Association of Atherogenic Index of plasma with Serum Uric Acid in US adults: A cross-sectional study from NHANES 2007-20216

Association of Atherogenic Index of plasma with Serum Uric Acid in US adults: A cross-sectional study from NHANES 2007-20216 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Atherogenic Index of plasma with Serum Uric Acid in US adults: A cross-sectional study from NHANES 2007-20216 Bingchao Hu, Wanqian Yu, Huiming Zou, Ping Li This is a preprint; it has not been peer reviewed by a journal.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-4995196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: 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. Methods: 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. Results: 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 in1 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. Conclusion: Theis 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. Atherogenic Index of Plasma serum uric acid NHANES Cross-sectional study Relationship 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[2–5]. 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[10–14].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[16–18]. 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 Figure 1. Figure 1. Flowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; AIP, Atherogenic Index of plasma; SUA, serum uric acid. 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]. 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). TABLE 1 Weighted baseline characteristics of participants. 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) 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. 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. Table 2: Correlation between AIP and SUA (mg/dl). 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 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. 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. Figure 2. Curve fitting analysis of AIPand SUA. 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. TABLE 3 Threshold effect analysis of AIP on SUA using the two-piecewise linear regression model. AIP Adjusted β (95% CI) P values fitting model by standard linear regression 0.68 (0.60, 0.76) <0.0001 fitting model by two-piecewise linear regression Inflection point 0.87 ≤ 0.87 0.81 (0.72, 0.89) 0.87 -0.55 (-0.91, -0.18) 0.0031 Log likelihood ratio test <0.001 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, cholesterol prescription, total cholesterol, hypertension, and diabetes. 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[28–31]. 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[34–36], 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[37–39]. 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]. 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. 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. Declarations Acknowledgements The author thanks the staff and the participants of the NHANES study for their valuable contributions. Author contributions 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. Funding Supported by funding from the following: the projects of the Natural Science Foundation of Jiangxi Province (20202ACBL206004); and the projects of the National Natural Science Foundation of China (82460079) Clinical trial number: not applicable Data availability Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/ nhanes/. Ethics approval and consent to participate The National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES program and released its documents for public use. 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Lipids Health Dis 17:11 Zhu X-W, Deng F-Y, Lei S-F (2015) 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 Shi Y, Wen M (2023) 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 Shin HR, Song S, Cho JA, Ly SY (2022) 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 He P, Li H, Liu C, et al (2022) U-shaped association between dietary copper intake and new-onset hypertension. Clin Nutr 41:536–542 Casagrande SS, Lee C, Stoeckel LE, Menke A, Cowie CC (2021) Cognitive function among older adults with diabetes and prediabetes, NHANES 2011-2014. Diabetes Res Clin Pract 178:108939 Phillips JA (2021) Dietary Guidelines for Americans, 2020-2025. Workplace Health Saf 69:395 Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, Curtin LR (2013) National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat 2 1–24 Jang AY, Lim S, Jo S-H, Han SH, Koh KK (2021) New Trends in Dyslipidemia Treatment. Circ J 85:759–768 Cui N, Cui J, Sun J, et al (2020) Triglycerides and Total Cholesterol Concentrations in Association with Hyperuricemia in Chinese Adults in Qingdao, China. Risk Manag Healthc Policy 13:165–173 Zhang Y, Wei F, Chen C, et al (2018) Higher triglyceride level predicts hyperuricemia: A prospective study of 6-year follow-up. J Clin Lipidol 12:185–192 Wang Z, Wu M, Du R, Tang F, Xu M, Gu T, Yang Q (2024) The relationship between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hyperuricaemia. Lipids Health Dis 23:187 Chen Y, Chang Z, Liu Y, Zhao Y, Fu J, Zhang Y, Liu Y, Fan Z (2022) 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 Drexel H, Larcher B, Mader A, Vonbank A, Heinzle CF, Moser B, Zanolin-Purin D, Saely CH (2021) The LDL-C/ApoB ratio predicts major cardiovascular events in patients with established atherosclerotic cardiovascular disease. Atherosclerosis 329:44–49 Zhang X, Zhang X, Li X, Feng J, Chen X (2019) 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 Sheng G, Liu D, Kuang M, Zhong Y, Zhang S, Zou Y (2022) 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 Fernández-Macías JC, Ochoa-Martínez AC, Varela-Silva JA, Pérez-Maldonado