Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Download PDF
Download ePub

Systemic inflammation partially mediates the association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and chronic cough

Lipids in Health and Disease volume 24, Article number: 85 (2025) Cite this article

Abstract

Background

Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), a new biomarker, reflects blood lipid status. Nevertheless, the association between NHHR and chronic cough remains uncertain.

Method

This investigation included 9725 individuals from the NHANES. This research employed multiple statistical models to illustrate the association between NHHR and chronic cough. These models included logistic regression models, the Shapley Additive Explanations (SHAP) model, trend tests, mediation analysis, restricted cubic splines (RCS), and subgroup analyses.

Result

The logistic regression model, adjusting all covariables, showed a positive association between NHHR with chronic cough (OR: 1.08; 95% CI: 1.02–1.14). Trend tests and RCS further proved that NHHR and chronic cough had a linear and positive association. The mediation analysis proved that systemic immune inflammation index (SII) and systemic inflammatory response index (SIRI) partially mediated the association between NHHR and chronic cough. The SHAP model suggested that the top five important markers for predicting chronic cough were SII, smoking, NHHR, BMI, and SIRI.

Conclusion

This investigation discovered that NHHR was positively associated with chronic cough. Regular NHHR monitoring may serve as a potential tool for identifying individuals at higher risk of chronic cough.

Introduction

Chronic cough is a frequent presentation in respiratory clinics and often signifies underlying respiratory or non-respiratory conditions. While it was previously defined epidemiologically as a cough persisting for more than three months, current clinical guidelines classify it as a cough lasting longer than eight weeks as the primary symptom [1, 2]. A retrospective meta-analysis reported that the prevalence of chronic cough around the world has been estimated to vary from 2 to 18% in diverse geographical regions [3]. If left untreated and unresolved, prolonged coughing can trigger a variety of detrimental effects on both the physical and psychological well-being of individuals, ultimately diminishing quality of life [4,5,6]. Aside from that, chronic cough is often seen merely as a symptom rather than a standalone clinical condition, it can lessen the diagnosis precision. For this reason, individuals suffering from chronic cough are likely to experience an elevated economic burden, predominantly encompassing direct costs such as medical consultations, medications, diagnosis, and hospitalization, as well as indirect costs including lowered productivity and absenteeism [7,8,9].

Notwithstanding the paucity of clinical research exploring the link between dyslipidemia and chronic cough, multiple studies have delved into the sophisticated connection between lipid metabolism and inflammatory lung disease. The risk of respiratory conditions may be exacerbated by dyslipidemia through inflammatory responses and oxidative stress [10]. As previously illustrated, variations in lipid metabolism may contribute to the development and advancement of COPD [11]. A 10-year follow-up retrospective cohort study revealed that individuals with hyperlipidemia had a 1.48-fold increased risk of developing COPD compared to those without it [12]. An inverse link between asthma incidence and levels of serum cholesterol as well as non-high-density lipoprotein cholesterol (NHDL-C) was found after serum cholesterol measurements were analyzed in 7,005 participants by Michael et al. Conversely, no statistically significant association was observed between asthma and high-density lipoprotein cholesterol (HDL-C) levels [13]. Nonetheless, a bidirectional two-sample Mendelian randomization study arrived at an utterly dissimilar conclusion that the elevated LDL is casually bound up with cholesterol levels, lowered HDL levels and heightened risk of asthma [14].

The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), a novel composite index, serves as an effective measure of atherogenic lipid profiles. Previous studies have demonstrated that NHHR outperforms conventional lipid parameters in predicting the risk of cardiovascular disease, insulin resistance, and metabolic syndrome [15,16,17].

