Higher atherogenic index of plasma is associated with hyperuricemia: a national longitudinal study

doi:10.21203/rs.3.rs-3099595/v1

Background

The association between atherogenic index of plasma (AIP) and hyperuricemia remains indistinct. We aimed to examine the relationship between the level of AIP and hyperuricemia among the middle aged and the elderly Chinese population.

Methods

The dataset were retrieved from the China Health and Retirement Longitudinal Study (CHARLS) survey conducted in 2011 and 2015. 13,021 participants in the CHARLS in 2011, and 7,017 participants involved in 2011 and 2015 were included in the cross-sectional and longitudinal analyses, respectively. The measurement of AIP and hyperuricemia was based on the test of fasting blood. The association between AIP and hyperuricemia was assessed by logistic regression, and the non-linear association was examined by restricted cubic splines (RCS).

Results

In the section of cross-sectional study, a positive association between AIP and hyperuricemia was found. The Odds ratios (ORs) of hyperuricemia were 1.00 (reference), 1.52(1.10–2.10), 1.80(1.31–2.47) and 3.81(2.84–5.11). Non-linear association was not detected using RCS analysis. There were 664 hyperuricemia cases during four years follow-up. The hyperuricemia prevalence was 9.5%. In a fully adjusted regression model, across the quartiles of AIP, the ORs for hyperuricemia were 1.00 (reference), 1.00(0.74–1.37), 1.59(1.20–2.11), 2.55(1.94–3.35).

Conclusion

AIP can predict the prevalence of hyperuricemia in Chinese middle-aged and elderly population.

atherogenic index of plasma

hyperuricemia

China Health and Retirement Longitudinal Study

Hyperuricemia, which refers to the elevation of uric acid in the plasma, is a common disorder that affects patients of all ages and genders[1]. Nowadays, it has become a major public health problem that threatens human health, and the prevalence keep rise constantly[2]. Among US adults the prevalence was even up to 21.2% in male and 21.6% in female in the past years[3]. The most common manifestation of hyperuricemia is gout, which can cause the renal dysfunction in the end stage, and be very painful but easily treatable[1]. In recent years, it was also demonstrated that hyperuricemia is also closely correlated with cardiovascular disease[4, 5]. Data from adult Korean reported hyperuricemia was associated with a nearly tripled risk for heart rate irregularity, which reflecting total arrhythmia[6]. The risk and all-cause mortality of coronary heart disease rise when participants suffering hyperuricemia[7]. Besides, the high uric acid level can be applied to predict the severity of coronary artery disease[8]. Hyperuricemia also has relationship with a significant increased risk of hypertension and congestive heart failure [9–11].

Dyslipidemia, a cluster of blood-lipid disorder diseases, is also an independent risk factor for cardiovascular disease[12]. Previous study documented hypercholesterolemia is one of the principal driving forces in the development of atherosclerotic cardiovascular diseases[13]. A large amount of evidence from genetic, epidemiologic and clinical intervention studies has already firmed that high serum levels of low-density lipoprotein cholesterol cause the development of atherosclerotic plaque[14]. More than that, hyperuricemia is often accompanied by abnormal lipid metabolism, including abnormal apolipoprotein A and low-density lipoprotein cholesterol levels; besides, the ratio of apolipoprotein B to A was found strongly associated with serum uric acid levels in US people[15].

AIP, calculated as log (triglyceride/high-density lipoprotein), is proposed for the first time by Dobiásová, which has closely correlation with lipo-protein particle size and esterification rate in apoB-lipoprotein-depleted plasma[16]. It is a lipid metabolism indicator, which was identified a novel indicator of cardiovascular disease among different population[17]. As an independent risk factor, AIP is useful for improving the diagnosis and prevention of subclinical coronary artery disease[18]. Low level of AIP even can predict a decrease in all-cause mortality of hospitalized patients with acute myocardial infarction[19].

Given that uric acid has closely relationship with dyslipidemia, the association between metabolism indicator AIP and hyperuricemia was examined in this article. To our knowledge, this has rarely been researched in Chinese middle aged and elderly people.

2.1 Data Source and Analytical Sample

In the present paper, dataset was retrieved from the CHARLS 2011 and 2015 survey. CHARLS is a longitudinal project initiated from 2011 and followed up every 2 years in China. It is conducted by the Chinese Center for Disease Control and Prevention (CCDCP). The project involves home interview, medical examination, etc., which was collected from 28 provinces and 150 counties in the whole China. Information such as health information, demographics, socioeconomic status as well as physical and physiological measurements were recorded. Specific descriptions regarding the CHARLS could be accessed via the official study website (http://charls.pku.edu.cn/) or publications[20].

