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

Association and biological pathways between metabolic syndrome and incident Parkinson's disease: A prospective cohort study of 289,150 participants

The relationship between metabolic syndrome (MetS) and Parkinson's disease (PD) remains uncertain due to inconsistent findings in previous studies. This prospective cohort study investigated the association between MetS and PD risk, along with potential biological mechanisms, using data from 289,150 PD-free participants in the UK Biobank. MetS was defined by the presence of at least three of the following components, while preMetS included one or two: increased waist circumference, elevated triglycerides (TG), high blood pressure (BP), elevated HbA1c, or reduced high-density lipoprotein cholesterol (HDL-C). Cox proportional hazards models were utilized to assess the risk of PD, and mediation analyses explored the role of blood biomarkers. Over a median follow-up of 13.1 years, 1682 participants developed PD. PreMetS (HR: 1.24, 95 % CI: 1.02-1.51, P = 0.028) and MetS (HR: 1.32, 95 % CI: 1.08-1.61, P = 0.008) were associated with an increased PD risk, with Kaplan-Meier analysis showing risk escalation with more MetS components. Among individual MetS components, increased waist circumference, elevated HbA1c, and reduced HDL-C were significantly associated with higher PD risk, while elevated TG and BP showed no significant association. Mediation analysis indicated that biomarkers of liver function (alkaline phosphatase) and kidney function (cystatin C) partially mediated the MetS-PD relationship. These findings highlight a significant link between MetS and higher PD risk, with possible mediation through specific blood biomarkers, though temporal ambiguity warrants cautious interpretation.

Dynamic changes in metabolic syndrome components and chronic kidney disease risk: a population-based prospective cohort study

OBJECTIVE

To investigate the relationships between dynamic changes in metabolic syndrome (MetS) components and chronic kidney disease (CKD) risk.

METHODS

Data from the UK Biobank, including baseline assessments from 2006 to 2010, repeat assessments in 2012-2013, and linked national health records, were analyzed. MetS components consisted of abdominal obesity, elevated blood pressure (BP), fasting blood glucose (FBG), serum uric acid (SUA), and lipid abnormalities. The Kaplan-Meier method and log-rank test were used to analyze CKD incidence and group differences. Cox regression models assessed the association between dynamic changes in MetS components and CKD risk.

RESULTS

The study enrolled 455,060 participants (45.7% male, 18.4% aged 65 years or older) with a median follow-up of 12.68 years. Those with MetS had a significantly higher 10-year CKD cumulative incidence probability of CKD than those without MetS (4.14% VS 1.14%). Multivariate analysis showed all baseline metabolic abnormalities were linked to CKD risk with HRs from 1.40(1.35-1.45) to 1.85 (1.78-1.92), and MetS strongly associated with CKD (HR: 2.31). CKD risk rose with more MetS components and progression stages. Notably, with FBG being the exception, the four MetS components that shifted from normal at baseline to abnormal at follow - up were associated with elevated CKD risk, with HRs (95% CI) ranging from 1.21 (1.00-1.48) to 1.73 (1.34-2.24). Participants with high baseline SUA, even if it normalized at follow - up, still faced a 1.30 - fold higher CKD risk (95% CI: 1.25-1.35), distinct from other components. For those developing one and ≥ 2 new MetS components at follow - up, the CKD risk HRs (95% CI) were 1.49 (1.00-2.35) and 2.26 (1.21-4.24) respectively.

CONCLUSION

MetS and its component changes are significantly associated with CKD risk, in a dose - response pattern. Incorporating SUA into MetS assessments enhances risk identification, especially noting females' higher susceptibility to elevated SUA. Dynamic monitoring of MetS components is crucial for assessing and predicting CKD risk.

CLINICAL TRIAL NUMBER

Not applicable.

Association between Body Mass Index and Acute Kidney Injury in Patients who Underwent Coronary Revascularization: A Retrospective Cohort Study from the MIMIC-IV Database