IN (2019) Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch Med Res 50:285–294 Zhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W, Feng Y, Yi F, Song Y (2015) Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol 73:931–938 Li Y-W, Kao T-W, Chang P-K, Chen W-L, Wu L-W (2021) 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 Yin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y (2023) Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol 22:157 Dobiásová M (2006) [AIP--atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. Vnitr Lek 52:64–71 He H, Wang S, Xu T, Liu W, Li Y, Lu G, Tu R (2023) Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study. Lipids Health Dis 22:38 Zhang Y, Zhang M, Yu X, Wei F, Chen C, Zhang K, Feng S, Wang Y, Li W-D (2020) Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study. J Transl Med 18:409 Xu Y, Dong H, Zhang B, Zhang J, Ma Q, Sun H (2022) Association between dyslipidaemia and the risk of hyperuricaemia: a six-year longitudinal cohort study of elderly individuals in China. Ann Med 54:2402–2410 Raja V, Aguiar C, Alsayed N, et al (2023) Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook. Atherosclerosis 383:117312 Vuorinen-Markkola H, Yki-Järvinen H (1994) Hyperuricemia and insulin resistance. J Clin Endocrinol Metab 78:25–29 Smith DA (2007) Treatment of the dyslipidemia of insulin resistance. Med Clin North Am 91:1185–1210, x Rizzo M, Kotur-Stevuljevic J, Berneis K, Spinas G, Rini GB, Jelic-Ivanovic Z, Spasojevic-Kalimanovska V, Vekic J (2009) Atherogenic dyslipidemia and oxidative stress: a new look. Transl Res 153:217–223 Al Shanableh Y, Hussein YY, Saidwali AH, Al-Mohannadi M, Aljalham B, Nurulhoque H, Robelah F, Al-Mansoori A, Zughaier SM (2022) 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 Conen D, Wietlisbach V, Bovet P, Shamlaye C, Riesen W, Paccaud F, Burnier M (2004) Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing country. BMC Public Health 4:9 Deedwania PC, Stone PH, Fayyad RS, Laskey RE, Wilson DJ (2015) 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 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4995196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":390804729,"identity":"4dd8a100-2216-402f-85f4-77ff53bdfafc","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":390804730,"identity":"2d89ba3c-0968-4a02-87c8-6e031b9387a7","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":390804731,"identity":"fcd0f516-bf7c-49d0-86bf-6ad073cd1528","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":390804732,"identity":"8f1858d7-0977-4816-b6ca-dea5038a0e93","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":"2024-08-29 06:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4995196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4995196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72751355,"identity":"45b935dc-6af1-41ef-ba7d-2f6f7b5e4223","added_by":"auto","created_at":"2025-01-01 15:04:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44277,"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":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4995196/v1/acef809d4cb30f2180dc1a2c.jpg"},{"id":72751354,"identity":"8a3dda72-6b8e-4de1-8739-16bf28d7fbbc","added_by":"auto","created_at":"2025-01-01 15:04:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCurve fitting analysis of AIP and SUA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4995196/v1/df7f17b7e580aa63f9f14f1d.jpg"},{"id":72751353,"identity":"cb1c0980-76bf-4190-ba26-b94c9fdfbdd6","added_by":"auto","created_at":"2025-01-01 15:04:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis for the association between AIP and SUA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4995196/v1/c5332d554dc74674d8d195b0.jpg"},{"id":91615569,"identity":"2bf00ab2-f000-4083-9221-ffea6c4b5195","added_by":"auto","created_at":"2025-09-18 10:32:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":752859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4995196/v1/71328f54-e21c-4362-8e5d-998cfa3674f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Atherogenic Index of plasma with Serum Uric Acid in US adults: A cross-sectional study from NHANES 2007-20216","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[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\u0026ndash;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.\u003c/p\u003e\n\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[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\u0026ndash;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\u0026ndash;18].\u003c/p\u003e\n\u003cp\u003eGiven 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.\u003c/p\u003e\n\u003cp\u003eThis 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.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData Source:\u0026nbsp;\u003c/p\u003e\n\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\u0026nbsp;questionnaires\u0026nbsp;to collect data. The study design utilizes a\u0026nbsp;stratified multiple phases probability sampling method\u0026nbsp;to guarantee the selection of samples that are highly representative.\u0026nbsp;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\u0026nbsp;research\u0026nbsp;can be\u0026nbsp;accessed\u0026nbsp;from the\u0026nbsp;public\u0026nbsp;NHANES website (https://www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003eStudy Population:\u003c/p\u003e\n\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 = 10,247) with complete AIP, SUA, and other covariate data.\u003c/p\u003e\n\u003cp\u003eThe flowchart of the investigation is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003eFigure 1. Flowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; AIP, Atherogenic Index of plasma; SUA, serum uric acid.