Trends and risk factors in severe hypoglycemia among individuals with type 2 diabetes in Korea

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS

The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.

METHODS

Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.

RESULTS

For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.

CONCLUSIONS/INTERPRETATION

Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland: A nationwide cohort study 2016-2022

AIMS

We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016-2022, stratifying by sociodemographics.

METHODS

Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016-2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv.

RESULTS

Rates for those under 20 with type 1 diabetes reached their minimum at the 2020-2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20-49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08-1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27-2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08-2.62) in type 2 diabetes.

CONCLUSIONS

Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.

Medication-Related Hospital Admissions and Emergency Department Visits in Older People with Diabetes: A Systematic Review

Limited data are available regarding adverse drug reactions (ADRs) and medication-related hospitalisations or emergency department (ED) visits in older adults with diabetes, especially since the emergence of newer antidiabetic agents. This systematic review aimed to explore the nature of hospital admissions and ED visits that are medication-related in older adults with diabetes. The review was conducted according to the PRISMA guidelines. Studies in English that reported on older adults (mean age ≥ 60 years) with diabetes admitted to the hospital or presenting to ED due to medication-related problems and published between January 2000 and October 2023 were identified using Medline, Embase, and International Pharmaceutical Abstracts databases. Thirty-five studies were included. Medication-related hospital admissions and ED visits were all reported as episodes of hypoglycaemia and were most frequently associated with insulins and sulfonylureas. The studies indicated a decline in hypoglycaemia-related hospitalisations or ED presentations in older adults with diabetes since 2015. However, the associated medications remain the same. This finding suggests that older patients on insulin or secretagogue agents should be closely monitored to prevent potential adverse events, and newer agents should be used whenever clinically appropriate.

Multimorbidity, Polypharmacy, Severe Hypoglycemia, and Glycemic Control in Patients Using Glucose-Lowering Drugs for Type 2 Diabetes: A Retrospective Cohort Study Using Health Insurance Claims in Japan

INTRODUCTION

This study aimed to understand the actual status of multimorbidity and polypharmacy among patients with type 2 diabetes using glucose-lowering drugs, and to assess the effects of patient characteristics on severe hypoglycemia and glycemic control.

METHODS

We designed a retrospective cohort study using health insurance claims and medical checkup data in Japan from April 2016 to February 2021 and identified patients with type 2 diabetes who were prescribed glucose-lowering drugs. We analyzed data on patient characteristics, including multimorbidity and polypharmacy, calculated the incidence rate for severe hypoglycemic events, applied a negative binomial regression model to explore factors that affected severe hypoglycemia, and analyzed the status of glycemic control in the subcohort for which HbA1c data were available.

RESULTS

Within the analysis population (n = 93,801), multimorbidity was present in 85.5% and mean ± standard deviation for oral drug prescriptions was 5.6 ± 3.5 per patient, while for those aged 75 years or older these numbers increased to 96.3% and 7.1 ± 3.5, respectively. The crude incidence rate for severe hypoglycemia was 5.85 (95% confidence interval 5.37, 6.37) per 1000 person-years. Risk factors for severe hypoglycemia included younger and older age, prior severe hypoglycemia, use of insulin, sulfonylurea, two-drug therapy including sulfonylurea or glinides, three-or-more-drug therapy, excessive polypharmacy, and comorbidities including end-stage renal disease (ESRD) requiring dialysis. Subcohort analysis (n = 26,746) showed that glycemic control is not always maintained according to guidelines.

CONCLUSION

Patients with type 2 diabetes, particularly older patients, experienced high multimorbidity and polypharmacy. Several risk factors for severe hypoglycemia were identified, most notably younger age, ESRD, history of severe hypoglycemia, and insulin therapy.

TRIAL REGISTRATION

The University Hospital Medical Information Network Clinical Trials Registry (UMIN000046736).

Comparison of Operational Definition of Type 2 Diabetes Mellitus Based on Data from Korean National Health Insurance Service and Korea National Health and Nutrition Examination Survey

BACKGROUND

We evaluated the validity and reliability of the operational definition of type 2 diabetes mellitus (T2DM) based on the Korean National Health Insurance Service (NHIS) database.

METHODS

Adult subjects (≥40 years old) included in the Korea National Health and Nutrition Examination Survey (KNHANES) from 2008 to 2017 were merged with those from the NHIS health check-up database, producing a cross-sectional dataset. We evaluated the sensitivity, specificity, accuracy, and agreement of the NHIS criteria for defining T2DM by comparing them with the KNHANES criteria as a standard reference.