Aim    Acute kidney injury (AKI) remains a common complication of coronary artery revascularization surgery and is associated with adverse outcomes in critically ill surgical patients. Body mass index (BMI) is associated with various diseases. This study aimed to evaluate the association between BMI and the risk of AKI in patients undergoing coronary artery revascularization surgery.Material and methods    In this retrospective cohort study, data were extracted from the Medical Information Mart for Intensive Care (MIMIC) - IV database from 2008 to 2019 for patients undergoing coronary artery revascularization surgery. The outcome was the occurrence of AKI after ICU admission. Covariates were selected using LASSO regression. Univariable and multivariable logistic regression models were utilized to assess the association between BMI and the odds of developing AKI in patients undergoing coronary artery revascularization surgery, with results presented as odds ratios (OR) and 95 % confidence intervals (CI). Subgroup analyses were performed based on age, surgery, anticoagulant use, and the Sequential Organ Failure Assessment (SOFA) score was computed to further explore the association between BMI and AKI.Results    This study included 3017 patients who underwent coronary artery revascularization surgery, of whom 2172 (72.8 %) developed AKI. Increasing BMI was significantly associated with elevated odds of AKI in patients undergoing coronary revascularization (OR = 1.10, 95 % CI: 1.08-1.12), indicating a 10 % increase in AKI risk for each unit increase in BMI, adjusted for demographic variables (age and gender) in Model 1. After further adjustment in Model 2 for significant baseline characteristics including comorbidities (type 2 diabetes, heart failure, malignant tumors, and chronic kidney disease) and ICU scoring systems (SOFA, APS III, SAPS II, OASIS, and CCI), the association remained significant with an 11 % increased risk of AKI per BMI unit increase (OR = 1.11, 95 % CI: 1.08-1.13).Conclusion    BMI may be a promising parameter for assessing the risk of AKI in paty revascularization surgery, providing valuable information for risk stratification and management of ICU patients undergoing such procedures.

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, hypercholesterolemia, and diabetes, are at increased risk for cerebral infarction (CI) and TIA. In resource-limited settings, diagnosing CI and TIA can be particularly difficult due to a lack of advanced imaging and specialized expertise. Therefore, we aim to develop a simple, convenient, blood-based approach that could assist clinicians in diagnosing CI and TIA, especially in regions where advanced imaging or stroke-specific expertise is limited. All study subjects were patients admitted to the First Hospital of Xiamen University and healthy check-up populations between January 2018 and September 2023. This study employed five machine learning methods alongside 21 blood routine indicators, 30 blood biochemical indicators, age, and gender to construct predictive models for CI and TIA in both healthy individuals and those with dysmetabolism. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) served as the metric to assess the comprehensive predictive capability of the models. Subsequently, the SHAP package was employed for model interpretation. Extreme Gradient Boosting (XGBoost) outperforms other models in all predictive models. In the models predicting CI and TIA among healthy, the AUC is 0.9958 (0.9947-0.9969) and 0.9928 (0.9899-0.9951), respectively. Among the nine shared key features of the two models are indicators of glucose metabolism, lipid metabolism, and liver metabolism. In the models for predicting CI and TIA among patients with hypertension, hypercholesterolemia, diabetes, and those with all three metabolic disorders combined, the AUCs ranged from 0.6990 to 0.8591. We found that the indicators K significantly contributed to predict CI and TIA from those with dysmetabolism. Additionally, metabolic-related indicators, such as glucose (GLU) and high-density lipoprotein cholesterol (HDL-C), are ranked highly among the top ten contributing features. XGBoost performed the best in all models. It can effectively differentiate CI and TIA from healthy and dysmetabolic patients by combining blood routine and blood biochemical indicators. Moreover, it can also differentiate CI from TIA. Although any suspicious findings from this model would still require confirmatory imaging, the simplicity and low cost of blood-based testing may offer a practical adjunct for clinicians-particularly in areas lacking advanced imaging or extensive stroke expertise-and could facilitate earlier diagnostic decision-making.

Linear positive association of metabolic score for insulin resistance with stroke risk among American adults: a cross-sectional analysis of National Health and Nutrition Examination Survey datasets

BACKGROUND

Insulin Resistance (IR) is associated with stroke. This study aimed to investigate the correlation between metabolic score for insulin resistance (METS-IR) level, a new biomarker for assessing IR, and stroke.

METHODS

This is a cross-sectional study based on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020 and included participants aged ≥ 20 years. All participants provided complete stroke and METS-IR related data. The study employed statistical techniques, including multivariate logistic regression analysis, restricted cubic splines (RCS), and stratified analyses to evaluate the relationship between the amounts of METS-IR and the risk of stroke.

RESULTS

The study included 14,029 participants aged 20 years or older. The fully adjusted model revealed a statistically significant correlation between METS-IR and stroke (OR=1.21, 95% CI: 1.00, 1.46; P<0.05). Specifically, for every 10-unit increase in METS-IR, there was a 21% increase in the prevalence of stroke. The prevalence of stroke was 60% higher in the Q4 group compared to the Q1 group, as indicated by a significant association with METS-IR (OR=1.60, 95% CI: 1.01, 2.54; P<0.05). The RCS analysis revealed a strong linear correlation between METS-IR and the incidence of stroke (P<0.05). Subgroup analyses showed that gender, age, race, alcohol consumption, smoking, diabetes, and hypertension exhibited correlation with this positive association, and a significant interaction was observed in age (P for interaction < 0.05).

CONCLUSIONS

The findings of this study indicate that elevated METS-IR levels are strongly linked to a greater risk of stroke in adults.

Designing machine learning for big data: A study to identify factors that increase the risk of ischemic stroke and prognosis in hypertensive patients

Background

Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients.

Method

Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors.

Results

The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients.

Conclusion

The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.