\u003c/p\u003e\n\u003cp\u003eEvaluation of the Atherogenic Index of Plasma (Exposure Variable):\u003c/p\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e\n\u003cp\u003eEvaluation of Serum Uric Acid (Outcome Variable):\u003c/p\u003e\n\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\n\u003cp\u003eEvaluation of Other Covariates:\u003c/p\u003e\n\u003cp\u003eIn accordance with previous research\u0026nbsp;as well as\u0026nbsp;clinical expertise,\u0026nbsp;we have included the following factors that may affect the relationship between AIP and SUA. The\u0026nbsp;investigation\u0026nbsp;took into account\u0026nbsp;what follows\u003c/p\u003e\n\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\n\u003cp\u003e\u0026nbsp;(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)\u0026nbsp;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\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[22].\u003c/p\u003e\n\u003cp\u003eStatistical Analysis:\u003c/p\u003e\n\u003cp\u003eThe CDC\u0026apos;s recommendations were followed in the performance of all statistical analyses.\u0026nbsp;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].\u003c/p\u003e\n\u003cp\u003eThe baseline data for continuous variables were reported as the mean \u0026plusmn; standard deviation (SD), while for categorical variables, they were reported as frequency (%). For AIP\u0026apos;s effect on SUA, we evaluated \u0026beta; 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.\u0026nbsp;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\n\u003cp\u003eSubgroup analysis also employed weighted multivariate logistic regression.\u0026nbsp;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.\u0026nbsp;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\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics of Participants:\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\u003eTABLE 1 Weighted baseline characteristics of participants.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eVariables#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 381px;\"\u003e\n \u003cp\u003eQuintile categories of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 43px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eAIP quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eQ1(-0.79-0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eQ2(0.08-0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eQ3(0.30-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eQ4(0.52-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e45.81 (44.78 ,46.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e46.81 (45.88 ,47.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e48.38 (47.57 ,49.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e48.74 (47.96 ,49.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSedentary time (minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e376.16 (360.67 ,391.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e400.60 (371.82 ,429.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e399.43 (380.05 ,418.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e391.82 (379.71 ,403.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eserum creatinine(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.84 (0.83 ,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.87 (0.86 ,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.90 (0.88 ,0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.92 (0.90 ,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eBlood urea nitrogen (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e13.17 (12.86 ,13.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e13.37 (13.11 ,13.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e13.49 (13.23 ,13.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.73 (13.48 ,13.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eTC(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e184.61 (182.75 ,186.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e188.87 (187.09 ,190.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e192.32 (190.14 ,194.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e206.69 (204.50 ,208.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSUA(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4.91 (4.84 ,4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.33 (5.27 ,5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e5.71 (5.63 ,5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.15 (6.07 ,6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSex(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e34.88 (32.64 ,37.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e46.05 (43.86 ,48.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e53.97 (51.27 ,56.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e64.15 (61.78 ,66.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e65.12 (62.81 ,67.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e53.95 (51.75 ,56.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e46.03 (43.36 ,48.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e35.85 (33.54 ,38.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e65.63 (61.71 ,69.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e70.92 (67.15 ,74.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e69.56 (66.07 ,72.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e72.08 (68.70 ,75.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e16.69 (13.95 ,19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e11.17 (9.45 ,13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e8.67 (7.30 ,10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.43 (4.39 ,6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5.47 (4.30 ,6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.42 (5.91 ,9.