Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study

Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study Xia Guo, Haoxuan Chu, Hanchi Xu, Shipeng Wang, Jiahuan He, Yushi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4576816/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The non-high-density lipoprotein cholesterol to high density lipoprotein cholesterol ratio (NHHR), which stands for the proportion of non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C), is a strong lipid marker that is linked to atherogenic features. The aim of the research was to investigate the potential association between NHHR and the prevalence of gout. Methods This study investigated the correlation between NHHR levels and gout by analyzing information gathered by the National Health and Nutrition Examination Survey (NHANES), a research study conducted in the United States from 2007 to 2018. NHHR is a mathematical formula that calculates the ratio of non-HDL-C to HDL-C. Gout assessment was conducted through a questionnaire. Multifactorial logistic regression analysis, subgroup analysis, and smoothed curve fitting were employed in this study. Results The study included 30,482 subjects. The fully adjusted models showed that for each unit rise in NHHR in continuous variables, there was an 11% higher likelihood of gout prevalence (OR: 1.11, 95% CI: 1.07–1.15). The analysis of the NHHR quartile variable revealed that those in the highest quartile of NHHR had a notably greater risk of developing gout in comparison to with people in the lowest quartile. (Q4 vs. Q1, OR: 1.43, 95% CI: 1.21–1.68). The subgroup analyses yielded consistent results across categories, indicating a substantial positive correlation between NHHR and gout. Interaction tests showed that gender, race, level of education, marital relationship, PIR, hypertension, BMI, smoking habits, and diabetes had no discernible effect on this association. The p-values for all the interactions were more than 0.05. Nevertheless, the relationship between NHHR and gout was substantially affected by the age of the participants (interaction p-value < 0.05). Conclusion Among adults in the United States, elevated NHHR levels are correlated with an increased odds of gout prevalence. Effectively managing NHHR levels may reduce the occurrence of gout. NHHR Gout NHANES Cross-sectional study Figures Figure 1 Figure 2 Introduction Gout is a common form of inflammatory arthritis that is distinguished by the long-term accumulation of urate crystals in and around the joints, caused by high levels of uric acid 1 . With improved living standards, its incidence and prevalence have risen globally, significantly impacting human health 2 . Gout is often associated with various comorbidities, including hypertension, cardiovascular diseases, obesity, hyperlipidemia, and metabolic syndrome 3 . Gout remains a significant global health issue, with an incidence of 0.58–2.89 cases per 1,000 person-years 4 . An research conducted in the United States found that the most prevalent coexisting medical conditions among individuals with gout and hyperuricemia were hypertension (57.9%), dyslipidemia (45.3%), respiratory and thoracic symptoms (35.3%), and diabetes mellitus (19.9%) 5 . Recent studies have identified a new composite indicator of atherogenicity: non-high-density lipoprotein cholesterol to high density lipoprotein cholesterol ratio (NHHR) 6 . The NHHR is an advanced and comprehensive tool used to evaluate atherogenic lipids. It surpasses existing lipid indices in accurately forecasting the likelihood of developing cardiovascular illness 7 . The NHHR, a recently identified lipoprotein ratio, takes into consideration the combined effects of high-density lipoprotein cholesterol (HDL-C) and non-HDL-C. This overcomes the prior constraint of researching lipids separately. This study documented the correlation and prognostic significance of NHHR in different medical conditions, including depression, periodontitis, cardiovascular disease, acute stroke, kidney stones, and diabetes mellitus 8–13 . For example, Hong et al. conducted a study that revealed a connection between NHHR and the occurrence of kidney stones. The investigation discovered that for every unit rise in NHHR, there was a 4% higher chance of acquiring kidney stones. Additionally, each unit rise in NHHR was connected with a 9% higher risk of recurrence of kidney stones 12 . Collectively, these studies suggest that NHHR is a useful predictor of metabolism-related diseases. Moreover, several investigations have established a robust correlation between dyslipidemia and the occurrence of gout or increased amounts of uric acid in the blood. A study has examined the relationship between common lipoproteins, including HDL-C and total cholesterol (TC), and the occurrence of gout. It has been shown that HDL-C is thought to have a protective effect in individuals with gout, whereas TC is positively associated with levels of blood uric acid (UA) 14,15 . Multiple studies have shown a significant association between serum uric acid (SUA) and lipid levels, such as triglycerides (TG), TC, low-density lipoprotein cholesterol (LDL-C), and HDL-C 16–21 . Nevertheless, the correlation between NHHR and gout remains uninvestigated. We completed a cross-sectional study utilizing the NHANES 2007–2018 dataset to examine the correlation between NHHR and the prevalence of gout in American adults. Investigating this correlation might yield significant knowledge on the connection between lipid metabolism and serum uric acid, which is crucial for devising efficient therapies to avert negative health consequences. Positioning NHHR as a new marker may offer innovative approaches for assessing and managing the prevalence of gout. Materials and methods Study population In this analysis, data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018 were utilized. NHANES is a comprehensive research initiative designed to assess the physical well-being and dietary condition of individuals, including adults and children, residing in the United States. The program guarantees that all members undertake a biennial health and nutrition examination. The NCHS study Ethics Review Committee has granted approval for the study protocol of the database. The data can be accessed by the public on the NHANES website. This study utilized data from the NHANES 2005–2018 period, initially including a total of 59,842 participants. Exclusions were made based on certain demographic characteristics: (a) those with missing NHHR data (n = 16,003), (b) those who did not provide data on gout (n = 12,491), and (c) those with missing data on covariates, including education level (n = 32), marital status (n = 10), hypertension (n = 39), diabetes (n = 767), and smoking status (n = 18). For the analysis, a grand number of 30,482 people were finally included. Figure 1 depicts the flowchart representing this procedure. Exposure definition The NHHR variable was included as the independent variable for assessing exposure. It was determined by dividing non-HDL cholesterol by HDL cholesterol. The calculation of non-HDL cholesterol involves removing HDL cholesterol from total cholesterol (TC). The study classified individuals into four tiers according to their NHHR readings. Outcome definition Gout was considered an outcome indicator for this study. This indicator was derived from the Health Status Questionnaire used in NHANES, which relied on individuals' self-reported responses to specific health queries during personal interviews. The question posed to all participants was, "Have you ever received a diagnosis of gout from a doctor or other healthcare professional?" Participants were instructed to indicate their reaction by selecting either 'yes' or 'no'. They were then categorized into gouty and non-gouty participants. Previous studies have demonstrated the increased accuracy of gout information obtained using self-report methods. Covariables In order to examine the separate correlation between NHHR and gout, we took into account possible factors that might influence the results and made adjustments for the following variables in our analysis: The variables of interest include sex, age, diabetes, race, level of education, smoking habits, marital status, income to poverty ratio (PIR), alcohol consumption, body mass index (BMI), and hypertension. The smoking status was ascertained using the question, "Have you consumed a minimum of 100 cigarettes in your lifetime?" Alcohol use was assessed based on the 'mean number of alcoholic beverages consumed each day throughout the previous 12 months.' There are three levels of PIR: '1', '1–3', and '3 or more.' Statistical analysis Continuous variables were described using either the standard deviation or the mean, while categorical variables were reported as percentages. In the categorical model, NHHR was categorized into quartiles, with the lowest quartile (Q1) serving as the reference group. The relationship between NHHR and gout was analyzed using multifactorial logistic regression models. NHHR, both in continuous and quartile form, was included as an independent variable in the models to explore potential correlations with gout. IThe study investigated the independent relationships between non-high-density lipoprotein cholesterol (NHHR) and gout using three distinct models. Model 1 did not account for variables. Model 2 included modifications to account for gender, age, and ethnicity. Model 3 incorporated further modifications for factors such as relationship status, educational level, PIR, BMI, drinking habits, smoking status, diabetes, and hypertension. Stratified multivariate regression models were used to do subgroup analysis. The stratification was based on many factors including age, gender, race, marital status, education level, PIR, BMI, diabetes, hypertension, the average number of drinks per day in the preceding 12 months, and smoking status. Potential differences between populations were explored in depth. Subsequently, the smoothed curve fitting approach (Fig. 2 ) was employed to analyze the nonlinear correlation between NHHR and gout. Statistical significance was determined at p < 0.05. The statistical study was conducted using R version 3.4.3 with d EmpowerStats. Results Baseline characteristics of participants Table 1 presents a summary of the key characteristics of participants selected from NHANES between 2007 and 2018, classified based on NHHR quartile. The study had 30,482 individuals, with a median age of 49.55 years at the beginning. The male participants accounted for 48.44% of the total, while the female participants accounted for 51.56%. The occurrence of gout rose as the NHHR increased (Q1: 4.06%; Q2: 4.31%; Q3: 4.90%; Q4: 5.44%; P < 0.01). Participants with higher levels of NHHR were more prone to male, Mexican American or Other Hispanic, have an educational attainment below the 11th grade, be married, have a PIR between 1–3, have a higher BMI, and have diabetes, hypertension, and a history of smoking more than 100 cigarettes in their lifetime (all P < 0.05). NHHR quartiles differed significantly by age, smoking, gender, race, HDL-C, TC, education level, marital status, diabetes, PIR, alcohol consumption, BMI, gout, and hypertension (P < 0.01). Table 1 Characteristics of the study population Characteristic Q1 Q2 Q3 Q4 P-value N 7618 7615 7627 7622 Age (years) 49.23 ± 19.42 50.18 ± 18.42 49.94 ± 17.04 48.86 ± 15.61 < 0.001 Sex (%) < 0.001 male 2694 (35.36%) 3210 (42.15%) 4008 (52.55%) 4852 (63.66%) Female 4924 (64.64%) 4405 (57.85%) 3619 (47.45%) 2770 (36.34%) Race (%) < 0.001 Mexican American 831 (10.91%) 1056 (13.87%) 1303 (17.08%) 1481 (19.43%) Other Hispanic 608 (7.98%) 790 (10.37%) 866 (11.35%) 953 (12.50%) Non-Hispanic White 3146 (41.30%) 3109 (40.83%) 3117 (40.87%) 3228 (42.35%) Non-Hispanic Black 2049 (26.90%) 1702 (22.35%) 1433 (18.79%) 1068 (14.01%) Other Races 984 (12.92%) 958 (12.58%) 908 (11.91%) 892 (11.70%) Education level (%) < 0.001 Less than 9th grade 576 (7.56%) 769 (10.10%) 880 (11.54%) 1018 (13.36%) 9–11th grade 960 (12.60%) 979 (12.86%) 1084 (14.21%) 1234 (16.19%) High school graduate/GED 1616 (21.21%) 1694 (22.25%) 1821 (23.88%) 1813 (23.79%) Some college or AA degree 2311 (30.34%) 2288 (30.05%) 2228 (29.21%) 2168 (28.44%) College graduate or above 2155 (28.29%) 1885 (24.75%) 1614 (21.16%) 1389 (18.22%) Marry status (%) < 0.001 Married 3442 (45.18%) 3842 (50.45%) 4133 (54.19%) 4266 (55.97%) Widowed 716 (9.40%) 660 (8.67%) 574 (7.53%) 447 (5.86%) Divorced 829 (10.88%) 797 (10.47%) 836 (10.96%) 843 (11.06%) Separated 267 (3.50%) 251 (3.30%) 252 (3.30%) 270 (3.54%) Never married 1755 (23.04%) 1460 (19.17%) 1260 (16.52%) 1109 (14.55%) Living with partner 609 (7.99%) 605 (7.94%) 572 (7.50%) 687 (9.01%) PIR (%) < 0.001 < 1 1428 (18.75%) 1475 (19.37%) 1453 (19.05%) 1662 (21.81%) 1–3 3445 (45.22%) 3559 (46.74%) 3770 (49.43%) 3775 (49.53%) ≥ 3 2745 (36.03%) 2581 (33.89%) 2404 (31.52%) 2185 (28.67%) Alcohol use (drinks) 3.40 ± 31.53 2.86 ± 22.90 3.65 ± 33.52 3.36 ± 24.62 < 0.001 BMI (kg/m2) 28.45 ± 2.98 28.86 ± 2.86 29.08 ± 3.04 29.11 ± 2.70 < 0.001 HDL-C(mg/dL) 1.80 ± 0.43 1.44 ± 0.28 1.23 ± 0.23 1.00 ± 0.20 < 0.001 Total Cholesterol(mg/dL) 4.39 ± 0.90 4.72 ± 0.90 5.05 ± 0.91 5.73 ± 1.11 < 0.001 NHHR 1.48 ± 0.32 2.28 ± 0.21 3.10 ± 0.28 4.86 ± 1.44 < 0.001 Diabetes (%) 0.006 No 6640 (87.16%) 6623 (86.97%) 6561 (86.02%) 6515 (85.48%) Yes 978 (12.84%) 992 (13.03%) 1066 (13.98%) 1107 (14.52%) Gout (%) < 0.001 No 7309 (95.94%) 7287 (95.69%) 7253 (95.10%) 7207 (94.56%) Yes 309 (4.06%) 328 (4.31%) 374 (4.90%) 415 (5.44%) Smoking habit (%) < 0.001 No 4526 (59.41%) 4479 (58.82%) 4270 (55.99%) 3789 (49.71%) Yes 3092 (40.59%) 3136 (41.18%) 3357 (44.01%) 3833 (50.29%) Hypertension (%) < 0.001 No 5089 (66.80%) 4900 (64.35%) 4812 (63.09%) 4792 (62.87%) Yes 2529 (33.20%) 2715 (35.65%) 2815 (36.91%) 2830 (37.13%) The association between NHHR and gout Table 2 illustrates the relationship between NHHR and gout. The study results demonstrated a clear and direct relationship between elevated NHHR levels and an increased incidence of gout in the continuous models. The link between NHHR and the odds of gout prevalence was evident as well as the baseline model without any adjustments and the fully adjusted model. In the model that accounted for all relevant factors, the chances of gout prevalence increased by 11% for every unit of rise in NHHR (OR = 1.11; 95% CI: 1.07, 1.15). In the categorical model, specifically in the partially adjusted model (model 2), the fourth quartile (Q4) exhibited a 54% higher prevalence of gout (OR = 1.54; 95% CI: 1.31, 1.81) compared with the lowest NHHR quartile (Q1). The odds ratios (OR) for the risk of gout in the second, third, and fourth quartiles compared to the lowest reference quartile were 1.05 (95% CI: 0.89, 1.24), 1.19 (95% CI: 1.01, 1.41), and 1.43 (95% CI: 1.21, 1.68), correspondingly. These results were supported by a test for trend (P < 0.05) after extensive adjustments. Table 2 Odd ratios and 95% confidence intervals for gout Exposure Model 1 Model 2 Model 3 OR(95%CI), P-value OR(95%CI), P-value OR(95%CI), P-value Continuous 1.09 (1.05, 1.12) 1.13 (1.10, 1.17) 1.11 (1.07, 1.15) NHHR quartile Q1 Reference Reference Reference Q2 1.06 (0.91, 1.25) 1.06 (0.90, 1.25) 1.05 (0.89, 1.24) Q3 1.22 (1.05, 1.42) 1.26 (1.07, 1.48) 1.19 (1.01, 1.41) Q4 1.36 (1.17, 1.58) 1.54 (1.31, 1.81) 1.43 (1.21, 1.68) P for trend < 0.001 < 0.001 < 0.001 Male Continuous 0.99 (0.95, 1.03) 1.11 (1.07, 1.15) 1.10 (1.05, 1.14) NHHR quartile Q1 Reference Reference Reference Q2 0.87 (0.71, 1.06) 0.95 (0.77, 1.16) 0.93 (0.76, 1.15) Q3 0.90 (0.75, 1.09) 1.16 (0.95, 1.41) 1.11 (0.91, 1.35) Q4 0.82 (0.68, 0.98) 1.29 (1.06, 1.57) 1.23 (1.00, 1.50) P for trend 0.059 0.001 0.011 Female Continuous 1.16 (1.09, 1.23) 1.20 (1.12, 1.27) 1.13 (1.06, 1.21) NHHR quartile Q1 Reference Reference Reference Q2 1.23 (0.94, 1.60) 1.28 (0.97, 1.67) 1.21 (0.92, 1.59) Q3 1.29 (0.98, 1.70) 1.40 (1.05, 1.85) 1.25 (0.94, 1.66) Q4 1.92 (1.46, 2.51) 2.17 (1.64, 2.86) 1.79 (1.35, 2.38) P for trend < 0.001 < 0.001 < 0.001 Notes: Model 1: Non-adjusted. Model 2: Adjusted for sex, age, and race. Model 3: Adjusted for sex, age, and race, educational level, smoking, drinking, marital status, PIR, diabetes, hypertension, and BMI. The sex variables were not adjusted in the stratified analysis of sex. NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio; BMI, body mass index; HDL-C, high density lipoprotein cholesterol; PIR, poverty Income ratio. Subgroup analyses The subgroup analysis data are presented in Table 3 . The findings indicate that age modifies the association between NHHR and gout (p < 0.05), while variables such as gender, level of education, race, marital relationship, PIR, BMI, diabetes, hypertension, and smoking habits do not affect this association. The p-values for the interaction of these variables (all p > 0.05) suggest that the likelihood of an increase in the prevalence of NHHR with gout is consistent across subgroups except for age. Table 3 Subgroup analysis of the association between NHHR and gout. NHHR OR(95%CI) P-value P for interaction Age 0.0343 < 40 1.20 (1.11, 1.29) < 0.0001 40–60 