Skip to main content
Download PDF
Download ePub

Association between neutrophil to high-density lipoprotein cholesterol ratio and hearing loss: a cross-sectional study from NHANES

Journal of Health, Population and Nutrition volume 44, Article number: 110 (2025) Cite this article

Abstract

Background

This study aimed to investigate the relationship between the neutrophil-to-HDL cholesterol ratio (NHR) and the risk of hearing loss, as well as to evaluate the potential of the NHR as a biomarker for hearing loss.

Methods

The U.S. National Health and Nutrition Examination Survey (NHANES) data covering 2005–2012 and 2015–2020 were analyzed. A weighted multivariate logistic regression model assessed the correlation between NHR and speech-frequency hearing loss (SFHL) and high-frequency hearing loss (HFHL). Restricted cubic spline (RCS) regression analysis was utilized to investigate the nonlinear correlation. Additionally, subgroup analysis was performed to identify differences among subgroups. A receiver operating characteristic (ROC) analysis was conducted to evaluate the efficacy of NHR in predicting hearing loss.

Results

A total of 10,436 participants were involved. After comprehensive adjustments for confounding factors, NHR was linearly correlated with SFHL and HFHL. Subgroup analysis revealed that race and the poverty index ratio (PIR) significantly modified the association between NHR and hearing loss. ROC analysis demonstrated the predictive capability of NHR for hearing loss.

Conclusion

NHR is positively correlated with the risk of hearing loss. This study suggests that NHR may serve as a potential biomarker for predicting and assessing hearing loss, demonstrating significant clinical application value. However, this cross-sectional study limits the ability to establish causality. Future longitudinal studies are needed to confirm these findings and explore potential mechanisms.

Background

Hearing loss is a significant public health concern. According to the World Health Organization (WHO), more than 1.5 billion individuals are affected by hearing loss, of whom approximately 430 million worldwide experience moderate or higher levels of hearing loss [1]. Hearing loss is not merely a sensory impairment that ranks as the fifth leading cause of disability worldwide and is linked to various comorbidities, including cognitive decline, social isolation, and increased medical expenses [2]. In 2017, the WHO estimated that unaddressed hearing loss incurred an annual cost of approximately $750 billion globally, underscoring its significant economic burden [3]. Hearing loss can be classified into three categories: conductive, sensorineural, and mixed. Conductive hearing loss is generally more amenable to improvement, whereas sensorineural hearing loss currently lacks effective medication approved by the FDA [4]. As the population ages, the incidence is anticipated to increase further [1]. Consequently, the development of effective interventions for prevention and treatment is essential to address this escalating global health challenge.

The risk factors for hearing loss are diverse, including demographic characteristics, health conditions, environmental exposures, and lifestyle choices. Age and sex are significant demographic determinants, as the prevalence of hearing loss increases with age, and men are at a higher risk than women [5]. Risk factors for cardiovascular diseases, such as obesity, hypertension, and diabetes are significantly associated with hearing impairment [6]. Lifestyle factors such as noise exposure, smoking, and poor dietary habits can adversely affect hearing health [5, 7]. Furthermore, individuals with a family history of hearing impairment are at a greater risk of developing hearing loss, indicating a potential genetic influence [8]. In recent years, dyslipidemia, chronic low-grade inflammation, and oxidative stress have emerged as contributing factors to hearing loss [10,11,12].

Previous studies have demonstrated associations between inflammatory markers and hearing impairment. Cross-sectional studies reported correlations of white blood cell count (WBCC), neutrophil count, interleukin-6 (IL-6), and C-reactive protein (CRP) with hearing loss. Large longitudinal cohorts, including studies by Lassale C et al. and Nash SD et al., further confirmed that systemic inflammation, reflected by WBCC and CRP, predicts hearing decline in the elderly [12, 13]. Additionally, dyslipidemia-related impaired cochlear blood flow may contribute to hearing loss [14]. Therefore, we aimed to identify composite indicators that capture the combined impact of inflammation and dyslipidemia on hearing.

It was reported that the NHR has emerged as a potential cost-effective biomarker of systemic inflammation [15, 16], which has been proven associated with cardiovascular disease, metabolic syndrome, and certain cancers [18,19,20]. These findings indicate that NHR possesses considerable predictive validity for various systemic diseases. The potential impact of inflammation and dyslipidemia on hearing loss prompted us to investigate whether NHR could serve as a biomarker.

