Hyperglycemia and hypoglycemia exposure are differentially associated with micro- and macrovascular complications in adults with Type 1 Diabetes

Metabolic syndrome in type 1 diabetes: higher time above range and glycemic variability revealed by continuous glucose monitoring (CGM)

AIMS

To investigate the glucose profile of Chinese individuals with type 1 diabetes (T1D) who also have metabolic syndrome.

MATERIALS AND METHODS

Type 1 diabetes participants from Peking University People's Hospital were recruited from Jan 2017 to Jan 2024. The diagnosis of metabolic syndrome was developed based on the updated National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) criteria. Demographic data, anthropometric measurements, clinical information and continuous glucose monitoring (CGM) data were collected and compared between participants with metabolic syndrome and those without.

RESULTS

The median age of the participants was 50.0 years (IQR 35.0-63.3), and the median duration was 10.0 years (IQR 2.0-17.0). Compared to those without metabolic syndrome, participants with metabolic syndrome were older (63.0 years, IQR 41.0-69.0 vs. 48.5 years, IQR 35.0-60.0; P < 0.001) and had a longer duration (13.0 years, IQR 5.0-22.0 vs. 9.0 years, IQR 2.0-15.0; P = 0.011). The comparison of CGM metrics suggested significantly higher time above range (TAR, 48.9%, IQR 35.3-59.5 vs. 32.8%, IQR 16.1-47.6; P < 0.001), standard deviation (SD, 3.6 ± 0.9 mmol/L vs. 3.2 ± 1.0 mmol/L; P = 0.022) and interquartile range (IQR, 4.2 mmol/L, IQR 3.2-4.8 vs. 3.7 mmol/L, IQR 3.0-4.5; P = 0.046) in those with metabolic syndrome. And the Logistic regression analysis showed that TAR (OR 1.53, 95% CI 1.02-2.23, per 20% increase), SD ( OR 1.75, 95% CI 1.07-2.84, P = 0.025) and IQR (OR 1.50, 95% CI 1.03-2.19, P = 0.036) were positively associated with metabolic syndrome after adjusting for age, sex, diabetes duration, BMI and complication status.

CONCLUSIONS

Our findings suggested that in T1D participants, metabolic syndrome was associated with higher glucose level and glycemic variability. Personalized diabetes education including optimal meal planning and sufficient physical activity should be emphasized to improve glycemic control in T1D with metabolic syndrome.

The related factors affecting the relationship between HbA1c and glucose management indicator in adult T2D patients with good glycemic control

PURPOSE

To explore the relationship between glucose management indicator (GMI) and HbA1c and find the affecting factors in adult T2D patients with good glycemic control.

METHODS

Adult T2D patients with both HbA1c < 7% and time in range (TIR) > 70% were retrospectively analyzed. A significant difference between GMI and HbA1c was defined as an absolute value of hemoglobin glycation index (|HGI|, HbA1c minus GMI) ≥ 0.5%. Factors associated with high |HGI| were determined by logistic regression analysis. The performance of possible factors in predicting high |HGI| was verified by ROC curve analysis. And the linear relationship between GMI and HbA1c was also investigated.

RESULTS

Of all the 94 patients (median HbA1c 6.18%, mean GMI 6.34%) included, 28.72% had an |HGI | ≥ 0.5% and only 15.96% had an |HGI | < 0.1%. Standard deviation of blood glucose (SDBG), a glycemic variability index, affected |HGI| (OR = 3.980, P = 0.001), and showed the best performance in predicting high |HGI| (AUC = 0.712, cutoff value = 1.63 mmol/L, P = 0.001). HbA1c was linearly correlated with GMI (β = 0.295, P = 0.004). Their correlation weakened after further adjusting for SDBG (β = 0.232, P = 0.012). Linear correlation between them was closer in patients with smaller SDBG ( < 1.63 mmol/L) than those with larger SDBG (P = 0.004).

