Association Between Uric Acid and Metabolic Syndrome in Elderly Women

Uric acid to HDL ratio: A marker for predicting incidence of metabolic syndrome in patients with type II diabetes

BACKGROUND AND AIM

Metabolic syndrome (MetS) refers to existence of a set of risk factors for developing cardiovascular disease and diabetes. Recently, uric acid to HDL ratio has attracted attention in predicting development of diabetes and incidence of metabolic syndrome in diabetic individuals. This study was performed to investigate UHR in patients with type II diabetes as a predicting factor for metabolic syndrome.

METHODS AND RESULTS

This descriptive-analytical study examined 300 patients with type II diabetes referring to diabetes clinic of Ali-Ibn Abi-Talib hospital, Rafsanjan city in 2021, selected through convenient sampling. Data were collected by a research-made checklist (including demographic, clinical and laboratory parameters) and analyzed by SPSS 24 and chi-square, Fisher exact test, independent paired t-test, one-way analysis of variance (ANOVA), Mann-Whitney U, and receiver operating characteristic (ROC). The findings revealed that 74.33 % (95 % CI: 69 to 79.18 %) of the patients had metabolic syndrome. Further, patients with metabolic syndrome demonstrated a significantly higher mean UHR ratio and triglyceride levels compared to their counterparts without the syndrome (p = 0.002 and p < 0.001, respectively). The ROC analysis identified a UHR threshold of 8.118 % with a sensitivity of 70.32 % and specificity of 55.08 % for diagnosing metabolic syndrome development in type II diabetes (AUC: 0.621, P = 0.001, 95%CI: 0.549-0.694).

CONCLUSION

The results of the present study revealed that the prevalence of metabolic syndrome was high in diabetic patients. UHR showed good sensitivity in the diagnosis of metabolic syndrome in type II diabetes. Thus, regular monitoring of laboratory tests is recommended in patients with diabetes.

Metabolic syndrome and transaminases: systematic review and meta-analysis

BACKGROUND

Metabolic syndrome (MetS) is a group of metabolic abnormalities characterised by hypertension, central obesity, dyslipidaemia and dysregulation of blood glucose, associated with the risk of diabetes, cardiovascular disease and overall mortality. The presence of elevated liver enzymes may precede the development of MetS, with alterations of the liver being observed that are directly related to metabolic problems. The study aims to provide the best evidence on the association between liver enzymes (ALT, AST, GGT) and MetS by determining the effect size of these biomarkers.

METHODS

A systematic review and meta-analysis of studies indexed in PubMed and Scopus databases were performed. Study quality was assessed using the STROBE tool. The Grade Pro tool was used to evaluate the evidence, and the quantitative synthesis was performed using RevMan (Cochrane Collaboration).

RESULTS

Seventeen articles comparing liver enzyme concentrations between 76,686 with MetS (MetS+) and 201,855 without MetS (MetS-) subjects were included. The concentration of ALT, AST and GGT in the MetS + subjects was significantly higher than in the control group 7.13 IU/L (CI95% 5.73-8.54; p < 0.00001; I2 = 96%), 2.68 IU/L (CI95% 1.82-3.54; p < 0.00001; I2 = 96%) and 11.20 IU/L (CI95% 7.11-15.29; p < 0.00001; I2 = 96%), respectively.

CONCLUSIONS

The evaluation of the relationship of liver enzymes in the pathophysiological process of MetS could lead to new insights into early diagnosis.

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study

BACKGROUND

The rapid increase of single-person households in South Korea is leading to an increase in the incidence of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases, due to lifestyle changes. It is necessary to analyze the complex effects of metabolic syndrome risk factors in South Korean single-person households, which differ from one household to another, considering the diversity of single-person households.

OBJECTIVE

This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning techniques and categorically characterize the risk factors through latent class analysis (LCA).

METHODS

This cross-sectional study included 10-year secondary data obtained from the National Health and Nutrition Examination Survey (2009-2018). We selected 1371 participants belonging to single-person households. Data were analyzed using SPSS (version 25.0; IBM Corp), Mplus (version 8.0; Muthen & Muthen), and Python (version 3.0; Plone & Python). We applied 4 machine learning algorithms (logistic regression, decision tree, random forest, and extreme gradient boost) to identify important factors and then applied LCA to categorize the risk groups of metabolic syndromes in single-person households.