This research aimed to explore the utility of the NHR as a potential biomarker for hearing loss. We focused our study on the hypothesis that an elevated NHR is associated with an increased likelihood of developing hearing loss. Rigorous statistical approaches were used to analyze cross-sectional data collected from the NHANES during 2005–2012 and 2012–2020 to evaluate this hypothesis and account for potential confounding factors.

Methods

Study population

Data were collected from the NHANES which utilizes intricate multistage probability sampling techniques and reflects the health of the U.S. population. Using a cross-sectional study approach, we analyzed NHANES data from 2005 to 2012 and 2015–2020, focusing on participants with complete NHR and hearing information to investigate the association between them. Of the original 75,575 participants, they were excluded for following conditions: (1) were younger than 20 years old or pregnant (N = 33,093), (2) were missing binaural speech frequencies (0.5, 1, 2, and 4 kHz) and binaural pure tone average (PTA) data at high frequencies (3, 4, 6, and 8 kHz) (N = 31,413), or (3) lacked NHR data (N = 663). Thus, the final cohort analyzed consisted of 10,436 individuals (Fig. 1).

Fig. 1
figure 1

Inclusion and exclusion criteria for participants in this study

Full size image

Data assessment

The neutrophil count (10^3 cells/µL) is divided by the HDL-C value (mmol/L). Two metrics were obtained from laboratory tests, to derive the NHR. These test results adhered to procedure manuals specified in the NHANES, thereby ensuring the accuracy and reproducibility of measurement results.

Audiometry in NHANES employed pure-tone air conduction audiometry by trained examiners in a calibrated soundproof room using an audiometer equipped with standard earphones and inserted earphones. Participants’ hearing thresholds were assessed at frequencies 0.5, 1, 2, 3, 4, 6, and 8 kHz, with intensities ranging from − 10 to 120 dB. Each ear was examined twice at 1 kHz. If the difference between the two measurement results exceeded 10 dB, the result was excluded. All subjects’ left and right pure tone average (PTA) results were compared, and the ear with better hearing was designated the hearing index. According to the WHO’s report [1], hearing loss is defined as PTA ≥ 20 dB. Therefore, PTA values of ≥ 20 dB for speech frequencies (0.5, 1, 2, and 4 kHz) are classified as speech-frequency hearing loss (SFHL), while those for high frequencies (3, 4, 6, and 8 kHz) are categorized as high-frequency hearing loss (HFHL) [20]. Audiometric testing in NHANES was conducted in sound-isolated booths using standardized protocols consistent with guidelines from the American Speech-Language-Hearing Association. The audiometers were regularly calibrated to meet ANSI S3.6 specifications for audiometric equipment. Quality control procedures included daily biological checks and regular monitoring of equipment function to ensure the accuracy and reliability of hearing threshold measurements [21, 22].

Covariates

Covariates included sociodemographic data, such as age, sex, race, educational level, poverty income ratio (PIR), and marital status. Lower socioeconomic status is associated with higher systemic inflammation, making PIR a relevant factor in assessing the relationship between NHR and hearing loss [23]. Additionally, the covariates included risk factors for hearing loss, such as body mass index (BMI), smoking habits, alcohol consumption, and noise exposure. The classification of all covariates can be found in Additional file 1 [24]. Notably, an individual’s noise exposure (yes or no) was determined by whether they were exposed to noise during work, after-work activities, or when using firearms.

Statistical analysis

R software (V4.3.1) was used to perform the statistical analyses. P-values < 0.05 (two-tailed) were defined as statistically significant. For data with missing covariates, multiple imputation was performed using the “MICE” package. In descriptive data for baseline characteristics, categorical variables were presented as frequencies (n) and weighted percentages, and continuous variables were expressed as weighted means accompanied by standard deviations. The t-test and chi-square test were used to evaluate differences between diseased and non-diseased. Multiple weighted logistic regression examined the relationship between NHR and hearing loss. Three models were constructed based on adjustment for confounders: model 1 (unadjusted for any covariates), model 2 (adjusted for sociodemographic characteristics), and model 3 (adjusted for all covariates). Continuous variable NHR was divided into four quartiles to create categorical variables and calculated p-values for trend. Subsequently, RCS regression was utilized to illustrate the nonlinear relationship. In addition, we executed interaction tests and subgroup analyses for various covariates. Finally, we used the ROC curve to determine AUC values to evaluate NHR as a predictor of hearing loss. The Youden index (sensitivity + specificity − 1) was used method to determine the best cutoff. This assessment illustrated the balance between sensitivity and specificity of the model at different NHR thresholds. Values of AUC close to 1 indicated a higher predictive accuracy of the model.

