An association of smoking with serum urate and gout: A health paradox

The role of body mass index on the association between the energy-adjusted dietary inflammatory index and hyperuricemia: a mediation analysis based on NHANES (2007-2016)

BACKGROUND

The Energy-Adjusted Dietary Inflammatory Index (E-DII) is related to both body mass index (BMI) and hyperuricemia. However, the association among BMI, hyperuricemia and DII is yet to be fully elucidated. The purpose of this study is to explore the role of BMI in the relationship between E-DII and hyperuricemia in the American population.

METHODS

A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2016, with a sample size of 10,571 participants. The study used a weighted logistic regression model and a generalized additive model (GAM) to explore the associations among BMI, hyperuricemia and E-DII. Furthermore, mediation analysis was utilized to illustrate the mediating relationships among these variables.

RESULTS

The results of the study indicated that a higher E-DII was related to an increased risk of hyperuricemia. The association between E-DII and hyperuricemia was partially mediated by BMI.

CONCLUSIONS

E-DII is associated with hyperuricemia. BMI mediates the relationship between E-DII and hyperuricemia among Americans, which provides crucial information for the prevention of hyperuricemia.

The relationship between education attainment and gout, and the mediating role of modifiable risk factors: a Mendelian randomization study

Objective

To investigate the causal relationship between educational attainment (EA) and gout, as well as the potential mediating effects of individual physical status (IPS) such as body mass index (BMI) and systolic blood pressure (SBP) and lifestyle habits (LH) including alcohol intake frequency (drinking), current tobacco smoking (smoking), and time spent watching television (TV).

Methods

Utilizing two-sample Mendelian randomization (MR), we analyzed the causal effects of EA on gout risk, and of IPS (BMI and SBP) and LH (smoking, drinking, and TV time) on gout risk. Multivariable MR (MVMR) was employed to explore and quantify the mediating effects of IPS and LH on the causal relationship between EA and gout risk.

Results

An elevation of educational attainment by one standard deviation (4.2 years) exhibited a protective effect against gout (odds ratio 0.724, 95% confidence interval 0.552-0.950; p = 0.020). We did not observe a causal relationship between smoking and gout, but BMI, SBP, drinking, and TV time were found to be causal risk factors for gout. Moreover, BMI, SBP, drinking, and TV time acted as mediating factors in the causal relationship between EA and gout risk, explaining 27.17, 14.83, 51.33, and 1.10% of the causal effects, respectively.

Conclusion

Our study indicates that having a genetically predicted higher level of EA may provide protection against gout. We found that this relationship is influenced by IPS factors such as BMI and SBP, as well as LH including drinking and TV time.

The Relationship Between Psychosocial Behavior and the Quality of Life of Male Gout Patients in Southwest China: A Cross-Sectional Study Based on an Information-Motivation-Behavioral Skills Model

Purpose

Gout is more severe in men, leading to a poor quality of life. Previous studies did not sufficiently pay attention to the quality of life and related factors in gout patients in Southwest China. This study aimed to investigate the quality of life of men with gout in Southwest China and explore the relationship between psychosocial factors and health-related quality of life from the perspective of an information-motivation-behavioral skill model.

Patients and Methods

This was a cross-sectional study conducted in the West China Hospital of Sichuan University located in Southwest China. In total, 230 male patients with gout were enrolled. The health-related quality of life of patients was assessed using the gout impact scale. The gout knowledge questionnaire was used to assess patients' information. The positive psycap questionnaire was used to assess motivation. The gout patients' self-management assessment scale was used to assess behavioral skills. Multiple linear regression was used to identify the factors associated with the health-related quality of life of patients.

Results

The overall mean gout impact scale score was 52.7±15.3 (maximum possible = 100). Factors associated with the total gout impact scale score were tophi (β=0.138, P=0.050), pain (β=0.255, P<0.001), and resiliency (β=-0.282, P<0.001). In addition, demographic characteristics (educational level, smoking and marital status), clinical characteristics (tophi, pain, number of attacks over half a year, and number of affected joints) and psychosocial behavior variables (resiliency, hope, disease treatment management, diet management) were associated with several dimensions of the gout impact scale.

