The Association Between Atherogenic Index of plasma and Serum Uric Acid: A Cross-Sectional Study

The Association Between Atherogenic Index of plasma and Serum Uric Acid: 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 Article The Association Between Atherogenic Index of plasma and Serum Uric Acid: A Cross-Sectional Study Bingchao Hu, Wanqian Yu, Huiming Zou, Ping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945254/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 Metabolic diseases are significantly correlated with the Atherogenic Index of Plasma (AIP). However, there is currently no conclusive data establishing a direct connection between AIP and serum uric acid (SUA) levels. Data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2007 to 2016 were used in this cross-sectional investigation. 10,247 people in all participated in the study. By using the logarithm (base 10) of the ratio of triglycerides to high-density lipoprotein cholesterol, AIP was calculated. The concentration of SUA was the dependent variable. The connection between AIP and SUA levels was tested using a multi-factor logistic regression model and a limited three-sample technique. Sub-group analysis and interaction testing were also carried out. In the completely adjusted model, the study found a curvilinear relationship between AIP and the chance of higher SUA levels. Serum uric acid (SUA) levels were directly correlated with an increase in AIP values when the atherogenic index of plasma (AIP) was less than 0.81. Nevertheless, serum uric acid (SUA) levels consistently decreased with increasing AIP values when the atherogenic index of plasma (AIP) exceeded 0.81. Moreover, the probability of having high SUA levels was significantly higher in those in the top 25% of AIP than in those in the lowest 25% of AIP (β = 0.6195, 95% CI: 0.54–0.68, P < 0.001). This association was consistent for every category.there is an inverted U-shaped nonlinear relationship between SUA levels and AIP among adult US population. This suggests that higher AIP levels could lead to higher SUA levels. Health sciences/Medical research/Epidemiology Health sciences/Diseases Health sciences/Medical research AIP1 SUA2 NHANES3 Cross-sectional study4 Relationship5 Figures Figure 1 Figure 2 Figure 3 Introduction As a consequence of purine metabolism, serum uric acid (SUA) plays a vital role in the body as an antioxidant, effectively removing harmful free radicals 1 . However, gout and associated comorbidities like metabolic syndrome, hypertension, and diabetes can arise from abnormally high serum uric acid (SUA) levels, posing serious health risks 2 , 3 , 4 , 5 . Serum uric acid (SUA) levels has risen recently, increasing the significant public health concern of hyperuricemia. Based on past research, it is estimated that 20% of Americans suffer from hyperuricemia 6 . In addition, there has been a notable increase in hyperuricemia prevalence worldwide, which has had a significant effect on a global level 7 , 8 . Still, there is not enough treatment for it. AIP is a new lipid marker proposed by Dobiásová that is calculated as the logarithm of the ratio of TG to HDL-C, or high-density lipoprotein cholesterol 9 . AIP serves as a reliable diagnostic for small dense low-density lipoprotein cholesterol (sd-LDL-C) since it is negatively connected with lipoprotein particle size 9 . According to recent studies, AIP is more accurate in predicting the risk of cardiovascular disease than traditional lipid indicators 10 , 11 , 12 , 13 , 14 .It is interesting to note that AIP is not only highly correlated with atherosclerosis but also functions as a marker of the degree of insulin resistance (IR) in individuals 15 . Previous research indicates a strong correlation between AIP and metabolic diseases, including obesity, diabetes, hypertension, and atherosclerotic cardiovascular disease 16 , 17 , 18 . Given the strong connection between AIP and metabolic indicators 19 , it is crucial to examine the possible correlation between AIP and SUA in order to identify practical and readily available markers for screening individuals with hyperuricemia. This work used a cross-sectional analysis of NHANES (2007–2016) data to examine the association between AIP and SUA, with the potential to mitigate hyperuricemia and enhance its prognosis. Materials and Methods Data Source: The National Health and Nutrition Examination Survey (NHANES) database is a comprehensive countrywide study that was created to evaluate the health as well as nutritious condition of Americans who do not live in institutions. Under the auspices of the National Center for Health Statistics (NCHS), NHANES employs examinations and questionnaires to collect data. The study design utilizes a stratified multiple phases probability sampling method to guarantee the selection of samples that are highly representative. The NHANES procedure was accepted by the NCHS Ethics and Research Assessment Board following a comprehensive examination. All participants willingly and consciously gave written consent. At present, we lack any resources that would enable us to personally identify any individual. Hence, there is no need for any additional ethical assessment, and the complete dataset of the research can be accessed from the public NHANES website ( https://www.cdc.gov/nchs/nhanes/ ). Study Population: Between 2007 and 2016, 50,588 participants in this study took engagement in NHANES cycles. Of the participants, 49,695 were at least 20 years old. We eliminated 37,081 individuals with incomplete AIP data and 47 instances with incomplete SUA data in addition to 2,320 patients with missing covariate data. Finally, we expanded our follow-up analysis to include participants (n = 10,247) with complete AIP, SUA, and other covariate data.The flowchart of the investigation is presented in Fig. 1 . Evaluation of the Atherogenic Index of Plasma (Exposure Variable): The AIP was calculated using the fasting TG to HDL-C ratio and logarithmic transformation, as follows: AIP = log10 (TG / HDL-C) 9 . Subsequently, each individual was classed into one of four groups according to their AIP quartiles: Q1 (-0.79-0.08), Q2 (0.08–0.30), Q3 (0.30–0.52), and Q4 (0.52–2.06). Evaluation of Serum Uric Acid (Outcome Variable): Data on SUA were obtained by taking serum samples from NHANES participants at Mobile Examination Centers (MEC).The SUA concentration was computed using the timed terminal approach. The levels of SUA was determined by analyzing the change in the absorption of the chromosomal product formed from the reaction between uric acid and 3,5-dichlorohydroxybenzenesulfonate, with the assistance of 4-aminoantipyrine as a catalyst. The purpose of SUA levels in our examination was to serve as a results variable. Evaluation of Other Covariates: In accordance with previous research as well as clinical expertise, we have included the following factors that may affect the relationship between AIP and SUA. The investigation took into account what follows variables, continuous variables: age (age), long sitting time (minutes), acetaminotransferase (ALT, U/L), amino transferase in the winter (AST, U / L), mercury (mg/dl), uric nitrogen in the blood (mg / dl) and total cholesterol (mg/dl). The classification variables consist of gender (male/female), ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Negro/other race), educational level (higher or higher than high school/under high school), smoking status (yes/no), alcohol consumption status, hypertension, diabetes, cholesterol prescription (PFC), and household income/poverty ratio (PIR). Hypertension was defined as the use of antihypertensive medication and having an average systolic blood pressure of 140 mmHg or higher, or a diastolic blood pressure of 90 mmHg or above 20 . Diabetes was diagnosed based on three criteria: (1) evidence from a healthcare professional; (2) a HbA1c level higher than 6.5%; and (3) fasting plasma glucose levels of 126 mg/dL or greater 21 . The income groups categorized according to PIR were as follows: low (PIR less than 1.3), moderate (PIR between 1.3 and 3.5), and high (PIR greater than 4). Regarding marital status, there were four distinct categories: the people who had never been married, those who were widowed, those who were divorced, and those who were cohabiting with a partner. Drinking can be categorized into two groups: current drinking and not participating from drinking entirely. Less than 25 kg/m 2 was regarded normal, 25–30 kg/m 2 was deemed overweight, and 30 kg/m 2 or beyond was considered obese 22 . 2.6 Statistical Analysis: The CDC's recommendations were followed in the performance of all statistical analyses. In order to ensure the applicability of the results to the population of the United States, a sample strata, regions, and weights obtained by NHANES were utilized in conjunction with the complicated multistage clustering survey methodology 23 . The baseline data for continuous variables were reported as the mean ± standard deviation (SD), while for categorical variables, they were reported as frequency (%). For AIP's effect on SUA, we evaluated β and its 95% confidence interval using weighted multivariate linear regression models. The AIP variable was examined in both continuous and categorical forms (quartiles). Baseline characteristics of the AIP quartiles were analyzed using weighted one-way ANOVA for continuous variables and the weighted chi-square test for categorical variables. No factors were updated in model 1. Model 2 considered age, gender, and race as factors. Model 3 included and adjusted for numerous variables, such as age, gender, race, BMI, education level, PIR, blood urea nitrogen, AST, ALT, creatinine, sedentary time, drinking and smoking behaviors, diabetes, hypertension, total cholesterol, and PFC. Subgroup analysis also employed weighted multivariate logistic regression. If the P value for the interaction was not statistically significant, the results across strata were valid; alternatively, particular populations might exist. We utilized generalized additive models (GAM) to analyze the nonlinear association between AIP and SUA, employing smooth curve fitting. The critical turning points of the AIP-SUA connection were determined by a recursive method after the presence of non-linearity was detected. A threshold effect research was then carried out to compare the logistic regression model with the two-part logistic regression model. The statistical analyses were conducted using R (version 4.2.2, http://www.R-project.org ) and EmpowerStats (version 4.2, www.empowerstats.com ). Statistical significance was determined using a bilateral P-value of less than 0.05. Results Baseline Characteristics of Participants: Table 1 presents the initial