Risk factors for age-related hearing loss | CIA

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Back to Journal »Clinical Interventions for Aging» Volume 16

Multi-dimensional risk factors for age-related hearing loss in the elderly living in the Malaysian community

Author Ooi TC, Ishak WS, Sharif R, Shahar S, Rajab NF, Singh DKA, Mukari SZMS

Published on December 8, 2021, the 2021 volume: 16 pages 2033-2046

DOI https://doi.org/10.2147/CIA.S340432

Single anonymous peer review

Editor approved for publication: Professor Nandu Goswami

Theng Choon Ooi, 1 Wan Syafira Ishak, 1 Razinah Sharif, 1 Suzana Shahar, 1 Nor Fadilah Rajab, 1 Devinder Kaur Ajit Singh, 1 Siti Zamratol-Mai Sarah Mukari 2 1 Healthy Aging and Wellness Center, School of Health Sciences, Kebangsaan, Malaysia, Kuala Lumpur, 50300, Malaysia; 2 Institute of Ear, Hearing and Speech, National University of Malaysia, Kuala Lumpur, 50300, Malaysia Address: Wan Syafira Ishak Hearing Project, Center for Healthy Aging and Health, National University of Malaysia, School of Health Sciences, Jalan Raja Muda Abdul Aziz, Kuala Lumpur, 50300, Malaysia Tel +60 392895011 Fax +60 392897161 Email [email protected] Purpose: This study assesses the prevalence and multidimensional risk factors of age-related hearing loss (ARHL) among elderly people in Malaysian communities. Patients and methods: A total of 253 participants 60 years and older participated in this cross-sectional study. Participants were assessed by pure tone audiometry. The hearing threshold is calculated for the better ears and is divided into 0.5 to 4 kHz octave pure tone average (PTA) and 2 to 8 kHz octave frequency pure tone average (HFA). Then, multiple logistic regression analysis was used to determine the risk factors associated with PTA hearing loss (HL) and HFAHL. Results: The prevalence of ARHL based on PTA and HFA among the elderly in the community was 75.5% and 83.0%, respectively. After multi-factor adjustment, older age (OR: 1.239; 95% CI: 1.062–1.445), higher waist circumference (OR: 1.158; 95% CI: 1.015–1.322), and lower intake of niacin (OR: 0.909) ; 95% CI: 0.831–0.988) and potassium (OR: 0.998; 95% CI: 0.996–1.000) and low RAVLT T5 scores (OR: 0.905; 95% CI: 0.838–0.978) were identified as the risk of PTAHL factor. At the same time, the older the age (OR: 1.117; 95% CI: 1.003-1.244), the increase in carbohydrate intake (OR: 1.018; 95% CI: 1.006-1.030) and the decrease in potassium intake (OR: 0.998; 95 % CI: 0.997) – 0.999), and lower RAVLT T5 scores (OR: 0.922; 95% CI: 0.874–0.973) are associated with an increased risk of HFAHL. Conclusion: Increased age, higher waist circumference, reduced niacin and potassium intake, increased carbohydrate intake, and lower RAVLT T5 score are associated with an increased risk of ARHL. Modifying these risk factors may benefit the prevention and management strategies of ARHL in the elderly. Keywords: Cognitive function, dietary intake, hearing loss, elderly, prevalence, risk factors

Age-related hearing loss (ARHL) is an age-related degenerative disease characterized by progressive symmetric sensorineural hearing loss (HL) that mainly occurs at higher frequencies. 1 Approximately 1.5 billion people in the world suffer from some degree of HL, and more than 42% of them are seniors 60 years and older. 2 According to reports, more than 65% of people over 60 have HL, and the prevalence in the elderly population increases exponentially with age. 3 The prevalence of ARHL is expected to increase dramatically. As has been observed in the current global population shift trend, an increasingly aging population worldwide. 2

The root cause of ARHL is complex and multi-factorial. Its development and progress can be attributed to susceptibility to genetic and biological risk factors, comorbidities, lifestyle behaviors and environmental insults. 1,4 Susceptibility genetic factors play a crucial role in determining the incidence and severity of ARHL. At the same time, men are more likely to develop ARHL than women of the same age, which may be due to a higher risk of exposure to noise or other environmental risk factors. 2 Individuals with certain health conditions such as cardiovascular disease, diabetes, and kidney disease are at higher risk of ARHL. The risks of ARHL. 4 Use of ototoxic drugs, continuous exposure to occupational hazards such as noise and chemicals, and unhealthy lifestyle habits (for example, smoking, excessive drinking, unhealthy eating habits, nutritional deficiencies, and excessive loudness in entertainment environments) appear as such sensory defects Common risk factors for 1,4 All these risk factors can initiate and exacerbate the progression of ARHL independently or in concert.

