Abstract
Background:
The discovery of new metabolic markers may be helpful for early diagnosis of acute pediatric appendicitis (APA). However, no studies have been reported regarding identification of potential metabolic markers for the APA diagnosis by metabonomics.
Methods:
Serum samples of APA (n=32), non-appendicitis inflammation (NAI, n=32) and healthy children (HS, n=65) were analyzed by the 1H NMR-based metabonomics. A logistic regression model was established to screen the most efficient markers combinations for classification. Forty double-blind samples were further validated the model.
Results:
Nine blood metabolites that were different in the APA group from other groups were identified. To differentiate APA from HS, single variable of acetate, formate, white blood cell (WBC) and C-reactive protein (CRP) showed a high diagnostic value (area under the receiver operating characteristic [AUROC]<0.92), while they had a weak diagnostic value (AUROC<0.77) for identifying the APA and NAI. By contrast, the AUROC values of leucine (0.799) were higher than that of WBC and CRP. A combination of five variables, i.e. leucine, lactate, betaine, WBC and CRP, showed a high diagnostic value (AUROC=0.973) for the APA discriminating from the NAI, and the sensitivity and specificity were 93.8% and 93.7%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 85% for 40 unknown samples.
Conclusions:
The current study provides useful information in our understanding of the metabolic alterations associated with APA and indicates that measurement of these metabolites in serum effectively aids in the clinical identification of APA.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: The work was supported by a grant from the National Natural Science Foundation of China (grant nos. 81560631 and 81460118), The Distinguished Young Scholars Foundation of Jiangxi Province (grant no. 20162BCB23022) and the Innovation Fund Designated for Graduate Students of Nanchang University (grant no. cx2017271).
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Wu HP, Yang WC, Wu KH, Chen CY, Fu YC. Diagnosing appendicitis at different time points in children with right lower quadrant pain: comparison between pediatric appendicitis score and the alvarado score. World J Surg 2012;36:216–21.10.1007/s00268-011-1310-5Search in Google Scholar PubMed
2. van den Bogaard VA, Euser SM, van der Ploeg T, de Korte N, Sanders DG, de Winter D, et al. Diagnosing perforated appendicitis in pediatric patients: a new model. J Pediatr Surg 2016;51:444–8.10.1016/j.jpedsurg.2015.10.054Search in Google Scholar PubMed
3. Corwin MT, Chang M, Fananapazir G, Seibert A, Lamba R. Accuracy and radiation dose reduction of a limited abdominopelvic CT in the diagnosis of acute appendicitis. Abdom Imaging 2015;40:1177–82.10.1007/s00261-014-0280-0Search in Google Scholar PubMed
4. Kearl YL, Claudius I, Behar S, Cooper J, Dollbaum R, Hardasmalani M, et al. Accuracy of magnetic resonance imaging and ultrasound for appendicitis in diagnostic and nondiagnostic studies. Acad Emerg Med 2016;23:179–85.10.1111/acem.12873Search in Google Scholar PubMed
5. Augustin T, Cagir B, Vandermeer TJ. Characteristics of perforated appendicitis: effect of delay is confounded by age and gender. J Gastrointest Surg 2011;15:1223–31.10.1007/s11605-011-1486-xSearch in Google Scholar PubMed
6. Bickell NA, Aufses AH Jr, Rojas M, Bodian C. How time affects the risk of rupture in appendicitis. J Am Coll Surg 2006;202: 401–6.10.1016/j.jamcollsurg.2005.11.016Search in Google Scholar PubMed
7. Papandria D, Goldstein SD, Rhee D, Salazar JH, Arlikar J, Gorgy A, et al. Risk of perforation increases with delay in recognition and surgery for acute appendicitis. J Surg Res 2013;184:723–9.10.1016/j.jss.2012.12.008Search in Google Scholar PubMed PubMed Central
