Additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality: A longitudinal nationwide population-based study

Additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality: A longitudinal nationwide population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality: A longitudinal nationwide population-based study Ling Li, Lan Li, Chenchen Qin, Qian Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5301331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular diseases (CVD) and hearing loss (HL) are significant public health concerns, sharing common pathological mechanisms and being associated with severe health outcomes. This study investigates the impact of CVD and HL on all-cause and cardiovascular mortality. Methods Data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 were analyzed, along with mortality data from the National Death Index (NDI) up to December 2019. Initially, we explored the correlation between different types of HL and CVD. Participants were categorized into four groups based on the presence of CVD and HL, and mortality outcomes were analyzed accordingly. Results Among 10,614 participants, 6,039 (56.9%) had neither CVD nor HL (CVD-/HL-), 3,465 (32.6%) had HL only (CVD-/HL+), 279 (2.6%) had CVD only (CVD+/HL-), and 831 (7.8%) had both CVD and HL (CVD+/HL+). Compared to individuals without HL, those with overall frequency HL (OR = 1.49, 95% CI: 1.14–1.96, p = 0.004) and high-frequency HL (OR = 1.41, 95% CI: 1.04–1.90, p = 0.03) showed a positive correlation with CVD, while low-frequency HL (OR = 1.04, 95% CI: 0.86–1.26, p = 0.71) showed no significant association. In terms of mortality, compared to the CVD-/HL- group, the CVD+/HL- group (HR = 1.88, 95% CI: 1.29–2.73, p = 0.001) and the CVD+/HL + group (HR = 2.19, 95% CI: 1.69–2.83, p < 0.0001) had increased all-cause mortality risks, whereas the CVD-/HL + group did not show statistical significance (HR = 1.24, 95% CI: 0.98–1.57, p = 0.07). The CVD+/HL- group (HR = 3.66, 95% CI: 2.00–6.71, p < 0.0001) and the CVD+/HL + group (HR = 2.91, 95% CI: 1.89–4.47, p < 0.0001) had increased cardiovascular mortality risks, while the CVD-/HL + group did not show statistical significance (HR = 1.24, 95% CI: 0.98–1.57, p = 0.07). Conclusion The simultaneous presence of CVD and HL significantly raised the likelihood of death from any cause and cardiovascular events. Patients with either condition may need more vigilant treatment to avoid the onset of the other condition and lower the risk of death. Health sciences/Diseases/Cardiovascular diseases Health sciences/Cardiology/Cardiovascular biology Cardiovascular diseases hearing loss All-cause mortality Cardiovascular mortality NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction In recent years, the prevalence of hearing loss (HL) has been steadily increasing due to global population aging, heightened environmental noise pollution, and prolonged use of auditory devices[ 1 ]. According to 2019 WHO statistics, over 1.5 billion people worldwide suffer from disabling HL, with this number projected to rise to 2.5 billion by 2050, making HL the third leading cause of disability globally[ 2 ]. HL can be categorized into conductive and sensorineural types, with sensorineural HL being more prevalent and constituting the majority of HL cases[ 3 ]. HL is not only a communicative disorder but also a significant condition that severely impacts quality of life[ 4 , 5 ]. Beyond its association with comorbidities such as depression and anxiety[ 6 ], HL is independently related to cognitive dysfunction[ 7 ]. Established risk factors for adult HL include aging, noise exposure, genetic predispositions, autoimmune diseases, chronic ear conditions, and the use of ototoxic drugs such as aminoglycosides and cisplatin[ 8 – 11 ]. Recent studies have also indicated that HL is linked to various cardiovascular diseases and risk factors[ 12 , 13 ], which may cause microvascular damage, oxidative stress, and impaired electrical signal transmission within the cochlea[ 12 ]. Furthermore, the WHO report highlights that the aging population and the increasing prevalence of cardiovascular and metabolic diseases worldwide could further elevate the incidence of HL[ 14 ]. Previous epidemiological investigations have explored whether HL increases mortality, yielding inconsistent findings[ 15 ]. Some research has shown a positive correlation between HL and mortality. For instance, a meta-analysis of 26 studies with 1,213,756 mainly Western participants found that HL was linked to an increased risk of both all-cause and CVD mortality[ 16 ]. However, other studies reported no such association, highlighting the variability in findings. A notable study from Iceland identified that dichotomized HL was notably linked to later CVD mortality, yet it showed no significant association with all-cause mortality in older adults[ 17 ]. Many studies are constrained by small sample sizes and rely on self-reported HL rather than audiometric tests, potentially introducing measurement errors or misclassification that weaken the observed associations[ 15 ]. Additionally, most research has primarily focused on older populations and overall mortality without detailed analysis of specific causes of death[ 15 ]. HL is linked to several cardiovascular risk factors, such as smoking, diabetes, hypertension, hyperlipidemia, and obesity, and is often related to cardiovascular diseases themselves[ 18 – 23 ]. However, the impact of combined CVD and HL on all-cause and cardiovascular mortality remains unclear. Understanding this relationship is crucial for determining whether patients with CVD or HL require preventive measures and treatment for HL or CVD. Therefore, this research leveraged data from the National Health and Nutrition Examination Survey (NHANES) collected between 1999 and 2018 to explore the influence of CVD and HL on all-cause and cardiovascular mortality. 2 Methods 2.1 Study design and population The design of the study and the characteristics of the participants information for this research were gathered from the NHANES, which uses a complex, multi-step probability sampling plan to effectively depict the U.S. civilian non-institutionalized population. Since 1999, NHANES has conducted annual measurements, adding approximately 5,000 participants to the database each year. NHANES gathers a broad range of health-related data by conducting thorough interviews and physical exams, as outlined on their official site. Audiometric data was collected, along with other forms of data. Ethical approval was granted for the research protocol, with the requirement that all participants give written informed consent. The analysis included information from eight NHANES cycles between 1999 and 2018 covering the years: 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2009–2010, 2011–2012, 2015–2016, and 2017–2018. The dataset from the US Centers for Disease Control and Prevention's National Center for Health Statistics is well-documented in the NHANES Documentation Files, which can be accessed at https://www.cdc.gov/nchs/nhanes/ as of December 15, 2021. Eligibility for our study was determined based on inclusion criteria applied to the 1999–2018 NHANES cohorts. Participants were considered if they were: (1) 20 years of age or older, (2) had measurements for audiometric information, and (3) possessed the requisite data for diagnosing CVD, mortality follow-up information, and covariates. Initially enrolling 49,312 participants, our final analysis included 10,614 eligible individuals with complete data on HL, CVD diagnosis, and mortality, as depicted in Fig. 1 . 2.2 Definition of Hearing loss, cardiovascular disease and other covariates Hearing was quantitatively assessed through pure-tone air conduction audiometry at seven different frequencies between 500 Hz and 8 kHz. Skilled health technicians performed the measurements in a calibrated sound booth (Acoustic Systems, model Delta 142). HL was characterized by an average pure-tone (APT) hearing threshold above 20 dB, with the exception of potential mixed or conductive hearing loss cases. All-frequency HL was characterized in our study as an APT threshold above 20 decibels for all frequencies examined. Low-frequency (0.5, 1, 2, and 4 kHz) and high-frequency (3, 4, and 8 kHz) HL were defined according to the WHO definition of HL (APT threshold above 20 dB)[ 24 – 26 ]. CVD was detected by medical surveys and self-reported diagnoses which included any signs of coronary artery disease, congestive heart failure, myocardial infarction, angina, or cerebrovascular accident. Demographic, physical examination, and laboratory data were extracted from the NHANES database across eight cycles. Demographic factors such as age, gender, ethnicity, household earnings, educational attainment, marital situation, smoking habits, and alcohol use were determined based on particular guidelines[ 27 ]. Medical conditions such as diabetes mellitus (DM) and hypertension were identified through a combination of self-reports, laboratory results, and medication use, with specific diagnostic criteria for each condition. The physical exam and lab results covered measurements such as body mass index (BMI), waist circumference, total bilirubin, aspartate aminotransferase (AST), triglycerides (TG), total cholesterol, high-density lipoprotein cholesterol (HDL-cholesterol), creatinine, glycosylated hemoglobin (HbA1c), and urinary albumin-creatinine ratio (UACR). We selected all-frequency HL as the criterion for defining the HL population. Study participants were then stratified into four distinct groups based on the occurrence of CVD and HL: 1) neither CVD nor HL present (CVD-/HL-), 2) HL present without CVD (CVD-/HL+), 3) CVD present without HL (CVD+/HL-), and 4) both CVD and HL present (CVD+/HL+). 2.3 Study outcome Data on deaths were gathered from the NDI, including details on mortality status and causes of death, connected to NHANES participants via the NDI’s accessible records until December 31, 2019. The length of the follow-up period was measured in months, starting from the day the participant was examined at the NHANES mobile examination center and continuing until either the date of death or the end of the mortality follow-up period. Causes of death were categorized according to the International Classification of Diseases, Tenth Revision (ICD-10). The research concentrated on the main results of death from any cause and cardiovascular-related death (codes 100–109, 111, 113, 120–151, and 160–169). 