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Association between triglyceride-glucose index and fractional exhaled nitric oxide in adults with asthma from NHANES 2007–2012

Lipids in Health and Disease volume 23, Article number: 335 (2024) Cite this article

Abstract

Background

Several studies have shown a potential relationship between triglyceride-glucose index (TGI) and asthma. However, limited research has been conducted on the relationship between TGI and fractional exhaled nitric oxide (FeNO).

Methods

A total of 1,910 asthmatic individuals from the National Health and Nutrition Examination Survey (NHANES) database were included in this study. Linear regression analyses were used to investigate the relationship between TGI and FeNO in patients with asthma. Subsequently, a trend test was applied to verify whether there was a linear relationship between the TGI and FeNO. Finally, a subgroup analysis was performed to confirm the relationship among the different subgroup populations.

Results

Multivariable linear regression analyses showed that TGI was linearly related to FeNO in the asthmatic population. The trend test additionally validated the positive linear relationship between TGI and FeNO. The result of XGBoost revealed the five most influential factors on FeNO in a ranking of contrasted importance: eosinophil (EOS), body mass index (BMI), poverty-to-income ratio (PIR), TGI, and white blood cell count (WBC).

Conclusions

This investigation revealed a positive linear relationship between TGI and FeNO in patients with asthma. This finding suggests a potential relationship between TGI and airway inflammation in patients with asthma, thereby facilitating the prompt identification of irregularities and providing a basis for clinical decision making. This study provides a novel perspective on asthma management.

Introduction

Asthma is a prevalent health problem worldwide that affects individuals of all ages and has become the second most common cause of mortality in chronic airway disorders [1]. It is characterized by chronic inflammation with distinct symptoms, including shortness of breath, cough, chest distress, and/or wheezing. Global population surveys report that the current prevalence of asthma is approximately 11.5% [2]. Asthma caused 0.461 million deaths in 2019, resulting in an age-standardized mortality rate of 5.8 per 100,000 individuals. Despite a 51.3% decline in asthma incidence and death rates between 1990 and 2019, the total number of asthma cases continues to increase owing to population expansion [3, 4]. It is a highly prevalent chronic non-contagious disease associated with substantial morbidity, mortality, and loss of productivity [5].

Nitric oxide (NO) is widely acknowledged as a cellular messenger with a short half-life that contributes to several biological processes, including inflammation, immunity, cell survival, and apoptosis [6, 7]. NO plays a pathophysiological role in the complex environments of airways and lungs and induces airway hyperresponsiveness (AHR) by inflammatory mediation [8]. Under physiological conditions, NO weakly relaxes smooth muscles and protects against AHR [8]. During respiration, NO generates from the airway lining. IL-13 increases NO synthase levels in bronchial epithelial cells, a process associated with inflammation [9]. Accordingly, exhaled NO can be considered an indirect marker of elevated airway inflammation, indicative of exacerbation risk. Nowadays, fractional exhaled nitric oxide (FeNO) testing is a rising, convenient, available, and non-intrusive method. This useful tool evaluates asthmatic patients by objectively measuring cytokines, chemokines, and alarm signaling of ongoing type 2 airway inflammation, which are targets of standard anti-inflammatory therapies, such as omalizumab, dupilumab, and tezepelumab [10,11,12].

Inflammation and metabolism are extensively studied areas in lung disease research. Metabolic syndrome (MetS), characterized by insulin resistance (IR) and several comorbidities, has been linked to an increased incidence, prevalence, and severity of asthma [13]. Moreover, Peters et al. found that IR was associated with decreased lung function in patients with asthma [14]. TGI, an effective measure of evaluating IR [15], is associated with an increased risk of pulmonary disorders and has gradually become a biomarker for impaired lung function [16]. Zhou et al. discovered that asthmatic patients with critical illness who had a TGI greater than 4.8 had a higher likelihood of death, indicating that measuring the TGI could aid in assessing the risk and predicting the prognosis of extremely ill asthmatic patients [17].

Research on the relationship between TGI and FeNO remains scarce. Therefore, this study aimed to evaluate the relation between TGI and FeNO in asthmatic populations using NHANES data.

Materials and methods

Data source and study population

Every two years, the CDC conducts the NHANES to collect data on the nutritional and health conditions of the American population. Using a complicated and stratified approach, the NHANES carefully selects an accurately representative sample of non-institutionalized citizens. This study utilized data from 2007 to 2012, including three NHANES cycles. The following individuals were excluded: (1) those without asthma (2), those aged < 18 years (3), those with missing FeNO data, and (4) those with missing TGI data. Finally, the study included 1,910 people with asthma. The screening flowchart is shown in Fig. 1.