In the present study, there are 17,705 participants were involved in the 2011 baseline. After eliminating the missing data of age, gender or uric acid testing, a total of 13,021 eligible participants was enrolled for cross sectional study. Respondents who participated both in CHARLS 2011 and 2015 waves was selected from CHARLS2015. Besides, those with hyperuricemia in 2011 baseline were excluded. At last, 7,017 participants were reserved for longitudinal analysis. The detail is shown in Fig. 1. According to the quartiles of AIP, the study population was allocated to 4 groups and showed as Q1 (< 0.29), Q2 (0.29–0.77), Q3 (0.77–1.30), Q4 (> 1.30).

2.2. Data collection and Covariates

In the CHARLS 2015 survey, questionnaires containing basic information such as marital status, behavior and history of diseases, etc. were performed by experienced interviewers [20]. Fasting venous blood from the respondents was acquired and centrifuged to gain plasma. The plasma was then immediately stored under appropriate frozen condition (-20℃) until transporting to the CCDCP in Beijing for testing within 2 weeks. The medical laboratory results were obtained by testing these specimens. Blood biomarkers including uric acid, triglyceride (TG), high-density lipoprotein (HDL), blood urea nitrogen (BUN), cystatin (Cyc) C, glucose, low-density lipoprotein (LDL), etc., were determined using enzymatic colorimetric tests[20]. AIP index was calculated as log (TG/HDL) and was classified into four groups. The definition of hyperuricemia was serum uric acid (SUA) ≥ 357 µmol/L for females and ≥ 417 µmol/L for males[21]. The covariates in the present study consisted of age, gender, marital status, sleep duration, afternoon napping, cigarette consumption, alcohol consumption, body mass index (BMI), depression reflected by the Center for Epidemiologic Studies Depression (CESD) scale − 10 questionnaire, BUN, LDL, hepatic disease, renal disease, digestive disease and arthritis disease. Age was categorized into 4 groups: 40–50 years, 50-60years, 60-70years, and > 70years; marital status was categorized into individuals living together or living alone. Sleeping time was classified into three groups: sleeping less than 6 hours, between 6–8 hours and more than 8 hours. Afternoon napping was defined according whether participants had the custom of afternoon napping or not. Smoking status consisted of three status smoking, never and quit smoking. Alcohol consumption was categorized as more than once a month, less than once a month, and never. BMI was divided into three groups: < 18.5 Kg/m², < 24 Kg/m², < 28 Kg/m², ≥ 28 Kg/m². Depression was assessed by CESD-10, and participants got score > 10 were identified as depressive state[22]. LDL is classified into two groups > 120 mg/dl and < 120 mg/dl. Hepatic disorders included hepatitis, liver cyst, hepatic aneurysm, etc. except for fatty liver, tumors, and cancer. Renal disorders included kidney stones, CKDs, etc. excluding tumor or cancer. Digestive diseases covered digestive ulcer, gastritis, etc. but not contained tumor or cancer. The established regression models were displayed as follows. Model 1: univariate regression; Model 2: adjusted by age, gender, marital status, sleeping time and afternoon napping based on model 1; Model 3: further adjusted by alcohol and cigarette consumption, BMI and depression; Model 4: further adjusted by BUN and LDL; Model 4: further adjusted by hepatic disease, digestive disease, renal disease and arthritis disease.

2.3. Statistical analyses

Data analysis was conducted using Stata 16.0. Continuous data are presented as the mean ± standard deviation (s.d.), and categorical data are displayed as proportions (%). Analysis of variance was used to evaluate the differences in baseline characteristics, and Student’s t-test and the Chi-square were adopted for continuous data and categorical data, respectively. Logistic regression was adopted to assess the ORs for the association between AIP and hyperuricemia. Besides, subgroup analysis was employed to explore the association in particular groups. A restricted cubic spline was adopted to model and visualize the relationship between AIP and hyperuricemia index with 3 knots. Longitudinal analysis was further used to access the association between AIP and hyperuricemia. Figures were drawn by Stata and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA). P < 0.05 (two-sided) was considered statistically significant.