Results

Baseline characteristics

A total of 10,436 individuals were included, with outcomes categorized into the SFHL and HFHL groups (Additional file 1). Among them, 3,018 (28.9%) individuals were diagnosed with SFHL, while 5,227 (50.1%) were diagnosed with HFHL. Notably, there were no significant differences in smoking habits between patients with two types of hearing loss and those without. However, all other covariates exhibited significant differences between hearing loss and normal groups. Patients with SFHL or HFHL were generally older and tended to be male, non-Hispanic white, and widowed/divorced/separated. They were inclined to lower levels of education, lower household incomes, and a personal history of smoking and alcohol consumption. In terms of noise exposure, individuals with a history of it were more likely to experience HFHL. In particular, participants with both types of hearing loss exhibited higher neutrophil counts, while levels of HDL-C did not differ significantly between hearing loss and normal participants. Furthermore, participants with hearing loss generally demonstrated higher NHR, which was particularly significant among those with SFHL.

The relationship between NHR and hearing loss

The weighted multiple logistic regression indicated that NHR was associated with SFHL and HFHL (Table 1). In Model 3, NHR was positively correlated with both SFHL and HFHL, with ORs of 1.06 (95% CI: 1.01 to 1.11) for both, p = 0.017 and p = 0.021, respectively. The positive associations observed between the Q2 and Q3 of NHR and SFHL did not demonstrate statistical significance compared to the Q1 (Q2: OR = 1.02, 95% CI: 0.83 to 1.24; Q3: OR = 1.26, 95% CI: 0.99 to 1.61). However, the Q4 was significantly positively related to the risk of SFHL, with an OR of 1.48 (95% CI: 1.18 to 1.86). Regarding the HFHL, the positive correlations observed in the Q2 and Q3 of NHR did not reach statistical significance (Q2: OR = 1.00, 95% CI: 0.83 to 1.20; Q3: OR = 1.20, 95% CI: 0.99 to 1.45), and Q4 could be considered as marginally significant (Q4: OR = 1.21, 95% CI: 1.00 to 1.46, p = 0.054). Additionally, adjusted models consistently showed an increasing trend in NHR quartiles associated with SFHL and HFHL (p for trend < 0.001, p = 0.036, respectively).

Table 1 The associations of NHR with SFHL and HFHL in weighted logistic regression models
Full size table

Restricted cubic spline regression (RCS) analysis

We used the RCS model to investigate the nonlinear correlation between NHR and hearing loss (Fig. 2). RCS regression analysis did not detect a non-linear association, suggesting that the risk of hearing loss increases linearly with higher NHR levels within the observed range.

Fig. 2
figure 2

RCS analysis. (a) The nonlinear association between NHR and SFHL. (b) The nonlinear association between NHR and HFHL

Full size image

Subgroup analysis

Participants were categorized based on all covariates mentioned above. Subgroup analysis and interaction tests were consequently conducted. The results showed a marked correlation between NHR and hearing loss, in speech-frequency subgroups based on race (P = 0.016) (Table 2). Among HFHL, race and PIR exhibited statistically significant differences (P = 0.023 and P = 0.041) (Table 3). Other confounding factors did not show significant differences. This underscores the importance of accounting for these variables in future studies to enhance predictive accuracy.

