Longitudinal Average Glucose Levels and Variance and Risk of Stroke: A Chinese Cohort Study

Higher triglyceride‑glucose index is associated with increased risk of stroke among middle-aged and elderly Chinese: a national longitudinal study

Stroke is a severe cerebrovascular disease. This study aimed to determine the association between triglyceride‑glucose (TyG) index and stroke among middle-aged and elderly Chinese. Data was extracted from China Health and Retirement Longitudinal Study survey 2015 and survey 2018. Logistic regression, trend test and subgroup analysis were conducted to assess the association. Possible nonlinear relationships were explored with restricted cubic spline (RCS). Propensity score matching (PSM) was conducted to attenuate the effect of confounding factors. ORs of stroke was positively associated with TyG index. The ORs in RCS analysis also increased with the rising TyG, though p for non-linearity was bigger than 0.05. After PSM, the ORs in the full adjusted models were 1.28 (1.01, 1.62). TyG was suggested as an independent risk factor for stroke in the middle aged and elderly Chinese.

Metabolic syndrome parameters' variability and stroke incidence in hypertensive patients: evidence from a functional community cohort

BACKGROUND

Stroke is a common complication of hypertension, but the predictive value of metabolic syndrome parameters' variability on stroke risk in individuals with hypertension remains unclear. Therefore, our objective was to investigate the relationship between metabolic syndrome parameters' variability and the risk of total stroke and its subtypes in hypertensive patients.

METHODS

This prospective cohort study included 17,789 individuals with hypertension from the Kailuan study since 2006. Metabolic syndrome parameters, including waist circumference (WC), fasting blood glucose (FBG), systolic blood pressure (SBP), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG), were collected at three follow-up visits in the 2006, 2008, and 2010 surveys. We assess the variability utilizing the coefficient of variation (CV), standard deviation (SD), average real variation (ARV), and variability independent of the mean (VIM), with CV initially assessed. Participants were categorized based on the number of high-variability metabolic syndrome parameters (0, 1, 2, ≥ 3). Stroke cases were identified by reviewing medical records. The associations between variability in metabolic syndrome parameters and the risk of total stroke and its subtypes were analyzed using Cox proportional hazard regression models.

RESULTS

During a median follow-up of 9.32 years, 1223 cases of stroke were recorded. Participants with ≥ 3 high-variability metabolic syndrome parameters had an increased risk of total stroke (HR: 1.29, 95%CI 1.09-1.52), as well as an increased risk of ischemic stroke (HR: 1.31, 95%CI 1.05-1.63) compared to those without high-variability parameters. The study also examined variability in each metabolic syndrome parameter, and significant associations with an increased risk of total stroke were observed for variability in SBP (HR: 1.24, 95%CI 1.05-1.46) and HDL-C (HR: 1.34, 95%CI 1.09-1.64).

CONCLUSIONS

Long-term fluctuations in metabolic syndrome parameters significantly increase the risk of total stroke, especially ischemic stroke. Maintaining low variability in metabolic syndrome parameters could benefit health, and hypertensive individuals must be regularly monitored.

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

Background

Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.

Methods

A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.

Results

After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.

Conclusion

MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

Continuous Intravenous versus Subcutaneous Administration of Insulin for Glycemic Variability in Acute Ischemic Stroke

BACKGROUND

Continuous intravenous infusion (IV) or subcutaneous injection (SC) of insulin was widely applied to control hyperglycemia after ischemic stroke. However, the impact of different administration modes on glycemic variability was unknown.

METHODS

Consecutive stroke patients treated with intravenous thrombolysis were screened. Subjects who received insulin treatment were included and entered into the IV or SC group according to the respective administration mode. Blood glucose was closely monitored within the first 72 hours, and the target range of glucose was from 7.7 to 10.0 mmol/L for all patients. The variabilities of glucose, assessed using standard deviation of the mean, variable coefficient and range from the maximum to the minimum value, were compared between the two groups.

RESULTS

A total of 130 patients were enrolled with 66 in the IV groups and 64 in the SC group. Compared with the SC group, the IV group had higher glycemic variability evaluated as either standard deviation (2.7 ± 0.7 mmol/L vs 2.2 ± 0.9 mmol/L, p = 0.002), variable coefficient (0.26 ± 0.06 vs 0.23 ± 0.08, p = 0.011) or range (10.0 ± 3.6 mmol/L vs 8.1 ± 3.1 mmol/L, p = 0.001). Multivariate logistic regression analyses found that continuous intravenous infusion was associated with higher level of the standard deviation (adjusted OR 3.01, 95% CI 1.29-7.28, p = 0.011), variable coefficient (adjusted OR 5.97, 95% CI 2.55-13.96, p < 0.001) and range (adjusted OR 6.08, 95% CI 2.63-14.05, p < 0.001).

CONCLUSION

Continuous intravenous infusion of insulin was associated with higher glycemic variability than subcutaneous injection in acute stroke patients receiving thrombolysis.

Association between fasting blood glucose levels and stroke events: a large-scale community-based cohort study from China

OBJECTIVES

Diabetes mellitus has been associated with stroke. However, the association between fasting blood glucose (FBG) and stroke risk in a general population remains not clear. The purpose of our study was to examine the FBG levels on subsequent stroke risk in a community-based cohort in China.

DESIGN

Prospective cohort study, employing Cox proportional hazard model to analyse the association of FBG levels with stroke risk.

SETTING

A community-based cohort study included adults participating in a baseline survey conducted in 2013 in Changshu, eastern China.

PARTICIPANTS

16 113 participants were recruited with a multistage sampling method, excluding participants with severe disability, severe cancer, severe psychiatric disturbance or previous stroke before enrolment.

PRIMARY OUTCOME MEASURES

Stroke events.

RESULTS

During a median follow-up of 5.5 years, 417 incident cases of stroke were identified. The adjusted HR for total and ischaemic stroke for participants in the fourth quartile of FBG compared with the first quartile was 1.44 (95% CI 1.07 to 1.94) and 1.57 (95% CI 1.11 to 2.21), respectively. FBG levels of ≥7.0 mmol/L were associated with an increased risk of stroke based on two clinical classifications (American Diabetes Association: 1.68 (1.24 to 2.27); WHO: 1.62 (1.21, 2.13)). In stratified analyses, risk associations existed in women (HR: 1.92, 95% CI 1.22 to 3.01) and postmenopausal women (HR: 1.68, 95% CI 1.06 to 2.68) for the fourth quartile versus the first. More importantly, the meta-analysis observed a positive association between FBG levels and stroke risk (pooled HR: 1.70, 95% CI 1.27 to 2.29; n=7)).

CONCLUSIONS

Higher FBG level was independently associated with an increased risk of stroke in Chinese adults, especially significant in women.

Admission Glucose Levels May Increase the Risk for Early Neurological Deterioration in Females With Acute Ischemic Stroke

Background and purpose: Early neurological deterioration (END) is associated with poor outcome for patients with acute ischemic stroke (AIS). Patients with hyperglycemia have increased risk for stroke and tend to have poor outcome with and without diabetes after stroke. The present study aimed to determine if blood glucose was associated with END and if sex difference was present in the development of END in AIS patients.

Methods: A total of 220 consecutive patients (both males and females) with AIS between 2012 and 2015 were screened for this retrospective study. After exclusion, 213 patients were included for analysis. Propensity-score matching was used for normalization of variables including stroke severity, time from symptom onset to treatment, and treatment methods.

Results: END was present in 68 patients (31.9%). Multivariate regression analysis showed that the risk of END was significantly higher in males with AIS than in females (P < 0.001), and admission blood glucose level was independently associated with END (P < 0.001). However, subgroup analysis demonstrated that admission glucose levels were significantly associated with increased risk for END only in females, but not in males (P = 0.008). When the cutoff value of 107.1 mg/dL was used, the admission blood glucose level had a significant predictive value for END prediction with a sensitivity of 100% and a specificity of 53% in female patients.

Conclusions: The data demonstrated that sex difference was present for the development of END in AIS patients with an increased risk for males. The present study also showed that admission glucose level could be an important predicting factor for END in female patients with AIS.