ARHL is the third leading cause of years of disability (YLD) in 2019 and the main cause of YLD for people over 70 years old. 3 Due to its high global prevalence, ARHL has had a significant economic impact on society. 5 In addition, unresolved elderly people with HL may cause cognitive, physical, and psychosocial problems, such as social isolation, loss of function, depression, cognitive impairment, and physical weakness. 1,4 It is reported that early screening of ARHL and the use of hearing aids can reduce the adverse effects and improve the quality of life of the affected. However, the effectiveness of such interventions is limited by several factors, including the inability to obtain relevant medical institutions that provide professional hearing care, stigma, and the inability to afford hearing aids. 5 Identifying risk factors, especially changeable risk factors (for example, lifestyle habits) and environmental insults), may help plan and develop prevention strategies to reduce the progress and severity of ARHL. However, Malaysia's research focus on ARHL is limited. The National Ear and Hearing Impairment Survey completed in 2005 found that 69.9% of the elderly 60 years and older have HL. 6 Although the study reported on the demographic trends of HL, it did not provide a detailed survey of the risk factors that HL can change. Therefore, the main purpose of this study is to determine the multi-dimensional risk factors for ARHL among the elderly living in Malaysian communities.

This study is part of a prospective population-based aging study [Long Term Research Funding Program-Towards a Useful Aging (LRGS-TUA)]. The data was collected from the first wave of LRGS-TUA research conducted in 2013. Seniors aged 60 and over were recruited from four different states in Malaysia through a stratified random sampling method. These states are Selangor (Central Malaysia), Perak (Northwest), Kelantan (Northeast) and Johor (South) . However, the hearing assessment data is only applicable to participants from Selangor. A total of 573 seniors met the inclusion criteria and agreed to participate. However, only 253 participants (44.2%) completed all the tests and were included in this study. The sampling method, inclusion criteria and exclusion criteria of this study are described in detail by Shahar et al. 7

This study was approved by the Medical Research and Ethics Committee of the University of Malaysia (UKM and was carried out in accordance with the Declaration of Helsinki. Participants obtained written informed consent before participating.

Participants' sociodemographics, medical history, dietary status, cognitive function, psychosocial and functional status data were obtained by trained field staff through face-to-face interviews using standardized questionnaires. 7 Anthropometry, body composition, blood pressure data, physical fitness, biochemical and hearing tests are evaluated using the protocol described by Shahar et al. 7

The information obtained includes age, gender, ethnicity, smoking and drinking habits, years of education, and self-reported medical history (hypertension, hypercholesterolemia, diabetes, cardiovascular disease, and tinnitus).

Use the portable SECA 206 portable body measuring instrument (Seca, Hamburg, Germany) and Tanita digital lithium scale (Tanita, Tokyo, Japan) to measure height and weight. Then, calculate the body mass index (BMI) by using the formula "weight (kg) / height (m) 2". Waist circumference, hip circumference and calf circumference are measured with Lufkin tape, and the reading is accurate to 0.1 cm. Use bioelectrical impedance analysis Inbody S10 (Biospace, Seoul, Korea) to analyze body composition (fat mass, fat-free mass, skeletal muscle mass, and body fat percentage). An automatic digital sphygmomanometer (OMRON, Kyoto, Japan) was used to continuously measure the systolic and diastolic blood pressure twice to obtain the average reading.

The participants’ usual food and beverage intake during the week was recorded using a validated questionnaire on the eating habits of the elderly. 7 Then, use the dietitian ProTM software to analyze the diet records of each participant to obtain their respective nutritional intake.

Participants were asked to fast overnight before collecting blood samples. The fasting venous blood was then collected from the participants by a well-trained phlebotomist and sent to an accredited medical laboratory for biochemical analysis. Parameters included in the analysis include fasting blood glucose, hemoglobin, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, and albumin levels.

The physical fitness of the participants was assessed through the elderly physical fitness test. 7 The elderly physical fitness test is a series of tests, including a 2-minute step test, a chair standing test, a chair sitting test, a timed get-up test, a hand grip test, and a back scratch test. These fitness tests are used to measure aerobic endurance, lower extremity muscle strength, lower extremity flexibility, flexibility and balance, upper extremity muscle strength and upper extremity flexibility.

