Introduction

Serum uric acid (SUA) is the end product of purine metabolism in body and independently associated with variety of chronic diseases1,2,3. Hyperuricemia is a metabolic disease due to disturbances in purine metabolism and elevated SUA concentrations4. With the improvement of living standards, the incidence of hyperuricemia is gradually increasing and there is a gradual trend of under-ageing5. The overall prevalence of hyperuricemia was 21% in the United States, 16.6% in Australia, and 25% in Ireland6,7,8. With rapid economic development and lifestyle changes, the incidence of hyperuricemia in China is also rising rapidly, from 1.4% in 1980s to 8.4% in 2009–20109,10. The prevalence of hyperuricemia varies in different regions and hyperuricemia has become an important public health burden that seriously affects human health. Hyperuricemia is closely associated with many diseases such as diabetes and hypertension and SUA levels are an independent risk factor for cardiovascular events11. China currently has about 120 million patients with hyperuricemia and the annual growth rate is about 9.7%. Hyperuricemia has become the “fourth high” following hypertension, hyperlipidemia and hyperglycemia12. Novel anthropometric indices such as body adiposity index (BAI), a body shape index (ABSI), body roundness index (BRI), visceral adiposity index (VAI), lipid accumulation product (LAP) index, atherogenic index of plasma (AIP), and cardiometabolic index (CMI) are more closely related to metabolic abnormalities than traditional anthropometric indices such as body mass index (BMI), waist circumference (WC), and waist-to-body ratio (WHtR)13,14. Fewer studies have been conducted based on the relationship between novel anthropometric indicators and hyperuricemia. This study was to analyze the SUA levels of adults and the relationship between hyperuricemia and these new indicators in Su-Xi-Chang area of China, in order to provide a theoretical basis for the management of SUA levels in patients with hyperuricemia.

Methods

Study participants

This is a cross-sectional study. A total of 14,834 participants aged 18 years or older population who had health examination in Taihu Sanatorium of Jiangsu Province from March 2020-April 2021 were enrolled and they had resided locally for more than 5 years in Su-Xi-Chang area of China. Excluding those with previous myocardial infarction, personal history of malignant tumor, history of stroke, secondary hypertension, severe hepatic and renal insufficiency, those who had taken hormones, diuretics, allopurinol and other medications affecting SUA levels within six months before, and those who excluded incomplete data related to this study. A total of 9788 cases of males and 5046 cases of females were enrolled, with a median age of 50.61 ± 11.50 years. The study was approved by the Ethics Committee of our hospital (SGLL2020005) and the subjects signed an informed consent form. All methods were carried out in accordance with relevant guidelines and regulations (e.g. Helsinki guidelines).

Questionnaires

The survey was conducted by uniformly trained medical staff and included: age, smoking, alcohol consumption, exercise and history of previous diseases (hypertension, diabetes mellitus, coronary heart disease, stroke and other cardiovascular diseases and medication use).

The definition of smoking, drinking, diet and exercise

Smoking was defined as having smoked cigarettes in the 30 days prior to the survey; alcohol consumption was defined as more than 1 drink per week on average, regardless of the type of alcohol; healthy diet was defined as meeting 2 out of the following 3 criteria: consuming ≥ 5 servings of fruits and/or vegetables per day; ≥2 servings of fish (instead of red meat) per week, and 3) < 1500 mg of sodium per day; and physical activity was defined as exercising more than 3 times per week for a cumulative total of more than 90 min.

Calculation of indicators15

BMI = Weight/Height2;WHtR = WC/Height;

AIP = log (TG [mmol/L]HDL-C[mmol/l]);

CMI = TG/HDL-C×WHtR;

LAP (females) = TG(mmol/L) × (WC[cm]-58);

LAP (males) = TG(mmol/L)×(WC[cm]-65);

TyG = ln[TG(mg/dl) ×FPG (mg/dl)/2];

VAI(females) = WC/(36.58 + 1.89×BMI)×(TG/0.81)×(1.52/HDL-C);

VAI(males) = WC/(39.68 + 1.88×BMI)×(TG/1.03)×(1.31/HDL-C);

BRI = 364.2-{365.5×[1-(WC/2π)2/(0.5×height)2]}1/2;

ABSI = WC(cm)/(height[cm])1/2 ×(BMI2)1/3.