2.4 Statistical analysis In accordance with NHANES analytical guidelines, our statistical analyses incorporated the survey's complex sampling design and associated sampling weights. Participant demographics and clinical characteristics were summarized using weighted means and standard errors for continuous variables and counts with weighted percentages for categorical variables. Weighted linear regression analysis was utilized for continuous variables, while categorical variables were examined through design-adjusted chi-square tests. We first used multivariate logistic regression analysis to determine odds ratios (OR) and 95% confidence intervals (CI) in order to investigate the association between different forms of HL (all-frequency HL, low-frequency HL, and high-frequency HL) and CVD. The crude model was unadjusted. Age and sex were taken into account when adjusting Model 1. Model 2 included adjustments for age, sex, race, family income, and education. Additional adjustments were made to Model 3 for factors including age, gender, ethnicity, household earnings, educational background, alcohol consumption, smoking habits, DM, and hypertension. The Cox proportional hazards model was then used to determine the hazard ratios (HR) and 95% CI for mortality based on the presence of CVD and HL. Age and sex were taken into account when adjusting Model 1. Model 2 included race, level of education, family income ratio, and marital status in addition to the variables in Model 1. Model 3 incorporated extra modifications for BMI, waist circumference, total bilirubin, AST, TG, total cholesterol, HDL-cholesterol, HbA1c, UACR, creatinine, and habits of drinking and smoking. Furthermore, the study also included interaction and subgroup analyses for both all-cause and cardiovascular mortality, which were stratified by age (using 40 years as a cutoff point), gender, DM, and hypertension, following the specifications of Model 3. In order to strengthen the trustworthiness of our findings, we performed two sensitivity analyses: one that omitted participants who passed away within a two-year period, and another that excluded individuals younger than 40. The threshold for statistical significance was established as p < 0.05. R software version 4.3.1 (R Foundation, Vienna, Austria) was utilized for all analyses. 3 Results 3.1 Baseline characteristics Among the 10,614 participants analyzed, 6,039 (56.9%) were categorized as having neither CVD nor HL (CVD-/HL-), 3,465 (32.6%) had only HL (CVD-/HL+), 279 (2.6%) had only CVD (CVD+/HL-), and 831 (7.8%) were diagnosed with both conditions (CVD+/HL+). The baseline characteristics of these groups are shown in Table 1 . Significantly older individuals were observed in the CVD+/HL + group compared to the other groups, with mean ages of 39.85, 59.15, 51.78, and 66.84 years for the CVD-/HL-, CVD-/HL+, CVD+/HL-, and CVD+/HL + groups, respectively (p < 0.0001). Furthermore, the CVD+/HL + group exhibited significantly elevated levels of BMI, waist circumference, AST, HbA1c, UACR, and creatinine compared to the other groups (all p < 0.0001). Individuals who had both cardiovascular disease and high cholesterol were more inclined to be male, possess a moderate household income, primarily identify as Non-Hispanic White, have completed high school, and have a history of smoking. They also had higher rates of diabetes and hypertension in contrast to those without either condition. Table 1 Baseline characteristics of the study participants Variable Total (n = 10614) CVD-/HL- (n = 6039) CVD-/HL+ (n = 3465) CVD+/HL- (n = 279) CVD+/HL+ (n = 831) P value Age, years, mean (SD) 47.44(0.31) 39.85(0.29) 59.15(0.39) 51.78(1.03) 66.84(0.58) < 0.0001 BMI, kg/m2, mean (SD) 28.78(0.10) 28.42(0.13) 29.02(0.15) 30.41(0.47) 30.77(0.36) < 0.0001 Waist circumference, cm, mean (SD) 98.73(0.26) 96.41(0.30) 101.58(0.40) 102.15(1.05) 107.39(0.80) < 0.0001 AST, U/L, mean (SD) 25.21(0.17) 24.55(0.17) 26.39(0.37) 24.81(0.74) 26.42(0.99) < 0.0001 Total bilirubin, µmol/L, mean (SD) 11.44(0.11) 11.37(0.12) 11.61(0.18) 11.15(0.36) 11.55(0.27) 0.28 Triglycerides, mmol/L, mean (SD) 1.74(0.03) 1.64(0.03) 1.91(0.05) 1.92(0.13) 1.85(0.06) < 0.0001 Total cholesterol, mmol/L, mean (SD) 5.11(0.02) 5.07(0.02) 5.27(0.03) 5.09(0.09) 4.76(0.05) < 0.0001 HDL-cholesterol, mmol/L, mean (SD) 1.38(0.01) 1.39(0.01) 1.37(0.01) 1.35(0.05) 1.28(0.02) < 0.0001 HbA1c, %, mean (SD) 5.60(0.01) 5.44(0.01) 5.80(0.03) 5.91(0.09) 6.17(0.06) < 0.0001 UACR, mg/g, median(Q1, Q3) 6.38(4.23,11.53) 5.80(4.00, 9.74) 7.36(4.71,14.12) 7.26(4.29,14.57) 10.68(5.56,26.67) < 0.0001 Creatinine, µmol/L, median(Q1, Q3) 73.37(61.88,88.40) 70.72(61.88, 83.10) 78.68(64.53, 88.40) 76.02(61.90, 91.05) 86.63(70.72,102.54) < 0.0001 Sex, n (%) < 0.0001 Female 5297(51.35) 3324(55.87) 1504(43.60) 165(55.82) 304(40.86) Male 5317(48.65) 2715(44.13) 1961(56.40) 114(44.18) 527(59.14) Ethnicity, n (%) < 0.0001 Mexican American 1819(7.63) 1104(8.83) 580(6.06) 31(4.40) 104(4.22) Non-Hispanic Black 2241(10.03) 1403(11.28) 598(7.16) 105(20.02) 135(7.41) Non-Hispanic White 4548(70.13) 2266(66.47) 1677(76.05) 97(61.89) 508(81.99) Other Hispanic 901(5.84) 531(6.60) 301(5.05) 20(3.61) 49(2.68) Other Race - Including Multi-Racial 1105(6.37) 735(6.83) 309(5.67) 26(10.07) 35(3.71) Educational level, n (%) < 0.0001 9-11th Grade (Includes 12th grade with no diploma) 1510(10.47) 764(9.26) 546(11.99) 49(12.88) 151(14.64) College Graduate or above 2536(30.74) 1672(33.66) 687(27.37) 61(26.03) 116(18.91) High School Grad/GED or Equivalent 2394(22.55) 1287(20.75) 821(24.75) 67(27.13) 219(28.59) Less Than 9th Grade 1188(5.33) 463(3.71) 561(7.90) 21(3.39) 143(10.15) Some College or AA degree 2986(30.91) 1853(32.61) 850(28.00) 81(30.58) 202(27.72) Family income, n (%) < 0.0001 High 3358(43.81) 2070(45.37) 1028(43.15) 74(38.60) 186(33.08) Low 3193(20.83) 1746(20.55) 1069(20.31) 112(29.78) 266(22.82) Medium 4063(35.36) 2223(34.07) 1368(36.54) 93(31.62) 379(44.10) Marital status, n (%) < 0.0001 Living with partner 816(8.10) 627(10.06) 148(5.13) 20(6.60) 21(2.99) Married 5745(57.99) 3097(55.41) 2032(62.65) 141(58.21) 475(61.83) Never married 1803(16.40) 1447(21.81) 286(7.75) 31(11.78) 39(4.58) other 2250(17.50) 868(12.71) 999(24.48) 87(23.42) 296(30.61) Alcohol user, n (%) < 0.0001 former 1833(14.30) 734(10.38) 757(18.75) 74(24.07) 268(29.22) heavy 2031(20.71) 1399(24.19) 503(15.32) 43(17.34) 86(12.44) mild 3584(36.57) 1984(35.19) 1217(39.50) 82(31.89) 301(38.21) moderate 1537(16.67) 1030(18.98) 411(13.77) 33(12.72) 63(8.59) never 1629(11.75) 892(11.27) 577(12.66) 47(13.97) 113(11.54) Smoking status, n (%) < 0.0001 former 2713(25.86) 1101(20.81) 1159(32.58) 81(28.72) 372(43.93) never 5733(53.15) 3673(58.31) 1642(46.88) 116(40.41) 302(35.49) now 2168(20.99) 1265(20.88) 664(20.55) 82(30.87) 157(20.58) DM, n (%) < 0.0001 no 8736(86.65) 5471(92.89) 2599(79.62) 182(72.40) 484(62.17) yes 1878(13.35) 568(7.11) 866(20.38) 97(27.60) 347(37.83) Hypertension, n (%) < 0.0001 no 6102(63.16) 4351(75.13) 1505(48.31) 82(33.86) 164(23.37) yes 4512(36.84) 1688(24.87) 1960(51.69) 197(66.14) 667(76.63) Mortality, n (%) < 0.0001 no 9160(90.01) 5781(96.45) 2700(82.98) 226(83.00) 453(60.86) yes 1454(9.99) 258(3.55) 765(17.02) 53(17.00) 378(39.14) CVD: cardiovascular diseases; HL: hearing loss; BMI: body mass index; AST: aspartate aminotransferase; HDL-cholesterol: high density lipoprotein cholesterol; HbA1c: glycosylated hemoglobin; UACR: urinary albumin creatinine ratio; DM: diabetes 3.2 The association between HL and CVD Model 3 findings indicate a positive association between all-frequency HL (OR = 1.49, 95% CI 1.14–1.96, p = 0.004) and high-frequency HL (OR = 1.41, 95% CI 1.04–1.90, p = 0.03) with CVD when compared to individuals without HL. However, low-frequency HL (OR = 1.04, 95% CI: 0.86–1.26, p = 0.71) was not associated with CVD. Table 2 presents the detailed correlation results. Table 2 Association HL with CVD Character Crude model Model 1 Model 2 OR Model 3 OR OR 95%CI P OR 95%CI P OR 95%CI P OR 95%CI P All-frequency HL no ref ref ref ref yes 5.82(4.61,7.35) < 0.0001 1.82(1.39,2.37) < 0.0001 1.68(1.28,2.20) < 0.001 1.49(1.14,1.96) 0.004 Low-frequency HL no ref ref ref ref yes 3.46(2.94,4.06) < 0.0001 1.15(0.95,1.41) 0.16 1.04(0.85,1.27) 0.71 1.04(0.86,1.26) 0.71 High-frequency HL no ref ref ref ref yes 5.96(4.65,7.64) < 0.0001 1.71(1.28,2.29) < 0.001 1.58(1.16,2.14) 0.004 1.41(1.04,1.90) 0.03 Crude model was adjusted for no covariates;Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, race, family income and education; Model 3 was adjusted for age, sex, race, family income, education, alcohol user, smoking status, diabetes and hypertension. 3.3 All-cause and cause-specific mortality according to cardiovascular diseases and hearing loss Over a period of 8.1 years (with a range of 4.3 to 15.8 years), there were 1,454 total deaths, including 462 deaths linked to cardiovascular problems. Figure 2 shows survival curves for the four groups, indicating notable variations in overall and cause-specific survival rates (both log-rank p < 0.0001), with the CVD-/HL- group having the best survival and the CVD+/HL + group having the worst. Table 3 and Fig. 3 outline the HR for all-cause and CVD mortality based on CVD and HL status. In comparison to the CVD-/HL- baseline group, there was a higher risk of death from any cause in the CVD+/HL- group (HR = 1.88, 95% CI 1.29–2.73, p = 0.001) and the CVD+/HL + group (HR = 2.19, 95% CI 1.69–2.83, p < 0.0001), while the CVD-/HL + group did not show a statistically significant difference (HR = 1.24, 95% CI 0.98–1.57, p = 0.07). Cardiovascular mortality was higher in the CVD+/HL- (HR = 3.66, 95% CI 2.00–6.71, p < 0.0001) and CVD+/HL+ (HR = 2.91, 95% CI 1.89–4.47, p < 0.0001) groups compared to the CVD-/HL- group. However, the CVD-/HL + group did not show a statistically significant difference in risk (HR = 1.24, 95% CI 0.98–1.57, p = 0.07). Table 3 Risks of all-cause and cardiovascular disease mortality according to the presence of CVD or HL status Character Crude model Model 1 Model 2 Model 3 HR 95%CI P HR 95%CI P HR 95%CI P HR 95%CI P All-cause mortality groups CVD-/HL- ref ref ref ref CVD-/HL+ 5.62(4.60, 6.86) < 0.0001 1.53(1.23,1.90) < 0.001 1.40(1.11,1.76) 0.005 1.24(0.98,1.57) 0.07 CVD+/HL- 5.44(3.79, 7.81) < 0.0001 2.67(1.85,3.87) < 0.0001 2.49(1.71,3.61) < 0.0001 1.88(1.29,2.73) 0.001 CVD+/HL+ 17.12(13.93,21.04) < 0.0001 3.22(2.53,4.09) < 0.0001 2.76(2.15,3.56) < 0.0001 2.19(1.69,2.83) < 0.0001 p for trend < 0.0001 < 0.0001 < 0.0001 < 0.0001 Cardiovascular diseases mortality groups CVD-/HL- ref ref ref ref CVD-/HL+ 7.48(5.44,10.29) < 0.0001 1.51(1.04,2.20) 0.03 1.42(0.98,2.07) 0.06 1.26(0.88,1.81) 0.2 CVD+/HL- 12.14(6.90,21.35) < 0.0001 5.11(2.79,9.38) < 0.0001 4.68(2.56,8.55) < 0.0001 3.66(2.00,6.71) < 0.0001 CVD+/HL+ 33.74(23.16,49.15) < 0.0001 4.37(2.75,6.96) < 0.0001 3.82(2.38,6.14) < 0.0001 2.91(1.89,4.47) < 0.0001 p for trend < 0.0001 < 0.0001 < 0.0001 < 0.0001 Crude model, was adjusted for no covariates;Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, race and ethnicity, educational level, family income and marital status; Model 3 was adjusted for age, sex, race and ethnicity, educational level, family income, marital status, smoking status, alcohol user, body mass index, waist circumference, aspartate aminotransferase, total bilirubin, triglycerides, total cholesterol, high density lipoprotein cholesterol, glycosylated hemoglobin, Creatinine and urinary albumin creatinine ratio. 