High HbA1c is associated with higher risk of ischaemic stroke in Pakistani population without diabetes

Association of Glycated Hemoglobin With Acute Ischemic Stroke in a Tertiary Care Center in a Tribal Region of Jharkhand

BACKGROUND

Increased glycated hemoglobin (HbA1c) levels have shown an association with an increased risk of stroke in patients admitted to a tertiary care center in Jharkhand.

OBJECTIVES

To find out and estimate the risk of acute ischemic stroke in patients with increased HbA1c levels compared with controls.

METHODS

This observational case-control study was conducted on patients admitted to the department of general medicine at a tertiary care center in Ranchi from June 2021 to November 2022. The patients included in this study were those aged 18 years or older and who were clinically and radiologically diagnosed with acute ischemic stroke. Only patients with a first episode of stroke were included, and patients with hemorrhagic stroke or transient ischemic attack were excluded from this study. An equal number of control participants were also included. Ion exchange high-performance liquid chromatography was used to perform the HbA1c tests. The same method was used to measure HbA1c levels in the controls. All findings were recorded in a Microsoft Excel sheet (Microsoft Corporation, Redmond, WA), and the data were analyzed using SPSS version 22.0 software (IBM Corp., Armonk, NY). After performing a descriptive statistical analysis, the findings were classified over a range of values and described accordingly. For each variable, an independent t-test was performed to compare the cases with the controls. A multivariable logistic regression analysis was used to choose the appropriate potential factors to determine the association in the multivariable analysis.

RESULTS

A total of 185 cases and 185 controls were included. The mean age of the cases with ischemic stroke was 63.77 ± 10.312, and that of the controls was 53.18 ± 11.35 (p < 0.01). The mean HbA1c level in the patients of acute ischemic stroke was 6.97 ± 1.84, and that of the controls was 5.99 ± 1.69 (p < 0.01). The mean random blood sugar (RBS) value in the ischemic stroke cases was 170.21 ± 84.16, and that of the controls was 150.03 ± 82.25 (p = 0.02). To compare the factors that were determined to be statistically significant between ischemic stroke cases and controls, a multivariable logistic regression analysis was performed. The HbA1c p-value was 0.01, the odds ratio (OR) was 1.280, and the 95% CI was 1.11-1.48. The other variables apart from HbA1c that were statistically significant between the ischemic stroke cases and the controls were age (p < 0.01, OR: 1.280, 95% CI: 1.06-1.11), hypertension (p = 0.618, OR: 1.130, 95% CI: 0.70-1.83), and high-density lipoprotein (p = 0.055, OR: 0.975, 95% CI: 0.95-1.00). When other cofounders were considered, it was concluded that with a 1% increase in HbA1c, the risk of stroke increases by 28% (OR: 1.28, 95% CI: 1.11-1.48). To compare the variables that were determined to be statistically significant between the control and ischemic stroke case groups, a multivariable logistic regression was used. The area under the receiver operating characteristic curve for HbA1c was 0.773 and RBS was 0.600.

CONCLUSION

This study shows that higher HbA1c levels in patients increase the risk of ischemic stroke. This study brings to light the need to screen the population periodically for diabetes by routinely testing for Hba1c in those who are at high risk of diabetes. Stroke risk can be reduced with early management and intervention. This study also concludes that HbA1c is a better predictor for assessing the risk of ischemic stroke than RBS levels.

Association and pathways of baseline and longitudinal hemoglobin A1c with the risk of incident stroke: A nationwide prospective cohort study

AIMS

To investigate the association of baseline and long-term mean hemoglobin A1c (HbA1c) with the risk of stroke.

METHODS

A total of 11,220 participants aged over 45 years and without stroke at baseline were enrolled from the China Health and Retirement Longitudinal Study. Mean HbA1c was calculated as the mean of HbA1c at all previous visits before stroke occurred or the end of follow-up. Multivariable-adjusted Cox regressions and Bayesian network were used for the analysis.

RESULTS

During a median follow-up of 7.50 years, a total of 626 cases of stroke occurred. The risk of stroke increased with quintiles of baseline and mean HbA1c, the hazard ratio (HR) in Q5 versus Q1 was 1.30 (95 % confidence interval [CI],1.03-1.64) and 1.79 (95 % CI, 1.38-2.34), respectively. Per 1 unit increase in baseline and mean HbA1c was associated with 10 % (HR, 1.10; 95 % CI, 1.02-1.18) an 12 % (HR, 1.12; 95 % CI, 1.05-1.19) higher risk of stroke. Bayesian network analysis showed that the pathway from HbA1c to stroke was through hypertension, dyslipidemia, obesity, and inflammation.

CONCLUSIONS

Elevated levels of both baseline and long-term HbA1c were associated with increased risk of stroke, and hypertension and obesity played an important role in the pathway.

