
Glucose series complexity at the threshold of diabetes 糖尿病阈值的血糖序列的复杂性
Author(s) -
Varela Manuel,
Rodriguez Carmen,
Vigil Luis,
Cirugeda Eva,
Colas Ana,
Vargas Borja
Publication year - 2015
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.12182
Subject(s) - medicine , diabetes mellitus , glycemic , body mass index , detrended fluctuation analysis , correlation , series (stratigraphy) , metabolic syndrome , statistics , mathematics , endocrinology , paleontology , geometry , scaling , biology
Background One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well‐accepted method to measure this phenomenon is detrended fluctuation analysis ( DFA ). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus ( T2DM ). Methods We report on the clinical and glucometric characteristics of a sample of 103 patients at increased risk of developing T2DM . All patients had HbA1c levels 5%–6.4% and met at least one of the following criteria: body mass index ( BMI ) > 30 kg/m 2 , essential hypertension or a first‐degree relative with T2DM . For each patient, a 24‐h glucose time series was obtained, and the clinical and glucometric variables were compared. Results There was a significant correlation between the number of N ational C holesterol E ducation P rogram – A dult T reatment P anel ( ATP III ) metabolic syndrome ( MS )‐defining criteria and DFA (ρ = 0.231, P = 0.019), and DFA differed significantly between patients meeting or not the ATP III definition of MS (1.443 vs 1.399, respectively; P = 0.018). The DFA was not correlated with HbA1c. Depending on how it was calculated, the area under the log( F n)∼log(n) curve correlated with HbA1c levels or the number of MS criteria. Conventional variability metrics (mean amplitude of glycemic excursions) did not differ between patients complying or not with the definition of MS . Conclusions Complexity analysis is capable of detecting differences in variables related to the risk of developing T2DM and could be a useful tool to study the initial phases of glucoregulatory dysfunction leading to T2DM .