Conclusion

The health-related quality of life of male patients with gout in Southwest China was at a medium level. We found that demographic characteristics, clinical characteristics, and psychosocial factors were associated with health-related quality of life of patients with gout. These findings can be used as a reference to improve health-related quality of life of patients with gout.

Women with Metabolic Syndrome and Unhealthy Lifestyle Factors Are at a Higher Risk for Hyperuricemia

Hyperuricemia (HUA) has become a significant medical concern due to its complications and links to metabolic syndrome (MetS) and cardiovascular disease (CVD), which result in increased mortality. The pathogenic processes associated with unhealthy behaviors, MetS, and HUA can be cooperative and potentially synergistic in the activation of risk factors. Recent research has shown sex-based differences in the relationship between HUA and its associated risk factors. This study aimed to investigate these differences, particularly in the context of MetS and CVD risk factors and unhealthy lifestyles. We also aimed to evaluate the joint effects of these factors based on sex. We conducted a cross-sectional study using nationally representative survey data from the Korean National Health and Nutritional Examination Survey 2016-2018. We performed multivariable logistic regression analysis, calculating adjusted odds ratios (ORs) with their 95% confidence intervals (CIs). We also conducted subgroup analyses based on sex and the presence of MetS with or without unhealthy lifestyle factors (tobacco use, alcohol intake). We found sex-based differences in the relationships between HUA and MetS, CVD risk factors, and lifestyle behaviors. Our major finding was a significant association between MetS and HUA in both men and women, regardless of alcohol consumption and smoking status, and this association was stronger in women. We also observed a synergistic effect of MetS and lifestyle factors on the risk of HUA, particularly in women, in whom the risk of HUA increased up to four times compared to the reference group. A sex-based clinical strategy for HUA is necessary to reduce related complications and their socio-economic burden.

Healthy lifestyle scores associate with incidence of type 2 diabetes mediated by uric acid

BACKGROUND

Whether and to what extent serum uric acid (SUA) mediates the association between combined lifestyle behaviors and type 2 diabetes mellitus (T2DM) remain unclear. This study aimed to investigate the role of SUA in the relationship between healthy lifestyle scores (HLS) and the incidence of T2DM.

METHODS

This prospective study used data from Zhejiang Metabolic Syndrome cohort. A HLS (5-point scale including healthy waist circumference (WC), never smoking, high physical activity, healthy diet and moderate alcohol intake) was estimated in 13,919 participants, who had SUA at baseline examination in 2009-2014, and were followed-up to 2021-2022 to ascertain incident of T2DM. Cox proportional hazards models and mediation analysis were used to examine the associations between HLS, SUA and T2DM.

RESULTS

We included 13,919 participants aged 18 years or older without diabetes at baseline (mean age 54.6 [SD 13.9] years, 58.7% female). During a median follow-up of 9.94 years, 645 cases of T2DM occurred. Compared with participants with a poor HLS, those with 4-5 low-risk lifestyle factors showed a 60% reduction in the risk of developing T2DM (adjusted HR, 0.40; 95% CI: 0.28-0.57). Further, the population-attributable risk percent (95% CI) of T2DM for poor adherence to the overall healthy lifestyle (< 4 low-risk factors) was 43.24% (30.02%, 56.46%). The HLS was inversely associated with SUA level. With per score increased in HLS, the beta (95% CI) of SUA (log transformed) was - 0.03 (- 0.03, - 0.02), and the odds ratio (95% CI) of hyperuricemia was 0.82 (0.77, 0.86). The relationship between the HLS and risk of T2DM was mediated by SUA with a 13.06% mediation effect. There was no significant combined effect of HLS and SUA on risk of T2DM (P = 0.097).

CONCLUSIONS

The relationship between overall healthy lifestyle behaviors and T2DM was reconfirmed and the association appeared to be mediated by SUA. The mediation effect of baseline SUA was more pronounced among women who were below 60 years old.