The Malay version of the Simple Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess global functions. Although they have similarities in cognitive function assessment, the MoCA test is more sensitive than MMSE in detecting mild cognitive impairment and has a smaller ceiling effect. 8 At the same time, a subset of the Wexler Adult Intelligence Scale was used to evaluate information processing, visual movement speed, visual memory and coordination, as well as attention, attention and memory. Then, use the Rey Auditory Language Learning Test (RAVLT) to determine short-term language memory and language learning. Cognitive function assessment is described in detail by Shahar et al. 7

Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), and World Health Organization Disability Assessment Form 2.0 (WHODAS 2.0) are used to assess the functions of self-care, independent living skills, and disability, respectively. At the same time, the Medical Outcome Social Support (MOSS) survey is used to measure functional social support. The Eysenck Personality Questionnaire and the Geriatric Depression Scale were used to determine personality disorders and depressive symptoms. The three-item loneliness scale, the four-item perceived stress scale, the eight-item flourishing scale and the life satisfaction scale were used to evaluate loneliness, pressure perception, successful self-perception and life satisfaction. Shahar et al. 7 described psychosocial and functional status assessment in detail.

Hearing assessment is performed with slight modifications in the method of Mukari et al. 9 In short, the hearing status of the participants was performed by trained personnel using a calibrated Madsen Itera II diagnostic audiometer and TDH 39 earphones in a sound-processed mobile compartment. The air conduction thresholds for the left and right ears are obtained per ear at frequencies of 0.5, 1, 2, 3, 4, 6 and 8 kHz. In this study, we classified the hearing threshold of better ears into 0.5 to 4 kHz octave pure tone average (PTA) and 2 to 8 kHz octave high frequency pure tone average (HFA). HL is defined as the threshold average value greater than 25 dB hearing level.

The statistical package for the Social Sciences version 25.0 (IBM Corp, Armonk, New York, USA) was used to analyze the data. The prevalence of HL is calculated by dividing the number of HL participants based on PTA (PTAHL) or HFA (HFAHL) by the total number of participants in this study. Next, by using independent T test for continuous variables or chi-square test for categorical variables, compare the variables between participants with normal hearing and the HL of each PTA category. Variables that appeared to be significant (p <0.05) in the univariate test were further analyzed by binary logistic regression. Then, variables that are significantly (p <0.05) associated with HL are included in the final multiple binary logistic regression model, and other well-known confounding factors (age, gender, race, years of education, smoking and drinking habits, Incidence and BMI). In the final multiple logistic regression model, significant (p<0.05) variables were identified as potential risk factors for PTAHL or HFAHL.

The characteristics of the participants included and excluded in the analysis are shown in Table 1. Except for the ethnicity ratio (p <0.01), most characteristics are not significantly different. The proportion of Malay population is higher, and the proportion of Chinese and Chinese is lower. The Indian population among the excluded participants. Table 1 Characteristics of participants included and excluded in the study

Table 1 Characteristics of participants included and excluded in the study

Among the elderly living in the community, hearing loss based on HFA (83%) is more common than hearing loss based on PTA standards (75.5%). Table 2 shows the characteristics of participants based on hearing status. Compared with participants with normal hearing, participants with PTAHL were more likely (p <0.05) to be male (47.6%), elderly (69.49 ± 6.07 years), Chinese (59.7%), and suffering from cardiovascular disease (16.8). %), high waist circumference (91.24 ± 11.93 cm), high body fat (25.21 ± 8.89 kg), low vitamin C (124.83 ± 69.11 mg/day), niacin (9.55 ± 3.50 mg/day) ), potassium (1355.39 ± 425.50 mg/day), zinc (3.30 ± 1.45 mg/day) and copper (0.61 ± 0.27 mg/day), higher fasting blood glucose levels (6.44 ± 2.27 mmol/day) L), lower chair sitting test (9.61 ± 12.39 cm), timing test (10.44 ± 3.21 seconds) and back scratch test (19.05 ± 13.98 cm) scores, in MMSE (23.47 ± 4.94), MoCA (19.39 ± 6.12) ), number sign test (5.91 ± 3.05), number span test (7.67 ± 2.68) and RAVLT T5 score (37.97 ± 11.41), with high WHODAS 2.0 (3.90 ± 6.30) and prosperity scale (14.89 ± 7.26) score . Table 2 General characteristics and hearing status of participants 60 years and older

