Diabetes Mellitus Is Associated with a Lower Risk of Gout: A Meta-Analysis of Observational Studies

Association between physical activity and the prevalence of gout among patients with type 2 diabetes mellitus and hyperuricemia: a two-center population-based cross-sectional study

INTRODUCTION

Diabetes mellitus (DM) and gout cohabitation severely reduces patient life quality while raising financial burden on individual and society. The aim of this study was to elucidate the association between physical activity (PA) and the prevalence of gout among type 2 DM (T2DM) and hyperuricemia (HUA) patients.

METHODS

In all, we recruited 2291 T2DM patients with HUA. Among them, 448 had gout and 1843 did not. We collected patient data, such as anthropometry, laboratory reports, and medical history, for our analyses. The PA assessment was based on the Chinese version of International PA Questionnaire-short (IPAQ). Moreover, the relationship between PA and gout risk was examined using multivariate logistic regression models.

RESULTS

Total PA was markedly low among gout patients, relative to controls (p < 0.05). Based on the IPAQ categorical score, 38.2% exhibited "low," 26.8% "moderate," and 35.0% "high" PA among gout patients. In comparison, 12.4% performed "low," 53.8% "moderate," and 33.8% "high" PA among controls. Multivariate analysis revealed that, after adjustment of confounding factors, both low (OR 6.382) and high PA (OR 2.048) had a higher prevalence of gout, as compared to moderate PA. Moreover, we revealed that the male sex, age, HUA duration, serum uric acid, glycated hemoglobin, dyslipidemia history, and drinking status were also independent indicators of the prevalence of gout. Furthermore, stratification analyses revealed results consistent with our prior results.

CONCLUSIONS

PA intensity was associated with the prevalence of gout among T2DM and HUA patients, and the lowest prevalence was achieved from moderate intensity PA. Key Points • PA intensity was associated with the prevalence of gout among T2DM and HUA patients. • The lowest prevalence of gout was achieved from moderate intensity PA.

Relationships between obesity and prevalence of gout in patients with type 2 diabetes mellitus: a cross-sectional population-based study

BACKGROUND

The purpose of this study was to investigate the relationships between generalized, abdominal, and visceral fat obesity and the prevalence of gout in patients with type 2 diabetes mellitus (T2DM).

METHODS

Data were obtained from the electronic medical databases of the National Metabolic Management Center (MMC) of Yuhuan Second People's Hospital and Taizhou Central Hospital (Taizhou University Hospital) between September 2017 and June 2023. Four obesity indicators were analyzed: waist circumference (WC), waist-to-hip ratio (WHR), body mass index (BMI), and visceral fat area (VFA). The relationships between these parameters and gout prevalence were analyzed using multivariate logistic regression and restricted cubic spline (RCS) analyses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of the four parameters for gout.

RESULTS

This cross-sectional study enrolled 10,535 participants (600 cases and 9,935 controls). Obesity was more common in patients with gout, and the obesity indicators were markedly higher in this group. After adjustment for confounders, obesity, as defined by BMI, WC, WHR, and VFA, was found to be associated with greater gout prevalence, with odds ratios (OR) of 1.775, 1.691, 1.858, and 1.578, respectively (P < 0.001). The gout odds ratios increased markedly in relation to the obesity indicator quartiles (P-value for trend < 0.001), and the obesity indicators were positively correlated with gout prevalence, as shown using RCS. The area under the ROC curve values for BMI, WC, WHR, and VFA were 0.629, 0.651, 0.634, and 0.633, respectively.

CONCLUSION

Obesity-whether general, abdominal, or visceral fat obesity-was positively linked with elevated gout risk. But uncovering the causality behind the relationship requires further prospective study. Obesity indicators (BMI, WC, WHR, and VFA) may have potential value for diagnosing gout in clinical practice.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

Emodin, a Natural Anthraquinone, Increases Uric Acid Excretion in Rats with Potassium Oxonate-Induced Hyperuricemia

The treatment of hyperuricemia and gout is mostly based on lowering serum uric acid levels using drugs, such as allopurinol, or increasing urinary excretion of uric acid. However, some patients still experience adverse reactions to allopurinol and turn to Chinese medicine as an alternative. Therefore, it is crucial to design a preclinical study to obtain more convincing data on the treatment of hyperuricemia and gout with Chinese medicine. This study aimed to explore the therapeutic effect of emodin, a Chinese herbal extract, in a rat model of hyperuricemia and gout. In this study, we used 36 Sprague-Dawley rats, which were randomly divided into six groups for experimentation. Hyperuricemia was induced in rats by intraperitoneal injections of potassium oxonate. The efficacy of emodin in reducing serum uric acid levels was demonstrated by comparing the positive control group with groups treated with three different concentrations of emodin. The inflammatory profiles, including interleukin (IL)-1β, IL-6, and tumor necrosis factor-α levels, were unaffected by emodin treatment. In the experimental results, it was observed that the serum uric acid concentration in the vehicle control group was 1.80 ± 1.14, while the concentrations in the moderate and high concentration emodin groups were 1.18 ± 0.23 and 1.12 ± 0.57, resulting in no significant difference in uric acid concentration between these treatment groups and the control group, indicating that emodin has a therapeutic effect on hyperuricemia. The increase in the fractional excretion of uric acid (FEUA) demonstrated that emodin promoted urinary uric acid excretion without significantly affecting the inflammatory profile. Thus, emodin reduced the serum uric acid concentration to achieve effective treatment of hyperuricemia and gout by increasing urinary excretion. These results were supported by the measured serum uric acid and FEUA levels. Our data have potential implications for the treatment of gout and other types of hyperuricemia in clinical practice.