Biochemical tests

Before blood sampling, all subjects fasted for 8–12 h. About 5 mL of elbow venous blood was drawn on an empty stomach, and serum was separated for total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triacylglycerol (TG), FPG, and SUA. The diagnostic criterion for hyperuricemia was a fasting SUA level of > 420 µmol/L in males, and a SUA level of > 360 µmol/L in females9.

Statistical processing

SPSS statistical software (version 18.0, IBM Corp, Armonk, NY, USA) was used to test the normal distribution and chi-square test of the measurement data. Measurement data conforming to the normal distribution were expressed as mean ± standard deviation (± s) and independent samples t-test was used for comparison between two groups; one-way analysis of variance (ANOVA) was used for comparison of the count data between multiple groups, and then LSD was used for comparison between two groups, and Tamhane′s method was used in case of chi-square test; non-normally distributed measurement data were expressed as median (P25, P75), and Mann-Whitney test was used for comparison between two groups. Normally distributed measures were expressed as median (IQR), and the Mann-Whitney test was used for comparisons between groups; the chi-square test was used for measurement data; the new anthropometric indices were categorized by quartiles, and one-way ANOVA was used to compare the levels of SUA across quartiles, and the trend of the chi-square test was used to analyze the results. To analyze changes in the incidence of hyperuricemia when novel anthropometric indices were classified in different quartiles. Partial correlation analysis was applied to analyze the correlation between the novel anthropometric indices and SUA levels in male and female populations, with model a unadjusted; model b adjusted for age, SBP, DBP, FPG, smoking, alcohol consumption, exercise, and diet. logistic regression analysis was used to examine the relationship between the novel anthropometric indices and hyperuricemia, with model a unadjusted; model b adjusted for age, SBP, DBP, FPG, smoking, alcohol consumption, exercise, and diet. The predictive ability of the new obesity indices for hyperuricemia was assessed using subject work characteristics (ROC) analysis and area under the ROC curve (AUC), and the optimal threshold value for each of the new indices was determined. The optimal cut-off value was determined using the maximum Yoden indicator value (SEN + SPE-1). The area under the ROC curve for LAP, TyG, and ABSI indices was calculated and compared using the DeLong method in MedCalc Statistical Software version 19.0.4 (MedCalc Software bvba, Ostend, Belgium; https://www.medcalc.org; 2019)16. Two-tailed analyses were conducted and P value < 0.05 was considered statistically significant.

Results

Basic characteristics of the study sample stratified by sex status

A total of 14,834 participants were enrolled, with a mean age of 50.61 ± 11.50 years, 9,788 males and 5,046 females. The SUA levels of the subjects were 341.89 ± 84.18 µmol/L, which was higher in males (375.37 ± 74.68 µmol/L) than in females (276.93 ± 60.19 µmol/L) (P < 0.01). The prevalence of hyperuricemia in the subjects was 19.8%, which was higher in men (25.4%) than in women (9.0%) (P < 0.001). WC, SBP, DBP, FPG, TG, HDL, LDL, UA, BAI, CI, ABSI, BRI, VAI, LAP and CMI levels as well as the proportion of hyperuricemia, smoking, alcohol consumption, sensible diet, hypertension, and diabetes mellitus were higher in men than in women, and the difference was statistically significant (P < 0.001) (Table 1).

Table 1 Basic characteristics of the study sample stratified by sex status.
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SUA levels changes in participants quartiles by new anthropometric indicator

All participants were stratified by sex and then grouped according to BAI, CI, ABSI, BRI, VAI, LAP, and CMI quartiles. Both males and females showed a gradual trend of increasing SUA levels in participants with increasing quartile groups for each indicator (P < 0.001). Trend chi-square analysis showed that all participants stratified by sex and grouped according to BAI, CI, ABSI, BRI, VAI, LAP, and CMI quartiles, both males and females showed a gradual increasing trend in the proportion of participants with hyperuricemia as the quartile grouping of each indicator increased (P < 0.001) (Tables 2 and 3; Fig. 1).