3.1 Demographic characteristics of the study population

A total of 13,021 participants at CHARLS 2011 baseline were included in the cross-sectional analyses. Among them, 6,146 were female and 5,367 were male. Across the quartiles, the mean (SD) of AIP is -0.07 ± 0.28, 0.54 ± 0.13, 1.02 ± 0.15 and 1.87 ± 0.54, respectively. The mean age of the enrolled participants was 59.21 ± 9.71, and the age showed decreased trend with the AIP increasing. Table 1 shows that the number of individuals whose BMI < 24 Kg/m² apparently decreased across the quartiles of AIP, but the number increased across the quartiles in the participants with BMI ≥ 24 Kg/m². The prevalence of current smoking was also lower among those with high AIP. It is less likely to have higher AIP in the participants without napping and with digestive disease. The detail of baseline clinical profiles attending the 2011 CHARLS survey are shown in Table 1.

Table 1

**Baseline clinical profiles of participants attending the 2011 CHARLS survey**
Clinical characteristics	Total participants	Q1	Q2	Q3	Q4	P
Clinical characteristics	N = 13021	N = 3275	N = 3236	N = 3246	N = 3264	P
Age, mean (SD)	59.21 (9.71)	59.83 (10.10)	59.38 (9.91)	59.11 (9.52)	58.51 (9.26)	< 0.001
Gender						< 0.001
Male	5367 (46.6%)	1497 (51.9%)	1320 (46.0%)	1258 (43.6%)	1292 (45.0%)
Female	6146 (53.4%)	1390 (48.1%)	1550 (54.0%)	1625 (56.4%)	1581 (55.0%)
Marital status						0.003
With spouse present	9528 (82.8%)	2374 (82.2%)	2354 (82.0%)	2358 (81.8%)	2442 (85.0%)
Others	1985 (17.2%)	513 (17.8%)	516 (18.0%)	525 (18.2%)	431 (15.0%)
Cigarette consumption						< 0.001
Current-smoker	3512 (30.5%)	982 (34.0%)	886 (30.9%)	830 (28.8%)	814 (28.3%)
Non-smoker	7026 (61.1%)	1660 (57.5%)	1760 (61.4%)	1823 (63.3%)	1783 (62.1%)
Ever-smoker	970 (8.4%)	244 (8.5%)	222 (7.7%)	229 (7.9%)	275 (9.6%)
Alcohol consumption						< 0.001
Drink more than once a month	2870 (24.9%)	935 (32.4%)	670 (23.4%)	620 (21.5%)	645 (22.5%)
Drink less than once a month	900 (7.8%)	227 (7.9%)	229 (8.0%)	213 (7.4%)	231 (8.0%)
None of These	7736 (67.2%)	1724 (59.7%)	1969 (68.7%)	2048 (71.1%)	1995 (69.5%)
Body mass index						< 0.001
< 18.5 Kg/m2	669 (6.9%)	290 (11.7%)	215 (8.8%)	121 (5.0%)	43 (1.8%)
≥ 18.5 & < 24 Kg/m2	5035 (51.9%)	1661 (66.8%)	1373 (56.3%)	1120 (46.4%)	881 (37.3%)
≥ 24 & < 28 Kg/m2	2851 (29.4%)	438 (17.6%)	652 (26.7%)	824 (34.1%)	937 (39.7%)
≥ 28 Kg/m2	1148 (11.8%)	99 (4.0%)	199 (8.2%)	348 (14.4%)	502 (21.2%)
Nap						0.004
No	5154 (46.7%)	1347 (48.5%)	1300 (47.2%)	1298 (47.2%)	1209 (43.8%)
Yes	5889 (53.3%)	1432 (51.5%)	1455 (52.8%)	1451 (52.8%)	1551 (56.2%)
Sleep duration						0.30
< 6 hours	5548 (50.6%)	1422 (51.4%)	1386 (50.7%)	1405 (51.6%)	1335 (48.7%)
6–8 hours	4486 (40.9%)	1118 (40.4%)	1105 (40.4%)	1088 (40.0%)	1175 (42.9%)
> 8 hours	928 (8.5%)	226 (8.2%)	243 (8.9%)	229 (8.4%)	230 (8.4%)
Low density lipoprotein						< 0.001
< 120 mg/dL	6586 (57.3%)	1894 (65.6%)	1551 (54.0%)	1396 (48.4%)	1745 (61.2%)
≥ 120 mg/dL	4905 (42.7%)	992 (34.4%)	1319 (46.0%)	1486 (51.6%)	1108 (38.8%)
Total cholesterol						< 0.001
< 200 mg/dL	6991 (60.7%)	1934 (67.0%)	1850 (64.5%)	1711 (59.4%)	1496 (52.1%)
≥ 200 mg/dL	4519 (39.3%)	952 (33.0%)	1019 (35.5%)	1171 (40.6%)	1377 (47.9%)
Depression	3980 (38.1%)	1039 (39.7%)	982 (37.8%)	1003 (38.5%)	956 (36.5%)	0.12
Arthritis	4032 (35.0%)	979 (33.9%)	1004 (35.0%)	1057 (36.7%)	992 (34.5%)	0.15
Digestive diseases	2659 (23.1%)	729 (25.3%)	679 (23.7%)	650 (22.5%)	601 (20.9%)	0.001
Renal diseases	772 (6.7%)	205 (7.1%)	184 (6.4%)	188 (6.5%)	195 (6.8%)	0.73
Hepatic diseases	451 (3.9%)	117 (4.1%)	101 (3.5%)	117 (4.1%)	116 (4.0%)	0.66