Table 2 Subgroup analysis for the association between NHR and SFHL with different clinical characteristics
Full size table
Table 3 Subgroup analysis for the association between NHR and HFHL with different clinical characteristics
Full size table

Receiver operating characteristic (ROC) analysis

The ROC curve illustrated the predictive capability of NHR for SFHL (Fig. 3a) with an area under the curve (AUC) of 0.871. This indicated good predictive performance when NHR was combined with demographic and other clinical variables, consistent with previous weighted multivariate logistic regression results, suggesting a significant association between elevated NHR and SFHL occurrence. The ROC curve for NHR in predicting HFHL showed a higher AUC of 0.906 (Fig. 3b) which suggested that NHR combined with other variables was more effective in predicting the occurrence of HFHL. Additionally, analysis using NHR alone yielded AUC values of 0.533 for predicting SFHL and 0.521 for HFHL, with cutoff values of 2.934 and 2.475, respectively. (Additional file 2). In summary, the ROC curve analysis further validated NHR as a potential biomarker for assessing the risk of hearing loss.

Fig. 3
figure 3

ROC analysis. (a) ROC curve and diagnostic value of NHR in SFHL. (b) ROC curve and diagnostic value of NHR in HFHL

Full size image

Discussion

In this study, we conducted a range of statistical analyses of NHR as predictors of hearing loss. Weighted multiple logistic regression showed that an increased NHR was significantly related to SFHL and HFHL. Additionally, independent NHR levels demonstrated a negative impact on SFHL after adjusting for key confounding variables. As the NHR quartiles increased, the effect of NHR on HFHL showed marginal significance. RCS regression analysis showed no nonlinear correlation between NHR and hearing loss. Subgroup analyses indicated that factors like race and PIR influenced the correlation. ROC analysis illustrated the trade-off relationship between the model’s sensitivity and specificity across various classification thresholds.

Longitudinal cohort studies highlighted the impact of inflammation on hearing loss. Nash SD et al. measured CRP, IL-6, and hearing impairment at multiple time points over 22 years. They found that inflammatory markers at a single time point were not associated with increased hearing loss risk. However, adults under 60 with persistently elevated CRP levels had twice the risk of developing hearing impairment [13]. Another prospective study further demonstrated the association between CRP and hearing loss risk in older adults. Additionally, elevated WBCC appeared to have predictive value for hearing impairment in the elderly [12].

Cochlear hair cells in the inner ear play a crucial role in efficiently converting sound energy into nerve signals and transmitting them to the brain during the normal auditory perception process. However, once these cells are damaged, they cannot regenerate [25]. Inflammatory factors can impair the microcirculatory function of the inner ear through various mechanisms. Firstly, they may increase capillary permeability, resulting in the obstruction of nutrient transport to inner ear tissues, which can lead to hypoxia and malnutrition of hair cells and neurons [26]. Additionally, inflammatory factors such as IL-1β and TNF-α can exert direct effects on the vascular endothelial cells within the inner ear, leading to their dysfunction and promoting apoptosis of hair cells [27, 28]. The activation of the NLRP3 inflammasome pathway is associated with noise-induced cochlear damage. This activation results in the production of additional inflammatory cytokines which exacerbate the inflammatory response and contribute to the destruction of cochlear hair cells [29]. Furthermore, chronic inflammatory states can induce neuroinflammation along the central auditory pathway, thereby playing a role in the development of sensorineural hearing loss [30]. Chan J et al. found that a long-term high-fat diet can trigger a localized inflammatory response in the cochlea, characterized by increased expression of inflammatory markers and the recruitment of macrophages to the cochlea [31]. This suggests that changes in systemic metabolic and inflammatory status may indirectly influence hearing health by altering the microenvironment of the inner ear. Additionally, oxidative stress is also a significant mechanism contributing to hearing loss. Both inflammation and lipid metabolism disorders trigger excessive production of reactive oxygen species (ROS), leading to oxidative stress (OS) [32, 33]. OS can cause hair cell apoptosis and necrosis by destroying intracellular macromolecules and impairing mitochondrial function, ultimately leading to sensorineural hearing loss [9, 34]. In short, systemic inflammatory status plays a role in sensorineural hearing loss through various pathways and mechanisms. According to our results, participants with hearing loss exhibited elevated neutrophil counts. As an important cell type of the innate immune system, the increased number of neutrophils often indicates a rise in systemic inflammation levels and tissue injury [35, 36]. A study by Bae SH et al. found that neutrophils were recruited in the spiral ligament instead of the stria vascularis (SV) region and Corti organ in LPS-induced cochlear inflammation [37]. Previous studies have shown that HDL possesses anti-inflammatory effects [38, 39]. However, in this study, no significant difference in HDL levels was observed between participants with and without hearing loss. Therefore, single biochemical marker may not adequately represent the intricate influence of systemic metabolic and inflammation on hearing loss. Considering that the anti-inflammatory effect of HDL may influence neutrophils, NHR reflects the level of systemic inflammation more comprehensively than a single indicator [18]. From a physiological perspective, an increase in NHR may indicate that the body is experiencing chronic hyperinflammation [40]. As previously mentioned, inflammatory factors contribute to the decline in hearing function through multiple pathways.