1.07 (1.01, 1.13) 0.0183 ≥ 60 1.06 (1.01, 1.12) 0.0207 Sex 0.4957 Male 1.10 (1.06, 1.15) < 0.0001 Female 1.13 (1.06, 1.20) 0.0002 Race 0.2946 Mexican American 1.18 (1.06, 1.30) 0.0016 Other Hispanic 1.03 (0.89, 1.19) 0.7062 Non-Hispanic White 1.12 (1.07, 1.18) < 0.0001 Non-Hispanic Black 1.04 (0.95, 1.14) 0.3577 Other Race 1.13 (1.02, 1.26) 0.0230 Education level 0.9215 Less than 9th grade 1.10 (0.98, 1.23) 0.0963 9-11th grade 1.10 (1.03, 1.19) 0.0086 High school graduate/GED 1.09 (1.02, 1.17) 0.0146 Some college or AA degree 1.13 (1.07, 1.21) < 0.0001 College graduate or above 1.09 (1.00, 1.19) 0.0586 Marital status 0.2321 Married 1.15 (1.10, 1.20) < 0.0001 Widowed 1.02 (0.90, 1.16) 0.7294 Divorced 1.04 (0.94, 1.16) 0.4215 Separated 1.03 (0.88, 1.20) 0.7380 Never married 1.07 (0.93, 1.23) 0.3353 Living with partner 1.13 (0.98, 1.29) 0.0869 PIR 0.5587 < 1 1.10 (1.03, 1.18) 0.0038 1–3 1.10 (1.04, 1.15) 0.0004 ≥ 3 1.13 (1.06, 1.21) 0.0002 BMI 0.7894 < 25 1.17 (0.90, 1.52) 0.2506 25–30 1.11 (1.07, 1.15) < 0.0001 ≥ 30 1.14 (1.02, 1.27) 0.0188 Smoking status 0.7632 No 1.10 (1.04, 1.16) 0.0007 Yes 1.11 (1.07, 1.16) < 0.0001 Hypertension 0.2827 No 1.13 (1.07, 1.20) < 0.0001 Yes 1.09 (1.04, 1.14) 0.0001 Diabetes 0.1345 No 1.13 (1.08, 1.17) < 0.0001 Yes 1.07 (1.00, 1.13) 0.0441 Discussion The current research conducted a cross-sectional study of 30,482 people from the United States to examine the connection between NHHR (non-high-density lipoprotein cholesterol) and the prevalence of gout in a sample that is typical of the entire country. Our findings indicate that NHHR is positively associated with increased gout prevalence in model 3. More precisely, in the analysis of continuous variables, each additional unit of NHHR was shown to be correlated with an 11% higher occurrence of gout. Additional subgroup analysis provided more evidence of the consistent nature of this positive correlation. Crucially, the association remained unaffected by factors such as gender, race, degree of education, marital status, PIR, BMI, smoking habits, hypertension, or diabetes. Furthermore, the relationship between NHHR and gout prevalence was affected by age. As NHHR increases, individuals younger than 40 years of age are more likely to develop gout. Moreover, the smoothed curve fitting study revealed a direct relationship between NHHR and the occurrence of gout. Therefore, NHHR can function as an early predictor of the probability of developing gout, which is essential for promptly preventing, diagnosis, and treating those at a heightened risk. Notably, this article includes an initial investigation into the correlation between NHHR and gout. Emerging evidence suggests that NHHR is an accurate indicator of risk for lipid-related diseases 22 . Nevertheless, there is a lack of research specifically examining the influence of non-high-density lipoprotein cholesterol (NHHR) on gout, despite a significant amount of literature exploring the relationship between other lipid-related factors and gout. Liang et al. conducted a cross-sectional research involving 653 patients with gout and discovered a notable association between uric acid (UA) and triglycerides in women (p = 0.039). Additionally, they observed higher levels of HDL-C in women (p < 0.05) 23 . The Dietary Intervention Randomized Controlled Trial (DIRECT) conducted with 235 participants indicated a potential link between higher levels of HDL-C and a decreased risk of gout. Additionally, an improved ratio of total cholesterol to HDL-C was also linked with a reduced prevalence of gout 24 . A Mendelian randomization study based on publicly available GWAS pooled statistics demonstrated that lower HDL levels were positively correlated with a higher chance of gout and higher serum uric acid concentrations. Specifically, the data showed that a one standard deviation (SD) rise in HDL (~ 12.26 mg/dL) was associated with a decrease in gout risk of approximately 25% and a reduction in serum urate of 0.09 mg/dL. There was a direct relationship between high levels of triglycerides (TG) and high quantities of uric acid in the blood. The findings showed that for every one standard deviation rise in TG (approximately 112.33 mg/dL), there was a corresponding increase of 0.10 mg/dL in serum uric acid concentration 25 . Although these findings do not provide direct evidence of an association between NHHR and gout, they indirectly support a positive correlation between the two. This study contributes to the expanding field of research on the relationship between lipid metabolism and the onset of gout by utilizing innovative lipid profiles. NHHR, a novel composite indicator of atherogenic lipid composition 7 , is superior to conventional single lipid variables in evaluating the extent of atherosclerosis 26 . It also serves as an important lipid marker for the prevention of plaque formation 27 . Additionally, findings from the NAGALA longitudinal cohort research carried out in Japan indicate that NHHR is a more effective indicator for forecasting the likelihood of developing diabetes in comparison to traditional lipid markers 7 . Lin et al. conducted a research on a sample of 9,764 Chinese volunteers and discovered that NHHR was a more powerful indicator of insulin resistance and diabetes compared to other traditional lipid markers 28 . The diagnostic value of NHHR has also surpassed conventional lipid biomarkers in predicting non-alcoholic fatty liver disease (NAFLD) 29 and metabolic syndrome 30 . In conclusion, NHHR has demonstrated excellent predictive ability in several studies. Therefore, NHHR holds great potential for extensive clinical application as a widely used biomarker. Study strengths and limitations This investigation possesses several merits. First, the study is founded upon a substantial sample size of 30,482 people. Second, adjustments for confounding covariates were made to ensure more reliable results. Third, subgroup examinations were conducted to assess the strength and reliability of the link between NHHR and the odds of gout prevalence in various groups. Finally, smoothed curve fitting analysis was employed to investigate the positive association between NHHR and kidney stones. Nevertheless, this study has certain constraints. First, because of its cross-sectional design, the study was unable to establish a causal relationship between NHHR and gout. Second, although multiple covariates were considered, it was not feasible to exclude all potential factors that might influence the study results. Third, age differences among participants were present, suggesting that future studies should be conducted with larger and more age-homogeneous samples. Finally, the design of the NHANES database may have introduced selection bias, potentially affecting the results. Conclusions The data we have collected clearly shows a significant correlation between higher NHHR scores and the probability of gout being present in individuals in the United States. This highlights the necessity of identifying individuals at risk for gout by monitoring aberrant NHHR levels. While the cross-sectional form of this study hinders the ability to make causal inferences, it does offer a foundation for future prospective investigations to confirm the causal association between NHHR and gout. Declarations Data availability statement The data that substantiate the conclusions of this investigation are readily accessible at https://www.cdc.gov/nches/nhanes. Ethics approval and consent to participate The NHANES procedures received approval from the National Center for Health Statistics Ethics Review Board of the U.S. CDC, and all participants granted written informed permission. Author contributions This study was designed and funded by YW. XG played a key role in the methodology and writing review. The manuscript was drafted by HC and HX, and the figures were prepared by SW. Each author made contributions to the paper and gave their approval for the final version that was submitted. Acknowledgements We express our gratitude to the CDC's National Center for Health Statistics for their role in creating, gathering, and overseeing the NHANES data, as well as for its accessibility to the general public. Conflict of interest The authors assert that they have no conflict of interest. Funding This study did not get any financial support from any public, commercial, or non-profit organization. References Dalbeth, N.; Merriman, T. R.; Stamp, L. K. Gout. Lancet 2016 , 388 (10055), 2039–2052. https://doi.org/10.1016/S0140-6736(16)00346-9. Peng, X.; Li, X.; Xie, B.; Lai, Y.; Sosnik, A.; Boucetta, H.; Chen, Z.; He, W. Gout Therapeutics and Drug Delivery. 