characteristics of each participant. The study had a total of 10,247 individuals, at a median age of 47.44 years. Of the participants, 49.76% were male and 50.24% were female. The participants were divided into Q1-Q4 groups based on their AIP quartiles. Significant differences were observed in age, gender, race, education level, PIR, marital status, BMI, drinking status, smoking status, hypertension, cholesterol prescription, diabetes, ALT, AST, SUA, creatinine, and total cholesterol among the highest quartile (Q4) of AIP compared to other subgroups (all P < 0.05). Relationship Between AIP and SUA: The findings of the multivariate regression analysis between AIP and SUA are displayed in Table 2. The study's findings suggest that the chance of SUA occurring increases with plasma's Atherogenic Index score. In the unadjusted model 1, a positive connection was found between AIP and SUA (β = 1.2995, 95% CI: 1.21 - 1.37, P < 0.001). After adjusting for age, gender, and race, there remained a significant positive association in model 2 (β = 1.0495, 95% CI: 0.97 - 1.12, P < 0.001). The positive link persisted in the entirely adjusted model 3 (β = 0.6895, 95% CI: 0.60 - 0.76, P < 0.001). The association between AIP and SUA remained statistically significant even when we categorized AIP into quartiles instead of treating it as a continuous variable. Individuals in the upper quartile of AIP exhibited a 0.61 μmol/L increase in SUA compared to those in the smallest quartile (model 3, β = 0.6195, 95% CI: 0.54 - 0.68) while accounting for confounding variables. Furthermore, the outcomes utilizing generalized additive models and smooth fit curves hint to a non-linear correlation between SUA risk and AIP. Non-Linear Relationship: The study's findings, as shown in Figure 2, demonstrate a non-linear relationship association between AIP with SUA, as indicated by the GAM and smooth fitting of curves analysis. The log-likelihood ratio test revealed a P value of less than 0.001 when comparing the linear regression model and the two-piecewise linear regression model, This suggests that the two-piecewise linear regression model provides a superior fit to the data. The results of the investigation, employing the recursive algorithm and two-piecewise linear regression model, are presented in Table 3. The inflection point of the inverted U-shaped correlation between AIP (atherogenic index of plasma) and SUA (serum uric acid) was determined to be 0.87. The effect size of 0.81 (95% CI: 0.72 - 0.89) and P value of less than 0.001 on the left side of the inflection point show a strong favorable connection. Nonetheless, there was a substantial negative correlation between AIP and SUA on the right side of the second inflection point. The study findings reveal that there was a positive association between AIP levels and SUA levels. The effect size was -0.55 (95% CI: -0.91 - 0.18) and the statistical significance was shown by a P value of 0.003. Subgroup Analysis: In order to examine the link between AIP and SUA levels more thoroughly and provide data, a subgroup analysis was performed. So as to evaluate the effects of various variables, interaction tests were also performed. AIP and SUA consistently exhibited a positive correlation in the subgroup analysis results, demonstrating the stability of the link between the two variables. Interestingly, there was no significant interaction found for age, gender, BMI, diabetes, or hypertension, indicating that these variables are not necessary for this link to exist (interaction P > 0.05 for all). Discussion This first population-based study looked into the relationship between AIP and the likelihood of high SUA levels. AIP was directly connected with serum uric acid levels when compared to conventional lipid markers. TG is often the principal objective for treating dyslipidemia 24 . Previous studies have also shown a distinct relationship between the risk of high uric acid levels and TG content 25 , 26 . Furthermore, a number of studies have discovered that lowered HDL-C levels are a significant risk factor for high uric acid 27 . According to recent studies, lipid ratios may be important indicators of several diseases, including diabetes, metabolic syndrome, and cardiovascular ailments 28 , 29 , 30 , 31 . AIP has been proposed as a potential biomarker for early CVD event detection in developing countries 32 . Studies show that AIP performs significantly better in assessing atherosclerosis than traditional lipid measures 33 . Similarly, studies on metabolic disorders have shown that AIP, as opposed to single lipid indicators like TG and HDL-C 34 , 35 , 36 , is a stronger predictor of type 2 diabetes, hypertension, insulin resistance (IR), metabolic syndrome, and metabolic syndrome. However, whether AIP can be a useful marker for predicting uric acid levels is uncertain. Our study's RCS analysis shows a non-linear, inverted U-shaped connection between AIP and the chance of high SUA levels. This work fills a void in the literature by expanding the applicability of lipid ratios and raises the prospect that AIP could be a valuable marker for SUA level prediction. Furthermore, cohort studies have substantiated a robust association between dyslipidemia and elevated levels of uric acid 37 , 38 , 39 . Therefore, from the perspective of lipid management, managing and preventing hyperuricemia offers substantial clinical benefits. The present study aims to demonstrate the critical role of non-HDL-C in lipid control 40 . To find out if AIP is a novel target for lipid management, more research is required. In the end, the inability of subgroup analysis and interaction testing to identify specific groups indicates that this relationship is robust across a variety of categories. Further research is needed for other demographics, such as older Asians. IR, oxidative stress, inflammation, lifestyle, and the use of lipid-lowering drugs are some of the conceivable factors that may demonstrate a relationship between the concentrations of dyslipidemia markers and the probability of elevated SUA levels. Insulin resistance (IR), which lowers the body's ability to use insulin as needed and causes issues with lipid metabolism and increased uric acid production, is linked to both illnesses 41 , 42 . Furthermore, oxidative stress—which can result in uric acid accumulation and lipid peroxidation—can be brought on by an imbalance between antioxidants and free radicals 43 . Chronic inflammation is common in dyslipidemia patients, and this can lead to an increase in uric acid generation and a decrease in its excretion 44 .Additionally, researchers have found that individuals with hyperlipidemia and hyperuricemia share similar dietary patterns, such as consuming high fat and alcohol-containing foods in excess 45 . It's interesting to note that using lipid-lowering drugs is associated with variations in SUA levels 46 . Strengths and Limitations of the Study This study has a number of benefits. It is based on data from the large sample size, nationally representative NHANES database. The accuracy of the findings was enhanced by the study's effective management of any confounding variables. Furthermore, RCS analysis was used to look at non-linear correlations, and subgroup analysis was done to assess how consistently the results held true for different populations. There are, however, some issues with the current study. In order to determine the causative nature of these associations, prospective cohort studies and intervention trials are required as the cross-sectional design precludes inferring causation. Second, there are other potential confounding factors that this study did not consider but that could have skewed the results, such as the use of uric acid-lowering drugs or dietary patterns marked by heavy alcohol and high-fat food intake. Furthermore, additional study is required to see whether the results apply to other populations because the sample is derived from the US population. Conclusion In summary, the inverted U-shaped non-linear relationship between the two variables suggests that there may be a causal relationship between higher AIP and higher SUA levels in the adult US population. These findings underline the vital requirement of following and regulating patients' AIP levels to prevent the worsening of hyperuricemia. Additional study is essential to confirm our findings and investigate the possible mechanism behind this connection, and huge-scale, high-quality randomized studies are crucial to this goal. Declarations Competing interests Te authors declare no competing interests. Funding Supported by funding from the following: the National Key Research and Development Program(2020YFC2002902). Author Contribution BCH participated in literature search , study design , data collection, data analysis , data interpretation , and wrote the manuscript . WQY, HMZ. Conceived of the study and participated in its design , coordination , data collection and analysis . P.L. participated in study design and provided the critical revision . All authors read and approved the final manuscript. Acknowledgements The author thanks the staff and the participants of the NHANES study for their valuable contributions. Data Availability Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/ nhanes/.If anyone wants to get data from this study, please contact [email protected] Data availability Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/ . If anyone wants to get data from this study, please contact [email protected] References Pasalic, D., Marinkovic, N. & Feher-Turkovic, L. Uric acid as one of the important factors in multifactorial disorders–facts and controversies. Biochem. Med. (Zagreb) . 22 , 63–75 (2012). Gherghina, M. E. et al. Uric Acid and Oxidative Stress-Relationship with Cardiovascular, Metabolic, and Renal Impairment. Int. J. Mol. Sci. 23 , 3188 (2022). Dalbeth, N. et al. Enhanced osteoclastogenesis in patients with tophaceous gout: urate crystals promote osteoclast development through interactions with stromal cells. Arthritis Rheum. 58 , 1854–1865 (2008). Han, Y. et al. Serum Uric Acid Might Be Positively Associated With Hypertension in Chinese Adults: An Analysis of the China Health and Nutrition Survey. Front. Med. (Lausanne) . 8 , 755509 (2021). Battelli, M. G., Bortolotti, M., Polito, L. & Bolognesi, A. The role of xanthine oxidoreductase and uric acid in metabolic syndrome. Biochim. Biophys. Acta Mol. Basis Dis. 1864 , 2557–2565 (2018). Chen-Xu, M., Yokose, C., Rai, S. K., Pillinger, M. H. & Choi, H. K. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007–2016. Arthritis Rheumatol. 71 , 991–999 (2019). Dehlin, M., Jacobsson, L. & Roddy, E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat. Rev. Rheumatol. 