Abstract
Uric acid (UA) acts as an antioxidant but, when elevated, contributes to gout. Higher body mass index (BMI) is consistently linked to increased UA, and dyslipidemia correlates with UA levels. However, the extent to which these lipid fractions mediate the BMI–UA relationship remains limited, especially in the context of aging populations in China. This study aims to investigate the association between BMI and UA among Chinese adults and to quantify the mediating roles of triglycerides (TG), total cholesterol (TC), high‐density lipoprotein cholesterol (HDLC), and low‐density lipoprotein cholesterol (LDLC) in this relationship using nationally representative China Health and Retirement Longitudinal Study (CHARLS) data. We analyzed cross-sectional data from 8238 participants in the 2011 wave of the CHARLS, serum lipids were measured via standard CHARLS protocols. Covariates included demographics, lifestyle, clinical history, blood pressure, fasting glucose, sleep, and physical activity. Two multivariable linear regression models estimated the BMI–UA association before and after adjusting for TG, TC, HDLC, and LDLC. A parallel mediation analysis decomposed BMI’s total effect on UA into direct and indirect components via each lipid parameter, using 5000 bootstrapped samples. Multivariable linear regression showed that BMI was positively associated with UA (β = 0.12, P < .001, adjusted R2 = 0.19). After adjusting for lipid mediators, the association remained significant but attenuated (β = 0.09, P < .001, adjusted R2 = 0.23). Mediation analysis revealed that high serum TG, TC, and HDLC explain a meaningful part (28.4%) of the BMI–UA association (total indirect effect = 0.714, 95% confidence interval: 0.557–0.881), whereas LDLC shows no significant mediating role (indirect effect = −0.118, 95% confidence interval:−0.267–0.021). Serum TG, TC, and HDLC explain a meaningful part of the BMI–UA association, whereas LDLC shows no significant mediating role. These results highlight lipid metabolism as one pathway linking adiposity with urate levels and warrant confirmation in larger cohorts and mechanistic investigations.
Keywords: body mass index, HDL cholesterol, LDL cholesterol, serum lipids, total cholesterol, triglycerides, uric acid
1. Introduction
The global aging population is accompanied by significant metabolic and physiological challenges, particularly among older adults. Among various metabolic markers, uric acid (UA) has garnered considerable attention for its dual roles in human health. On the one hand, UA acts as an antioxidant, significantly contributes to tissue antioxidant capacity by scavenging reactive oxygen species and converting to allantoin[1,2]; on the other hand, hyperuricemia, resulting from disruptions in urate homeostasis, is a major contributor to gout and is strongly associated with cardiovascular, neurological, and metabolic diseases through inflammatory and oxidative mechanisms.[3–5] Simultaneously, body mass index (BMI), a widely recognized indicator of body fat, has been extensively studied in relation to metabolic health and chronic disease risks.[6,7] Numerous studies have demonstrated a positive association between higher BMI and elevated UA levels.[8–10] Gaining a clearer understanding of lipid’s role is essential for identifying actionable targets to mitigate hyperuricemia and its associated comorbidities in aging populations.
Dyslipidemia has been independently associated with hyperuricemia, as individual serum lipid fractions (including TC, high-density lipoprotein cholesterol [HDLC], low-density lipoprotein cholesterol [LDLC], and triglycerides [TG]) demonstrate significant correlations with serum UA concentrations and related metabolic disturbances.[11] It has been established that serum TG levels are positively correlated with serum UA levels, with the relationship exhibiting distinct patterns between sexes.[12] Recent longitudinal evidence shows a stronger link between the hypertriglyceridemic-waist phenotype and incident hyperuricemia in females than in males, with significant multiplicative interaction (P = .006) and higher odds ratios in women (2.36 vs 1.29).[13] However, research gaps persist regarding the integrated pathways through which various lipid fractions mediate the BMI-UA relationship. Most studies focus on single markers, failing to account for the parallel mediating effects of a comprehensive lipid profile. Furthermore, such multi-pathway evidence is particularly scarce for aging Chinese populations with distinct metabolic characteristics.