CONCLUSIONS

Even in adult T2D patients with good glycemic control, the discrepancy between GMI and HbA1c existed. Their relationship was affected by glycemic variability. SDBG mainly accounted for this consequence.

TRIAL REGISTRATION

Chinese clinical trial registry ( www.chictr.org.cn ), ChiCTR2000034884, 2020-07-23.

Positive Islet Cell Cytoplasmic Antibody and Long-Term Use of Lipid-Lowering Agents Are Positively Correlated With Peripheral Atherosclerosis in Patients With Autoimmune Diabetes: A Cross-Sectional Study

Aims: This cross-sectional study is aimed at determining whether systemic inflammation, diabetic autoantibodies, and islet β cell dysfunction play a role in the progression of macrovascular complications in patients with autoimmune diabetes. Methods: 202 patients with autoimmune diabetes aged ≥ 35 years and hospitalized in Peking Union Medical College Hospital were enrolled in this study. The patients were divided into three groups based on the severity of peripheral atherosclerosis. Biomarkers of systemic inflammation, diabetes autoantibodies, islet β cell function, and other covariates validated to be associated with macrovascular complications were collected. Correlations between the severity of peripheral atherosclerosis and systemic inflammation, diabetic autoantibodies, and islet β cell function were examined using an ordinal logistic regression model. Results: Of the enrolled patients, 39.1% were male, with a median age of 53 (43, 60) years and a diabetes duration of 96 (36, 216) months. 58 patients had no lesions in the peripheral arteries, 72 had atherosclerosis in the carotid or lower extremity arteries, and the rest had lesions in both arteries. In the multifactor ordinal logistic regression test, positive islet cell cytoplasmic antibody (ICA) and long-term use of lipid-lowering agents were independently associated with peripheral atherosclerosis after adjusting for age and diabetes duration. Conclusions: The correlation between positive ICA and atherosclerosis suggests inflammation at an early stage plays a role in macrovascular complications in autoimmune diabetes. The association between long-term use of lipid-lowering agents and atherosclerosis suggests the need for early screening and intervention for dyslipidemia in patients with autoimmune diabetes.

Glucose interpretation meaning and action (GIMA): Insights to blood glucose user interface interpretation in type 1 diabetes

Background

Current glucose monitoring user interfaces (UIs) are problematic for people with Type 1 Diabetes Mellitus (T1DM) in maintaining recommended blood glucose levels effectively. However, there is a lack of in-depth investigation into this problem when these individuals interpret and make real-time decisions based on the glucose monitoring devices they use daily.

Objectives

We aim to investigate problems associated with glucose monitoring UIs by observing users' interpretation and decision-making while reading their Continuous Glucose Monitoring (CGM), Flash Glucose Monitoring (Flash) or Self-monitoring of Blood Glucose (SMBG).

Methods

A mixed-method study was conducted. The Think Aloud protocol was used to capture participants' decision-making process while reading various device UIs. Their responses were evaluated using standard clinical guidance to assess their accuracy. Additionally, a survey was distributed to gather their perceptions of self-management practices.

Results

Twenty-seven participants (17 patients and 10 carers) were recruited. Interpretation accuracy averaged 38.0% ± 11.1% for CGM, 39.5% ± 8.8% for Flash, and 33.3% ± 7.8% for SMBG group. Treatment action accuracy was 21.5% ± 15.6% for CGM, 21.2% ± 14.0% for Flash, and 18.0% ± 13.2% for SMBG group. Despite this, 75.0% of all participants expressed very high confidence in their self-management.

Conclusions

Interpreting and making decisions using glucose monitoring UIs remains significantly challenging for people with T1DM despite their self-perceived performance. Improving such UIs is crucial to reduce misinterpretation and help these individuals make better treatment decisions without relying on their potentially inaccurate interpretations.

Heterogeneity of glycaemic phenotypes in type 1 diabetes

AIMS/HYPOTHESIS

Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.

METHODS

In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.

