Diagnostic value of exhaled volatile organic compounds in pulmonary cystic fibrosis: A systematic review_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YU Xiaoping ¹ , SU Zhixia ² , YAN Kai ² , SHA Taining ² , HE Yuhang ³ , ZHANG Yanyan ⁴ , TAO Yujian ⁵ , GUO Hong ⁶ , LU Guangyu ² ,  GONG Weijuan ⁷

1. Department of Health Management Center, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China;
2. School of Public Health, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China;
3. School of Nursing, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China;
4. Testing Center of Yangzhou University, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China;
5. Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China;
6. Department of Thoracic Surgery, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China;
7. Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China;

Corresponding author：

GONG Weijuan, Email: wjgong@yzu.edu.cn

Keywords：

Cystic fibrosis; volatile organic compounds; exhaled breath; biomarker; systematic review

DOI：

10.7507/1007-4848.202406075

Video：

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Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST).Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high bias risk, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

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3.	Mall MA, Mayer-Hamblett N, Rowe SM. Cystic fibrosis: Emergence of highly effective targeted therapeutics and potential clinical implications. Am J Respir Crit Care Med, 2020, 201(10): 1193-1208.
4.	Stanford GE, Dave K, Simmonds NJ. Pulmonary exacerbations in adults with cystic fibrosis: A grown-up issue in a changing cystic fibrosis landscape. Chest, 2021, 159(1): 93-102.
5.	Ratjen F, Bell SC, Rowe SM, et al. Cystic fibrosis. Nat Rev Dis Primers, 2015, 1: 15010.
6.	Sanders DB, Fink AK. Background and epidemiology. Pediatr Clin North Am, 2016, 63(4): 567-584.
7.	囊性纤维化诊断与治疗中国专家共识编写组, 中国罕见病联盟呼吸病学分会, 中国支气管扩张症临床诊治与研究联盟. 囊性纤维化诊断与治疗中国专家共识(2023版). 中华结核和呼吸杂志, 2023, 46(4): 352-372.
8.	Guo X, Liu K, Liu Y, et al. Clinical and genetic characteristics of cystic fibrosis in CHINESE patients: A systemic review of reported cases. Orphanet J Rare Dis, 2018, 13(1): 224.
9.	Ozgenc A. The most common preanalytic problem of sweat testing: Insufficient sweat volume. Int J Med Biochem, 2020.
10.	Drabińska N, Flynn C, Ratcliffe N, et al. A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome. J Breath Res, 2021, 15(3): 33761469.
11.	Ratcliffe N, Wieczorek T, Drabińska N, et al. A mechanistic study and review of volatile products from peroxidation of unsaturated fatty acids: An aid to understanding the origins of volatile organic compounds from the human body. J Breath Res, 2020, 14(3): 034001.
12.	Barker M, Hengst M, Schmid J, et al. Volatile organic compounds in the exhaled breath of young patients with cystic fibrosis. Eur Respir J, 2006, 27(5): 929-936.
13.	Smith D, Sovová K, Dryahina K, et al. Breath concentration of acetic acid vapour is elevated in patients with cystic fibrosis. J Breath Res, 2016, 10(2): 021002.
14.	Gaisl T, Bregy L, Stebler N, et al. Real-time exhaled breath analysis in patients with cystic fibrosis and controls. J Breath Res, 2018, 12(3): 036013.
15.	Woollam M, Siegel AP, Grocki P, et al. Preliminary method for profiling volatile organic compounds in breath that correlate with pulmonary function and other clinical traits of subjects diagnosed with cystic fibrosis: A pilot study. J Breath Res, 2022, 16(2): 35120338.
16.	Bruderer T, Gaisl T, Gaugg MT, et al. On-line analysis of exhaled breath focus review. Chem Rev, 2019, 119(19): 10803-10828.