RESULTS

Through LCA, participants were classified into 4 groups (group 1: intense physical activity in early adulthood, group 2: hypertension among middle-aged female respondents, group 3: smoking and drinking among middle-aged male respondents, and group 4: obesity and abdominal obesity among middle-aged respondents). In addition, age, BMI, obesity, subjective body shape recognition, alcohol consumption, smoking, binge drinking frequency, and job type were investigated as common factors that affect metabolic syndrome in single-person households through machine learning techniques. Group 4 was the most susceptible and at-risk group for metabolic syndrome (odds ratio 17.67, 95% CI 14.5-25.3; P<.001), and obesity and abdominal obesity were the most influential risk factors for metabolic syndrome.

CONCLUSIONS

This study identified risk groups and factors affecting metabolic syndrome in single-person households through machine learning techniques and LCA. Through these findings, customized interventions for each generational risk factor for metabolic syndrome can be implemented, leading to the prevention of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases. In conclusion, this study contributes to the prevention of metabolic syndrome in single-person households by providing new insights and priority groups for the development of customized interventions using classification.

High-Fructose Diet-Induced Hyperuricemia Accompanying Metabolic Syndrome-Mechanisms and Dietary Therapy Proposals

Fructose is often used as a food ingredient due to its low production costs and sweetening power. In recent years, it has been noticed that people on a Western diet high in fructose have high levels of uric acid in their blood. It was recognized that the specific metabolism of fructose in the body might cause increased production of uric acid, which then may affect the intensification of lipogenesis and the development of metabolic syndrome (MetS), insulin resistance, gout, cardiovascular diseases, leptin resistance, or non-alcoholic fatty liver disease. So far, to treat hyperuricemia, it has been recommended to use a low-purine diet characterized by limiting protein-containing products. However, this recommendation often leads to an increased intake of carbohydrate-rich foods that may contain fructose. Increased fructose consumption may enhance the secretion of uric acid again and, consequently, does not have therapeutic effects. Therefore, instead of a low-purine diet, using healthy diets, such as DASH or the Mediterranean diet, which can benefit metabolic parameters, could be a better proposal. This article provides an overview of this approach, focusing on MetS and hyperuricemia among high-fructose dieters.

Association between metabolic syndrome and uric acid: a systematic review and meta-analysis

This systematic review and meta-analysis aim to provide the best evidence on the association between metabolic syndrome (MetS) and uric acid (UA) by determining the size of the effect of this biomarker on MetS. The review protocol is registered with PROSPERO (CRD42021231124). The search covered the PubMed and Scopus databases. Methodological quality was assessed with the STROBE tool, overall risk of bias with RevMan (Cochrane Collaboration) and quality of evidence with Grade Pro. Initially, 1582 articles were identified. Then, after excluding duplicates and reviewing titles and abstracts, 1529 articles were excluded from applying the eligibility criteria. We included 43 papers (56 groups) comparing UA concentrations between subjects 91,845 with MetS and 259,931 controls. Subjects with MetS had a higher mean UA of 0.57 mg/dl (95% CI 0.54-0.61) (p < 0.00001). Given the heterogeneity of the included studies, the researchers decided to perform subgroups analysis. Men with MetS have a higher UA concentration mg/dl 0.53 (95% CI 0.45-0.62, p < 0.00001) and women with MetS 0.57 (95% CI 0.48-0.66, p < 0.00001) compared to subjects without MetS. Assessment of UA concentration could provide a new avenue for early diagnosis of MetS, as a new biomarker and the possibility of new therapeutic targets.

Which Clusters of Metabolic Syndrome Are the Most Associated with Serum Uric Acid?