Table 2 Changes in uric acid levels in male adults in quartiles of a new anthropometric indicator.
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Table 3 Changes in uric acid levels in female adults in quartiles of a new anthropometric indicator.
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Fig. 1
figure 1

Analysis of the percentage of hyperuricemia in quartiles of a new anthropometric indicator. Data are expressed as frequencies (percentages) for categorical variables. AIP atherogenic index of plasma, CMI cardiometabolic index, LAP lipid accumulation product, VAI visceral adiposity index, TyG index-triglyceride glucose index, ABSI A body shape index, BRI body roundness index.

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The correlation between novel anthropometric indicators and SUA levels

The novel anthropometric parameters BAI, CI, ABSI, BRI, VAI, LAP, and CMI correlated with SUA levels in both men and women and this correlation persisted after adjusting for age, SBP, DBP, FPG, smoking, alcohol consumption, exercise, and diet, with correlation coefficients for SUA levels in men of 0.286, 0.198, 0.253, 0.191, respectively. 0.293, 0.056, 0.174 (P < 0.001); correlation coefficients with SUA levels in women 0.274, 0.168, 0.229, 0.161, 0.271, 0.036, 0.167 (P < 0.001) (Table 4).

Table 4 Analysis of the biased correlation between novel anthropometric indicators and uric acid levels in adults.
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The effect of novel anthropometric indicators on hyperuricemia

In male participants, CMI, LAP, TyG, BRI were independent risk factors for participants’ SUA levels, adjusting for SBP, DBP, FPG, smoking, alcohol consumption, exercise and diet, LAP, TyG, BRI were independent risk factors for SUA levels in the male population, and the risk of developing hyperuricemia in the LAP, TyG, and BRI Q4 group was respectively 4.96, 3.98, and 1.83 times (P < 0.001); among female participants, LAP, TyG, and BRI were independent risk factors for SUA levels in the participants, and after adjusting for age, SBP, DBP, FPG, smoking, alcohol, exercise, and diet, LAP, TyG, and BRI were independent risk factors for SUA levels in the female population, and the risk of hyperuricemia in the LAP, TyG, and BRI Q4 groups was 3.62, 5.52, and 2.91 times higher than that of the Q1 group, respectively (P < 0.001) (Table 5; Fig. 2).

Table 5 Logistic regression analysis of the effect of novel anthropometric indicators on hyperuricemia.
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Fig. 2
figure 2

The novel anthropometric indicators on hyperuricemia. Model a unadjusted; model b adjusted for age, SBP, DBP, FPG, smoking, alcohol consumption, exercise, and diet. AIP atherogenic index of plasma, CMI cardiometabolic index, LAP lipid accumulation product, VAI visceral adiposity index, TyG index-triglyceride glucose index, ABSI A body shape index, BRI body roundness index.

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Novel anthropometric indicators on hyperuricemia for prediction of hyperuricemia

The novel anthropometric indices LAP, TyG, and BRI had predictive value for hyperuricemia in both male and female participants. In women, LAP predicted hyperuricemia with an AUC of 0.767, sensitivity of 0.725, and specificity of 0.687 (P < 0.001); TyG predicted hyperuricemia with an AUC of 0.746, sensitivity of 0.666, and specificity of 0.720 (P < 0.001); BRI predicted hyperuricemia with an AUC of 0.716, sensitivity of 0.699 and specificity of 0.627 (P < 0.001). Traditional obesity indicators BMI, WC, and WHtR predicted hyperuricemia AUC of 0.720, 0.721, and 0.728, respectively (P < 0.001). In men, LAP predicted hyperuricemia AUC of 0.694, sensitivity of 0.629, and specificity of 0.659 (P < 0.001); TyG predicted hyperuricemia AUC of 0.661, sensitivity of 0.689, and specificity of 0.560 (P < 0.001); BRI predicted hyperuricemia AUC of 0.599, sensitivity of 0.742 and specificity of 0.406 (P < 0.001). Traditional obesity indicators BMI, WC, and WHtR predicted hyperuricemia AUC of 0.642, 0.638, and 0.626, respectively (P < 0.001) (Table 6).

