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

doi:10.21203/rs.3.rs-4945254/v1

Metabolic diseases are significantly correlated with the Atherogenic Index of Plasma (AIP). However, there is currently no conclusive data establishing a direct connection between AIP and serum uric acid (SUA) levels. Data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2007 to 2016 were used in this cross-sectional investigation. 10,247 people in all participated in the study. By using the logarithm (base 10) of the ratio of triglycerides to high-density lipoprotein cholesterol, AIP was calculated. The concentration of SUA was the dependent variable. The connection between AIP and SUA levels was tested using a multi-factor logistic regression model and a limited three-sample technique. Sub-group analysis and interaction testing were also carried out. In the completely adjusted model, the study found a curvilinear relationship between AIP and the chance of higher SUA levels. Serum uric acid (SUA) levels were directly correlated with an increase in AIP values when the atherogenic index of plasma (AIP) was less than 0.81. Nevertheless, serum uric acid (SUA) levels consistently decreased with increasing AIP values when the atherogenic index of plasma (AIP) exceeded 0.81. Moreover, the probability of having high SUA levels was significantly higher in those in the top 25% of AIP than in those in the lowest 25% of AIP (β = 0.6195, 95% CI: 0.54–0.68, P < 0.001). This association was consistent for every category.there is an inverted U-shaped nonlinear relationship between SUA levels and AIP among adult US population. This suggests that higher AIP levels could lead to higher SUA levels.

Health sciences/Medical research/Epidemiology

Health sciences/Diseases

Health sciences/Medical research

AIP1

SUA2

NHANES3

Cross-sectional study4

Relationship5

As a consequence of purine metabolism, serum uric acid (SUA) plays a vital role in the body as an antioxidant, effectively removing harmful free radicals¹. However, gout and associated comorbidities like metabolic syndrome, hypertension, and diabetes can arise from abnormally high serum uric acid (SUA) levels, posing serious health risks^2,3,4,5. Serum uric acid (SUA) levels has risen recently, increasing the significant public health concern of hyperuricemia. Based on past research, it is estimated that 20% of Americans suffer from hyperuricemia⁶. 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 cholesterol⁹. AIP serves as a reliable diagnostic for small dense low-density lipoprotein cholesterol (sd-LDL-C) since it is negatively connected with lipoprotein particle size⁹. According to recent studies, AIP is more accurate in predicting the risk of cardiovascular disease than traditional lipid indicators^{10,11,12,13,14}.It is interesting to note that AIP is not only highly correlated with atherosclerosis but also functions as a marker of the degree of insulin resistance (IR) in individuals¹⁵. Previous research indicates a strong correlation between AIP and metabolic diseases, including obesity, diabetes, hypertension, and atherosclerotic cardiovascular disease^16,17,18.

Given the strong connection between AIP and metabolic indicators¹⁹, 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.

Data Source:

The National Health and Nutrition Examination Survey (NHANES) database is a comprehensive countrywide study that was created to evaluate the health as well as nutritious condition of Americans who do not live in institutions. Under the auspices of the National Center for Health Statistics (NCHS), NHANES employs examinations and questionnaires to collect data. The study design utilizes a stratified multiple phases probability sampling method to guarantee the selection of samples that are highly representative. The NHANES procedure was accepted by the NCHS Ethics and Research Assessment Board following a comprehensive examination. All participants willingly and consciously gave written consent. At present, we lack any resources that would enable us to personally identify any individual. Hence, there is no need for any additional ethical assessment, and the complete dataset of the research can be accessed from the public NHANES website (https://www.cdc.gov/nchs/nhanes/).

Study Population:

Between 2007 and 2016, 50,588 participants in this study took engagement in NHANES cycles. Of the participants, 49,695 were at least 20 years old. We eliminated 37,081 individuals with incomplete AIP data and 47 instances with incomplete SUA data in addition to 2,320 patients with missing covariate data. Finally, we expanded our follow-up analysis to include participants (n = 10,247) with complete AIP, SUA, and other covariate data.The flowchart of the investigation is presented in Fig. 1.