RESULTS

In the study population (n=13,006), two algorithms were devised to determine from the NHIS dataset whether the diagnostic claim codes for T2DM were accompanied by prescription codes for anti-diabetic drugs (algorithm 1) or not (algorithm 2). Using these algorithms, the prevalence of T2DM was 14.9% (n=1,942; algorithm 1) and 20.8% (n=2,707; algorithm 2). Good reliability in defining T2DM was observed for both algorithms (Kappa index, 0.73 [algorithm 1], 0.63 [algorithm 2]). However, the accuracy (0.93 vs. 0.89) and specificity (0.96 vs. 0.90) tended to be higher for algorithm 1 than for algorithm 2. The validity (accuracy, ranging from 0.91 to 0.95) and reliability (Kappa index, ranging from 0.68 to 0.78) of defining T2DM by NHIS criteria were independent of age, sex, socioeconomic status, and accompanied hypertension or dyslipidemia.

CONCLUSION

The operational definition of T2DM based on population-based NHIS claims data, including diagnostic codes and prescription codes, could be a valid tool to identify individuals with T2DM in the Korean population.

Reproductive Life Span and Severe Hypoglycemia Risk in Postmenopausal Women with Type 2 Diabetes Mellitus

BACKGROUND

Estrogen promotes glucose homeostasis, enhances insulin sensitivity, and maintains counterregulatory responses in recurrent hypoglycemia in women of reproductive age. Postmenopausal women with type 2 diabetes mellitus (T2DM) might be more vulnerable to severe hypoglycemia (SH) events. However, the relationship between reproductive factors and SH occurrence in T2DM remains unelucidated.

METHODS

This study included data on 181,263 women with postmenopausal T2DM who participated in a national health screening program from January 1 to December 31, 2009, obtained using the Korean National Health Insurance System database. Outcome data were obtained until December 31, 2018. Associations between reproductive factors and SH incidence were assessed using Cox proportional hazards models.

RESULTS

During the mean follow-up of 7.9 years, 11,279 (6.22%) postmenopausal women with T2DM experienced SH episodes. A longer reproductive life span (RLS) (≥40 years) was associated with a lower SH risk compared to a shorter RLS (<30 years) (adjusted hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.69 to 0.80; P for trend <0.001) after multivariable adjustment. SH risk decreased with every 5-year increment of RLS (with <30 years as a reference [adjusted HR, 0.91; 95% CI, 0.86 to 0.95; P=0.0001 for 30-34 years], [adjusted HR, 0.80; 95% CI, 0.76 to 0.84; P<0.001 for 35-39 years], [adjusted HR, 0.74; 95% CI, 0.68 to 0.81; P<0.001 for ≥40 years]). The use of hormone replacement therapy (HRT) was associated with a lower SH risk than HRT nonuse.

CONCLUSION

Extended exposure to endogenous ovarian hormone during lifetime may decrease the number of SH events in women with T2DM after menopause.

Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record-Based Machine Learning: Development and Validation

BACKGROUND

Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed.

OBJECTIVE

The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes.

METHODS

We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used as the main criteria to evaluate model performance.

RESULTS

We included 29,843 patients with type 2 diabetes, of whom 2804 patients (9.4%) developed hypoglycemia. In this study, the embedding machine learning model (XGBoost3) showed the best performance among all the models. The AUC and the accuracy of XGBoost are 0.82 and 0.93, respectively. The XGboost3 was also superior to other models in DCA.

CONCLUSIONS

The Paragraph Vector-Distributed Memory model can effectively extract features and improve the performance of the XGBoost model, which can then effectively predict hypoglycemia in patients with type 2 diabetes.

Trends in the Incidence of Hospitalization for Major Diabetes-Related Complications in People With Type 1 and Type 2 Diabetes in Australia, 2010-2019

OBJECTIVE

To determine trends in the incidence of major diabetes-related complications in Australia.

RESEARCH DESIGN AND METHODS

This study included 70,885 people with type 1 and 1,089,270 people with type 2 diabetes registered on the Australian diabetes registry followed from July 2010 to June 2019. Outcomes (hospitalization for myocardial infarction [MI], stroke, heart failure [HF], lower-extremity amputation [LEA], hypoglycemia, and hyperglycemia) were obtained via linkage to hospital admissions databases. Trends over time in the age-adjusted incidence of hospitalizations were analyzed using joinpoint regression and summarized as annual percent changes (APCs).