16 , 380–390 (2020). Danve, A., Sehra, S. T. & Neogi, T. Role of Diet in Hyperuricemia and Gout. Best Pract. Res. Clin. Rheumatol. 35 , 101723 (2021). Dobiásová, M. & Frohlich, J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin. Biochem. 34 , 583–588 (2001). Bora, K., Pathak, M. S., Borah, P., Hussain, M. I. & Das, D. Association of the Apolipoprotein A-I Gene Polymorphisms with Cardiovascular Disease Risk Factors and Atherogenic Indices in Patients from Assam, Northeast India. Balkan J. Med. Genet. 20 , 59–70 (2017). Won, K. B. et al. Atherogenic index of plasma and the risk of rapid progression of coronary atherosclerosis beyond traditional risk factors. Atherosclerosis . 324 , 46–51 (2021). Li, Y. W., Kao, T. W., Chang, P. K., Chen, W. L. & Wu, L. W. Atherogenic index of plasma as predictors for metabolic syndrome, hypertension and diabetes mellitus in Taiwan citizens: a 9-year longitudinal study. Sci. Rep. 11 , 9900 (2021). Zhu, X. et al. Atherogenic index of plasma is a novel and better biomarker associated with obesity: a population-based cross-sectional study in China. Lipids Health Dis. 17 , 37 (2018). Guo, Q. et al. The sensibility of the new blood lipid indicator–atherogenic index of plasma (AIP) in menopausal women with coronary artery disease. Lipids Health Dis. 19 , 27 (2020). Fernández-Macías, J. C., Ochoa-Martínez, A. C. & Varela-Silva, J. A. Pérez-Maldonado, I. N. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch. Med. Res. 50 , 285–294 (2019). Shen, S. W. et al. Atherogenic index of plasma is an effective index for estimating abdominal obesity. Lipids Health Dis. 17 , 11 (2018). Zhu, X. W., Deng, F. Y. & Lei, S. F. Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim. Care Diabetes . 9 , 60–67 (2015). Shi, Y. & Wen, M. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011–2018 population. Cardiovasc. Diabetol. 22 , 19 (2023). Shin, H. R., Song, S., Cho, J. A. & Ly, S. Y. Atherogenic Index of Plasma and Its Association with Risk Factors of Coronary Artery Disease and Nutrient Intake in Korean Adult Men: The 2013–2014 KNHANES. Nutrients 14, 1071 (2022). He, P. et al. U-shaped association between dietary copper intake and new-onset hypertension. Clin. Nutr. 41 , 536–542 (2022). Casagrande, S. S., Lee, C., Stoeckel, L. E., Menke, A. & Cowie, C. C. Cognitive function among older adults with diabetes and prediabetes, NHANES 2011–2014. Diabetes Res. Clin. Pract. 178 , 108939 (2021). Phillips, J. A. Dietary Guidelines for Americans, 2020–2025. Workplace Health Saf. 69 , 395 (2021). Johnson, C. L. et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat. 2 , 1–24 (2013). Jang, A. Y., Lim, S., Jo, S. H., Han, S. H. & Koh, K. K. New Trends in Dyslipidemia Treatment. Circ. J. 85 , 759–768 (2021). Cui, N. et al. Triglycerides and Total Cholesterol Concentrations in Association with Hyperuricemia in Chinese Adults in Qingdao, China. Risk Manag Healthc. Policy . 13 , 165–173 (2020). Zhang, Y. et al. Higher triglyceride level predicts hyperuricemia: A prospective study of 6-year follow-up. J. Clin. Lipidol. 12 , 185–192 (2018). Wang, Z. et al. The relationship between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hyperuricaemia. Lipids Health Dis. 23 , 187 (2024). Chen, Y. et al. Triglyceride to high-density lipoprotein cholesterol ratio and cardiovascular events in the general population: A systematic review and meta-analysis of cohort studies. Nutr. Metab. Cardiovasc. Dis. 32 , 318–329 (2022). Drexel, H. et al. The LDL-C/ApoB ratio predicts major cardiovascular events in patients with established atherosclerotic cardiovascular disease. Atherosclerosis . 329 , 44–49 (2021). Zhang, X., Zhang, X., Li, X., Feng, J. & Chen, X. Association of metabolic syndrome with atherogenic index of plasma in an urban Chinese population: A 15-year prospective study. Nutr. Metab. Cardiovasc. Dis. 29 , 1214–1219 (2019). Sheng, G. et al. Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk. Diabetes Metab. Syndr. Obes. 15 , 1677–1686 (2022). Fernández-Macías, J. C., Ochoa-Martínez, A. C. & Varela-Silva, J. A. Pérez-Maldonado, I. N. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch. Med. Res. 50 , 285–294 (2019). Zhu, L. et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol. 73 , 931–938 (2015). Li, Y. W., Kao, T. W., Chang, P. K., Chen, W. L. & Wu, L. W. Atherogenic index of plasma as predictors for metabolic syndrome, hypertension and diabetes mellitus in Taiwan citizens: a 9-year longitudinal study. Sci. Rep. 11 , 9900 (2021). Yin, B. et al. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc. Diabetol. 22 , 157 (2023). Dobiásová, M. AIP–atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. Vnitr Lek . 52 , 64–71 (2006). He, H. et al. Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study. Lipids Health Dis. 22 , 38 (2023). Zhang, Y. et al. Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study. J. Transl Med. 18 , 409 (2020). Xu, Y. et al. Association between dyslipidaemia and the risk of hyperuricaemia: a six-year longitudinal cohort study of elderly individuals in China. Ann. Med. 54 , 2402–2410 (2022). Raja, V. et al. Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook. Atherosclerosis . 383 , 117312 (2023). Vuorinen-Markkola, H. & Yki-Järvinen, H. Hyperuricemia and insulin resistance. J. Clin. Endocrinol. Metab. 78 , 25–29 (1994). Smith, D. A. Treatment of the dyslipidemia of insulin resistance. Med. Clin. North. Am. 91 , 1185–1210 (2007). Rizzo, M. et al. Atherogenic dyslipidemia and oxidative stress: a new look. Transl Res. 153 , 217–223 (2009). Al Shanableh, Y. et al. Prevalence of asymptomatic hyperuricemia and its association with prediabetes, dyslipidemia and subclinical inflammation markers among young healthy adults in Qatar. BMC Endocr. Disord . 22 , 21 (2022). Conen, D. et al. Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing country. BMC Public. Health . 4 , 9 (2004). Deedwania, P. C., Stone, P. H., Fayyad, R. S., Laskey, R. E. & Wilson, D. J. Improvement in Renal Function and Reduction in Serum Uric Acid with Intensive Statin Therapy in Older Patients: A Post Hoc Analysis of the SAGE Trial. Drugs Aging . 32 , 1055–1065 (2015). Tables TABLE 1 Weighted baseline characteristics of participants. Note : Values for categorical variables are given as weighted percentage (standard error); for continuous variables, as weighted mean ± standard error. Weighted Student’s t-test and chi-squared test were used. Abbreviations: BMI, body mass index; ALT, alanine transaminase; AST, aspartate transaminase; TC, total cholesterol; SUA, serum uric acid; AIP, atherogenic index of plasma Variables# Quintile categories of AIP P-value AIP quartile Q1(-0.79-0.08) Q2(0.08-0.30) Q3(0.30-0.52) Q4(0.52-2.06) Participants 2558 2565 2562 2562 AGE(years) 45.81 (44.78 ,46.84) 46.81 (45.88 ,47.74) 48.38 (47.57 ,49.20) 48.74 (47.96 ,49.53) 0.0001 Sedentary time (minute) 376.16 (360.67 ,391.65) 400.60 (371.82 ,429.37) 399.43 (380.05 ,418.81) 391.82 (379.71 ,403.94) 0.244 serum creatinine(mg/dl) 0.84 (0.83 ,0.86) 0.87 (0.86 ,0.88) 0.90 (0.88 ,0.91) 0.92 (0.90 ,0.94) <0.0001 Blood urea nitrogen (mg/dl) 13.17 (12.86 ,13.49) 13.37 (13.11 ,13.64) 13.49 (13.23 ,13.76) 13.73 (13.48 ,13.99) 0.055 TC(mg/dl) 184.61 (182.75 ,186.47) 188.87 (187.09 ,190.65) 192.32 (190.14 ,194.51) 206.69 (204.50 ,208.89) <0.0001 SUA(mg/dl) 4.91 (4.84 ,4.97) 5.33 (5.27 ,5.39) 5.71 (5.63 ,5.80) 6.15 (6.07 ,6.23) <0.0001 SEX(%) <0.0001 Male 34.88 (32.64 ,37.19) 46.05 (43.86 ,48.25) 53.97 (51.27 ,56.64) 64.15 (61.78 ,66.46) Female 65.12 (62.81 ,67.36) 53.95 (51.75 ,56.14) 46.03 (43.36 ,48.73) 35.85 (33.54 ,38.22) Race (%) <0.0001 Mexican American 65.63 (61.71 ,69.35) 70.92 (67.15 ,74.41) 69.56 (66.07 ,72.84) 72.08 (68.70 ,75.22) Other Hispanic 16.69 (13.95 ,19.83) 11.17 (9.45 ,13.15) 8.67 (7.30 ,10.26) 5.43 (4.39 ,6.69) Non-Hispanic White 5.47 (4.30 ,6.93) 7.42 (5.91 ,9.27) 8.94 (7.22 ,11.03) 9.80 (7.96 ,12.03) Non-Hispanic Black 4.48 (3.34 ,5.98) 4.83 (3.71 ,6.28) 5.57 (4.52 ,6.84) 6.02 (4.70 ,7.69) Other races 7.73 (6.42 ,9.28) 5.66 (4.58 ,6.98) 7.26 (6.16 ,8.55) 6.67 (5.49 ,8.08) Education Level (%) <0.0001 Less than high school 3.29 (2.51 ,4.31) 5.06 (4.11 ,6.20) 4.88 (4.05 ,5.86) 7.02 (5.88 ,8.35) High school 8.24 (6.78 ,9.98) 10.15 (8.70 ,11.80) 12.48 (10.30 ,15.04) 13.59 (12.18 ,15.13) Above high school 88.47 (86.37 ,90.28) 84.80 (82.90 ,86.52) 82.65 (79.83 ,85.14) 79.40 (77.19 ,81.45) poverty ratio (%) 0.0002 3.5 45.81 (42.55 ,49.11) 43.50 (39.96 ,47.11) 38.72 (35.53 ,42.02) 39.29 (35.74 ,42.95) Marital status (%) 0.0001 Married/Living with partner 62.15 (59.18 ,65.04) 62.46 (59.44 ,65.39) 64.73 (62.08 ,67.29) 67.24 (64.04 ,70.29) Widowed/Divorced/Separated 15.83 (13.91 ,17.97) 18.62 (16.74 ,20.66) 17.82 (16.22 ,19.55) 18.14 (15.98 ,20.52) Never married 22.02 (19.80 ,24.41) 18.92 (16.42 ,21.69) 17.45 (15.18 ,19.97) 14.62 (12.43 ,17.12) BMI (kg/m 2 ) (%) <0.0001 =30 18.34 (16.51 ,20.33) 32.20 (29.62 ,34.89) 43.05 (40.47 ,45.66) 53.83 (51.19 ,56.45) Smoking status(%) <0.0001 Never 62.44 (59.32 ,65.46) 57.77 (55.21 ,60.28) 51.96 (49.22 ,54.69) 47.09 (44.76 ,49.43) Smoking former 22.40 (20.11 ,24.88) 24.14 (21.86 ,26.57) 26.98 (24.46 ,29.67) 28.63 (26.26 ,31.12) Smoking now 15.16 (13.20 ,17.34) 18.09 (16.03 ,20.35) 21.06 (18.60 ,23.74) 24.29 (22.35 ,26.33) Drinking status (%) <0.0001 Never 11.65 (10.16 ,13.33) 12.01 (10.37 ,13.86) 10.86 (9.36 ,12.57) 9.21 (7.79 ,10.85) Drinking former 77.66 (75.13 ,80.00) 74.46 (72.07 ,76.71) 73.62 (71.26 ,75.85) 71.63 (68.85 ,74.25) Drinking now 10.69 (9.16 ,12.45) 13.53 (12.14 ,15.06) 15.52 (13.83 ,17.38) 19.16 (17.00 ,21.53) HYPERTENSION(%) <0.0001 No 69.88 (67.16 ,72.47) 61.15 (58.31 ,63.93) 55.86 (53.27 ,58.41) 47.84 (44.90 ,50.79) Yes 30.12 (27.53 ,32.84) 38.85 (36.07 ,41.69) 44.14 (41.59 ,46.73) 52.16 (49.21 ,55.10) Diabetes(%) <0.0001 No 93.23 (91.98 ,94.30) 90.27 (88.87 ,91.52) 85.14 (83.05 ,87.01) 77.67 (74.99 ,80.14) Yes 6.77 (5.70 ,8.02) 9.73 (8.48 ,11.13) 14.86 (12.99 ,16.95) 22.33 (19.86 ,25.01) Table 2 : Correlation between AIP and SUA (mg/dl).SUA, serum uric acid; 95% CI, 95% confidence interval; Model 1 , no covariates were adjusted; Model 2 , adjusted for gender, age, race; Model 3 , adjusted for gender, age, race,sedentary time, ALT, AST, creatinine, blood urea nitrogen, education level, the ratio of family income to poverty, marital status, body mass index, alcohol status, smoking status, hypertension, total cholesterol，cholesterol prescription, total cholesterol, and diabetes. AIP SUA, mg/dL, β (95%CI), P-value Model 1 Model 2 Model 3 Continuous 1.29 (1.21, 1.37) <0.001 1.04 (0.97, 1.12) <0.001 0.68 (0.60, 0.76) <0.001 Categories Q1(-0.79-0.08) Ref Ref Ref Q2(0.08-0.30) 0.41 (0.34, 0.49) <0.001 0.33 (0.26, 0.40) <0.001 0.20 (0.13, 0.26) <0.001 Q3(0.30-0.52) 0.77 (0.70, 0.85) <0.001 0.63 (0.56, 0.70) <0.001 0.42 (0.35, 0.48) <0.001 Q4(0.52-2.06) 1.14 (1.07, 1.22) <0.001 0.93 (0.86, 1.00) <0.001 0.61 (0.54, 0.68) <0.001 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. 