3.4 Subgroup analyses and sensitivity analyses The subgroup analyses in Table 4 and Supplementary Tables S1 show that the CVD+/HL + group has a greater risk of all-cause mortality compared to the CVD-/HL- group in different subgroups for both all-cause and cardiovascular diseases mortality. Notably, increased mortality risks were observed in participants aged 41–85 years (HR = 5.65; 95% CI = 4.34–7.36), aged 20–40 years (HR = 13.5; 95% CI = 1.92–95.4), females (HR = 9.99; 95% CI = 7.12-14.0), males (HR = 6.30; 95% CI = 4.40–9.04), those with and without DM (HR = 4.88; 95% CI = 3.22–7.39 and HR = 7.39; 95% CI = 5.44–10.02, respectively), and with and without hypertension (HR = 5.80; 95% CI = 4.41–7.63 and HR = 7.58; 95% CI = 5.09–11.29, respectively). Moreover, there was a notable correlation between diabetes status and the likelihood of all-cause mortality, with a p-value for interaction of 0.016. Supplementary Table S1 provides a comprehensive overview of the findings from the subgroup analyses on mortality related to cardiovascular diseases. Table 4 Subgroup of all-cause mortality according to according to the presence of CVD or HL status Subgroup CVD-/HL- CVD-/HL+ p CVD+/HL- p CVD+/HL+ p p for trend p for interaction AGE 0.345 41–85 ref 2.642(2.072,3.369) < 0.0001 2.814(1.881,4.210) < 0.0001 5.651(4.340,7.358) < 0.0001 < 0.0001 20–40 ref 2.037(1.003, 4.138) 0.049 1.410(0.343, 5.803) 0.634 13.515(1.915,95.360) 0.009 0.004 sex 0.133 Male ref 3.373(2.436,4.669) < 0.0001 2.142(1.048,4.378) 0.037 6.299(4.391,9.035) < 0.0001 < 0.0001 Female ref 3.569(2.613, 4.876) < 0.0001 4.178(2.641, 6.610) < 0.0001 9.993(7.123,14.020) < 0.0001 < 0.0001 DM 0.016 no ref 3.592(2.802, 4.605) < 0.0001 2.449(1.447, 4.147) < 0.001 7.385(5.444,10.016) < 0.0001 < 0.0001 yes ref 2.093(1.388,3.158) < 0.001 2.933(1.551,5.545) < 0.001 4.879(3.220,7.394) < 0.0001 < 0.0001 Hypertension 0.127 yes ref 2.766(2.134,3.584) < 0.0001 2.377(1.473,3.835) < 0.001 5.804(4.414,7.631) < 0.0001 < 0.0001 no ref 3.097(2.155, 4.452) < 0.0001 3.126(1.376, 7.098) 0.006 7.580(5.089,11.289) < 0.0001 < 0.0001 DM: diabetes mellitus;HR: hazard ratio; CI: confidence interval Each stratification was adjusted for race and ethnicity, educational level, family income, marital status, smoking status, alcohol user, body mass index, waist circumference, aspartate aminotransferase, total bilirubin, triglycerides, total cholesterol, high density lipoprotein cholesterol, glycosylated hemoglobin, Creatinine and urinary albumin creatinine ratio. Supplementary Tables S2A, S2B, S3A , and S3B , as well as Supplementary Figures S1A and S1B , present initial features of distinct categories, results from diverse models, and findings from Kaplan-Meier sensitivity analyses. Participants under the age of 40 were excluded in the sensitivity analysis. The research discovered increased chances of death from any cause in the CVD+/HL- (HR = 1.98; 95% CI 1.34–2.93) and CVD+/HL+ (HR = 2.11; 95% CI 1.62–2.77) categories, while no significant difference was observed in the CVD-/HL+ (HR = 1.21; 95% CI 0.95–1.54) group when compared to the CVD-/HL- group. Higher cardiovascular mortality risks were noted in the CVD+/HL- (HR = 4.38; 95% CI: 2.30–8.33) and CVD+/HL+ (HR = 3.05; 95% CI: 1.81–5.17) groups, but not in the CVD-/HL+ (HR = 1.35; 95% CI: 0.86–2.12) group. A different sensitivity analysis was conducted by removing individuals who passed away within the first two years of the study period. The research discovered increased chances of death from any cause in the groups with CVD-/HL+ (HR = 1.29; 95% CI 1.03–1.63), CVD+/HL- (HR = 1.92; 95% CI 1.27–2.89), and CVD+/HL+ (HR = 2.11; 95% CI 1.61–2.75) when compared to the CVD-/HL- group. Higher cardiovascular mortality risks were noted in the CVD+/HL- (HR = 3.13; 95% CI: 1.61–6.11) and CVD+/HL+ (HR = 2.56; 95% CI: 1.62–4.07) groups, but not in the CVD-/HL+ (HR = 1.15; 95% CI: 0.80–1.66) group. 4 Discussion In this extensive cohort study, individuals with combined CVD and HL were at an increased risk for all-cause and cardiovascular mortality. Compared to participants without HL, those with all-frequency HL and high-frequency HL were positively associated with CVD, while low-frequency HL showed no significant association. The risk of all-cause mortality was notably higher in the CVD+/HL- and CVD+/HL + groups, with the CVD+/HL + group presenting the highest overall mortality risk. Cardiovascular mortality was also significantly elevated in the CVD+/HL- and CVD+/HL + groups. Our findings suggest that individuals with both CVD and HL are at a heightened risk of mortality, underscoring the need for integrated healthcare approaches to manage these coexisting conditions. Our study is the first to analyze the risk of all-cause and cardiovascular mortality in individuals with both CVD and HL. Previous epidemiologic research has explored the connection between HL and overall mortality, yielding inconsistent results[ 28 – 33 ]. These discrepancies may stem from variations in sample sizes, study population demographics, methods for assessing hearing status, reference group usage, and the extent of adjustments for comorbidities and other confounders[ 15 ]. Furthermore, there is a scarcity of studies investigating the association between HL and mortality related to specific causes, such as cardiovascular mortality[ 15 ]. For instance, a study in Iceland involving 4,926 adults aged 67 and older found that demonstrated a significant association with elevated rates of cardiovascular mortality but not with overall mortality[ 17 ]. In contrast, a more recent investigation involving 50,462 Norwegians, showed a positive correlation between HL and both overall and cardiovascular mortality, with no observed association with mortality related to injuries[ 34 ]. Another study by Lee et al.[ 15 ] involving 580,798 relatively young participants found a significant increase in all-cause and cardiovascular mortality risks with a graded relationship starting from mild HL, and a notably higher risk of injury-related mortality in those with moderate-to-severe HL. However, a study conducted in Japan found a significant link between self-reported HL and all-cause mortality, while no significant association was observed with cardiovascular disease mortality[ 35 ]. The precise mechanism behind the observed link between HL and elevated mortality rates remains incompletely understood, but a number of potential rationales exist. HL is associated with various adverse outcomes, including difficulties in disabilities, social isolation, falls, injuries, depression, and cognitive dysfunction[ 36 , 37 ]. Individuals with moderate to severe HL tend to experience social isolation and diminished comprehension of their health conditions and treatments[ 38 ]. These aspects potentially contribute to higher rates of overall and injury-related mortality. Moreover, epidemiologic investigations have established connections between HL and cardiovascular disease as well as associated risk factors like hypertension, hyperlipidemia, obesity, and diabetes mellitus[ 39 , 40 ]. For instance, Lu et al.[ 41 ] found that patients with sudden HL had significantly higher concentrations of total cholesterol, triglyceride, and lipoprotein A compared to controls. Chang et al.[ 42 ] reported that individuals with hypercholesterolemia had a 1.60-fold increased risk of sudden HL compared to those with normal cholesterol levels. The internal auditory artery, responsible for supplying the cochlea without collateral anastomotic networks, is highly vulnerable to ischemia[ 43 ]. Older individuals often exhibit histological arteriosclerotic changes across the auditory system, alongside microvascular issues affecting the cochlea’s stria vascularis, which can coincide with systemic vascular diseases linked closely to cardiovascular risk factors[ 44 , 45 ]. Additionally, subclinical atherosclerosis in the carotid intima has been associated with a five-year onset of hearing impairment in predominantly middle-aged groups, suggesting that HL might mirror cardiovascular health and be influenced by cardiovascular risk factors, as well as both subclinical and clinical cardiovascular diseases[ 45 ]. Contrary to our findings, however, other studies have indicated that low-frequency HL is typical in patients with vascular disease[ 23 ]. Further research is needed to validate the relationship between HL and the onset of cardiovascular disease and to elucidate the mechanisms underpinning the heightened risk of overall and cardiovascular mortality in individuals with HL. 5 Conclusion In conclusion, the coexistence of CVD and HL significantly amplifies the risk of both all-cause and cardiovascular mortality. Our findings suggest that individuals with either condition may benefit from more comprehensive and proactive management strategies to prevent the onset of the other condition, thereby reducing overall mortality risks. Integrating regular hearing assessments and cardiovascular health monitoring could be essential in managing these populations effectively. Abbreviations CVD Cardiovascular diseases HL hearing loss NHANES National Health and Nutrition Examination Survey APT average pure-tone DM diabetes mellitus BMI body mass index AST aspartate aminotransferase TG triglycerides HDL-cholesterol high-density lipoprotein cholesterol HbA1c glycosylated hemoglobin UACR urinary albumin-creatinine ratio ICD-10 International Classification of Diseases, Tenth Revision OR odds ratios CI confidence intervals HR hazard ratios Declarations Ethics approval and consent to participate: Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes/ . The National Center for Health Statistics Research Ethics Review Board has approved NHANES data collection, and participants provide written informed consent. Consent for publication: Not applicable. Competing Interests: The authors declare no competing financial interests. Funding: Not applicable. Author Contribution Conceptualization: Ling Li. Data curation: Ling Li and Lan Li. Formal analysis: Ling Li and Chenchen Qin. Writing-original draft: Ling Li and Qian Zhong. Writing-review & editing: Lan Li and Chenchen Qin. Acknowledgments: We thank all the contributors and participants in the National Health and Nutrition Examination Survey and the original genome-wide association studies. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Nieman, C. L. & Oh, E. S. Hearing Loss. Ann. Intern. Med. 173 Itc81-itc96 (2020). Collaborators, G. H. L. Hearing loss prevalence and years lived with disability, 1990–2019: findings from the Global Burden of Disease Study 2019. Lancet . 397 , 996–1009. 10.1016/s0140-6736(21)00516-x (2021). Zahnert, T. The differential diagnosis of hearing loss. Dtsch. Arztebl Int. 108 , 433–443. 10.3238/arztebl.2011.0433 (2011). quiz 444. Pinsonnault-Skvarenina, A., de Lacerda, A. B. 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Supplementary Files FigureS1A.jpg FigureS1B.jpg TableS1.SubgroupofcardiovasculardiseasesmortalityaccordingtoaccordingtothepresenceofCVDorHLstatus.xlsx TableS2A.Baselinecharacteristicsofthestudywithoutparticipantsundertheageof40.xlsx TableS2B.Baselinecharacteristicsofthestudywithoutparticipantswhodiedwithintwoyears.xlsx TableS3A.RisksofallcauseandcardiovasculardiseasemortalityaccordingtothepresenceofCVDorHLstatuswithoutparticipantsundertheageof40..xlsx TableS3B.RisksofallcauseandcardiovasculardiseasemortalityaccordingtothepresenceofCVDorHLstatuswithoutparticipantswhodiedwithintwoyears.