Hyperuricaemia elevates risk of short-term readmission and mortality in patients with heart failure.

BACKGROUND: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Serum uric acid (SUA), a product of purine metabolism, has been implicated in HF progression. However, the association between hyperuricaemia and the short-term readmission and mortality in patients with HF remains controversial. METHODS: In this retrospective cohort study, we analysed data from a HF database specific to the Chinese population. The primary endpoint was short-term readmission or all-cause mortality within 90 days. Participants with HF were categorised into normouricaemia group (NUA) and hyperuricaemia group (HUA) based on a SUA threshold of 420 µmol/L. The association between SUA and primary endpoint was evaluated using Kaplan-Meier survival curves and Cox regression analysis. RESULTS: Baseline characteristics revealed significant differences between NUA and HUA groups, with the latter exhibiting a higher prevalence of males, chronic kidney disease (CKD) and elevated levels of various biomarkers. During a 90-day follow-up, 493 (26.6%) participants reached the primary endpoint, with a higher incidence observed in the HUA group at 31.2%, compared with 20.1% in the NUA group. When a threshold effect was identified at 420 µmol/L, a non-linear association was observed between SUA and the primary endpoint. After adjusting for gender, age, New York Heart Association class, CKD, systolic blood pressure (SBP) and potassium, the HUA group exhibited a higher risk for the primary endpoint compared with the NUA group (HR: 1.40, 95% CI: 1.14 to 1.72, p=0.001). Additionally, the risk increased across quartiles of SUA (P for trend=0.002). Furthermore, stratified analyses indicated a stronger association in patients without CKD (P interaction=0.033). CONCLUSION: Hyperuricaemia is independently associated with an increased risk of short-term readmission and mortality in patients with HF. Our findings suggest that monitoring and managing SUA could be crucial in improving patient with HF outcomes.

Interaction of serum uric acid with overweight on hypertension: findings from the China Health and Nutrition Survey.

BACKGROUND: Both serum uric acid (SUA) levels and body mass index (BMI) are recognized as important risk factors for hypertension. The current study aimed to investigate the interaction effects between SUA levels and overweight (defined as BMI ≥ 24 kg/m2 in Chinese) on the incidence of hypertension among Chinese adults. METHODS: 1124 hypertensive participants and 7283 non-hypertensive participants, extracted from the China Health and Nutrition Survey (CHNS), were analyzed. Participants were categorized based on their SUA levels and BMI, to investigate the interaction effects between SUA levels and overweight on hypertension. RESULTS: In comparison with the reference group (BMI < 24 kg/m2 and in the 1st quintile of SUA), multivariable adjusted analysis demonstrated that the odds ratio (OR) (95% confidence interval, 95% CI) of hypertension for participants with overweight alone was 2.18 (1.41-3.37); for elevated SUA levels alone, the ORs (95% CIs) were 1.57 (1.08-2.30), 1.84 (1.24-2.74), 2.21 (1.47-3.32), and 2.48 (1.55-3.96) across SUA quintiles; and for the combined effect of higher SUA levels and overweight, the ORs (95% CIs) were 3.25 (2.19-4.82), 3.73 (2.51-5.55), 5.17 (3.42-7.80), and 6.21 (4.01-9.60). The relative excess risk due to interaction (RERI) was 3.26 (1.43-5.09) at the 5th quintile of SUA, indicating the presence of additive interaction between overweight and SUA levels on hypertension. CONCLUSION: Interaction between SUA levels and overweight on hypertension exists specifically at the highest quintile (Q5, > 6.39 mg/dL) of SUA among Chinese adults. Therefore, strategies to lower SUA levels could be considered as a potential approach to mitigate hypertension risk in overweight individuals within this specific subgroup.

Kidney function, uric acid, and risk of atrial fibrillation: experience from the AMORIS cohort.