Table 6 ROC curve analysis of novel anthropometric indicators on hyperuricemia for prediction of hyperuricemia.
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Comparison of predictive ability of LAP, TyG index, WHtR and BIR for hyperuricemia

For the comparison of predictive ability for hyperuricemia in the male population, LAP was higher than TyG and WHtR, with an AUC area difference of 0.022 and 0.039 (P < 0.001), respectively, and the difference was not statistically significant when compared to TyG index and WHtR; in the female population, LAP was higher for the prediction of hyperuricemia in this population compared to TyG and BMI, with an AUC difference of 0.033 and 0.052 (P < 0.001), and the difference in predictive ability was not statistically significant compared to TyG and WHtR. In both men and women, LAP had a higher predictive ability for hyperuricemia than other obesity indicators in this population (P < 0.001), with TyG having the next highest predictive ability (Table 7).

Table 7 Comparison of predictive ability of LAP, TyG index, WHtR and BMI for hyperuricemia.
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Discussion

The prevalence of hyperuricemia was 25% in men aged 40–74 years in Shanghai and 14.9% in Nanjing, with a higher prevalence in men than in women5,10. This study showed that the prevalence of hyperuricemia in the adult population was 19.8%, 25.4% in males and 9.0% in females, which was lower than Shanghai and similar to Nanjing. Nanjing, Shanghai, and Suxi-Xi-Chang area belong to the Yangtze River Delta plain and the Taihu River system in China. Local resident have similar diets and cultures. Shanghai is the economic center of China, with an economic aggregate equivalent to two or three times that of Nanjing, and Suxi-Xi-Chang area is representative among these several cities. The prevalence of hyperuricemia varies among populations in different regions of China, possibly due to differences in the level of economic and lifestyles. Interestingly, in addition to its association with chronic disease, hyperuricemia may also increase mortality, especially in women over 50 years of age17. Hyperuricemia has become an important public health burden that deserves more attention.

Previous studies have used traditional anthropometric indices such as BMI, WC, and WHtR, and the positive correlation between excess body fat accumulation and hyperuricemia has been well established. Kim et al.18 demonstrated that the prevalence of hyperuricemia increased with increasing BMI, and that overweight people accounted for 61.7% of the patients with hyperuricemia. A cross-sectional study of 1426 participants in Mongolia, China, showed that the prevalence of hyperuricemia and SUA levels were most strongly correlated with WC and triglycerides19. BMI and alcohol consumption were associated with SUA levels with a significant interaction. BMI preceded hyperuricemia and the latter partially mediated the relationship between BMI and hypertension, independent of behavioral and other metabolic factors20. The relationship between hyperuricemia and BMI is bidirectional, SUA and its production can be involved in the progressive reduction in tissue insulin sensitivity, increased blood pressure and atherogenic dyslipidemia that can largely contribute to the excess in the risk of cardiovascular disease21. Higher BMI may increase SUA levels through renal impairment, causing a decrease in renal processing of uric acid22. Control of BMI is beneficial in reducing the risk of hyperuricemia, with individuals with increased weight or WC showing a higher propensity for hyperuricemia. BMI, WC, and WHtR are traditional anthropometric measures commonly used for weight management with some limitations. BMI has a weak ability to differentiate between muscle and fat accumulation5,6, and WC and WHtR are considered specific alternatives for assessing abdominal obesity23, as well as weak ability to differentiate between visceral and subcutaneous fat24.

US study finds LAP superior to BMI in identifying adults at cardiovascular risk and may be a better predictor of cardiovascular disease25. At the same time, LAP can better predict the risk of metabolic syndrome and diabetes which is superior to BMI and WC in identifying insulin resistance in nondiabetic individuals26. Gao et al.27compared the ability of BMI and LAP to predict the risk of hypertension in Inner Mongolian males. LAP was found to be more strongly associated with hypertension risk than BMI and may be the anthropometric measure of choice for predicting hypertension risk in men. There are fewer studies on the relationship between LAP and hyperuricemia in humans. The study in Liaoning of China, on the relationship between a novel anthropometric measure and hyperuricemia in a population found LAP was a better predictor of hyperuricemia than BMI (AUC, 0.568)28. A recent Korean study also found that LAP was superior to BMI and WC in predicting hyperuricemia in men, but similar to BMI and WC in predicting hyperuricemia in women29. The present study demonstrated the predictive value of LAP for hyperuricemia in both male and female participants. In females, LAP predicted hyperuricemia with an AUC of 0.767, sensitivity of 0.725, and specificity of 0.687. In males, LAP predicted hyperuricemia with an AUC of 0.694, sensitivity of 0.629, and specificity of 0.659, which suggests that LAP is more closely associated with hyperuricemia in females. The mechanisms underlying the predictive ability of LAP for hyperuricemia in this population need further study.