Given its established utility, investigating the relationship between NHHR and chronic cough may offer valuable insights into the interplay between lipid metabolism and lung diseases. Studies have highlighted the potential role of systemic inflammation and metabolic dysregulation in the pathogenesis of chronic cough [9, 18]. Inflammation plays a central role in the pathogenesis of chronic cough, including local airway inflammation, systemic inflammation, and inflammation-mediated cough hypersensitivity [19, 20]. Interestingly, earlier studies have reported an inverse association between NHHR levels and asthma prevalence, while a positive association has been observed with COPD risk. However, the potential link between NHHR and chronic cough remains underexplored. To address this gap, this study utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the association between NHHR and chronic cough.

Materials and methods

Study population

The purpose of NHANES is to provide policymakers with nutritional and medical information on American adults and children. It is freely available to the public and updated frequently. The data analyzed of the investigation originated from the 2007–2012 NHANES. Exclusion criteria included: [1] those missing chronic cough data; [2] people with either HDL-C or NHDL-C missing; and [3] people with missing covariables. In the end, 9725 individuals were assigned to this investigation. The analyzed populations’ screening process diagram was apparent in Fig. 1.

Fig. 1
figure 1

Screening process diagram for analyzed population

Full size image

Assessment of NHHR and chronic cough

The University of Minnesota received the prepared and preserved blood samples for examination. Blood HDL-C and total blood cholesterol concentrations were quantified using enzymatic techniques. NHHR = (Total cholesterol - HDL-C) / HDL-C. This study determined whether the subjects had a chronic or non-chronic cough using the standard questionnaire. A physician or other health care provider asked the participant, “Do you often experience a cough on most days for a duration of three continuous months or longer each year?” When the participant responded “yes,” the participant was determined to have a chronic cough.

Covariables

Some covariables were incorporated into the investigation to reduce the potential effect caused by confounding factors. This study included the following confounding factors: sex, race, age, smoking, alcohol, education, marriage, body mass index (BMI), hypertension, diabetes, cardiovascular disorder (CVD), asthma, emphysema, chronic bronchitis, cancer, systemic immune inflammation index (SII = platelet number * neutrophil number/lymphocyte number), and systemic inflammation response index (SIRI = monocyte number * neutrophil number/lymphocyte number) [21].

Statistical analysis

This investigation applied the chi-square test to acquire the P-value for categorical data and the Kruskal-Wallis rank-sum test to acquire the P-value for continuous data. For non-normally distributed continuous data, this investigation utilized the median and IQR, whereas for those with normal distribution, this investigation employed the mean and SE. Percentages were implemented to describe categorical data. This investigation initially adopted three logistic regression analyses and trend tests to assess the relationship between NHHR and chronic cough. Restrictive cubic splines (RCS), based on logistic regression models, were employed to better quantify the dose-response association between NHHR and chronic cough. Subsequently, this investigation implemented the mediation effect model to assess the contribution of systematic inflammation to the association between NHHR and chronic cough. Afterwards, this study used the SHAP model with XGBoost to determine the importance of each variable identifying associations with chronic cough. Ultimately, this research performed subgroup analyses and sensitivity analyses using data derived from multiple imputations. This survey applied “MICE” package to handle the lack of covariables (education, marriage, BMI, smoking, alcohol, hypertension, diabetes, CVD, asthma, chronic bronchitis, emphysema, cancer, SII, and SIRI) with multiple imputations, and the percentage of each missing covariables was below 10%. Version 4.4.1 of R was employed to conduct all statistical analyses. This study defined statistical significance as P-value < 0.05.

Results

Baseline characteristic

The basic characteristic of the studied populations were summed up in Table 1. The average age of 9725 persons in this study was 59.60 years, and 1065 of them had a chronic cough. In the comparison of people without chronic cough versus people with chronic cough, several variables exhibited statistically significant differences, such as race, age, education, marriage, smoking, hypertension, diabetes, CVD, asthma, emphysema, chronic bronchitis, cancer, SII, SIRI, and NHHR.