N: number; Q: quartile; CHARLS: China Health and Retirement Longitudinal Study

3.2 The association between AIP index and hyperuricemia

As shown in Table 2, the prevalence of hyperuricemia was 3.26%, 4.56%, 4.96% and 10.55% in the four groups respectively, which indicated an increasing trend across the quartiles of AIP. In Model I, the ORs of univariate regression at Q2, Q3 and Q4 were 1.42(1.08–1.86), 1.55(1.19–2.02) and 3.50(2.76–4.44), all p values have statistical significance. In the other multivariate regression models, the ORs displayed the same elevating tendency as Model 1. Especially, all p values were less than 0.01 in Model 5. In this model containing full adjusting variables, the ORs were 1.52(1.10–2.10), 1.80(1.31–2.47) and 3.81(2.84–5.11). The risk of hyperuricemia increased by 250%, 318%, 245%, 281% and 281% at Q4 in the five models, respectively. The prevalence of hyperuricemia in the four groups together with ORs across the quartiles in the five models suggested a rising risk of hyperuricemia with the increasing AIP; all p values of tendency test were less than 0.01.

Table 2

Odds ratio (95% CI) for prevalent hyperuricemia by quartiles of AIP
Quartiles of AIP	Q1	Q2	Q3	Q4	P for trend
Cases	94	131	143	303	-
Prevalence (%)	3.26	4.56	4.96	10.55	-
Model 1	1.00 (reference)	1.42(1.08–1.86)*	1.55(1.19–2.02)***	3.50(2.76–4.44)***	< 0.001
Model 2	1.00 (reference)	1.52(1.15–2.02)**	1.79(1.36–2.36)***	4.18(3.26–5.36)***	< 0.001
Model 3	1.00 (reference)	1.47(1.07–2.01)	1.73(1.27–2.36)***	3.45(2.60–4.65)***	< 0.001
Model 4	1.00 (reference)	1.52(1.11–2.10)**	1.82(1.32–2.49)***	3.81(2.84–5.11)***	< 0.001
Model 5	1.00 (reference)	1.52(1.10–2.10)**	1.80(1.31–2.47)***	3.81(2.84–5.11)***	< 0.001
Multivariate logistic regression to examine the association between AIP index and hyperuricemia. The Q1 was set as the reference group. Model 1: univariate regression; Model 2: adjusted by age, gender, marital status, sleeping time and afternoon napping based on model 1; Model 3: further adjusted by alcohol and cigarette consumption, BMI and depression; Model 4: further adjusted by blood uric acid and LDL; Model 4: further adjusted by hepatic disease, digestive disease, renal disease and arthritis disease. p < 0.05; p < 0.01; **p < 0.001.

3.3 The subgroup analysis and interaction effect

Multivariate logistic regression was employed to further examine the association between AIP and hyperuricemia in the subgroup analyses. The increasing trend still existed in the subgroups of participants without renal disease and sleeping less than 6 hours. It is not observed any significant effects of gender, renal disease, cigarette consumption, depression and sleep duration on the association between AIP and hyperuricemia, while drinking more than once a month was detected to exacerbate the association (P-interaction = 0.02). Besides, in these subgroups, all ORs at Q3 and Q4 generally exhibit an increasing trend. Details were exhibited in Table 3.

