Long-term glucose variability and incident cardiovascular diseases and all-cause mortality events in subjects with and without diabetes: Tehran Lipid and Glucose Study

Advances and Opportunities of luminescence Nanomaterial for bioanalysis and diagnostics

Luminescence nanomaterials (LNMs) have recently received great attention in biological analysis and sensing owing to their key advances in easy design and functionalization with high photostability, luminescence stability, low autofluorescence, and multiphoton capacity. The number of publications surrounding LNMs for biological applications has grown rapidly. LNMs based on Stokes and anti-Stokes shifts are powerful tools for biological analysis. Especially, unique properties of anti-Stokes luminescence such as upconversion nanoparticles (UCNPs) with an implementation strategy to use longer-wavelength excitation sources such as near-infrared (NIR) light can depth penetrate to biological tissue for bioanalysis and bioimaging. We observed that the LNMs-based metal-organic frameworks (MOFs) have been developed and paid attention to the field of bioimaging and luminescence-based sensors, because of their structural flexibility, and multifunctionality for the encapsulation of luminophores. This article provides an overview of innovative LNMs such as quantum dots (QDs), UCNPs, and LMOFs. A brief summary of recent progress in design strategies and applications of LNMs including pH and temperature sensing in biologically responsive platforms, pathogen detection, molecular diagnosis, bioimaging, photodynamic, and radiation therapy published within the past three years is highlighted. It was found that the integrated nanosystem of lab-on-a-chip (LOC) with nanomaterials was rapidly widespread and erupting in interest after the COVID-19 pandemic. The simple operation and close processes of the integration nanosystem together with the optimized size and low energy and materials consumption of biochips and devices allow their trend study and application to develop portable and intelligent diagnostics tools. The last part of this work is the introduction of the utilization use of LNMs in LOC applications in terms of microfluidics and biodevices.

Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning

BACKGROUND

The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourable prognosis, and abnormal GV is prevalent in ICUs. However, the impact of GV on the prognosis of AF patients in the ICU remains uncertain. This study aimed to evaluate the relationship between GV and all-cause mortality after ICU admission at short-, medium-, and long-term intervals in AF patients.

METHODS

Data was obtained from the Medical Information Mart for Intensive Care IV 3.0 database, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models, and restricted cubic spline analyses were used to assess the associations between GV and mortality outcomes. Subsequently, GV and other clinical features were used to construct machine learning (ML) prediction models for 30-day all-cause mortality after ICU admission.

RESULTS

The primary analysis cohort included 8989 AF patients (age 76.5 [67.7-84.3] years; 57.8% male), while the external validation cohort included 837 AF patients (age 72.9 [65.3-80.2] years; 67.4% male). Multivariate Cox proportional hazards models revealed that higher GV quartiles were associated with higher risk of 30-day (Q3: HR 1.19, 95%CI 1.04-1.37; Q4: HR 1.33, 95%CI 1.16-1.52), 90-day (Q3: HR 1.25, 95%CI 1.11-1.40; Q4: HR 1.34, 95%CI 1.29-1.50), and 360-day (Q3: HR 1.21, 95%CI 1.09-1.33; Q4: HR 1.33, 95%CI 1.20-1.47) all-cause mortality, compared with lowest GV quartile. Moreover, our data suggests that GV needs to be contained within 20.0%. Among all ML models, light gradient boosting machine had the best performance (internal validation: AUC [0.780], G-mean [0.551], F1-score [0.533]; external validation: AUC [0.788], G-mean [0.578], F1-score [0.568]).

CONCLUSION

GV is a significant predictor of ICU short-term, mid-term, and long-term all-cause mortality in patients with AF (the potential risk stratification threshold is 20.0%). ML models incorporating GV demonstrated high efficiency in predicting short-term mortality and GV was ranked anterior in importance. These findings underscore the potential of GV as a valuable biomarker in guiding clinical decisions and improving patient outcomes in this high-risk population.

Mediation analysis of brain magnetic resonance imaging variables with all-cause and cardiovascular disease-specific mortalities in persons with type 2 diabetes

AIM

Glucose variation (GV) has emerged as a predictor of morbidity and mortality in persons with diabetes. However, no study has examined whether brain magnetic resonance imaging (MRI) variables mediated the association between mortality and GV.