Environmental factors and risk of gout

Gout is a chronic disease with inflammatory arthritis caused by monosodium urate (MSU) crystals deposition, an elevated serum urate level (hyperuricaemia) is the critical factor leading to MSU crystals deposition and promoting the progression of gout. The onset and development of gout is generally the result of multiple factors, such as diet, heredity and environmental factors. Although genetics and diet are thought to play as major factors, a growing body of research evidence has highlighted that environmental factors also play a significant role in the onset and exacerbation of gout. Recent studies have shown that air pollutants such as particulate matter, sulfur dioxide (SO₂) and carbon monoxide (CO) may increase the risk of hospitalizations for gout, and that the changes in temperature and humidity may affect uric acid (UA) levels. There is also seasonal trend in gout. It has been demonstrated that environmental factors may induce or accelerate the production and release of pro-inflammatory mediators, causing an unbalance oxidative stress and systemic inflammation, and then participating in the overall process or a certain link of gout. Moreover, several environmental factors have shown the ability to induce the production urate and regulate the innate immune pathways, involving in the pathogenesis of gout. Nevertheless, the role of environmental factors in the etiology of gout remains unclear. In this review, we summarized the recent literatures and aimed to discuss the relationship between environmental factors (such as microclimate, season, ambient/indoor air pollution and extreme weather) and gout. We further discussed the inflammatory mechanisms of environmental factors and gout and the comprehensive effects of environmental factors on gout. We also made a prospect of the management and treatment of gout, with special consideration to environmental factors associated with gout.

Prevalence of hyperuricemia and the population attributable fraction of modifiable risk factors: Evidence from a general population cohort in China

Data on updated hyperuricemia prevalence in Beijing-Tianjin-Hebei (BTH) region in China, which is one of the world-class urban agglomerations, is sparse. Overweight/obesity, alcohol consumption, smoking and sedentary behavior are modifiable risk factors (MRFs) for elevated serum uric acid (SUA), but their population attributable fractions (PAFs) for hyperuricemia is still unclear. Using baseline data from the BTH Physical Examination General Population Cohort, we calculated the crude- and adjusted-prevalence of hyperuricemia based on the 30,158 participants aged 18–80 years. Hyperuricemia was defined as SUA >420 μmol/L in men and >360 μmol/L in women, or currently use of uric acid lowering drugs. Overweight/obesity, alcohol consumption, smoking and sedentary behavior were considered as MRFs and their adjusted PAFs were estimated. The prevalence of hyperuricemia was 19.37%, 27.72% in men and 10.69% in women. The PAFs and 95% confidence intervals for overweight, obesity were 16.25% (14.26–18.25%) and 12.08% (11.40–12.77%) in men, 13.95% (12.31–15.59%) and 6.35% (5.97–6.74%) in women, respectively. Alcohol consumption can explain 4.64% (2.72–6.56%) hyperuricemia cases in men, but with no statistical significance in women. Cigarette smoking contributed to 3.15% (1.09–5.21%) cases in men, but a much lower fraction in women (0.85%, 0.49–1.22%). Compared with sedentary time <2 h per day, the PAFs of 2–4 h, 4–6 h, and more than 6 h per day were 3.14% (1.34–4.93%), 6.72% (4.44–8.99%) and 8.04% (4.95–11.13%) in men, respectively. Sedentary time was not found to be associated with hyperuricemia in women. These findings concluded that hyperuricemia is prevalent in this representative Chinese adult general population with substantial sex difference. Four MRFs (overweight/obesity, alcohol consumption, cigarette smoking and sedentary behavior) accounted for a notable proportion of hyperuricemia cases. The PAF estimations enable the exploration of the expected proportion of hyperuricemia cases that could be prevented if the MRFs were removed, which warrants the public health significance of life-style intervention.

Healthy lifestyle counteracts the risk effect of genetic factors on incident gout: a large population-based longitudinal study

BACKGROUND

Risk genes linked to the development of gout have been identified, and lifestyle factors are related to gout risk. It remains unclear whether healthy lifestyle factors can mitigate the genetic risk of gout. Therefore, we aimed to explore whether and to what extent a healthy lifestyle can mitigate the risk of gout related to genetic factors.