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5301331","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379027738,"identity":"f67e9925-493a-40bc-a5df-43f237ac7050","order_by":0,"name":"Ling Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie2QsQrCQAxAUwrR4bSbHDj0F04K4tCPiUsnB0c3B6GTOlsE/QU/oRJw0i9wqbg4nltdxOvWRXqj4D1ISCCPuwTA4fhBur5JNJPztLXIc11aKFgpxTn21uI0PmZLG8WEd0sTbycnEbfRRml1+EHIPsJEMwgIg17e9LFuMiLBiHA58HQEg2xLTYoYKpIs0FsdeCOA1NVKUSxNURjRTokKokQhCrBWhkB5TChQmSPL5l2C4Bw9X29J4f5+17qMw6DfoFQPyVojv47V8bXVmMPhcPwvH6pYPzJ8h25tAAAAAElFTkSuQmCC","orcid":"","institution":"People’s Hospital of Deyang City","correspondingAuthor":true,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":379027739,"identity":"6395e36f-201c-49d4-9b9b-ab9656431075","order_by":1,"name":"Lan Li","email":"","orcid":"","institution":"Affiliated Hospital of Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Li","suffix":""},{"id":379027740,"identity":"0ba2d481-759a-4451-bab0-2fff0b57a6c7","order_by":2,"name":"Chenchen Qin","email":"","orcid":"","institution":"People’s Hospital of Deyang City","correspondingAuthor":false,"prefix":"","firstName":"Chenchen","middleName":"","lastName":"Qin","suffix":""},{"id":379027741,"identity":"d855f79d-8440-4e16-8aab-9394c0b8e020","order_by":3,"name":"Qian Zhong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-10-21 05:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5301331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5301331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70404441,"identity":"af7f99e0-98ea-432e-af1f-e3a696e1a9e3","added_by":"auto","created_at":"2024-12-02 23:01:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the selection strategy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe criteria and detailed numbers of excluded participants are showed in this figure. After data cleansing, 10641 participants were included to investigate the additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality.\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5301331/v1/1779a36ef7bc49eeb877e5ab.jpeg"},{"id":70405230,"identity":"d9499bfb-4049-44a1-a0c4-53db89222ff9","added_by":"auto","created_at":"2024-12-02 23:09:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves show all-cause and cardiovascular mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier curves in Figure 2 illustrate the changes in survival rates over the follow-up period for both all-cause and cardiovascular mortality, categorized by the presence of cardiovascular diseases and hearing loss.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5301331/v1/a6af1a20d11afc517e30a5ef.jpeg"},{"id":70404444,"identity":"1158a7db-42b4-4ccb-9692-ced4100390a4","added_by":"auto","created_at":"2024-12-02 23:01:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot for all-cause and cardiovascular mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis forest plot presents the HRs with 95% CIs for all-cause mortality and CVD mortality, based on the presence or absence of CVD and HL, using the CVD-/HL- group as a 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23:01:29","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14572,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2B.Baselinecharacteristicsofthestudywithoutparticipantswhodiedwithintwoyears.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5301331/v1/5f5b21300b91aea87fe5576f.xlsx"},{"id":70404450,"identity":"4f9cb723-dab7-4c10-bee8-4be8220cf299","added_by":"auto","created_at":"2024-12-02 23:01:29","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14991,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3A.RisksofallcauseandcardiovasculardiseasemortalityaccordingtothepresenceofCVDorHLstatuswithoutparticipantsundertheageof40..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5301331/v1/9802b8e4d4785cbbde24474b.xlsx"},{"id":70405232,"identity":"3bfbaa92-2a4d-4d4f-b0c3-ec03a9126765","added_by":"auto","created_at":"2024-12-02 23:09:29","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15043,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3B.RisksofallcauseandcardiovasculardiseasemortalityaccordingtothepresenceofCVDorHLstatuswithoutparticipantswhodiedwithintwoyears.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5301331/v1/7e54e6a6c5e091cb7045b27a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality: A longitudinal nationwide population-based study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent years, the prevalence of hearing loss (HL) has been steadily increasing due to global population aging, heightened environmental noise pollution, and prolonged use of auditory devices[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to 2019 WHO statistics, over 1.5\u0026nbsp;billion people worldwide suffer from disabling HL, with this number projected to rise to 2.5\u0026nbsp;billion by 2050, making HL the third leading cause of disability globally[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HL can be categorized into conductive and sensorineural types, with sensorineural HL being more prevalent and constituting the majority of HL cases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HL is not only a communicative disorder but also a significant condition that severely impacts quality of life[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Beyond its association with comorbidities such as depression and anxiety[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], HL is independently related to cognitive dysfunction[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Established risk factors for adult HL include aging, noise exposure, genetic predispositions, autoimmune diseases, chronic ear conditions, and the use of ototoxic drugs such as aminoglycosides and cisplatin[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies have also indicated that HL is linked to various cardiovascular diseases and risk factors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which may cause microvascular damage, oxidative stress, and impaired electrical signal transmission within the cochlea[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the WHO report highlights that the aging population and the increasing prevalence of cardiovascular and metabolic diseases worldwide could further elevate the incidence of HL[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious epidemiological investigations have explored whether HL increases mortality, yielding inconsistent findings[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some research has shown a positive correlation between HL and mortality. For instance, a meta-analysis of 26 studies with 1,213,756 mainly Western participants found that HL was linked to an increased risk of both all-cause and CVD mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, other studies reported no such association, highlighting the variability in findings. A notable study from Iceland identified that dichotomized HL was notably linked to later CVD mortality, yet it showed no significant association with all-cause mortality in older adults[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Many studies are constrained by small sample sizes and rely on self-reported HL rather than audiometric tests, potentially introducing measurement errors or misclassification that weaken the observed associations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, most research has primarily focused on older populations and overall mortality without detailed analysis of specific causes of death[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. HL is linked to several cardiovascular risk factors, such as smoking, diabetes, hypertension, hyperlipidemia, and obesity, and is often related to cardiovascular diseases themselves[\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the impact of combined CVD and HL on all-cause and cardiovascular mortality remains unclear. Understanding this relationship is crucial for determining whether patients with CVD or HL require preventive measures and treatment for HL or CVD.\u003c/p\u003e \u003cp\u003eTherefore, this research leveraged data from the National Health and Nutrition Examination Survey (NHANES) collected between 1999 and 2018 to explore the influence of CVD and HL on all-cause and cardiovascular mortality.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and population\u003c/h2\u003e \u003cp\u003eThe design of the study and the characteristics of the participants information for this research were gathered from the NHANES, which uses a complex, multi-step probability sampling plan to effectively depict the U.S. civilian non-institutionalized population. Since 1999, NHANES has conducted annual measurements, adding approximately 5,000 participants to the database each year. NHANES gathers a broad range of health-related data by conducting thorough interviews and physical exams, as outlined on their official site. Audiometric data was collected, along with other forms of data. Ethical approval was granted for the research protocol, with the requirement that all participants give written informed consent.\u003c/p\u003e \u003cp\u003eThe analysis included information from eight NHANES cycles between 1999 and 2018 covering the years: 1999\u0026ndash;2000, 2001\u0026ndash;2002, 2003\u0026ndash;2004, 2005\u0026ndash;2006, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2015\u0026ndash;2016, and 2017\u0026ndash;2018. The dataset from the US Centers for Disease Control and Prevention's National Center for Health Statistics is well-documented in the NHANES Documentation Files, which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e as of December 15, 2021.\u003c/p\u003e \u003cp\u003eEligibility for our study was determined based on inclusion criteria applied to the 1999\u0026ndash;2018 NHANES cohorts. Participants were considered if they were: (1) 20 years of age or older, (2) had measurements for audiometric information, and (3) possessed the requisite data for diagnosing CVD, mortality follow-up information, and covariates. Initially enrolling 49,312 participants, our final analysis included 10,614 eligible individuals with complete data on HL, CVD diagnosis, and mortality, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of Hearing loss, cardiovascular disease and other covariates\u003c/h2\u003e \u003cp\u003eHearing was quantitatively assessed through pure-tone air conduction audiometry at seven different frequencies between 500 Hz and 8 kHz. Skilled health technicians performed the measurements in a calibrated sound booth (Acoustic Systems, model Delta 142). HL was characterized by an average pure-tone (APT) hearing threshold above 20 dB, with the exception of potential mixed or conductive hearing loss cases. All-frequency HL was characterized in our study as an APT threshold above 20 decibels for all frequencies examined. Low-frequency (0.5, 1, 2, and 4 kHz) and high-frequency (3, 4, and 8 kHz) HL were defined according to the WHO definition of HL (APT threshold above 20 dB)[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCVD was detected by medical surveys and self-reported diagnoses which included any signs of coronary artery disease, congestive heart failure, myocardial infarction, angina, or cerebrovascular accident.