Hidden noise in immunologic parameters might explain rapid progression in early-onset periodontitis
Autoři:
George Papantonopoulos aff001; Chryssa Delatola aff002; Keiso Takahashi aff003; Marja L. Laine aff002; Bruno G. Loos aff002
Působiště autorů:
Center for Research and Applications of Nonlinear Systems, Department of Mathematics, University of Patras, Patras, Greece
aff001; Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
aff002; Department of Conservative Dentistry, School of Dentistry, Ohu University, Fukushima, Fukushima, Japan
aff003
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224615
Souhrn
To investigate in datasets of immunologic parameters from early-onset and late-onset periodontitis patients (EOP and LOP), the existence of hidden random fluctuations (anomalies or noise), which may be the source for increased frequencies and longer periods of exacerbation, resulting in rapid progression in EOP. Principal component analysis (PCA) was applied on a dataset of 28 immunologic parameters and serum IgG titers against periodontal pathogens derived from 68 EOP and 43 LOP patients. After excluding the PCA parameters that explain the majority of variance in the datasets, i.e. the overall aberrant immune function, the remaining parameters of the residual subspace were analyzed by computing their sample entropy to detect possible anomalies. The performance of entropy anomaly detection was tested by using unsupervised clustering based on a log-likelihood distance yielding parameters with anomalies. An aggregate local outlier factor score (LOF) was used for a supervised classification of EOP and LOP. Entropy values on data for neutrophil chemotaxis, CD4, CD8, CD20 counts and serum IgG titer against Aggregatibacter actinomycetemcomitans indicated the existence of possible anomalies. Unsupervised clustering confirmed that the above parameters are possible sources of anomalies. LOF presented 94% sensitivity and 83% specificity in identifying EOP (87% sensitivity and 83% specificity in 10-fold cross-validation). Any generalization of the result should be performed with caution due to a relatively high false positive rate (17%). Random fluctuations in immunologic parameters from a sample of EOP and LOP patients were detected, suggesting that their existence may cause more frequently periods of disease activity, where the aberrant immune response in EOP patients result in the phenotype “rapid progression”.
Klíčová slova:
Cytokines – Entropy – Chemotaxis – Lymphocytes – Monocytes – Neutrophils – principal component analysis – Periodontitis
Zdroje
1. Loos BG, Papantonopoulos GH, Jespen S, Laine M. What is the contribution of genetics to periodontal risk? Dent Clin North Am 2015;59:761–780. doi: 10.1016/j.cden.2015.06.005 26427567
2. Loos BG, van Dyke TE. The role of inflammation and genetics in periodontal disease. Periodontol 2000, 2019, In press. https://www.researchgate.net/publication/333376521
3. Reynolds AM. Modifiable risk factors in periodontitis: at the intersection of aging and disease. Periodontol 2000 2014;64:7–19. doi: 10.1111/prd.12047 24320953
4. Nicolis G, Nicolis C. Foundations of complex systems: emergence, information and prediction, 2nd edition. Singapore: World Scientific, 2012: 1–26.
5. Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Aggressive periodontitis defined by recursive partitioning analysis of immunologic factors. J Periodontol 2013; 84:974–984. doi: 10.1902/jop.2012.120444 23003914
6. Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLOS ONE 2014;9(3): e89757. doi: 10.1371/journal.pone.0089757 24603408
7. Armitage GC. Development of a classification system for periodontal diseases and conditions. Ann Periodontol 1999;4:1–6. doi: 10.1902/annals.1999.4.1.1 10863370
8. Van der Velden U. Purpose and problems of periodontal disease classificationPeriodontol 2000 2005;39:13–21. doi: 10.1111/j.1600-0757.2005.00127.x 16135060
9. Fine DH, Patil AG, Loos BG. Classification and diagnosis of aggressive periodontitis. J Clin Periodontol 2018;45(Suppl20):S95–S111.
10. Schaefer AS, Richter GM, Nothnagel M, et al. A genome-wide association study identifies GLT6D1 as a susceptibility locus for periodontitis. Hum Mol Genet 2010;19(3):553–62. doi: 10.1093/hmg/ddp508 19897590
11. Kebschull M, Guarnieri P, Demmer RT, Boulesteix A-L, Pavlidis P, Papapanou PN. Molecular Differences between Chronic and Aggressive Periodontitis. J Dent Res 2013;92:1081–1088. doi: 10.1177/0022034513506011 24122488
12. Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J Clin Periodontol 2018;45(Suppl 20):S149–S161.
13. Papapanou PN, Sanz M, Buduneli N, et al. Periodontitis: Consensus report of Workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri‐Implant Diseases and Conditions. J Clin Periodontol 2018;45(Suppl 20):S162–S170.
14. Billings M, Holtfreter B, Papapanou PN, Mitnik GL, Kocher T, Dye BA. Age‐dependent distribution of periodontitis in two countries: findings from NHANES 2009‐2014 and SHIP‐TREND 2008‐2012. J Clin Periodontol 2018;45(Suppl 20):S130–S148.
