PMID- 31851681
OWN - NLM
STAT- MEDLINE
DCOM- 20200401
LR  - 20200401
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 14
IP  - 12
DP  - 2019
TI  - Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in 
      patients at risk: Model optimization and comparison with other metrics.
PG  - e0225817
LID - 10.1371/journal.pone.0225817 [doi]
LID - e0225817
AB  - 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.
FAU - Colas, Ana
AU  - Colas A
AUID- ORCID: 0000-0003-2784-4150
AD  - Department of Internal Medicine, Hospital 12 de Octubre, Madrid, Spain.
FAU - Vigil, Luis
AU  - Vigil L
AD  - Department of Internal Medicine, Hospital Universitario de Mostoles, Mostoles, 
      Madrid, Spain.
FAU - Vargas, Borja
AU  - Vargas B
AUID- ORCID: 0000-0002-5553-8372
AD  - Department of Internal Medicine, Hospital Universitario de Mostoles, Mostoles, 
      Madrid, Spain.
FAU - Cuesta-Frau, David
AU  - Cuesta-Frau D
AD  - Technological Institute of Informatics, Universitat Politecnica de Valencia, 
      Alcoi Campus, Alcoi, Spain.
FAU - Varela, Manuel
AU  - Varela M
AD  - Department of Internal Medicine, Hospital Universitario de Mostoles, Mostoles, 
      Madrid, Spain.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20191218
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (Blood Glucose)
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Aged
MH  - Aged, 80 and over
MH  - Algorithms
MH  - Blood Glucose/*analysis
MH  - Datasets as Topic
MH  - Diabetes Mellitus, Type 2/*diagnosis
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Risk Factors
MH  - Young Adult
PMC - PMC6919578
COIS- The authors have declared that no competing interests exist.
EDAT- 2019/12/19 06:00
MHDA- 2020/04/02 06:00
PMCR- 2019/12/18
CRDT- 2019/12/19 06:00
PHST- 2018/10/27 00:00 [received]
PHST- 2019/11/13 00:00 [accepted]
PHST- 2019/12/19 06:00 [entrez]
PHST- 2019/12/19 06:00 [pubmed]
PHST- 2020/04/02 06:00 [medline]
PHST- 2019/12/18 00:00 [pmc-release]
AID - PONE-D-18-31170 [pii]
AID - 10.1371/journal.pone.0225817 [doi]
PST - epublish
SO  - PLoS One. 2019 Dec 18;14(12):e0225817. doi: 10.1371/journal.pone.0225817. 
      eCollection 2019.