BACKGROUND: Uric acid closely relates to both kidney disease and atrial fibrillation (AF), yet the extent to which it influences the kidney-AF association remains uncertain. We examined the relationship between kidney function and risk of AF, accounting for uric acid levels. METHODS: A total of 308,509 individuals in the Swedish Apolipoprotein-Related Mortality Risk (AMORIS) cohort were included and their serum creatinine and uric acid were measured during 1985-1996. Ten-year incident AF was identified via linkage with the national registers. Glomerular filtration rate (eGFR) (ml/min/1.73 m2) was calculated with the 2009 Chronic Kidney Disease Epidemiology Collaboration equation. Hyperuricemia was defined as > 420 µmol/L for men and > 360 µmol/L for women. RESULTS: Over a mean follow-up of 9.4 years, 10,007 (3.2%) incident AF cases occurred. After adjusting for age, sex, cardiovascular diseases, total cholesterol, triglycerides, and glucose, individuals with low eGFR (< 30 and 30-59 ml/min/1.73 m2 ) had a higher risk of AF compared to those with normal eGFR (60-89) (hazard ratio (HR) = 1.72, 95% confidence interval (CI):1.29-2.30; HR = 1.10, 95% CI: 1.03-1.18, respectively). After further adjusting for uric acid levels, the association disappeared (HR = 0.97, 95% CI: 0.72-1.30; HR = 0.93, 95% CI: 0.86-1.00, respectively). When stratifying by hyperuricemia yes/no, eGFR < 30 ml/min/1.73 m2 was associated with higher AF risk in a small group of individuals without hyperuricemia (HR = 2.58, 95% CI: 1.64-4.07). CONCLUSION: Uric acid largely accounted for the relationship between eGFR and AF in this study. However, in individuals without hyperuricemia, eGFR in the lowest range (< 30 ml/min/1.73 m2) was still associated with increased risk of AF.

Serum uric acid: an independent risk factor for cardiovascular disease in Pakistani Punjabi patients.

BACKGROUND: It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort. METHODS: This is a retrospective observational study in which 502 human subjects having CVD, hypertension and/or diabetes were grouped based on SUA levels as normouricemia (n = 266) and hyperuricemia (n = 236). Role of SUA was assessed in increasing the risk of CVD independent of other key confounding factors (i.e. age, gender, dyslipidemia, hypertension, diabetes, dietary and life-style habits). All clinical and biochemical data were analyzed in SPSS (ver. 20). RESULTS: Subjects aged 55 ± 13 years were of both genders (males: 52%). SUA levels were significantly different among clinical subtypes of CVD [i.e. acute coronary syndrome (ACS), myocardial infarction (MI) and heart failure (HF)]. Spearman correlation showed a significantly positive association between CVD and SUA (rho = 0.149, p < 0.001). Multivariate logistic regression of SUA quartiles showed that hyperuricemia is associated with CVD [3rd quartile: OR: 1.78 (CI: 1.28-2.48), p = 0.001 and 4th quartile: OR: 2.37 (CI: 1.72-3.27), p < 0.001]. Moreover, this association remained significant even after adjusting for confounding factors. CONCLUSION: This study showed that SUA is positively associated with CVD, thus it can act as an independent risk factor for CVD.

Development and validation of a renal involvement model for patients with hyperuricemia: A cross-sectional study.

OBJECTIVE: To construct a prediction model for renal involvement in patients with hyperuricemia (HUA) based on logistic regression analysis, to achieve early risk stratification. METHOD: In this cross-sectional study, we collected data from the National Health and Nutrition Examination Survey (NHANES), and constructed a predicted model for renal involvement in HUA patients. The discriminative ability of the model was assessed using the receiver operating characteristic (ROC) curve. Model accuracy was evaluated using the Hosmer-Lemeshow test and calibration curve, while clinical utility was assessed using decision curve analysis (DCA). Furthermore, internal and external validation cohorts were also applied to validate the model. RESULTS: A total of 1669 patients from NHANES between 2007 and 2010 were included in the final analysis for modeling and validation. Six predictive factors including age, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Cr, Uric Acid (UA), and sex were identified by binary logistic regression analysis for renal involvement in HUA patients and used to construct a nomogram with good consistency and accuracy. The AUC values for the predictive model, internal validation, and external validation were 0.881 (95% CI: 0.836-0.926), 0.908 (95% CI: 0.871-0.944), and 0.927 (95% CI: 0.897-0.957), respectively. The calibration curves demonstrated consistency between the nomogram and observed values. The DCA curves of the model and validation cohort indicated good clinical utility. CONCLUSION: This study developed a predictive model for renal involvement in hyperuricemia patients with strong predictive performance and validated by internal and external cohorts, aiding in the early detection of high-risk populations for renal involvement.

Identifying reliable obesity indices for hyperuricemia among middle-aged and elderly populations: a longitudinal study.