15. Goodson JM, Tanner AC, Haffajee AD, Sornberger GC, Socransky SS. Patterns of progression and regression of advanced destructive periodontal disease. J Clin Periodontol 1982;9:472–481. doi: 10.1111/j.1600-051x.1982.tb02108.x 6960023
16. Raj A, van Oudenaarden A. Stochastic gene expression and its consequences Cell 2008;135:216–226. doi: 10.1016/j.cell.2008.09.050
17. Viney M, Reece SE. Adaptive noise. Proc R Soc B 2013;280:20131104. doi: 10.1098/rspb.2013.1104 23902900
18. Reinius B, Sandberg R. Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat Rev Genet 2015;16:653–664. doi: 10.1038/nrg3888 26442639
19. Tian T, Burrage K. Stochastic models for regulatory networks of the genetic toggle switch. Proc Nat Acad Sci 2006;103:8372–8377. doi: 10.1073/pnas.0507818103 16714385
20. Löe H, Ånerud A, Boysen H, Morrison E. Natural history of periodontal disease in man. Rapid, moderate and no loss of attachment in Sri Lankan laborers 14 to 46 years of age. J Clin Periodontol 1986;13:431–445. doi: 10.1111/j.1600-051x.1986.tb01487.x 3487557
21. Takahashi K, Ohyama H, Kitanaka M, et al. Heterogeneity of host immunologic risk factors in patients with aggressive periodontitis. J Periodontol 2001;72:425–437. doi: 10.1902/jop.2001.72.4.425 11338294
22. Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Comp Surv 2009;9:1–72.
23. Delatola C, Loos BG, Levin E, Laine ML. At least three phenotypes exist among periodontitis patients J Clin Periodontol 2017;44:1068–1076. doi: 10.1111/jcpe.12797 28800144
24. Sanchez A, Golding I. Genetic determinants and cellular constraints in noisy gene expression. Science 2013, 342(6163): 1188–1193. doi: 10.1126/science.1242975 24311680
25. Raghuraman S, Donkin I, Versteyhe S, Barres R, Simar D. The emerging role of epigenetics in inflammation and immunometabolism. Trends Endocrinol Metab 2016;27:782–795. doi: 10.1016/j.tem.2016.06.008 27444065
26. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157–1182.
27. Lamont RJ, Koo H, Hajishengallis G. The oral microbiota: dynamic communities and host interaction. Nat Microbiol 2018;16:745–759.
28. Garlet GP. Destructive and protective roles of cytokines in periodontitis: A re-appraisal from host defense and tissue destruction viewpoints. J Dent Res 2010;89:1349–1363. doi: 10.1177/0022034510376402 20739705
29. Åberg HC, Peyman K, Johansson A. Aggregatibacter Actinomycetemcomitans: Virulence of its leukotoxin and association with aggressive periodontitis. Virulence 2015;6:188–195. doi: 10.4161/21505594.2014.982428 25494963
30. Haubek D, Ennibi OK, Poulsen K, Vaeth M, Poulsen S, Kilian M. Risk of aggressive periodontitis in adolescent carriers of the JP2 clone of Aggregatibacter (Actinobacillus) actinomycetemcomitans in Morocco: a prospective longitudinal cohort study. Lancet 2008 19;371(9608):237–242. doi: 10.1016/S0140-6736(08)60135-X 18207019
31. Mombelli A, Casagni F, Madianos PN. Can presence or absence of periodontal pathogens distinguish between subjects with chronic and aggressive periodontitis? A systematic review. J Clin Periodontol 2002;29(Suppl 3):10–21.
32. Bartold PM, Van Dyke TE. An appraisal of the role of specific bacteria in the initial pathogenesis of periodontitis. J Clin Periodontol 2019;46:6–11.
33. Naginyte M, Do T, Meade J, Devine DA, Marsh PD. Enrichment of periodontal pathogens from the biofilms of healthy adults. Sci Rep 2019;9:5491. doi: 10.1038/s41598-019-41882-y 30940882
34. Futosi K, Fodor S, Mócsai A. Neutrophil cell surface receptors and their intercellular signal transduction pathways. Inter Immunopharm 2013;17:638–650.
35. Seymour JG, Berglundh T, Trombelli L. Pathogenesis of Periodontitis. In: Lang NP, Lindhe J, editors. Clinical Periodontology and Implant Dentistry, 6th edition. London; Wiley Blackwell, 2015: 256–312.
36. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316(22):2402–10. doi: 10.1001/jama.2016.17216 27898976
37. Weng SF, Vaz L, Qureshi N, Kai J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLOS ONE 2019;14(3): e0214365. doi: 10.1371/journal.pone.0214365 30917171
38. Parmar JD, Patel JD. Anomaly detection in data mining. IJARCCE 2017;7(4):32–40.
39. Goldstein M, Uscida S. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLOS ONE 2016;11:e0152173. doi: 10.1371/journal.pone.0152173 27093601
40. Lakhina A, Crovella M, Diot C. Diagnosing network-wide traffic anomalies. In: Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications. Portland; Oregon USA, 2004: 1–23.
41. Shih M-Y, Jheng J-W, Lai L-F. A two-step method for clustering mixed categorical and numerical data. Tamkang J Sci Eng 2010;13:11–19.
42. Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: Identifying Density-Based Local Outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. Dallas, Texas; ACM Press, 2000: 93–104.
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PLOS One
2019 Číslo 11
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