Our data demonstrated a positive linear correlation between NHR and hearing loss. This suggests a focus not only on the nutritional supply to the inner ear’s neurovascular system but also on the levels of systemic inflammation when preventing and treating hearing loss. This finding offers significant reference value for the clinical identification of high-risk populations for hearing loss and the implementation of targeted intervention strategies. Subgroup analysis showed that the association between higher NHR and hearing loss was more pronounced in non-Hispanic White participants. This finding aligns with a large population-based epidemiological study reporting a lower prevalence of hearing loss among Black participants compared to Whites [41]. Such differences may be attributed to racial variations in baseline systemic inflammation, genetic susceptibility, environmental exposures, and access to healthcare. Mechanistically, cochlear melanin deposition has been suggested to play a protective role against hearing loss [42]. We also observed that low PIR, reflecting lower socioeconomic status, significantly strengthened the association between NHR and hearing loss. This may result from the combined effects of chronic stress, poor nutrition, limited access to healthcare, and higher environmental risk exposures commonly faced by socioeconomically disadvantaged populations. These factors could exacerbate systemic inflammation and amplify the biological impact of elevated NHR on cochlear and auditory function. Consequently, future research should investigate the role of sociodemographic characteristics in the pathogenesis of hearing impairment to inform the development of targeted intervention measures.

Using nationally representative NHANES data, we applied weighted multivariable logistic regression, RCS regression, and subgroup analyses to minimize the inherent limitations of cross-sectional studies and enhance the reliability of the observed associations while controlling for potential confounders. Although causality cannot be established, cross-sectional studies are well-suited for identifying strong associations and generating hypotheses for future research. Before the NHR can be recommended as a screening tool for hearing loss risk, further longitudinal studies are necessary to validate its predictive value over time and to assess its clinical utility in early detection and prevention strategies.

The study has the following limitations. On the one hand, the cross-sectional design employed did not allow for the determination of a causal relationship between the two factors, highlighting the need for longitudinal cohort studies to track the dynamic relationship between changes in inflammatory indicators and changes in hearing. On the other hand, the study focused exclusively on NHR as a comprehensive inflammation indicator and did not thoroughly evaluate other inflammatory cytokines, oxidative stress, and additional biomarkers, which could help elucidate the specific mechanisms of inflammation. Participants primarily consist of individuals from the U.S., resulting in a relatively concentrated distribution of race and socioeconomic status. Expanding the sample range could enhance the generalizability of the results. Finally, the study did not fully account for potential confounding factors, such as past medical history, medication use, and genetic predisposition, which may interfere with an accurate assessment of the relationship between inflammatory indicators and hearing loss through their potential impact on hearing. While this study contributes new evidence regarding the significant role of systemic inflammation in hearing health, the research design requires further optimization, and the observation indicators and population range should be expanded to better elucidate the intrinsic relationship.

Conclusion

This cross-sectional study showed a positive correlation between NHR and two types of hearing loss, including SFHL and HFHL. This association was consistently confirmed across diverse population characteristics. These findings underscore the potential of controlling systemic inflammation to reduce the risk of hearing damage. Moreover, they provide new evidence for exploring factors affecting hearing health from the perspectives of inflammation and metabolism, while also seeking potential biomarkers for the early identification and prevention of hearing loss.

Data availability

This study analyzes publicly available datasets. These data can be found here: National Health and Nutrition Examination Survey Database (https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm).

References

  1. World report on. Hearing: executive summary. Geneva: World Health Organization 2021. Licence. CC BY-NC-SA 3.0 IGO.

  2. Disease GBD, Injury I, Prevalence C. Global, regional, and National incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet Oct. 2016;8(10053):1545–602. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(16)31678-6.