The China Health and Retirement Longitudinal Study (CHARLS) offers a robust platform for investigating this complex relationship. As a nationally representative longitudinal survey, CHARLS collects comprehensive health, socioeconomic, and biomarker data from middle-aged and older Chinese adults.[14] To address the aforementioned gaps, this study utilizes the CHARLS dataset to evaluate the potential mediating pathways through which different lipid components influence the association between BMI and UA. By leveraging this dataset, it is possible to explore the associations between UA, BMI, and TG with greater specificity, providing valuable insights into metabolic health dynamics among the aging population. Such studies not only enhance the understanding of BMI-UA interactions but also aid in identifying modifiable risk factors for metabolic disorders in older adults.
Therefore, this study aimed to assess the association between BMI and UA and to explicitly investigate the parallel mediating roles of serum lipids (including TG, TC, HDLC, and LDLC) in modulating this relationship in a nationally representative cohort of Chinese adults aged ≥ 45 years. By focusing on this demographic, which faces a high burden of both dyslipidemia and hyperuricemia, this study addresses the lack of multi-pathway evidence in aging Chinese populations and offers nuanced insights into age-related metabolic perturbations. Ultimately, by elucidating these mediating pathways, this work aims to support more effective UA‐lowering strategies and further reduce gout incidence in China’s aging population.
2. Method
2.1. Data source and study population
The CHARLS is a nationally representative biennial survey designed to collect comprehensive data on the health and well-being of individuals aged 45 years and older in China (http://charls.pku.edu.cn/). To ensure representativeness, CHARLS employs a multistage probability sampling strategy.[14] This study used data from the 2011 wave of CHARLS to examine the mediating role of blood lipid components in the association between BMI and UA. The CHARLS program was reviewed and approved by the Ethical Review Committee at Peking University in 2008 (IRB00001052-11015), and written informed consent was obtained from all participants prior to their enrollment. All procedures in this study complied with the ethical guidelines and regulations of CHARLS. The data were accessed on November 25, 2024, from the official CHARLS website for research purposes. All data used in this study were fully de-identified prior to public release, and the authors did not have access to any information that could identify individual participants during or after data collection.
Participants were included if they met the following criteria: age ≥ 45 years; available and valid data on key sociodemographic and health variables, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), gender, residential location (urban/rural), marital status, smoking and drinking status, sleep duration, educational attainment, BMI, and physical activity volume (PAV), and self-reported history of diabetes, kidney disease, and hypertension; complete biochemical data at baseline, including serum UA, TG, total cholesterol (TC), HDLC, low-density lipoprotein cholesterol (LDLC), and fasting glucose.
Meanwhile, participants were excluded if they had a self-reported history of dyslipidemia or receiving any treatment for dyslipidemia (including traditional Chinese medicine, Western medication, or other treatments). After applying these criteria, a total of 8238 participants were included in the final analysis (Table 1).
Table 1.
Inclusion and exclusion criteria of study participants.
| Category | Criteria |
|---|---|
| Inclusion Criteria | |
| 1 | Age ≥ 45 yr |
| 2 | Available and valid data on key sociodemographic and clinical variables: SBP, DBP, sex, residential location (urban/rural), marital status, smoking status, drinking status, sleep duration, educational level, BMI, PAV, and self-reported history of diabetes, kidney disease, and hypertension |
| 3 | Complete baseline biochemical measurements: UA, TG, TC, HDLC, LDLC, and fasting glucose |
| Exclusion Criteria | |
| 1 | Self-reported history of dyslipidemia |
| 2 | Receiving any treatment for dyslipidemia (including traditional Chinese medicine, Western medication, or other lipid-lowering therapies) |
BMI = body mass index, DBP = diastolic blood pressure, HDLC = high-density lipoprotein cholesterol, LDLC = low-density lipoprotein cholesterol, PAV = physical activity volume, SBP = systolic blood pressure, TC = total cholesterol, TG = triglycerides, UA = uric acid.
2.2. Measures
2.2.1. Serum lipids
Serum lipid profiles were assessed to evaluate their potential mediating role in the association between BMI and UA levels. The lipid parameters included TC, HDLC, LDLC, and TG. All lipid concentrations were converted from mg/dL to mmol/L using standard conversion factors: TC, HDLC, and LDLC values were divided by 38.67, while TG values were multiplied by 0.01129. Following the 2023 Chinese Guideline for Lipid Management, the clinical cutoff values for identifying high or abnormal lipid levels are defined as follows: High TG ≥ 2.3 mmol/L, High TC ≥ 6.2 mmol/L, High LDLC ≥ 4.1 mmol/L, and Low HDLC < 1.0 mmol/L.[15] These standardized lipid measures were incorporated into the mediation analysis to explore their intermediary effects on the relationship between BMI and UA.
