Poincaré plot quantification for assessing glucose variability from continuous glucose monitoring systems and a new risk marker for hypoglycemia: application to type 1 diabetes patients switching to continuous subcutaneous insulin infusion

Impact of the complexity of glucose time series on all-cause mortality in patients with type 2 diabetes

CONTEXT

Previous studies suggest that the complexity of glucose time series may serve as a novel marker of glucose homeostasis.

OBJECTIVE

We aimed to investigate the relationship between the complexity of glucose time series and all-cause mortality in patients with type 2 diabetes.

METHODS

Prospective data of 6000 adult inpatients with type 2 diabetes from a single center was analyzed. The complexity of glucose time series index (CGI) based on continuous glucose monitoring (CGM) was measured at baseline with refined composite multi-scale entropy. Participants were stratified by the tertiles of CGI: < 2.15, 2.15-2.99, and ≥ 3.00. Cox proportional hazards regression models were used to assess the relationship between CGI and all-cause mortality.

RESULTS

During a median follow-up of 9.4 years, 1217 deaths were identified. A significant interaction between glycated hemoglobin A1c (HbA1c) and CGI in relation to all-cause mortality was noted (P for interaction = 0.016). The multivariable-adjusted hazard ratios for all-cause mortality at different CGI levels [≥ 3.00 (reference group), 2.15-2.99, and < 2.15] were 1.00, 0.76 (95% CI 0.52-1.12), and 1.47 (95% CI 1.03-2.09) in patients with HbA1c < 7.0%, while the association was nonsignificant in those with HbA1c ≥ 7.0%. The restricted cubic spline regression revealed a non-linear (P for non-linearity = 0.041) relationship between CGI and all-cause mortality in subjects with HbA1c < 7.0% only.

CONCLUSIONS

Lower CGI is associated with an increased risk of all-cause mortality among patients with type 2 diabetes achieving the HbA1c target. CGI may be a new indicator for the identification of residual risk of death in well-controlled type 2 diabetes.

Nonlinear Dynamics of Reaction Time and Time Estimation during Repetitive Test

(1) Background: In this research, we aimed to investigate a computational model of repetitive reaction time (RT) and virtual reaction time (VRT) testing. (2) Methods: The study involved 180 subjects (50 men, 130 women, mean age 31.61 ± 13.56 years). The data were statistically analyzed through the coefficient of variation (CV) and the Poincaré plot indicators. (3) Results: We obtained an excellent level of reliability for both sessions of testing and we put into evidence a relationship of association of the RT and VRT with the subjects’ age, which was more pregnant for RT (p < 0.05). For both RT and VRT data series, we determined a consistent closer association between CV and the Poincaré plot descriptors SD1, SD2 (SD—standard deviation), and the area of the fitting ellipse (AFE) (p < 0.01). We reported an underestimation of the time interval of 2 s during the VRT session of testing, with an average value of CV of VRT, the equivalent of the Weber fraction, of 15.21 ± 8.82%. (4) Conclusion: The present study provides novel evidence that linear and nonlinear analysis of RT and VRT variability during serial testing bring complementary insights to the understanding of complex neurocognitive processes implied in the task execution.

Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data

BACKGROUND

Monitoring physical activity with consumers wearables is one of the possibilities to control a patient's self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking.

OBJECTIVE

This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine.

METHODS

Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices.

RESULTS

Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters.

CONCLUSIONS

Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.

The Applicability of the Poincaré Plot in the Analysis of Variability of Reaction Time during Serial Testing

(1) Background: This study aims to put into evince the relationship between the variability of the reaction time (RT) during repeated testing, expressed through indicators extracted by the Poincaré plot method, and the age of the participants, their self-reported health (SRH), and level of perceived anxiety. (2) Methods: The study was performed using computerized RT testing software. An observational cross-sectional study was performed on a group of 120 subjects (mean age 42.33 ± 21.12 years), sex ratio men to women 1.14:1. Data were processed through descriptive and inferential statistics. The Poincaré plot method was applied in the analysis of the RT series of data, by calculating the indicators SD₁, SD₂, SD₁/SD₂, and area of the fitting ellipse (AFE) (3) Results: We provided evidence of the excellent reliability of the web-based RT serial testing (Cronbach’s Alpha 0.991) with this sample group. Our results showed that age is an important predictor for mean values of RT, while SD₁, SD₂, and AFE indicators are for SRH (p < 0.01). (4) Conclusions: the variability of RT, expressed by the Poincaré plot indicators, reflects the health status rather than the aging of the subjects and is barely influenced by their level of anxiety.

Hand grip strength variability during serial testing as an entropic biomarker of aging: a Poincaré plot analysis

Background

The Poincaré plot method can be used for both qualitative and quantitative assessment of self-similarity in usually periodic functions, hence the idea of applying it to the study of homeostasis of living organisms. From the analysis of numerous scientific data, it can be concluded that hand functionality can be correlated with the state of the human body as a biological system exposed to various forms of ontogenetic stress.