BACKGROUND: Given the established link between obesity and hyperuricemia (HUA), the research want to investigate the relationship between different obesity indices and HUA, and further analyze which obesity index can better predict HUA. METHODS: The data were obtained from a longitudinal study involving middle-aged and elderly populations in Dalian, China. The research encompassed individuals who exhibited typical uric acid levels initially and tracked their progress over a three-year period. 8 obesity indices were evaluated retrospectively. Subgroup analyses were conducted to identify susceptible populations. Restricted cubic splines (RCS) were utilized to model the dose-response relationships between obesity indices and HUA. Receiver operating characteristic (ROC) curves were applied to visualize and compare the predictive value of both traditional and new obesity indices for HUA. RESULTS: Among 4,112 individuals with normal baseline uric acid levels, 950 developed HUA. Significant associations with HUA were observed for body mass index (BMI), waist circumference (WC), body roundness index (BRI), cardiometabolic index (CMI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), and abdominal volume index (AVI). Subgroup analysis indicated that all obesity indices proved more effective in assessing the onset of HUA in women without Metabolic Syndrome (MetS). Further analysis using RCS revealed non-linear dose-response relationships between LAP, CMI, VAI, and HUA in males, with similar non-linear relationships observed for all indices in females. The results from the ROC curves indicate that LAP may serve as a better predictor of HUA in males, and CVAI may serve as a better predictor in females. CONCLUSION: HUA is closely associated with obesity indices. Among females, CVAI emerges as the preferred predictive index for HUA. In males, LAP emerges as the preferred predictive index for HUA.

Uric acid and metabolic syndrome: Importance of hyperuricemia cut-off.

BACKGROUND: The relationship between HyperUricemia (HU) and Metabolic Sindrome (MS) and if Uric Acid (UA) should be inserted into MS definitions is a matter of debate. Aim of our study was to evaluate the correlation between UA and HU with Insulin Resistance (IR) and MS in a population of hypertensive patients. HU was defined with two cut-offs (the classic one of ≥6 mg/dL for women and ≥ 7 for men; the newly proposed URRAH one with ≥5.6 mg/dL for both sexes). METHODS: We enrolled 473 Hypertensive patients followed by the Hypertension Unit of San Gerardo Hospital (Monza, Italy). IR was defined through TG/HDL ratio and NCEP-ATP-III criteria were used for MS diagnosis. RESULTS: MS was found in 33.6 % while HU affected 14.8 % of subjects according to the traditional cut-off and 35.9 % with the URRAH cut-off. 9.7 % (traditional cut-off) and 17.3 % (URRAH's threshold) of the subjects had both HU and MS. UA level was significantly higher in MS group (5.7 vs 4.9 mg/dL, p < 0.0001) as well as for HU (29.0 vs 7.6 % and 51.6 vs 28.0 %, for classic and URRAH cut-off respectively, p < 0.0001 for both comparison). Logistic multivariable regression models showed that UA is related to MS diagnosis (OR = 1.608 for each 1 mg/dL), as well as HU with both cut-off (OR = 5.532 and OR = 3.379, p < 0.0001 for all comparison, for the classic cut-off and the URRAH one respectively). CONCLUSIONS: The main finding of our study is that UA and HU significantly relate to IR and MS. The higher the values of UA and the higher the cut-off used, the higher the strength of the relationship.

Hyperuricemia in Cardiac Rehabilitation Patients: Prevalence and Association with Functional Improvement and Left Ventricular Ejection Fraction.

INTRODUCTION: The role of uric acid (UA) and Hyper Uricemia (HU) in cardiac rehabilitation (CR) patients have been very little studied. AIM: To evaluate the prevalence of HU and if it is associated to the functional improvement obtained or the left ventricular Ejection Fraction (EF) in CR patients after Acute or Chronic Coronary Syndrome (ACS and CCS respectively). METHODS: We enrol 411 patients (62.4 ± 10.2 years; males 79.8%) enrolled in the CR program at Niguarda Hospital (Milan) from January 2012 to May 2023. HU was defined both as the classic cut-off (> 6 for females, > 7 mg/dL for males) and with the newly identified one by the URRAH study (> 5.1 for females, > 5.6 mg/dL for males). All patients performed a 6MWT and an echocardiography at the beginning and at the end of CR program. RESULTS: Mean UA values were within the normal range (5.6 ± 1.4 mg/dL) with 19.5% (classic cut-off) HU patients with an increase to 47.4% with the newer one. Linear regression analysis showed no role for UA in determining functional improvement, while UA and hyperuricemia (classic cut-off) were associated to admission and discharge EF. The same was not with the URRAH cut-off. CONCLUSIONS: HU is as frequent in CR patients as in those with ACS and CCS. UA didn't correlate with functional recovery while it is associated with admission and discharge EF as also is for HU (classic cut-off). Whit the URRAH cut-off HU prevalence increases significantly, however, it doesn't show any significant association with EF.