Fig. 1
figure 1

Flow chart for choosing populations for study

Full size image

Measurement of TGI and FeNO

TGI = Ln (fasting triglyceride [mg/dL] × fasting glucose [mg/dL]/2). Triglycerides and fasting glucose were quantified using enzymatic assays on a Roche Modular P and Roche/Hitachi Cobas C 501 chemical analyzer employing a hexokinase-mediated reaction, respectively. Aerocrine, a company based in Stockholm, Sweden, created a portable NIOXMINO device that utilizes an electrochemical sensor to quantify the FeNO level. We computed the average value after each participant completed two repeated FeNO tests. The average of two reproducible FeNO measurements was used for the analysis.

Covariables

The investigation incorporated covariables to reduce the potential influence of confounding factors. Confounders in the analysis were age, sex, race, PIR, education, marriage, BMI, smoking, alcohol intake, hypertension, diabetes, cardiovascular disease (CVD), and other chronic airway disease (CAD), glucocorticoids (GCS) usage (whether used within 1 month), inhaled corticosteroids (ICS) (whether used within 2 days), and lipid-lowering medications (whether within a month), serum cholesterol, WBC, NEU, EOS.

Statistical analysis

First, FeNO was classified into three tertiles. The weighted chi-square test was used to determine the P-value for categorical variables. For continuous variables, the Kruskal–Wallis rank-sum test was applied to calculate the P-value. We calculated the median and IQR for non-normally distributed continuous variables and the mean and standard error (SE) for normally distributed variables. Categorical variables were described using proportions. Moreover, in the asthmatic population, three linear regression models were used to ascertain the relationship between TGI and FeNO. Model X controlled for no covariables; Model Y controlled for sex, age, race, education, marriage, PIR, and BMI; Model Z controlled for sex, age, race, education, marriage, PIR, BMI, smoking, alcohol, hypertension, diabetes, CVD, history of other CAD, GCS use, ICS use, lipid-lowering drug use, cholesterol, WBC, NEU, and EOS. The multicollinearity of the variables was evaluated using variance inflation factor (VIF) values. A VIF greater than 10 indicates the presence of multicollinearity. Covariables were selected in the multivariable regression model to meet the following requirement: if adjusted in the model, the covariable would change the effect estimate by at least 10%. Trend tests were used to investigate whether the association between TGI and FeNO was linear. Subgroup analyses and interaction tests were performed to analyze the relationship between TGI and FeNO in various groups. Finally, the XGBoost model was applied to analyze the influence of various variables on FeNO. Multiple imputations (MICE package) were applied to address variables with missing values, and the overall percentage of missing values for each covariable was < 10%. The specific numbers of missing variables were as follows: other CAD (n = 149), marriage (n = 145), smoking (n = 144), PIR (n = 137), alcohol intake (n = 62), BMI (n = 14), NEU (n = 8), EOS (n = 8), WBC (n = 6), and education (n = 1). All missing covariables were randomly absent. Sample weights were implemented to accommodate the intricate sample design of the NHANES. The R program (version 4.3.1) was used to perform statistical analyses. Statistical significance was defined as a P-value of less than 0.05.

Results

Baseline characteristic

The study population had a mean age of 42.27 years, and most participants were white people (Table 1). This study found significant variations in the distribution of sex, race, education, PIR, smoking status, CVD, other CAD, ICS usage within 2 days, WBC, BNEU, and BEOS. However, the different FeNO tertile groups did not show any statistically significant variations in age, marital status, BMI, alcohol intake, hypertension, diabetes, GCS usage within a month, lipid-lowering drug within a month, cholesterol, or TGI.

Table 1 Baseline characteristics of investigation’s population
Full size table

Relationship between TGI and FENO

Multivariable linear regression analyses (models Y and Z) showed a positive relationship between TGI and FeNO in patients with asthma (Table 2). In Model Z, after controlling for all confounders, FeNO exhibited an increase of 2.32 ppb for each additional unit of TGI. The trend tests proved a linear relationship between the TGI and FeNO in models Y and Z.

Table 2 Association between TGI and FeNO in asthmatic populations
Full size table

Subgroup analysis

Subgroup analysis was performed to determine the relationship between the TGI and FeNO in various subgroup populations. Supplementary Table 1 presents the results grouped by sex, age, race, BMI, CVD, hypertension, diabetes, other CAD, usage of GCS within a month, ICS within 2 days, and lipid-lowering drug within a month. Subgroup analysis findings demonstrated that a statistically significant positive relationship between TGI and FeNO existed among female and white individuals over 40 years with a BMI≥ 30 and without CVD, diabetes, or other CAD, and without using lipid-lowering medications, GCS, or ICS. Additionally, we observed the absence of interaction effects in each subgroup analysis.