METHODS

Within the UK Biobank, 416,481 gout-free participants (aged 37-74) were identified at baseline. Polygenic risk for gout was assessed and categorized as low (lowest tertile), middle (tertile 2), and high (highest tertile). Healthy lifestyle factors included no/moderate alcohol consumption, no smoking, physical activity, and a healthy diet. Participants were categorized into three groups according to their number of healthy lifestyle factors: unfavorable (0 or 1), intermediate (any 2), and favorable (3 or 4). Data were analyzed using Cox proportional hazard models.

RESULTS

Over the follow-up (median: 12.1 years), 6206 participants developed gout. Compared to low genetic risk, the hazard ratios (HRs) and 95% confidence intervals (CIs) of gout was 1.44 (1.35-1.54) for middle and 1.77 (1.66-1.89) for high genetic risk. The HRs (95% CIs) of gout were 0.63 (0.59-0.67) for a favorable lifestyle and 0.79 (0.75-0.85) for an intermediate lifestyle, compared to an unfavorable lifestyle. In joint effect analysis, compared to participants with low genetic predisposition and a favorable lifestyle, the HRs (95% CIs) of gout were 2.39 (2.12-2.70)/3.12 (2.79-3.52) in those with middle and high genetic predisposition plus unfavorable lifestyle profiles, and 1.53 (1.35-1.74)/1.98 (1.75-2.24) for those with middle and high genetic predisposition plus favorable lifestyle profiles, respectively. Moreover, compared to an unfavorable lifestyle, the HRs of gout related to a favorable lifestyle was 0.64 (95% CI, 0.56-0.73) for low genetic risk, 0.65 (95% CI, 0.58-0.72) for middle genetic risk, and 0.62 (95% CI, 0.57-0.69) for high genetic risk. There was a significant additive interaction between unfavorable lifestyle and high genetic risk on gout.

CONCLUSIONS

Healthy lifestyle was associated with a lower risk of gout and may attenuate the risk of gout related to genetic factors by almost a third.

Incident Gout: Risk of Death and Cause-Specific Mortality in Western Sweden: A Prospective, Controlled Inception Cohort Study

Background

Excess mortality in gout has been attributed to cardiovascular diseases (CVD). Considering the decline in CVD mortality in the general population, we wanted to evaluate overall mortality in gout and cause-specific contributions to mortality beyond CVD and temporal trends.

Methods

All incident cases of gout between 2006 and 2015 in western Sweden and 5 population controls per case matched for age, sex, and county were identified. Comorbidities were identified for 5 years preceding the index date. Follow-up ended at death, migration, or end of study on December 2017. Effect of gout on death risk was calculated using COX regression on the whole population and stratified by sex, adjusted for demographics, and comorbidities. Death incidence rates were compared between the two time periods, 2006-2010 and 2011-2015.

Results

We identified 22,055 cases of incident gout and 98,946 controls, median age (Q1, Q3) 69-68 (57, 79/56, 78) years and 67.6-66.5% males. Except for dementia, all comorbidities were significantly more common at baseline among gout cases. Overall, the risk for death in incident gout was neither increased overall nor in men, but women had a 10% elevated risk. In adjusted models for cause-specific mortality, death from CVD, renal disease, and digestive system diseases were significantly increased in the total gout population while death from dementia, cancer, and lung diseases were significantly decreased. There were no significant differences in overall incident death rate ratios between cases and controls in the two time periods examined.

Conclusions

An increased risk for CVD, renal disease, and diseases of the digestive system in patients with gout highlights the importance of addressing CVD risk factors in gout management. Gout was associated with reduced mortality from dementia, which may have implications on urate lowering therapy and possible effects on dementia risk.

Lifestyle factors and comorbidities in gout patients compared to the general population in Western Sweden: results from a questionnaire study

OBJECTIVE

This study aimed to identify lifestyle factors associated with gout in patients with prevalent gout compared to the general population.

METHOD

Adult patients with gout identified in primary and secondary care in Western Sweden between 2015 and 2017 were sent a questionnaire asking about demographics, lifestyle, and comorbidities. Five age- and gender-matched controls were identified in a random sample of 52 348 individuals aged 16-84 years who participated in the National Public Health survey in Sweden, year 2015. Logistic regression models were used to compare cases and controls with regard to lifestyle factors and comorbidities.