Evaluation of the Atherogenic Index of Plasma (Exposure Variable):

The AIP was calculated using the fasting TG to HDL-C ratio and logarithmic transformation, as follows: AIP = log10 (TG / HDL-C)⁹. Subsequently, each individual was classed into one of four groups according to their AIP quartiles: Q1 (-0.79-0.08), Q2 (0.08–0.30), Q3 (0.30–0.52), and Q4 (0.52–2.06).

Evaluation of Serum Uric Acid (Outcome Variable):

Data on SUA were obtained by taking serum samples from NHANES participants at Mobile Examination Centers (MEC).The SUA concentration was computed using the timed terminal approach. The levels of SUA was determined by analyzing the change in the absorption of the chromosomal product formed from the reaction between uric acid and 3,5-dichlorohydroxybenzenesulfonate, with the assistance of 4-aminoantipyrine as a catalyst. The purpose of SUA levels in our examination was to serve as a results variable.

Evaluation of Other Covariates:

In accordance with previous research as well as clinical expertise, we have included the following factors that may affect the relationship between AIP and SUA. The investigation took into account what follows

variables, continuous variables: age (age), long sitting time (minutes), acetaminotransferase (ALT, U/L), amino transferase in the winter (AST, U / L), mercury (mg/dl), uric nitrogen in the blood (mg / dl) and total cholesterol

(mg/dl). The classification variables consist of gender (male/female), ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Negro/other race), educational level (higher or higher than high school/under high school), smoking status (yes/no), alcohol consumption status, hypertension, diabetes, cholesterol prescription (PFC), and household income/poverty ratio (PIR). Hypertension was defined as the use of antihypertensive medication and having an average systolic blood pressure of 140 mmHg or higher, or a diastolic blood pressure of 90 mmHg or above²⁰. 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 greater²¹. 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/m² was regarded normal, 25–30 kg/m² was deemed overweight, and 30 kg/m² or beyond was considered obese²².

2.6 Statistical Analysis:

The CDC's recommendations were followed in the performance of all statistical analyses. In order to ensure the applicability of the results to the population of the United States, a sample strata, regions, and weights obtained by NHANES were utilized in conjunction with the complicated multistage clustering survey methodology²³.

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.

Baseline Characteristics of Participants:

Table 1 presents the initial characteristics of each participant. The study had a total of 10,247 individuals, at a median age of 47.44 years. Of the participants, 49.76% were male and 50.24% were female. The participants were divided into Q1-Q4 groups based on their AIP quartiles. Significant differences were observed in age, gender, race, education level, PIR, marital status, BMI, drinking status, smoking status, hypertension, cholesterol prescription, diabetes, ALT, AST, SUA, creatinine, and total cholesterol among the highest quartile (Q4) of AIP compared to other subgroups (all P < 0.05).

Relationship Between AIP and SUA:

The findings of the multivariate regression analysis between AIP and SUA are displayed in Table 2. The study's findings suggest that the chance of SUA occurring increases with plasma's Atherogenic Index score. In the unadjusted model 1, a positive connection was found between AIP and SUA (β = 1.2995, 95% CI: 1.21 - 1.37, P < 0.001). After adjusting for age, gender, and race, there remained a significant positive association in model 2 (β = 1.0495, 95% CI: 0.97 - 1.12, P < 0.001). The positive link persisted in the entirely adjusted model 3 (β = 0.6895, 95% CI: 0.60 - 0.76, P < 0.001). The association between AIP and SUA remained statistically significant even when we categorized AIP into quartiles instead of treating it as a continuous variable. Individuals in the upper quartile of AIP exhibited a 0.61 μmol/L increase in SUA compared to those in the smallest quartile (model 3, β = 0.6195, 95% CI: 0.54 - 0.68) while accounting for confounding variables. Furthermore, the outcomes utilizing generalized additive models and smooth fit curves hint to a non-linear correlation between SUA risk and AIP.