Table 1 Baseline characteristics of study populations
Full size table

Association between NHHR and chronic cough

This research also employed three logistic regressions to quantify the association between NHHR with chronic cough (Table 2). Higher NHHR was associated with increased odds of chronic cough, as demonstrated by Models A, B, and C. And Model C showed the odds of chronic cough increased by 8% for each additional unit of NHHR, adjusting for all covariates. The results of the trend test (Table 2) indicated that the NHHR group with the highest tertile (T3) had an increased likelihood of chronic cough compared to the NHHR group with the lowest tertile (T1). The P for trend = 0.0216 indicated statistical significance in the trend test and suggested a linear association between NHHR and chronic cough. Subsequently, this study further confirmed the linear association between NHHR and chronic cough using RCS (P-non-linear = 0.160) (Fig. 2). Mediation analysis proved that SII (1.62%) and SIRI (2.97%) partially mediated the association between NHHR and the probability of chronic cough (Figure S1).

Table 2 Association between NHHR and chronic cough
Full size table
Fig. 2
figure 2

Dose-response association of NHHR and chronic cough based on models A (A), B (B), and C (C). Model A adjusted for none. Model B adjusted for sex, age, and race. Model C = Model Y + adjusted for education, marriage, BMI, smoking, alcohol, hypertension, diabetes, CVD, asthma, chronic bronchitis, emphysema, cancer, SII, and SIRI

Full size image

SHAP Model

Moreover, this study also used SHAP based on the XGBoost model to evaluate the contribution and importance of each variable (sex, age, race, education, marriage, BMI, smoking, alcohol, hypertension, diabetes, CVD, asthma, chronic bronchitis, emphysema, cancer, SII, SIRI, and NHHR) in predicting chronic cough. The SHAP model indicated that the top five variables that contributed the most to predicting chronic cough were SII, smoking, NHHR, BMI, and SIRI (Fig. 3A). Among them, NHHR was positively associated with chronic cough (Fig. 3B).

Fig. 3
figure 3

The SHAP model based on XGBoost provided the relative importance of each variable on chronic cough

Full size image

Subgroup and sensitivity analysis

Subgroup analyses were carried out to discuss the association between NHHR with chronic cough in various groups (Table S1). White women over 60 years old, without asthma, chronic bronchitis, or emphysema, exhibited the positive association between elevated NHHR with probability of chronic cough. This investigation also conducted several sensitivity analyses to prove the dependability and accuracy of previous outcomes. After the multiple imputation of all missing covariables, the primary results obtained from the imputed data were predominantly consistent with previous outcomes (Table S2 and Figure S2).

Discussion

Novel insights into the associations between NHHR and the probability of chronic cough were provided by our study. Multiple statistical models were employed in this study, revealing that the probability of chronic cough increased as NHHR levels rose. Mediation analysis proved that SII and SIRI partially mediated the association between NHHR and chronic cough. As evidently suggested by the SHAP model, SII, smoking, NHHR, BMI, and SIRI were identified as the top five key predictors for the probability of chronic cough.