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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-4945254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":355453322,"identity":"1629a67a-9ca6-45d8-93bb-fe166ddd411e","order_by":0,"name":"Bingchao Hu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Bingchao","middleName":"","lastName":"Hu","suffix":""},{"id":355453323,"identity":"06b50cee-95e1-4ec4-a403-8d7a3bc9825b","order_by":1,"name":"Wanqian Yu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wanqian","middleName":"","lastName":"Yu","suffix":""},{"id":355453324,"identity":"857b8b54-6823-48f7-8da8-8c32b87374b2","order_by":2,"name":"Huiming Zou","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Huiming","middleName":"","lastName":"Zou","suffix":""},{"id":355453325,"identity":"f729c711-0b76-428e-98fb-c9e3850fa1d9","order_by":3,"name":"Ping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACPmYGhgNgFoj8YGBjR1ALG7IWxhkFacmEtcBZQC3MPB8OMTYQ1MLOe/Bwwa+6xL7bzcekbQwOMDOwHz66Ab/D+BIOz+w7nDjzzrE06RyDO3wMPGlpN/Br4TE4zNtzIHHDjRwzoJZnzAwSPGbEaKkDasn/Jm1hcJixgSgtPD+YQbawSTMQpwXoF96Gw8Yzb6QZW/YYpCWzEfILP//Zw595/tTJ9t1Ifnjjxx8bO372w8fwamFg4AFGYRuDYwMDA4sE2F78yqFaGP4w2ANJ5g+EVY+CUTAKRsFIBAAszE0NIUthTgAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-20 13:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4945254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4945254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64828541,"identity":"414718d0-d919-4437-bf06-5224ec4bef21","added_by":"auto","created_at":"2024-09-19 09:10:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76058,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; AIP, Atherogenic Index of plasma; SUA, serum uric acid.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4945254/v1/60b9af4331a514bbe734e0f6.png"},{"id":64828544,"identity":"abe707f2-9647-40f5-8d3a-70951331e60f","added_by":"auto","created_at":"2024-09-19 09:10:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41756,"visible":true,"origin":"","legend":"\u003cp\u003eCurve fitting analysis of AIP and SUA.\u003c/p\u003e\n\u003cp\u003eAIP: Adjusted β (95% CI) \u0026nbsp;\u0026nbsp;P values\u003c/p\u003e\n\u003cp\u003efitting model by standard linear \u0026nbsp;\u0026nbsp;regression: 0.68 (0.60, 0.76) \u0026lt;0.0001\u003c/p\u003e\n\u003cp\u003efitting odel by two-piecewise linear \u0026nbsp;\u0026nbsp;regression\u003c/p\u003e\n\u003cp\u003eInflection point: 0.87\u003c/p\u003e\n\u003cp\u003e≤0.87:\t0.81 (0.72, 0.89) \u0026lt;0.0001\u003c/p\u003e\n\u003cp\u003e\u0026gt; 0.87:\t-0.55 (-0.91, -0.18) 0.0031\u003c/p\u003e\n\u003cp\u003eLog likelihood ratio test:\t\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4945254/v1/1b7d552296ba73e63f988f98.png"},{"id":64828570,"identity":"eaa09dc7-1838-426d-ac68-fcd84922fd9a","added_by":"auto","created_at":"2024-09-19 09:10:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148809,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between AIP and SUA.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4945254/v1/87f946dfad22e7c38628400f.png"},{"id":72190657,"identity":"3d647e12-31bb-4195-9d21-1b82b6f7b30b","added_by":"auto","created_at":"2024-12-23 14:09:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841557,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945254/v1/af107bbb-88ae-4f01-8792-2d015e5f3817.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Atherogenic Index of plasma and Serum Uric Acid: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs a consequence of purine metabolism, serum uric acid (SUA) plays a vital role in the body as an antioxidant, effectively removing harmful free radicals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, gout and associated comorbidities like metabolic syndrome, hypertension, and diabetes can arise from abnormally high serum uric acid (SUA) levels, posing serious health risks\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Serum uric acid (SUA) levels has risen recently, increasing the significant public health concern of hyperuricemia. Based on past research, it is estimated that 20% of Americans suffer from hyperuricemia\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, there has been a notable increase in hyperuricemia prevalence worldwide, which has had a significant effect on a global level\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Still, there is not enough treatment for it.\u003c/p\u003e \u003cp\u003eAIP is a new lipid marker proposed by Dobi\u0026aacute;sov\u0026aacute; that is calculated as the logarithm of the ratio of TG to HDL-C, or high-density lipoprotein cholesterol\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. AIP serves as a reliable diagnostic for small dense low-density lipoprotein cholesterol (sd-LDL-C) since it is negatively connected with lipoprotein particle size\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. According to recent studies, AIP is more accurate in predicting the risk of cardiovascular disease than traditional lipid indicators\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.It is interesting to note that AIP is not only highly correlated with atherosclerosis but also functions as a marker of the degree of insulin resistance (IR) in individuals\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Previous research indicates a strong correlation between AIP and metabolic diseases, including obesity, diabetes, hypertension, and atherosclerotic cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the strong connection between AIP and metabolic indicators\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, it is crucial to examine the possible correlation between AIP and SUA in order to identify practical and readily available markers for screening individuals with hyperuricemia.\u003c/p\u003e \u003cp\u003eThis work used a cross-sectional analysis of NHANES (2007\u0026ndash;2016) data to examine the association between AIP and SUA, with the potential to mitigate hyperuricemia and enhance its prognosis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source:\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) database is a comprehensive countrywide study that was created to evaluate the health as well as nutritious condition of Americans who do not live in institutions. Under the auspices of the National Center for Health Statistics (NCHS), NHANES employs examinations and questionnaires to collect data. The study design utilizes a stratified multiple phases probability sampling method to guarantee the selection of samples that are highly representative. The NHANES procedure was accepted by the NCHS Ethics and Research Assessment Board following a comprehensive examination. All participants willingly and consciously gave written consent. At present, we lack any resources that would enable us to personally identify any individual. Hence, there is no need for any additional ethical assessment, and the complete dataset of the research can be accessed from the public NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population:\u003c/h2\u003e \u003cp\u003eBetween 2007 and 2016, 50,588 participants in this study took engagement in NHANES cycles. Of the participants, 49,695 were at least 20 years old. We eliminated 37,081 individuals with incomplete AIP data and 47 instances with incomplete SUA data in addition to 2,320 patients with missing covariate data. Finally, we expanded our follow-up analysis to include participants (n\u0026thinsp;=\u0026thinsp;10,247) with complete AIP, SUA, and other covariate data.The flowchart of the investigation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Atherogenic Index of Plasma (Exposure Variable):\u003c/h2\u003e \u003cp\u003eThe AIP was calculated using the fasting TG to HDL-C ratio and logarithmic transformation, as follows: AIP\u0026thinsp;=\u0026thinsp;log10 (TG / HDL-C)\u003csup\u003e9\u003c/sup\u003e. Subsequently, each individual was classed into one of four groups according to their AIP quartiles: Q1 (-0.79-0.08), Q2 (0.08\u0026ndash;0.30), Q3 (0.30\u0026ndash;0.52), and Q4 (0.52\u0026ndash;2.06).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Serum Uric Acid (Outcome Variable):\u003c/h2\u003e \u003cp\u003eData on SUA were obtained by taking serum samples from NHANES participants at Mobile Examination Centers (MEC).The SUA concentration was computed using the timed terminal approach. The levels of SUA was determined by analyzing the change in the absorption of the chromosomal product formed from the reaction between uric acid and 3,5-dichlorohydroxybenzenesulfonate, with the assistance of 4-aminoantipyrine as a catalyst. The purpose of SUA levels in our examination was to serve as a results variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Other Covariates:\u003c/h2\u003e \u003cp\u003eIn accordance with previous research as well as clinical expertise, we have included the following factors that may affect the relationship between AIP and SUA. The investigation took into account what follows\u003c/p\u003e \u003cp\u003evariables, continuous variables: age (age), long sitting time (minutes), acetaminotransferase (ALT, U/L), amino transferase in the winter (AST, U / L), mercury (mg/dl), uric nitrogen in the blood (mg / dl) and total cholesterol\u003c/p\u003e \u003cp\u003e(mg/dl). The classification variables consist of gender (male/female), ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Negro/other race), educational level (higher or higher than high school/under high school), smoking status (yes/no), alcohol consumption status, hypertension, diabetes, cholesterol prescription (PFC), and household income/poverty ratio (PIR). Hypertension was defined as the use of antihypertensive medication and having an average systolic blood pressure of 140 mmHg or higher, or a diastolic blood pressure of 90 mmHg or above\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Diabetes was diagnosed based on three criteria: (1) evidence from a healthcare professional; (2) a HbA1c level higher than 6.5%; and (3) fasting plasma glucose levels of 126 mg/dL or greater\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The income groups categorized according to PIR were as follows: low (PIR less than 1.3), moderate (PIR between 1.3 and 3.5), and high (PIR greater than 4). Regarding marital status, there were four distinct categories: the people who had never been married, those who were widowed, those who were divorced, and those who were cohabiting with a partner. Drinking can be categorized into two groups: current drinking and not participating from drinking entirely. Less than 25 kg/m\u003csup\u003e2\u003c/sup\u003e was regarded normal, 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e was deemed overweight, and 30 kg/m\u003csup\u003e2\u003c/sup\u003e or beyond was considered obese\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis:\u003c/h2\u003e \u003cp\u003e The CDC's recommendations were followed in the performance of all statistical analyses. In order to ensure the applicability of the results to the population of the United States, a sample strata, regions, and weights obtained by NHANES were utilized in conjunction with the complicated multistage clustering survey methodology\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe baseline data for continuous variables were reported as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while for categorical variables, they were reported as frequency (%). For AIP's effect on SUA, we evaluated β and its 95% confidence interval using weighted multivariate linear regression models. The AIP variable was examined in both continuous and categorical forms (quartiles). Baseline characteristics of the AIP quartiles were analyzed using weighted one-way ANOVA for continuous variables and the weighted chi-square test for categorical variables. No factors were updated in model 1. Model 2 considered age, gender, and race as factors. Model 3 included and adjusted for numerous variables, such as age, gender, race, BMI, education level, PIR, blood urea nitrogen, AST, ALT, creatinine, sedentary time, drinking and smoking behaviors, diabetes, hypertension, total cholesterol, and PFC.