Table 2 General characteristics and hearing status of participants 60 years and older

At the same time, HFAHL participants were more likely to be male (48.1%), older (69.30 ± 5.99 years), suffering from cardiovascular disease (15.7%), higher waist circumference (90.94 ± 11.91 cm), and higher carbohydrate intake. High (220.31 ± 62.05 g/day), vitamin C (126.29 ± 69.42 mg/day), potassium (1376.04 ± 453.15 mg/day), magnesium (135.51 ± 54.32 mg/day), zinc (3.36 ± 1.46 mg/day) And copper (0.62 ± 0.31 mg/day), higher fasting blood glucose level (6.38 ± 2.20 mmol/L), lower HDL level (1.40 ± 0.35 mmol/L), with lower chair Sit and stretch test (9.43 ± 12.22 cm), timing test (10.29 ± 3.15 seconds) and back scratch test (18.92 ± 13.99 cm) scores, MMSE (23.65 ± 4.86), MoCA (19.63 ± 6.04), number symbol test ( 6.06 ± 3.06) score is low, digital span test (7.71 ± 2.68) and RAVLT T5 score (38.15 ± 11.33) and higher score in WHODAS 2.0 (3.60 ± 6.11).

Table 3 lists the variables that were significantly correlated with PTAHL and HFAHL using binary logistic regression analysis. These variables were then input into the final multiple logistic regression model to determine the risk factors associated with the two types of HL (Table 4). After multi-factor adjustment, older age (OR: 1.239; 95% CI: 1.062–1.445), higher waist circumference (OR: 1.158; 95% CI: 1.015–1.322), and lower intake of niacin (OR: 0.909) ; 95% CI: 0.831–0.988) and potassium (OR: 0.998; 95% CI: 0.996–1.000) and lower RAVLT T5 scores (OR: 0.905; 95% CI: 0.838–0.978) are identified as the risk of PTAHL factor. At the same time, as age increases (OR: 1.117; 95% CI: 1.003–1.244), carbohydrate intake increases (OR: 1.018; 95% CI: 1.006–1.030), and potassium intake decreases (OR : 0.998; 95% CI): 0.997–0.999), and low scores in RAVLT T5 (OR: 0.922; 95% CI: 0.874–0.973) are associated with an increased risk of HFAHL. Older age, lower potassium intake, and lower RAVLT T5 score appear to be common risk factors for PTAHL and HFAHL. Table 3 Univariate scores of individual risk factors for PTAHL and HFAHL Table 4 Risk factors associated with PTAHL and HFAHL among elderly people in the community

Table 3 Univariate scores of individual risk factors for PTAHL and HFAHL

Table 4 Risk factors associated with PTAHL and HFAHL among the elderly in the community

We found that the prevalence of PTAHL in Malaysians aged 60 and above is 75.5%. As reported in previous studies, this prevalence is in the range of 69.9-76.2%. 6,10,11 However, the 2018 National Health Incidence Survey (NHMS) showed that the prevalence of self-reported hearing disability among 60-year-old Malaysians was only 6.4%. 12 The significant difference in prevalence between our current survey results and NHMS 2018 can be attributed to different methods of detecting hearing impairment. This study uses pure tone audiometry to assess the hearing status of participants, which is the gold standard test of hearing acuity. 2 At the same time, NHMS 2018 uses the self-reported Washington Disability Group Questionnaire to assess the hearing disability of the elderly. 12 Therefore, changes may occur because the outcome measures of these two assessments are viewed from two different perspectives. Although the use of self-reported listening questionnaires has significant advantages over PTA assessment in terms of cost and ease of management, there may be deviations due to differences in perceptions and beliefs about hearing impairment among individuals, leading to underestimation or overestimation of HL.13, 14 The accuracy of self-reported HL is also affected by various factors, such as the degree of HL, demographics, socioeconomic and psychological factors. 15 In addition, the prevalence of HFAHL (83.0%) is slightly higher than that of PTAHL (75.5%). This observation is possible because ARHL, especially the sensory Alzheimer's subtype, first develops in the high frequency area and then deteriorates to the low frequency area of ​​the cochlea. 16

Our current research results indicate that increasing age is a common risk factor for PTAHL and HFAHL. The association between age and HL is well documented. It is predicted that starting from the second decade of life, the prevalence of HL will double for every 10 years of age increase. 60 and 69 years old account for 58.2% of people over 90 in the world. 2 The progression of HL with age is mainly due to cochlear and neurodegeneration that affect auditory processing and sound interpretation. 17 In addition, since continuous exposure to various HL risk factors may increase throughout the life cycle, as people's life span increases, the risk of HL is higher.

