Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics
Autoři:
Ana Colás aff001; Luis Vigil aff002; Borja Vargas aff002; David CuestaFrau aff003; Manuel Varela aff002
Působiště autorů:
Department of Internal Medicine, Hospital 12 de Octubre, Madrid, Spain
aff001; Department of Internal Medicine, Hospital Universitario de Móstoles, Móstoles, Madrid, Spain
aff002; Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, Alcoi, Spain
aff003
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0225817
Souhrn
Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24-hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two “pre-diabetic behaviours” (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.
Klíčová slova:
Principal component analysis – Algorithms – Glucose – Ellipses – Interpolation – Glucose tolerance – White noise
Zdroje
1. Goldstein B, Fiser D, Kelly M, Mickelsen D, Ruttimann U, Pollack M. Decomplexification in critical illness and injury: Relationship between heart rate variability, severity of illness, and outcome. Crit Care Med. 1998;26(2):352–357. doi: 10.1097/00003246-199802000-00040 9468175
2. Churruca J, Vigil L, Luna E, Ruiz-Galiana J, Varela M. The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes, metabolic syndrome and obesity: Targets and therapy. 2008;1:3–11. doi: 10.2147/DMSO.S3812
3. Vikman S, Mäkikallio TH, Yli-Mäyry S, Pikkujämsä S, Koivisto AM, Reinikainen P, et al. Altered Complexity and Correlation Properties of RR Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation. Circulation. 1999;100(20):2079–2084. doi: 10.1161/01.cir.100.20.2079 10562264
4. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: A systematic analysis for the Global Burden of Disease Study 2015. The Lancet. 2016;388(10053):1459–1544. https://doi.org/10.1016/S0140-6736(16)31012-1
5. Saudek C, Derr R, Kalyani R. Assessing glycemia in diabetes using self-monitoring blood glucose and hemoglobin a1c. JAMA. 2006;295(14):1688–1697. doi: 10.1001/jama.295.14.1688 16609091
6. Monnier L, Colette C, Owens DR. Glycemic variability: The third component of the dysglycemia in diabetes. Is it important? How to measure it? Journal of diabetes science and technology. 2008;2 6:1094–100. doi: 10.1177/193229680800200618 19885298
7. Abdul-Ghani M, Tripathy D, DeFronzo R. Contributions of Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care. 2006;5(29):1130–1139. doi: 10.2337/dc05-2179
8. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2018. Diabetes Care. 2018;41(Supplement 1):S13–S27. doi: 10.2337/dc18-S002 29222373
9. Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. Prediabetes: a high-risk state for diabetes development. The Lancet. 2012;379(9833):2279—2290. https://doi.org/10.1016/S0140-6736(12)60283-9
10. DeFronzo RA, Banerji MA, Bray GA, Buchanan TA, Clement S, Henry RR, et al. Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia. 2010;53(3):435–445. doi: 10.1007/s00125-009-1614-2 20012012
11. Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, et al. Impaired Fasting Glucose and Impaired Glucose Tolerance. Diabetes Care. 2007;30(3):753–759. doi: 10.2337/dc07-9920 17327355
12. Ogata H, Tokuyama K, Nagasaka S, Tsuchita T, Kusaka I, Ishibashi S, et al. The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism. 2012;61(7):1041—1050. https://doi.org/10.1016/j.metabol.2011.12.007 22304838
13. Costa M, Henriques T, Munshi MN, Segal A, Goldberger A. Dynamical glucometry: Use of multiscale entropy analysis in diabetes. 2014;24:033139.
14. Kohnert KD, Heinke P, Vogt L, Salzsieder E. Utility of different glycemic control metrics for optimizing management of diabetes. World journal of diabetes. 2015;6(1):17—29. doi: 10.4239/wjd.v6.i1.17 25685275
15. García Maset L, Blasco González L, Llop Furquet G, Montes F, Hernandez Marco R. Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes. Diabetes Technology and Therapeutics. 2016;18(11). doi: 10.1089/dia.2016.0208 27728773
16. Monnier L, Colette C, Owens DR. The application of simple metrics in the assessment of glycaemic variability. Diabetes and Metabolism,. 2018; https://doi.org/10.1016/j.diabet.2018.02.008
17. Service FJ, O’Brien PC, Rizza RA. Measurements of Glucose Control. Diabetes Care. 1987;10(2):225–237. doi: 10.2337/diacare.10.2.225 3582083
18. Mcdonnell C, Donath S, Vidmar SI, Werther G, Cameron FJ. A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic Variation. 2005;7:253–63.
19. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PC, Peng CK, Stanley HE. Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences. 2002;99(suppl 1):2466–2472. doi: 10.1073/pnas.012579499
20. Crenier L, Lytrivi M, Van Dalem A, Keymeulen B, Corvilain B. Glucose Complexity Estimates Insulin Resistance in Either Non Diabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology and Metabolism. 2016;101. doi: 10.1210/jc.2015-4035 26859105
21. Rodriguez de Castro C, Vigil L, Vargas B, Garcia Delgado E, Garcia-Carretero R, Ruiz-Galiana J, et al. Glucose time series complexity as a predictor of type 2 Diabetes. Diabetes Metab Res Rev. 2017;30(2). doi: 10.1002/dmrr.2831
22. Wever C, Schnell O. The assessment of glycemic variability and its impact on diabetes–related complications: An overview. Diabetes technology and therapeutics. 2009;11(10):623—633. doi: 10.1089/dia.2009.0043
23. Pincus S, Gladstone I, Ehrenkranz R. A regularity statistic for medical data analysis. J of Clin Monit and Comput. 1991;7(4):335–345. doi: 10.1007/BF01619355
24. Richman JS. Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics—Theory and Methods. 2007;36(5):1005–1019. doi: 10.1080/03610920601036481
25. Platiša MM, Bojić T, Pavlović SU, Radovanović NN, Kalauzi A. Generalized Poincaré Plots-A New Method for Evaluation of Regimes in Cardiac Neural Control in Atrial Fibrillation and Healthy Subjects. Frontiers in Neuroscience. 2016;10:38. doi: 10.3389/fnins.2016.00038 26909018
26. García-Puig J, Ruilope L, Luque M, Fernández J, Ortega R, Dal-Ré R, et al. Glucose metabolism in patients with essential hypertension. Am J Med. 2006;119(4):318–326. doi: 10.1016/j.amjmed.2005.09.010 16564774
27. Lepot M, Aubin JB, Clemens FHLR. Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water. 2017;9(10). doi: 10.3390/w9100796
28. Eke A, Hermán P, Bassingthwaighte JB, Raymond GM, Percival DB, Cannon M, et al. Physiological time series: distinguishing fractal noises from motions. Pflugers Arch. 2000;439:403–415. doi: 10.1007/s004249900135 10678736
29. Eke A, Herman P, Kocsis L, Kozak LR. Fractal characterization of complexity in temporal physiological signals. Physiol Meas. 2002;23:R1–R38. doi: 10.1088/0967-3334/23/1/201 11876246
30. Sedgwick P. Cox proportional hazards regression. BMJ. 2013;347.
31. King AB, Philis-Tsimikas A, Kilpatrick ES, Langbakke IH, Begtrup K, Vilsbøll T. A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges. Diabetes Technology & Therapeutics. 2017;19(4):255–264. doi: 10.1089/dia.2016.0405
32. Colas A, Vigil L, Rodríguez de Castro C, Vargas B, Varela M. New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews. 2018;0(0):e3002. doi: 10.1002/dmrr.3002
33. Henriques T, Munshi MN, Segal AR, Costa MD, Goldberger AL. “Glucose-at-a-Glance”: New Method to Visualize the Dynamics of Continuous Glucose Monitoring Data. Journal of Diabetes Science and Technology. 2014;8(2):299–306. doi: 10.1177/1932296814524095 24876582
34. Hinton PR. 22: Complex Analysis. In: Statistics Explained. Routledge; 2004. p. 306.
35. Cauter EV, Blackman JD, Roland D, Spire JP, Refetoff S, Polonsky KS. Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. The Journal of Clinical Investigation. 1991;88(3):934–942. doi: 10.1172/JCI115396 1885778
36. Qian J, Scheer FAJL. Circadian System and Glucose Metabolism: Implications for Physiology and Disease. Trends in Endocrinology and Metabolism. 2016;27(5):282—293. https://doi.org/10.1016/j.tem.2016.03.005 27079518
37. Hwa RC, Ferrée TC. Scaling properties of fluctuations in the human electroencephalogram. Physical review E, Statistical, nonlinear, and soft matter physics. 2002;66 2 Pt 1:021901. doi: 10.1103/PhysRevE.66.021901 12241208
Článok vyšiel v časopise
PLOS One
2019 Číslo 12
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Masturbační chování žen v ČR − dotazníková studie
- Nejasný stín na plicích – kazuistika
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Somatizace stresu – typické projevy a možnosti řešení
Najčítanejšie v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts