Stroke Risk Factors in United States: An Analysis of the 2013-2018 National Health and Nutrition Examination Survey

Disease burden of stroke and its subtypes attributable to low dietary fiber in China, 1990-2019

This study aimed to assess the current status and changing trends of the disease burden of stroke and its subtypes due to low dietary fiber intake in China from 1990 to 2019. In cases of stroke and its subtypes attributable to low dietary fiber, deaths, disability-adjusted life-years (DALYs), age-standardized mortality rates (ASMR), age-standardized DALYs rates (ASDR), and percentage change were used to assess disease burden. Data were obtained from the 2019 global burden of disease study. Trends were assessed using Joinpoint regression and age-period-cohort analysis. Between 1990 and 2019, there was a declining trend in stroke and its subtypes, ASDR and ASMR, as well as the corresponding number of deaths and DALYs, due to low dietary fiber intake in China. Subarachnoid hemorrhage (SH) showed the greatest decrease, followed by intracerebral hemorrhage (IH) and ischemic stroke (IS). Local drift curves showed a U-shaped distribution of stroke, IS, and IH DALYs across the whole group and sex-based groups. For mortality, the overall and male trends were similar to those for DALYs, whereas female stroke, IH, and IS showed an upward trend. The DALYs for stroke and IH showed a clear bimodal distribution, IS showed an increasing risk with age. For mortality, the SH subtype showed a decreasing trend, whereas other subtypes showed an increasing risk with age. Both the period and cohort rates of stroke DALYs and motality due to low dietary fiber have declined. Males had a higher risk of DALYs and mortality associated with low fiber levels. The burden of stroke and its subtypes associated with a low-fiber diet in China has been declining over the past 30 years, with different patterns of change for different stroke subtypes and a higher burden for males, highlighting the differential impact of fiber intake on stroke and its subtypes.

Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models

Objective

Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS.

Methods

In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models.

Results

Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969).

Conclusion

In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.

Triglyceride-glucose index and the risk of stroke in American adults: findings from the atherosclerosis risk in communities study

OBJECTIVES

The associations between the triglyceride-glucose (TyG) index with subsequent stroke in American adults are unknown. The aim of this study was to determine the associations between baseline and trajectories of TyG index with subsequent stroke in American adults.

METHODS

A total of 10,132 participants free of a history of stroke at baseline were included. We quantified the association of baseline and trajectories of TyG index with incident total stroke, ischemic stroke and intracerebral hemorrhage using Cox regression, restricted cubic splines and logistic regression analysis.

RESULTS

There were 909 incident stroke cases over a median follow-up of 26.6 years. After adjustment for potential confounders, each unit increase in the TyG index was associated with a 32.1% higher risk of incident stroke. Compared with participants in the lowest quartile of the baseline TyG index, those in the highest quartile had a greater risk of incident stroke [HR (95% CI) 1.254 (1.014-1.552)]. Restricted cubic splines showed that the risk of stroke increased in participants with a higher TyG index, especially when the TyG index was > 8.6. Results were similar for incident ischemic stroke. Compared with participants in the lowest quartile of the baseline TyG index, those in the second quartile had a lower risk of intracerebral hemorrhage [HR (95% CI) 0.494 (0.262-0.931)]. Five discrete trajectories with stable TyG indexes at various levels at follow-up visits were identified, and parallel results were observed for the associations of trajectories of TyG index with outcomes.

CONCLUSIONS

The TyG index independently predict stroke progression.

Association between the weight-adjusted waist index and stroke: a cross-sectional study

BACKGROUND

The weight-adjusted waist index (WWI) is a new measure of obesity, and this study aimed to determine the association between the WWI and stroke.

METHODS

Using the National Health and Nutrition Examination Survey (NHANES) 2011-2020 dataset, cross-sectional data from 23,389 participants were analysed. The correlation between the WWI and stroke was investigated through multivariate logistic regression and smoothing curve fitting. Subgroup analysis and interaction tests were also carried out.

RESULTS

The research involved 23,389 participants, of whom 893 (3.82%) had a stroke. The fully adjusted model revealed a positive correlation between the WWI and stroke [1.25 (1.05, 1.48)]. Individuals who were in the highest quartile of WWI exhibited a 62% higher likelihood of experiencing a stroke than those in the lowest quartile [1.62 (1.06, 2.48)]. Subgroup analysis and interaction tests revealed that this positive correlation was similar in different population settings (all P for interaction  > 0.05).

