Poor glycemic control impacts linear and non-linear dynamics of heart rate in DM type 2

Bassi, Daniela; Arakelian, Vivian Maria; Mendes, Renata Gonçalves; Caruso, Flávia Cristina Rossi; Júnior, José Carlos de Melo Vieira; Zangrando, Katiany Thays Lopes; Oliveira, Cláudio Ricardo de; Haus, Jacob M.; Arena, Ross; Borghi‐Silva, Audrey

doi:10.1590/1517-869220152104150003

Cited by 9 publications

(5 citation statements)

References 40 publications

(44 reference statements)

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“…The unexpectedly weak correlation across the variability domains suggests that the indices are not interchangeable. These correlations are in a way comparable with those found for the cardiac interbeat time series ( Bassi et al, 2015 ) because the information retrieved from PCP and from DFA analysis show structural correlations of the underlying dynamics. By the multivariate regression analyses, we disclosed that the measures of glucose profile dynamics are independent predictors of the quality of glucose control, as defined by the time spent in target range (TIR).…”

Section: Discussionsupporting

confidence: 79%

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Kohnert

Heinke

Vogt³

et al. 2018

Front. Physiol.

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Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (α1, α2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and α exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol2/l2): 21.71 ± 9.82 vs. 7.25 ± 5.92, and α1: 1.94 ± 0.12 vs. 1.75 ± 0.12, α2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol2/l2), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents α1 and α2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (β = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though α1 and MSE failed. In the regression models, including SFE, AFE, and α2 (β = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.

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Section: Discussionsupporting

confidence: 79%

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Kohnert

Heinke

Vogt³

et al. 2018

Front. Physiol.

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“…Poorly controlled patients presented with lower HRV measures of the time domain range (SDNN and SDANN) which are components representing the overall variability but mostly the sympathetic activity as previously hypothesized [ 7 , 8 ]. We did not find significant difference in elements of the frequency domain range such as, LF, HF.…”

Section: Main Textmentioning

confidence: 60%

Poor glycemic control impacts heart rate variability in patients with type 2 diabetes mellitus: a cross sectional study

et al. 2018

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ObjectiveWe aimed to determine and compare HRV parameters in poorly and well controlled type 2 diabetes. 54 normotensive type 2 diabetes patients without clinical signs of CAN were enrolled; 29 poorly controlled (HbA1c ≥ 7%) and 25 controls matched for age, sex and BMI. HRV analysis was performed using 24-h ambulatory ECG, with automatic estimation of the time and frequency domain ranges. Comparisons were performed using Mann–Whitney test.ResultsWe included 54 participants (26 males) aged 56 years [43–62], with known duration of diabetes 3 years [1–7]. HbA1c was 10.1% [9.1–11.9] vs 5.3% [5.1–6.3] (p < 0.001). Blood pressure was 126 mmHg [121–130] vs 124 mmHg [113–133] in the poorly controlled group and the well-controlled group respectively (p = 0.5). 24-h mean heart rate was significantly higher in poorly controlled vs well controlled patients (79 bpm [77–83] vs 75 bpm [69–79], p = 0.006). In the time domain analysis, markers of the overall variability were lower and thus altered in the poorly controlled group (SDNN: 102 ms [90.5–111.1] vs 112.3 ms [104.4–131.2], p = 0.01 and SDANN 88 ms [72.9–99.7] vs 97.8 ms [91.8–114.5], p = 0.01). The frequency domain analysis showed trends towards lower values of sympathovagal balance markers in the poorly controlled group. Reduced HRV is associated with poorly controlled type 2 diabetes mellitus and may be an early marker in clinical practice.

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“…Recently, our group verified that patients with DMT2 with poor glycemic control are more susceptible to poor autonomic nervous control of HR, demonstrated by linear and nonlinear indices. 31 However, the present study is the first to analyze the coexistence of HTN and DMT2 by means of linear and nonlinear HRV analysis.…”

Section: Discussionmentioning

confidence: 93%

“…HRV is reduced in patients with DMT2 31 as well as in patients with HTN 32 and its reduction is associated with poor cardiovascular prognosis. 33 Autonomic imbalance may be a final common pathway to increased morbidity and mortality in the presence of various conditions, including CVD.…”

Section: Discussionmentioning

confidence: 99%

Effects of Coexistence Hypertension and Type II Diabetes on Heart Rate Variability and Cardiorespiratory Fitness

Bassi¹,

Cabiddu²,

Mendes³

et al. 2018

Arquivos Brasileiros De Cardiologia

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BackgroundType 2 diabetes Mellitus (T2DM) is associated with cardiac autonomic dysfunction, which is an independent predictor of mortality in chronic diseases. However, whether the coexistence of systemic arterial hypertension (HTN) with DMT2 alters cardiac autonomic modulation remains unknown.ObjectiveTo evaluate the influence of HTN on cardiac autonomic modulation and cardiorespiratory fitness in subjects with DMT2.Methods60 patients of both genders were evaluated and allocated to two groups: DMT2 patients (n = 32; 51 ± 7.5 years old) and DMT2 + HTN patients (n = 28; 51 ± 6.9 years old). RR intervals were obtained during rest in supine position. Linear and nonlinear indices of heart rate variability (HRV) were computed using Kubios HRV software. Pulmonary gas exchange was measured breath-by-breath, using a portable telemetric system during maximal incremental exercise testing on a cycle ergometer. Statistical analysis included Shapiro-Wilk test followed by Student’s t Test, Pearson correlation and linear regression.ResultsWe found that patients in the DMT2+HTN group showed lower values of mean RR intervals (801.1 vs 871.5 ms), Shannon entropy (3 vs 3.2) and fractal dimension SD 1 (9.5 vs 14.5), when contrasted with patients in the DMT2 group. Negative correlations were found between some HRV nonlinear indices and exercise capacity indices.ConclusionHTN negatively affects the cardiac autonomic function in diabetic patients, who are already prone to develop autonomic dysfunction. Strategies are need to improve cardiac autonomic functionality in this population.

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Poor glycemic control impacts linear and non-linear dynamics of heart rate in DM type 2

Cited by 9 publications

References 40 publications

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Poor glycemic control impacts heart rate variability in patients with type 2 diabetes mellitus: a cross sectional study

Effects of Coexistence Hypertension and Type II Diabetes on Heart Rate Variability and Cardiorespiratory Fitness

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