RESULTS

We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.

CONCLUSIONS/INTERPRETATION

Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21, 5273

The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones.

Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making.

Influence of obesity on blood glucose control using continuous glucose monitoring data among patients with type 1 diabetes

INTRODUCTION

The global increase in the prevalence rates of overweight or obesity has also affected patients with type 1 diabetes (T1D), where this disease had traditionally been associated with a lean phenotype. On the other hand, the effect of obesity on new glycemic control metrics obtained from continuous glucose monitoring (CGM) in T1D is poorly understood. We wanted to assess whether there is any relationship between BMI (body mass index) and the different CGM metrics or HbA1c.

METHODS

Two hundred and twenty-five patients with T1D (47.1% ♀, mean age 42.9±14.7 years) with a CGM for a minimum of 6 months were analysed by downloading their CGM and collecting clinical and anthropometric variables.

RESULTS

35.1% (79/225) of the T1D patients had overweight and 17.3% (39/225) lived with obesity, while the remaining 47.6% had a normal weight. A negative correlation was found between GMI (glucose management indicator) and BMI (-0.2; p=0.008) and HbA1c (-0.2; p=0.01). In contrast, a positive correlation was observed between the total dose of insulin and the BMI (0.3; p<0.0001). No significant correlations were found between BMI and other CGM metrics.

CONCLUSIONS

Overweight or obesity do not imply worse glycemic control in patients with T1D or less use of CGM. Possibly, and in order to achieve a good glycemic control, more units of insulin are necessary in these patients which, in turn, makes weight control more difficult.

The Relationship Between Glycated Hemoglobin and Time in Range in a Pediatric Population

In adults with type 1 diabetes (T1D), time in range (TIR) [70-180 mg/dL] has been proposed as an additional metric besides glycated hemoglobin (HbA1c). This retrospective monocentric cohort study determined the correlation between HbA1c and TIR during the 2, 4, and 12 weeks (TIR2w, TIR4w, and TIR12w) before consultation in a pediatric T1D population. A total of 168 children with T1D were included. Continuous glucose monitoring data, HbA1c, and demographic variables were collected. We found strong linear correlations between HbA1c and TIR2w (R = -0.571), HbA1c and TIR4w (R = -0.603), and between HbA1c and TIR12w (R = -0.624). A strong correlation exists between TIR2w and TIR12w, HbA1c and time above range (TAR), and between TIR and TAR at different time points. In conclusion, a strong correlation was found between HbA1c and TIR, making TIR a potentially complementary metric to HbA1c. TIR2w seems a viable alternative to TIR12w. TAR also seems promising in assessing glycemic control.

The effect of health quotient and time management skills on self-management behavior and glycemic control among individuals with type 2 diabetes mellitus

Objective

The aim of this study was to evaluate the present status of self-management behavior and glycemic control in individuals diagnosed with Type 2 Diabetes Mellitus (T2D), as well as to examine the impact of health quotient (HQ) and time management skills on both self-management behavior and glycemic control.

Methods

Between October 2022 and March 2023, a purposive sampling method had been utilized to select 215 participants with type T2D. The survey concluded a general information questionnaire, an HQ scale, a diabetes time management questionnaire and a self-management behavior questionnaire. The health quotient(HQ)encompasses the individuals' knowledge, attitude toward health, and the ability to maintain their own well-being. The diabetes time management questionnaire was reverse-scored, with higher scores indicating an enhanced competence in time management. The path among variables was analyzed using structural equation modeling(SEM).

Results

SEM showed that the direct effect of HQ on time management was -0.566 (p < 0.05), the direct effect of time management on the effect of self-management was -0.617 (p < 0.05), the direct effect of HQ on self-management was 0.156, and the indirect effect was 0.349 (p < 0.05); the relationship between health quotient and self-management was partially mediated by time management, with a mediating effect size of 68.8%. In addition, self-management had a direct effect on HbAlc, with a size of -0.394 (p < 0.05); The impacts of both HQ and time management on HbAlc were found to be mediated by self-management, with HQ demonstrating an indirect effect of -0.199 (p < 0.05) and time management showing an indirect effect of 0.244 (p < 0.05).