CONCLUSION

A higher WWI was associated with a higher prevalence of stroke. The results of this study underscore the value of the WWI in stroke prevention and management.

Polymorphisms of TRIB1 Genes for Coronary Artery Disease and Stroke Risk: A Systematic Review and Meta-analysis

BACKGROUND AND AIMS

In recent years, the results of the association between Tribbles Pseudokinase 1 (TRIB1) gene polymorphism and the risk of coronary artery disease (CAD) and stroke are inconsistent. This study aimed to systematically review the literature on TRIB1 gene polymorphisms and susceptibility to coronary atherosclerotic heart disease (CAD) and stroke.

METHODS

This study collected studies published until May 2022 through a systematic search of PubMed, Web of Science, and Google Scholar databases. After a systematic literature search, pooled odds ratio (OR) and their corresponding 95% confidence interval (CI) were used to assess the strength of the association.

RESULTS

We identified 6 studies on rs17321515, including 12892 controls and 4583 patients, and 3 on rs2954029, including 1732 controls and 1305 patients. In different genetic models, the rs2954029 genetic polymorphism significantly increased the risk of CAD and stroke. In the codominant model, the AA genotype increased the risk of CAD and stroke (OR=1.74, 95% CI=1.39-2.17, P<0.001); the TA genotype also increased the prevalence of CAD and stroke risk (OR=1.39, 95% CI=1.18-1.64, P<0.001). Compared with the control group, the TT+TA genotype increased the risk of CAD and stroke in the dominant genetic model (OR=1.46, 95%CI=1.25-1.71, P<0.001), and in the recessive model, the TA+AA genotype increased the risk of CAD and stroke (OR=1.41, 95% CI=1.15-1.72, P<0.001). In addition, the TRIB1 rs17321515 polymorphism was not found to be associated with the risk of CAD and stroke, which may be related to other factors such as race.

CONCLUSIONS

The rs2954029 A allele was significantly associated with an increased risk of CAD and stroke, according to the present meta-analysis. However, the association of rs17321515 polymorphism with susceptibility to CAD and stroke has not been found in this study.

American Association of Clinical Endocrinology Consensus Statement: Comprehensive Type 2 Diabetes Management Algorithm - 2023 Update

OBJECTIVE

This consensus statement provides (1) visual guidance in concise graphic algorithms to assist with clinical decision-making of health care professionals in the management of persons with type 2 diabetes mellitus to improve patient care and (2) a summary of details to support the visual guidance found in each algorithm.

METHODS

The American Association of Clinical Endocrinology (AACE) selected a task force of medical experts who updated the 2020 AACE Comprehensive Type 2 Diabetes Management Algorithm based on the 2022 AACE Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan and consensus of task force authors.

RESULTS

This algorithm for management of persons with type 2 diabetes includes 11 distinct sections: (1) Principles for the Management of Type 2 Diabetes; (2) Complications-Centric Model for the Care of Persons with Overweight/Obesity; (3) Prediabetes Algorithm; (4) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Dyslipidemia; (5) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Hypertension; (6) Complications-Centric Algorithm for Glycemic Control; (7) Glucose-Centric Algorithm for Glycemic Control; (8) Algorithm for Adding/Intensifying Insulin; (9) Profiles of Antihyperglycemic Medications; (10) Profiles of Weight-Loss Medications (new); and (11) Vaccine Recommendations for Persons with Diabetes Mellitus (new), which summarizes recommendations from the Advisory Committee on Immunization Practices of the U.S. Centers for Disease Control and Prevention.

CONCLUSIONS

Aligning with the 2022 AACE diabetes guideline update, this 2023 diabetes algorithm update emphasizes lifestyle modification and treatment of overweight/obesity as key pillars in the management of prediabetes and diabetes mellitus and highlights the importance of appropriate management of atherosclerotic risk factors of dyslipidemia and hypertension. One notable new theme is an emphasis on a complication-centric approach, beyond glucose levels, to frame decisions regarding first-line pharmacologic choices for the treatment of persons with diabetes. The algorithm also includes access/cost of medications as factors related to health equity to consider in clinical decision-making.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.