    Article  Google Scholar 

  3. Global costs of unaddressed hearing loss and cost-effectiveness of interventions: a WHO report. 2017. Geneva: World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO.

  4. Michels TC, Duffy MT, Rogers DJ. Hearing loss in adults: differential diagnosis and treatment. Am Fam Physician Jul. 2019;15(2):98–108.

    Google Scholar 

  5. Hoffman HJ, Dobie RA, Losonczy KG, Themann CL, Flamme GA. Declining prevalence of hearing loss in US adults aged 20 to 69 years. JAMA Otolaryngol Head Neck Surg Mar. 2017;1(3):274–85. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamaoto.2016.3527.

    Article  Google Scholar 

  6. Tan HE, Lan NSR, Knuiman MW, et al. Associations between cardiovascular disease and its risk factors with hearing loss-A cross-sectional analysis. Clin Otolaryngol Feb. 2018;43(1):172–81. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/coa.12936.

    Article  CAS  Google Scholar 

  7. Garcia Morales EE, Ting J, Gross AL, et al. Association of cigarette smoking patterns over 30 years with audiometric hearing impairment and Speech-in-Noise perception: the atherosclerosis risk in communities study. JAMA Otolaryngol Head Neck Surg Mar. 2022;1(3):243–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamaoto.2021.3982.

    Article  Google Scholar 

  8. Kochhar A, Hildebrand MS, Smith RJ. Clinical aspects of hereditary hearing loss. Genet Med Jul. 2007;9(7):393–408. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/gim.0b013e3180980bd0.

    Article  Google Scholar 

  9. Paciello F, Pisani A, Rolesi R, et al. Oxidative stress and inflammation cause auditory system damage via glial cell activation and dysregulated expression of gap junction proteins in an experimental model of styrene-induced oto/neurotoxicity. J Neuroinflammation Jan. 2024;4(1):4. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12974-023-02996-3.

    Article  CAS  Google Scholar 

  10. Jamesdaniel S, Rosati R, Westrick J, Ruden DM. Chronic lead exposure induces cochlear oxidative stress and potentiates noise-induced hearing loss. Toxicol Lett Aug. 2018;292:175–80. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.toxlet.2018.05.004.

    Article  CAS  Google Scholar 

  11. Nguyen PTT, Song H, Kim B, et al. Age-related hearing loss was accelerated by apoptosis of spiral ganglion and stria vascularis cells in ApoE KO mice with hyperglycemia and hyperlipidemia. Front Neurol. 2022;13:1016654. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fneur.2022.1016654.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lassale C, Vullo P, Cadar D, Batty GD, Steptoe A, Zaninotto P. Association of inflammatory markers with hearing impairment: the english longitudinal study of ageing. Brain Behav Immun Jan. 2020;83:112–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.bbi.2019.09.020.

    Article  CAS  Google Scholar 

  13. Nash SD, Cruickshanks KJ, Zhan W, et al. Long-term assessment of systemic inflammation and the cumulative incidence of age-related hearing impairment in the epidemiology of hearing loss study. J Gerontol Biol Sci Med Sci Feb. 2014;69(2):207–14. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/gerona/glt075.

    Article  CAS  Google Scholar 

  14. Peng Z, Wu Q, Zhao CL, Gong SS. Lipid metabolism and hearing loss: association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with adolescent hearing health. Lipids Health Dis Oct. 2024;19(1):340. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02331-6.

    Article  CAS  Google Scholar 

  15. Korkmaz SA, Kizgin S. Neutrophil/high-density lipoprotein cholesterol (HDL), monocyte/hdl and platelet/hdl ratios are increased in acute mania as markers of inflammation, even after controlling for confounding factors. Curr Med Res Opin Oct. 2023;39(10):1383–90. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/03007995.2023.2260302.

    Article  CAS  Google Scholar 

  16. Wei Y, Gao H, Luo Y, et al. Systemic inflammation and oxidative stress markers in patients with unipolar and bipolar depression: A large-scale study. J Affect Disord Feb. 2024;1:346:154–66. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jad.2023.10.156.

    Article  CAS  Google Scholar 

  17. Huang JB, Chen YS, Ji HY, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids Health Dis Apr. 2020;4(1):59. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-020-01238-2.