8. Weber TR, Keller MA, Bower RJ, Spinner G, Vierling K. Is delayed operative treatment worth the trouble with perforated appendicitis is children? Am J Surg 2003;186:685–9.10.1016/j.amjsurg.2003.08.027Search in Google Scholar PubMed
9. Bolandparvaz S, Vasei M, Owji AA, Ata-Ee N, Amin A, Daneshbod Y, et al. Urinary 5-hydroxy indole acetic acid as a test for early diagnosis of acute appendicitis. Clin Biochem 2004;37:985–9.10.1016/j.clinbiochem.2004.07.003Search in Google Scholar PubMed
10. Dalal I, Somekh E, Bilker-Reich A, Boaz M, Gorenstein A, Serour F. Serum and peritoneal inflammatory mediators in children with suspected acute appendicitis. Arch Surg 2005;140:169–73.10.1001/archsurg.140.2.169Search in Google Scholar PubMed
11. Groselj-Grenc M, Repse S, Vidmar D, Derganc M. Clinical and laboratory methods in diagnosis of acute appendicitis in children. Croat Med J 2007;48:353–61.Search in Google Scholar
12. Murphy CG, Glickman JN, Tomczak K, Wang YY, Beggs AH, Shannon MW, et al. Acute appendicitis is characterized by a uniform and highly selective pattern of inflammatory gene expression. Mucosal Immunol 2008;1:297–308.10.1038/mi.2008.13Search in Google Scholar PubMed PubMed Central
13. Schellekens DH, Hulsewe KW, van Acker BA, van Bijnen AA, de Jaegere TM, Sastrowijoto SH, et al. Evaluation of the diagnostic accuracy of plasma markers for early diagnosis in patients suspected for acute appendicitis. Acad Emerg Med 2013;20:703–10.10.1111/acem.12160Search in Google Scholar PubMed
14. Andersson RE. Meta-analysis of the clinical and laboratory diagnosis of appendicitis. Br J Surg 2004;91:28–37.10.1002/bjs.4464Search in Google Scholar PubMed
15. Garcia Pena BM, Cook EF, Mandl KD. Selective imaging strategies for the diagnosis of appendicitis in children. Pediatrics 2004;113:24–8.10.1542/peds.113.1.24Search in Google Scholar PubMed
16. Poortman P, Lohle PN, Schoemaker CM, Oostvogel HJ, Teepen HJ, Zwinderman KA, et al. Comparison of CT and sonography in the diagnosis of acute appendicitis: a blinded prospective study. AJR Am J Roentgenol 2003;181:1355–9.10.2214/ajr.181.5.1811355Search in Google Scholar PubMed
17. Bro R, Kamstrup-Nielsen MH, Engelsen SB, Savorani F, Rasmussen MA, Hansen L, et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 2015;11:1376–1380.10.1007/s11306-015-0793-8Search in Google Scholar PubMed PubMed Central
18. Fages A, Duarte-Salles T, Stepien M, Ferrari P, Fedirko V, Pontoizeau C, et al. Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort. BMC Med 2015; 13:242.10.1186/s12916-015-0462-9Search in Google Scholar PubMed PubMed Central
19. Kobayashi T, Nishiumi S, Ikeda A, Yoshie T, Sakai A, Matsubara A, et al. A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiol Biomarkers Prev 2013;22:571–9.10.1158/1055-9965.EPI-12-1033Search in Google Scholar PubMed
20. Ryoo I, Kwon H, Kim SC, Jung SC, Yeom JA, Shin HS, et al. Metabolomic analysis of percutaneous fine-needle aspiration specimens of thyroid nodules: potential application for the preoperative diagnosis of thyroid cancer. Sci Rep 2016; 6:30075.10.1038/srep30075Search in Google Scholar PubMed PubMed Central
21. Wojakowska A, Chekan M, Widlak P, Pietrowska M. Application of metabolomics in thyroid cancer research. Int J Endocrinol 2015;2015:258763.10.1155/2015/258763Search in Google Scholar PubMed PubMed Central
22. Xie B, Liu A, Zhan X, Ye X, Wei J. Alteration of gut bacteria and metabolomes after glucaro-1,4-lactone treatment contributes to the prevention of hypercholesterolemia. J Agric Food Chem 2014;62:7444-51.10.1021/jf501744dSearch in Google Scholar PubMed