MATERIALS AND METHODS

This study was a retrospective cohort comprising 3,961 individuals with type 2 diabetes (T2D), whose electronic medical records were retrieved from a medical center between January 2001 and October 2021. GV was quantified using coefficient of variation of fasting plasma glucose (FPG-CV) and glycated hemoglobin (HbA1c). The MRI variables included the presence or absence of cerebrovascular abnormality and white matter hyperintensity (WMH). All deaths and deaths resulting from expanded cardiovascular disease (CVD) were identified through annual record linkage with National Death Datasets. Cox proportional hazards models were applied to evaluate associations of MRI variable or GV with mortality. Mediation analyses were performed to assess the relative contributions of MRI variables for GV on mortality.

RESULTS

Among 3,961 patients, 2,114 patients (53.4%) had cerebrovascular abnormality and 1,888 patients (47.7%) had WMH. The results showed cerebrovascular abnormality and WMHs were significantly associated with all-cause and expanded CVD mortality after considering GV. The largest mediated effects of GV on all-cause and expanded CVD mortality were observed by cerebrovascular abnormality (5.26% and 8.49%, respectively).

CONCLUSIONS

Our study suggests cerebrovascular abnormality and WMHs are important predictors of mortality in patients with T2D after considering GV. In addition, MRI variables of cerebrovascular abnormality expressed weak but significant mediation effect on the associations between GV and mortality.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

The association between index-year, average, and variability of the triglyceride-glucose index with health outcomes: more than a decade of follow-up in Tehran lipid and glucose study

BACKGROUND

The association between baseline triglyceride glucose index (TyG index) and incident non-communicable diseases, mainly in Asian populations, has been reported. In the current study, we aimed to evaluate the association between index-year, average, and visit-to-visit variability (VVV) of the TyG index with incident type 2 diabetes mellitus (T2DM), hypertension, cardiovascular disease (CVD), and all-cause mortality among the Iranian population.

METHODS

The study population included 5220 participants (2195 men) aged ≥ 30 years. TyG index was calculated as Ln (fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2). Average values of the TyG index and also VVV (assessed by the standard deviation (SD) and variability independent of mean) were derived during the exposure period from 2002 to 2011 (index-year). Multivariable Cox proportional hazards regression models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of the TyG index for incident different health outcomes.

RESULTS

During more than 6 years of follow-up after the index year, 290, 560, 361, and 280 events of T2DM, hypertension, CVD, and all-cause mortality occurred. 1-SD increase in the TyG index values at the index-year was independently associated with the incident T2DM [HR (95% CI) 2.50 (2.13-2.93)]; the corresponding values for the average of TyG index were 2.37 (2.03-2.76), 1.12 (0.99-1.26, pvalue = 0.05), 1.18 (1.01-1.36), and 1.29 (1.08-1.53) for incident T2DM, hypertension, CVD, and all-cause mortality, respectively. Compared to the first tertile, tertile 3 of VVV of the TyG index was independently associated with incident hypertension [1.33 (1.07-1.64), Ptrend <0.01]. Likewise, a 1-SD increase in VVV of the TyG index was associated with an 11% excess risk of incident hypertension [1.11 (1.02-1.21)]. However, no association was found between the VVV of the TyG index and other outcomes. Moreover, the impact of index-year and average values of the TyG index was more prominent among women regarding incident CVD (P for interactions < 0.05).

CONCLUSION

Although the higher TyG index at index-year and its VVV were only associated with the incident T2DM and hypertension, respectively, its average value was capable of capturing the risk for all of the health outcomes.

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Effect of glucose variability on the mortality of adults aged 75 years and over during the first year of the COVID-19 pandemic

BACKGROUND

To our knowledge, only one study has examined the association between glucose variability (GV) and mortality in the elderly population with diabetes. GV was assessed by HbA1c, and a J-shaped curve was observed in the relationship between HbA1c thresholds and mortality. No study of GV was conducted during the COVID-19 pandemic and its lockdown. This study aims to evaluate whether GV is an independent predictor of all-cause mortality in patients aged 75 years or older with and without COVID-19 who were followed during the first year of the COVID-19 pandemic and its lockdown measures.

METHODS

This was a retrospective cohort study of 407,492 patients from the AGED-MADRID dataset aged 83.5 (SD 5.8) years; 63.2% were women, and 29.3% had diabetes. GV was measured by the coefficient of variation of fasting plasma glucose (CV-FPG) over 6 years of follow-up (2015-2020). The outcome measure was all-cause mortality in 2020. Four models of logistic regression were performed, from simple (age, sex) to fully adjusted, to assess the effect of CV-FPG on all-cause mortality.

