Prognostic impact of type 1 and type 2 diabetes mellitus in atrial fibrillation and the effect of severe hypoglycaemia: A nationwide cohort study

Estimating risk of consequences following hypoglycaemia exposure using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS

Whether hypoglycaemia increases the risk of other adverse outcomes in diabetes remains controversial, especially for hypoglycaemia episodes not requiring assistance from another person. An objective of the Hypoglycaemia REdefining SOLutions for better liVEs (Hypo-RESOLVE) project was to create and use a dataset of pooled clinical trials in people with type 1 or type 2 diabetes to examine the association of exposure to all hypoglycaemia episodes across the range of severity with incident event outcomes: death, CVD, neuropathy, kidney disease, retinal disorders and depression. We also examined the change in continuous outcomes that occurred following a hypoglycaemia episode: change in eGFR, HbA1c, blood glucose, blood glucose variability and weight.

METHODS

Data from 84 trials with 39,373 participants were pooled. For event outcomes, time-updated Cox regression models adjusted for age, sex, diabetes duration and HbA1c were fitted to assess association between: (1) outcome and cumulative exposure to hypoglycaemia episodes; and (2) outcomes where an acute effect might be expected (i.e. death, acute CVD, retinal disorders) and any hypoglycaemia exposure within the last 10 days. Exposures to any hypoglycaemia episode and to episodes of given severity (levels 1, 2 and 3) were examined. Further adjustment was then made for a wider set of potential confounders. The within-person change in continuous outcomes was also summarised (median of 40.4 weeks for type 1 diabetes and 26 weeks for type 2 diabetes). Analyses were conducted separately by type of diabetes.

RESULTS

The maximally adjusted association analysis for type 1 diabetes found that cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of neuropathy, kidney disease, retinal disorders and depression, with risk ratios ranging from 1.55 (p=0.002) to 2.81 (p=0.002). Associations of a similar direction were found when level 1 episodes were examined separately but were significant for depression only. For type 2 diabetes cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of death, acute CVD, kidney disease, retinal disorders and depression, with risk ratios ranging from 2.35 (p<0.0001) to 3.00 (p<0.0001). These associations remained significant when level 1 episodes were examined separately. There was evidence of an association between hypoglycaemia episodes of any kind in the previous 10 days and death, acute CVD and retinal disorders in both type 1 and type 2 diabetes, with rate ratios ranging from 1.32 (p=0.017) to 2.68 (p<0.0001). These associations varied in magnitude and significance when examined separately by hypoglycaemia level. Within the range of hypoglycaemia defined by levels 1, 2 and 3, we could not find any evidence of a threshold at which risk of these consequences suddenly became pronounced.

CONCLUSIONS/INTERPRETATION

These data are consistent with hypoglycaemia being associated with an increased risk of adverse events across several body systems in diabetes. These associations are not confined to severe hypoglycaemia requiring assistance.

Cardiovascular disease in type 1 diabetes, an underestimated danger: Epidemiological and pathophysiological data

Cardiovascular disease (CV) is a common complication of type 1 diabetes (T1D) and a leading cause of death. T1D patients are more likely to develop CV disease (CVD) early in life and show a reduction of life expectancy of at least 11 years. Patients with a young age of T1D onset have a substantially higher CV risk. The reasons for increased atherosclerosis in T1D patients are not entirely explained. In addition to the typical CV risk factors, long-term hyperglycemia has a significant impact by inducing oxidative stress, vascular inflammation, monocyte adhesion, arterial wall thickening and endothelial dysfunction. Additionally, CVD in T1D is also associated with nephropathy. However, CVD risk is still significantly increased in T1D patients, in good glycemic control without additional CV risk factors, indicating the involvement of supplementary potential factors. By increasing oxidative stress, vascular inflammation, and endothelial dysfunction, hypoglycemia and glucose variability may exacerbate CVD. Moreover, significant qualitative and functional abnormalities of lipoproteins are present in even well-controlled T1D patients and are likely to play a role in the development of atherosclerosis and the promotion of CVD. According to recent research, immune system dysfunction, which is typical of auto-immune T1D, may also promote CVD, likely via inflammatory pathways. In addition, T1D patients who are overweight or obese exhibit an additional CV risk due to pathophysiological mechanisms that are similar to those seen in T2D.

A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study

BACKGROUND AND OBJECTIVE

Patients with both coronary artery disease (CAD) and atrial fibrillation (AF) are at a high risk of major adverse cardiovascular and cerebrovascular events (MACCE) during hospitalization. Accurate prediction of MACCE can help identify high-risk patients and guide treatment decisions. This study was to elaborate and validate a dynamic nomogram for predicting the occurrence of MACCE during hospitalization in Patients with CAD combined with AF.

METHODS

A total of 3550 patients with AF and CAD were collected. They were randomly assigned to a training group and a validation group in a ratio of 7 : 3. Univariate and multivariate analyses were utilized to identify risk factors (P < 0.05). To avoid multicollinearity and overfit of the model, the least absolute shrinkage and selection operator was conducted to further screen the risk factors. Calibration curves, receiver operating characteristic curves, and decision curve analyses are employed to assess the nomogram. For external validation, a cohort consisting of 249 patients was utilized from the Medical Information Mart for Intensive Care IV Clinical Database, version 2.2.

RESULTS

Eight indicators with statistical differences were screened by univariate analysis, multivariate analysis, and the least absolute shrinkage and selection operator method (P < 0.05). The prediction model based on eight risk factors demonstrated good prediction performance in the training group, with an area under the curve (AUC) of 0.838. This performance was also maintained in the internal validation group (AUC = 0.835) and the external validation group (AUC = 0.806). Meanwhile, the calibration curve indicates that the nomogram was well-calibrated, and decision curve analysis revealed that the nomogram exhibited good clinical utility.

CONCLUSION

The nomogram we constructed may aid in stratifying the risk and predicting the prognosis for patients with CAD and AF.

Diabetes and risk of heart failure in people with and without cardiovascular disease: systematic review and meta-analysis

BACKGROUND

People with diabetes have an increased risk of heart failure (HF), compared to those without diabetes. However, no comprehensive systematic review and meta-analysis has explored whether these associations could differ in relation to prevalent cardiovascular disease (CVD).

AIMS

To estimate the association between diabetes and incident heart failure (HF), compared to without diabetes, in individuals with and without CVD.

METHODS

PubMed, Scopus, and Web of Science were searched for observational cohort studies from the earliest dates to 22nd March 2023. A random-effects model calculated the pooled relative risk (RR).

RESULTS

Of 11,609 articles, 31 and 6 studies reported data in people with type 2 diabetes (T2D) and type 1 diabetes (T1D) respectively. Individuals with T2D had an increased risk of HF irrespective of CVD prevalence: 1.61 (95% CI: 1.35-1.92) in those with CVD; 1.78 (1.60-1.99) without CVD; and 2.02 (1.75-2.33) with unspecified CVD prevalence. Meta-regression did not identify a significant difference comparing HF risk in T2D individuals with vs. without CVD (p = 0.232).

CONCLUSION

Peoplewith T2D, compared to those without diabetes, have similar increased risk of HF, regardless of CVD prevalence. Strategiesproven to lower HF risk in T2D individuals should be prioritized for those with and without CVD.