RESULTS

Of the 1589 invited gout patients, 868 (55%) responded. After matching for age and gender, 728 were included in the analysis (82.4% male; mean ± sd age 69.3 ± 10.5 years for men and 71.8 ± 9.9 years for women with gout). Male and female gout patients were significantly more likely to be overweight or obese (men 79% vs 66%; women 78.5% vs 65.3%), to have binge-drinking behaviour (men 29.9% vs 11%; women 13.7% vs 2.9%), and to be ex-smokers, compared to controls. Moreover, male gout patients reported lower levels of physical activity, while diabetes and hypertension were more common in both genders with gout than in controls.

CONCLUSION

In this questionnaire study, gout patients reported significantly more obesity and binge-drinking behaviour and less physical activity than controls. This suggests that there are great unmet needs for the management of lifestyle factors, particularly regarding overweight/obesity and binge drinking, in patients with gout.

Monocyte to high-density lipoprotein cholesterol ratio and serum uric acid in Chinese adults: a cross-sectional study

BACKGROUND

Previous studies have shown that the monocyte to high-density lipoprotein cholesterol (HDL-C) ratio (MHR) is a predictor of various diseases such as coronary heart disease, diabetic microangiopathy, and metabolic syndrome. However, there are few scientific reports on the correlation between MHR and serum uric acid. The objective of this report is to explore the relationship between MHR and serum uric acid in Chinese adults.

METHODS

This cross-sectional study included 646 participants from southwest China who underwent a health examination at the Health Management Center of Deyang People's Hospital. The examination included blood pressure readings, routine blood tests (lipid, fasting glucose, serum transaminase, and serum uric acid levels), and various standardized questionnaires. We employed a generalized additive model and smoothed curve fitting to explore the relationship between MHR and serum uric acid levels. We then performed subgroup analyses to investigate the robustness of this relationship.

RESULTS

After adjusting for confounders (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, aspartate transaminase, alanine aminotransferase, fasting glucose, total cholesterol, low-density lipoprotein, smoking, drinking, and exercise status), MHR was found to be positively correlated with serum uric acid levels (P < 0.001). The smoothing curve showed an approximately linear correlation between MHR and serum uric acid levels, and the linear correlation coefficient was 146.74 (95% CI 96.16-197.33, P < 0.0001). The subgroup analyses showed that the effect of MHR on serum uric acid levels was smaller in occasional smokers and smokers than in nonsmokers (P = 0.0194).

CONCLUSION

MHR was significantly and positively correlated with serum uric acid levels. Additionally, the effect of MHR on serum uric acid levels was lower in the individuals who smoked more.

Association of low-level environmental exposure to cadmium and lead with gout flare using a cohort study design

Cadmium (Cd) and lead (Pb) are toxic heavy metals with endocrine-disrupting properties. We investigated the associations of low-level environmental exposure to Cd/Pb and gout status (intercritical gout, gout flare and combined gout) in a cohort study. We measured by ICP-MS the levels of Cd and Pb in blood (Cd-B and Pb-B) and urine (Cd-U and Pb-U) from 408 participants with blood and 346 participants with urine samples recruited from a hospital gout clinic. The median levels of Cd-B and Pb-B (in μg/L) in the gout flare group were 0.87 (range 0.41-2.49) and 31.54 (25.38-41.46), respectively, and the median levels of Cd-U and Pb-U in the gout flare group were 1.05 (0.69-1.91) and 3.86 (3.49-4.44), respectively. These medians were significantly higher than those in the control or intercritical groups (P < 0.05). For Cd-B in tertile 2 (T2) and Cd-U in tertile 3, Cd levels were significantly associated with gout flare status compared to the reference tertile 1 (OR = 4.3, P = 0.041 and OR = 25.1, P = 0.002, respectively) after adjustment under Model 3. For Pb-U, the risk of gout flare status was significantly higher in T2 (OR = 51.0, P = 0.002) compared to the T1 under Model 3. Our results show that median levels of Cd-B, Pb-B, Cd-U and Pb-U in the gout flare group were significantly higher than participants without gout or with gout but in the intercritical period. We provide evidence that the risk of gout flare status is associated with increased Cd levels, and that blood and urine levels of Cd are a risk factor for gout flare status.

Second-Hand Smoke and Its Synergistic Effect with a Body-Mass Index of >24.9 kg/m2 Increase the Risk of Gout Arthritis in Indonesia

To analyze the association between smoking status (active smoking and exposure to Second-Hand Smoking (SHS)) and the synergistic effect of smoking status and BMI with gout risk, a community-based case-control design was undertaken among 385 participants, including 304 healthy controls and 81 gout patients from seven community health services. Adjusted Odd Ratios (AORs) and 95% Confidence Interval (CIs) of gout for active smoking and SHS were 3.26 (95% CI = 1.07~9.90) and 4.67 (95% CI = 2.18~10.00) compared to non-smokers. Time-dependent manner of active smoking and SHS significantly increased gout risk with AORs and 95% CIs of 5.95 (1.41~25.03) and 10.12 (3.51~29.14). Dose-dependency of active smokers and SHS showed AORs and 95% CIs of 5.15 (1.28~20.63) and 4.37 (1.33~14.28). Smoking 20 cigarettes (one pack) per day for one year is equivalent to one pack-year. Active smoking >20 pack-year and SHS > 26.5 pack-year increased gout risk with AORs and 95% CIs of 7.18 (1.53~33.67) and 9.95 (3.64~27.22). Participants who smoked (active smoking and SHS) and with Body Mass Index (BMI) of > 24.9 kg/m2 synergistically increased gout risk, with an AOR of 9.65 and 95% CI of 3.25~28.65, compared to BMI ≤ 24.9 kg/m2 and non-smoker. Smoking status (active smoking and SHS) and the synergistic effect of smoking status and BMI increased gout risk in Indonesia.

Behavioral Characteristics of Gout Patients and Their Impact on the Results of Urate Lowering Therapy

Background. Gout is frequently accompanied by hypertension, obesity, and/or impaired glucose tolerance, which are often complicated with heavy smoking and dietary violations, especially in male patients.

Objective. To determine the behavioral characteristics of gout patients that could impact the results of urate lowering therapy.

Subjects and methods. The relationship between behavioral characteristics and results of urate lowering therapy were investigated in 74 male gout patients of Chengdu Rheumatism Hospital. The results of treatment were evaluated using serum uric acid contents before and after treatment, VAS pain score before and after treatment. Behavioral characteristics for the last ten years (smoking status, daily volume of drinking water, etc.) were accessed by means of retrospective survey.

Results. In gout patients with poor treatment response, serum uric acid at the beginning of the treatment was already significantly lower (365.76 ± 163.06 μmol/L); this trend was also noted in a “slow progress” group, while patients with higher serum uric acid before treatment had notably better response to urate lowering therapy. During further analysis age negatively correlated with serum uric acid (r = –0.328; p = 0.002) and uric acid clearance ratio (r = –0.299; p = 0.002).

In patients with uric acid clearance ratio above 40 % the prevalence of kidney diseases (stones or dysfunction) was significantly lower compared to other groups. There was no significant influence of hypertension, diabetes mellitus, fatty liver or hyperlipidaemia on uric acid clearance ratio (p > 0.05).

Amount of drinking water also influenced the serum uric acid clearance ratio. Bigger amount of patients in the “fast progress” group (40.0 % compared to 30.44 and 25.0 %) tended to drink more water.

Conclusion. In most gout patients, serum uric acid levels before treatment acted as the reliable predictor of good response to urate lowering therapy. Treatment response (serum uric acid clearance ratio) correlated positively with the hyperuricemia and drinking sufficient amounts of water, negatively – with prolonged smoking (more than 10 years) and age.