17.	McGrath LT, Patrick R, Mallon P, et al. Breath isoprene during acute respiratory exacerbation in cystic fibrosis. Eur Respir J, 2000, 16(6): 1065-1069.
18.	van Mastrigt E, Reyes-Reyes A, Brand K, et al. Exhaled breath profiling using broadband quantum cascade laser-based spectroscopy in healthy children and children with asthma and cystic fibrosis. J Breath Res, 2016, 10(2): 026003.
19.	Weber R, Perkins N, Bruderer T, et al. Identification of exhaled metabolites in children with cystic fibrosis. Metabolites, 2022, 12(10): 980.
20.	Robroeks CM, van Berkel JJ, Dallinga JW, et al. Metabolomics of volatile organic compounds in cystic fibrosis patients and controls. Pediatr Res, 2010, 68(1): 75-80.
21.	van Horck M, Smolinska A, Wesseling G, et al. Exhaled volatile organic compounds detect pulmonary exacerbations early in children with cystic fibrosis: Results of a 1 year observational pilot study. J Breath Res, 2021, 15(2): 026012.
22.	Mustafina M, Silantyev A, Krasovskiy S, et al. Exhaled breath analysis in adult patients with cystic fibrosis by real-time proton mass spectrometry. Clin Chim Acta, 2024, 560: 119733.
23.	Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010, 25(9): 603-605.
24.	Modesti PA, Reboldi G, Cappuccio FP, et al. Panethnic differences in blood pressure in Europe: A systematic review and meta-analysis. PLoS One, 2016, 11(1): e0147601.
25.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
26.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
27.	Petrocheilou A, Moudaki A, Kaditis AG. Inflammation and infection in cystic fibrosis: Update for the clinician. Children (Basel), 2022, 9(12): 1898.
28.	van Mastrigt E, de Jongste JC, Pijnenburg MW. The analysis of volatile organic compounds in exhaled breath and biomarkers in exhaled breath condensate in children-clinical tools or scientific toys? Clin Exp Allergy, 2015, 45(7): 1170-1188.
29.	Houston CJ, Alkhatib A, Einarsson GG, et al. Diminished airway host innate response in people with cystic fibrosis who experience frequent pulmonary exacerbations. Eur Respir J, 2024, 63(2): 2301228.
30.	Rang C, Keating D, Wilson J, et al. Re-imagining cystic fibrosis care: Next generation thinking. Eur Respir J, 2020, 55(5): 1902443.
31.	Stephenson AL, Stanojevic S, Sykes J, et al. The changing epidemiology and demography of cystic fibrosis. Presse Med, 2017, 46(6 Pt 2): e87-e95.
32.	Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.
33.	Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
34.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
35.	张秀秀, 王慧, 田双双, 等. 高维数据回归分析中基于LASSO的自变量选择. 中国卫生统计, 2013, 30(6): 922-926.
36.	谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型: 模型的建立. 中国循证心血管医学杂志, 2019, 11(1): 14-16,23.
37.	Fernandez-Felix BM, García-Esquinas E, Muriel A, et al. Bootstrap internal validation command for predictive logistic regression models. Stata J, 2021, 21(2): 498-509.
38.	Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
39.	Kothalawala DM, Kadalayil L, Weiss VBN, et al. Prediction models for childhood asthma: A systematic review. Pediatr Allergy Immunol, 2020, 31(6): 616-627.
40.	Gelin MF, Blokhin AP, Ostrozhenkova E, et al. Theory helps experiment to reveal VOCs in human breath. Spectrochim Acta A Mol Biomol Spectrosc, 2021, 258: 119785.
41.	Roe T, Silveira S, Luo Z, et al. Particles in exhaled air (PExA): Clinical uses and future implications. Diagnostics (Basel), 2024, 14(10): 972.
42.	Czippelová B, Nováková S, Šarlinová M, et al. Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS). J Breath Res, 2024, 18(3): 38701772.

