The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes

Figures & data

Table 1 Patient’s characteristics

	Healthy controls (H)	Metabolic syndrome (MS)	Type 2 diabetes (DM)
Age (yr)	45.2 (17.3)	50.7 (11.9)	59.7 (7.2)	F2,27^Table Footnote* = 4.08, p = 0.028	H-DM p = 0.022
BMI (Kg/m2)	26.2 (3.0)	33.3 (4.9)	29.8 (5.8)	F2,27^Table Footnote* = 5.49, p = 0.010	H-MS p = 0.007
Waist (cm)	94 (16.4)	112.4 (13.6)	107.2 (11.6)	F2,27^Table Footnote* = 5.34, p = 0.011	nsd
Systolic BP (mm Hg)	142 (28.1)	148 (20.6)	144 (25.1)	nsd
Diastolic BP (mm Hg)	83 (9.3)	90 (13.4)	83 (11.7)	nsd
Glucose (mmol/L)	5.46 (0.40)	5.97 (0.69)	7.47 (1.31)	F2,27^Table Footnote* = 14.37, p < 0.001	H-DM p = 0.001; MS-DM p = 0.002
HDL-C (mmol/l)	1.33 (0.21)	1.19 (0.21)	1.39 (0.39)	nsd
Triglycerides (mmol/L)	1.11 (0.37)	1.50 (0.61)	1.49 (0.68)	nsd
Glycosylated Hb (%)	5.3 (0.5)	5.9 (0.3)	6.8 (1.1)	F2,27^Table Footnote* = 10.68, p < 0.001	H-DM p = 0.001; MS-DM p = 0.030
Insulin (pmol/L)	51 (16)	107 (83)	101 (77)	KW nsd
HOMA-IR	2.1 (0.7)	4.6 (3.2)	5.6 (4.5)	KW† Chi2 = 8.43, p = 0.015
DFA	1.39 (0.10)	1.39 (0.07)	1.42 (0.10)	F2,27^Table Footnote* = 9.94, p = 0.001	H-DM p = 0.001; MS-DM p = 0.006
MAGE	30.0 (14.3)	45.5 (17.2)	62.7 (27.5)	F2,27^Table Footnote* = 6.38, p = 0.005	H-DM p = 0.004

Abbreviations: BMI, body mass index; BP, blood pressure; DFA, detrended fluctuation analysis; HDL-C, high-density lipoprotein-cholesterol; HOMA-IR, Homeostatic Assessment Model of Insulin Resistance; MAGE, mean amplitude of glycemic excursions.

Notes:

* ANOVA,

† Kruskal-Wallis.

Units conversion: Glucose (mg/dl) = Glucose (mmol/L) * 18.02; HDL-C (mg/dl) = HDL-C (mmol/L) * 38.61; Triglycerides (mg/dl) = Triglycerides (mmol/L) * 88.50; Insulin (uU/ml) = Insulin (pmol/L) * 0.1667.

Figure 1 Example of a glycemic profile of a patient of each group.

Notes: *DFA, detrended fluctuation analysis; ^†MAGE, mean amplitude of glycemic excursions.

Figure 2 Differences in detrended fluctuation analysis among healthy subjects, patients with the metabolic syndrome and patients with type 2 diabetes.

Figure 3 Correlation between detrended fluctuation analysis and mean amplitude of glycemic excursions.

Figure 4 Detrended fluctuation analysis (1). From the original time series, an integrated curve is obtained:$y(k)={\sum }_{i=1}^k(G_i-G_{\mathit{\text{mean}}})$

Notes: G_i, each individual point; G_mean, mean of the series as a whole. This integrated curve will be utilized for further calculations.

Figure 5 Detrended fluctuation analysis (DFA) (2). The integrated curve is divided into progressively smaller time segments (A, B, C, etc). A regression line is calculated for each segment, and the total difference between the integrated curve and the regression lines is calculated for each time window (F(n), gray area). The smaller the time window, the better the fit of the regression line and the lower the value of F(n). Finally, a plot is drawn (D) with log(F(n)) in the y-axis and log(time-window) in the x-axis. A good fit reveals the presence of scaling (self-similarity). DFA is the slope of the regression line. It displays the scaling exponent, and is an indicator of the degree of complexity of the curve.