The notion that NHHR is a superior indicator of lipid-relevant disease risk is strongly supported by mushrooming evidence. Although rare studies have directly dug into the possible link between a chronic cough and lipid metabolism for the time being, multitudinous studies have suggested that problems with regard to lipid metabolism can worsen respiratory diseases like COPD, asthma, and community-acquired pneumonia (CAP) [22]. In general, respiratory disorders are featured by airflow limitation and chronic airway inflammation. What’s more, inflammation presents a profound association with disorders of lipid metabolism [23]. As apparently demonstrated by current theory, high levels of plasma free fatty acids (FFA) and LDL-C can transform into oxidized LDL, which damages lung tissues even more by causing oxidative stress and apoptosis for more damage [24]. And evidence suggests that oxidative stress may be a key factor in triggering the vicious cycle of chronic cough [25]. Airway wall thickening and airflow limitation can be precipitated by aberrant lipid accumulation within the airways, thereby exacerbating the risk of airway injury in parallel [26]. As displayed by a 5-year follow-up prospective cohort study, an augment of 100 mg in triglyceride levels revealed a certain link with a 42% elevation in mortality rates among COPD patients [27]. Another nationwide population study in Taiwan has demonstrated a 1.48-fold elevated risk of COPD in individuals suffering from hyperlipidaemia in comparison with those without hyperlipidaemia [12]. A potential association between asthma and dyslipidemia, especially with elevated triglyceride levels, is being pointed out by accumulating evidence. As clearly suggested by an exploratory study, dyslipidemia contributes to the pathogenesis of asthma. An independent association between elevated serum triglyceride levels and asthma in obese patients was reported accordingly [28]. It’ s noteworthy that a link between composite biomarkers, such as the monocyte-to-HDL cholesterol ratio and the triglyceride-glucose index, has been shown by cross-sectional studies [29, 30]. The problem of lipid indices not fully describing fat utilization in organisms and the asthma patient population has been addressed by these studies. More recent retrospective analyses have unveiled a positive link between elevated levels of triglycerides, total cholesterol, and LDL-C being associated with the development of asthma [31]. As indicated by an all-round review of the literature on sepsis, plasma cholesterol is a paramount factor in severe bacterial infections [32]. On top of that, within the field of sepsis research, evidence in an increasing amount underscores the vital importance of plasma cholesterol in severe bacterial infections. A modest-scale investigation exhibits a major finding that hypocholesterolemia is linked with augmented mortality from severe CAP [33].

Respiratory conditions, such as chronic bronchitis (a type of COPD), asthma, and pathogenic infections, are widely recognized as common causes of chronic cough [34]. While numerous studies have suggested a potential link between dyslipidemia and respiratory conditions, evidence remains limited. To date, only a small number of studies have directly explored the association between chronic cough and dyslipidemia. The study’s findings, which delved into the lipid profiles of individuals with chronic cough, indicated that no statistically significant differences were found in cholesterol, triglyceride, VLDL, and HDL values when the patient group and the control group were compared systematically [35]. Nevertheless, our exploration suggested that NHHR displayed positive connection with an augmented prevalence of chronic cough.

NHHR stands for a new marker of blood lipid status. As evidently illustrated by a multiplying number of studies, NHHR can independently throw light upon the risk of lipid-relevant diseases in adults, including Non-alcoholic fatty liver disease, kidney stones, diabetes, and suicidal thoughts [36,37,38,39]. It has been proven true even when accounting for errors in LDL and HDL levels. A negative association between NHHR and asthma incidence was demonstrated among American people, with gender influences being noted [40]. We also observed that DII-mediated, higher NHHR levels can bring about an augment in COPD prevalence [41]. It’s noteworthy that a linear positive association between NHHR and chronic cough was found in our investigation, which aligns with the conclusions drawn from their studies. As a result, NHHR serves as a valuable tool for predicting the likelihood of chronic cough. Altogether, excellent predictive efficacy has been demonstrated by the NHHR in a range of studies. Furthermore, distinguished by its non-invasive nature, ease of accessibility, and cost-effectiveness, the NHHR therefore has been extensively utilized, which adequately exhibits exceptional potential for clinical implementation.

Notwithstanding the fact that the link between NHHR and chronic cough through various statistical models, some limitations still exist in our survey. First and foremost, the diagnosis of chronic cough in NHANES is based on standard medical questionnaires rather than international diagnostic criteria, and some people lost questionnaire information, which may cause some bias. And due to database limitations, this survey did not include information on other potential chronic cough diseases, such as gastroesophageal reflux disease and interstitial lung disease. Secondly, we failed to prove causation as much attributable to the study’s cross-sectional approach. It’s exceedingly pivotal to carry out longitudinal studies, so as to confirm the causal link between NHHR and chronic cough. In addition, despite the adjustment for a wide range of covariables, the limitation inherent in NHANES data prevents the exclusion of potential confounders arising from unmeasured variables, such as dietary factors or occupational exposures. Last, certain drugs that affect chronic cough may have been excluded from the study population ascribed to the limitations of the NHANES database. Further studies incorporating clinical evaluations are needed to validate our results.