XGBoost model

The XGBoost model was applied to analyze the importance of the variables in FeNO. According to the XGBoost model, FeNO was mainly affected by five variables listed in the following decreasing order of significance: EOS, BMI, PIR, TGI, and WBC (Fig. 2).

Fig. 2
figure 2

The XGBoost model provided the relative importance of each variable on FeNO

Full size image

Discussion

This is the first cross-sectional study to quantitatively assess the relationship between TGI and FeNO in asthmatic patients. Multivariable regression analyses revealed a positive linear relationship between TGI and FeNO, which was revalidated using the trend test. We then performed subgroup analyses to find a positive linear association between TGI and FeNO among female and white individuals over 40 years of age with a BMI exceeding 30 and no CVD, diabetes, other CAD, lipid-lowering drugs, GCS, or ICS. The XGBoost results showed the five most influential factors on FeNO, in the following ranking of contrasted importance: EOS, BMI, PIR, TGI, and WBC.

NO is a short-lived signaling molecule with a straightforward structure that varies in function according to its manufacturing site, concentration, and interactions with other molecules and proteins. NO functions as a bronchodilator and an inhibitor of inflammatory cell signaling proteins. Conversely, NO plays a pathological role by acting as a pro-inflammatory mediator. When a large amount of NO is present in the oxidative environment of the asthmatic airway, it damages and swells the airway, creating toxic reactive nitrogen species (RNS) and breaking down proteins. This makes the airways more sensitive, a characteristic of asthma [8, 9]. Furthermore, NO concentrations in exhaled air in asthmatic individuals are three times higher than those in normal individuals [8]. Over time, FeNO has become an important tool for increasing the probability of asthma diagnosis. Recently, researchers have discovered that FeNO serves as a valuable indicator for identifying type-2 asthma, determining the risk of exacerbation, and predicting the effectiveness of ICS and biologics [11, 18, 19]. The American Thoracic Society suggests that individuals with asthma undergo FeNO testing along with regular treatment, and FeNO-guided management can evidently reduce the number of asthma flare-ups [20]. Price et al. found that FeNO levels above 50 ppb can indicate eosinophilic airway inflammation and the likelihood that corticosteroids will work [21]. Bacharier et al. found that FeNO is a clinically useful biomarker of asthma exacerbations and dupilumab responses in children with uncontrolled moderate-to-severe asthma [22]. In addition, FeNO serves as a practical and nonintrusive method for monitoring the frequency of asthma attacks and is one of the primary methods of contemporary asthma management [23].

Abnormalities in glucose metabolism such as IR and diabetes are associated with increased contractility and proliferation of airway smooth muscles, identified risk factors for asthma exacerbation [24]. TGI, a recently developed marker for assessing IR, offers a more feasible alternative to previous methods, such as the homeostasis model assessment of IR. Studies have demonstrated a strong relationship between elevated TGI levels and an increased risk of vascular diseases [25,26,27]. Wu et al. found that higher TGI levels were directly associated with respiratory symptoms, self-reported chronic bronchitis, and restrictive spirometry patterns, suggesting the effectiveness of TGI as an indicator in pulmonary health assessment [16, 28]. Another study found a positive linear relationship between TGI and EOS in an American asthmatic population [29]. Furthermore, elevated TGI levels may be valuable predictive markers of all-cause mortality in critically ill patients with asthma or COPD. Comprehensive studies have also supported the relatively independent role of TGI, regardless of traditional predictors, in identifying patients with asthma who require intensive treatment [17, 30]. Preliminary detection of TGI can prompt patients to seek more standardized diagnoses and treatments at higher-level hospitals. Thus, the TGI has implications for further FeNO detection.

Few studies have examined the TGI and FeNO. This is the first investigation to establish a link of TGI and FeNO in patients with asthma. The relationship between TGI and FeNO levels across different groups was analyzed using subgroup analyses, accounting for confounders. We employed the XGBoost model to evaluate the importance of distinct variables associated with FeNO. Compared with other models, XGBoost handles large datasets, accommodates nonlinear relationships more efficiently, and provides more accurate predictions. However, this study had some limitations. First, the cross-sectional design of the investigation resulted in an inability to determine a cause-and-effect relationship. Second, this study did not include data on the severity of asthma or sputum eosinophil counts because of database limitations. Furthermore, the survey did not clearly identify the acute attack or remission period of asthma when the individuals participated. This study identified individuals with asthma using a questionnaire instead of a bronchial provocation or relaxation test.