\u003c/p\u003e \u003cp\u003eDemographic, physical examination, and laboratory data were extracted from the NHANES database across eight cycles. Demographic factors such as age, gender, ethnicity, household earnings, educational attainment, marital situation, smoking habits, and alcohol use were determined based on particular guidelines[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Medical conditions such as diabetes mellitus (DM) and hypertension were identified through a combination of self-reports, laboratory results, and medication use, with specific diagnostic criteria for each condition. The physical exam and lab results covered measurements such as body mass index (BMI), waist circumference, total bilirubin, aspartate aminotransferase (AST), triglycerides (TG), total cholesterol, high-density lipoprotein cholesterol (HDL-cholesterol), creatinine, glycosylated hemoglobin (HbA1c), and urinary albumin-creatinine ratio (UACR).\u003c/p\u003e \u003cp\u003eWe selected all-frequency HL as the criterion for defining the HL population. Study participants were then stratified into four distinct groups based on the occurrence of CVD and HL: 1) neither CVD nor HL present (CVD-/HL-), 2) HL present without CVD (CVD-/HL+), 3) CVD present without HL (CVD+/HL-), and 4) both CVD and HL present (CVD+/HL+).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study outcome\u003c/h2\u003e \u003cp\u003eData on deaths were gathered from the NDI, including details on mortality status and causes of death, connected to NHANES participants via the NDI\u0026rsquo;s accessible records until December 31, 2019. The length of the follow-up period was measured in months, starting from the day the participant was examined at the NHANES mobile examination center and continuing until either the date of death or the end of the mortality follow-up period. Causes of death were categorized according to the International Classification of Diseases, Tenth Revision (ICD-10). The research concentrated on the main results of death from any cause and cardiovascular-related death (codes 100\u0026ndash;109, 111, 113, 120\u0026ndash;151, and 160\u0026ndash;169).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003e In accordance with NHANES analytical guidelines, our statistical analyses incorporated the survey's complex sampling design and associated sampling weights. Participant demographics and clinical characteristics were summarized using weighted means and standard errors for continuous variables and counts with weighted percentages for categorical variables. Weighted linear regression analysis was utilized for continuous variables, while categorical variables were examined through design-adjusted chi-square tests.\u003c/p\u003e \u003cp\u003eWe first used multivariate logistic regression analysis to determine odds ratios (OR) and 95% confidence intervals (CI) in order to investigate the association between different forms of HL (all-frequency HL, low-frequency HL, and high-frequency HL) and CVD. The crude model was unadjusted. Age and sex were taken into account when adjusting Model 1. Model 2 included adjustments for age, sex, race, family income, and education. Additional adjustments were made to Model 3 for factors including age, gender, ethnicity, household earnings, educational background, alcohol consumption, smoking habits, DM, and hypertension. The Cox proportional hazards model was then used to determine the hazard ratios (HR) and 95% CI for mortality based on the presence of CVD and HL. Age and sex were taken into account when adjusting Model 1. Model 2 included race, level of education, family income ratio, and marital status in addition to the variables in Model 1. Model 3 incorporated extra modifications for BMI, waist circumference, total bilirubin, AST, TG, total cholesterol, HDL-cholesterol, HbA1c, UACR, creatinine, and habits of drinking and smoking.\u003c/p\u003e \u003cp\u003eFurthermore, the study also included interaction and subgroup analyses for both all-cause and cardiovascular mortality, which were stratified by age (using 40 years as a cutoff point), gender, DM, and hypertension, following the specifications of Model 3. In order to strengthen the trustworthiness of our findings, we performed two sensitivity analyses: one that omitted participants who passed away within a two-year period, and another that excluded individuals younger than 40.\u003c/p\u003e \u003cp\u003eThe threshold for statistical significance was established as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. R software version 4.3.1 (R Foundation, Vienna, Austria) was utilized for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eAmong the 10,614 participants analyzed, 6,039 (56.9%) were categorized as having neither CVD nor HL (CVD-/HL-), 3,465 (32.6%) had only HL (CVD-/HL+), 279 (2.6%) had only CVD (CVD+/HL-), and 831 (7.8%) were diagnosed with both conditions (CVD+/HL+). The baseline characteristics of these groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significantly older individuals were observed in the CVD+/HL\u0026thinsp;+\u0026thinsp;group compared to the other groups, with mean ages of 39.85, 59.15, 51.78, and 66.84 years for the CVD-/HL-, CVD-/HL+, CVD+/HL-, and CVD+/HL\u0026thinsp;+\u0026thinsp;groups, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Furthermore, the CVD+/HL\u0026thinsp;+\u0026thinsp;group exhibited significantly elevated levels of BMI, waist circumference, AST, HbA1c, UACR, and creatinine compared to the other groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Individuals who had both cardiovascular disease and high cholesterol were more inclined to be male, possess a moderate household income, primarily identify as Non-Hispanic White, have completed high school, and have a history of smoking. They also had higher rates of diabetes and hypertension in contrast to those without either condition.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10614)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCVD-/HL-\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6039)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVD-/HL+\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3465)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCVD+/HL-\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;279)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCVD+/HL+\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;831)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.44(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.85(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.15(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.78(1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.84(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m2, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.78(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.42(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.02(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.41(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.77(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ecm, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.73(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.41(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.58(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.15(1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.39(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eU/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.21(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.55(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.39(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.81(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.42(0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal bilirubin, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026micro;mol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.44(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.37(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.61(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.15(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.55(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emmol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol, mmol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.11(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.09(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.76(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-cholesterol, mmol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c, %, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.60(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.80(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.91(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.17(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUACR, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emg/g, median(Q1, Q3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.38(4.23,11.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.80(4.00, 9.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.36(4.71,14.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26(4.29,14.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.68(5.56,26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026micro;mol/L, median(Q1, Q3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.37(61.88,88.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.72(61.88, 83.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.68(64.53, 88.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.02(61.90, 91.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.63(70.72,102.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5297(51.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3324(55.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1504(43.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165(55.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e304(40.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5317(48.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2715(44.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1961(56.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114(44.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e527(59.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1819(7.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1104(8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e580(6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104(4.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2241(10.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1403(11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e598(7.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105(20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135(7.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4548(70.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2266(66.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1677(76.