23. Xie B, Gong T, Gao R, Liu J, Zuo J, Wang X, et al. Development of rat urinary HPLC-UV profiling for metabonomic study on Liuwei Dihuang Pills. J Pharm Biomed Anal 2009;49:492–7.10.1016/j.jpba.2008.10.022Search in Google Scholar PubMed
24. Yin P, Wan D, Zhao C, Chen J, Zhao X, Wang W, et al. A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Mol Biosyst 2009;5:868–76.10.1039/b820224aSearch in Google Scholar PubMed
25. Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, et al. Plasma metabonomics as a novel diagnostic approach for major depressive disorder. J Proteome Res 2012;11: 1741–1748.10.1021/pr2010082Search in Google Scholar PubMed
26. Mamtimin B, Xia G, Mijit M, Hizbulla M, Kurbantay N, You L, et al. Metabolic differentiation and classification of abnormal Savda Munziq’s pharmacodynamic role on rat models with different diseases by nuclear magnetic resonance-based metabonomics. Pharmacogn Mag 2015;11: 698–706.10.4103/0973-1296.165551Search in Google Scholar PubMed PubMed Central
27. Schisterman EF, Faraggi D, Reiser B, Trevisan M. Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error. Am J Epidemiol 2001;154:174–9.10.1093/aje/154.2.174Search in Google Scholar PubMed
28. He XY, Zhong J, Wang SW, Zhou YF, Wang L, Zhang YP, et al. Serum metabolomics differentiating pancreatic cancer from new-onset diabetes. Oncotarget 2017;8:29116–29124.10.18632/oncotarget.16249Search in Google Scholar PubMed PubMed Central
29. Itoi T, Sugimoto M, Umeda J, Sofuni A, Tsuchiya T, Tsuji S, et al. Serum metabolomic profiles for human pancreatic cancer discrimination. Int J Mol Sci 2017;18:767.10.3390/ijms18040767Search in Google Scholar PubMed PubMed Central
30. Playdon MC, Ziegler RG, Sampson JN, Stolzenberg-Solomon R, Thompson HJ, Irwin ML, et al. Nutritional metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr 2017;106:637–49.10.3945/ajcn.116.150912Search in Google Scholar PubMed PubMed Central
31. Kentsis A, Lin YY, Kurek K, Calicchio M, Wang YY, Monigatti F, et al. Discovery and validation of urine markers of acute pediatric appendicitis using high-accuracy mass spectrometry. Ann Emerg Med 2010;55:62–70 e4.10.1016/j.annemergmed.2009.04.020Search in Google Scholar PubMed PubMed Central
32. Capati A, Ijare OB, Bezabeh T. Diagnostic applications of nuclear magnetic resonance-based urinary metabolomics. Magn Reson Insights 2017;10:1178623X17694346.10.1177/1178623X17694346Search in Google Scholar
33. Demircan M, Cetin S, Uguralp S, Sezgin N, Karaman A, Gozukara EM. Plasma D-lactic acid level: a useful marker to distinguish perforated from acute simple appendicitis. Asian J Surg 2004;27:303–5.10.1016/S1015-9584(09)60056-7Search in Google Scholar
34. Xie B, Waters MJ, Schirra HJ. Investigating potential mechanisms of obesity by metabolomics. J Biomed Biotechnol 2012;2012:805683.10.1155/2012/805683Search in Google Scholar PubMed PubMed Central
35. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 2009;9:311–26.10.1016/j.cmet.2009.02.002Search in Google Scholar PubMed PubMed Central
36. Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen M, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 2013;36:648–55.10.2337/dc12-0895Search in Google Scholar PubMed PubMed Central
37. Zhai G, Wang-Sattler R, Hart DJ, Arden NK, Hakim AJ, Illig T, et al. Serum branched-chain amino acid to histidine ratio: a novel metabolomic biomarker of knee osteoarthritis. Ann Rheum Dis 2010;69:1227–31.10.1136/ard.2009.120857Search in Google Scholar PubMed
38. Craig A, Cloarec O, Holmes E, Nicholson JK, Lindon JC. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem 2006;78:2262–7.10.1021/ac0519312Search in Google Scholar PubMed
Supplemental Material:
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2017-0858).
©2018 Walter de Gruyter GmbH, Berlin/Boston