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e8.94 (7.22 ,11.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.80 (7.96 ,12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4.48 (3.34 ,5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.83 (3.71 ,6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e5.57 (4.52 ,6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.02 (4.70 ,7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eOther races\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e7.73 (6.42 ,9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.66 (4.58 ,6.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e7.26 (6.16 ,8.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.67 (5.49 ,8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eEducation Level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3.29 (2.51 ,4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.06 (4.11 ,6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4.88 (4.05 ,5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.02 (5.88 ,8.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8.24 (6.78 ,9.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e10.15 (8.70 ,11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12.48 (10.30 ,15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.59 (12.18 ,15.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e88.47 (86.37 ,90.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e84.80 (82.90 ,86.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e82.65 (79.83 ,85.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.40 (77.19 ,81.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003epoverty ratio (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;=1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e19.94 (17.63 ,22.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e21.28 (19.00 ,23.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e22.54 (20.00 ,25.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24.42 (21.94 ,27.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e34.24 (31.72 ,36.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e35.23 (32.35 ,38.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e38.74 (35.97 ,41.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e36.29 (33.65 ,39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026gt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e45.81 (42.55 ,49.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e43.50 (39.96 ,47.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e38.72 (35.53 ,42.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e39.29 (35.74 ,42.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMarried/Living\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ewith partner\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e62.15 (59.18 ,65.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e62.46 (59.44 ,65.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e64.73 (62.08 ,67.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e67.24 (64.04 ,70.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eWidowed/Divorced/Separated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e15.83 (13.91 ,17.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18.62 (16.74 ,20.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17.82 (16.22 ,19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e18.14 (15.98 ,20.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e22.02 (19.80 ,24.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18.92 (16.42 ,21.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17.45 (15.18 ,19.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.62 (12.43 ,17.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e51.33 (48.35 ,54.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e33.81 (30.65 ,37.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e21.79 (19.29 ,24.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11.70 (10.34 ,13.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e25-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e30.33 (28.20 ,32.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e33.99 (31.85 ,36.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e35.16 (32.93 ,37.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34.46 (32.22 ,36.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026gt;=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e18.34 (16.51 ,20.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e32.20 (29.62 ,34.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e43.05 (40.47 ,45.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e53.83 (51.19 ,56.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSmoking status(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e62.44 (59.32 ,65.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e57.77 (55.21 ,60.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e51.96 (49.22 ,54.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e47.09 (44.76 ,49.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSmoking former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e22.40 (20.11 ,24.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e24.14 (21.86 ,26.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e26.98 (24.46 ,29.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e28.63 (26.26 ,31.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSmoking now\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e15.16 (13.20 ,17.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18.09 (16.03 ,20.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e21.06 (18.60 ,23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24.29 (22.35 ,26.