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(61.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e508(81.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901(5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531(6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301(5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1105(6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e735(6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309(5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(10.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35(3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th Grade (Includes 12th grade with no diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1510(10.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e764(9.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546(11.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49(12.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151(14.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2536(30.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1672(33.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e687(27.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(26.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116(18.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2394(22.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1287(20.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e821(24.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(27.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219(28.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess Than 9th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1188(5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463(3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561(7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143(10.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome College or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2986(30.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1853(32.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850(28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(30.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202(27.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily income, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3358(43.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2070(45.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1028(43.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74(38.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186(33.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3193(20.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746(20.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1069(20.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112(29.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e266(22.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4063(35.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2223(34.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1368(36.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93(31.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e379(44.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e816(8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e627(10.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148(5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5745(57.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3097(55.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2032(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141(58.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e475(61.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1803(16.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1447(21.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286(7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(11.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2250(17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e868(12.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e999(24.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87(23.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e296(30.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol user, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1833(14.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e734(10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e757(18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74(24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268(29.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2031(20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1399(24.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e503(15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43(17.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86(12.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3584(36.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1984(35.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1217(39.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(31.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e301(38.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1537(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1030(18.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411(13.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(12.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63(8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1629(11.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e892(11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577(12.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(13.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113(11.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2713(25.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1101(20.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1159(32.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(28.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e372(43.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5733(53.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3673(58.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1642(46.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116(40.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e302(35.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2168(20.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1265(20.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e664(20.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(30.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157(20.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8736(86.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5471(92.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2599(79.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182(72.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e484(62.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1878(13.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e568(7.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e866(20.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(27.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e347(37.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6102(63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4351(75.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1505(48.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(33.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164(23.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4512(36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1688(24.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1960(51.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197(66.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e667(76.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9160(90.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5781(96.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2700(82.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e226(83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e453(60.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1454(9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258(3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e765(17.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53(17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e378(39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCVD: cardiovascular diseases; HL: hearing loss; BMI: body mass index; AST: aspartate aminotransferase; HDL-cholesterol: high density lipoprotein cholesterol; HbA1c: glycosylated hemoglobin; UACR: urinary albumin creatinine ratio; DM: diabetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The association between HL and CVD\u003c/h2\u003e \u003cp\u003eModel 3 findings indicate a positive association between all-frequency HL (OR\u0026thinsp;=\u0026thinsp;1.49, 95% CI 1.14\u0026ndash;1.96, p\u0026thinsp;=\u0026thinsp;0.004) and high-frequency HL (OR\u0026thinsp;=\u0026thinsp;1.41, 95% CI 1.04\u0026ndash;1.90, p\u0026thinsp;=\u0026thinsp;0.03) with CVD when compared to individuals without HL. However, low-frequency HL (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.86\u0026ndash;1.26, p\u0026thinsp;=\u0026thinsp;0.71) was not associated with CVD. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the detailed correlation results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation HL with CVD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2 OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3 OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-frequency HL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82(4.61,7.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82(1.39,2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68(1.28,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49(1.14,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-frequency HL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46(2.94,4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.95,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.85,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04(0.86,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-frequency HL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.96(4.65,7.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71(1.28,2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58(1.16,2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41(1.04,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCrude model was adjusted for no covariates;Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, race, family income and education; Model 3 was adjusted for age, sex, race, family income, education, alcohol user, smoking status, diabetes and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 All-cause and cause-specific mortality according to cardiovascular diseases and hearing loss\u003c/h2\u003e \u003cp\u003eOver a period of 8.1 years (with a range of 4.3 to 15.8 years), there were 1,454 total deaths, including 462 deaths linked to cardiovascular problems. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows survival curves for the four groups, indicating notable variations in overall and cause-specific survival rates (both log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with the CVD-/HL- group having the best survival and the CVD+/HL\u0026thinsp;+\u0026thinsp;group having the worst.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outline the HR for all-cause and CVD mortality based on CVD and HL status. In comparison to the CVD-/HL- baseline group, there was a higher risk of death from any cause in the CVD+/HL- group (HR\u0026thinsp;=\u0026thinsp;1.88, 95% CI 1.29\u0026ndash;2.73, p\u0026thinsp;=\u0026thinsp;0.001) and the CVD+/HL\u0026thinsp;+\u0026thinsp;group (HR\u0026thinsp;=\u0026thinsp;2.19, 95% CI 1.69\u0026ndash;2.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while the CVD-/HL\u0026thinsp;+\u0026thinsp;group did not show a statistically significant difference (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI 0.98\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.07). Cardiovascular mortality was higher in the CVD+/HL- (HR\u0026thinsp;=\u0026thinsp;3.66, 95% CI 2.00\u0026ndash;6.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and CVD+/HL+ (HR\u0026thinsp;=\u0026thinsp;2.91, 95% CI 1.89\u0026ndash;4.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) groups compared to the CVD-/HL- group. However, the CVD-/HL\u0026thinsp;+\u0026thinsp;group did not show a statistically significant difference in risk (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI 0.98\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisks of all-cause and cardiovascular disease mortality according to the presence of CVD or HL status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR 95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egroups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD-/HL-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD-/HL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.62(4.60, 6.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53(1.23,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40(1.11,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24(0.98,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD+/HL-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.44(3.79, 7.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67(1.85,3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49(1.71,3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.88(1.29,2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD+/HL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.12(13.93,21.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22(2.53,4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.76(2.15,3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.19(1.69,2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular diseases mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egroups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD-/HL-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD-/HL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.48(5.44,10.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51(1.04,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42(0.98,2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26(0.88,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD+/HL-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.14(6.90,21.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11(2.79,9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.68(2.56,8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.66(2.00,6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD+/HL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.74(23.16,49.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.37(2.75,6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.82(2.38,6.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.91(1.89,4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCrude model, was adjusted for no covariates;Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, race and ethnicity, educational level, family income and marital status; Model 3 was adjusted for age, sex, race and ethnicity, educational level, family income, marital status, smoking status, alcohol user, body mass index, waist circumference, aspartate aminotransferase, total bilirubin, triglycerides, total cholesterol, high density lipoprotein cholesterol, glycosylated hemoglobin, Creatinine and urinary albumin creatinine ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analyses and sensitivity analyses\u003c/h2\u003e \u003cp\u003eThe subgroup analyses in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary \u003cb\u003eTables S1\u003c/b\u003e show that the CVD+/HL\u0026thinsp;+\u0026thinsp;group has a greater risk of all-cause mortality compared to the CVD-/HL- group in different subgroups for both all-cause and cardiovascular diseases mortality. Notably, increased mortality risks were observed in participants aged 41\u0026ndash;85 years (HR\u0026thinsp;=\u0026thinsp;5.65; 95% CI\u0026thinsp;=\u0026thinsp;4.34\u0026ndash;7.36), aged 20\u0026ndash;40 years (HR\u0026thinsp;=\u0026thinsp;13.5; 95% CI\u0026thinsp;=\u0026thinsp;1.92\u0026ndash;95.4), females (HR\u0026thinsp;=\u0026thinsp;9.99; 95% CI\u0026thinsp;=\u0026thinsp;7.12-14.0), males (HR\u0026thinsp;=\u0026thinsp;6.30; 95% CI\u0026thinsp;=\u0026thinsp;4.40\u0026ndash;9.04), those with and without DM (HR\u0026thinsp;=\u0026thinsp;4.88; 95% CI\u0026thinsp;=\u0026thinsp;3.22\u0026ndash;7.39 and HR\u0026thinsp;=\u0026thinsp;7.39; 95% CI\u0026thinsp;=\u0026thinsp;5.44\u0026ndash;10.02, respectively), and with and without hypertension (HR\u0026thinsp;=\u0026thinsp;5.80; 95% CI\u0026thinsp;=\u0026thinsp;4.41\u0026ndash;7.63 and HR\u0026thinsp;=\u0026thinsp;7.58; 95% CI\u0026thinsp;=\u0026thinsp;5.09\u0026ndash;11.29, respectively). Moreover, there was a notable correlation between diabetes status and the likelihood of all-cause mortality, with a p-value for interaction of 0.016. Supplementary \u003cb\u003eTable S1\u003c/b\u003e provides a comprehensive overview of the findings from the subgroup analyses on mortality related to cardiovascular diseases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup of all-cause mortality according to according to the presence of CVD or HL status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCVD-/HL-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCVD-/HL+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCVD+/HL-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCVD+/HL+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.642(2.072,3.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.814(1.881,4.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.651(4.340,7.358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.037(1.003, 4.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.410(0.343, 5.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.515(1.915,95.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.373(2.436,4.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.142(1.048,4.378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.299(4.391,9.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.569(2.613, 4.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.178(2.641, 6.610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.993(7.123,14.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.592(2.802, 4.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.449(1.447, 4.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.385(5.444,10.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.093(1.388,3.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.933(1.551,5.545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.879(3.220,7.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.766(2.134,3.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.377(1.473,3.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.804(4.414,7.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.097(2.155, 4.452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.126(1.376, 7.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.580(5.089,11.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eDM: diabetes mellitus;HR: hazard ratio; CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eEach stratification was adjusted for race and ethnicity, educational level, family income, marital status, smoking status, alcohol user, body mass index, waist circumference, aspartate aminotransferase, total bilirubin, triglycerides, total cholesterol, high density lipoprotein cholesterol, glycosylated hemoglobin, Creatinine and urinary albumin creatinine ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSupplementary \u003cb\u003eTables S2A, S2B, S3A\u003c/b\u003e, and \u003cb\u003eS3B\u003c/b\u003e, as well as Supplementary \u003cb\u003eFigures S1A and S1B\u003c/b\u003e, present initial features of distinct categories, results from diverse models, and findings from Kaplan-Meier sensitivity analyses. Participants under the age of 40 were excluded in the sensitivity analysis. The research discovered increased chances of death from any cause in the CVD+/HL- (HR\u0026thinsp;=\u0026thinsp;1.98; 95% CI 1.34\u0026ndash;2.93) and CVD+/HL+ (HR\u0026thinsp;=\u0026thinsp;2.11; 95% CI 1.62\u0026ndash;2.77) categories, while no significant difference was observed in the CVD-/HL+ (HR\u0026thinsp;=\u0026thinsp;1.21; 95% CI 0.95\u0026ndash;1.54) group when compared to the CVD-/HL- group. Higher cardiovascular mortality risks were noted in the CVD+/HL- (HR\u0026thinsp;=\u0026thinsp;4.38; 95% CI: 2.30\u0026ndash;8.33) and CVD+/HL+ (HR\u0026thinsp;=\u0026thinsp;3.05; 95% CI: 1.81\u0026ndash;5.17) groups, but not in the CVD-/HL+ (HR\u0026thinsp;=\u0026thinsp;1.35; 95% CI: 0.86\u0026ndash;2.12) group.\u003c/p\u003e \u003cp\u003eA different sensitivity analysis was conducted by removing individuals who passed away within the first two years of the study period. The research discovered increased chances of death from any cause in the groups with CVD-/HL+ (HR\u0026thinsp;=\u0026thinsp;1.29; 95% CI 1.03\u0026ndash;1.63), CVD+/HL- (HR\u0026thinsp;=\u0026thinsp;1.92; 95% CI 1.27\u0026ndash;2.89), and CVD+/HL+ (HR\u0026thinsp;=\u0026thinsp;2.11; 95% CI 1.61\u0026ndash;2.75) when compared to the CVD-/HL- group. Higher cardiovascular mortality risks were noted in the CVD+/HL- (HR\u0026thinsp;=\u0026thinsp;3.13; 95% CI: 1.61\u0026ndash;6.11) and CVD+/HL+ (HR\u0026thinsp;=\u0026thinsp;2.56; 95% CI: 1.62\u0026ndash;4.07) groups, but not in the CVD-/HL+ (HR\u0026thinsp;=\u0026thinsp;1.15; 95% CI: 0.80\u0026ndash;1.66) group.