The absolute risk of gout by clusters of gout-associated comorbidities and lifestyle factors-30 years follow-up of the Malmö Preventive Project

Background

Gout is predicted by a number of comorbidities and lifestyle factors. We aimed to identify discrete phenotype clusters of these factors in a Swedish population-based health survey. In these clusters, we calculated and compared the incidence and relative risk of gout.

Methods

Cluster analyses were performed to group variables with close proximity and to obtain homogenous clusters of individuals (n = 22,057) in the Malmö Preventive Project (MPP) cohort. Variables clustered included obesity, kidney dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking, and the use of diuretics. Incidence rates and hazard ratios (HRs) for gout, adjusted for age and sex, were computed for each cluster.

Results

Five clusters (C1–C5) were identified. Cluster C1 (n = 16,063) was characterized by few comorbidities. All participants in C2 (n = 750) had kidney dysfunction (100%), and none had CVD. In C3 (n = 528), 100% had CVD and most participants were smokers (74%). C4 (n = 3673) had the greatest fractions of obesity (34%) and dyslipidemia (74%). In C5 (n = 1043), proportions with DM (51%), hypertension (54%), and diuretics (52%) were highest. C1 was by far the most common in the population (73%), followed by C4 (17%). These two pathways included 86% of incident gout cases. The four smaller clusters (C2–C5) had higher incidence rates and a 2- to 3-fold increased risk for incident gout.

Conclusions

Five distinct clusters based on gout-related comorbidities and lifestyle factors were identified. Most incident gout cases occurred in the cluster of few comorbidities, and the four comorbidity pathways had overall a modest influence on the incidence of gout.

Occupational exposure to inorganic dust and risk of gout: a population-based study

Background

Risk factors operating independently of hyperuricemia could be of importance in determining why only a minority of people with hyperuricemia develop gout. Exposure to inorganic dust has been linked to other inflammatory diseases and could influence the development of gout.

Objectives

To evaluate if occupational exposure to inorganic dust increases the risk of gout.

Methods

Individuals aged 30–65 years with a first gout diagnosis in 2006–2012 in the population-based healthcare database of the Western Swedish Healthcare Region (VEGA) and population controls matched by age and sex were included. Data on occupation was collected from the Swedish occupational register. Exposure status was assigned by means of a job exposure matrix. Data on gout-related comorbidities was collected from VEGA. Alcohol use disorder and obesity were related both to gout and exposure to inorganic dust and were adjusted for in multivariate analyses. ORs were calculated using conditional logistic regression.

Results

5042 gout cases and 20 682 controls were included. Exposure to inorganic dust was associated with gout in both unadjusted (OR 1.12, 95% CI 1.04 to 1.20) and multivariate (OR 1.08, 95% CI 1.00 to 1.16) analyses of the whole population. In sex-stratified multivariate analyses, dust exposure was significantly associated with gout in women (adjusted OR 1.26, 95% CI 1.05 to 1.51), but not in men (adjusted OR 1.05, 95% CI 0.97 to 1.13).

Conclusions

We describe for the first time an association between exposure to inorganic dust and gout. After adjusting for confounders, the findings were statistically significant for women but not for men.

Hyperuricemia versus lifestyle in men and women of the Russian Federation population

Objective ― Analyzing gender-related specificities of hyperuricemia associations with lifestyle choices in the Russian Federation (RF) population. Material and Methods ― Cross-sectional survey was performed within the guidelines of the Russian national multicenter study ESSE-RF. The final sample size was 20781 people 25-64 years old. The effect of age, body mass index (BMI), diet, smoking status, and alcohol consumption on the incidence of hyperuricemia was evaluated. The analysis was carried out separately for men and women, using logistic regression. To build a model for predicting hyperuricemia, classification trees were used. Results ― Multidirectional associations of hyperuricemia were revealed both in women (with BMI, age, ‘Smart’ dietary pattern, alcohol consumption) and in men (with BMI, ‘smart’ dietary pattern, alcohol consumption, smoking status). The predictive accuracy of hyperuricemia incidence models was 60.4% in women vs. 65.7% in men. In both gender groups, BMI had the highest contribution to predicting the hyperuricemia incidence, while the next predictors, in terms of significance, differed among men and women. In women, the greatest impact, in decreasing order, was exerted by age, dietary patterns and drinking, whereas in men, by smoking status, dietary patterns and drinking. Conclusion ― In the RF population, there are multiple gender-specific associations of hyperuricemia with lifestyle choices, particularly nutrition, smoking and alcohol consumption.

