How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

Fasting Glucose Variability as a Risk Indicator for End-Stage Kidney Disease in Patients with Diabetes: A Nationwide Population-Based Study

Given the fact that diabetes remains a leading cause of end-stage kidney disease (ESKD), multi-aspect approaches anticipating the risk for ESKD and timely correction are crucial. We investigated whether fasting glucose variability (FGV) could anticipate the development of ESKD and identify the population prone to the harmful effects of GV. We included 777,192 Koreans with diabetes who had undergone health examinations more than three times in 2005-2010. We evaluated the risk of the first diagnosis of ESKD until 2017, according to the quartile of variability independent of the mean (VIM) of FG using multivariate-adjusted Cox proportional hazards analyses. During the 8-year follow-up, a total of 7290 incidents of ESKD were found. Subjects in the FG VIM quartile 4 had a 27% higher risk for ESKD compared to quartile 1, with adjustment for cardiovascular risk factors and the characteristics of diabetes. This effect was more distinct in patients aged < 65 years; those with a long duration of diabetes; the presence of hypertension or dyslipidemia; and prescribed angiotensin-converting enzyme inhibitors, metformin, sulfonylurea, α-glucosidase inhibitors, and insulin. In contrast, the relationship between baseline FG status and ESKD risk showed a U-shaped association. FGV is an independent risk factor for kidney failure regardless of FG.

In-Hospital Mortality and Glycemic Control in Patients with Hospital Hyperglycemia

BACKGROUND

Stress-induced hyperglycemia is a phenomenon that occurs typically in patients hospitalized for acute disease and resolves spontaneously after regression of the acute illness. However, it can also occur in diabetes patients, a fact that is sometimes overlooked. It is thus important to make a proper diabetes diagnosis if hospitalized patients with episodes of hyperglycemia with and without diabetes are studied.

AIMS

To estimate the extent of the association between stress-induced hyperglycemia and in-hospital mortality in patients with hospital hyperglycemia (HH), and to explore potential differences between patients diagnosed with diabetes (HH-DBT) and those with stress-induced hyperglycemia (SH), but not diagnosed with diabetes.

METHODS

A cohort of adults with hospital hyperglycemia admitted to a tertiary, university hospital in Buenos Aires, Argentina, was analyzed retrospectively.

RESULTS

In the study, 2,955 patients were included and classified for analysis as 1,579 SH and 1,376 HH-DBT. Significant differences were observed in glycemic goal (35.53% SH versus 25.80% HH-DBT, p < 0.01), insulin use rate (26.66% SH versus 46.58% HH-DBT, p < 0.01), and severe hypoglycemia rate (1.32% SH versus 1.74% HH-DBT, p < 0.01). There were no differences in hypoglycemia rate (8.23% SH versus 10.53% HH-DBT) and hospital mortality. There was no increase in risk of mortality in the SH group adjusted for age, non-scheduled hospitalization, major surgical intervention, critical care, hypoglycemia, oncological disease, cardiovascular comorbidity, and prolonged hospitalization.

CONCLUSIONS

In this study, we observed better glycemic control in patients with SH than in those with HH-DBT, and there was no difference in hospital mortality.

Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study

BACKGROUND

Previous research regarding long-term glucose variability over several years which is an emerging indicator of glycemic control in diabetes showed several limitations. We investigated whether variability in long-term fasting plasma glucose (FG) can predict the development of stroke, myocardial infarction (MI), and all-cause mortality in patients with diabetes.

METHODS

This is a retrospective cohort study using the data provided by the Korean National Health Insurance Corporation. A total of 624,237 Koreans ≥ 20 years old with diabetes who had undergone health examinations at least twice from 2005 to 2008 and simultaneously more than once from 2009 to 2010 (baseline) without previous histories of stroke or MI. As a parameter of variability of FG, variability independent of mean (VIM) was calculated using FG levels measured at least three times during the 5 years until the baseline. Study endpoints were incident stroke, MI, and all-cause mortality through December 31, 2017.

RESULTS

During follow-up, 25,038 cases of stroke, 15,832 cases of MI, and 44,716 deaths were identified. As the quartile of FG VIM increased, the risk of clinical outcomes serially increased after adjustment for confounding factors including duration and medications of diabetes and the mean FG. Adjusted hazard ratios (95% confidence intervals) of FG VIM quartile 4 compared with quartile 1 were 1.20 (1.16-1.24), 1.20 (1.15-1.25), and 1.32 (1.29-1.36) for stroke, MI and all-cause mortality, respectively. The impact of FG variability was higher in the elderly and those with a longer duration of diabetes and lower FG levels.