Table 3

Subgroup analysis of association between quartiles of AIP and hyperuricemia
Factors	Q1	Q2	Q3	Q4	P for interaction
Gender					0.80
Male	1.00	1.41(0.92–2.16)	1.82(1.20–2.77)**	3.78(2.55–5.62)***
Female	1.00	1.60(0.97–2.62)	1.68(1.03–2.75)*	3.07(1.3–4.90)***
Renal disease					0.16
Yes	1.00	2.04(0.55–7.54)	5.38(1.61–17.87)**	4.86(1.44–16.35)
No	1.00	1.50(1.08–2.09)*	1.63(1.17–2.27)**	3.40(2.50–4.64)***
Alcohol consumption					0.02
More than once a month	1.00	1.72(0.97–3.03)	1.92(1.08–3.39)*	4.71(2.79–7.94)***
Less than once a month	1.00	1.08(0.29–4.06)	0.99(0.24-4.00)	2.15(0.62–7.38)
Non-drinker	1.00	1.40(0.93–2.12)	1.66(1.11–2.48)*	2.94(2.00-4.31)***
Cigarette consumption					0.53
Current-smoker	1.00	1.24(0.72–2.12)	1.35(0.78–2.33)	2.64(1.58–4.41)***
Non-smoker	1.00	1.81(1.16–2.82)	1.97(1.27–3.06)**	3.51(2.30–5.35)***
Ever-smoker	1.00	0.59(0.22–1.58)	1.06(0.44–2.55)	3.21(1.47-7.00)**
Depression					0.78
Yes	1.00	1.64(0.95–2.84)	2.29(1.36–3.87)**	3.57(2.14–5.95)***
No	1.00	1.44(0.97–2.15)	1.51(1.00-2.25)*	3.40(2.36–4.92)***
Sleep duration					0.97
< 6 hours	1.00	1.92(1.26–2.92)**	1.82(1.19–2.81)**	3.29(2.19–4.95)***
6–8 hours	1.00	1.31(0.76–2.26)	1.72(1.02–2.91)*	3.73(2.30–6.06)***
> 8 hours	1.00	0.40(0.08–2.12)	1.96(0.62–6.19)	3.87(1.23–12.18)*
Association between quartiles of AIP and hyperuricemia stratified by gender, renal disease, alcohol consumption, cigarette consumption, depression, sleep duration. Models were adjusted for the same covariates as model 5 in Table 2. Stratification variables were not adjusted in the corresponding models. Q: quartile; p < 0.05; p < 0.01; *p < 0.001. P < 0.05, P < 0.01, *P < 0.001

3.4 Restricted cubic spline regression ROC analysis

Restricted cubic splines analysis was employed to flexibly model and visualize the association between AIP and hyperuricemia. As shown in Fig. 2, there is no non-linear relationship was detected. With the increases in AIP, the ORs for hyperuricemia increased synchronously. P for non-linearity were 0.45, 0.26 and 0.81, respectively in total, male and female groups. P for overall were all less than 0.001.

3.5 The longitudinal association between AIP and hyperuricemia

Participants with hyperuricemia in CHARLS 2011 survey were excluded. Then longitudinal analysis was performed in participants involved in both CHARLS 2011 and 2015 survey. Result showed that the prevalence of hyperuricemia was 9.5%. Across the quartiles of AIP, the incidence gradually increased, and were 5.92%, 6.48%, 10.37% and 15.48%. Besides, the increasing trend still exist in the ORs across the AIP quartiles in every model, and all p for trend were less than 0.001 (Table 4). Subgroup analyses displayed that there were no significant effects of gender, renal disease, alcohol consumption, depression and sleep duration on the association between AIP and hyperuricemia. Among participants who never or ever smoked, higher AIP was associated with an increased risk of hyperuricemia; the OR were 2.74(1.90–3.95) and 5.49(1.84–16.41) compared with the first quartile of AIP (Table 5).