Association of hyperuricemia with risk of cardiovascular disease according to the number of risk factors within target range.

BACKGROUND AND AIMS: Risk factor modification may decrease the risk of cardiovascular disease (CVD). Whether risk factor modification can mitigate the effect of hyperuricemia on CVD is unclear. This study aimed to investigate the risk of CVD among individuals with hyperuricemia, according to risk factors on target, compared with controls without hyperuricemia. METHODS AND RESULTS: This prospective study included 91,722 participants free of CVD at baseline (2006-2007) of the Kailuan study. Individuals with hyperuricemia were categorized according to the number of seven selected risk factors within the guideline-recommended target range (nonsmoking, physical activity, healthy diet, guideline-recommended levels of body mass index, blood pressure, fasting blood glucose, and total cholesterol). During a median follow-up of 13.00 years, 671 out of 6740 individuals (9.96%) with hyperuricemia and 6301 out of 84,982 control subjects (7.41%) had incident CVD. Compared with control subjects without hyperuricemia, individuals with hyperuricemia who had 4 or 5 to 7 risk factors on target had no significant excess CVD risk, the hazard ratio (HR) (95% confidence internal [CI]) was 0.93 (0.79-1.10) and 0.88 (0.71-1.10), respectively. Among individuals with hyperuricemia, excess CVD risk decreased stepwise for a higher number of risk factors on target, the HR of CVD associated with per additional risk factor within target range was 0.82 (95% CI, 0.77-0.87). Similar results were yielded for CVD subtypes. CONCLUSIONS: Among individuals with hyperuricemia, excess CVD risk decreased stepwise for a higher number of risk factors within target.

Serum uric acid, body mass index, and cardiovascular diseases: A multiple two-step Mendelian randomization study.

BACKGROUND AND AIMS: A number of health issues, including high serum uric acid (SUA) and cardiovascular disease (CVD), have been linked to obesity based on observational evidence, though it's currently unclear how these issues are causally related. In order to determine whether obesity mediates this association, we set out to investigate the causal relationship between SUA, obesity, and CVD. METHODS AND RESULTS: From publicly available genome-wide association studies, we acquired instrumental variables that had a strong correlation to SUA and body mass index (BMI). We employed multiple two-step Mendelian randomization (MR) analyses, using genetic and clinical data from various publicly available biological databases. The mediating role of BMI was examined through mediation analysis. SUA was genetically correlated with BMI [OR = 1.080, 95% CI: 1.024-1.139, P = 0.005]. There was a positive causal effect of SUA on AF [OR = 0.892, 95% CI: 0.804-0.990, P = 0.032], CAD [OR = 0.942, 95% CI: 0.890-0.997, P = 0.037], and EHT [OR = 1.080, 95% CI: 1.024-1.139, P = 0.005]. Among them, BMI mediated the effects of SUA on AF (42.2%; 95% CI, 35.3%-51.9%), CAD (76.3%; 95% CI, 63.4%-92.0%), and EHT (10.0%; 95% CI, 0%-20.0%). CONCLUSION: Our research revealed a causal relationship between high SUA exposure and an increased risk of obesity. Additionally, a high SUA level was linked to an increased risk of various CVDs. Given that individuals with high SUA are more likely to be susceptible to AF, CAD, and EHT, attention must be given to their weight status.

Linear associations of Chinese visceral adiposity index and its change with hyperuricemia: A prospective cohort study.

BACKGROUND AND AIMS: This study aims to investigate the association of Chinese visceral adiposity index (CVAI) with incident hyperuricemia (HUA). METHODS AND RESULTS: We included 5186 adults aged ≥45 years from China Health and Retirement Longitudinal Study. Modified Poisson regression model was used to estimate the relative risks (RRs) of incident HUA associated with baseline CVAI, and logistic model was used to estimate the odds ratios (ORs) of HUA for CVAI change. Restricted cubic splines analysis was adopted to model the dose-response associations. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the predictive value of CVAI. During 4-year follow-up, a total of 510 (9.8%) HUA cases were identified. The RRs (95%CIs) of incident HUA were 3.75 (2.85-4.93) for quartile 4 versus quartile 1 and 1.56 (1.45-1.69) for per-standard deviation increase in baseline CVAI. For the analyses of CVAI change, compared with stable group, participants in decreased group had 34% lower risk (OR 0.66, 95%CI 0.49-0.87) and those in increased group had 35% (1.35, 1.03-1.78) higher risk of HUA. Linear associations of baseline CVAI and its change with HUA were observed (Pnonlinear >0.05). Besides, the AUC value for HUA was 0.654 (0.629-0.679), which was higher than other five obesity indices. CONCLUSIONS: Our study found linear associations between baseline CVAI and its change and risk of HUA. CVAI had the best predictive performance in predicting incident HUA. These findings suggest CVAI as a reliable obesity index to identify individuals with higher HUA risk.