Conclusion

Health quotient and time management in patients with T2D serve as catalysts for self-management behavior. They affect HbAlc level indirectly through self-management practices. The suggestion is to prioritize the cultivation of rational time organization and management skills in T2D patients, as well as enhance their health quotient level. This can facilitate a more effective improvement in patients' self-management behaviors, ultimately achieving the objective of maintaining optimal glycemic control.

Glucose sensor with predictive alarm for hypoglycaemia: Improved glycaemic control in adolescents with type 1 diabetes

AIM

Hypoglycaemic events are linked to microvascular and macrovascular complications in people with type 1 diabetes. We aimed to evaluate the efficacy of glucose sensor [real-time continuous glucose monitoring (RT-CGM)] with predictive alarm (PA) in reducing the time spent below the range (%TBR <70 mg/dl) in a group of adolescents with type 1 diabetes (AwD).

MATERIALS AND METHODS

This was a crossover, monocentric and randomized study. RT-CGM was set with Alarm on Threshold (AoT) at 70 mg/dl) or PA for hypoglycaemia (20 m before threshold). Twenty AwD were enrolled and randomized to either a PA/AoT or AoT/PA treatment sequence, in a 1:1 ratio. The two groups (PA vs. AoT) were compared using two-way repeated measures ANOVA taking account of the carryover effect.

RESULTS

AwD using PA for hypoglycaemia spent less time in severe hypoglycaemia (%TBR2 <54 mg/dl; 0.32 ± 0.31 vs. 0.91 ± 0.90; p < .02) and hypoglycaemia (%TBR <70 mg/dl; 1.68 ± 1.06 vs. 2.90 ± 2.05; p < .02), with better glycaemia risk index (51.3 ± 11.0 vs. 61.5 ± 12.6; p ≤ .01).

CONCLUSION

The use of RT-CGM with PA for hypoglycaemia technology in AwD using multiple daily insulin injection treatment could significantly reduce the risk of having hypoglycaemic events resulting in an improved quality of glucose control.

CLINICAL TRIAL REGISTRATION NUMBER

NCT05574023.

Severe hypoglycemia and hypoglycemia awareness are associated with preclinical atherosclerosis in patients with type 1 diabetes without an estimated high cardiovascular risk

AIMS

To explore the relationship between severe hypoglycemia (SH) and hypoglycemia awareness with preclinical atherosclerosis in type 1 diabetes (T1D).

MATERIALS AND METHODS

Cross-sectional study in patients with T1D without cardiovascular disease (CVD), and with ≥1 of the following: ≥40 years, diabetic kidney disease, or ≥10 years of T1D duration with another risk factor. CVD risk was estimated with the Steno T1 Risk Engine (Steno-Risk). Carotid plaque was evaluated using standardised ultrasonography protocol. Logistic regression models adjusted for CVD risk factors were constructed to test the independent associations with SH or hypoglycemia awareness assessed by the Clarke questionnaire (Clarke). The inclusion of SH and Clarke in Steno-Risk was further evaluated.

RESULTS

We included 634 patients (52.4% men, age 48.3 ± 10.8 years, T1D duration 27.4 ± 11.1 years, 39.9% harbouring plaque). A stepped increase in the presence of plaque according to Steno-Risk was observed (13.5%, 37.7%, and 68.7%, for low, moderate, and high risk, respectively; p < 0.001). SH history (OR 4.4 [1.3-14.6]) and Clarke score (OR 1.7 [1.2-2.2]) were associated with plaque in low-risk patients (n = 192). Clarke score was also associated with plaque burden in low-moderate-risk participants (n = 436; ≥2 plaques: OR 1.2 [1.0-1.5], p = 0.031; ≥3 plaques: OR 1.4 [1.1-2.0], p = 0.025). The inclusion of SH and Clarke scores in Steno-Risk significantly improved the identification of low-risk individuals with atherosclerosis (area under the curve: 0.658 vs. 0.576; p = 0.036).

