1. Introduction
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Cardiometabolic multimorbidity (CMM) refers to the coexistence of two or more cardiometabolic diseases (CMD), such as diabetes, cardiovascular disease (CVD), and stroke
1–2. With the accelerating aging of the global population, the prevalence of CMM has risen significantly, establishing it as a major public health issue that threatens the health of middle-aged and older adults
3–4. Compared to individuals with a single CMD, those with CMM face substantially higher risks of all-cause mortality, recurrent cardiovascular events, and healthcare resource utilization, imposing a considerable burden on healthcare systems
5–6. Therefore, identifying modifiable risk factors for CMM and developing early intervention strategies are of significant clinical importance.
Hearing impairment (HI) is a highly prevalent sensory disorder in older adults and represents the third leading cause of disability globally7–8, and its incidence increases with age. Approximately 1.57 billion people globally experienced some degree of hearing loss (HL) in 2019, representing 19.3% of the world's population9. Among adults aged 60 years and older, the prevalence of disabling HL surpasses 25%10. In recent years, the association between HI and systemic chronic disorders has garnered increasing attention, particularly in the field of cardiometabolic health. Previous studies have indicated that individuals with HI have a 20% increased risk of developing CVD compared to those with normal hearing11, the underlying mechanisms may be associated with pathophysiological pathways such as cochlear microvascular ischemia, chronic inflammation, and oxidative stress12. Furthermore, a cross-sectional study revealed a significantly higher risk of CMM among individuals with dual impairment of sensory vision and hearing (odds ratio [OR] = 1.862, 95% confidence interval [CI]: 1.387–2.500)13. However, the independent association between HI with CMM and their dose-response relationship remain unclear. This study employs nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) to systematically examine the association between HI and CMM, aiming to establish a scientific foundation for early prevention and screening of high-risk populations for CMM.
2. Materials and methods
2.1 Data and sample sources
The data for this study were derived from the CHARLS. CHARLS is a nationally representative prospective cohort study of middle-aged and older adults (≥ 45 years) in China. Its sampling framework spans 28 provinces, 150 county-level units, and 450 village-level units, ensuring strong regional and population representativeness
14. The study encompasses comprehensive assessments of sociodemographic characteristics (e.g., age, sex, education level), health status and functioning (e.g., history of chronic diseases, hearing function, and activities of daily living), and socioeconomic status (e.g., income, health insurance coverage). CHARLS data collection began with the baseline survey in 2011, and follow-up surveys were conducted in 2013, 2015, 2018, and 2020. The baseline survey was initiated in 2011, with follow-up surveys conducted in 2013, 2015, 2018, and 2020.
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The CHARLS was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015).
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All participants provided written informed consent. The datasets can be accessed through the official website (
http://charls.pku.edu.cn/en/).
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This study employed data from the CHARLS spanning from 2011 to 2018 to analysis. The inclusion criteria were as follows: (1) age ≥ 45 years; (2) completion of hearing function assessment and cardiometabolic-related evaluations at baseline survey; and (3) availability of complete clinical data. The exclusion criteria were as follows: (1) age < 45 years (n = 649); (2) missing data on key covariate information (e.g., sex, marital status, education level, smoking history and alcohol use history, n = 3952); (3) incomplete data on chronic disease history (including hypertension, diabetes, heart disease, stroke, and chronic kidney disease [CKD], n = 217); (4) pre-existing CMM at baseline survey (n = 400); and (5) death or loss to follow-up during the study period (n = 3445). Ultimately, a total of 9,035 participants were included in the analysis. The detailed screening process is illustrated in Fig. 1.
2.2. Assessment of Hearing status and CMM
Hearing function was evaluated with a standardized question from the CHARLS questionnaire: “Is your hearing excellent, very good, good, fair, or poor (with using hearing aid if they use)?” 15. Hearing status was classified into three groups based on established diagnostic criteria7, 16: normal hearing (self-reported “excellent,” “very good,” or “good”), mild HI (self-reported “fair”), and severe HI (self-reported “poor” or daily use of hearing aid).
The presence of CMD was assessed through self-reported physician diagnosis of any of the following conditions: (1) diabetes or hyperglycemia; (2) stroke; (3) CVD (including coronary heart disease, angina, congestive heart failure, or other cardiac disorders)17. CMM was defined as the concurrent presence of two or more CMD during follow-up.