Background and Objectives: Metabolic syndrome is defined as three or more of five components; therefore, there are 16 possible different clusters of metabolic risk factors that are under one diagnosis of metabolic syndrome. In this study, we evaluated the different clusters of metabolic syndrome (MetS) across serum uric acid (SUA) quartiles and analyzed the association of these clusters with SUA levels, respectively, in both men and women. Materials and Methods: A total of 606 subjects were recruited to a cross-sectional study from the ongoing Lithuanian High Cardiovascular Risk primary prevention program (LitHiR). All of the study subjects were diagnosed with MetS (according to the 2005 National Cholesterol Education Program Adult Treatment Panel III MetS definition). Results: In the middle-aged population of patients with MetS living in Lithuania, a high proportion of hyperuricaemia was detected—35.5% (95% Cl 31.7% to 39.4%). For women possessing all five MetS components, the chances of having hyperuricaemia are 2.807 higher than for women with three risk factors (p < 0.001). However, men do not have a statistically significantly higher chance of having hyperuricaemia, depending on the number of MetS components in our population. Using multivariable models, the statistically significant chance of having hyperuricaemia was observed only in women possessing all five MetS components (OR = 2.386, p < 0.0001), compared to any other of 15 MetS clusters. After adjustment for age and sex, the chance of having hyperuricaemia for individuals with the cluster of all five MetS components, compared to any other of 15 MetS clusters, remained (OR = 1.982, p = 0.001). Also, a lower probability (OR = 0.653, p = 0.039) of having hyperuricaemia was observed for individuals having the combination of abnormal plasma glucose, blood pressure, and waist circumference. Conclusions: Patients with the clustering of all five metabolic syndrome components are at higher risk for having hyperuricaemia than patients with any other combination of MetS clusters. This risk is even higher for women. It could be beneficial for patients presented with all five MetS components to be screened for SUA concentration in the primary CVD prevention program.

Serum uric acid is independently associated with metabolic syndrome and systemic hypertension in women from northeast Brazil

Hyperuricemia (HU) has been associated with cardiovascular risk and metabolic syndrome (MS) worldwide. However, inconsistencies about this relation are still reported, and it is not clear whether hyperuricemia is an independent risk factor for MS. The aim of this study was to determine hyperuricemia associations with systemic hypertension and MS in women from northeast Brazil. The study included 301 women. Hyperuricemia was considered for serum uric acid (SUA) ≥6 mg/dL. Insulin resistance (IR) was measured by TyG index (TyG ≥ 4.55). Fisher test and Multivariate logistic regression analyses estimated the association between hyperuricemia (or SUA level) and systemic hypertension and MS. Hyperuricemia association with systemic hypertension was independent of age, body-mass index (BMI), smoking and alcoholism (OR: OR: 4.6050; p = .000256), and MS components (OR: 4.1296; IC95% 1.8330_9.3033; p = .000621). Hyperuricemia increased risk of systemic hypertension by 4,6 -fold. SUA level was associated with MS, independently of other classic component factors of the syndrome (OR:1.34, p = .0129). Hyperuricemia and high SUA levels were associated with MS and systemic hypertension. Effect of hyperuricemia in systemic hypertension is independent of age, BMI, lifestyle, and MS factors. SUA levels are independently associated with MS.

Investigating the relationship between serum uric acid to high-density lipoprotein ratio and metabolic syndrome

Aims

This study aimed to determine a parameter to more easily diagnose metabolic syndrome and predict its probability of occurrence in high‐risk individuals.

Methods

In this cross‐sectional study, data related to the study population in the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS) were examined. Subjects were divided into two groups with and without metabolic syndrome, and the relevant factors such as the ratios of uric acid to high‐density lipoprotein (HDL) (UHR) in these two groups were compared, and the best cut‐off point was determined.

Results

Data related to 817 people including 96 people with metabolic syndrome and 721 people without metabolic syndrome were analysed. The mean UHR was significantly higher in patients with metabolic syndrome (14.76 ± 6.33%) compared with those without metabolic syndrome (10.0 ± 3.10%) (p < .001). People with high UHR are 2.9 times more at risk of metabolic syndrome and the best cut‐off point was 9.50% with 86% sensitivity and 55% specificity.

Conclusions

According to our study, UHR is also helpful in diagnosing metabolic syndrome and can also be used to screen people at risk for metabolic syndrome.

Urine Uric Acid Excretion Levels are Positively Associated with Obesity and Abdominal Obesity in Type 2 Diabetes Patients without Chronic Kidney Disease

PURPOSE

We aimed to investigate whether urine uric acid excretion (UUAE) levels are associated with obesity and abdominal obesity in patients with type 2 diabetes (T2D).