    Article  CAS  Google Scholar 

  18. Chen T, Chen H, Xiao H, et al. Comparison of the value of neutrophil to High-Density lipoprotein cholesterol ratio and lymphocyte to High-Density lipoprotein cholesterol ratio for predicting metabolic syndrome among a population in the Southern Coast of China. Diabetes Metab Syndr Obes. 2020;13:597–605. https://doiorg.publicaciones.saludcastillayleon.es/10.2147/dmso.S238990.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shi K, Hou J, Zhang Q, Bi Y, Zeng X, Wang X. Neutrophil-to-high-density-lipoprotein-cholesterol ratio and mortality among patients with hepatocellular carcinoma. Front Nutr. 2023;10:1127913. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnut.2023.1127913.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang S, Luo J, Zhang F, et al. Association between blood volatile organic aromatic compound concentrations and hearing loss in US adults. BMC Public Health Feb. 2024;27(1):623. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-024-18065-0.

    Article  CAS  Google Scholar 

  21. National Health and Nutrition Examination Survey; AUDIOMETRY PROCEDURES MANUAL. 2003. doi:https://wwwn.cdc.gov/nchs/data/nhanes/public/2003/manuals/AU.pdf

  22. National Health and Nutrition Examination Survey (NHANES); Audiometry Procedures Manual. 2011. doi:https://wwwn.cdc.gov/nchs/data/nhanes/public/2011/manuals/Audiometry_Procedures_Manual.pdf

  23. Muscatell KA, Brosso SN, Humphreys KL. Socioeconomic status and inflammation: a meta-analysis. Mol Psychiatry Sep. 2020;25(9):2189–99. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41380-018-0259-2.

    Article  Google Scholar 

  24. Sousa-Santos AR, Afonso C, Santos A, et al. The association between 25(OH)D levels, frailty status and obesity indices in older adults. PLoS ONE. 2018;13(8):e0198650. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0198650.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cunningham LL, Tucci DL. Hearing loss in adults. N Engl J Med Dec. 2017;21(25):2465–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMra1616601.

    Article  Google Scholar 

  26. Scherer EQ, Yang J, Canis M, et al. Tumor necrosis factor-α enhances microvascular tone and reduces blood flow in the cochlea via enhanced sphingosine-1-phosphate signaling. Stroke Nov. 2010;41(11):2618–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/strokeaha.110.593327.

    Article  CAS  Google Scholar 

  27. Fuentes-Santamaria V, Alvarado JC, Melgar-Rojas P, Gabaldon-Ull MC, Miller JM, Juiz JM. The role of glia in the peripheral and central auditory system following noise overexposure: contribution of TNF-alpha and IL-1beta to the pathogenesis of hearing loss. Front Neuroanat. 2017;11:9. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnana.2017.00009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen D, Jia G, Zhang Y, et al. Sox2 overexpression alleviates noise-induced hearing loss by inhibiting inflammation-related hair cell apoptosis. J Neuroinflammation Feb. 2022;28(1):59. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12974-022-02414-0.

    Article  CAS  Google Scholar 

  29. Li P, Li S, Wang L, et al. Mitochondrial dysfunction in hearing loss: oxidative stress, autophagy and NLRP3 inflammasome. Front Cell Dev Biol. 2023;11:1119773. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fcell.2023.1119773.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Manohar S, Dahar K, Adler HJ, Dalian D, Salvi R. Noise-induced hearing loss: neuropathic pain via Ntrk1 signaling. Mol Cell Neurosci Sep. 2016;75:101–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.mcn.2016.07.005.

    Article  CAS  Google Scholar 

  31. Chan J, Telang R, Kociszewska D, Thorne PR, Vlajkovic SM. A High-Fat diet induces Low-Grade cochlear inflammation in CD-1 mice. Int J Mol Sci May. 2022;6(9). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms23095179.

  32. Li K, Deng Y, Deng G, et al. High cholesterol induces apoptosis and autophagy through the ROS-activated AKT/FOXO1 pathway in tendon-derived stem cells. Stem Cell Res Ther Mar. 2020;20(1):131. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13287-020-01643-5.

    Article  CAS  Google Scholar 

  33. Mittal M, Siddiqui MR, Tran K, Reddy SP, Malik AB. Reactive oxygen species in inflammation and tissue injury. Antioxid Redox Signal Mar. 2014;1(7):1126–67. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/ars.2012.5149.