CONCLUSIONS

In diabetes, long-term glucose variability showed a dose-response relationship with the risk of stroke, MI, and all-cause mortality in this nationwide observational study.

A GLP-1 Analog Liraglutide Reduces Intimal Hyperplasia After Coronary Stent Implantation via Regulation of Glycemic Variability and NLRP3 Inflammasome/IL-10 Signaling in Diabetic Swine

Objective

This study aimed to explore whether treatment with the glucagon-like peptide-1 (GLP-1) analog liraglutide reduces intimal hyperplasia after coronary stent implantation via regulation of glycemic variability, the NLRP3 inflammasome, and IL-10 in diabetic swine.

Methods

Fifteen pigs were divided into a diabetes mellitus (DM) group (n = 6), a DM + liraglutide treatment group (L group) (n = 6) and a sham group (n = 3). A total of 24 everolimus-eluting stents were implanted in the left anterior descending and right coronary arteries at 3 weeks. A novel continuous glucose monitoring system (GMS) was used for 2 weeks. The means and standard deviations (SDs) were measured and calculated by the GMS. At 22 weeks, the lumen area (LA), neointimal thickness (NIT), neointimal area (NIA), and percent area stenosis (%AS) were analyzed by optical coherence tomography. Plasma tumor necrosis factor-α, interleukin-6, and interleukin-10 were assayed by ELISA. The intima protein expression levels of NLRP3, interleukin-1β, interleukin-18 and interleukin-10 were examined using Western blot analysis. Histology was used to evaluate the healing response. In an in vitro study, THP-1 cells were divided into control, high glucose (HG), HG + liraglutide, and HG + liraglutide + Exe(9-39) (a GLP-1 receptor inhibitor) groups.

Results

The L group had a lower SD, NIT, NIA, and %AS; a larger LA; reduced inflammation and injury scores; lower expression levels of tumor necrosis factor-α, interleukin-6, NLRP3, interleukin-1β, and interleukin-18; and higher expression of interleukin-10 compared with those of the DM group (p < 0.05). In the in vitro study, similar results were obtained in the HG + liraglutide group, and Exe(9-39) abolished the effect of liraglutide (p < 0.05).

Conclusions

Liraglutide treatment reduces intimal hyperplasia after stent implantation via regulation of glycemic variability, the NLRP3 inflammasome, and IL-10 in diabetic pigs in a GLP-1 receptor-dependent manner. Reducing the inflammation induced by glycemic variability may be one of the cardioprotective mechanisms of liraglutide.

Independent Association of Glucose Variability With Hospital Mortality in Adult Intensive Care Patients: Results From the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Binational Registry

Supplemental Digital Content is available in the text.

Wide variations in blood glucose excursions in critically ill patients may influence adverse outcomes such as hospital mortality. However, whether blood glucose variability is independently associated with mortality or merely captures the excess risk attributable to hyperglycemic and hypoglycemic episodes is not established. We investigated whether blood glucose variability independently predicted hospital mortality in nonhyperglycemic critical care patients.

Glucose Variability and Coronary Artery Disease

Fasting blood glucose, postprandial blood glucose and glycated haemoglobin are considered three important indicators for diabetes treatment. There is increasing evidence that glucose variability has more detrimental effects on the coronary arteries than does chronic sustained hyperglycaemia. This overview summarises recent findings in the field of glucose variability and its possible relationship with coronary artery disease. Glucose variability may be a marker of increased progression of coronary disease and plaque vulnerability. It might be a potential new therapeutic target for secondary prevention of coronary artery disease. Future studies will focus on the early detection and control of glucose variability to improve the clinical outcomes in patients with coronary artery disease.

Relationship of Glucose Variability With Glycated Hemoglobin and Daily Mean Glucose: A Post Hoc Analysis of Data From 5 Phase 3 Studies

BACKGROUND

The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG).

METHODS

Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics.

RESULTS

Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group.

CONCLUSIONS

Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.