Our research shows that a higher waist circumference is associated with an increased risk of HL in the elderly. There is evidence that compared with BMI, which reflects the overall degree of body obesity, the measurements of waist circumference, abdominal obesity, and central obesity are more prominent in predicting HL. 18,19 The impact of BMI on hearing level is still inconclusive. According to reports, obese adults have a higher risk of hearing impairment than adults with normal BMI. 20 This may be because obesity can lead to oxidative stress caused by obesity, which may damage the auditory structure of the inner ear. 21 In addition, obesity and other related comorbidities, such as hypertension, cardiovascular disease and diabetes, can cause changes in the blood vessels and capillaries that supply the ears, disrupt the blood circulation of the inner ear, and damage hair cells. 20 In contrast, Kim et al. found that underweight people are more likely to develop HL than people with normal BMI. 22 These contradictory findings may be because fat deposits around the abdomen are more strongly associated with other obesity-related comorbidities, which may contribute to the development and progression of HL compared with overall obesity. twenty three

In this study, we demonstrated that higher carbohydrate intake is associated with an increased risk of HL. Previously, Gopinath et al. reported that the high glycemic load diet and total carbohydrate intake of the elderly are associated with an increased risk of HL. 24 Similarly, Rosenhall et al. reported that high intake of low-molecular-weight carbohydrates is associated with poor hearing threshold. 25 In addition, people with high carbohydrate intake, especially those with simple carbohydrates and added sugars, have more May become obese and suffer from other complications such as cardiovascular disease, dyslipidemia, and diabetes. 26 As mentioned earlier, obesity and complications such as cardiovascular disease may disrupt the blood flow in the inner ear, thereby affecting hearing. 20

Contrary to our current research results, Spankovich et al. proved that higher carbohydrate intake has the effect of preventing HL in the elderly. 27 This is possible because complex carbohydrates such as grain fiber, whole wheat, and vegetables are foods with low glycemic index, which may lead to the 28 Reduced obesity is related. 29 Therefore, carbohydrates from different sources may have different biological effects on the body and affect hearing ability differently.

Niacin, or more widely known as vitamin B3, is the precursor of two biologically active coenzymes, namely nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP). 30 In addition to the well-known physiological functions of energy production, the role of cell signaling, DNA repair and central nervous system, niacin in the auditory function is still unclear. To date, only a few studies have reported an association between niacin intake and HL. Our findings indicate that insufficient intake of niacin may increase the risk of ARHL. Previously, Kang et al. reported that only in a univariate linear regression model, niacin intake was correlated with better hearing acuity, while Kim and Chung demonstrated that the higher the niacin intake, the higher the risk of ARHL in the elderly. The lower the risk. 31,32 However, a recent study conducted by Choi et al. showed that niacin intake has nothing to do with the development of ARHL. 33 The possible mechanism of niacin's protective effect on HL is that niacin can prevent the degeneration of spiral ganglion neurons and maintain synaptic contact with ARHL. As demonstrated in vivo, spiral ganglion neurons and hair cells are activated by activating the NAD+-SIRT3 pathway, as shown in vivo. 34 Therefore, further research is needed to confirm the role of niacin in preventing ARHL.

Consistent with the results of our current study, Jung et al. demonstrated that higher potassium intake is associated with better hearing levels and lower prevalence of adult HL. 35 Potassium is one of the essential nutrients that help maintain fluid and electrolyte balance, and nerve impulse transmission. 36 A high concentration of potassium ions is found in the cochlear endolymph, which is necessary for the generation of the cochlear potential (EP) that drives the sensory transduction of hair cells of. 37 During sound transduction, the vibration of the basement membrane causes the specialized transduction channels to open on the static cilia of the hair cells. Due to the presence of EP, potassium ions are driven into the hair cells, thereby stimulating the hair cells. Due to the degeneration of stria vascularis (a special ion transport structure in the cochlea), the decrease in the expression of specific potassium ion channels, and the mutation of the gene encoding potassium regulatory protein in the cochlea, the destruction of potassium homeostasis in the endolymph and sensorineural HL Related. 38, 39