RESULTS

In type 1 diabetes, the incidence of all complications remained stable, except for stroke, which increased from 2010-2011 to 2018-2019 (financial years; APC: +2.5% [95% CI 0.1, 4.8]), and hyperglycemia, which increased from 2010-2011 to 2016-2017 (APC: +2.7% [1.0, 4.5]). In type 2 diabetes, the incidence of stroke remained stable, while the incidence of MI decreased from 2012-2013 to 2018-2019 (APC: -1.7% [95% CI -2.8, -0.5]), as did the incidence of HF and hypoglycemia from 2010-2011 to 2018-2019 (APCs: -0.8% [-1.5, 0.0] and -5.3% [-6.7, -3.9], respectively); the incidence of LEA and hyperglycemia increased (APCs: +3.1% [1.9, 4.4], and +7.4% [5.9, 9.0]). Most trends were consistent by sex, but differed by age; in type 2 diabetes most improvements were confined to individuals aged ≥60 years.

CONCLUSIONS

Trends in admissions for diabetes-related complications were largely stable in type 1 diabetes. In type 2 diabetes, hospitalization rates for MI, HF, and hypoglycemia fell over time, while increasing for LEA and hyperglycemia.

The Risk Factors of Severe Hypoglycemia in Older Patients with Dementia and Type 2 Diabetes Mellitus

BACKGROUND

The adequate glycemic control and risk factors for hypoglycemia in older patients with dementia and type 2 diabetes mellitus (T2DM) remain unclear. This study aimed to analyze the status of glycemic control and determine the risk of hypoglycemia among these groups.

METHODS

A hospital admission record due to hypoglycemia through an emergency room with glucose supplementation in the Chang Gung Memorial Hospital was identified as a hypoglycemic event. Patients with dementia and T2DM without hypoglycemic events throughout the study period were defined as the control group. We gathered patients aged ≥65 years with a diagnosis of Alzheimer's dementia (AD) and T2DM between 2001 and 2018 in the Chang Gung Research Database (CGRD). We extracted data included medication use, diagnoses, and biochemistry data from hospital records.

RESULTS

A total of 3877 older patients with dementia and T2DM with regular visits to the outpatient department were enrolled in this study. During the two-year follow-up period, 494 participants (12.7%) experienced hypoglycemia. Multivariable logistic multivariable regression models for hypoglycemic events showed that metformin had a protective effect (odds ratio (OR) = 0.75, p = 0.023), insulin had the highest risk (OR = 4.64, p < 0.001). Hemoglobin A1c (HbA1c) levels were not correlated with hypoglycemic events (OR = 0.95, p = 0.140). Patients with hypoglycemic episodes had a significantly higher proportion of ≥2 Charlson Comorbidity Index scores than those without hypoglycemic episodes (83.2% versus 56.4%, p < 0.001).

CONCLUSIONS

Drug regimen affects hypoglycemic episodes but not HbA1c in older patients with dementia and T2DM. In addition, patients with more comorbidities experience an increased risk of hypoglycemia.

Association of long-term visit-to-visit variability of HbA1c and fasting glycemia with hypoglycemia in type 2 diabetes mellitus

BACKGROUND

Self-management of blood glucose levels to avoid hypoglycemia is vital for patients with type 2 diabetes mellitus (T2DM). The association between specific metrics of glycemic variability (glycosylated hemoglobin A1c [HbA1c] and fasting plasma glucose [FPG]) and severe hypoglycemia has not been fully studied in patients with T2DM.

METHODS

In this post hoc analysis, patients with established T2DM with a high risk of cardiovascular disease were included in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. The Cox proportional hazards model was used to investigate the relationship between glycemic variability and hypoglycemia requiring medical assistance (HMA) and hypoglycemia requiring any third-party assistance (HAA). The prognostic value of HbA1c/FPG variability for our predefined outcomes was compared using Harrell's C method.

RESULTS

After adjusting for confounders, each increase in HbA1c variability of 1 standard deviation (SD) indicated a higher risk of HAA (hazard ratio [HR]: 1.10; 95% confidence interval [CI]: 1.03-1.16; P < 0.01) and HMA events (HR: 1.11; 95% CI: 1.03-1.20; P < 0.01). Meanwhile, each increase in FPG variability of 1 SD increased the risk of HAA (HR: 1.40; 95% CI: 1.31-1.49; P < 0.01) and HMA events (HR: 1.46; 95% CI: 1.35-1.57; P < 0.01). Meanwhile, models, including FPG variability, had better prognostic value for our predefined outcomes than HbA1c variability (P < 0.01).

CONCLUSIONS

Increased visit-to-visit variability in HbA1c and fasting glycemia is associated with a greater risk of severe hypoglycemic events in T2DM patients. FPG variability is a more sensitive indicator than HbA1c variability.

TRIAL REGISTRATION

http://www.clinicaltrials.gov. Unique identifier: NCT00000620.