Table 4

Odds ratio (95% CI) for prevalent hyperuricemia by quartiles of AIP in longitudinal analysis
Quartiles of AIP	Q1	Q2	Q3	Q4	P for trend
Cases	107	114	185	258	-
Prevalence (%)	5.92	6.48	10.37	15.48	-
Model 1	1.00 (reference)	1.10(0.84–1.45)	1.84(1.44–2.36)***	2.91(2.30–3.69)***	< 0.001
Model 2	1.00 (reference)	1.17(0.86–1.55)	1.95(1.51–2.52)***	3.21(2.52–4.10)***	< 0.001
Model 3	1.00 (reference)	1.04(0.77–1.40)	1.65(1.25–2.17)***	2.58(1.97–3.38)***	< 0.001
Model 4	1.00 (reference)	1.01(0.75–1.37)	1.59(1.20–2.10)***	2.53(1.93–3.33)***	< 0.001
Model 5	1.00 (reference)	1.00(0.74–1.37)	1.59(1.20–2.11)***	2.55(1.94–3.35)***	< 0.001
The adjusting variables were the same as Table 2. p < 0.05; p < 0.01; **p < 0.001.

Table 5

Subgroups analysis of association between quartiles of AIP and hyperuricemia in longitudinal analysis
Factors	Q1	Q2	Q3	Q4	P for interaction
Gender					0.09
Male	1.00	1.20(0.80–1.80)	1.67(1.13–2.49)*	2.32(1.57–3.46)***
Female	1.00	0.81(0.51–1.30)	1.53(1.01–2.30)*	2.63(1.77–3.92)***
Renal disease					0.06
Yes	1.00	0.72(0.17–3.16)	2.81(0.77–10.25)	7.60(2.37–24.38)***
No	1.00	1.02(0.75–1.40)	1.53(1.14–2.01)**	2.26(1.69–3.01)***
Alcohol consumption					0.09
More than once a month	1.00	1.11(0.66–1.87)	1.52(0.91–2.54)	2.20(1.31–3.70)**
Less than once a month	1.00	1.16(0.43–3.15)	0.98(0.33–2.91)	2.79(1.07–7.23)*
Non-drinker	1.00	0.95(0.63–1.44)	1.74(1.20–2.52)**	2.65(1.84–3.82)***
Cigarette consumption					0.02
Current-smoker	1.00	1.04(0.64–1.71)	1.15(0.70–1.89)	1.69(1.04–2.75)*
Non-smoker	1.00	1.01(0.67–1.52)	1.78(1.22–2.60)**	2.74(1.90–3.95)***
Ever-smoker	1.00	0.73(0.18–2.90)	2.85(0.93–8.76)	5.49(1.84–16.41)**
Depression					0.88
Yes	1.00	1.12(0.69–1.83)	1.35(0.85–2.15)	2.38(1.51–3.74)***
No	1.00	0.96(0.65–1.43)	1.75(1.22–2.62)**	2.61(1.83–3.72)***
Sleep duration					0.87
< 6 hours	1.00	0.97(0.62–1.51)	1.65(1.11–2.47)**	2.76(1.86–4.09)***
6–8 hours	1.00	0.93(0.58–1.50)	1.40(0.90–2.17)	2.15(1.41–3.27)***
> 8 hours	1.00	1.83(0.61–5.49)	2.51(0.88–7.18)	2.90(0.97–8.65)
Models were adjusted for the same covariates as model 5 in Table 2. Stratification variables were not adjusted in the corresponding models. Q: quartile; p < 0.05; p < 0.01; **p < 0.001.

In this nationally longitudinal study targeting the middle aged and elderly population, we found that AIP index was positively correlated with hyperuricemia prevalence, which is independent of sociodemographic and lifestyle factors, such as sleep duration. Though non-linear relationship was not found, the curve of restricted cubic splines analysis also exhibited a positive relationship between AIP and hyperuricemia.

With the improvement of living standards, high purine and protein diet increase, thus the prevalence of hyperuricemia has been increasing in recent decades worldwide[23]. A national survey conducted in 2009–2010 in Chinese adults displayed that the prevalence of hyperuricemia was up to 8.4%[24]. Available evidence claims that uric acid can uphold the atherosclerosis process via disturbing lipid metabolism, which indicate that hyperuricemia is intrinsically connected with dyslipidemia[25]. AIP, as a lipid metabolism index, is a least studied risk factor in hyperuricemia. In our study, the cross-sectional analysis demonstrated an apparently positive association between AIP and the prevalence of hyperuricemia. In the final model, the risk of hyperuricemia in the highest AIP quartile is 3.81 times bigger than the reference group. This outcome is partially in accordance with a cross-sectional study, in which the AIP of hyperuricemia group was higher than that of normal uric acid group (0.17 ± 0.30 vs. −0.08 ± 0.29), while hyperuricemia was detected as an independent risk factor for high AIP level[26]. Moreover, in the subgroup analysis, we confirmed that drinking can exacerbate the relationship between AIP and hyperuricemia risk.