Can SGLT2 inhibitors prevent incident gout? A systematic review and meta-analysis

PURPOSE

To collate the effect of SGLT2 inhibitors (SGLT2i) on adverse gout events in people with type 2 diabetes mellitus (T2DM).

METHODS

PubMed/MEDLINE, Embase, and Web of Science databases were systematically searched using appropriate keywords/MeSH/Emtree terms till January 25, 2022, to identify observational studies, randomized controlled trials (RCTs) or post hoc analysis reporting incident gout events and/or commencement of anti-gout drug in people with T2DM receiving SGLT2i versus those not receiving SGLT2i. Subgroup analyses were performed using comparators as placebo/other antidiabetic drugs and presence/absence of baseline hyperuricemia (uric acid ≥ 7 or < 7 mg/dl). Hazard ratios (HR) with 95% confidence intervals (CI) were calculated.

RESULTS

We identified 5 studies (3 observational, 2 post hoc analysis of RCTs) pooling data retrieved from 568,010 people with T2DM. Pooled analysis showed that SGLT2i use was associated with 30% reduction in incident gout events/gout flares (HR 0.70, 95% CI: 0.59, 0.84, p < 0.001, I² = 84%). Sensitivity analysis after excluding the retrospective observational study showed similar estimates (HR 0.65, 95% CI: 0.60, 0.70, p < 0.001, I² = 0%). Subgroup analysis of data retrieved only from RCTs also showed significant benefits (HR 0.74, 95% CI: 0.55, 0.98, p = 0.03, I² = 0%). Pooled analysis of data from 2 studies showed that SGLT2i use led to a significant reduction in the need for commencement of new anti-gout drug (pooled HR 0.58, 95% CI: 0.48, 0.71, p < 0.001, I² = 0%). Consistent benefits were also observed for subgroup without baseline hyperuricemia (pooled HR 0.65, 95% CI: 0.47, 0.89, p < 0.01, I² = 0%).

CONCLUSIONS

SGLT2i may potentially prevent gout-related adverse events in people with T2DM.

Genetic Predisposition to Type 2 Diabetes and Insulin Levels Is Positively Associated With Serum Urate Levels

PURPOSE

Previous epidemiological evidence showed that type 2 diabetes (T2D) is related with gout. However, the causality and the direction of this association are still not definitely elucidated. We investigated bidirectional associations of T2D and glycemic traits with serum urate concentrations and gout using a Mendelian randomization approach.

METHODS

Summary statistics from the large-scale genomewide association studies conducted for T2D (Ncase = 62 892, Ncontrol = 596 424), fasting glucose (N = 133 010), fasting insulin (N = 133 010), hemoglobin A1c (N = 123 665), homeostasis model assessment of insulin resistance (N = 46 186), urate (N = 110 347), and gout (Ncase = 2115, Ncontrol = 67 259) among participants of European ancestry were analyzed. For each trait of interest, independent genomewide significant (P < 5 × 10-8) single nucleotide polymorphisms were selected as instrumental variables. The inverse-variance weighted method was used for the primary analyses.

RESULTS

Genetic predisposition to higher risk of T2D [beta = 0.042; 95% confidence interval (CI) = 0.016-0.068; P = 0.002] and higher levels of fasting insulin (beta = 0.756; 95% CI = 0.408-1.102; P = 1.96e-05) were significantly associated with increased serum urate concentrations. Moreover, we found suggestively significant evidence supporting a causal role of fasting insulin on risk of developing gout (odds ratio = 3.06; 95% CI = 0.88-10.61; P = 0.078). In the reverse direction analysis, genetic predisposition to both urate and gout were not associated with T2D or any of 4 glycemic traits being investigated.

CONCLUSIONS

This study provides supportive evidence on causal associations of T2D and fasting insulin with serum urate concentrations and a suggestive association of fasting insulin with risk of gout. Future research is required to examine the underlying biological mechanisms on such relationships.

Cell lineage-specific methylome and genome alterations in gout

In this study, we examined data from 69 gout patients and 1,455 non-gout controls using a MethylationEPIC BeadChip assay and Illumina HiSeq platform to identify lineage-specific epigenetic alterations and associated genetic factors that contributed to gouty inflammation. Cell lineage-specific differentially methylated sites were identified using CellDMC after adjusting for sex, age, alcohol drinking, smoking status, and smoking history (total pack-years). Different cell lineages displayed distinct differential methylation. Ingenuity Pathway Analysis and NetworkAnalyst indicated that many differential methylated sites were associated with interleukin-1β expression in monocytes. On the UCSC Genome Browser and WashU Epigenome Browser, metabolic trait, cis-methylation quantitative trait loci, genetic, and functional annotation analyses identified nine methylation loci located in interleukin-1β-regulating genes (PRKCZ, CIDEC, VDAC1, CPT1A, BIRC2, BRCA1, STK11, and NLRP12) that were associated specifically with gouty inflammation. All nine sites mapped to active regulatory elements in monocytes. MoLoTool and ReMap analyses indicated that the nine methylation loci overlapped with binding sites of several transcription factors that regulated interleukin-1β production and gouty inflammation. Decreases in PRKCZ and STK11 methylation were also associated with higher numbers of first-degree relatives who also had gout. The gouty-inflammation specific methylome and genome alterations could potentially aid in the identification of novel therapeutic targets.