Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

The Contribution of Postprandial Glucose Levels to Hyperglycemia in Type 2 Diabetes Calculated from Continuous Glucose Monitoring Data: Real World Evidence from the DIALECT-2 Cohort

BACKGROUND/OBJECTIVES

Traditional glycemic monitoring in type 2 diabetes is limited, whereas continuous glucose monitoring (CGM) offers better insights into glucose fluctuations. This study aimed to determine the correlations and relative contributions of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels to hyperglycemia.

METHODS

We utilized CGM and recorded carbohydrate intake data from lifestyle diaries of 59 patients enrolled in the Diabetes and Lifestyle Cohort Twente (DIALECT-2). Correlations between FPG and the glucose management indicator (GMI), FPG and Time Above Range (TAR), PPG and GMI, and PPG and TAR were conducted. Daily and mealtime relative contributions of PPG and FPG to glycated hemoglobin (HbA1c) and GMI were determined, considering two ranges: on target (<7.0%, 53 mmol/mol) and not on target (≥7.0%, 53 mmol/mol). Correlations between mealtime PPG and carbohydrate consumption were examined.

RESULTS

FPG and PPG correlated with GMI (r = 0.82 and 0.41, respectively, p < 0.05). The relative contribution of PPG in patients with HbA1c, GMI, and TAR values not on target was lower than in patients with HbA1c, GMI, and TAR values on target. When analyzing different mealtimes, patients with target GMI values had a higher PPG (73 ± 21%) than FPG after breakfast (27 ± 21%, p < 0.001). Individuals with elevated GMI levels had lower PPG after lunch (30 ± 20%), dinner (36 ± 23%), and snacks (34 ± 23%) than FPG. PPG after breakfast positively correlated (r = 0.41, p < 0.01) with breakfast carbohydrate intake.

CONCLUSIONS

Both PPG and FPG contribute to hyperglycemia, with PPG playing a larger role in patients with better glycemic control, especially after breakfast. Targeting PPG may be crucial for optimizing glucose management.

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study

BACKGROUND

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

OBJECTIVE

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

METHODS

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

RESULTS

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

CONCLUSIONS

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Serum T50 predicts cardiovascular mortality in individuals with type 2 diabetes: A prospective cohort study

BACKGROUND AND AIMS

Individuals with type 2 diabetes (T2D) have a higher risk of cardiovascular disease, compared with those without T2D. The serum T50 test captures the transformation time of calciprotein particles in serum. We aimed to assess whether serum T50 predicts cardiovascular mortality in T2D patients, independent of traditional risk factors.

METHODS

We analyzed 621 individuals with T2D in this prospective cohort study. Cox regression models were performed to test the association between serum T50 and cardiovascular and all-cause mortality. Causes of death were categorized according to ICD-10 codes. Risk prediction improvement was assessed by comparing Harrell's C for models without and with T50. RESULTS: The mean age was 64.2 ± 9.8 years, and 61% were male. The average serum T50 time was 323 ± 63 min. Higher age, alcohol use, high-sensitive C-reactive protein, and plasma phosphate were associated with lower serum T50 levels. Higher plasma triglycerides, venous bicarbonate, sodium, magnesium, and alanine aminotransferase were associated with higher serum T50 levels. After a follow-up of 7.5[5.4-10.7] years, each 60 min decrease in serum T50 was associated with an increased risk of cardiovascular (fully adjusted HR 1.32, 95% CI 1.08-1.50, and p = 0.01) and all-cause mortality (HR 1.15, 95%CI 1.00-1.38, and p = 0.04). Results were consistent in sensitivity analyses after exclusion of individuals with estimated glomerular filtration rate <45 or <60 mL/min/1.73 m2 and higher plasma phosphate levels.

CONCLUSIONS

Serum T50 improves prediction of cardiovascular and all-cause mortality risk in individuals with T2D. Serum T50 may be useful for risk stratification and to guide therapeutic strategies aiming to reduce cardiovascular mortality in T2D.

Correlation between time on target and glycated hemoglobin in people with diabetes mellitus: systematic review

to analyze the correlation between time on target and glycated hemoglobin in people living with diabetes mellitus and carrying out continuous blood glucose monitoring or self-monitoring of capillary blood glucose.

systematic review of etiology and risk based on JBI guidelines and reported according to Preferred Reporting Items for Systematic Reviews and Meta- Analyses, covering six databases and grey literature. The sample included 16 studies and methodological quality was assessed using JBI tools. Protocol registered in the Open Science Framework, available at https://doi.org/10.17605/OSF.IO/NKMZB.

time on target (70-180 mg/dl) showed a negative correlation with glycated hemoglobin, while time above target (>180 mg/dl) showed a positive correlation. Correlation coefficients ranged between -0.310 and -0.869 for time on target, and between 0.66 and 0.934 for time above target. A study was carried out on a population that performed self-monitoring.

there is a statistically significant correlation between time on target and time above target with glycated hemoglobin. The higher the proportion in the adequate glycemic range, the closer to or less than 7% the glycated hemoglobin will be. More studies are needed to evaluate this metric with data from self-monitoring of blood glucose.

Impact of cumulative hyperglycemic burden on the pancreatic cancer risk: A nationwide cohort study

AIMS

We aimed to investigate how much cumulative hyperglycemia exposure increases pancreatic cancer risk.

METHODS

This study used the National Health Insurance Service Database of Claims and included 3,138,099 individuals who underwent four consecutive annual health screenings between 2009 and 2013. We defined hyperglycemic burden in two ways. First, the hyperglycemic burden was given a score from 0 to 4, with one point assigned for each time blood glucose was ≥100 mg/dL or the use of an antidiabetic drug. Furthermore, we performed semiquantitative scoring of a pre-diabetic (100-125; 1 point) and diabetic level (≥126; 2 points) and categorized into one of nine groups (hyperglycemic score 0-8).