    Article  CAS  Google Scholar 

  34. Tan WJT, Song L. Role of mitochondrial dysfunction and oxidative stress in sensorineural hearing loss. Hear Res Jul. 2023;434:108783. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.heares.2023.108783.

    Article  Google Scholar 

  35. Poposki JA, Klingler AI, Stevens WW, et al. Elevation of activated neutrophils in chronic rhinosinusitis with nasal polyps. J Allergy Clin Immunol May. 2022;149(5):1666–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jaci.2021.11.023.

    Article  CAS  Google Scholar 

  36. Kaiser R, Gold C, Joppich M, et al. Peripheral priming induces plastic transcriptomic and proteomic responses in Circulating neutrophils required for pathogen containment. Sci Adv Mar. 2024;22(12):eadl1710. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/sciadv.adl1710.

    Article  CAS  Google Scholar 

  37. Bae SH, Yoo JE, Choe YH, et al. Neutrophils infiltrate into the spiral ligament but not the stria vascularis in the cochlea during lipopolysaccharide-induced inflammation. Theranostics. 2021;11(6):2522–33. https://doiorg.publicaciones.saludcastillayleon.es/10.7150/thno.49121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Thacker SG, Zarzour A, Chen Y, et al. High-density lipoprotein reduces inflammation from cholesterol crystals by inhibiting inflammasome activation. Immunol Nov. 2016;149(3):306–19. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/imm.12638.

    Article  CAS  Google Scholar 

  39. Fotakis P, Kothari V, Thomas DG, et al. Anti-Inflammatory effects of HDL (High-Density Lipoprotein) in macrophages predominate over Proinflammatory effects in atherosclerotic plaques. Arterioscler Thromb Vasc Biol Dec. 2019;39(12):e253–72. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/ATVBAHA.119.313253.

    Article  CAS  Google Scholar 

  40. Sotiropoulos C, Giormezis N, Pertsas V, Tsirkas T. Biomarkers and data visualization of insulin resistance and metabolic syndrome: an applicable approach. Life (Basel) Sep. 2024;21(9). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/life14091197.

  41. Lin FR, Niparko JK, Ferrucci L. Hearing loss prevalence in the united States. Arch Intern Med Nov. 2011;14(20):1851–2. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/archinternmed.2011.506.

    Article  Google Scholar 

  42. Murillo-Cuesta S, Contreras J, Zurita E, et al. Melanin precursors prevent premature age-related and noise-induced hearing loss in albino mice. Pigment Cell Melanoma Res Feb. 2010;23(1):72–83. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1755-148X.2009.00646.x.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the workers who contributed to the collection of NHANES, making it possible for scientists and doctors worldwide to share these data.

Funding

This study was supported by the Postgraduate Research Innovation Project of Hunan Province (2024ZZTS0027), and the National Natural Science Foundation of China (Nos. 81974424).

Author information

Authors and Affiliations

  1. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China

    Yuxuan Yu, Zhe Shen, Yong Liu & Xin Zhang

  2. Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China

    Yuxuan Yu, Zhe Shen, Yong Liu & Xin Zhang

  3. Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China

    Yuxuan Yu, Zhe Shen, Yong Liu & Xin Zhang

  4. National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, 410008, Hunan, China

    Xin Zhang

Authors
  1. Yuxuan Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhe Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Y. Y. led the study’s conception, overall design, main data analysis, and paper writing. Z. S. contributed to data analysis and provided advice on paper revision. Y. L. checked and revised the manuscript. Z. X. made significant contributions to study initiation, supervision, project, management, manuscript writing, and final revision. The final manuscript was read and approved by all authors.

Corresponding author

Correspondence to Xin Zhang.

Ethics declarations

Ethics approval and consent to participate

Data for this study was collected from the NHANES database. Approval was obtained from the Research Ethics Review Board of the National Center for Health Statistics (NCHS). All participants provided written informed consent, ensuring compliance with relevant local laws and regulations to uphold the legality of the research.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Additional file 1

Additional file 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Shen, Z., Liu, Y. et al. Association between neutrophil to high-density lipoprotein cholesterol ratio and hearing loss: a cross-sectional study from NHANES. J Health Popul Nutr 44, 110 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00851-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00851-0

Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

Contact us