\u003c/p\u003e \u003cp\u003eSubgroup analysis also employed weighted multivariate logistic regression. If the P value for the interaction was not statistically significant, the results across strata were valid; alternatively, particular populations might exist. We utilized generalized additive models (GAM) to analyze the nonlinear association between AIP and SUA, employing smooth curve fitting. The critical turning points of the AIP-SUA connection were determined by a recursive method after the presence of non-linearity was detected. A threshold effect research was then carried out to compare the logistic regression model with the two-part logistic regression model.\u003c/p\u003e \u003cp\u003eThe statistical analyses were conducted using R (version 4.2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and EmpowerStats (version 4.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was determined using a bilateral P-value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics of Participants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the initial characteristics of each participant. The study had a total of 10,247 individuals, at a median age of 47.44 years. Of the participants, 49.76% were male and 50.24% were female. The participants were divided into Q1-Q4 groups based on their AIP quartiles. Significant differences were observed in age, gender, race, education level, PIR, marital status, BMI, drinking status, smoking status, hypertension, cholesterol prescription, diabetes, ALT, AST, SUA, creatinine, and total cholesterol among the highest quartile (Q4) of AIP compared to other subgroups (all P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between AIP and SUA: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of the multivariate regression analysis between AIP and SUA are displayed in Table 2. The study\u0026apos;s findings suggest that the chance of SUA occurring increases with plasma\u0026apos;s Atherogenic Index score. In the unadjusted model 1, a positive connection was found between AIP and SUA (\u0026beta; = 1.2995, 95% CI: 1.21 - 1.37, P \u0026lt; 0.001).\u0026nbsp;After adjusting for age, gender, and race, there remained a significant positive association in model 2 (\u0026beta; = 1.0495, 95% CI: 0.97 - 1.12, P \u0026lt; 0.001). The positive link persisted in the entirely adjusted model 3 (\u0026beta; = 0.6895, 95% CI: 0.60 - 0.76, P \u0026lt; 0.001).\u0026nbsp;The association between AIP and SUA remained statistically significant even when we categorized AIP into quartiles instead of treating it as a continuous variable. Individuals in the upper quartile of AIP exhibited a 0.61 \u0026mu;mol/L increase in SUA compared to those in the smallest quartile (model 3, \u0026beta; = 0.6195, 95% CI: 0.54 - 0.68) while accounting for confounding variables. Furthermore, the outcomes utilizing generalized additive models and smooth fit curves hint to a non-linear\u0026nbsp;correlation\u0026nbsp;between SUA risk and AIP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-Linear Relationship: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s findings, as shown in Figure 2, demonstrate a non-linear relationship\u0026nbsp;association\u0026nbsp;between AIP\u0026nbsp;with\u0026nbsp;SUA, as indicated by the GAM and smooth fitting of curves analysis. The log-likelihood ratio test revealed a P value of less than 0.001 when comparing the linear regression model and the two-piecewise linear regression model,\u0026nbsp;This suggests that the two-piecewise linear regression model provides a superior fit to the data.\u003c/p\u003e\n\u003cp\u003eThe results of the investigation,\u0026nbsp;employing\u0026nbsp;the recursive algorithm and two-piecewise linear regression model, are presented in Table 3. The inflection point of the inverted U-shaped correlation between AIP (atherogenic index of plasma) and SUA (serum uric acid) was determined to be 0.87. The effect size of 0.81 (95% CI: 0.72 - 0.89) and P value of less than 0.001 on the left side of the inflection point show a strong favorable connection. Nonetheless, there was a substantial negative correlation between AIP and SUA on the right side of the second inflection point. The study findings reveal that there was a positive association between AIP levels and SUA levels. The effect size was -0.55 (95% CI: -0.91 - 0.18) and the statistical significance was shown by a P value of 0.003.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to examine the link between AIP and SUA levels more thoroughly and provide data, a subgroup analysis was performed. So as to evaluate the effects of various variables, interaction tests were also performed. AIP and SUA consistently exhibited a positive correlation in the subgroup analysis results, demonstrating the stability of the link between the two variables. Interestingly, there was no significant interaction found for age, gender, BMI, diabetes, or hypertension, indicating that these variables are not necessary for this link to exist (interaction P \u0026gt; 0.05 for all).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis first population-based study looked into the relationship between AIP and the likelihood of high SUA levels. AIP was directly connected with serum uric acid levels when compared to conventional lipid markers. TG is often the principal objective for treating dyslipidemia\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Previous studies have also shown a distinct relationship between the risk of high uric acid levels and TG content\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Furthermore, a number of studies have discovered that lowered HDL-C levels are a significant risk factor for high uric acid\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. According to recent studies, lipid ratios may be important indicators of several diseases, including diabetes, metabolic syndrome, and cardiovascular ailments\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. AIP has been proposed as a potential biomarker for early CVD event detection in developing countries\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Studies show that AIP performs significantly better in assessing atherosclerosis than traditional lipid measures\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, studies on metabolic disorders have shown that AIP, as opposed to single lipid indicators like TG and HDL-C\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, is a stronger predictor of type 2 diabetes, hypertension, insulin resistance (IR), metabolic syndrome, and metabolic syndrome. However, whether AIP can be a useful marker for predicting uric acid levels is uncertain. Our study's RCS analysis shows a non-linear, inverted U-shaped connection between AIP and the chance of high SUA levels. This work fills a void in the literature by expanding the applicability of lipid ratios and raises the prospect that AIP could be a valuable marker for SUA level prediction.\u003c/p\u003e \u003cp\u003eFurthermore, cohort studies have substantiated a robust association between dyslipidemia and elevated levels of uric acid\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Therefore, from the perspective of lipid management, managing and preventing hyperuricemia offers substantial clinical benefits. The present study aims to demonstrate the critical role of non-HDL-C in lipid control\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. To find out if AIP is a novel target for lipid management, more research is required. In the end, the inability of subgroup analysis and interaction testing to identify specific groups indicates that this relationship is robust across a variety of categories. Further research is needed for other demographics, such as older Asians.\u003c/p\u003e \u003cp\u003eIR, oxidative stress, inflammation, lifestyle, and the use of lipid-lowering drugs are some of the conceivable factors that may demonstrate a relationship between the concentrations of dyslipidemia markers and the probability of elevated SUA levels. Insulin resistance (IR), which lowers the body's ability to use insulin as needed and causes issues with lipid metabolism and increased uric acid production, is linked to both illnesses\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Furthermore, oxidative stress\u0026mdash;which can result in uric acid accumulation and lipid peroxidation\u0026mdash;can be brought on by an imbalance between antioxidants and free radicals\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Chronic inflammation is common in dyslipidemia patients, and this can lead to an increase in uric acid generation and a decrease in its excretion\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.Additionally, researchers have found that individuals with hyperlipidemia and hyperuricemia share similar dietary patterns, such as consuming high fat and alcohol-containing foods in excess\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. It's interesting to note that using lipid-lowering drugs is associated with variations in SUA levels\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations of the Study\u003c/h2\u003e \u003cp\u003eThis study has a number of benefits. It is based on data from the large sample size, nationally representative NHANES database. The accuracy of the findings was enhanced by the study's effective management of any confounding variables. Furthermore, RCS analysis was used to look at non-linear correlations, and subgroup analysis was done to assess how consistently the results held true for different populations. There are, however, some issues with the current study. In order to determine the causative nature of these associations, prospective cohort studies and intervention trials are required as the cross-sectional design precludes inferring causation. Second, there are other potential confounding factors that this study did not consider but that could have skewed the results, such as the use of uric acid-lowering drugs or dietary patterns marked by heavy alcohol and high-fat food intake. Furthermore, additional study is required to see whether the results apply to other populations because the sample is derived from the US population.