RESULTS

During follow-up, 34,925 patients died (14,999 women and 19,926 men), with an all-cause mortality rate of 822.3 per 10,000 person-years (95% confidence interval (CI), 813.7 to 822.3) (739 per 10,000; 95% CI 728.7 to 739.0 in women and 967.1 per 10,000; 95% CI 951.7 to 967.2 in men). The highest quartile of CV-FPG was significantly more common in the deceased group (40.1% vs. 23.6%; p < 0.001). In the fully adjusted model including dementia (Alzheimer's disease) and basal FPG, the odds ratio for mortality ranged from 1.88 to 2.06 in patients with T2DM and from 2.30 to 2.61 in patients with normoglycaemia, according to different sensitivity analyses.

CONCLUSIONS

GV has clear implications for clinical practice, as its assessment as a risk prediction tool should be included in the routine follow-up of the elderly and in a comprehensive geriatric assessment. Electronic health records can incorporate tools that allow its calculation, and with this information, clinicians will have a broader view of the medium- and long-term prognosis of their patients.

Antihyperglycemic activity of 14-deoxy, 11, 12-didehydro andrographolide on streptozotocin-nicotinamide induced type 2 diabetic rats

BACKGROUND

Diabetic Mellitus is characterized by a lack or failure of insulin to bind to its target receptor or failure of the pancreas to yield insulin. This study evaluated the antihyperglycemic activity of 14-deoxy, 11, 12-didehydro andrographolide on streptozotocin-nicotinamide-induced type 2 diabetic rats. Diabetic conditions were induced by administering streptozotocin at a dosage of 45 mg/kg body weight and nicotinamide at a dosage of 110 mg/kg body weight through intraperitoneal injection.

MATERIALS AND METHODS

Diabetic-induced rats were treated with 14-deoxy, 11, 12-didehydro andrographolide concentrations between 10 and 500 mg/kg body weight. The blood glucose level and body weight of the rats were periodically examined. The pancreas was isolated and the histopathological staining was performed after making fine sections of the pancreas using a microtome. The influence of 14-deoxy, 11, 12-didehydro andrographolide on the expression level of various insulin signaling cascades was determined with q-PCR and western blotting.

RESULTS

The blood glucose level of the diabetic-induced rats was significantly (p < 0.05) higher when compared with the control group and resulted in a drop in the blood glucose level of the diabetic rats. Oral glucose level was also reduced in the treatment group and no significant reduction was noted in the untreated. The lipid profiling revealed that the atherogenic index and cholesterol ratio was increased in the diabetic group over the control group. Upregulation of the insulin cascades like IRTK and GLUT4 was observed by the q-PCR and upregulation of GLUT4 and IR-β was observed by the western blot analysis.

CONCLUSION

Overall, the finding indicates that 14-deoxy, 11, 12-didehydro andrographolide exhibited antihyperglycemic activity by modulating the expression of insulin cascades.

Bedtime, sleep pattern, and incident cardiovascular disease in middle-aged and older Chinese adults: The dongfeng-tongji cohort study

OBJECTIVES

To investigate the associations of bedtime and a low-risk sleep pattern with incident cardiovascular disease (CVD).

METHODS

A total of 31,500 retirees were included from the Dongfeng-Tongji cohort in 2008-2010 and 2013. Sleep information was collected by questionnaires. CVD events were identified through the health care system until December 31, 2018. Cox proportional hazards regression models were performed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

During an average follow-up of 7.2 years, 8324 cases of incident CVD, including 6557 coronary heart disease (CHD) and 1767 stroke, were documented. U-shaped associations of bedtime with the risks of incident CVD and stroke were observed. Compared with bedtime between 10:01 p.m.-11:00 p.m., the HR (95% CI) for CVD was 1.10 (1.01-1.20) for ≤9:00 p.m., 1.07 (1.01-1.13) for 9:01 p.m.-10:00 p.m., and 1.32 (1.11-1.58) for >12:00 a.m., respectively, mainly driven by stroke risk (22%, 14%, and 70% higher for ≤9:00 p.m., 9:01 p.m.-10:00 p.m., and >12:00 a.m., respectively). The number of low-risk sleep factors, namely bedtime between 10:01 p.m.-12:00 a.m., sleep duration of 7-< 8 h/night, good/fair sleep quality, and midday napping ≤60 min, exhibited dose-dependent relationships with CVD, CHD, and stroke risks. Participants with 4 low-risk sleep factors had a respective 24%, 21%, and 30% lower risk of CVD, CHD, and stroke than those with 0-1 low-risk sleep factor.