However, although it plays an important physiological role in sound transduction, the direct relationship between potassium intake and HL remains unclear. Previously, the consumption of a high-potassium diet has been shown to increase the level of aldosterone in the body, which subsequently leads to an increase in the expression of Na+/-K+ ATPase and NKCC1 in the stria vascularis, indicating that higher potassium intake helps maintain EP and endolymph. Prevent ARHL. 40-43 In addition, a high-potassium diet can also indirectly prevent HL through its beneficial effects on hypertension and blood sugar control. 36

Current research results also show that elderly people with HL score lower on the RAVLT cognitive test. RAVLT is a neuropsychological tool widely used to assess the functions of attention, memory and learning in the auditory-language field. 44 Although hearing impairment is widely regarded as one of the causes of cognitive impairment in the elderly, this association may be due to the fact that HL and cognitive impairment have the same risk factors and pathogenesis, such as cardiovascular disease, microcirculation disorders, and oxidative stress. Irritation and inflammation. 45,46 However, the auditory-verbal memory of hearing impaired participants should be carefully analyzed to avoid any misleading interpretations. It is recommended that the listening ability of participants may affect the results of the RAVLT test, because the management of RAVLT requires communication between the evaluator and the participant, and the test items are presented to the participants orally. 47 Cognitively complete HL adults performed significantly worse than normal people-hearing the corresponding hearing in the auditory-verbal memory test, thus underestimating the actual cognitive performance of participants diagnosed with HL. 48

In this study, HL participants also generally performed lower on other cognitive tests. However, in the final multiple logistic regression model, only the RAVLT T5 score seems to be related to HL. Therefore, we cannot rule out the possibility that HL participants performed poorly in the RAVLT test, rather than being considered cognitively impaired, because they were unable to correctly listen to the verbal commands and test items given by the evaluator. In addition, a recent study conducted by Füllgrabe (2020) showed that although the test item has perfect audibility, the cognitive test performance of participants is affected under simulated HL conditions. 49 This may be due to the need for additional cognitive resources to simulate auditory perception processing under HL conditions, which limits the remaining cognitive resources for performing other cognitive processes. 46 In the future, a prospective cohort population needs to be studied to verify the relationship between the RAVLT score and HL.

Since HL cannot be cured, hearing aids are one of the options to reduce the adverse effects of HL. However, in Malaysia, the hearing aid adoption rate for elderly people with HL is only 2.7%. 50 Therefore, the determination of the changeable HL risk factors proposed in this study may help audiologists or policy makers design more comprehensive and effective hearing aids. Hearing health awareness or HL prevention program.

In summary, increased age, increased waist circumference, decreased niacin and potassium intake, increased carbohydrate intake, and decreased RAVLT T5 score are associated with an increased risk of ARHL. Identifying these risk factors may help develop prevention and management strategies for ARHL in the elderly. Nonetheless, this study has demonstrated its superiority in using objective measures to assess the association between HL and its wide range of health factors. However, due to the cross-sectional nature of this study, the causal relationship between risk factors and ARHL is still inconclusive, and longitudinal cohort studies are needed in the future.

ADL, activities of daily living; ARHL, age-related hearing loss; BMI, body mass index; EP, cochlear potential; HDL, high-density lipoprotein; HFA, high-frequency pure tone average; HFAHL, hearing loss based on high-frequency pure tone average; HL, hearing loss; IADL, instrumental activities of daily living; low-density lipoprotein, low-density lipoprotein; LRGS-TUA, long-term research grant program-towards useful aging; MMSE, simple mental status check; MoCA, Montreal recognized Knowledge assessment; MOSS, medical achievement social support; NAD, nicotinamide adenine dinucleotide; NADP, nicotinamide adenine dinucleotide phosphate; NHMS, national health incidence survey; PTA, pure tone average; PTAHL, based on pure tone Average hearing loss; RAVLT, Rey Auditory Language Learning Test; WHODAS 2.0, World Health Organization Disability Assessment Table 2.0; YLD, years of disability.

The data supporting the results of this study can be obtained from the corresponding author upon reasonable request.

The author thanks participants and research team members for their participation and support.

All authors have made significant contributions to the work of the report, whether in terms of concept, research design, execution, data acquisition, analysis, and interpretation, or in all these areas; participating in drafting, revising, or critically reviewing articles; final approval requirements Published version; agreed on the journal to which the article was submitted; and agreed to be responsible for all aspects of the work.

This work was supported by the long-term research grant programs of the Ministry of Higher Education of Malaysia (LRGS/1/2019/UM-UKM/1/4 and LRGS/BU/2012/UKM-UKM/K/01).

The authors report no conflicts of interest in this work.

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