Conclusion

This investigation revealed a positive linear relationship between TGI and FeNO in patients with asthma. This finding suggests a potential relationship between TGI and airway inflammation in patients with asthma, thereby facilitating the prompt identification of irregularities and providing a basis for clinical decision making. This study provides a novel perspective on asthma management.

Availability of data and materials

No datasets were generated or analysed during the current study.

References

  1. Xu Q, Zhou Q, Chen J, Li T, Ma J, Du R, et al. The incidence of asthma attributable to temperature variability: an ecological study based on 1990–2019 GBD data. Sci Total Environ. 2023;904: 166726.

    Article  PubMed  CAS  Google Scholar 

  2. Patel KB, Mims JW, Clinger JD. The Burden of asthma and allergic rhinitis: epidemiology and health care costs. Otolaryngol Clin North Am. 2024;57(2):179–89.

    Article  PubMed  Google Scholar 

  3. Shin YH, Hwang J, Kwon R, Lee SW, Kim MS, Shin JI, et al. Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: a systematic analysis for the global burden of Disease Study 2019. Allergy. 2023;78(8):2232–54.

    Article  PubMed  CAS  Google Scholar 

  4. Cao Y, Chen S, Chen X, Zou W, Liu Z, Wu Y, et al. Global trends in the incidence and mortality of asthma from 1990 to 2019: an age-period-cohort analysis using the global burden of disease study 2019. Front Public Health. 2022;10: 1036674.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wang Z, Li Y, Gao Y, Fu Y, Lin J, Lei X, et al. Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: a systematic analysis for the global burden of Disease Study 2019. Respir Res. 2023;24(1):169.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Kiss H, Örlős Z, Gellért Á, Megyesfalvi Z, Mikáczó A, Sárközi A, et al. Exhaled biomarkers for point-of-care diagnosis: recent advances and new challenges in breathomics. Micromachines. 2023;14(2):391.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nathan C, Xie QW. Nitric oxide synthases: roles, tolls, and controls. Cell. 1994;78(6):915–8.

    Article  PubMed  CAS  Google Scholar 

  8. Ghosh S, Erzurum SC. Nitric oxide metabolism in asthma pathophysiology. Biochim Biophys Acta. 2011;1810(11):1008–16.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Guo FH, Comhair SA, Zheng S, Dweik RA, Eissa NT, Thomassen MJ, et al. Molecular mechanisms of increased nitric oxide (NO) in asthma: evidence for transcriptional and post-translational regulation of NO synthesis. J Immunol. 2000;164(11):5970–80 (Baltimore, Md: 1950).

    Article  PubMed  CAS  Google Scholar 

  10. Kolkhir P, Akdis CA, Akdis M, Bachert C, Bieber T, Canonica GW, et al. Type 2 chronic inflammatory diseases: targets, therapies and unmet needs. Nat Rev Drug Discovery. 2023;22(9):743–67.

    Article  PubMed  CAS  Google Scholar 

  11. Maniscalco M, Fuschillo S, Mormile I, Detoraki A, Sarnelli G, Paulis A, et al. Exhaled nitric oxide as Biomarker of Type 2 diseases. Cells. 2023;12(21):2518.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Celis-Preciado CA, Lachapelle P, Couillard S. Exhaled nitric oxide (FeNO): bridging a knowledge gap in asthma diagnosis and treatment. Clin Experimental Allergy: J Br Soc Allergy Clin Immunol. 2023;53(8):791–3.

    Article  Google Scholar 

  13. Serafino-Agrusa L, Spatafora M, Scichilone N. Asthma and metabolic syndrome: current knowledge and future perspectives. World J Clin Cases. 2015;3(3):285–92.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Peters MC, Schiebler ML, Cardet JC, Johansson MW, Sorkness R, DeBoer MD, et al. The impact of insulin resistance on loss of lung function and response to treatment in Asthma. Am J Respir Crit Care Med. 2022;206(9):1096–106.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC, et al. Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: a systematic review. Int J Endocrinol. 2020;2020:4678526.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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  PubMed  PubMed Central  CAS  Google Scholar 

  17. Zhou WQ, Song X, Dong WH, Chen Z. Independent effect of the triglyceride-glucose index on all-cause mortality in critically ill patients with chronic obstructive pulmonary disease and asthma: a retrospective cohort study. Chronic Resp Dis. 2024;21:14799731241245424.