CONCLUSIONS

In patients with T1D without an estimated high CVD risk, SH and hypoglycemia awareness assessment score were independently associated with preclinical atherosclerosis and improved identification of patients who would benefit from an intensive approach.

Neutrophil-to-lymphocyte ratio is independently associated with carotid atherosclerosis burden in individuals with type 1 diabetes

BACKGROUND AND AIMS

Recent studies have identified a relationship between innate versus. Adaptative immunity and cardiovascular disease (CVD) in the general population, but information on type 1 diabetes (T1D) is lacking. We aimed to study the relationship between inflammatory biomarkers and preclinical atherosclerosis in this population.

METHODS AND RESULTS

Cross-sectional study in T1D individuals without CVD and with ≥1 of the following: ≥40 years, diabetic kidney disease, or ≥10 years of diabetes duration with classical CVD risk factors. Carotid plaques were evaluated by ultrasonography. C-reactive protein, total leukocyte count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio and systemic immune-inflammation index were assessed as inflammatory markers. Multivariate-adjusted models including age, sex, and other CVD risk factors were constructed to test their independent associations with atherosclerosis burden. We included 602 subjects (52.8% men, 48.7 ± 10.2 years old and 27.0 ± 10.5 years of diabetes duration). Carotid plaques were found in 41.2% of the individuals (12.8%, ≥3 plaques). The number of carotid plaques (none, 1-2, ≥3 plaques), was directly associated with the leukocyte count (6570 [5445-8050], 6640 [5450-8470] and 7310 [5715-8935] per mm3, respectively; p for trend = 0.021) and the NLR (1.63 [1.28-2.13], 1.78 [1.38-2.25] and 2.14 [1.58-2.92], respectively; p for trend <0.001), but only the NLR remained directly associated in fully-adjusted models (presence of plaques; OR 1.285 [1.040-1.587]; ≥3 plaques, OR 1.377 [1.036-1.829]).

CONCLUSIONS

The NLR was independently and directly associated with carotid plaque burden in T1D individuals. Our data support the role of innate versus. Adaptative immunity in atherosclerosis also among the T1D population.

Safety and performance of a hybrid closed-loop insulin delivery system with carbohydrate suggestion in adults with type 1 diabetes prone to hypoglycemia

AIMS

To evaluate the safety and performance of a hybrid closed-loop (HCL) system with automatic carbohydrate suggestion in adults with type 1 diabetes (T1D) prone to hypoglycemia.

METHODS

A 32-hour in-hospital pilot study, including a night period, 4 meals and 2 vigorous unannounced 45-minute aerobic sessions, was conducted in 11 adults with T1D prone to hypoglycemia. The primary outcome was the percentage of time in range 70-180 mg/dL (TIR). Main secondary outcomes were time below range < 70 mg/dL (TBR < 70) and < 54 (TBR < 54). Data are presented as median (10th-90th percentile ranges).

RESULTS

The participants, 6 (54.5%) men, were 24 (22-48) years old, and had 22 (9-32) years of T1D duration. All of them regularly used an insulin pump and a continuous glucose monitoring system. The median TIR was 78.7% (75.6-91.2): 92.7% (68.2-100.0) during exercise and recovery period, 79.3% (34.9-100.0) during postprandial period, and 95.4% (66.4-100.0) during overnight period. The TBR < 70 and TBR < 54 were 0.0% (0.0-6.6) and 0.0% (0.0-1.2), respectively. A total of 4 (3-9) 15-g carbohydrate suggestions were administered per person. No severe acute complications occurred during the study.

CONCLUSIONS

The HCL system with automatic carbohydrate suggestion performed well and was safe in this population during challenging conditions in a hospital setting.