Relationship between coffee consumption and serum uric acid level in the general Korean population: A nationwide cross-sectional study

OBJECTIVE

Coffee is one of the world's most consumed beverages, and its consumption is increasing. Serum uric acid levels are affected by dietary factors, and increased levels can cause a variety of diseases, including gout. This study aimed to investigate the relationship between coffee consumption and serum uric acid levels in the general Korean population.

METHODS

A nationwide cross-sectional study was conducted using data from the Korean National Health and Nutrition Examination Survey in 2016. A total of 3005 participants who checked serum uric acid levels and a completed coffee consumption survey were included (1146 men and 1859 women). The amount of coffee consumption was examined via self-reporting and categorized as none, <1 cup, 1-2 cups, 2-5 cups, and ≥5 cups of coffee consumed daily.

RESULTS

The mean serum uric acid level of the men was 5.91 ± 1.24 mg/dL and that of women was 4.29 ± 0.97 mg/dL. In the multiple linear regression analysis, there were no significant differences in the serum uric acid levels between the non-coffee-drinking group and the coffee-drinking group (<1 cup, 1-2 cups, 2-5 cups, and ≥5 cups of coffee) in both men and women (P = .569, .258, .466, and .751, respectively, in men; .185, .520, .116, and .302, respectively, in women).

CONCLUSIONS

There was no significant relationship between coffee consumption and serum uric acid levels in the general Korean population.

Assessing the causal association between smoking behavior and risk of gout using a Mendelian randomization study

This study aimed to examine whether smoking behavior is causally related to gout. Summary statistics of publicly available data from genome-wide association studies (GWAS) of smoking behavior (n = 85,997) served as the exposure dataset, while meta-analysis results of 14 studies including 2115 cases and 67,259 controls of European descent served as the outcome dataset. The data were subjected to two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. Five single-nucleotide polymorphisms (SNPs) from GWAS of smoking behavior were selected as instrumental variables (IVs) to improve inference: CHRNA3 (rs1051730), PDE1C (rs215614), CYP2A6 (rs4105144), CHRNB3 (rs6474412), and CYP2B6 (rs7260329). The IVW data did not support a causal association between smoking behavior and gout (beta = - 0.035, SE = 0.036, p = 0.333). MR-Egger regression indicated that directional pleiotropy did not bias the result (intercept = 0.021; p = 0.560). MR-Egger analysis revealed no causal association between smoking behavior and gout (beta = - 0.074, SE = 0.070, p = 0.366). The weighted median approach did not support a causal association between smoking behavior and gout (beta = - 0.043, SE = 0.040, p = 0.279). Cochran's Q test indicated no evidence of heterogeneity between IV estimates based on individual variants. The results of "leave one out" analysis demonstrated that no single SNP drove the IVW point estimate. MR estimates using IVW, weighted median, and MR-Egger analysis were consistent and did not support a causal inverse association between smoking behavior and gout.

Association between smoking and gout: a meta-analysis

Previous studies showed that smoking is linked with the decreased risk of gout, but the results remain controversial. The aim of this meta-analysis was to determine the associations between smoking and the risk of gout. A systematic literature search of PubMed and EMBASE was conducted. Data were extracted by two independent reviewers. The pooled odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of associations, using random effects and fixed effects models. Five studies with a total of 17,915,507 participants with 16,880 gouts were enrolled. Heterogeneity among the effect sizes of five studies was reported as I² = 87.9%. Our meta-analysis indicated that smoking (ever- vs non-, OR = 0.87, 95% CI = 0.67-1.12) was not associated with the risk of gout in random effects model, whereas there was an association between smoking (ever- vs non-, OR = 0.70, 95% CI = 0.65-0.74) and gout risk in fixed effects model. Our finding indicates that smoking may not be involved in the risk of gout. However, further studies are still needed to confirm our results.