Methods

We used the Poincaré plot method to analyze the variability of hand grip strength (HGS), as an entropic biomarker of aging, during 60 repetitive tests of the dominant and nondominant hand, in young and older healthy subjects. An observational cross-sectional study was performed on 80 young adults (18–22 years old, mean age 20.01 years) and 80 older people (65–69 years old, mean age 67.13 years), with a sex ratio of 1:1 for both groups. For statistical analysis, we applied univariate descriptive statistics and inferential statistics (Shapiro–Wilk test, Mann–Whitney U-test for independent large samples, with the determination of the effect size coefficient r, and simple linear regression. We calculated the effect of fatigue and the Poincaré indices SD₁, SD₂, SD₁/SD₂ and the area of the fitting ellipse (AFE) for the test values of each subject.

Results

The analysis of the differences between groups revealed statistically significant results for most HGS-derived indices (p ≤ 0.05), and the magnitude of the differences indicated, in most situations, a large effect size (r > 0.5). Our results demonstrate that the proposed repetitive HGS testing indicates relevant differences between young and older healthy subjects. Through the mathematical modeling of data and the application of the concept of entropy, we provide arguments supporting this new design of HGS testing.

Conclusions

Our results indicate that the variability of HGS during serial testing, which reflects complex repetitive biomechanical functions, represents an efficient indicator for differentiation between young and older hand function patterns from an entropic perspective. In practical terms, the variability of HGS, evaluated by the new serial testing design, can be considered an attractive and relatively simple biomarker to use for gerontological studies.

Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates

OBJECTIVE

To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (CGM).

STUDY DESIGN

We retrospectively analyzed CGM traces obtained from 50 very preterm neonates, grouped into two sub-cohorts started on CGM within 12 and 48 h of birth, respectively. A CGM linked to an Artificial Intelligence Risk (CLAIR) index was developed to quantify glucose variability during the first 72 h of life in neonates with and without IVH. Brain-US was performed at least twice a day for the first 5 days of birth. An integrated remote monitoring platform was developed to capture major clinical events in real time and gather data for the risk index. The new score performance was further compared with other measures of glucose variability (coefficient of variation [CV] and standard deviation [SD]) and with a clinical risk index for babies II (CRIB-II) as a predictor of IVH event. The two cohorts were analyzed separately for internal validation of the method.

RESULTS

The primary cohort consisted of 26 neonates (gestational age 30 [28, 31] weeks; BW1275 g[1090, 1750]). Controls (n = 23) exhibited higher CLAIR index than cases (P = 0.004). A cut-off of 0.69 for the new CLAIR index allowed a 100% sensitivity and an 83% specificity for IVH prediction. The CLAIR index was the sole significant predictor for IVH (P = 0.003) when compared with clinical variables, CV, SD, and CRIB-II. In a subgroup analysis in very low-birth-weight infants, the CLAIR index was the sole variable significantly associated with IVH (P = 0.009). Analysis on the secondary cohort (five cases and 16 controls) confirmed a higher CLAIR index in the controls (P = 0.008), in the absence of a difference for CV, SD, and CRIB-II between the two groups.

CONCLUSION

CGM, combined with the AI-algorithm, provides a high-sensitivity index for risk detection of IVH that reflects the glycemic impairment preceding IVH.

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

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.

Glucose Variability: A Review of Clinical Applications and Research Developments

Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes

AIMS

The dynamical structure of glucose fluctuation has largely been disregarded in the contemporary management of diabetes.

METHODS

In a retrospective study of patients with diabetes, we evaluated the relationship between glucose dynamics, antihyperglycemic therapy, glucose variability, and glucose exposure, while taking into account potential determinants of the complexity index. We used multiscale entropy (MSE) analysis of continuous glucose monitoring data from 131 subjects with type 1 (n = 18), type 2 diabetes (n = 102), and 11 nondiabetic control subjects. We compared the MSE complexity index derived from the glucose time series among the treatment groups, after adjusting for sex, age, diabetes duration, body mass index, and carbohydrate intake.

RESULTS

In type 2 diabetic patients who were on a diet or insulin regimen with/without oral agents, the MSE index was significantly lower than in nondiabetic subjects but was lowest in the type 1 diabetes group (p < 0.001). The decline in the MSE complexity across the treatment groups correlated with increasing glucose variability and glucose exposure. Statistically, significant correlations existed between higher MSE complexity indices and better glycemic control. In multivariate regression analysis, the antidiabetic therapy was the most powerful predictor of the MSE (β = -0.940 ± 0.242, R ² = 0.306, p < 0.001), whereas the potential confounders failed to contribute.