2.3. Assessment of Covariates
The covariates included in this study encompassed demographic characteristics (including age, sex, education level, marital status), lifestyle factors (including smoking history, alcohol use history), body mass index (BMI), and medical history (including hypertension, CKD). Education level was reclassified from the original 12 categories into four levels: illiterate, primary school, junior high school, and high school or above. Marital status was categorized into married/cohabiting and other (including divorced, widowed, and unmarried). Smoking and alcohol use histories were recorded as binary variables (yes/no). BMI was calculated as weight (kg) divided by the square of height (m). Medical history of hypertension and CKD was determined based on self-reported physician diagnoses. All covariate data were obtained from standardized questionnaires and physical examination records to ensure data consistency and completeness.
2.4. Statistical Analysis
Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and compared using the t-test. Non-normally distributed continuous variables were expressed as median and interquartile range and compared using the Mann-Whitney U test. Categorical variables were presented as frequencies and percentages and compared between groups using Fisher’s exact test or Pearson’s chi-square test, as appropriate. The Kaplan–Meier survival curves were used to describe the cumulative incidence risk of CMM for different hearing status groups, and differences between groups were compared using the log-rank test. Cox proportional hazards regression model were used to calculate hazard ratio (HR) and 95% CI for the association between hearing status and CMM. The following three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age and sex; and Model 3 was further adjusted for smoking history, alcohol use history, education level, marital status, BMI, hypertension, and CKD based on Model 2.
To verify the robustness of the results, subgroup analyses were stratified by age (45–60 years/≥60 years) and sex (male/female) to explore the heterogeneity of the association between HI and CMM across different populations. All tests were two-sided, and a P-value < 0.05 was considered statistically significant. Data analysis was performed using Zstats software (version 1.0; http://www.zstats.net) and R (version 4.3.3).
4. Discussion
Using a nationally representative sample from the CHARLS database, this study included 9,035 participants to investigate the association between HI and the risk of CMM in middle-aged and older adults. The results demonstrated that HI was associated with an elevated risk of CMM, and this association was more pronounced among females and individuals aged 60 years or older. These findings provide new insights into the identification of risk factors for CMM and the early screening of high-risk populations.
This national cohort study first identified HI as an independent risk factor for CMM. In the fully adjusted model, individuals with mild and severe HI showed a 30% and 53% elevated risk of CMM, respectively. This indicated that HI may be an early warning signal for the development of CMM. This finding aligns with previous studies suggesting an association between HL and CVD12. A previous meta-analysis demonstrated that HL significantly increases the risk of cardiovascular mortality by 28% (HR = 1.28, 95% CI: 1.10–1.50)18. A dose-response relationship was also identified, showing that each 30-dB increment in audiometric thresholds doubles the hazard for all-cause mortality (HR = 2.05, 95% CI: 1.45–2.90)18. The results confirm the observed dose-response relationship between HL severity and risk elevation, where the hazard increases progressively with higher audiometric thresholds in a dose-dependent relationship. Kim et al. confirmed a significant positive correlation between the degree of HL and the risk of CVD in occupational noise-exposed populations19. A Korean longitudinal study further substantiated this association within specific populations. For instance, among individuals with diabetes and comorbid hearing impairment, the risk of myocardial infarction rose by 11.7% and that of stroke by 13.4%20.
This study demonstrates that HI was an independent risk factor for the development of CMM in middle-aged and older adults, and its underlying mechanisms involve the synergistic effects of multiple pathophysiological pathways, including behavioral patterns, vascular pathology, inflammatory stress, and psychoneuroendocrine responses. Firstly, changes in behavioral patterns represent important driving factors. Specifically, communication barriers caused by moderate to severe hearing loss can lead to social isolation, which in turn may discourage participation in physical activities21, and the decrease in physical activities can lead to the development of metabolic disorders, including obesity and insulin resistance22−23. These findings indicate that exercise interventions for individuals with HI might lower the risk of CMM. Secondly, CVD and HI are directly related through a common vascular pathological basis. The high metabolic characteristics of microcirculation in the inner ear make stria vascularis highly sensitive to ischemia and hypoxia22. Vascular dysfunction represents a core pathological mechanism in CMM, which can simultaneously compromise cochlear perfusion and disrupt cardiovascular and cerebral blood supply. Furthermore, after adjusting for factors including smoking status and hypertension, the association remained significant, suggesting that pathological mechanisms independent of established vascular risk factors may be involved. Thirdly, chronic inflammation and oxidative stress play pivotal roles in this process. The elevation of inflammatory markers such as interleukin-6 and C-reactive protein is associated not only with HL, but also serves as a key trigger for the development of CMM24−26. HL may trigger systemic inflammation through persistent sensory damage or by reducing anti-inflammatory factors due to social withdrawal. This establishes a vicious cycle of "inflammation-metabolic disorders-vascular injury," which exacerbates hearing deterioration and increases cardiometabolic risk. Finally, psychological and neuroendocrine disorders exert a cumulative effect. Negative emotions such as depression and anxiety caused by HI, promote abnormal vasoconstriction and hemorheological alterations through neuroendocrine pathways involving imbalance of the 5-hydroxytryptamine and norepinephrine, ultimately inducing tissue ischemia and hypoxia and increasing the risk of cardiovascular events27. The overlapping risk factors, including advanced age and unhealthy diet, further amplify these effects through multisystem interactions.