CONCLUSIONS

Individuals with early or late bedtimes had a higher CVD risk, especially stroke. Having low-risk sleep habits is associated with lower CVD risks.

Visit-to-visit HbA1c variability is associated with aortic stiffness progression in participants with type 2 diabetes

BACKGROUND

Glycemic variability plays an important role in the development of cardiovascular disease (CVD). This study aims to determine whether long-term visit-to-visit glycemic variability is associated with aortic stiffness progression in participants with type 2 diabetes (T2D).

METHODS

Prospective data were obtained from 2115 T2D participants in the National Metabolic Management Center (MMC) from June 2017 to December 2022. Two brachial-ankle pulse wave velocity (ba-PWV) measurements were performed to assess aortic stiffness over a mean follow-up period of 2.6 years. A multivariate latent class growth mixed model was applied to identify trajectories of blood glucose. Logistic regression models were used to determine the odds ratio (OR) for aortic stiffness associated with glycemic variability evaluated by the coefficient of variation (CV), variability independent of the mean (VIM), average real variability (ARV), and successive variation (SV) of blood glucose.

RESULTS

Four distinct trajectories of glycated hemoglobin (HbA1c) or fasting blood glucose (FBG) were identified. In the U-shape class of HbA1c and FBG, the adjusted ORs were 2.17 and 1.21 for having increased/persistently high ba-PWV, respectively. Additionally, HbA1c variability (CV, VIM, SV) was significantly associated with aortic stiffness progression, with ORs ranging from 1.20 to 1.24. Cross-tabulation analysis indicated that the third tertile of the HbA1c mean and VIM conferred a 78% (95% confidence interval [CI] 1.23-2.58) higher odds of aortic stiffness progression. Sensitivity analysis demonstrated that the SD of HbA1c and the highest HbA1c variability score (HVS) were significantly associated with the adverse outcomes independent of the mean of HbA1c during the follow-up.

CONCLUSIONS

Long-term visit-to-visit HbA1c variability was independently associated with aortic stiffness progression, suggesting that HbA1c variability was a strong predictor of subclinical atherosclerosis in T2D participants.

The association between fasting plasma glucose variability and incident eGFR decline: evidence from two cohort studies

BACKGROUND

Glycemic variability (GV) is developing as a marker of glycemic control, which can be utilized as a promising predictor of complications. To determine whether long-term GV is associated with incident eGFR decline in two cohorts of Tehran Lipid and Glucose Study (TLGS) and Multi-Ethnic Study of Atherosclerosis (MESA) during a median follow-up of 12.2 years.

METHODS

Study participants included 4422 Iranian adults (including 528 patients with T2D) aged ≥ 20 years from TLGS and 4290 American adults (including 521 patients with T2D) aged ≥ 45 years from MESA. The Multivariate Cox proportional hazard models were used to assess the risk of incident eGFR decline for each of the fasting plasma glucose (FPG) variability measures including standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) both as continuous and categorical variables. The time of start for eGFR decline and FPG variability assessment was the same, but the event cases were excluded during the exposure period.

RESULTS

In TLGS participants without T2D, for each unit change in FPG variability measures, the hazards (HRs) and 95% confidence intervals (CI) for eGFR decline ≥ 40% of SD, CV, and VIM were 1.07(1.01-1.13), 1.06(1.01-1.11), and 1.07(1.01-1.13), respectively. Moreover, the third tertile of FPG-SD and FPG-VIM parameters was significantly associated with a 60 and 69% higher risk for eGFR decline ≥ 40%, respectively. In MESA participants with T2D, each unit change in FPG variability measures was significantly associated with a higher risk for eGFR decline ≥ 40%.Regarding eGFR decline ≥ 30% as the outcome, in the TLGS, regardless of diabetes status, no association was shown between FPG variability measures and risk of eGFR decline in any of the models; however, in the MESA the results were in line with those of GFR decline ≥ 40%.Using pooled data from the two cohorts we found that generally FPG variability were associated with higher risk of eGFR decline ≥ 40% only among non-T2D individuals.

CONCLUSIONS

Higher FPG variability was associated with an increased risk of eGFR decline in the diabetic American population; however, this unfavorable impact was found only among the non-diabetic Iranian population.

