Impact of the Complexity of Glucose Time Series on All-Cause Mortality in Patients With Type 2 Diabetes

Jinghao Cai; Qing Yang; Jingyi Lu; Yun Shen; Chunfang Wang; Lei Chen; Lei Zhang; Wei Lu; Wei Zhu; Tian Xia; Jian Zhou

J Clin Endocrinol Metab. 2023;108(5):1093-1100.

Abstract and Introduction

Abstract

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 were analyzed. The complexity of glucose time series index (CGI) based on continuous glucose monitoring (CGM) was measured at baseline with refined composite multiscale entropy. Participants were stratified by CGI tertiles of: < 2.15, 2.15 to 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 nonlinear (P for nonlinearity = 0.041) relationship between CGI and all-cause mortality in subjects with HbA1c < 7.0% only.

Conclusion: 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.

Introduction

With the rapid growth of technologies, continuous glucose monitoring (CGM) plays an increasingly important role in glycemic management.^[1] Compared with conventional glucose monitoring methods, CGM can provide a large amount of glucose data varying with time, which enables more accurate assessment of glycemic fluctuations. Glucose time series data from CGM contains complex and nonlinear information about glycemic fluctuations. However, traditional glycemic variability metrics such as coefficient of variation (CV) and SD only assess the amplitude changes in glycemic fluctuations regardless of the time dimension.^[2,3] Furthermore, as these linear methods simplify data from glucose dynamics for the assessment of glycemic fluctuations, they miss abundant nonlinear characteristics of dynamical changes related to glucose regulation, which determine the complexity of glucose time series.

Recently, time series analyses have provided a new approach to evaluating the complexity of physiological time series. Nonlinear methods such as detrended fluctuation analysis (DFA),^[4] Poincaré plot,^[5] and multiscale entropy (MSE)^[6] have been developed. Of these, MSE analysis based on entropy is one of the popular time series analyses, which can quantify the complexity of time series data by calculating sample entropy over multiple time scales.^[6] The glucose homeostasis is maintained by multiple interconnected feedback loops over multiple time scales, involving hormones, diet intake, and muscle activity, among other factors. Therefore, the dynamics of glucose homeostasis system could be regarded as a complex system. In accord with this notion, glucose time series data with higher entropy indicates more irregularity and unpredictability, which means the data is more complex with more diverse patterns of glucose dynamic changes.

Previous studies revealed that patients with diabetes were characterized by a loss of complexity across multiple time scales compared with normal individuals.^[7–10] More recently, our post hoc analysis of a multicenter CGM study^[11] showed that the complexity of glucose time series decreased progressively across the glycemic continuum (from healthy to prediabetes to diabetes) and was significantly associated with measures of insulin sensitivity/secretion, implying that glucose time series complexity may serve as a new marker for assessing glucose homeostasis.

However, the clinical relevance of this metric remains to be determined. Therefore, the current study sought to examine the association between the complexity of glucose time series measured with refined composite multiscale entropy (RCMSE) analysis,^[12] extended from MSE for shorter time series, and all-cause mortality among patients with type 2 diabetes in a large prospective cohort.

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Abstract and Introduction
Materials and Methods
Results
Discussion
References

