U-Shaped Association Between Serum Uric Acid and Short-Term Mortality in Patients With Infective Endocarditis

Potential protective effects of increased serum uric acid concentration in sarcopenia: A meta-analysis and systematic review

BACKGROUND

Previous studies on the association between serum uric acid (UA) levels and sarcopenia have yielded contradictory results. This meta-analysis and literature review assessed the association between serum UA levels and sarcopenia. Moreover, we conducted a comparative analysis of the differences in serum UA concentrations between individuals with and without sarcopenia.

METHODS

A systematic search was conducted across various medical databases, namely PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, and Wanfang (from the start to August 20, 2023). This search focused on published studies that investigated the relationship between serum UA levels and sarcopenia. The relationship between serum UA concentration and the occurrence of sarcopenia was analyzed, and the differences in serum UA concentrations between individuals with sarcopenia and control groups were reviewed. Statistical analysis was performed using STATA 11.0 and R 4.1.3.

RESULTS

Sixteen studies were considered for our analysis. The results indicated a significant association between low serum UA concentration and a higher sarcopenia risk, particularly among male patients (adjusted odds ratio = 0.65, 95% confidence interval [CI] = 0.49, 0.87, P = .004, I2 = 0%). Individuals with sarcopenia exhibited decreased serum UA concentrations compared with those of the control group (mmol/L: weighted mean difference = -28.25, 95% CI = -40.45, -16.05, P < .001; mg/dL: weighted mean difference = -0.82, 95% CI = -1.05, -0.58, P < .001). Additionally, serum UA concentration was positively correlated with skeletal muscle mass index and handgrip strength (skeletal muscle index: correlation coefficient = 0.17, 95% CI = 0.11, 0.22, P < .001; handgrip strength: common odds ratios = 0.10, 95% CI = 0.06, 0.14, P < .001).

CONCLUSION

Individuals with sarcopenia have relatively low serum UA concentrations. A notable correlation between serum UA concentration and sarcopenia was observed. Hence, monitoring UA levels could aid in the early detection and treatment of sarcopenia, enabling timely intervention to preserve muscle mass and strength.

Hyperuricemia and associated factors: The case of outpatients at the Bafoussam Regional Hospital- Cameroon, an analytical cross-sectional study

Background and Aims

Hyperuricemia constitutes a major public health issue due to its implication in many chronic diseases and metabolic syndromes. We propose to study the prevalence and associated factors of hyperuricemia to diagnose asymptomatic patients and make prognoses on the state of health of the patients.

Methods

An analytic cross-sectional study has been carried out at the Bafoussam Regional Hospital and the Biochemistry laboratory of the Université des Montagnes over 2 months. Sociodemographic and anthropometric characteristic was obtained; a blood sample was collected from the chosen patients and a biochemical test (uric acid, creatinine, urea, total cholesterol, high density lipoproteins cholesterol, triglyceride) was analyzed by spectrophotometric method. Statistical tests were carried out using SPSS statistical software. Logistic regression analyses identified factors associated with variables of interest. The significance was measured by a p < 0.05 with a confidential level of 95%.

Results

The patient population was made up of 100 patients. The sex ratio was 1.22 in favor of men. The prevalence of hyperuricemia in our study was 28.0% with 31.1% in women and 27.3% in men. The mean average of uric acid in the hyperuricemia population was 7.50 ± 1.24 mg/L and the normal uricemia population was 4.69 ± 1.49 mg/L (p < 0.0001). The mean average triglyceride in the hyperuricemia population was 143 ± 14 and 117.55 ± 55.52 mg/dL in normal uricemia with p = 0.046. Age range [35-45] and hypertriglyceridemia have been associated with hyperuricemia with respectively (odds ratio [OR] = 4.07, p < 0.015) confidence interval, CI: [0.89: 97.0]) and ([OR = 2.50, p < 0.046] CI: [1.01: 6.09]).

Conclusion

The prevalence of hyperuricemia was relatively high and has been associated with metabolic disorders in the population. It is necessary to focus on early diagnoses, treatment, and early intervention in view to prevent chronic diseases associated with hyperuricemia.

Association of serum uric acid with all-cause and cardiovascular mortality in obstructive sleep apnea

The study investigated the association between Serum Uric Acid (SUA) levels and all-cause as well as cardiovascular mortality in patients with Obstructive Sleep Apnea (OSA). This prospective cohort study enrolled participants with OSA from four cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2008, and 2015-2018. A weighted Cox proportional hazards model was used to assess adjusted hazard ratios (aHRs) and their corresponding 95% confidence intervals (CI) for all-cause and cardiovascular mortality. Additionally, multivariable logistic regression and restricted cubic splines (RCS) models were employed to examine nonlinear relationships between SUA and all-cause and cardiovascular mortality. Among the 5,584 OSA participants included in the study, covering the four NHANES cycles and with a median follow-up of 4.333 years, a total of 537 deaths were observed, including 108 deaths attributed to cardiovascular disease. Comparing the fourth quartile (Q4) of uric acid levels, both the fifth quartile (Q5) (aHRs = 1.51, 95% CI [1.08, 2.12]) and the second quartile (Q2) (aHRs = 1.53, 95% CI [1.04, 2.25]) of uric acid levels were independently associated with an increased risk of all-cause mortality. Furthermore, comparing the fourth quartile (Q4) of uric acid levels, the second quartile (Q2) (aHRs = 2.40, 95% CI [1.08, 5.35]) of uric acid levels were independently associated with an increased risk of cardiovascular mortality. The RCS model demonstrated a U-shaped pattern in the association between SUA and all-cause mortality in OSA, with an inflection point observed at 5.83 mg/dl. The findings of this study suggest a U-shaped association between serum SUA levels and all-cause mortality and nonlinearity association between serum SUA levels and all-cause mortality. Further studies are warranted to determine the causal relationship between SUA levels and all-cause and cardiovascular mortality.

Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation

BACKGROUND

A number of models have been reported for predicting atrial fibrillation (AF) recurrence after catheter ablation. Although many machine learning (ML) models were developed among them, black-box effect existed widely. It was always difficult to explain how variables affect model output. We sought to implement an explainable ML model and then reveal its decision-making process in identifying patients with paroxysmal AF at high risk for recurrence after catheter ablation.

METHODS

Between January 2018 and December 2020, 471 consecutive patients with paroxysmal AF who had their first catheter ablation procedure were retrospectively enrolled. Patients were randomly assigned into training cohort (70%) and testing cohort (30%). The explainable ML model based on Random Forest (RF) algorithm was developed and modified on training cohort, and tested on testing cohort. In order to gain insight into the association between observed values and model output, Shapley additive explanations (SHAP) analysis was used to visualize the ML model.

RESULTS

In this cohort, 135 patients experienced tachycardias recurrences. With hyperparameters adjusted, the ML model predicted AF recurrence with an area under the curve of 66.7% in the testing cohort. Summary plots listed the top 15 features in descending order and preliminary showed the association between features and outcome prediction. Early recurrence of AF showed the most positive impact on model output. Dependence plots combined with force plots showed the impact of single feature on model output, and helped determine high risk cut-off points. The thresholds of CHA₂DS₂-VASc score, systolic blood pressure, AF duration, HAS-BLED score, left atrial diameter and age were 2, 130 mmHg, 48 months, 2, 40 mm and 70 years, respectively. Decision plot recognized significant outliers.

CONCLUSION

An explainable ML model effectively revealed its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation by listing important features, showing the impact of every feature on model output, determining appropriate thresholds and identifying significant outliers. Physicians can combine model output, visualization of model and clinical experience to make better decision.

The Therapeutic Effect and the Potential Mechanism of Flavonoids and Phenolics of Moringa oleifera Lam. Leaves against Hyperuricemia Mice

The aim of this study is to evaluate the anti-hyperuricemia effect and clarify the possible mechanisms of flavonoids and phenolics of MOL (MOL-FP) in mice. Hyperuricemia mice were generated via intraperitoneal (i.p.) administration of potassium oxonate (PO) and oral gavage (p.o.) of hypoxanthine (HX). Serum uric acid (UA), weight, serum XO activity, hepatic XO activity, urea nitrogen (BUN), creatinine (CRE), serum AST level, serum ALT level, mRNA expression of renal urate-anion transporter 1 (URAT1), glucose transporter 9 (GLUT9), organic anion transporters 1 (OAT1), organic anion transporters 3 (OAT3), and ATP-binding cassette transporter G2 (ABCG2) were determined. The molecular docking was conducted using AutoDock Vina 1.2.0 to screen potential XO inhibitors in MOL-FP. Serum metabolomics was established to collect the metabolic profiles of mice and explore the metabolic changes that occurred after MOL-FP treatment. MOL-FP could notably reduce the serum UA level of hyperuricemia mice by inhibiting XO activity and regulating renal urate transporters. Molecular docking studies indicated that 5-p-coumaroylquinic acid, 3-p-coumaroylquinic acid, and catechin could be potential XO inhibitors. Besides, MOL-FP prevented the pathological process of hyperuricemia by regulating biomarkers associated with purine metabolism, amino acid metabolism, and lipid metabolism.

U-shaped association of uric acid to overall-cause mortality and its impact on clinical management of hyperuricemia

Serum uric acid (SUA) is significantly elevated in obesity, gout, type 2 diabetes mellitus, and the metabolic syndrome and appears to contribute to the renal, cardiovascular and pulmonary comorbidities that are associated with these disorders. Most previous studies have focused on the pathophysiologic effects of high levels of uric acid (hyperuricemia). More recently, research has also shifted to the impact of hypouricemia, with multiple studies showing the potentially damaging effects that can be caused by abnormally low levels of SUA. Along with these observations, recent inconclusive data from human studies evaluating the treatment of hyperuricemia with xanthine oxidoreductase (XOR) inhibitors have added to the debate about the causal role of UA in human disease processes. SUA, which is largely derived from hepatic degradation of purines, appears to exert both systemic pro-inflammatory effects that contribute to disease and protective antioxidant properties. XOR, which catalyzes the terminal two steps of purine degradation, is the major source of both reactive oxygen species (O2.-, H2O2) and UA. This review will summarize the evidence that both elevated and low SUA may be risk factors for renal, cardiovascular and pulmonary comorbidities. It will also discuss the mechanisms through which modulation of either XOR activity or SUA may contribute to vascular redox hemostasis. We will address future research studies to better account for the differential effects of high versus low SUA in the hope that this will identify new evidence-based approaches for the management of hyperuricemia.