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the inverted U-shaped non-linear relationship between the two variables suggests that there may be a causal relationship between higher AIP and higher SUA levels in the adult US population. These findings underline the vital requirement of following and regulating patients' AIP levels to prevent the worsening of hyperuricemia. Additional study is essential to confirm our findings and investigate the possible mechanism behind this connection, and huge-scale, high-quality randomized studies are crucial to this goal.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eTe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eSupported by funding from the following: the National Key Research and Development Program(2020YFC2002902).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBCH participated in literature search , study design , data collection, data analysis , data interpretation , and wrote the manuscript . WQY, HMZ. Conceived of the study and participated in its design , coordination , data collection and analysis . P.L. participated in study design and provided the critical revision . All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author thanks the staff and the participants of the NHANES study for their valuable contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/ nhanes/.If anyone wants to get data from this study, please contact [email protected]\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIf anyone wants to get data from this study, please contact [email protected]\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePasalic, D., Marinkovic, N. \u0026amp; Feher-Turkovic, L. Uric acid as one of the important factors in multifactorial disorders\u0026ndash;facts and controversies. \u003cem\u003eBiochem. Med. (Zagreb)\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 63\u0026ndash;75 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGherghina, M. E. et al. Uric Acid and Oxidative Stress-Relationship with Cardiovascular, Metabolic, and Renal Impairment. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 3188 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalbeth, N. et al. Enhanced osteoclastogenesis in patients with tophaceous gout: urate crystals promote osteoclast development through interactions with stromal cells. \u003cem\u003eArthritis Rheum.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 1854\u0026ndash;1865 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, Y. et al. Serum Uric Acid Might Be Positively Associated With Hypertension in Chinese Adults: An Analysis of the China Health and Nutrition Survey. \u003cem\u003eFront. Med. (Lausanne)\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 755509 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBattelli, M. G., Bortolotti, M., Polito, L. \u0026amp; Bolognesi, A. The role of xanthine oxidoreductase and uric acid in metabolic syndrome. \u003cem\u003eBiochim. Biophys. Acta Mol. Basis Dis.\u003c/em\u003e \u003cb\u003e1864\u003c/b\u003e, 2557\u0026ndash;2565 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen-Xu, M., Yokose, C., Rai, S. K., Pillinger, M. H. \u0026amp; Choi, H. K. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007\u0026ndash;2016. \u003cem\u003eArthritis Rheumatol.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 991\u0026ndash;999 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehlin, M., Jacobsson, L. \u0026amp; Roddy, E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. \u003cem\u003eNat. Rev. Rheumatol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 380\u0026ndash;390 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanve, A., Sehra, S. T. \u0026amp; Neogi, T. Role of Diet in Hyperuricemia and Gout. \u003cem\u003eBest Pract. Res. Clin. Rheumatol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 101723 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobi\u0026aacute;sov\u0026aacute;, M. \u0026amp; Frohlich, J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). \u003cem\u003eClin. Biochem.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 583\u0026ndash;588 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBora, K., Pathak, M. S., Borah, P., Hussain, M. I. \u0026amp; Das, D. Association of the Apolipoprotein A-I Gene Polymorphisms with Cardiovascular Disease Risk Factors and Atherogenic Indices in Patients from Assam, Northeast India. \u003cem\u003eBalkan J. Med. Genet.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 59\u0026ndash;70 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWon, K. B. et al. Atherogenic index of plasma and the risk of rapid progression of coronary atherosclerosis beyond traditional risk factors. \u003cem\u003eAtherosclerosis\u003c/em\u003e. \u003cb\u003e324\u003c/b\u003e, 46\u0026ndash;51 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y. W., Kao, T. W., Chang, P. K., Chen, W. L. \u0026amp; Wu, L. W. Atherogenic index of plasma as predictors for metabolic syndrome, hypertension and diabetes mellitus in Taiwan citizens: a 9-year longitudinal study. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 9900 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, X. et al. Atherogenic index of plasma is a novel and better biomarker associated with obesity: a population-based cross-sectional study in China. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 37 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, Q. et al. The sensibility of the new blood lipid indicator\u0026ndash;atherogenic index of plasma (AIP) in menopausal women with coronary artery disease. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 27 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as, J. C., Ochoa-Mart\u0026iacute;nez, A. C. \u0026amp; Varela-Silva, J. A. P\u0026eacute;rez-Maldonado, I. N. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. \u003cem\u003eArch. Med. Res.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 285\u0026ndash;294 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, S. W. et al. Atherogenic index of plasma is an effective index for estimating abdominal obesity. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 11 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, X. W., Deng, F. Y. \u0026amp; Lei, S. F. Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. \u003cem\u003ePrim. Care Diabetes\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 60\u0026ndash;67 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, Y. \u0026amp; Wen, M. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011\u0026ndash;2018 population. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 19 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin, H. R., Song, S., Cho, J. A. \u0026amp; Ly, S. Y. Atherogenic Index of Plasma and Its Association with Risk Factors of Coronary Artery Disease and Nutrient Intake in Korean Adult Men: The 2013\u0026ndash;2014 KNHANES. \u003cem\u003eNutrients\u003c/em\u003e 14, 1071 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, P. et al. U-shaped association between dietary copper intake and new-onset hypertension. \u003cem\u003eClin. Nutr.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 536\u0026ndash;542 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasagrande, S. S., Lee, C., Stoeckel, L. E., Menke, A. \u0026amp; Cowie, C. C. Cognitive function among older adults with diabetes and prediabetes, NHANES 2011\u0026ndash;2014. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e178\u003c/b\u003e, 108939 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips, J. A. Dietary Guidelines for Americans, 2020\u0026ndash;2025. \u003cem\u003eWorkplace Health Saf.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 395 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, C. L. et al. National health and nutrition examination survey: analytic guidelines, 1999\u0026ndash;2010. \u003cem\u003eVital Health Stat.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 1\u0026ndash;24 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, A. Y., Lim, S., Jo, S. H., Han, S. H. \u0026amp; Koh, K. K. New Trends in Dyslipidemia Treatment. \u003cem\u003eCirc. J.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e, 759\u0026ndash;768 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, N. et al. Triglycerides and Total Cholesterol Concentrations in Association with Hyperuricemia in Chinese Adults in Qingdao, China. \u003cem\u003eRisk Manag Healthc. Policy\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 165\u0026ndash;173 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. et al. Higher triglyceride level predicts hyperuricemia: A prospective study of 6-year follow-up. \u003cem\u003eJ. Clin. Lipidol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 185\u0026ndash;192 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Z. et al. The relationship between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hyperuricaemia. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 187 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. et al. Triglyceride to high-density lipoprotein cholesterol ratio and cardiovascular events in the general population: A systematic review and meta-analysis of cohort studies. \u003cem\u003eNutr. Metab. Cardiovasc. Dis.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 318\u0026ndash;329 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrexel, H. et al. The LDL-C/ApoB ratio predicts major cardiovascular events in patients with established atherosclerotic cardiovascular disease. \u003cem\u003eAtherosclerosis\u003c/em\u003e. \u003cb\u003e329\u003c/b\u003e, 44\u0026ndash;49 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Zhang, X., Li, X., Feng, J. \u0026amp; Chen, X. Association of metabolic syndrome with atherogenic index of plasma in an urban Chinese population: A 15-year prospective study. \u003cem\u003eNutr. Metab. Cardiovasc. Dis.