Table 1. Characteristics of participants by tertiles of CGI
Characteristics	Total	CGI			P for trend
Characteristics	Total	<2.15	2.15–2.99	≥ 3.00	P for trend
Participants, n	6000	2010	1965	2025
Age, years	61.6 ± 11.9	62.2 ± 12.0	61.2 ± 11.9	61.4 ± 11.8	0.032
Men, n (%)	3272 (54.5)	1191 (59.3)	1094 (55.7)	987 (48.7)	<0.001
BMI, kg/m²	24.9 ± 3.5	24.6 ± 3.5	24.9 ± 3.6	25.2 ± 3.5	<0.001
Diabetes duration, years	10.0 (4.0, 15.0)	10.0 (4.0, 15.0)	10.0 (4.0, 15.0)	8.0 (3.0, 14.0)	<0.001
Systolic blood pressure, mmHg	132.9 ± 16.9	132.9 ± 17.2	132.8 ± 16.7	132.9 ± 16.8	0.917
Diastolic blood pressure, mmHg	79.8 ± 9.4	79.5 ± 9.4	80.1 ± 9.4	79.7 ± 9.4	0.601
Total cholesterol, mmol/L	4.7 ± 1.2	4.7 ± 1.2	4.7 ± 1.2	4.8 ± 1.2	0.482
Triglycerides, mmol/L	1.4 (0.9, 2.0)	1.2 (0.9, 1.9)	1.4 (1.0, 2.1)	1.4 (1.0, 2.1)	<0.001
HDL cholesterol, mmol/L	1.1 ± 0.3	1.1 ± 0.3	1.1 ± 0.3	1.1 ± 0.3	0.001
LDL cholesterol, mmol/L	3.0 ± 1.0	3.0 ± 1.0	2.9 ± 0.9	3.0 ± 0.9	0.878
HbA1c, %	8.9 ± 2.2	9.3 ± 2.3	8.9 ± 2.2	8.5 ± 2.0	<0.001
CGM metrics
CGI	2.7 ± 1.1	1.7 ± 0.3	2.6 ± 0.2	3.9 ± 0.8	<0.001
CV, %	26.5 ± 9.5	31.7 ± 9.7	26.2 ± 8.4	21.7 ± 7.7	<0.001
TIR, %	64.7 ± 24.2	60.2 ± 21.9	64.8 ± 24.1	69.2 ± 25.5	<0.001
MSG, mmol/L	9.2 ± 1.9	9.5 ± 1.9	9.2 ± 1.9	8.8 ± 1.9	<0.001
History of CVDs, n (%)	1273 (21.2)	440 (21.9)	400 (20.4)	433 (21.4)	0.695
History of cancer, n (%)	273 (4.6)	99 (4.9)	81 (4.1)	93 (4.6)	0.614
Current smoker, n (%)	1431 (23.8)	498 (24.8)	494 (25.1)	439 (21.7)	0.021
Medication, n (%)
Insulin	4014 (66.9)	1514 (75.3)	1343 (68.3)	1157 (57.1)	<0.001
Oral hypoglycemic drugs	4238 (70.6)	1312 (65.3)	1393 (70.9)	1533 (75.7)	<0.001
Antihypertensive drugs	3266 (54.4)	1098 (54.6)	1028 (52.3)	1140 (56.3)	0.285
Aspirin	2837 (47.3)	966 (48.1)	911 (46.4)	960 (47.4)	0.680
Statins	2327 (38.8)	775 (38.6)	748 (38.1)	804 (39.7)	0.454

Data shown are mean ± SD, median (interquartile range) or number (percentage) unless otherwise indicated.
Abbreviations: BMI, body mass index; CGI, complexity of glucose time series index; CGM, continuous glucose monitoring; CV, coefficient of variation; CVDs, cardiovascular diseases; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MSG, mean sensor glucose; TIR, time in range.

Table 2. HRs of all-cause mortality across tertiles of CGI among patients with type 2 diabetes
All-cause mortality	CGI
All-cause mortality	≥ 3.00	2.15–2.99	< 2.15
Participants/deaths, n	2025/371	1965/374	2010/472
Person-years	19524.3	18749.7	18543.5
Adjusted HRs (95% CIs)
Model 1	1.00	1.02 (0.88–1.17)	1.21 (1.06–1.39)**
Model 2	1.00	1.00 (0.87–1.16)	1.19 (1.04–1.36)*
Model 3	1.00	0.92 (0.80–1.06)	1.03 (0.89–1.18)

Model 1 adjusted for age and sex; Model 2 further adjusted for diabetes duration, BMI, systolic blood pressure, triglycerides, total cholesterol, current smoking status, history of cancer, and history of CVDs; Model 3 adjusted for the covariates in Model 2 plus HbA1c, use of insulin, and oral hypoglycemic drugs; *P < 0.05, **P < 0.01.
Abbreviations: BMI, body mass index; CGI, complexity of glucose time series index; CVDs, cardiovascular diseases; HbA1c, glycated hemoglobin A1c; HR, hazard ratio.