METHODS

There were 2785 type 2 diabetic patients in this cross-sectional study. Obesity was defined as BMI ≥ 25 kg/m², and abdominal obesity was defined as waist circumference (WC) ≥90 cm for men and WC ≥ 80 cm for women based on World Health Organization (WHO) recommendations for Asians. Chronic kidney disease (CKD) was defined as the estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m² and/or urinary albumin excretion (UAE) ≥300 mg/24h. 24-h UUAE was determined enzymatically using a single 24-hour urine collection. All the subjects were stratified into quartiles based on UUAE levels. Both obesity and abdominal obesity were compared among the UUAE quartile groups, respectively. Furthermore, the associations of UUAE with obesity and abdominal obesity were analyzed in both CKD and non-CKD patients, respectively.

RESULTS

There was an obvious increased trend in both obesity prevalence (36.2%, 41.5%, 46.3%, and 63.4%, respectively, p < 0.001 for trend) and abdominal obesity prevalence (58.1%, 61.2%, 64.7%, and 75.8%, respectively, p < 0.001 for trend) in patients with T2D across the UUAE quartiles after controlling for age, sex and diabetes duration. Multiple logistic regression analyses revealed independent associations between UUAE quartiles and obesity (p < 0.001) and abdominal obesity (p < 0.001) in all patients. However, UUAE was significantly associated with obesity and abdominal obesity only in the T2D patients without CKD (p < 0.001 in model 1, model 2, model 3 and model 4, respectively).

CONCLUSION

Increased UUAE levels were significantly associated with the presence of obesity, especially abdominal obesity in T2D patients without CKD.

27-Hydroxycholesterol regulates human SLC22A12 gene expression through estrogen receptor action

The excretion and reabsorption of uric acid both to and from urine are tightly regulated by uric acid transporters. Metabolic syndrome conditions, such as obesity, hypercholesterolemia, and insulin resistance, are believed to regulate the expression of uric acid transporters and decrease the excretion of uric acid. However, the mechanisms driving cholesterol impacts on uric acid transporters have been unknown. Here, we show that cholesterol metabolite 27‐hydroxycholesterol (27HC) upregulates the uric acid reabsorption transporter URAT1 encoded by SLC22A12 via estrogen receptors (ER). Transcriptional motif analysis showed that the SLC22A12 gene promoter has more estrogen response elements (EREs) than other uric acid reabsorption transporters such as SLC22A11 and SLC22A13, and 27HC‐activated SLC22A12 gene promoter via ER through EREs. Furthermore, 27HC increased SLC22A12 gene expression in human kidney organoids. Our results suggest that in hypercholesterolemic conditions, elevated levels of 27HC derived from cholesterol induce URAT1/SLC22A12 expression to increase uric acid reabsorption, and thereby, could increase serum uric acid levels.

Incidence of metabolic syndrome and determinants of its progression in Southern Iran: A 5-year longitudinal follow-up study

Background

Metabolic syndrome (MetS) is a cluster of conditions increasing the risk of serious diseases. This study aimed to define the predictors of MetS incident in a community-based cohort in Southern Iran, during a mean follow-up period of 5.1 years.

Materials and Methods

During the mean follow-up period of 5.1 years, a cohort study was conducted on 819 Iranian adults aged ≥18 years at baseline and followed to determine the incidence and predictors of MetS progression in Shiraz, a main urban region in the southern part of Iran. The International Diabetes Federation Guideline was used to detect the MetS. Multiple Cox's proportional hazards models were also used to estimate the predictors of new-onset MetS.

Results

The prevalence of MetS was 25.9% at baseline, and the overall incidence of subsequent MetS was 5.45% (95% confidence interval [CI]: 4.47-6.59). The incidence of MetS was significantly higher in women (7.12% [95% CI: 5.52-9.05]) than in men (3.92% [95% CI: 2.80-5.34]). Moreover, it increased by 5.02 (95% CI, 3.75-6.58) among individuals who had one metabolic component and by 12.65 (95% CI, 9.72-16.18) for those who had three or more components (P < 0001). The incidence of MetS was also analyzed using the multiple Cox's proportional hazards model for potential risk factors, and it was revealed that female gender (hazard ratio [HR] 2.45; 95% CI: 1.33, 4.50; P = 0.004), higher body mass index (HR 3.13; 95% CI: 1.43.6.84; P = 0.012), increased abdominal obesity (HR 1.45; 95% CI 0.85, 2.46; P = 0.045), smoking (HR 4.79; 95% CI 2.09, 10.97; P < 0.001), and lower high-density lipoprotein (HR 0.53; 95% CI: 0.29, 1.00; P = 0.044) significantly predicted the onset of MetS at baseline; however, age, systolic and diastolic blood pressure, serum uric acid, fasting blood glucose, cholesterol, triglyceride and creatinine, estimated glomerular filtration rate, marital status, level of education, and level of physical activity did not independently predict the onset of MetS when other covariates were considered.