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

Purpose: Stroke has sparked global concern as it seriously threatens people's life, bringing about dramatic health burdens on patients, especially for type 2 diabetes mellitus (T2DM) patients. Therefore, a risk scoring model is urgently valuable for T2DM patients to predict the risk of stroke incidence and for positive health intervention. Methods: We randomly divided 4,335 T2DM patients into two groups, training set (n = 3,252) and validation set (n = 1,083), at the ratio of 3:1. Characteristic variables were then selected based on the data of training set through least absolute shrinkage and selection operator regression. Three models were established to verify predictive ability. Foundation model was composed of basic information and physical indicators. Biochemical model consisted of biochemical indexes. Integrated model combined the above two models. Data of three models were then put into logistic regression analysis to form nomogram prediction models. Tools including C index, calibration plot, and curve analysis were implemented to test discrimination, calibration, and clinical use. To select the best predicting model, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were put into effect. Results: Eleven risk factors were determined, including age, duration of T2DM, estimated glomerular filtration rate, systolic blood pressure, diastolic blood pressure, low-density lipoprotein, high-density lipoprotein, triglyceride, body mass index, uric acid, and glycosylated hemoglobin A_1c, all with significant P-values through logistic regression analysis. In the training set, areas under the curve of three models were 0.810, 0.819, and 0.884, whereas in the validation set, they were 0.836, 0.832, and 0.909. Through calibration plot, the S:P values in the training set were 0.836, 0.754, and 0.621 and were 0.918, 0.682, and 0.666 separately in the validation set. In terms of the decision curve analysis, the risk thresholds were, respectively, 8-73%, 8-98%, and 8%~ in the training set and 8-70%, 8-90%, and 8-95% in the validation set. With the aid of NRI and IDI, integrated model is proved to be the best model in training set and validation set. Besides, internal validation was conducted on all the subjects in this study, and the C index was 0.890 (0.873-0.907). Conclusion: This study established a model predicting risk of stroke for T2DM patients through a community-based survey.

Hemoglobin A1C Levels are Independently Associated with the Risk of Coronary Atherosclerotic Plaques in Patients without Diabetes: A Cross-Sectional Study

AIM

Coronary atherosclerotic plaques can be detected in asymptomatic subjects and are related to low-density lipoprotein cholesterol (LDL) levels in patients with coronary artery disease. However, researchers have not yet determined the associations between various plaque characteristics and other lipid parameters, such as HDL-C and TG levels, in low-risk populations.

METHODS

One thousand sixty-four non-diabetic subjects (age, 57.86±9.73 years; 752 males) who underwent coronary computed tomography angiography (CCTA) were enrolled and the severity and patterns of atherosclerotic plaques were analyzed.

RESULTS

Statin use was reported by 25% of the study population, and subjects with greater coronary plaque involvement (segment involvement score, SIS) were older and had a higher body mass index (BMI), blood pressure, unfavorable lipid profiles and comorbidities. After adjusting for comorbidities, only age (β=0.085, p＜0.001), the male gender (β=1.384, p＜0.001), BMI (β=0.055, p=0.019) and HbA1C levels (β=0.894, p＜0.001) were independent factors predicting the greater coronary plaque involvement in non-diabetic subjects. In the analysis of significantly different (＞50%) stenosis plaque patterns, age (OR: 1.082, 95% CI: 10.47-1.118) and a former smoking status (OR: 2.061, 95% CI: 1.013-4.193) were independently associated with calcified plaques. For partial calcified (mixed type) plaques, only age (OR: 1.085, 95% CI: 1.052-1.119), the male gender (OR: 7.082, 95% CI: 2.638-19.018), HbA1C levels (OR: 2.074, 95% CI: 1.036-4.151), and current smoking status (OR: 1.848, 95% CI: 1.089-3.138) were independently associated with the risk of the presence of significant stenosis in mixed plaques.

CONCLUSIONS

A higher HbA1c levels is independently associated with the presence and severity of coronary artery atherosclerosis in non-diabetic subjects, even when LDL-C levels are tightly controlled.

Towards stroke prediction using electronic health records

Background

As of 2014, stroke is the fourth leading cause of death in Japan. Predicting a future diagnosis of stroke would better enable proactive forms of healthcare measures to be taken. We aim to predict a diagnosis of stroke within one year of the patient’s last set of exam results or medical diagnoses.

Methods

Around 8000 electronic health records were provided by Tsuyama Jifukai Tsuyama Chuo Hospital in Japan. These records contained non-homogeneous temporal data which were first transformed into a form usable by an algorithm. The transformed data were used as input into several neural network architectures designed to evaluate efficacy of the supplied data and also the networks’ capability at exploiting relationships that could underlie the data. The prevalence of stroke cases resulted in imbalanced class outputs which resulted in trained neural network models being biased towards negative predictions. To address this issue, we designed and incorporated regularization terms into the standard cross-entropy loss function. These terms penalized false positive and false negative predictions. We evaluated the performance of our trained models using Receiver Operating Characteristic.

Results

The best neural network incorporated and combined the different sources of temporal data through a dual-input topology. This network attained area under the Receiver Operating Characteristic curve of 0.669. The custom regularization terms had a positive effect on the training process when compared against the standard cross-entropy loss function.

Conclusions

The techniques we describe in this paper are viable and the developed models form part of the foundation of a national clinical decision support system.

Review of acute ischaemic stroke in Pakistan: progress in management and future perspectives

Ischaemic stroke is a major cause of neurological morbidity and mortality. The objective of this review article is to summarise facts pertaining to acute ischaemic stroke and its various aspects in a developing country like Pakistan, where resources are limited and the healthcare system is underdeveloped. No large-scale epidemiological studies are available to determine the true incidence of stroke in Pakistan. We reviewed the available literature on stroke from Pakistan and through this article we primarily aim to present the current acute ischaemic stroke management in Pakistan in juxtaposition to that of the developed world. We also intend to highlight areas for future development and improvement in management. The routine practice in Pakistan is that of using stat dose of aspirin in emergency (ER) at large with only a handful of centres offering thrombolytic therapy with recombinant tissue plasminogen activator for acute ischaemic stroke. This too is faced with the problem of long window periods before the patient reaches a proper stroke care centre. The facilities of interventional therapies like arterial thrombolysis and endovascular surgery are non-existent and rehabilitation facilities limited. The opportunities for training physicians in acute stroke are also scarce. Stroke in children is underdiagnosed and that in women not availing facilities at stroke care centres. While basic research has gained pace regarding local demographic data, advanced research and genetic studies are extremely limited. The field of stroke neurology needs to grow at a substantial pace in Pakistan to be at par with the developed world.