RESULTS

During the median 6.2 years of follow-up, groups with a hyperglycemic burden of 1, 2, 3, and 4 had a 15%, 30%, 26%, and 67% increased pancreatic cancer risk compared with normal subjects. In semiquantitative analyses, individuals with a pre-diabetic glucose level on at least one occasion had a 14% increased the risk. Furthermore, individuals with a burden score of 8 had an 89% higher risk than subjects with a normal range.

CONCLUSIONS

The pancreatic cancer incidence increased significantly according to the hyperglycemic burden, defined as sustained hyperglycemic exposure, including pre-diabetic levels.

The Importance of Continuous Glucose Monitoring-derived Metrics Beyond HbA1c for Optimal Individualized Glycemic Control

CONTEXT

Current guidelines recommend assessing glycemic control using continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) measurement.

OBJECTIVE

This study aimed to clarify the characteristics of patients who might benefit from CGM metrics in addition to HbA1c monitoring.

METHODS

CGM metrics, specifically time in range (TIR), time below range (TBR), and time above range (TAR), were determined in 999 outpatients with type 2 diabetes and compared between HbA1c categories (HbA1c < 53 mmol/mol [7.0%, HbA1c <  53], HbA1c 53-63 mmol/mol [7.0-7.9%, HbA1c 53-63], HbA1c 64-74 mmol/mol [8.0-8.9%, HbA1c 64-74], and HbA1c ≥ 75 mmol/mol [9.0%, HbA1c ≥  75]) and between patients with identical HbA1c categories who were stratified by age, types of antidiabetic agents, and renal function.

RESULTS

For HbA1c <  53 category, patients aged ≥ 65 years had a significantly higher nocturnal TBR than those aged < 65 years. For HbA1c <  53 and HbA1c 53-63 categories, patients receiving insulin and/or sulfonylureas had a significantly higher TAR and TBR, and a lower TIR than those not receiving these drugs, and for HbA1c 64-74 category, they had a significantly higher TBR. For HbA1c <  53, HbA1c 53-63, and HbA1c 64-74 categories, patients with an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 had a significantly higher TBR during some periods than those with an eGFR ≥ 60.

CONCLUSION

Higher HbA1c levels do not always protect against hypoglycemic episodes. Our data demonstrate that using CGM metrics to complement HbA1c monitoring is beneficial, especially in older people, users of insulin and/or sulfonylureas, and patients with chronic kidney disease.

Magnitude of Glycemic Improvement in Patients with Type 2 Diabetes Treated with Basal Insulin: Subgroup Analyses from the MOBILE Study

Objective: To determine if type 2 diabetes patients using basal insulin without prandial insulin with worse glycemic control at baseline would have the greatest benefit from using real-time continuous glucose monitoring (CGM). Methods: We conducted a post hoc analysis of the MOBILE Study, a multicenter trial examining the impact of CGM versus self-monitoring with a blood glucose meter (BGM) in patients with type 2 diabetes treated with basal insulin without prandial insulin. Participants were divided into subgroups based on baseline hemoglobin A1c (HbA1c) and baseline time-in-range 70-180 mg/dL (TIR). Change in TIR from baseline was calculated within each subgroup. Results: In subgroups based on baseline HbA1c, compared with the BGM group, the CGM group had 14% greater increase in TIR for participants with baseline HbA1c ≥8.5%, 14% greater increase for baseline HbA1c ≥9.0%, 22% greater increase for baseline HbA1c ≥9.5%, and 32% greater increase for baseline HbA1c ≥10.0% (P-value for interaction = 0.27). The time spent with glucose >250 mg/dL was significantly lower with CGM compared with BGM among participants with higher HbA1c values (P for interaction = 0.004). Results in subgroups based on baseline TIR paralleled the results in subgroups based on baseline HbA1c. Conclusion: While the benefit of CGM on TIR among patients with type 2 diabetes treated with basal insulin is apparent across the range of baseline glycemic control, the greatest impact of CGM is in those with the worst baseline glycemic control, particularly among those with HbA1c ≥10%. Clinical Trial Registration number: NCT03566693.

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Discrete Markov chains are frequently used to analyse transition behaviour in sequential data. Here, the transition probabilities can be estimated using varying order Markov chains, where order k specifies the length of the sequence history that is used to model these probabilities. Generally, such a model is fitted to the entire dataset, but in practice it is likely that some heterogeneity in the data exists and that some sequences would be better modelled with alternative parameter values, or with a Markov chain of a different order. We use the framework of Exceptional Model Mining (EMM) to discover these exceptionally behaving sequences. In particular, we propose an EMM model class that allows for discovering subgroups with transition behaviour of varying order. To that end, we propose three new quality measures based on information-theoretic scoring functions. Our findings from controlled experiments show that all three quality measures find exceptional transition behaviour of varying order and are reasonably sensitive. The quality measure based on Akaike’s Information Criterion is most robust for the number of observations. We furthermore add to existing work by seeking for subgroups of sequences, as opposite to subgroups of transitions. Since we use sequence-level descriptive attributes, we form subgroups of entire sequences, which is practically relevant in situations where you want to identify the originators of exceptional sequences, such as patients. We show this relevance by analysing sequences of blood glucose values of adult persons with diabetes type 2. In the experiments, we find subgroups of patients based on age and glycated haemoglobin (HbA1c), a measure known to correlate with average blood glucose values. Clinicians and domain experts confirmed the transition behaviour as estimated by the fitted Markov chain models.