Non-Linear Relationship:

The study's findings, as shown in Figure 2, demonstrate a non-linear relationship association between AIP with SUA, as indicated by the GAM and smooth fitting of curves analysis. The log-likelihood ratio test revealed a P value of less than 0.001 when comparing the linear regression model and the two-piecewise linear regression model, This suggests that the two-piecewise linear regression model provides a superior fit to the data.

The results of the investigation, employing the recursive algorithm and two-piecewise linear regression model, are presented in Table 3. The inflection point of the inverted U-shaped correlation between AIP (atherogenic index of plasma) and SUA (serum uric acid) was determined to be 0.87. The effect size of 0.81 (95% CI: 0.72 - 0.89) and P value of less than 0.001 on the left side of the inflection point show a strong favorable connection. Nonetheless, there was a substantial negative correlation between AIP and SUA on the right side of the second inflection point. The study findings reveal that there was a positive association between AIP levels and SUA levels. The effect size was -0.55 (95% CI: -0.91 - 0.18) and the statistical significance was shown by a P value of 0.003.

Subgroup Analysis:

In order to examine the link between AIP and SUA levels more thoroughly and provide data, a subgroup analysis was performed. So as to evaluate the effects of various variables, interaction tests were also performed. AIP and SUA consistently exhibited a positive correlation in the subgroup analysis results, demonstrating the stability of the link between the two variables. Interestingly, there was no significant interaction found for age, gender, BMI, diabetes, or hypertension, indicating that these variables are not necessary for this link to exist (interaction P > 0.05 for all).

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 dyslipidemia²⁴. 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 acid²⁷. According to recent studies, lipid ratios may be important indicators of several diseases, including diabetes, metabolic syndrome, and cardiovascular ailments^28,29,30,31. AIP has been proposed as a potential biomarker for early CVD event detection in developing countries³². Studies show that AIP performs significantly better in assessing atherosclerosis than traditional lipid measures³³.

Similarly, studies on metabolic disorders have shown that AIP, as opposed to single lipid indicators like TG and HDL-C^34,35,36, is a stronger predictor of type 2 diabetes, hypertension, insulin resistance (IR), metabolic syndrome, and metabolic syndrome. However, whether AIP can be a useful marker for predicting uric acid levels is uncertain. Our study's RCS analysis shows a non-linear, inverted U-shaped connection between AIP and the chance of high SUA levels. This work fills a void in the literature by expanding the applicability of lipid ratios and raises the prospect that AIP could be a valuable marker for SUA level prediction.

Furthermore, cohort studies have substantiated a robust association between dyslipidemia and elevated levels of uric acid^37,38,39. Therefore, from the perspective of lipid management, managing and preventing hyperuricemia offers substantial clinical benefits. The present study aims to demonstrate the critical role of non-HDL-C in lipid control⁴⁰. 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 radicals⁴³. Chronic inflammation is common in dyslipidemia patients, and this can lead to an increase in uric acid generation and a decrease in its excretion⁴⁴.Additionally, researchers have found that individuals with hyperlipidemia and hyperuricemia share similar dietary patterns, such as consuming high fat and alcohol-containing foods in excess⁴⁵. It's interesting to note that using lipid-lowering drugs is associated with variations in SUA levels⁴⁶.

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.

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.

Competing interests

Te authors declare no competing interests.

Funding

Supported by funding from the following: the National Key Research and Development Program(2020YFC2002902).

Author Contribution

BCH participated in literature search , study design , data collection, data analysis , data interpretation , and wrote the manuscript . WQY, HMZ. Conceived of the study and participated in its design , coordination , data collection and analysis . P.L. participated in study design and provided the critical revision . All authors read and approved the ﬁnal manuscript.

Acknowledgements

The author thanks the staff and the participants of the NHANES study for their valuable contributions.

Data Availability

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/ nhanes/.If anyone wants to get data from this study, please contact [email protected]

Data availability

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/.