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 1214\u0026ndash;1219 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, G. et al. Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk. \u003cem\u003eDiabetes Metab. Syndr. Obes.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1677\u0026ndash;1686 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as, J. C., Ochoa-Mart\u0026iacute;nez, A. C. \u0026amp; Varela-Silva, J. A. P\u0026eacute;rez-Maldonado, I. N. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. \u003cem\u003eArch. Med. Res.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 285\u0026ndash;294 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, L. et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. \u003cem\u003eKardiol Pol.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e, 931\u0026ndash;938 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y. W., Kao, T. W., Chang, P. K., Chen, W. L. \u0026amp; Wu, L. W. Atherogenic index of plasma as predictors for metabolic syndrome, hypertension and diabetes mellitus in Taiwan citizens: a 9-year longitudinal study. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 9900 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, B. et al. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 157 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobi\u0026aacute;sov\u0026aacute;, M. AIP\u0026ndash;atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. \u003cem\u003eVnitr Lek\u003c/em\u003e. \u003cb\u003e52\u003c/b\u003e, 64\u0026ndash;71 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, H. et al. Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 38 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. et al. Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 409 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, Y. et al. Association between dyslipidaemia and the risk of hyperuricaemia: a six-year longitudinal cohort study of elderly individuals in China. \u003cem\u003eAnn. Med.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 2402\u0026ndash;2410 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaja, V. et al. Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook. \u003cem\u003eAtherosclerosis\u003c/em\u003e. \u003cb\u003e383\u003c/b\u003e, 117312 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuorinen-Markkola, H. \u0026amp; Yki-J\u0026auml;rvinen, H. Hyperuricemia and insulin resistance. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e, 25\u0026ndash;29 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, D. A. Treatment of the dyslipidemia of insulin resistance. \u003cem\u003eMed. Clin. North. Am.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 1185\u0026ndash;1210 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizzo, M. et al. Atherogenic dyslipidemia and oxidative stress: a new look. \u003cem\u003eTransl Res.\u003c/em\u003e \u003cb\u003e153\u003c/b\u003e, 217\u0026ndash;223 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Shanableh, Y. et al. Prevalence of asymptomatic hyperuricemia and its association with prediabetes, dyslipidemia and subclinical inflammation markers among young healthy adults in Qatar. \u003cem\u003eBMC Endocr. Disord\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 21 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConen, D. et al. Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing country. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 9 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeedwania, P. C., Stone, P. H., Fayyad, R. S., Laskey, R. E. \u0026amp; Wilson, D. J. Improvement in Renal Function and Reduction in Serum Uric Acid with Intensive Statin Therapy in Older Patients: A Post Hoc Analysis of the SAGE Trial. \u003cem\u003eDrugs Aging\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e, 1055\u0026ndash;1065 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e Weighted baseline characteristics of participants.\u003cstrong\u003eNote\u003c/strong\u003e: Values for categorical variables are given as weighted percentage (standard error); for continuous variables, as weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Weighted Student\u0026rsquo;s t-test and chi-squared test were used. Abbreviations: BMI, body mass index; ALT, alanine transaminase; AST, aspartate transaminase; TC, total cholesterol; SUA, serum uric acid; AIP, atherogenic index of plasma\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eVariables#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.03676470588235%\" colspan=\"4\"\u003e\n \u003cp\u003eQuintile categories of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.952095808383234%\"\u003e\n \u003cp\u003eAIP quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.560878243512974%\"\u003e\n \u003cp\u003eQ1(-0.79-0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.36327345309381%\"\u003e\n \u003cp\u003eQ2(0.08-0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.161676646706585%\"\u003e\n \u003cp\u003eQ3(0.30-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.962075848303392%\"\u003e\n \u003cp\u003eQ4(0.52-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e2558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e2565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eAGE(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e45.81 (44.78 ,46.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e46.81 (45.88 ,47.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e48.38 (47.57 ,49.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e48.74 (47.96 ,49.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSedentary time (minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e376.16 (360.67 ,391.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e400.60 (371.82 ,429.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e399.43 (380.05 ,418.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e391.82 (379.71 ,403.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eserum creatinine(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e0.84 (0.83 ,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e0.87 (0.86 ,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.90 (0.88 ,0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e0.92 (0.90 ,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eBlood urea nitrogen (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e13.17 (12.86 ,13.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e13.37 (13.11 ,13.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e13.49 (13.23 ,13.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e13.73 (13.48 ,13.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eTC(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e184.61 (182.75 ,186.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e188.87 (187.09 ,190.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e192.32 (190.14 ,194.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e206.69 (204.50 ,208.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSUA(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e4.91 (4.84 ,4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e5.33 (5.27 ,5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e5.71 (5.63 ,5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e6.15 (6.07 ,6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSEX(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e34.88 (32.64 ,37.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e46.05 (43.86 ,48.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e53.97 (51.27 ,56.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e64.15 (61.78 ,66.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e65.12 (62.81 ,67.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e53.95 (51.75 ,56.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e46.03 (43.36 ,48.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e35.85 (33.54 ,38.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e65.63 (61.71 ,69.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e70.92 (67.15 ,74.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e69.56 (66.07 ,72.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e72.08 (68.70 ,75.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e16.69 (13.95 ,19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e11.17 (9.45 ,13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e8.67 (7.30 ,10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e5.43 (4.39 ,6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e5.47 (4.30 ,6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e7.42 (5.91 ,9.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e8.94 (7.22 ,11.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e9.80 (7.96 ,12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e4.48 (3.34 ,5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e4.83 (3.71 ,6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e5.57 (4.52 ,6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e6.02 (4.70 ,7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eOther races\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e7.73 (6.42 ,9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e5.66 (4.58 ,6.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e7.26 (6.16 ,8.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e6.67 (5.49 ,8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eEducation Level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e3.29 (2.51 ,4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e5.06 (4.11 ,6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e4.88 (4.05 ,5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e7.02 (5.88 ,8.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e8.24 (6.78 ,9.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e10.15 (8.70 ,11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e12.48 (10.30 ,15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e13.59 (12.18 ,15.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e88.47 (86.37 ,90.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e84.80 (82.90 ,86.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e82.65 (79.83 ,85.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e79.40 (77.19 ,81.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003epoverty ratio (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e\u0026lt;=1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e19.94 (17.63 ,22.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e21.28 (19.00 ,23.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e22.54 (20.00 ,25.