CONCLUSIONS

The loss of dynamical complexity in glucose homeostasis correlates more closely with therapy modalities and glucose variability than with clinical measures of glycemia. Thus, targeting the glucoregulatory system by adequate therapeutic interventions may protect against progressive worsening of diabetes control.

Glycemic Key Metrics and the Risk of Diabetes-Associated Complications

Prevention of diabetes-associated complications is closely linked to preventing and controlling hyperglycemia. Glycated hemoglobin (HbA1c), a glucose metric and a risk factor for chronic complications, is not reliable under certain clinical conditions, does not capture glyemic variability and glucose dynamics. There is evidence that glycemic variability is an independent predictor variable of hypoglycemia and a potential risk marker for vascular diabetes complications. Despite advanced glucose monitoring methods, monitoring of glucose with blood glucose meters remains indispensible as an adjunct to HbA1c measurements, because it gives direct feedback on short-term changes in glucose levels. Optimized diabetes treatment and prevention or delay of diabetes complications needs both key glucose control metrics on a daily basis, involving fasting, preprandial, and postprandial glucose levels as well as advanced, user-friendly monitoring methods. The broad application of systems for continuous glucose monitoring in clinical settings is partly hampered by lacking measures generally accepted for analysis of glucose profiles and as standards for reporting of glucose data. We performed a literature search, using PubMed and Scopus and included relevant literature published online up to March 1, 2016. In this review, we discuss the importance of several glucose measures for primary and secondary prevention of diabetes complications and possibilities for evaluation of monitored glucose data with special consideration of glycemic variability, glucose dynamics, and the utility of continuous glucose monitoring.

Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes

BACKGROUND

Time series analysis provides information on blood glucose dynamics that is unattainable with conventional glycemic variability (GV) indices. To date, no studies have been published on these parameters in pediatric patients with type 1 diabetes. Our aim is to evaluate the relationship between time series analysis and conventional GV indices, and glycosylated hemoglobin (HbA1c) levels.

METHODS

This is a transversal study of 41 children and adolescents with type 1 diabetes. Glucose monitoring was carried out continuously for 72 h to study the following GV indices: standard deviation (SD) of glucose levels (mg/dL), coefficient of variation (%), interquartile range (IQR; mg/dL), mean amplitude of the largest glycemic excursions (MAGE), and continuous overlapping net glycemic action (CONGA). The time series analysis was conducted by means of detrended fluctuation analysis (DFA) and Poincaré plot.

RESULTS

Time series parameters (DFA alpha coefficient and elements of the ellipse of the Poincaré plot) correlated well with the more conventional GV indices. Patients were grouped according to the terciles of these indices, to the terciles of eccentricity (1: 12.56-16.98, 2: 16.99-21.91, 3: 21.92-41.03), and to the value of the DFA alpha coefficient (> or ≤1.5). No differences were observed in the HbA1c of patients grouped by GV index criteria; however, significant differences were found in patients grouped by alpha coefficient and eccentricity, not only in terms of HbA1c, but also in SD glucose, IQR, and CONGA index.

CONCLUSIONS

The loss of complexity in glycemic homeostasis is accompanied by an increase in variability.

Glucose Variability: Comparison of Different Indices During Continuous Glucose Monitoring in Diabetic Patients

BACKGROUND

Glucose variability has been suspected to be a major factor of diabetic complications. Several indices have been proposed for measuring glucose variability, but their interest remains discussed. Our aim was to compare different indices.

METHODS

Glucose variability was studied in 150 insulin-treated diabetic patients (46% men, 42% type 1 diabetes, age 52 ± 11 years) using a continuous glucose monitoring system (668 ± 564 glucose values; mean glucose value 173 ± 38 mg/dL). Results from the mean, the median, different indices (SD, MAGE, MAG, glucose fluctuation index (GFI), and percentages of low [<60 mg/dL] and high [>180 mg/dL] glucose values), and ratios (CV = SD/m, MAGE/m, MAG/m, and GCF = GFI/m) were compared using Pearson linear correlations and a multivariate principal component analysis (PCA).

RESULTS

CV, MAGE/m (ns), GCF and GFI (P < .05), MAG and MAG/m (P < .01) were not strongly correlated with the mean. The percentage of high glucose values was mainly correlated with indices. The percentage of low glucose values was mainly correlated with ratios. PCA showed 3 main axes; the first was associated with descriptive data (mean, SD, CV, MAGE, MAGE/m, and percentage of high glucose values); the second with ratios MAG/m and GCF and with the percentage of low glucose values; and the third with MAG, GFI, and the percentage of high glucose values.

CONCLUSIONS

Indices and ratios provide complementary pieces of information associated with high and low glucose values, respectively. The pairs MAG+MAG/m and GFI+GCF appear to be the most reliable markers of glucose variability in diabetic patients.