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this extensive cohort study, individuals with combined CVD and HL were at an increased risk for all-cause and cardiovascular mortality. Compared to participants without HL, those with all-frequency HL and high-frequency HL were positively associated with CVD, while low-frequency HL showed no significant association. The risk of all-cause mortality was notably higher in the CVD+/HL- and CVD+/HL\u0026thinsp;+\u0026thinsp;groups, with the CVD+/HL\u0026thinsp;+\u0026thinsp;group presenting the highest overall mortality risk. Cardiovascular mortality was also significantly elevated in the CVD+/HL- and CVD+/HL\u0026thinsp;+\u0026thinsp;groups. Our findings suggest that individuals with both CVD and HL are at a heightened risk of mortality, underscoring the need for integrated healthcare approaches to manage these coexisting conditions.\u003c/p\u003e \u003cp\u003eOur study is the first to analyze the risk of all-cause and cardiovascular mortality in individuals with both CVD and HL. Previous epidemiologic research has explored the connection between HL and overall mortality, yielding inconsistent results[\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These discrepancies may stem from variations in sample sizes, study population demographics, methods for assessing hearing status, reference group usage, and the extent of adjustments for comorbidities and other confounders[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, there is a scarcity of studies investigating the association between HL and mortality related to specific causes, such as cardiovascular mortality[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, a study in Iceland involving 4,926 adults aged 67 and older found that demonstrated a significant association with elevated rates of cardiovascular mortality but not with overall mortality[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, a more recent investigation involving 50,462 Norwegians, showed a positive correlation between HL and both overall and cardiovascular mortality, with no observed association with mortality related to injuries[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Another study by Lee et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] involving 580,798 relatively young participants found a significant increase in all-cause and cardiovascular mortality risks with a graded relationship starting from mild HL, and a notably higher risk of injury-related mortality in those with moderate-to-severe HL. However, a study conducted in Japan found a significant link between self-reported HL and all-cause mortality, while no significant association was observed with cardiovascular disease mortality[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe precise mechanism behind the observed link between HL and elevated mortality rates remains incompletely understood, but a number of potential rationales exist. HL is associated with various adverse outcomes, including difficulties in disabilities, social isolation, falls, injuries, depression, and cognitive dysfunction[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Individuals with moderate to severe HL tend to experience social isolation and diminished comprehension of their health conditions and treatments[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These aspects potentially contribute to higher rates of overall and injury-related mortality. Moreover, epidemiologic investigations have established connections between HL and cardiovascular disease as well as associated risk factors like hypertension, hyperlipidemia, obesity, and diabetes mellitus[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For instance, Lu et al.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] found that patients with sudden HL had significantly higher concentrations of total cholesterol, triglyceride, and lipoprotein A compared to controls. Chang et al.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] reported that individuals with hypercholesterolemia had a 1.60-fold increased risk of sudden HL compared to those with normal cholesterol levels. The internal auditory artery, responsible for supplying the cochlea without collateral anastomotic networks, is highly vulnerable to ischemia[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Older individuals often exhibit histological arteriosclerotic changes across the auditory system, alongside microvascular issues affecting the cochlea\u0026rsquo;s stria vascularis, which can coincide with systemic vascular diseases linked closely to cardiovascular risk factors[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, subclinical atherosclerosis in the carotid intima has been associated with a five-year onset of hearing impairment in predominantly middle-aged groups, suggesting that HL might mirror cardiovascular health and be influenced by cardiovascular risk factors, as well as both subclinical and clinical cardiovascular diseases[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Contrary to our findings, however, other studies have indicated that low-frequency HL is typical in patients with vascular disease[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Further research is needed to validate the relationship between HL and the onset of cardiovascular disease and to elucidate the mechanisms underpinning the heightened risk of overall and cardiovascular mortality in individuals with HL.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, the coexistence of CVD and HL significantly amplifies the risk of both all-cause and cardiovascular mortality. Our findings suggest that individuals with either condition may benefit from more comprehensive and proactive management strategies to prevent the onset of the other condition, thereby reducing overall mortality risks. Integrating regular hearing assessments and cardiovascular health monitoring could be essential in managing these populations effectively.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehearing loss\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage pure-tone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-cholesterol\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycosylated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUACR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eurinary albumin-creatinine ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, Tenth Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The National Center for Health Statistics Research Ethics Review Board has approved NHANES data collection, and participants provide written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Ling Li. Data curation: Ling Li and Lan Li. Formal analysis: Ling Li and Chenchen Qin. Writing-original draft: Ling Li and Qian Zhong. Writing-review \u0026amp; editing: Lan Li and Chenchen Qin.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe thank all the contributors and participants in the National Health and Nutrition Examination Survey and the original genome-wide association studies.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNieman, C. L. \u0026amp; Oh, E. S. Hearing Loss. \u003cem\u003eAnn. Intern. Med.\u003c/em\u003e \u003cb\u003e173\u003c/b\u003e Itc81-itc96 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators, G. H. L. 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Subclinical atherosclerosis and increased risk of hearing impairment. \u003cem\u003eAtherosclerosis\u003c/em\u003e. \u003cb\u003e238\u003c/b\u003e, 344\u0026ndash;349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2014.12.031\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2014.12.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular diseases, hearing loss, All-cause mortality, Cardiovascular mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5301331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5301331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular diseases (CVD) and hearing loss (HL) are significant public health concerns, sharing common pathological mechanisms and being associated with severe health outcomes. This study investigates the impact of CVD and HL on all-cause and cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 were analyzed, along with mortality data from the National Death Index (NDI) up to December 2019. Initially, we explored the correlation between different types of HL and CVD. Participants were categorized into four groups based on the presence of CVD and HL, and mortality outcomes were analyzed accordingly.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 10,614 participants, 6,039 (56.9%) had neither CVD nor HL (CVD-/HL-), 3,465 (32.6%) had HL only (CVD-/HL+), 279 (2.6%) had CVD only (CVD+/HL-), and 831 (7.8%) had both CVD and HL (CVD+/HL+). Compared to individuals without HL, those with overall frequency HL (OR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.14\u0026ndash;1.96, p\u0026thinsp;=\u0026thinsp;0.004) and high-frequency HL (OR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.04\u0026ndash;1.90, p\u0026thinsp;=\u0026thinsp;0.03) showed a positive correlation with CVD, while low-frequency HL (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.86\u0026ndash;1.26, p\u0026thinsp;=\u0026thinsp;0.71) showed no significant association. In terms of mortality, compared to the CVD-/HL- group, the CVD+/HL- group (HR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.29\u0026ndash;2.73, p\u0026thinsp;=\u0026thinsp;0.001) and the CVD+/HL\u0026thinsp;+\u0026thinsp;group (HR\u0026thinsp;=\u0026thinsp;2.19, 95% CI: 1.69\u0026ndash;2.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) had increased all-cause mortality risks, whereas the CVD-/HL\u0026thinsp;+\u0026thinsp;group did not show statistical significance (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 0.98\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.07). The CVD+/HL- group (HR\u0026thinsp;=\u0026thinsp;3.66, 95% CI: 2.00\u0026ndash;6.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the CVD+/HL\u0026thinsp;+\u0026thinsp;group (HR\u0026thinsp;=\u0026thinsp;2.91, 95% CI: 1.89\u0026ndash;4.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) had increased cardiovascular mortality risks, while the CVD-/HL\u0026thinsp;+\u0026thinsp;group did not show statistical significance (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 0.98\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe simultaneous presence of CVD and HL significantly raised the likelihood of death from any cause and cardiovascular events. Patients with either condition may need more vigilant treatment to avoid the onset of the other condition and lower the risk of death.\u003c/p\u003e","manuscriptTitle":"Additive impact of cardiovascular diseases and hearing loss on all-cause and cardiovascular mortality: A longitudinal nationwide population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 23:01:24","doi":"10.21203/rs.3.rs-5301331/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2c9c255-c931-4dfd-a1a5-bb87df0716e4","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40360509,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":40360510,"name":"Health sciences/Cardiology/Cardiovascular biology"}],"tags":[],"updatedAt":"2025-02-18T08:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 23:01:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5301331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5301331","identity":"rs-5301331","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}