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eDrinking status (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e11.65 (10.16 ,13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e12.01 (10.37 ,13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e10.86 (9.36 ,12.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.21 (7.79 ,10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eDrinking former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e77.66 (75.13 ,80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e74.46 (72.07 ,76.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e73.62 (71.26 ,75.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e71.63 (68.85 ,74.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eDrinking now\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10.69 (9.16 ,12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e13.53 (12.14 ,15.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15.52 (13.83 ,17.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e19.16 (17.00 ,21.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eHypertension(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e69.88 (67.16 ,72.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e61.15 (58.31 ,63.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e55.86 (53.27 ,58.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e47.84 (44.90 ,50.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e30.12 (27.53 ,32.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e38.85 (36.07 ,41.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44.14 (41.59 ,46.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e52.16 (49.21 ,55.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eDiabetes(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e93.23 (91.98 ,94.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e90.27 (88.87 ,91.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e85.14 (83.05 ,87.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e77.67 (74.99 ,80.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6.77 (5.70 ,8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.73 (8.48 ,11.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14.86 (12.99 ,16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e22.33 (19.86 ,25.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\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\u003c/div\u003e\n\u003cp\u003e\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.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelationship Between AIP and SUA:\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\u003eTable 2: Correlation between AIP and SUA (mg/dl).\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\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\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\n\u003cp\u003eSUA, 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.\u003c/p\u003e\n\u003cp\u003eNon-Linear Relationship:\u0026nbsp;\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\u003eFigure 2. Curve fitting analysis of\u0026nbsp;AIPand\u0026nbsp;SUA.\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\u003eTABLE 3 Threshold effect analysis of AIP on SUA using the two-piecewise linear regression model.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted\u0026nbsp;\u0026beta;\u0026nbsp;(95% CI) P\u0026nbsp;values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efitting model by standard linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68 (0.60, 0.76) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efitting model by two-piecewise linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026le; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.72, 0.89) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.55 (-0.91, -0.18) 0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eadjusted 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, cholesterol prescription, total cholesterol, hypertension, and diabetes.\u003c/p\u003e\n\u003cp\u003eSubgroup Analysis:\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[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\u0026ndash;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].\u003c/p\u003e\n\u003cp\u003eSimilarly, studies on metabolic disorders have shown that AIP, as opposed to single lipid indicators like TG and HDL-C[34\u0026ndash;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\u0026apos;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\n\u003cp\u003eFurthermore, cohort studies have substantiated a robust association between dyslipidemia and elevated levels of uric acid[37\u0026ndash;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.\u003c/p\u003e\n\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\u0026apos;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\u0026mdash;which can result in uric acid accumulation and lipid peroxidation\u0026mdash;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\u0026apos;s interesting to note that using lipid-lowering drugs is associated with variations in SUA levels[46].\u003c/p\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\u0026apos; 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\n\u003cp\u003eStrengths and Limitations of the Study\u003c/p\u003e\n\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\u0026apos;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"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the staff\u0026nbsp;and the participants of the NHANES study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by funding from the following: the projects of the Natural Science Foundation of Jiangxi Province (20202ACBL206004); and\u0026nbsp;the projects of the National Natural Science Foundation of China (82460079)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/ nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES program and released its documents for public use. Written informed consent was obtained from each participant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePasalic D, Marinkovic N, Feher-Turkovic L (2012) Uric acid as one of the important factors in multifactorial disorders--facts and controversies. 