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Elevated Serum Uric Acid Increases Risks for Developing High LDL Cholesterol and Hypertriglyceridemia: A Five-Year Cohort Study in Japan. Int J Cardiol 2018 , 261 , 183–188. https://doi.org/10.1016/j.ijcard.2018.03.045. Peng, T.-C.; Wang, C.-C.; Kao, T.-W.; Chan, J. Y.-H.; Yang, Y.-H.; Chang, Y.-W.; Chen, W.-L. Relationship between Hyperuricemia and Lipid Profiles in US Adults. Biomed Res Int 2015 , 2015 , 127596. https://doi.org/10.1155/2015/127596. Xu, J.; Peng, H.; Ma, Q.; Zhou, X.; Xu, W.; Huang, L.; Hu, J.; Zhang, Y. Associations of Non-High Density Lipoprotein Cholesterol and Traditional Blood Lipid Profiles with Hyperuricemia among Middle-Aged and Elderly Chinese People: A Community-Based Cross-Sectional Study. Lipids Health Dis 2014 , 13 , 117. https://doi.org/10.1186/1476-511X-13-117. Kuwabara, M.; Niwa, K.; Hisatome, I.; Nakagawa, T.; Roncal-Jimenez, C. A.; Andres-Hernando, A.; Bjornstad, P.; Jensen, T.; Sato, Y.; Milagres, T.; Garcia, G.; Ohno, M.; Lanaspa, M. A.; Johnson, R. J. Asymptomatic Hyperuricemia Without Comorbidities Predicts Cardiometabolic Diseases: Five-Year Japanese Cohort Study. Hypertension 2017 , 69 (6), 1036–1044. https://doi.org/10.1161/HYPERTENSIONAHA.116.08998. Yang, S.; Zhong, J.; Ye, M.; Miao, L.; Lu, G.; Xu, C.; Xue, Z.; Zhou, X. Association between the Non-HDL-Cholesterol to HDL-Cholesterol Ratio and Non-Alcoholic Fatty Liver Disease in Chinese Children and Adolescents: A Large Single-Center Cross-Sectional Study. Lipids Health Dis 2020 , 19 (1), 242. https://doi.org/10.1186/s12944-020-01421-5. Liang, J.; Jiang, Y.; Huang, Y.; Huang, Y.; Liu, F.; Zhang, Y.; Yang, M.; Wu, J.; Xiao, M.; Cao, S.; Gu, J. Comorbidities and Factors Influencing Frequent Gout Attacks in Patients with Gout: A Cross-Sectional Study. Clin Rheumatol 2021 , 40 (7), 2873–2880. https://doi.org/10.1007/s10067-021-05595-w. Yokose, C.; McCormick, N.; Rai, S. K.; Lu, N.; Curhan, G.; Schwarzfuchs, D.; Shai, I.; Choi, H. K. Effects of Low-Fat, Mediterranean, or Low-Carbohydrate Weight Loss Diets on Serum Urate and Cardiometabolic Risk Factors: A Secondary Analysis of the Dietary Intervention Randomized Controlled Trial (DIRECT). Diabetes Care 2020 , 43 (11), 2812–2820. https://doi.org/10.2337/dc20-1002. Yu, X.; Wang, T.; Huang, S.; Zeng, P. Evaluation of the Causal Effects of Blood Lipid Levels on Gout with Summary Level GWAS Data: Two-Sample Mendelian Randomization and Mediation Analysis. J Hum Genet 2021 , 66 (5), 465–473. https://doi.org/10.1038/s10038-020-00863-0. Zhao, W.; Gong, W.; Wu, N.; Li, Y.; Ye, K.; Lu, B.; Zhang, Z.; Qu, S.; Li, Y.; Yang, Y.; Hu, R. Association of Lipid Profiles and the Ratios with Arterial Stiffness in Middle-Aged and Elderly Chinese. Lipids Health Dis 2014 , 13 , 37. https://doi.org/10.1186/1476-511X-13-37. Wang, A.; Li, Y.; Zhou, L.; Liu, K.; Li, S.; Zong, C.; Song, B.; Gao, Y.; Li, Y.; Tian, C.; Xing, Y.; Xu, Y.; Wang, L. Non-HDL-C/HDL-C Ratio Is Associated with Carotid Plaque Stability in General Population: A Cross-Sectional Study. Front Neurol 2022 , 13 , 875134. https://doi.org/10.3389/fneur.2022.875134. Lin, D.; Qi, Y.; Huang, C.; Wu, M.; Wang, C.; Li, F.; Yang, C.; Yan, L.; Ren, M.; Sun, K. Associations of Lipid Parameters with Insulin Resistance and Diabetes: A Population-Based Study. Clin Nutr 2018 , 37 (4), 1423–1429. https://doi.org/10.1016/j.clnu.2017.06.018. Kwok, R. M.; Torres, D. M.; Harrison, S. A. Vitamin D and Nonalcoholic Fatty Liver Disease (NAFLD): Is It More than Just an Association? Hepatology 2013 , 58 (3), 1166–1174. https://doi.org/10.1002/hep.26390. Kim, S. W.; Jee, J. H.; Kim, H. J.; Jin, S.-M.; Suh, S.; Bae, J. C.; Kim, S. W.; Chung, J. H.; Min, Y.-K.; Lee, M.-S.; Lee, M.-K.; Kim, K.-W.; Kim, J. H. Non-HDL-Cholesterol/HDL-Cholesterol Is a Better Predictor of Metabolic Syndrome and Insulin Resistance than Apolipoprotein B/Apolipoprotein A1. Int J Cardiol 2013 , 168 (3), 2678–2683. https://doi.org/10.1016/j.ijcard.2013.03.027. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4576816","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318313228,"identity":"9d1b0805-0ea0-45bd-b9bf-88e81d80f954","order_by":0,"name":"Xia Guo","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Guo","suffix":""},{"id":318313229,"identity":"b1ed2a04-bffb-4875-a014-02d703ed636b","order_by":1,"name":"Haoxuan Chu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Haoxuan","middleName":"","lastName":"Chu","suffix":""},{"id":318313230,"identity":"76b7b221-6633-4a26-b3b0-9ea8255f993d","order_by":2,"name":"Hanchi Xu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Hanchi","middleName":"","lastName":"Xu","suffix":""},{"id":318313231,"identity":"bab5fb84-3fdd-4c8c-adb7-14f3f76e75db","order_by":3,"name":"Shipeng Wang","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Shipeng","middleName":"","lastName":"Wang","suffix":""},{"id":318313232,"identity":"f9fc4dc8-f0a7-4349-8352-94f3be628d5d","order_by":4,"name":"Jiahuan He","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiahuan","middleName":"","lastName":"He","suffix":""},{"id":318313233,"identity":"cf28f035-1fd4-4bbb-b7e2-86540213033a","order_by":5,"name":"Yushi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYHACNoaECjYZEEuCBC1n2HhI1MLYxkCCFoMb6c8ePJzHx2NwgPngbR4GuzwitOSYGyRuYwNqYUu25mFILiZGC5sERAuPmTQPw4HEBmIcJpE4B6SF/xuxWhLMJBIbwLawEadF8swbM4mEY2w8kofZjC3nGCQT1sJ3PP2Z5I+aY3J8x5sf3nhTYUdYi8IBMHWMgYEZ7E5C6oFAHmJoDRFKR8EoGAWjYMQCAO8DNubvix8NAAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Yushi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-13 14:22:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4576816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4576816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60196969,"identity":"54612a7a-7a10-4f64-b634-1bb274da3ca9","added_by":"auto","created_at":"2024-07-13 01:52:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":484503,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart depicting sample selection for the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018；NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4576816/v1/dc80561d16914eaaad63aa9e.png"},{"id":60196968,"identity":"79545633-a9c8-4a84-ab6d-6e729ee956ef","added_by":"auto","created_at":"2024-07-13 01:52:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68571,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between NHHR and gout. The red solid line signifies the smooth curve fit between variables. The blue band represents the 95% confidence interval derived from the fit.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4576816/v1/c45c40b5e92399e79f5b275e.png"},{"id":62343591,"identity":"dad84673-17b7-4216-bdc0-ec59e67fbf55","added_by":"auto","created_at":"2024-08-13 06:55:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1332977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4576816/v1/e66d0c8d-78de-4042-a741-dd5680b4fd66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGout is a common form of inflammatory arthritis that is distinguished by the long-term accumulation of urate crystals in and around the joints, caused by high levels of uric acid\u003csup\u003e1\u003c/sup\u003e. With improved living standards, its incidence and prevalence have risen globally, significantly impacting human health\u003csup\u003e2\u003c/sup\u003e. Gout is often associated with various comorbidities, including hypertension, cardiovascular diseases, obesity, hyperlipidemia, and metabolic syndrome\u003csup\u003e3\u003c/sup\u003e. Gout remains a significant global health issue, with an incidence of 0.58\u0026ndash;2.89 cases per 1,000 person-years\u003csup\u003e4\u003c/sup\u003e. An research conducted in the United States found that the most prevalent coexisting medical conditions among individuals with gout and hyperuricemia were hypertension (57.9%), dyslipidemia (45.3%), respiratory and thoracic symptoms (35.3%), and diabetes mellitus (19.9%)\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have identified a new composite indicator of atherogenicity: non-high-density lipoprotein cholesterol to high density lipoprotein cholesterol ratio (NHHR)\u003csup\u003e6\u003c/sup\u003e. The NHHR is an advanced and comprehensive tool used to evaluate atherogenic lipids. It surpasses existing lipid indices in accurately forecasting the likelihood of developing cardiovascular illness\u003csup\u003e7\u003c/sup\u003e. The NHHR, a recently identified lipoprotein ratio, takes into consideration the combined effects of high-density lipoprotein cholesterol (HDL-C) and non-HDL-C. This overcomes the prior constraint of researching lipids separately. This study documented the correlation and prognostic significance of NHHR in different medical conditions, including depression, periodontitis, cardiovascular disease, acute stroke, kidney stones, and diabetes mellitus\u003csup\u003e8\u0026ndash;13\u003c/sup\u003e. For example, Hong et al. conducted a study that revealed a connection between NHHR and the occurrence of kidney stones. The investigation discovered that for every unit rise in NHHR, there was a 4% higher chance of acquiring kidney stones. Additionally, each unit rise in NHHR was connected with a 9% higher risk of recurrence of kidney stones\u003csup\u003e12\u003c/sup\u003e. Collectively, these studies suggest that NHHR is a useful predictor of metabolism-related diseases. Moreover, several investigations have established a robust correlation between dyslipidemia and the occurrence of gout or increased amounts of uric acid in the blood. A study has examined the relationship between common lipoproteins, including HDL-C and total cholesterol (TC), and the occurrence of gout. It has been shown that HDL-C is thought to have a protective effect in individuals with gout, whereas TC is positively associated with levels of blood uric acid (UA)\u003csup\u003e14,15\u003c/sup\u003e. Multiple studies have shown a significant association between serum uric acid (SUA) and lipid levels, such as triglycerides (TG), TC, low-density lipoprotein cholesterol (LDL-C), and HDL-C\u003csup\u003e16\u0026ndash;21\u003c/sup\u003e. Nevertheless, the correlation between NHHR and gout remains uninvestigated.\u003c/p\u003e \u003cp\u003eWe completed a cross-sectional study utilizing the NHANES 2007\u0026ndash;2018 dataset to examine the correlation between NHHR and the prevalence of gout in American adults. Investigating this correlation might yield significant knowledge on the connection between lipid metabolism and serum uric acid, which is crucial for devising efficient therapies to avert negative health consequences. Positioning NHHR as a new marker may offer innovative approaches for assessing and managing the prevalence of gout.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn this analysis, data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018 were utilized. NHANES is a comprehensive research initiative designed to assess the physical well-being and dietary condition of individuals, including adults and children, residing in the United States. The program guarantees that all members undertake a biennial health and nutrition examination. The NCHS study Ethics Review Committee has granted approval for the study protocol of the database. The data can be accessed by the public on the NHANES website.\u003c/p\u003e \u003cp\u003eThis study utilized data from the NHANES 2005\u0026ndash;2018 period, initially including a total of 59,842 participants. Exclusions were made based on certain demographic characteristics: (a) those with missing NHHR data (n\u0026thinsp;=\u0026thinsp;16,003), (b) those who did not provide data on gout (n\u0026thinsp;=\u0026thinsp;12,491), and (c) those with missing data on covariates, including education level (n\u0026thinsp;=\u0026thinsp;32), marital status (n\u0026thinsp;=\u0026thinsp;10), hypertension (n\u0026thinsp;=\u0026thinsp;39), diabetes (n\u0026thinsp;=\u0026thinsp;767), and smoking status (n\u0026thinsp;=\u0026thinsp;18). For the analysis, a grand number of 30,482 people were finally included. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the flowchart representing this procedure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExposure definition\u003c/h2\u003e \u003cp\u003eThe NHHR variable was included as the independent variable for assessing exposure. It was determined by dividing non-HDL cholesterol by HDL cholesterol. The calculation of non-HDL cholesterol involves removing HDL cholesterol from total cholesterol (TC). The study classified individuals into four tiers according to their NHHR readings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome definition\u003c/h2\u003e \u003cp\u003eGout was considered an outcome indicator for this study. This indicator was derived from the Health Status Questionnaire used in NHANES, which relied on individuals' self-reported responses to specific health queries during personal interviews. The question posed to all participants was, \"Have you ever received a diagnosis of gout from a doctor or other healthcare professional?\" Participants were instructed to indicate their reaction by selecting either 'yes' or 'no'. They were then categorized into gouty and non-gouty participants. Previous studies have demonstrated the increased accuracy of gout information obtained using self-report methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariables\u003c/h2\u003e \u003cp\u003eIn order to examine the separate correlation between NHHR and gout, we took into account possible factors that might influence the results and made adjustments for the following variables in our analysis: The variables of interest include sex, age, diabetes, race, level of education, smoking habits, marital status, income to poverty ratio (PIR), alcohol consumption, body mass index (BMI), and hypertension. The smoking status was ascertained using the question, \"Have you consumed a minimum of 100 cigarettes in your lifetime?\" Alcohol use was assessed based on the 'mean number of alcoholic beverages consumed each day throughout the previous 12 months.' There are three levels of PIR: '1', '1\u0026ndash;3', and '3 or more.'\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were described using either the standard deviation or the mean, while categorical variables were reported as percentages. In the categorical model, NHHR was categorized into quartiles, with the lowest quartile (Q1) serving as the reference group. The relationship between NHHR and gout was analyzed using multifactorial logistic regression models. NHHR, both in continuous and quartile form, was included as an independent variable in the models to explore potential correlations with gout. IThe study investigated the independent relationships between non-high-density lipoprotein cholesterol (NHHR) and gout using three distinct models. Model 1 did not account for variables. Model 2 included modifications to account for gender, age, and ethnicity. Model 3 incorporated further modifications for factors such as relationship status, educational level, PIR, BMI, drinking habits, smoking status, diabetes, and hypertension.\u003c/p\u003e \u003cp\u003eStratified multivariate regression models were used to do subgroup analysis. The stratification was based on many factors including age, gender, race, marital status, education level, PIR, BMI, diabetes, hypertension, the average number of drinks per day in the preceding 12 months, and smoking status. Potential differences between populations were explored in depth. Subsequently, the smoothed curve fitting approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was employed to analyze the nonlinear correlation between NHHR and gout. Statistical significance was determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The statistical study was conducted using R version 3.4.3 with d EmpowerStats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a summary of the key characteristics of participants selected from NHANES between 2007 and 2018, classified based on NHHR quartile. The study had 30,482 individuals, with a median age of 49.55 years at the beginning. The male participants accounted for 48.44% of the total, while the female participants accounted for 51.56%. The occurrence of gout rose as the NHHR increased (Q1: 4.06%; Q2: 4.31%; Q3: 4.90%; Q4: 5.44%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Participants with higher levels of NHHR were more prone to male, Mexican American or Other Hispanic, have an educational attainment below the 11th grade, be married, have a PIR between 1\u0026ndash;3, have a higher BMI, and have diabetes, hypertension, and a history of smoking more than 100 cigarettes in their lifetime (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). NHHR quartiles differed significantly by age, smoking, gender, race, HDL-C, TC, education level, marital status, diabetes, PIR, alcohol consumption, BMI, gout, and hypertension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e49.23\u0026thinsp;\u0026plusmn;\u0026thinsp;19.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e50.18\u0026thinsp;\u0026plusmn;\u0026thinsp;18.