Association of microalbuminuria and adverse outcomes in hypertensive patients: a meta-analysis

Hong, Zhibin; Jiang, Yabao; Liu, Peijun; Zhang, Li

doi:10.1007/s11255-021-02795-w

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Previous meta-analyses have suggested a link between albuminuria and an increased risk of mortality and poor cardiovascular outcomes in various populations. 25 , 34 , 35 , 36 Accordingly, this meta-analysis further added to the value of microalbuminuria as a predictor of ACM and CVM, especially in CHD patients.…”

Section: Discussionmentioning

confidence: 96%

Microalbuminuria and mortality in individuals with coronary heart disease: A meta-analysis of a prospective study

Govardi

Yulianda

Habib

et al. 2023

Indian Heart Journal

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Section: Discussionmentioning

confidence: 96%

Microalbuminuria and mortality in individuals with coronary heart disease: A meta-analysis of a prospective study

Govardi

Yulianda

Habib

et al. 2023

Indian Heart Journal

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“…Increased intraglomerular capillary pressure, which is related to systemic blood pressure as well as pre-and postglomerular resistance, is the most important determinant of MAU (21,22). Previous studies have reported that blood pressure is closely associated with albuminuria in patients with hypertension and in the general population (23)(24)(25). A study conducted among the Japanese population demonstrated that SBP exhibited an independent positive correlation with MAU (21).…”

Section: Discussionmentioning

confidence: 99%

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

et al. 2022

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ObjectiveMicroalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.MethodsThis cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.ResultsSystolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.ConclusionBased on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.

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“…A meta-analysis from 2014 showed a correlation between a decrease in the level of albumins in the urine and a decrease in cardiovascular events in patients with hypertension and/or diabetes [210]. In a meta-analysis from 2021, it was shown that microalbuminuria defined as urinary albumin excretion (30-300 mg/24 h urine) or albumin/creatinine ratio 30-300 mg/g from a spot urine or equivalent value, showed an independent relationship with all-cause mortality or major adverse cardiovascular events in hypertensive patients [211]. In a study by Milutinović et al (2019), there were no statistically significant changes in the level of albumin in the urine after a 3-month intervention with chokeberry juice [100].…”

Section: Impact Chokeberry On Uric Acid and Creatinine Levelmentioning

confidence: 99%

Chokeberry (A. melanocarpa (Michx.) Elliott)—A Natural Product for Metabolic Disorders?

2022

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Abnormal metabolism of substances in the body can result in metabolic disorders which include obesity, cardiovascular diseases, diabetes, hypertension, chronic kidney disease, liver disease, or cancer. Foods rich in antioxidants can help to prevent and treat various types of disorders. Chokeberry fruits are rich in polyphenols, especially cyanidins, and therefore, can show a beneficial health effect. The aim of this study was to summarize and systematize reports about the effects of chokeberry on various metabolic parameters. Studies from 2000 to 2021, published in the PubMed and Google Scholar databases, were reviewed. The review of studies shows that chokeberry may have a positive effect in dyslipidemia and hypertension and may increase the body’s antioxidant defense mechanisms. The anti-inflammatory effect, in turn, may translate into a reduction in the risk of metabolic disorders over a longer period of use. Changes in glucose levels were reported by studies in which the intervention lasted more than 10 weeks in patients with carbohydrate metabolism disorders. The effects of protecting the liver, inhibiting platelet aggregation, lowering uric acid levels, and having a protective effect on the kidneys require additional confirmation in human clinical trials. Consumption of chokeberry fruit did not impact on anthropometric measurements; however, it seems that chokeberry fruit can be recommended in many metabolic disorders due to the richness of bioactive ingredients.

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Association of microalbuminuria and adverse outcomes in hypertensive patients: a meta-analysis

Cited by 11 publications

References 25 publications

Microalbuminuria and mortality in individuals with coronary heart disease: A meta-analysis of a prospective study

Microalbuminuria and mortality in individuals with coronary heart disease: A meta-analysis of a prospective study

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

Chokeberry (A. melanocarpa (Michx.) Elliott)—A Natural Product for Metabolic Disorders?

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