Remnant Cholesterol and Its Visit-to-Visit Variability Predict Cardiovascular Outcomes in Patients With Type 2 Diabetes: Findings From the ACCORD Cohort

OBJECTIVE

Remnant cholesterol (remnant-C) predicts atherosclerotic cardiovascular disease, regardless of LDL-cholesterol (LDL-C) levels. This study assessed the associations between remnant-C and cardiovascular outcomes in type 2 diabetes.

RESEARCH DESIGN AND METHODS

This post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial used patient (type 2 diabetes >3 months) remnant-C and major adverse cardiovascular event (MACE) data from the study database. The associations between remnant-C and MACEs were evaluated using Cox proportional hazards regression analyses. We examined the relative MACE risk in remnant-C versus LDL-C discordant/concordant groups using clinically relevant LDL-C targets by discordance analyses.

RESULTS

The baseline analysis included 10,196 participants, with further visit-to-visit variability analysis including 9,650 participants. During follow-up (median, 8.8 years), 1,815 patients (17.8%) developed MACEs. After adjusting for traditional cardiovascular risk factors, each 1-SD increase in remnant-C was associated with a 7% higher MACE risk (hazard ratio [HR] 1.07, 95% CI 1.02-1.12, P = 0.004). In the fully adjusted model, the visit-to-visit remnant-C variability calculated using logSD (HR 1.41, 95% CI 1.18-1.69, P < 0.001) and logARV (HR 1.45, 95% CI 1.22-1.73, P < 0.001) was associated with MACEs. Residual lipid risk (remnant-C ≥31 mg/dL) recognized individuals at a higher MACE risk, regardless of LDL-C concentrations. Within each LDL-C subgroup (>100 or ≤100 mg/dL), high baseline remnant-C was associated with a higher MACE risk (HR 1.37, 95% CI 1.09-1.73, P = 0.007; HR 1.22, 95% CI 1.04-1.41, P = 0.015, respectively).

CONCLUSIONS

Remnant-C levels were associated with MACEs in patients with type 2 diabetes independent of LDL-C, and visit-to-visit remnant-C variability helped identify those with higher cardiovascular risk.

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.

Associations of hypoglycemia, glycemic variability and risk of cardiac arrhythmias in insulin-treated patients with type 2 diabetes: a prospective, observational study

Background

Insulin-treated patients with type 2 diabetes (T2D) are at risk of hypoglycemia, which is associated with an increased risk of cardiovascular disease and mortality. Using a long-term monitoring approach, we investigated the association between episodes of hypoglycemia, glycemic variability and cardiac arrhythmias in a real-life setting.

Methods

Insulin-treated patients with T2D (N = 21, [mean ± SD] age 66.8 ± 9.6 years, BMI 30.1 ± 4.5 kg/m², HbA1c 6.8 ± 0.4% [51.0 ± 4.8 mmol/mol]) were included for a one-year observational study. Patients were monitored with continuous glucose monitoring ([mean ± SD] 118 ± 6 days) and an implantable cardiac monitor (ICM) during the study period.

Results

Time spend in hypoglycemia was higher during nighttime than during daytime ([median and interquartile range] 0.7% [0.7–2.7] vs. 0.4% [0.2–0.8]). The ICMs detected 724 episodes of potentially clinically significant arrhythmias in 12 (57%) participants, with atrial fibrillation and pauses accounting for 99% of the episodes. No association between hypoglycemia and cardiac arrhythmia was found during daytime. During nighttime, subject-specific hourly incidence of cardiac arrhythmias tended to increase with the occurrence of hypoglycemia (incident rate ratio [IRR] 1.70 [95% CI 0.36–8.01]) but only slightly with increasing time in hypoglycemia (IRR 1.04 [95% CI 0.89–1.22] per 5 min). Subject-specific incidence of cardiac arrhythmias during nighttime increased with increasing glycemic variability as estimated by coefficient of variation whereas it decreased during daytime (IRR 1.33 [95% CI 1.05–1.67] and IRR 0.77 [95% CI 0.59–0.99] per 5% absolute increase, respectively).

Conclusions

Cardiac arrhythmias were common in insulin-treated patients with T2D and were associated with glycemic variability, whereas arrhythmias were not strongly associated with hypoglycemia.

Trial registration: NCT03150030, ClinicalTrials.gov, registered May 11, 2017. https://clinicaltrials.gov/ct2/show/NCT03150030

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01425-0.