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49.94\u0026thinsp;\u0026plusmn;\u0026thinsp;17.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e48.86\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2694 (35.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3210 (42.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4008 (52.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4852 (63.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4924 (64.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4405 (57.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3619 (47.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2770 (36.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e831 (10.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1056 (13.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1303 (17.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1481 (19.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e608 (7.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e790 (10.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e866 (11.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e953 (12.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3146 (41.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3109 (40.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3117 (40.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3228 (42.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2049 (26.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1702 (22.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1433 (18.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1068 (14.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984 (12.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e958 (12.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e908 (11.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e892 (11.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (7.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e769 (10.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e880 (11.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1018 (13.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e960 (12.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e979 (12.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1084 (14.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1234 (16.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1616 (21.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1694 (22.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1821 (23.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1813 (23.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2311 (30.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2288 (30.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2228 (29.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2168 (28.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2155 (28.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1885 (24.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1614 (21.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1389 (18.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarry status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3442 (45.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3842 (50.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4133 (54.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4266 (55.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e716 (9.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e660 (8.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e574 (7.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e447 (5.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e829 (10.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e797 (10.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e836 (10.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e843 (11.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (3.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251 (3.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252 (3.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270 (3.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1755 (23.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1460 (19.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1260 (16.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1109 (14.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e609 (7.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e605 (7.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e572 (7.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e687 (9.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1428 (18.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1475 (19.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1453 (19.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1662 (21.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3445 (45.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3559 (46.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3770 (49.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3775 (49.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2745 (36.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2581 (33.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2404 (31.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2185 (28.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use (drinks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;31.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;22.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;33.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;24.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e28.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e29.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e29.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Cholesterol(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNHHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6640 (87.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6623 (86.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6561 (86.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6515 (85.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978 (12.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992 (13.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1066 (13.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1107 (14.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGout (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7309 (95.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7287 (95.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7253 (95.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7207 (94.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 (4.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (4.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374 (4.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e415 (5.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking habit (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4526 (59.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4479 (58.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4270 (55.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3789 (49.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3092 (40.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3136 (41.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3357 (44.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3833 (50.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5089 (66.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4900 (64.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4812 (63.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4792 (62.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2529 (33.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2715 (35.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2815 (36.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2830 (37.