    Article  Google Scholar 

  18. Escamilla-Gil JM, Fernandez-Nieto M, Acevedo N. Understanding the Cellular sources of the Fractional exhaled nitric oxide (FeNO) and its role as a biomarker of type 2 inflammation in asthma. Biomed Res Int. 2022;2022:5753524.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Guida G, Carriero V, Bertolini F, Pizzimenti S, Heffler E, Paoletti G, et al. Exhaled nitric oxide in asthma: from diagnosis to management. Curr Opin Allergy Clin Immunol. 2023;23(1):29–35.

    Article  PubMed  CAS  Google Scholar 

  20. Khatri SB, Iaccarino JM, Barochia A, Soghier I, Akuthota P, Brady A, et al. Use of Fractional exhaled nitric oxide to Guide the treatment of Asthma: an official American thoracic Society Clinical Practice Guideline. Am J Respir Crit Care Med. 2021;204(10):e97-109.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Price DB, Buhl R, Chan A, Freeman D, Gardener E, Godley C, et al. Fractional exhaled nitric oxide as a predictor of response to inhaled corticosteroids in patients with non-specific respiratory symptoms and insignificant bronchodilator reversibility: a randomised controlled trial. Lancet Respiratory Med. 2018;6(1):29–39.

    Article  CAS  Google Scholar 

  22. Bacharier LB, Pavord ID, Maspero JF, Jackson DJ, Fiocchi AG, Mao X, et al. Blood eosinophils and fractional exhaled nitric oxide are prognostic and predictive biomarkers in childhood asthma. J Allergy Clin Immunol. 2024;154(1):101–10.

    Article  PubMed  CAS  Google Scholar 

  23. Wang K, Verbakel JY, Oke J, Fleming-Nouri A, Brewin J, Roberts N, et al. Using fractional exhaled nitric oxide to guide step-down treatment decisions in patients with asthma: a systematic review and individual patient data meta-analysis. Eur Respir J. 2020;55(5):1902150. https://doi.org/10.1183/13993003.02150-2019.

  24. Wu TD. Diabetes, insulin resistance, and asthma: a review of potential links. Curr Opin Pulm Med. 2021;27(1):29–36.

    Article  PubMed  CAS  Google Scholar 

  25. Tahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, et al. Challenges in the diagnosis of insulin resistance: focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metabolic Syndrome. 2022;16(8):102581.

    Article  PubMed  CAS  Google Scholar 

  26. Alizargar J, Bai CH, Hsieh NC, Wu SV. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. 2020;19(1):8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Liu D, Yang K, Gu H, Li Z, Wang Y, Wang Y. Predictive effect of triglyceride-glucose index on clinical events in patients with acute ischemic stroke and type 2 diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):280.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Chen XX, Rastogi D. Triglyceride-glucose index: a potential new biomarker for lung disease associated with metabolic dysregulation. Chest. 2021;160(3):801–2.

    Article  PubMed  Google Scholar 

  29. Wen J, Liao J, Wei C, Xia J, Giri M, Guo S. Relationship between triglyceride-glucose index and blood eosinophils among asthmatic individuals in the USA. Lipids Health Dis. 2024;23(1):149.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Staggers KA, Minard C, Byers M, Helmer DA, Wu TD. Metabolic dysfunction, triglyceride-glucose index, and risk of severe asthma exacerbation. J Allergy Clin Immunol Pract. 2023;11(12):3700-e52.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The National Center for Health Statistics (NCHS) authorized the NHANES database. The participants provided informed consent before implementing the data collection protocols and comprehensive health assessments. This study used the NHANES data and received approval from the NAHNES Institutional Review Board/NCHS Research Ethics Review Board (Protocol #2006-07, Protocol #2011-17).

Funding

This research was supported by Quanzhou Medical College research project (No. XYL2207).

Author information

Author notes

  1. Yao Pan, Lizhen Wu, Shiyi Yao and Jing Xia contributed equally to this work.

Authors and Affiliations

  1. Department of Respiratory and Critical Care Medicine, Jinjiang Municipal Hospital, Quanzhou, China

    Sanmei Zhuang

  2. Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China

    Yao Pan

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

    Shiyi Yao, Mohan Giri & Jun Wen

  4. Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China

    Lizhen Wu

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

    Jing Xia

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Contributions

Conceptualization: YP, LZW, JW, SYY, JX Data collection: YP, LZW Statistical analysis: YP, JW, JX Original draft: YP, LZW, SYY, JX Review & editing: JW, MG, SMZ Project administration: SMZ.

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Correspondence to Jun Wen or Sanmei Zhuang.

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Pan, Y., Wu, L., Yao, S. et al. Association between triglyceride-glucose index and fractional exhaled nitric oxide in adults with asthma from NHANES 2007–2012. Lipids Health Dis 23, 335 (2024). https://doi.org/10.1186/s12944-024-02323-6

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X