Conclusion

This study discovered that NHHR was positively associated with chronic cough. SII and SIRI partially mediated the association between NHHR and chronic cough. NHHR may be a valuable biomarker for predicting chronic cough. Regular NHHR monitoring may serve as a potential tool for identifying individuals at higher risk of chronic cough.

Data availability

The website (http://www.cdc.gov/nchs/nhanes/) contains all the data.

References

  1. Roe N, Lundy F, Litherland G, McGarvey L. Therapeutic targets for the treatment of chronic cough. Curr Otorhinolaryngol Rep. 2019;7:116–28.

    Article  Google Scholar 

  2. Morice AH, Millqvist E, Bieksiene K, Birring SS, Dicpinigaitis P, Ribas CD et al. ERS guidelines on the diagnosis and treatment of chronic cough in adults and children. Eur Respir J. 2020;55(1).

  3. Song WooJung SW, Chang YoonSeok CY, Faruqi S, Kim JuYoung KJ, Kang MinGyu KM et al. Kim SuJeong KS,. The global epidemiology of chronic cough in adults: a systematic review and meta-analysis. 2015.

  4. Alzaabi A, Idrees M, Behbehani N, Khaitov MR, Tunceli K, Urdaneta E, et al. editors. Cross-sectional study on asthma insights and management in the Gulf and Russia. Allergy and Asthma Proceedings; 2018: OceanSide Publications, Inc.

  5. Brien SB, Stuart B, Dickens AP, Kendrick T, Jordan RE, Adab P et al. Independent determinants of disease-related quality of life in COPD–scope for nonpharmacologic interventions? Int J Chronic Obstr Pulm Dis. 2018:247–56.

  6. Kubo T, Tobe K, Okuyama K, Kikuchi M, Chen Y, Schelfhout J, et al. Disease burden and quality of life of patients with chronic cough in Japan: a population-based cross-sectional survey. BMJ Open Respiratory Res. 2021;8(1):e000764.

    Article  Google Scholar 

  7. Mazzone SB, McGarvey L. Mechanisms and rationale for targeted therapies in refractory and unexplained chronic cough. Clin Pharmacol Ther. 2021;109(3):619–36.

    Article  PubMed  Google Scholar 

  8. Kyu HH, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and National disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1859–922.

    Article  Google Scholar 

  9. Song W-J, Chang Y-S, Faruqi S, Kim J-Y, Kang M-G, Kim S, et al. The global epidemiology of chronic cough in adults: a systematic review and meta-analysis. Eur Respir J. 2015;45(5):1479–81.

    Article  PubMed  Google Scholar 

  10. Isago H. The association between dyslipidemia and pulmonary diseases. J Atheroscler Thromb. 2024;31(9):1249–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chen H, Li Z, Dong L, Wu Y, Shen H, Chen Z. Lipid metabolism in chronic obstructive pulmonary disease. Int J Chronic Obstr Pulm Dis. 2019:1009–18.

  12. Yang H-Y, Hu L-Y, Chen H-J, Chen R-Y, Hu C-K, Shen C-C. Increased risk of chronic obstructive pulmonary disease in patients with hyperlipidemia: a nationwide population-based cohort study. Int J Environ Res Public Health. 2022;19(19):12331.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fessler MB, Massing MW, Spruell B, Jaramillo R, Draper DW, Madenspacher JH, et al. Novel relationship of serum cholesterol with asthma and wheeze in the united States. J Allergy Clin Immunol. 2009;124(5):967–74. e15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Liu YS, Lin YC, Lin MC, Wu CC, Wang TN. Association of blood lipid profiles and asthma: A bidirectional two-sample Mendelian randomization study. Ann Hum Genet. 2024;88(4):307–19.