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe association between NHHR and gout\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the relationship between NHHR and gout. The study results demonstrated a clear and direct relationship between elevated NHHR levels and an increased incidence of gout in the continuous models. The link between NHHR and the odds of gout prevalence was evident as well as the baseline model without any adjustments and the fully adjusted model. In the model that accounted for all relevant factors, the chances of gout prevalence increased by 11% for every unit of rise in NHHR (OR\u0026thinsp;=\u0026thinsp;1.11; 95% CI: 1.07, 1.15). In the categorical model, specifically in the partially adjusted model (model 2), the fourth quartile (Q4) exhibited a 54% higher prevalence of gout (OR\u0026thinsp;=\u0026thinsp;1.54; 95% CI: 1.31, 1.81) compared with the lowest NHHR quartile (Q1). The odds ratios (OR) for the risk of gout in the second, third, and fourth quartiles compared to the lowest reference quartile were 1.05 (95% CI: 0.89, 1.24), 1.19 (95% CI: 1.01, 1.41), and 1.43 (95% CI: 1.21, 1.68), correspondingly. These results were supported by a test for trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after extensive adjustments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdd ratios and 95% confidence intervals for gout\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI), P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95%CI), P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI), P-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.05, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (1.10, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.07, 1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.91, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.90, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.89, 1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.05, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.07, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.01, 1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (1.17, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54 (1.31, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.21, 1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.95, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.07, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.05, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.71, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.77, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.76, 1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.75, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.95, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.91, 1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.68, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.06, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.00, 1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.09, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.12, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.06, 1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.94, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.97, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (0.92, 1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (0.98, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.05, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (0.94, 1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92 (1.46, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17 (1.64, 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79 (1.35, 2.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Model 1: Non-adjusted. Model 2: Adjusted for sex, age, and race. Model 3: Adjusted for sex, age, and race, educational level, smoking, drinking, marital status, PIR, diabetes, hypertension, and BMI. The sex variables were not adjusted in the stratified analysis of sex. NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio; BMI, body mass index; HDL-C, high density lipoprotein cholesterol; PIR, poverty Income ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eThe subgroup analysis data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The findings indicate that age modifies the association between NHHR and gout (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while variables such as gender, level of education, race, marital relationship, PIR, BMI, diabetes, hypertension, and smoking habits do not affect this association. The p-values for the interaction of these variables (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) suggest that the likelihood of an increase in the prevalence of NHHR with gout is consistent across subgroups except for age.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis of the association between NHHR and gout.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.11, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.01, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.01, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.06, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.06, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.06, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.89, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.07, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.95, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.02, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.98, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.03, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.02, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.07, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.00, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.10, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.90, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.94, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.88, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.93, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (0.98, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.03, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.04, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.06, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.90, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.07, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.02, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.04, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.07, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.07, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.04, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.08, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.00, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current research conducted a cross-sectional study of 30,482 people from the United States to examine the connection between NHHR (non-high-density lipoprotein cholesterol) and the prevalence of gout in a sample that is typical of the entire country. Our findings indicate that NHHR is positively associated with increased gout prevalence in model 3. More precisely, in the analysis of continuous variables, each additional unit of NHHR was shown to be correlated with an 11% higher occurrence of gout. Additional subgroup analysis provided more evidence of the consistent nature of this positive correlation. Crucially, the association remained unaffected by factors such as gender, race, degree of education, marital status, PIR, BMI, smoking habits, hypertension, or diabetes. Furthermore, the relationship between NHHR and gout prevalence was affected by age. As NHHR increases, individuals younger than 40 years of age are more likely to develop gout. Moreover, the smoothed curve fitting study revealed a direct relationship between NHHR and the occurrence of gout. Therefore, NHHR can function as an early predictor of the probability of developing gout, which is essential for promptly preventing, diagnosis, and treating those at a heightened risk.\u003c/p\u003e \u003cp\u003eNotably, this article includes an initial investigation into the correlation between NHHR and gout. Emerging evidence suggests that NHHR is an accurate indicator of risk for lipid-related diseases\u003csup\u003e22\u003c/sup\u003e. Nevertheless, there is a lack of research specifically examining the influence of non-high-density lipoprotein cholesterol (NHHR) on gout, despite a significant amount of literature exploring the relationship between other lipid-related factors and gout. Liang et al. conducted a cross-sectional research involving 653 patients with gout and discovered a notable association between uric acid (UA) and triglycerides in women (p\u0026thinsp;=\u0026thinsp;0.039). Additionally, they observed higher levels of HDL-C in women (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e23\u003c/sup\u003e. The Dietary Intervention Randomized Controlled Trial (DIRECT) conducted with 235 participants indicated a potential link between higher levels of HDL-C and a decreased risk of gout. Additionally, an improved ratio of total cholesterol to HDL-C was also linked with a reduced prevalence of gout\u003csup\u003e24\u003c/sup\u003e. A Mendelian randomization study based on publicly available GWAS pooled statistics demonstrated that lower HDL levels were positively correlated with a higher chance of gout and higher serum uric acid concentrations. Specifically, the data showed that a one standard deviation (SD) rise in HDL (~\u0026thinsp;12.26 mg/dL) was associated with a decrease in gout risk of approximately 25% and a reduction in serum urate of 0.09 mg/dL. There was a direct relationship between high levels of triglycerides (TG) and high quantities of uric acid in the blood. The findings showed that for every one standard deviation rise in TG (approximately 112.33 mg/dL), there was a corresponding increase of 0.10 mg/dL in serum uric acid concentration\u003csup\u003e25\u003c/sup\u003e. Although these findings do not provide direct evidence of an association between NHHR and gout, they indirectly support a positive correlation between the two. This study contributes to the expanding field of research on the relationship between lipid metabolism and the onset of gout by utilizing innovative lipid profiles.