This study further explored the differences in the association between HI and CMM risk using stratified analyses by sex and age. Results from the sex subgroup analysis revealed that among females, the risk of CMM was 51% higher in those with mild HI than in those with normal hearing (OR = 1.51, 95% CI: 1.11-2.0), while severe HI was associated with a further elevated risk of 56% (OR = 1.56, 95% CI: 1.03–2.37). In contrast, no statistically significant association was observed among male participants. statistically significant association was observed among male participants. This observed sex difference aligns with findings from other studies on CVD associated with sensory impairments. A cross-sectional study from Bulgaria found that women exposed to self-reported occupational noise exhibited a 26% increased risk of heart disease (relative risk [RR] = 1.26, 95% CI: 0.53–3.01), whereas no significant risk change was observed in men (RR = 0.49, 95% CI: 0.14–1.65)28. In this study, the strong association between HI and the risk of CMM in women over 45 years of age may be related to sex differences and the regulatory role of estrogen. Compared with men, women exhibit higher peak amplitudes and shorter latencies of auditory evoked potentials, as well as lower hearing thresholds, suggesting physiological advantages in cochlear sensitivity and neural conduction velocity.29–30 Furthermore, estrogen exerts protective effects on both the cardiovascular system and auditory function31. Elevated circulating estrogen levels delay age-related HL and mitigate the risk of CVDs by maintaining cochlear tissue integrity, modulating metabolism, suppressing oxidative stress, and promoting angiogenesis32–33. Following menopause, the sharp decline in estrogen levels not only accelerates the deterioration of auditory function but may also exacerbate metabolic disorders and CVD risk through the aforementioned physiological pathways. This mechanism may significantly contribute to the more pronounced association between HI and CMM risk observed in females in this study. Age-stratified analysis revealed a 69% increased risk of CMM (OR = 1.69, 95% CI:1.14–2.51) in individuals aged ≥ 60 years with severe HI compared to those with normal hearing, while no significant association was observed among participants aged 45–60 years. This result may be associated with the age-dependent increase in CMM risk. With advancing age, the decline in metabolic function, accumulation of chronic inflammation, and reduction in physiological reserves across multiple systems collectively contribute to an elevated risk of CMM development.
As the first longitudinal cohort study in China to examine the association between HI and the incidence of CMM, this research addresses an important gap in epidemiological evidence and offers a theoretical foundation for identifying CMM risk factors and screening high-risk populations. Furthermore, this study draws on a nationally representative sample from the CHARLS database, which includes middle-aged and older adults across diverse regions, urban and rural settings, and socioeconomic strata in China. The results demonstrate strong extrapolation and effectively circumvent the regional selection bias inherent in single-center studies. This study employed a multifactorial Cox proportional hazards regression model to adjust for potential confounders, including demographic characteristics (including age, sex, education level, and marital status), lifestyle factors (including smoking history, alcohol use history), BMI, and medical history (including hypertension, CKD). Stratified analyses by sex and age were further conducted to assess the stability of the associations, which strengthens the internal validity of the findings. However, this study has several limitations. The definition of Hearing status relied on self-reported questionnaires or pure-tone audiometry, potentially introducing classification bias, and the self-reported data are inherently susceptible to subjective perception. In addition, the CHARLS database lacks information on specific etiologies of HL, such as occupational noise exposure or the use of ototoxic medications, making it impossible to further analyze the differences in the association between different types of HI and the risk of CMM. Although this study adjusted for multiple covariates, residual confounding from unmeasured factors (e.g., dietary patterns, physical activity intensity, or baseline cardiovascular health status) may persist, potentially influencing the observed associations. Thus, future intervention studies are needed to address these limitations and validate the findings.
In summary, this study provides epidemiological evidence linking HI to CMM. However, further large-scale, multicenter prospective studies are needed to clarify the causal relationship and identify potential interventional targets, which should incorporate etiological classification of HI and exploration of molecular mechanisms.