Conclusion

This study showed the high-incidence rates of MetS in males and females residing in Southern Iran. Therefore, the prevention through community-based lifestyle modification should be implemented to reduce the burden of MetS and its complications.

Association between serum uric acid and metabolic syndrome: a cross-sectional study in Bangladeshi adults

Elevated levels of serum uric acid (SUA) have been suggested to associate with cardiovascular disease, diabetes and metabolic syndrome (MetS). However, information is limited on the association between SUA and MetS in general adults. This study aimed to assess the relationship of SUA with MetS and its components in general adults in Bangladesh. A total of 420 participants were enrolled in this study and biochemical parameters including SUA, fasting blood glucose (FBG) and lipid profile were analyzed using standard methods. The NECP criteria were applied to define MetS. The association of SUA with MetS and its components were evaluated by multinomial logistic regression models. The overall prevalence of MetS was 22% with 21.9% in males and 22.1% in female participants. Male subjects had a high prevalence of elevated components of MetS than in the female subjects (p < 0.05 for all cases). The mean concentration of SUA was significantly higher in subjects of the MetS group compared to the non-MetS group (p < 0.05). The components of MetS were raised with the increasing concentrations of SUA across the quartiles. In regression analysis, SUA was significantly associated with the prevalence of MetS in Bangladeshi adults. In conclusion, elevated SUA was significantly associated with the prevalence of MetS and its components.

Sex- and age-specific variations, temporal trends and metabolic determinants of serum uric acid concentrations in a large population-based Austrian cohort

Little is known about sex- and age-specific variations and temporal trends in serum uric acid (SUA) concentrations, the prevalence of hyperuricemia and its association with metabolic risk factors in the general population. Between January 1, 1985 and June 30, 2005 146,873 participants (42% women) were recruited. Prevalence of hyperuricemia was estimated applying a common (SUA > 360 µmol/L) and sex-specific cut-off points (women > 340 µmol/L, men > 420 µmol/L). At baseline, mean age was 41.2 years in men and 51.5 years in women, mean SUA concentration was 314.8 µmol/L and 243.6 µmol/L, respectively. Applying a common cut-off point, the prevalence of hyperuricemia was 18.5% in men and 4.4% in women and by sex-specific cut-off points it was 15.1% and 13.8%, respectively. SUA levels increased by 6.7 µmol/L per decade in men, but remained constant in women until the age of 50 years with a sharp increase by approximately 22 µmol/L per decade thereafter. In men and women, hyperuricemia was associated with obesity, hypertriglyceridemia and elevated gamma-glutamyl transferase. With increasing age SUA levels and the prevalence of hyperuricemia rise in a sex-specific manner. Above the age of 65 years, the sex-specific prevalence of hyperuricemia in women outreaches that in men.

Association of Serum Uric Acid with cardio-metabolic risk factors and metabolic syndrome in seafarers working on tankers

BACKGROUND

There is still controversy over the clinical interpretation of the association between metabolic syndrome (MetS) and serum uric acid (SUA) levels. Therefore, the aim of this study was to investigate the association of SUA levels with MetS and other cardio-metabolic risk factors (CMRF) in seafarers working on tankers.

METHODS

This cross-sectional study was conducted in 2015 and included 234 male seafarers working on tankers. The participants were divided into three groups based on the tertiles of SUA. The report from of the National Committee of Obesity was used to define the MetS. The relationship between SUA, CMRF and MetS adjusted for age, educational level, job history, shift work, smoking and BMI was assessed by logistic regression analysis.

RESULTS

The subjects were aged 36.0 ± 10.3 years (mean ± SD). A notable upward trend was observed in mean weight, body mass index (BMI), triglyceride (TG), total cholesterol (TC), low density lipoprotein (LDL) and very low-density lipoprotein (VLDL) as tertiles of SUA increased (P < 0.001). In all models of the logistic regression analyses, the odds ratio (OR) of high TG for participants in the 3rd tertile of SUA was four times higher than that for participants in the 1st tertile of SUA (P < 0.001). The odds ratio of high TC and the SUA levels increased, so that the odds ratio of high TC for participants in the 2nd tertile was 2.47 (95% CI: 1.10-5.53) (P < 0.05) as compared with that for participants in the 1st tertile. Significant association was observed between MetS and the levels of SUA; 6.10 (95% CI: 1.77-20.94) (P < 0.05).