AIP was constructed as a lipid metabolism indicator consisting of TG and HDL. TG is an important component of blood lipid. A cross-sectional study demonstrated that elevated TG in men and women and TC in women were associated with increased hyperuricemia prevalence on the east coast of China[27].Yuntian Chu etc. identified the non-linear association of uric acid and TG levels with short children and adolescents[28]. Moreover, previous study identified that high TG may influence the renal function, which may reduce renal uric acid excretion, and increase the serum uric acid level[29–31]. All these finding revealed a closely positive association between TG and blood uric acid. Besides, a retrospective study showed HDL (OR = 0.217, 95%CI:0.074–0.637) may be an independent protective factor of hyperuricemia in bariatric surgery patients [32]. Another cross-sectional study indicates decreasing HDL-C were more likely to suffer hyperuricemia[33]. Fasting high-density lipoprotein cholesterol (HDL-C) levels were proved negatively associated with serum uric acid levels[34].Taken together, the aforementioned may partially explain bigger AIP index is connected with higher hyperuricemia risk.

Previous study indicated that AIP was associated with higher serum uric acid levels, while it was a cross-sectional research limited in the rural population of Liaoning province in northeast China[35]. In our study, after excluding the participants with hyperuricemia, the longitudinal analysis was conducted between CHARLS 2015 and 2011survey. The outcome showed that the ORs apparently increased in all models at the third and fourth quartiles of AIP, compared with the reference group. In addition, the surveys of CHARLS were on a national scale, and the participants were selected nationwide. This suggested AIP has the ability to predict the prevalence of hyperuricemia in the middle aged and elderly Chinese population. Moreover, integrating TG and HDL, AIP is a more comprehensive factor to predict hyperuricemia.

Generally, we found a practical indicator for predicting hyperuricemia prevalence, and it enriched the method alarming the hyperuricemia attack. Moreover, AIP is low-cost and feasible to acquire, for it can be directly acquired from the routine biochemical items conducted on admission. Given this, AIP index has important clinical implications and may be a promising indicator in clinical practice. Nevertheless, further exploration is still demanded. To the best of our knowledge, this is the first report to investigate the longitudinal relationship between AIP and hyperuricemia in the Chinese population.

Our study also had several limitations. First, cross-sectional study cannot exclude the possibility of residual confounders, which is limited to establish a causal relation between AIP and hyperuricemia. Therefore, large-scale prospective studies are needed to verify the present findings. Second, this study was based on the CHARLS survey in 2011 and 2015, which means it should be further verified in other countries or regions worldwide. Third, the information collected from the participants may suffer from recall bias on self-report variables. So, we still consider there is chance to cause deviation. In future studies, more objective measurements should be adopted.

Conclusively, results in this pilot study suggested that AIP can predict the prevalence of hyperuricemia in Chinese middle-aged and elderly population.

Ethics approval and consent to participation

The CHARLS study was approved by research ethics committees of Peking University. All participants provided written informed consent. No experimental interventions were performed. All procedures performed in this study were in accordance with the Declaration of Helsinki (revised in 2013) and were approved by the Ethics Committee of Peking University (IRB 00001052-11014).

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the CHARLS repository, http://charls.pku.edu.cn/.

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Author contributions

Feifei Xu performed the data analyses and wrote the manuscript. Shouping Wang, Chengyong Ma and Qin Li participants in preparing the figures. Zhongwei Zhang and Min He participated in the study design and helped draft the manuscript.

Acknowledgment

We thank the National Institute of Development Research of Peking University and the Chinese Center for Social Science Survey of Peking University for providing CHARLS data.

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No competing interests reported.

Higher atherogenic index of plasma is associated with hyperuricemia: a national longitudinal study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Data Source and Analytical Sample

2.2. Data collection and Covariates

2.3. Statistical analyses

3. Results

3.1 Demographic characteristics of the study population

3.2 The association between AIP index and hyperuricemia

3.3 The subgroup analysis and interaction effect

3.4 Restricted cubic spline regression ROC analysis

3.5 The longitudinal association between AIP and hyperuricemia

4. Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1