Mazidi et al.30 found there was a correlation between the TyG index and hyperuricemia in the Caucasian population. A cross-sectional study of 2243 participants in Xinjiang, China, found the TyG index was significantly associated with hyperuricemia and the TyG index was superior to obesity index in identifying hyperuricemia in a Chinese population undergoing medical examinations in Xinjiang, China even after adjusting for multivariable31. This study found TyG index was independent influence on hyperuricemia level in population, which was consistent with the results of previous studies. The TyG index is also of great value in identifying bisexual hyperuricemia, especially in women, who may have more complex endocrine factors related to female estrogen. Although our and other previous studies found an association between TyG index and hyperuricemia, it was unclear whether the specific mechanism was related to insulin resistance. Study north of Shanghai, found that compared with BMI, BRI was predictive of hypertension related complications in female individuals, but not in male individuals, and there was a sex difference32. This study found that BRI was an independent risk factor for hyperuricemia in Suxi-Chang area and had certain predictive power for hyperuricemia in both men and women. The relationship between BRI and hyperuricemia and the diagnostic ability of BRI need to be further explored.

The results of previous studies on the relationship between new anthropometric indicators and hyperuricemia in the population are also different. The study on the relationship between novel anthropometric measures and hyperuricemia in a population from Liaoning, China, which did not include TyG index, found that BAI, LAP and CMI were strong independent predictors of SUA28. A cross-sectional study of 2243 subjects in Xinjiang region of China found the AUC value of TyG index in predicting hyperuricemia was the highest in men (0.586). The AUC value of CMI in women was the highest, and the correlation between TyG index and hyperuricemia was stronger than that between obesity index in men and women, and TyG index was superior to other obesity indexes in identifying hyperuricemia in Xinjiang population31. In a recent cross-sectional study in Taiwan, comparing the predictive power of traditional obesity measures and new anthropometric measures for hyperuricemia, LAP had the highest area under the curve (0.691) in men, followed by TyG (0.661) and BMI (0.642). In the female population, the area under the curve (AUC) of LAP was also the highest (0.767), followed by TyG (0.746) and VAI (0.724), suggesting that obesity-related indexes were associated with hyperuricemia, and there were sex differences, and the association was higher in women than in men33. This is a large-scale cross-sectional study on the relationship between new anthropometric indicators and adult SUA level and hyperuricemia in Su-Xi-Chang areas of China. No significant relationship between CMI, ABSI and hyperuricemia level in the relevant population were found, which may be due to differences between regions. The traditional obesity index and the new anthropometric index have certain predictive ability for hyperuricemia in the adult population in Su-Wuxi-Chang area. Among the traditional obesity indicators WHtR had the highest predictive ability for hyperuricemia in women with an AUC of 0.728 and BMI had the highest predictive ability for hyperuricemia in men with an AUC of 0.642. These findings provide a new basis for comprehensive treatment of hyperuricemia in this regional people.

The present study did not find a significant relationship between CMI and ABSI and hyperuricemia in the relevant populations, with possible differences between populations. In this study, we found that traditional obesity indicators and novel anthropometric indices have certain predictive ability for hyperuricemia in adult populations in the Su-Wuxi-Chang area, and the predictive ability of the novel anthropometric indicators LAP and TyG is higher than that of the traditional obesity indicators. LAP has the highest area under the curve, followed by TyG. Among the traditional obesity indicators, WHtR had the highest predictive ability for hyperuricemia in females and BMI had the highest predictive ability for hyperuricemia in males. This result provides a new rationale for the comprehensive treatment of hyperuricemia in this population. Our results underlined the LAP as novel anthropometric indices, were strongly positively associated with hyperuricemia. In clinical practice, LAP which are obtainable and cost-effective could be potential monitoring indicators for hyperuricemia management in overweight/obese individuals. In addition, hyperuricemia can increase mortality, and the study of LAP and cardiovascular death events is also an interesting topic.

Limitations

This study was conducted on a health check-up population and was not based on a population-wide or community-based sample, so the representativeness has some limitations; this study was a cross-sectional study, which could not indicate the causal relationship between the new anthropometric indicators and hyperuricemia, and further prospective intervention studies are needed to clarify the relationship.