If anyone wants to get data from this study, please contact [email protected]

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TABLE 1 Weighted baseline characteristics of participants.Note: Values for categorical variables are given as weighted percentage (standard error); for continuous variables, as weighted mean ± standard error. Weighted Student’s t-test and chi-squared test were used. Abbreviations: BMI, body mass index; ALT, alanine transaminase; AST, aspartate transaminase; TC, total cholesterol; SUA, serum uric acid; AIP, atherogenic index of plasma

Variables#	Quintile categories of AIP				P-value
AIP quartile	Q1(-0.79-0.08)	Q2(0.08-0.30)	Q3(0.30-0.52)	Q4(0.52-2.06)	P-value
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
<=1.3	19.94 (17.63 ,22.47)	21.28 (19.00 ,23.75)	22.54 (20.00 ,25.29)	24.42 (21.94 ,27.08)
1.3-3.5	34.24 (31.72 ,36.86)	35.23 (32.35 ,38.22)	38.74 (35.97 ,41.58)	36.29 (33.65 ,39.02)
>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/m² ) (%)					<0.0001
<25	51.33 (48.35 ,54.30)	33.81 (30.65 ,37.13)	21.79 (19.29 ,24.52)	11.70 (10.34 ,13.22)
25-30	30.33 (28.20 ,32.54)	33.99 (31.85 ,36.20)	35.16 (32.93 ,37.46)	34.46 (32.22 ,36.78)
>=30	18.34 (16.51 ,20.33)	32.20 (29.62 ,34.89)	43.05 (40.47 ,45.66)	53.83 (51.19 ,56.45)
Smoking status(%)					<0.0001
Never	62.44 (59.32 ,65.46)	57.77 (55.21 ,60.28)	51.96 (49.22 ,54.69)	47.09 (44.76 ,49.43)
Smoking former	22.40 (20.11 ,24.88)	24.14 (21.86 ,26.57)	26.98 (24.46 ,29.67)	28.63 (26.26 ,31.12)
Smoking now	15.16 (13.20 ,17.34)	18.09 (16.03 ,20.35)	21.06 (18.60 ,23.74)	24.29 (22.35 ,26.33)
Drinking status (%)					<0.0001
Never	11.65 (10.16 ,13.33)	12.01 (10.37 ,13.86)	10.86 (9.36 ,12.57)	9.21 (7.79 ,10.85)
Drinking former	77.66 (75.13 ,80.00)	74.46 (72.07 ,76.71)	73.62 (71.26 ,75.85)	71.63 (68.85 ,74.25)
Drinking now	10.69 (9.16 ,12.45)	13.53 (12.14 ,15.06)	15.52 (13.83 ,17.38)	19.16 (17.00 ,21.53)
HYPERTENSION(%)					<0.0001
No	69.88 (67.16 ,72.47)	61.15 (58.31 ,63.93)	55.86 (53.27 ,58.41)	47.84 (44.90 ,50.79)
Yes	30.12 (27.53 ,32.84)	38.85 (36.07 ,41.69)	44.14 (41.59 ,46.73)	52.16 (49.21 ,55.10)
Diabetes(%)					<0.0001
No	93.23 (91.98 ,94.30)	90.27 (88.87 ,91.52)	85.14 (83.05 ,87.01)	77.67 (74.99 ,80.14)
Yes	6.77 (5.70 ,8.02)	9.73 (8.48 ,11.13)	14.86 (12.99 ,16.95)	22.33 (19.86 ,25.01)

Table 2: Correlation between AIP and SUA (mg/dl).SUA, serum uric acid; 95% CI, 95% confidence interval;

Model 1 , no covariates were adjusted; Model 2 , adjusted for gender, age, race; Model 3 , adjusted for gender, age, race,sedentary time, ALT, AST, creatinine, blood urea nitrogen, education level, the ratio of family income to poverty, marital status, body mass index, alcohol status, smoking status, hypertension, total cholesterol，cholesterol prescription, total cholesterol, and diabetes.

AIP	SUA, mg/dL, β (95%CI), P-value
AIP	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

No competing interests reported.

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

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Data Source:

Study Population:

Evaluation of the Atherogenic Index of Plasma (Exposure Variable):

Evaluation of Serum Uric Acid (Outcome Variable):

Evaluation of Other Covariates:

2.6 Statistical Analysis:

Results

Discussion

Strengths and Limitations of the Study

Conclusion

Declarations