CONCLUSIONS

Findings revealed that SUA levels were associated with MetS, high TG and high TC. Therefore, it is recommended that clinical attention should be given to symptoms related to elevated SUA - being one of the most important remediable risk factors for MetS - in the annual medical examinations of seafarers.

Prognostic Significance of Serum Uric Acid and Gamma-Glutamyltransferase in Patients with Advanced Gastric Cancer

PURPOSE

In this study, we aim to evaluate the prognostic role of serum uric acid and gamma-glutamyltransferase in advanced gastric cancer patients.

METHODS

A total of 180 patients pathologically diagnosed with advanced gastric cancer were included in this retrospective study. We used time-dependent receiver operating characteristic (ROC) curves to identify the optimal cut-off value of serum uric acid (UA) and gamma-glutamyltransferase (GGT). Survival analysis was performed using the Kaplan-Meier method and log-rank test, and multivariate Cox regression analyses were applied. A nomogram was formulated, and the calibration and discrimination of the nomogram were determined by calibration curve and concordance index (C-index). We validated the results using bootstrap resampling and a separate study on 60 patients collected from 2015 to 2017 using the same criteria in other medical center.

RESULTS

Both higher serum uric acid (>228 μmol/L) and higher gamma-glutamyltransferase (>14 U/L) had worse OS and PFS. Univariate analysis indicated that serum uric acid (UA) (p < 0.001 and p < 0.001) and gamma-glutamyltransferase (GGT) (p < 0.001 and p = 0.044) were significantly related to overall survival (OS) and progression-free survival (PFS), respectively. Multivariate analysis revealed serum uric acid (UA) and gamma-glutamyltransferase (GGT) were independent prognostic factors for OS (p = 0.012, p = 0.001). The optimal agreement between actual observation and nomogram prediction was shown by calibration curves. The C-indexes of the nomogram for predicting OS and PFS were 0.748 (95% CI: 0.70-0.79) and 0.728 (95% CI: 0.6741-0.7819), respectively. The results were confirmed in the validation cohort.

CONCLUSION

We observed that both serum UA and GGT were poor prognostic factors in patients with advanced gastric cancer. And we also formulated and validated a nomogram which can predict individual survival for advanced gastric cancer patients.

Dynamics of associations between perfluoroalkyl substances and uric acid across the various stages of glomerular function

National Health and Nutrition Examination Survey 2007-2014 data (N = 6844) for adults aged ≥ 20 years were analyzed to estimate associations of perfluoroalkyl substances (PFAS), namely, PFOA, PFOS, PFDA, PFHxS, and PFNA with uric acid across stages of declining glomerular function. The population was stratified by the estimated glomerular filtration rates (eGFR) stages accompanying kidney disease: GF-1 with eGFR > 90 mL/min/1.73 m²; GF-2 with eGFR 60-89 mL/min/1.73 m²; GF-3A with eGFR 45-59 mL/min/1.73 m²; and GF-3B/4 with eGFR 15-44 mL/min/1.73 m². Adjusted and unadjusted geometric means of uric acid increased from GF-1 to GF-3B/4 for males and females. Adjusted geometric means for uric acid were higher for males by 1.38, 1.03, and 0.62 mg/dL for GF-1, GF2, and GF-3 respectively but for GF-3B/4, females had higher adjusted geometric means than males by 0.16 mg/dL, revealing narrowing of sex differences in uric acid as glomerular function declines. The direction of association between PFAS and uric acid was positive for GF-1 and GF-2 for males and for every PFAS except PFDA for females. For males for GF-3B/4, association between every PFAS except PFHxS and uric acid was found to be negative (p < 0.01). For females, only PFHxS actually reverses its relationship with increasing stages of renal disease. Uric acid associations with PFAS reverse in males with advanced renal failure. An implication is that previously reported association of PFAS exposure with uric acid is not due to renal failure. Understanding of other biomarkers associated with both PFAS exposure and renal failure may benefit from similar evaluation.