Table 3. HRs for all-cause mortality according to tertiles of CGI among subpopulations
Subpopulations	CGI			P for interaction
Subpopulations	≥ 3.00	2.15–2.99	< 2.15	P for interaction
Age, years				>0.1
<60.0	1.00	0.79 (0.54–1.15)	1.11 (0.78–1.58)
≥60.0	1.00	0.97 (0.83–1.14)	1.05 (0.90–1.23)
Sex				>0.1
Men	1.00	1.03 (0.85–1.25)	1.09 (0.90–1.32)
Women	1.00	0.78 (0.62–0.97)*	0.94 (0.76–1.16)
Diabetes duration, years				0.058
<10.0	1.00	0.77 (0.61–0.98)*	1.02 (0.81–1.27)
≥10.0	1.00	1.03 (0.85–1.23)	1.04 (0.87–1.25)
Follow-up period, years				>0.1
<5.0	1.00	0.88 (0.69–1.13)	1.01 (0.81–1.27)
5.0–10.0	1.00	0.89 (0.72–1.08)	0.97 (0.80–1.19)
≥10.0	1.00	1.45 (0.94–2.24)	1.12 (0.71–1.77)
HbA1c, %				0.016
<7.0	1.00	0.76 (0.52–1.12)	1.48 (1.04–2.11)*
≥7.0	1.00	0.96 (0.82–1.12)	1.00 (0.86–1.17)
Current smokers				>0.1
Yes	1.00	0.98 (0.71–1.35)	1.05 (0.77–1.45)
No	1.00	0.90 (0.76–1.05)	1.01 (0.86–1.18)
History of CVDs				>0.1
Yes	1.00	0.94 (0.74–1.20)	0.89 (0.70–1.14)
No	1.00	0.91 (0.76–1.09)	1.10 (0.92–1.30)
History of cancer				>0.1
Yes	1.00	0.68 (0.37–1.27)	0.91 (0.52–1.62)
No	1.00	0.94 (0.81–1.09)	1.03 (0.89–1.20)
Use of insulin				0.093
Yes	1.00	0.88 (0.75–1.05)	0.93 (0.79–1.09)
No	1.00	0.99 (0.73–1.33)	1.50 (1.13–2.00)**
Use of oral hypoglycemic drugs				>0.1
Yes	1.00	0.99 (0.83–1.18)	1.09 (0.92–1.29)
No	1.00	0.84 (0.65–1.09)	0.98 (0.77–1.24)

Adjusted for age, sex, diabetes duration, BMI, systolic blood pressure, triglycerides, total cholesterol, current smoking status, history of cancer, history of CVDs, HbA1c, use of insulin, and oral hypoglycemic drugs, other than the variable for stratification; *P < 0.05, **P < 0.01.
Abbreviations: BMI, body mass index; CGI, complexity of glucose time series index; CVDs, cardiovascular diseases; HbA1c, glycated hemoglobin A1c; HR, hazard ratio.

Table 4. Hazard ratios for all-cause mortality according to tertiles of CGI among patients with type 2 diabetes across HbA1c categories
All-cause mortality	CGI
All-cause mortality	≥ 3.00	2.15–2.99	< 2.15
HbA1c < 7.0%
Participants/deaths, n	540/69	407/43	313/64
Person-years	5355.1	4029.8	2881.5
Adjusted HRs (95% CIs)
Model 1	1.00	0.79 (0.54–1.16)	1.51 (1.07–2.13)*
Model 2	1.00	0.79 (0.54–1.16)	1.54 (1.09–2.19)*
Model 3	1.00	0.76 (0.52–1.12)	1.47 (1.03–2.09)*
HbA1c ≥ 7.0%
Participants/deaths, n	1485/302	1558/331	1697/408
Person-years	14169.2	14719.9	15662.0
Adjusted HRs (95% CIs)
Model 1	1.00	1.03 (0.88–1.20)	1.11 (0.96–1.29)
Model 2	1.00	1.01 (0.86–1.18)	1.08 (0.93–1.25)
Model 3	1.00	0.94 (0.80–1.10)	0.96 (0.82–1.12)

Model 1 adjusted for age and sex; Model 2 further adjusted for diabetes duration, BMI, systolic blood pressure, triglycerides, total cholesterol, current smoking status, history of cancer, and history of CVDs; Model 3 adjusted for the covariates in Model 2 plus HbA1c, use of insulin, and oral hypoglycemic drugs; *P < 0.05.
Abbreviations: BMI, body mass index; CGI, complexity of glucose time series index; CVDs, cardiovascular diseases; HbA1c, glycated hemoglobin A1c; HR, hazard ratio.

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Impact of the Complexity of Glucose Time Series on All-Cause Mortality in Patients With Type 2 Diabetes

Abstract and Introduction

Abstract

Introduction

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Tables

References

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