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Vital Health Stat 2 1\u0026ndash;24\u003c/li\u003e\n\u003cli\u003eJang AY, Lim S, Jo S-H, Han SH, Koh KK (2021) New Trends in Dyslipidemia Treatment. Circ J 85:759\u0026ndash;768\u003c/li\u003e\n\u003cli\u003eCui N, Cui J, Sun J, et al (2020) Triglycerides and Total Cholesterol Concentrations in Association with Hyperuricemia in Chinese Adults in Qingdao, China. Risk Manag Healthc Policy 13:165\u0026ndash;173\u003c/li\u003e\n\u003cli\u003eZhang Y, Wei F, Chen C, et al (2018) Higher triglyceride level predicts hyperuricemia: A prospective study of 6-year follow-up. J Clin Lipidol 12:185\u0026ndash;192\u003c/li\u003e\n\u003cli\u003eWang Z, Wu M, Du R, Tang F, Xu M, Gu T, Yang Q (2024) The relationship between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hyperuricaemia. 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Nutr Metab Cardiovasc Dis 29:1214\u0026ndash;1219\u003c/li\u003e\n\u003cli\u003eSheng G, Liu D, Kuang M, Zhong Y, Zhang S, Zou Y (2022) Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk. Diabetes Metab Syndr Obes 15:1677\u0026ndash;1686\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as JC, Ochoa-Mart\u0026iacute;nez AC, Varela-Silva JA, P\u0026eacute;rez-Maldonado IN (2019) Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Arch Med Res 50:285\u0026ndash;294\u003c/li\u003e\n\u003cli\u003eZhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W, Feng Y, Yi F, Song Y (2015) Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol 73:931\u0026ndash;938\u003c/li\u003e\n\u003cli\u003eLi Y-W, Kao T-W, Chang P-K, Chen W-L, Wu L-W (2021) 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\u003c/li\u003e\n\u003cli\u003eYin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y (2023) Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol 22:157\u003c/li\u003e\n\u003cli\u003eDobi\u0026aacute;sov\u0026aacute; M (2006) [AIP--atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. Vnitr Lek 52:64\u0026ndash;71\u003c/li\u003e\n\u003cli\u003eHe H, Wang S, Xu T, Liu W, Li Y, Lu G, Tu R (2023) Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study. Lipids Health Dis 22:38\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang M, Yu X, Wei F, Chen C, Zhang K, Feng S, Wang Y, Li W-D (2020) Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study. J Transl Med 18:409\u003c/li\u003e\n\u003cli\u003eXu Y, Dong H, Zhang B, Zhang J, Ma Q, Sun H (2022) Association between dyslipidaemia and the risk of hyperuricaemia: a six-year longitudinal cohort study of elderly individuals in China. Ann Med 54:2402\u0026ndash;2410\u003c/li\u003e\n\u003cli\u003eRaja V, Aguiar C, Alsayed N, et al (2023) Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook. Atherosclerosis 383:117312\u003c/li\u003e\n\u003cli\u003eVuorinen-Markkola H, Yki-J\u0026auml;rvinen H (1994) Hyperuricemia and insulin resistance. J Clin Endocrinol Metab 78:25\u0026ndash;29\u003c/li\u003e\n\u003cli\u003eSmith DA (2007) Treatment of the dyslipidemia of insulin resistance. Med Clin North Am 91:1185\u0026ndash;1210, x\u003c/li\u003e\n\u003cli\u003eRizzo M, Kotur-Stevuljevic J, Berneis K, Spinas G, Rini GB, Jelic-Ivanovic Z, Spasojevic-Kalimanovska V, Vekic J (2009) Atherogenic dyslipidemia and oxidative stress: a new look. Transl Res 153:217\u0026ndash;223\u003c/li\u003e\n\u003cli\u003eAl Shanableh Y, Hussein YY, Saidwali AH, Al-Mohannadi M, Aljalham B, Nurulhoque H, Robelah F, Al-Mansoori A, Zughaier SM (2022) 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\u003c/li\u003e\n\u003cli\u003eConen D, Wietlisbach V, Bovet P, Shamlaye C, Riesen W, Paccaud F, Burnier M (2004) Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing country. BMC Public Health 4:9\u003c/li\u003e\n\u003cli\u003eDeedwania PC, Stone PH, Fayyad RS, Laskey RE, Wilson DJ (2015) 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\u0026ndash;1065\u003c/li\u003e\n\u003c/ol\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":"Atherogenic Index of Plasma, serum uric acid, NHANES, Cross-sectional study, Relationship","lastPublishedDoi":"10.21203/rs.3.rs-4995196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4995196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: 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.\u003c/p\u003e\n\u003cp\u003eMethods: 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.\u003c/p\u003e\n\u003cp\u003eResults: 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 in1 the top 25% of AIP than in those in the lowest 25% of AIP (β = 0.6195, 95% CI: 0.54-0.68, P \u0026lt; 0.001). This association was consistent for every category.\u003c/p\u003e\n\u003cp\u003eConclusion: Theis 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":"Association of Atherogenic Index of plasma with Serum Uric Acid in US adults: A cross-sectional study from NHANES 2007-20216","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 15:04:12","doi":"10.21203/rs.3.rs-4995196/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":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 15:04:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4995196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4995196","identity":"rs-4995196","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}