Lixisenatide reduces glycaemic variability in insulin-treated patients with type 2 diabetes

Chronic hyperglycaemia and glucose variability are associated with the development of chronic diabetes-related complications. We conducted a pooled analysis of patient-level data from three 24-week, randomized, phase III clinical trials to evaluate the impact of lixisenatide (LIXI) on glycaemic variability (GV) vs placebo as add-on to basal insulin. The main outcome GV measures were standard deviation (s.d.), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG) level, area under the curve for fasting glucose (AUC-F), and high (HBGI) and low blood glucose index (LBGI). The change in GV metrics over 24 weeks and relationships among baseline GV, patient characteristics and outcomes were assessed. Data were pooled from 1198 patients (665 LIXI, 533 placebo). Values for s.d., MAG level, MAGE, HBGI, and AUC-F significantly decreased with LIXI vs placebo, while LBGI values were unchanged. Higher baseline GV measures correlated with older age, longer type 2 diabetes duration, lower body mass index, higher baseline glycated/haemogobin, greater reduction in postprandial glucose (PPG) level, and higher rates of symptomatic hypoglycaemia. These data show that LIXI added to basal insulin significantly reduced GV and PPG excursions vs placebo, without increasing the risk of hypoglycaemia (LBGI).

Glucose Variability Measures as Predictors of Oral Feeding Intolerance in Acute Pancreatitis: A Prospective Pilot Study

BACKGROUND

Oral feeding intolerance (OFI) is a common complication in patients with acute pancreatitis (AP). Variations in blood glucose are associated with impaired gastrointestinal function but, to date, measures of glucose variability have not been investigated to predict OFI in patients with AP.

AIM

To investigate the usefulness of several glucose variability measures in predicting the occurrence of OFI early in the course of AP.

METHODS

In this prospective cohort study, six measures of glucose variability were calculated prior to the occurrence of OFI. Multivariate binary logistic regression analyses were conducted, and the diagnostic performance and accuracy of glucose variability measures were assessed.

RESULTS

Of the 95 prospectively enrolled patients, 21 (22%) developed OFI. After adjusting for confounders, admission blood glucose concentration and mean blood glucose concentration were significantly associated with OFI [odds ratio 1.49 (95% confidence interval 1.01-2.20) and odds ratio 1.67 (95% confidence interval 1.07-2.61), respectively]. Both admission blood glucose and mean blood glucose had an area under the curve of 0.83 and positive likelihood ratios of 6.45 and 10.19, respectively. Blood glucose concentration before refeeding, standard deviation of blood glucose concentration, coefficient of variation, and mean amplitude of glycemic excursions were not significantly associated with OFI.

CONCLUSION

In-hospital blood glucose concentrations are associated with subsequent development of OFI in patients with AP. In particular, admission blood glucose and mean blood glucose could be useful predictors of OFI in this setting.

Sensitivity of Traditional and Risk-Based Glycemic Variability Measures to the Effect of Glucose-Lowering Treatment in Type 2 Diabetes Mellitus

BACKGROUND

Here we assess associations between glycemic variability (GV) measures and outcomes from glucose-lowering therapy in patients with type 2 diabetes (T2DM) to identify the metrics most sensitive to treatment response.

METHODS

Data from 1699 patients in 6 previously reported studies in adults with T2DM treated with basal insulin and/or oral glucose-lowering drugs were included in a post hoc meta-analysis. Using 7-point blood glucose (BG) profiles we compared the GV metrics standard deviation (SD), mean amplitude of glycemic excursion (MAGE), mean absolute glucose (MAG), low and high BG risk indices (LBGI, HBGI), and average daily risk range (ADRR). Treatment-related changes in GV and risk status and associations between end-of-trial GV/risk metrics with treatment outcomes (end-of-trial glycated hemoglobin A1c[A1C] level ≥7.0%, hypoglycemia, and composite outcome of A1C <7.0% and no hypoglycemia), were evaluated.

RESULTS

Significant changes from baseline to end of treatment were observed in all measures (all P < .0001), with the largest reduction following treatment for HBGI (-65.5%) and ADRR (-43.3%). The baseline risk classification for hyperglycemia based on the risk categories of HBGI improved for 66.8%, remained unchanged for 29.8%, and deteriorated for 3.3% of patients (chi-square P < .0001), while the risk for hypoglycemia did not change. HBGI showed the strongest association with A1C ≥7.0% at the end of treatment, and LBGI showed the strongest association with symptomatic hypoglycemia.

CONCLUSIONS

During glucose-lowering therapy in T2DM, HBGI and LBGI offer insights into hyperglycemia and trends toward hypoglycemia, respectively; ADRR may be the optimal GV measure responsive to hypo- and hyperglycemic treatment effects.