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e24.42 (21.94 ,27.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e34.24 (31.72 ,36.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e35.23 (32.35 ,38.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e38.74 (35.97 ,41.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e36.29 (33.65 ,39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e\u0026gt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e45.81 (42.55 ,49.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e43.50 (39.96 ,47.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e38.72 (35.53 ,42.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e39.29 (35.74 ,42.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eMarried/Living\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ewith partner\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e62.15 (59.18 ,65.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e62.46 (59.44 ,65.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e64.73 (62.08 ,67.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e67.24 (64.04 ,70.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eWidowed/Divorced/Separated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e15.83 (13.91 ,17.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e18.62 (16.74 ,20.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e17.82 (16.22 ,19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e18.14 (15.98 ,20.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e22.02 (19.80 ,24.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e18.92 (16.42 ,21.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e17.45 (15.18 ,19.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e14.62 (12.43 ,17.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e ) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e51.33 (48.35 ,54.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e33.81 (30.65 ,37.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e21.79 (19.29 ,24.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e11.70 (10.34 ,13.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e25-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e30.33 (28.20 ,32.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e33.99 (31.85 ,36.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e35.16 (32.93 ,37.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e34.46 (32.22 ,36.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003e\u0026gt;=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e18.34 (16.51 ,20.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e32.20 (29.62 ,34.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e43.05 (40.47 ,45.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e53.83 (51.19 ,56.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSmoking status(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e62.44 (59.32 ,65.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e57.77 (55.21 ,60.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e51.96 (49.22 ,54.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e47.09 (44.76 ,49.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSmoking former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e22.40 (20.11 ,24.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e24.14 (21.86 ,26.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e26.98 (24.46 ,29.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e28.63 (26.26 ,31.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eSmoking now\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e15.16 (13.20 ,17.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e18.09 (16.03 ,20.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e21.06 (18.60 ,23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e24.29 (22.35 ,26.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eDrinking status (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e11.65 (10.16 ,13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e12.01 (10.37 ,13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e10.86 (9.36 ,12.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e9.21 (7.79 ,10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eDrinking former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e77.66 (75.13 ,80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e74.46 (72.07 ,76.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e73.62 (71.26 ,75.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e71.63 (68.85 ,74.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eDrinking now\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e10.69 (9.16 ,12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e13.53 (12.14 ,15.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e15.52 (13.83 ,17.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e19.16 (17.00 ,21.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eHYPERTENSION(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e69.88 (67.16 ,72.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e61.15 (58.31 ,63.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e55.86 (53.27 ,58.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e47.84 (44.90 ,50.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e30.12 (27.53 ,32.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e38.85 (36.07 ,41.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e44.14 (41.59 ,46.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e52.16 (49.21 ,55.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eDiabetes(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e93.23 (91.98 ,94.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e90.27 (88.87 ,91.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e85.14 (83.05 ,87.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e77.67 (74.99 ,80.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.014705882352942%\"\u003e\n \u003cp\u003e6.77 (5.70 ,8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.91176470588235%\"\u003e\n \u003cp\u003e9.73 (8.48 ,11.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.647058823529413%\"\u003e\n \u003cp\u003e14.86 (12.99 ,16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46323529411765%\"\u003e\n \u003cp\u003e22.33 (19.86 ,25.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.904411764705882%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Correlation between AIP and SUA (mg/dl).SUA, serum uric acid; 95% CI, 95% confidence interval;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1 , no covariates were adjusted; Model 2 , adjusted for gender, age, race; Model 3 , adjusted for gender, age, race,sedentary time, ALT, AST, creatinine, blood urea nitrogen, education level, the ratio of family income to poverty, marital status, body mass index, alcohol status, smoking status, hypertension, total cholesterol，cholesterol prescription, total cholesterol, and diabetes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eSUA, mg/dL, \u0026beta; (95%CI), P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29 (1.21, 1.37) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.97, 1.12) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68 (0.60, 0.76) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q1(-0.79-0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q2(0.08-0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41 (0.34, 0.49) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33 (0.26, 0.40) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.20 (0.13, 0.26) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q3(0.30-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77 (0.70, 0.85) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63 (0.56, 0.70) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42 (0.35, 0.48) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q4(0.52-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (1.07, 1.22) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.86, 1.00) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61 (0.54, 0.68) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"AIP1, SUA2, NHANES3, Cross-sectional study4, Relationship5","lastPublishedDoi":"10.21203/rs.3.rs-4945254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic diseases are significantly correlated with the Atherogenic Index of Plasma (AIP). However, there is currently no conclusive data establishing a direct connection between AIP and serum uric acid (SUA) levels. Data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2007 to 2016 were used in this cross-sectional investigation. 10,247 people in all participated in the study. By using the logarithm (base 10) of the ratio of triglycerides to high-density lipoprotein cholesterol, AIP was calculated. The concentration of SUA was the dependent variable. The connection between AIP and SUA levels was tested using a multi-factor logistic regression model and a limited three-sample technique. Sub-group analysis and interaction testing were also carried out. In the completely adjusted model, the study found a curvilinear relationship between AIP and the chance of higher SUA levels. Serum uric acid (SUA) levels were directly correlated with an increase in AIP values when the atherogenic index of plasma (AIP) was less than 0.81. Nevertheless, serum uric acid (SUA) levels consistently decreased with increasing AIP values when the atherogenic index of plasma (AIP) exceeded 0.81. Moreover, the probability of having high SUA levels was significantly higher in those in the top 25% of AIP than in those in the lowest 25% of AIP (β\u0026thinsp;=\u0026thinsp;0.6195, 95% CI: 0.54\u0026ndash;0.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association was consistent for every category.there is an inverted U-shaped nonlinear relationship between SUA levels and AIP among adult US population. This suggests that higher AIP levels could lead to higher SUA levels.\u003c/p\u003e","manuscriptTitle":"The Association Between Atherogenic Index of plasma and Serum Uric Acid: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 09:09:31","doi":"10.21203/rs.3.rs-4945254/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":"60939984-0eba-4e9d-ae27-e33deff0169d","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37786707,"name":"Health sciences/Medical research/Epidemiology"},{"id":37786708,"name":"Health sciences/Diseases"},{"id":37786709,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-12-23T14:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 09:09:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4945254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945254","identity":"rs-4945254","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}