1. Grasemann H, Ratjen F. Cystic fibrosis. N Engl J Med, 2023, 389(18): 1693-1707.
2. Bell SC, Mall MA, Gutierrez H, et al. The future of cystic fibrosis care: A global perspective. Lancet Respir Med, 2020, 8(1): 65-124.
3. Mall MA, Mayer-Hamblett N, Rowe SM. Cystic fibrosis: Emergence of highly effective targeted therapeutics and potential clinical implications. Am J Respir Crit Care Med, 2020, 201(10): 1193-1208.
4. Stanford GE, Dave K, Simmonds NJ. Pulmonary exacerbations in adults with cystic fibrosis: A grown-up issue in a changing cystic fibrosis landscape. Chest, 2021, 159(1): 93-102.
5. Ratjen F, Bell SC, Rowe SM, et al. Cystic fibrosis. Nat Rev Dis Primers, 2015, 1: 15010.
6. Sanders DB, Fink AK. Background and epidemiology. Pediatr Clin North Am, 2016, 63(4): 567-584.
7. 囊性纤维化诊断与治疗中国专家共识编写组, 中国罕见病联盟呼吸病学分会, 中国支气管扩张症临床诊治与研究联盟. 囊性纤维化诊断与治疗中国专家共识(2023版). 中华结核和呼吸杂志, 2023, 46(4): 352-372.
8. Guo X, Liu K, Liu Y, et al. Clinical and genetic characteristics of cystic fibrosis in CHINESE patients: A systemic review of reported cases. Orphanet J Rare Dis, 2018, 13(1): 224.
9. Ozgenc A. The most common preanalytic problem of sweat testing: Insufficient sweat volume. Int J Med Biochem, 2020.
10. Drabińska N, Flynn C, Ratcliffe N, et al. A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome. J Breath Res, 2021, 15(3): 33761469.
11. Ratcliffe N, Wieczorek T, Drabińska N, et al. A mechanistic study and review of volatile products from peroxidation of unsaturated fatty acids: An aid to understanding the origins of volatile organic compounds from the human body. J Breath Res, 2020, 14(3): 034001.
12. Barker M, Hengst M, Schmid J, et al. Volatile organic compounds in the exhaled breath of young patients with cystic fibrosis. Eur Respir J, 2006, 27(5): 929-936.
13. Smith D, Sovová K, Dryahina K, et al. Breath concentration of acetic acid vapour is elevated in patients with cystic fibrosis. J Breath Res, 2016, 10(2): 021002.
14. Gaisl T, Bregy L, Stebler N, et al. Real-time exhaled breath analysis in patients with cystic fibrosis and controls. J Breath Res, 2018, 12(3): 036013.
15. Woollam M, Siegel AP, Grocki P, et al. Preliminary method for profiling volatile organic compounds in breath that correlate with pulmonary function and other clinical traits of subjects diagnosed with cystic fibrosis: A pilot study. J Breath Res, 2022, 16(2): 35120338.
16. Bruderer T, Gaisl T, Gaugg MT, et al. On-line analysis of exhaled breath focus review. Chem Rev, 2019, 119(19): 10803-10828.
17. McGrath LT, Patrick R, Mallon P, et al. Breath isoprene during acute respiratory exacerbation in cystic fibrosis. Eur Respir J, 2000, 16(6): 1065-1069.
18. van Mastrigt E, Reyes-Reyes A, Brand K, et al. Exhaled breath profiling using broadband quantum cascade laser-based spectroscopy in healthy children and children with asthma and cystic fibrosis. J Breath Res, 2016, 10(2): 026003.
19. Weber R, Perkins N, Bruderer T, et al. Identification of exhaled metabolites in children with cystic fibrosis. Metabolites, 2022, 12(10): 980.
20. Robroeks CM, van Berkel JJ, Dallinga JW, et al. Metabolomics of volatile organic compounds in cystic fibrosis patients and controls. Pediatr Res, 2010, 68(1): 75-80.
21. van Horck M, Smolinska A, Wesseling G, et al. Exhaled volatile organic compounds detect pulmonary exacerbations early in children with cystic fibrosis: Results of a 1 year observational pilot study. J Breath Res, 2021, 15(2): 026012.
22. Mustafina M, Silantyev A, Krasovskiy S, et al. Exhaled breath analysis in adult patients with cystic fibrosis by real-time proton mass spectrometry. Clin Chim Acta, 2024, 560: 119733.
23. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010, 25(9): 603-605.
24. Modesti PA, Reboldi G, Cappuccio FP, et al. Panethnic differences in blood pressure in Europe: A systematic review and meta-analysis. PLoS One, 2016, 11(1): e0147601.
25. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
26. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
27. Petrocheilou A, Moudaki A, Kaditis AG. Inflammation and infection in cystic fibrosis: Update for the clinician. Children (Basel), 2022, 9(12): 1898.
28. van Mastrigt E, de Jongste JC, Pijnenburg MW. The analysis of volatile organic compounds in exhaled breath and biomarkers in exhaled breath condensate in children-clinical tools or scientific toys? Clin Exp Allergy, 2015, 45(7): 1170-1188.
29. Houston CJ, Alkhatib A, Einarsson GG, et al. Diminished airway host innate response in people with cystic fibrosis who experience frequent pulmonary exacerbations. Eur Respir J, 2024, 63(2): 2301228.
30. Rang C, Keating D, Wilson J, et al. Re-imagining cystic fibrosis care: Next generation thinking. Eur Respir J, 2020, 55(5): 1902443.
31. Stephenson AL, Stanojevic S, Sykes J, et al. The changing epidemiology and demography of cystic fibrosis. Presse Med, 2017, 46(6 Pt 2): e87-e95.
32. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.
33. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
34. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
35. 张秀秀, 王慧, 田双双, 等. 高维数据回归分析中基于LASSO的自变量选择. 中国卫生统计, 2013, 30(6): 922-926.
36. 谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型: 模型的建立. 中国循证心血管医学杂志, 2019, 11(1): 14-16,23.
37. Fernandez-Felix BM, García-Esquinas E, Muriel A, et al. Bootstrap internal validation command for predictive logistic regression models. Stata J, 2021, 21(2): 498-509.
38. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
39. Kothalawala DM, Kadalayil L, Weiss VBN, et al. Prediction models for childhood asthma: A systematic review. Pediatr Allergy Immunol, 2020, 31(6): 616-627.
40. Gelin MF, Blokhin AP, Ostrozhenkova E, et al. Theory helps experiment to reveal VOCs in human breath. Spectrochim Acta A Mol Biomol Spectrosc, 2021, 258: 119785.
41. Roe T, Silveira S, Luo Z, et al. Particles in exhaled air (PExA): Clinical uses and future implications. Diagnostics (Basel), 2024, 14(10): 972.
42. Czippelová B, Nováková S, Šarlinová M, et al. Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS). J Breath Res, 2024, 18(3): 38701772.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesDiagnostic value of exhaled volatile organic compounds in pulmonary cystic fibrosis: A systematic review

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