\u003c/p\u003e \u003cp\u003eNHHR, a novel composite indicator of atherogenic lipid composition\u003csup\u003e7\u003c/sup\u003e, is superior to conventional single lipid variables in evaluating the extent of atherosclerosis\u003csup\u003e26\u003c/sup\u003e. It also serves as an important lipid marker for the prevention of plaque formation\u003csup\u003e27\u003c/sup\u003e. Additionally, findings from the NAGALA longitudinal cohort research carried out in Japan indicate that NHHR is a more effective indicator for forecasting the likelihood of developing diabetes in comparison to traditional lipid markers\u003csup\u003e7\u003c/sup\u003e. Lin et al. conducted a research on a sample of 9,764 Chinese volunteers and discovered that NHHR was a more powerful indicator of insulin resistance and diabetes compared to other traditional lipid markers\u003csup\u003e28\u003c/sup\u003e. The diagnostic value of NHHR has also surpassed conventional lipid biomarkers in predicting non-alcoholic fatty liver disease (NAFLD) \u003csup\u003e29\u003c/sup\u003eand metabolic syndrome\u003csup\u003e30\u003c/sup\u003e. In conclusion, NHHR has demonstrated excellent predictive ability in several studies. Therefore, NHHR holds great potential for extensive clinical application as a widely used biomarker.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eThis investigation possesses several merits. First, the study is founded upon a substantial sample size of 30,482 people. Second, adjustments for confounding covariates were made to ensure more reliable results. Third, subgroup examinations were conducted to assess the strength and reliability of the link between NHHR and the odds of gout prevalence in various groups. Finally, smoothed curve fitting analysis was employed to investigate the positive association between NHHR and kidney stones. Nevertheless, this study has certain constraints. First, because of its cross-sectional design, the study was unable to establish a causal relationship between NHHR and gout. Second, although multiple covariates were considered, it was not feasible to exclude all potential factors that might influence the study results. Third, age differences among participants were present, suggesting that future studies should be conducted with larger and more age-homogeneous samples. Finally, the design of the NHANES database may have introduced selection bias, potentially affecting the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe data we have collected clearly shows a significant correlation between higher NHHR scores and the probability of gout being present in individuals in the United States. This highlights the necessity of identifying individuals at risk for gout by monitoring aberrant NHHR levels. While the cross-sectional form of this study hinders the ability to make causal inferences, it does offer a foundation for future prospective investigations to confirm the causal association between NHHR and gout.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that substantiate the conclusions of this investigation are readily accessible at https://www.cdc.gov/nches/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES procedures received approval from the National Center for Health Statistics Ethics Review Board of the U.S. CDC, and all participants granted written informed permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was designed and funded by YW. XG played a key role in the methodology and writing review. The manuscript was drafted by HC and HX, and the figures were prepared by SW. Each author made contributions to the paper and gave their approval for the final version that was submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the CDC\u0026apos;s National Center for Health Statistics for their role in creating, gathering, and overseeing the NHANES data, as well as for its accessibility to the general public.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not get any financial support from any public, commercial, or non-profit organization.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDalbeth, N.; Merriman, T. R.; Stamp, L. K. Gout. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e388\u003c/em\u003e (10055), 2039\u0026ndash;2052. https://doi.org/10.1016/S0140-6736(16)00346-9.\u003c/li\u003e\n\u003cli\u003ePeng, X.; Li, X.; Xie, B.; Lai, Y.; Sosnik, A.; Boucetta, H.; Chen, Z.; He, W. Gout Therapeutics and Drug Delivery. \u003cem\u003eJ Control Release\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e362\u003c/em\u003e, 728\u0026ndash;754. https://doi.org/10.1016/j.jconrel.2023.09.011.\u003c/li\u003e\n\u003cli\u003eStamp, L. K.; Chapman, P. T. Gout and Its Comorbidities: Implications for Therapy. \u003cem\u003eRheumatology (Oxford)\u003c/em\u003e \u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e52\u003c/em\u003e (1), 34\u0026ndash;44. https://doi.org/10.1093/rheumatology/kes211.\u003c/li\u003e\n\u003cli\u003eDehlin, M.; Jacobsson, L.; Roddy, E. 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The aim of the research was to investigate the potential association between NHHR and the prevalence of gout.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study investigated the correlation between NHHR levels and gout by analyzing information gathered by the National Health and Nutrition Examination Survey (NHANES), a research study conducted in the United States from 2007 to 2018. NHHR is a mathematical formula that calculates the ratio of non-HDL-C to HDL-C. Gout assessment was conducted through a questionnaire. Multifactorial logistic regression analysis, subgroup analysis, and smoothed curve fitting were employed in this study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 30,482 subjects. The fully adjusted models showed that for each unit rise in NHHR in continuous variables, there was an 11% higher likelihood of gout prevalence (OR: 1.11, 95% CI: 1.07\u0026ndash;1.15). The analysis of the NHHR quartile variable revealed that those in the highest quartile of NHHR had a notably greater risk of developing gout in comparison to with people in the lowest quartile. (Q4 vs. Q1, OR: 1.43, 95% CI: 1.21\u0026ndash;1.68). The subgroup analyses yielded consistent results across categories, indicating a substantial positive correlation between NHHR and gout. Interaction tests showed that gender, race, level of education, marital relationship, PIR, hypertension, BMI, smoking habits, and diabetes had no discernible effect on this association. The p-values for all the interactions were more than 0.05. Nevertheless, the relationship between NHHR and gout was substantially affected by the age of the participants (interaction p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAmong adults in the United States, elevated NHHR levels are correlated with an increased odds of gout prevalence. Effectively managing NHHR levels may reduce the occurrence of gout.\u003c/p\u003e","manuscriptTitle":"Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-13 01:52:30","doi":"10.21203/rs.3.rs-4576816/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"373147ec-9b18-4115-b4b2-a19aa026e890","owner":[],"postedDate":"July 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T06:47:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-13 01:52:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4576816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4576816","identity":"rs-4576816","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}