    Article  CAS  PubMed  Google Scholar 

  15. Kim SW, Jee JH, Kim HJ, Jin S-M, Suh S, Bae JC, et al. Non-HDL-cholesterol/HDL-cholesterol is a better predictor of metabolic syndrome and insulin resistance than Apolipoprotein B/apolipoprotein A1. Int J Cardiol. 2013;168(3):2678–83.

    Article  PubMed  Google Scholar 

  16. Lin D, Qi Y, Huang C, Wu M, Wang C, Li F, et al. Associations of lipid parameters with insulin resistance and diabetes: a population-based study. Clin Nutr. 2018;37(4):1423–9.

    Article  CAS  PubMed  Google Scholar 

  17. Cui Y, Choi M. Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and angina pectoris in US adults: a cross-sectional retrospective study based on NHANES 2009–2018. Lipids Health Dis. 2024;23(1):347.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wood LG, Gibson PG. Dietary factors lead to innate immune activation in asthma. Pharmacol Ther. 2009;123(1):37–53.

    Article  CAS  PubMed  Google Scholar 

  19. Chung KF, Pavord ID. Prevalence, pathogenesis, and causes of chronic cough. Lancet (London England). 2008;371(9621):1364–74.

    Article  PubMed  Google Scholar 

  20. Bonvini SJ, Belvisi MG. Cough and airway disease: the role of ion channels. Pulm Pharmacol Ther. 2017;47:21–8.

    Article  CAS  PubMed  Google Scholar 

  21. Wang R-H, Wen W-X, Jiang Z-P, Du Z-P, Ma Z-H, Lu A-L, et al. The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage. Front Immunol. 2023;14:1115031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kilk K, Aug A, Ottas A, Soomets U, Altraja S, Altraja A. Phenotyping of chronic obstructive pulmonary disease based on the integration of metabolomes and clinical characteristics. Int J Mol Sci. 2018;19(3):666.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ardestani ME, Zaerin O. Role of serum Interleukin 6, albumin and C-reactive protein in COPD patients. Tanaffos. 2015;14(2):134.

    Google Scholar 

  24. Shen Y, Yang T, Guo S, Li X, Chen L, Wang T, et al. Increased serum ox-LDL levels correlated with lung function, inflammation, and oxidative stress in COPD. Mediat Inflamm. 2013;2013(1):972347.

    Google Scholar 

  25. Koskela HO, Purokivi MK. Airway oxidative stress in chronic cough. Cough (London England). 2013;9(1):26.

    PubMed  Google Scholar 

  26. Higham A, Quinn AM, Cançado JED, Singh D. The pathology of small airways disease in COPD: historical aspects and future directions. Respir Res. 2019;20:1–11.

    Article  Google Scholar 

  27. Tanni S, Zamuner A, Schelini K, Coelho L, Caram L, Vale S, et al. Triglycerides are associated with five-year mortality in COPD patients. Eur Respiratory Soc; 2012.

  28. van Zelst CM, de Boer GM, Turk Y, van Huisstede A et al. Veen JC, Birnie E, editors. Association between elevated serum triglycerides and asthma in patients with obesity: An explorative study. Allergy and asthma proceedings; 2021: OCEAN SIDE PUBLICATIONS INC.

  29. Liu X, Yan G, Xu B, Sun M. Association between monocyte-to-high-density lipoprotein-cholesterol ratio and gallstones in US adults: findings from the National health and nutrition examination survey 2017–2020. Lipids Health Dis. 2024;23(1):173.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wu TD, Fawzy A, Brigham E, McCormack MC, Rosas I, Villareal DT, et al. Association of triglyceride-glucose index and lung health: a population-based study. Chest. 2021;160(3):1026–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Staggers KA, Minard C, Byers M, Helmer DA, Wu TD. Metabolic dysfunction, triglyceride-glucose index, and risk of severe asthma exacerbation. J Allergy Clin Immunology: Pract. 2023;11(12):3700–5. e2.