Association between triglyceride glucose index and hyperuricemia: a new evidence from China and the United States.

Background: Hyperuricemia (HUA) is a glo\bal public health problem. The etiology of HUA is complex and efficient and accurate assessment metrics are still lacking when conducting large-scale epidemiologic screening. The aim of this study was to evaluate the association of the triglyceride glucose (TyG) index, TyG-body mass index (BMI), TyG-waist-to-height ratio (WHtR) with the risk of HUA. Methods: Based on data collected from the National Health and Nutrition Examination Survey (NHANES) in the United States and the China Health and Aging Longitudinal Study (CHARLS) in China, a total of 14,286 U.S. adults and 4,620 Chinese adults were included in the analysis. The study examined the levels of TyG, TyG-BMI, TyG-WHtR, and TyG-WC. Multivariate logistic regression was utilized to investigate the relationships between these variables and hyperuricemia (HUA), separately. Additionally, the study used restricted cubic splines (RCS) to explore the linear associations of TyG, TyG-BMI, TyG-WHtR, TyG-WC, and HUA, separately. Results: The NHANES results showed that TyG [Q2, 1.58(1.26, 1.98); Q3, 2.36 (1.94, 2.88); Q4, 3.21 (2.61, 3.94)], TyG-BMI [Q2, 2.14 (1.74, 2.65); Q3, 3.38 (2.74, 4.17); Q4, 6.70 (5.55, 8.02)], TyG-WHtR [Q2, 1.92 (1.56, 2.36); Q3, 3.14 (2.56, 3.85); Q4, 6.28 (5.12, 7.69)], TyG-WC [Q2, 2.32 (1.85, 2.90); Q3, 3.51 (2.84, 4.34); Q4, 7.32 (5.95, 9.02)] were identified as risk factors for hyperuricemia (HUA). Similarly, the CHARLS results, when fully adjusted for covariates, indicated that TyG [Q4, 2.36 (1.08, 5.15)], TyG-BMI [Q3, 2.60 (1.05, 6.41); Q4, 3.70 (1.64, 8.32)], TyG-WHtR (Q4, 2.84 (1.23, 6.55), TyG-WC [Q4, 2.85 (1.23, 6.5)] were also risk factors for HUA. The predictive ability of each indicator for the risk of developing HUA was stronger in women than in men. Furthermore, there was an observed nonlinear relationship between TyG, TyG-BMI, TyG-WHtR, TyG-WC, and HUA in both the NHANES and CHARLS datasets (P-nonlinearity < 0.05). Conclusion: These findings suggest that TyG, TyG-BMI, TyG-WHtR and TyG-WC are associated with an increased risk of HUA. They are potential indicators for screening HUA status in the general population in China and the United States.

Correlation between hyperuricemia and thickened left ventricular wall in hypertensive young adults.

BACKGROUND: In this study, we examine the association between the hyperuricemia(HU) and hypertension(HTN) in Chinese young adults. Besides, the correlation between the occurrence of thickened left ventricular wall and HU was identified in patients with HTN. METHODS: In all, 360 patients with HTN and 1991 young adults with normal blood pressure(NBP) were enrolled in the study. Participant characteristics were collected. Univariable and multivariable logistic regression tests were utilized to identify the correlation between the presence of HU and HTN, and the correlation between the occurrence of thickened ventricular septum and HU in patients with HTN. RESULTS: The prevalence of HU in Chinese young adults with HTN was significantly higher than young adults with NBP(36.39% vs. 16.93%). Univariable analyses revealed that 8 factors were related with the presence of HTN with p value < 0.001, including HU, male, body mass index(BMI) ≥ 24 kg/m2, total cholesterol(TC) > 5.17mmol/L, triglyceride(TG) > 1.70mmol/L, high density lipoprotein cholesterol(HDL-C) < 1.0mmol/L, fasting blood glucose(FBG) > 6.10mmol/L and fatty liver. After adjusting these covariates, multivariable analysis revealed that HU[odds ratio(OR):1.47, 95% confidence interval(CI): 1.10-1.95, p = 0.008] remained independent association with HTN in young adults. Additionally, univariable and multivariable logistic analyses revealed that HU kept the independent effect on the presence of thickened interventricular septum(adjusted OR = 1.81, 95% CI: 1.05-3.11, P = 0.03) and thickened left ventricular posterior wall(adjusted OR = 2.28, 95% CI: 1.28-4.08, P = 0.005) in young adults with HTN. CONCLUSION: HU was independently associated with HTN in young adults. HU was independently correlated with thickened left ventricular wall, including interventricular septum and left ventricular posterior wall, in young adults with HTN.

