variables including education, area of residence,
smoking, drinking, central obesity, hypertension, dia-
betes, stroke, and LDL cholesterol, was only 3.5%. An
unknown category was used to represent missing data
for variables with missing rates greater than 1%, and
variables with missing rates lower than 1% were not
preprocessed as cases containing missing data were
automatically deleted during logistic regression ana-
lysis. Missing data in these variables may cause some,
albeit very limited, selection bias. Finally, the possibil-
ity of residual confounding exists.
Liu et al. BMC Nephrology (2020) 21:145 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Conclusions
This study indicated that reduced kidney function is in-
dependently associated with hearing loss in the middle-
aged and elderly Chinese and that patients with reduced
kidney function have higher odds of hearing loss. We
recommend that testing for hearing should be included
in the integrated management among patients with
CKD, and patients with CKD need to pay attention to
their hearing status from early stage to enable the imple-
mentation of suitable interventions at an appropriate
time and prevent the development of hearing loss.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12882-020-01810-z.
Additional file 1: Table S1. Multivariate Logistic regression analysis of
hearing loss
Acknowledgements
The authors thank Yu Lin and Jingyi Wu for their advice on data analysis and
manuscript drafting.
Authors’contributions
Conception and design: WL, LZ, GK; data acquisition and pre-processing: QM,
YW, YZ; data analysis and interpretation of results: WL, CY, LL, HW, ZS; manu-
script drafting: WL. GK, LZ and YZ contributed important intellectual content
during manuscript revision. All authors have read and approved the final
manuscript.
Funding
This study was supported by Grants from the National Natural Science
Foundation of China (Grant Nos. 81771938, 91846101, 81301296), from
Peking University (Grant Nos. BMU2018MX020, PKU2017LCX05), the National
Key Technology R&D Program of the Ministry of Science and Technology of
the People’s Republic of China (2016YFC1305400), and the University of
Michigan Health System-Peking University Health Science Center Joint Insti-
tute for Translational and Clinical Research (BMU20160466). The funding
body had no role in the design of the study and collection, analysis, and in-
terpretation of data and in writing the manuscript.
Availability of data and materials
The data that support the findings of this study are available from the
CHARLS project team but restrictions apply to the availability of these data,
which were used under license for the current study, and so are not publicly
available. Data are however available from the authors upon reasonable
request and with permission of the CHARLS project team.
Ethics approval and consent to participate
This study used secondary data from CHARLS for analysis. We were granted
permission by the CHARLS project team to access and use the data.
Consent for publication
Not applicable.
Competing interests
Luxia Zhang received research funding from AstraZeneca. The remaining
authors declare that they have no competing interests.
Author details
1
Department of Epidemiology and Biostatistics, School of Public Health,
Peking University, Beijing, China.
2
Institute of Social Science Survey, Peking
University, Beijing, China.
3
Renal Division, Department of Medicine, Peking
University First Hospital, Peking University Institute of Nephrology, 8 Xishiku
Street, Xicheng District, Beijing 100034, China.
4
National Institute of Health
Data Science, Peking University, 38 Xueyuan Road, Haidian District, Beijing
100191, China.
5
Center for Data Science in Health and Medicine, Peking
University, Beijing, China.
6
National School of Development, Peking
University, 5 Yiheyuan Road, Haidian District, Beijing 100871, China.
Received: 17 November 2019 Accepted: 15 April 2020
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