    CAS  Google Scholar 

  32. Taylor R, Zhang C, George D, Kotecha S, Abdelghaffar M, Forster T et al. Low circulatory levels of total cholesterol, HDL-C and LDL-C are associated with death of patients with sepsis and critical illness: systematic review, meta-analysis, and perspective of observational studies. EBioMedicine. 2024;100.

  33. Chien Y-F, Chen C-Y, Hsu C-L, Chen K-Y, Yu C-J. Decreased serum level of lipoprotein cholesterol is a poor prognostic factor for patients with severe community-acquired pneumonia that required intensive care unit admission. J Crit Care. 2015;30(3):506–10.

    Article  CAS  PubMed  Google Scholar 

  34. Alhajjaj MS, Sankari A, Bajaj P, Chronic Cough. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Abdulghani Sankari declares no relevant financial relationships with ineligible companies. Disclosure: Puneet Bajaj declares no relevant financial relationships with ineligible companies.: StatPearls publishing copyright © 2024. StatPearls Publishing LLC.; 2024.

  35. Madole MB, Dilip B, Mamatha M, Ankur P. Study of serum lactate dehydrogenase and lipid profile in patients with chronic cough. Int J Clin Biochem Res. 2016;3:409–12.

    Google Scholar 

  36. Yu B, Li M, Yu Z, Zheng T, Feng X, Gao A, et al. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of all-cause and cardiovascular mortality in US adults with diabetes or prediabetes: NHANES 1999–2018. BMC Med. 2024;22(1):317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hong H, He Y, Gong Z, Feng J, Qu Y. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and kidney stones: a cross-sectional study. Lipids Health Dis. 2024;23(1):102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen T, Cheng Y, Song Z, Zhang G, Zeng T, Chao H. Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and kidney stone: evidence from NHANES 2007–2018. BMC Public Health. 2024;24(1):1818.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang Y, Li S, An Z, Li S. The correlation between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with non-alcoholic fatty liver disease: an analysis of the population-based NHANES (2017–2018). Front Med (Lausanne). 2024;11:1477820.

    Article  PubMed  Google Scholar 

  40. Ying B, Liu X, Yang C, Xu J, Chen Y. Gender-specific association between a lipid composite index and asthma among US adults: insights from a population-based study. Lipids Health Dis. 2024;23(1):353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wu R, Gong H. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and chronic obstructive pulmonary disease: the mediating role of dietary inflammatory index. Front Nutr. 2024;11:1427586.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This investigation applied NHANES data and received approval from the NCHS Research Ethics Review Board (Protocol #2005-06, #2011-17).

Funding

Qianxinan Prefecture Medical Science Research Joint Project (2024-32).

Author information

Author notes

  1. Changfen Wang, Xuecheng Liao and Jun Wen contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, People’s Hospital of Qianxinan Prefecture, Xingyi City, Guizhou Province, China

    Changfen Wang, Xuecheng Liao & Jiulin Chen

  2. Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China

    Jun Wen

  3. Department of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan Province, China

    Ying Lan

Authors
  1. Changfen Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xuecheng Liao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiulin Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ying Lan
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jun Wen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization: CFW, JW, XCL, YL; Data collection: CFW, XCL, JW; Statistical analysis: JW, CFW; Original draft: CFW, XCL, JLC, JW; Review & editing: JW, YL; Project administration: CFW, YL, JW.

Corresponding author

Correspondence to Jun Wen.

Ethics declarations

Conflict of interest

All authors have reported that they have no conflicts of interest to disclose.

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.

Supplementary Material 1

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

Wang, C., Liao, X., Chen, J. et al. Systemic inflammation partially mediates the association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and chronic cough. Lipids Health Dis 24, 85 (2025). https://doi.org/10.1186/s12944-025-02498-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-025-02498-6

Keywords

Lipids in Health and Disease

ISSN: 1476-511X