Dietary polyunsaturated fatty acids intake is negatively associated with hyperuricemia: The National Health and Nutrition Examination Survey 2003-2015.

BACKGROUND AND AIMS: The objective of this research was to explore the associations between dietary PUFAs intake and hyperuricemia risk. METHODS AND RESULTS: Based on the National Health and Nutrition Examination Survey (NHANES) 2003-2015, all eligible individuals were divided into hyperuricemia and non-hyperuricemia groups based on diagnostic criteria for hyperuricemia (serum uric acid >420 µmol/L for men and >360 µmol/L for women). Multivariate-adjusted logistic regression was employed to explore the relationship between dietary PUFAs intake and hyperuricemia risk. Total PUFAs and their subtypes were modeled to isocalorically replace saturated fatty acids (SFAs) and monounsaturated fatty acids (MUFAs). Higher intake of n-3 PUFAs, n-6 PUFAs, linoleic acid (LA), alpha-linoleic acid (ALA), and non-marine PUFAs intake correlated with decreased hyperuricemia risk, with adjusted odds ratio (OR) and 95% confidence interval (95%CIs) were 0.77 (0.63, 0.93), 0.75 (0.61, 0.92), 0.75 (0.61, 0.91), 0.69 (0.55, 0.87), and 0.73 (0.59, 0.91), respectively. Replacing 5% of total energy intake from SFAs with isocaloric PUFAs was associated with decreased odds of hyperuricemia in men (0.69 (0.57, 0.84)) and in individuals (0.81 (0.71, 0.92)). Similar trends were observed in the substitution of SFAs with non-marine PUFAs in men (0.87 (0.80, 0.94)) and in all individuals (0.92 (0.88, 0.98)). Sensitivity analyses exhibited consistent results with primary analyses. CONCLUSION: Higher dietary intake of n-3 PUFAs, n-6 PUFAs, LA, ALA, and non-marine PUFAs was associated with decreased hyperuricemia risk. These results support the recommendation to substitute SFAs with PUFAs in diet.

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions.

Association between hyperuricemia and chronic total coronary occlusion in non-chronic kidney disease populations: a cross-sectional study.

BACKGROUND: Chronic total coronary occlusion (CTO) is an extremely hazardous condition that leads to various clinical phenomena and complications and results in social and economic burdens. Hyperuricemia (HU) is often associated with atherosclerosis. Few studies, however, have investigated the risk of CTO in individuals with HU and the role of traditional cardiovascular risk factors in this setting. METHODS: A cohort of 1245 individuals without chronic kidney disease from southwest China who underwent coronary angiography between February 2018 and June 2021 were enrolled. CTO was defined as a total occlusion of any coronary artery or arteries for more than 3 months. HU was defined as a serum uric acid level of ≥420 µmol/L in men and ≥360 µmol/L in women. Univariate and multivariate logistic regression models and subgroup analyses were applied to assess the relationship between HU and CTO. RESULTS: After adjustment, HU was noted to be associated with a 1.47-fold increase in the risk of CTO [odds ratio (OR), 1.47; 95% confidence interval (CI), 1.06-2.58; P  = 0.026]. As a continuous variable, uric acid was an independent predictor of CTO (OR, 1.002; 95% CI, 1.001-1.004; P  = 0.047). Subgroup analyses showed that the risk of CTO was higher among individuals under 65 years of age (OR, 2.77; 95% CI, 1.3-5.89), nonobese individuals (OR, 1.9; 95% CI, 1.16-3.1), and those with dyslipidemia (OR, 1.8; 95% CI, 1.04-3.11), while sex, smoking, hypertension, and diabetes did not show similar effects. Interaction analyses revealed no interaction among subgroups. CONCLUSION: Among individuals residing in southwest China, HU was associated with an increased risk of CTO in non-CKD individuals, especially those under 65 years of age and nonobese and dyslipidemic individuals.

A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. METHODS: A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. RESULTS: Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%. CONCLUSIONS: The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.