2.2.2. Physical activity
Physical activity was assessed using self-reported data from the 2011 wave of CHARLS, following a structure adapted from the International Physical Activity Questionnaire. Participants were asked whether they engaged in at least 10 minutes of vigorous, moderate, or low-intensity physical activity during a typical week. For those who responded affirmatively, follow-up questions assessed the number of days per week[1–7] and duration per day, with response options categorized as “≥ 10 minutes and < 30 minutes”, “≥ 30 minutes and < 2 hours”, “≥ 2 hours and < 4 hours”, or “≥ 4 hours”. Following previous studies, we converted these categories into midpoint values of 20, 75, 180, and 240 minutes, respectively.[16,17] The weekly duration for each physical activity level was calculated by multiplying the number of days per week by the corresponding daily duration. The total PAV was then computed in metabolic equivalent minutes per week (MET-min/wk) using the following formula[18]: PAV = (8.0 × weekly duration of vigorous activity) + (4.0 × weekly duration of moderate activity) + (3.3 × weekly duration of low-intensity activity). In accordance with International Physical Activity Questionnaire classification criteria, participants were grouped into 2 categories: < 600 MET-min/wk, indicating physical inactivity; and ≥ 600 MET-min/wk, indicating adequate activity.[19]
2.2.3. Covariates assessment
In this study, several control variables were included to account for potential confounding factors that may influence the serum UA. These control variables were selected based on their relevance to metabolic health and their potential association with the study outcomes. Demographic variables included gender (male/female), age (in years), education level (illiterate, primary school, secondary/high school, and college/university or above), marital status (unmarried/married), and place of residence (rural/urban). Lifestyle-related factors comprised smoking status (no/yes), drinking status (no/yes), and average sleep duration (in hours per night over the past month). Clinical variables included fasting blood glucose (mmol/L), blood pressure was represented by the mean of 3 SBP and 3 DBP readings (mm Hg), both treated as continuous variables. In addition, self-reported histories of diabetes, hypertension, and kidney disease were included as binary indicators (no/yes).
2.3. Statistical analysis
All statistical analyses were conducted in R (version 4.4.2). Descriptive statistics were initially computed to summarize the demographic and baseline characteristics of the sample. For continuous variables, the mean and standard deviation were reported, while categorical variables were summarized using frequencies and percentages. Spearman rank‐order correlations were then used to assess bivariate relationships among BMI, UA, TG, TC, HDLC, and LDLC. Thereafter, 2 sequential multivariable linear regression models were specified: Model 1 estimated the association of UA with BMI, adjusting for age, sex, education, marital status, smoking, alcohol use, SBP, DBP, sleep duration, fasting glucose, urban/rural residence, diabetes history, hypertension history, kidney disease history, and physical activity category; Model 2 expanded on Model 1 by additionally including TG, TC, HDLC, and LDLC as lipid mediators. Standardized β-coefficients were reported to facilitate comparison across predictors.
Finally, we conducted a parallel multiple-mediation analysis using a structural equation modeling approach with the lavaan package in R. In this model, BMI was specified as the independent variable, UA as the dependent variable, and TG, TC, HDLC, and LDLC were entered as parallel mediators. The analysis simultaneously adjusted for age, gender, education, marital status, smoking, drinking, blood pressure, sleep, blood glucose, residential address, histories of diabetes, hypertension, kidney disease, and physical activity. Direct, indirect (mediated), and total effects were estimated using nonparametric bootstrap resampling (5000 iterations).
3. Results
3.1. Characteristics of participants
Table 2 summarizes the baseline characteristics of the 8238 participants. The study population had a mean age of 59.4 years and a mean BMI of 23.3 kg/m2, with a slight female majority (52.7%). The cohort was characterized by a high proportion of individuals with primary education or below. The vast majority of the participants were married and reported healthy lifestyle habits, including being nonsmokers and nondrinkers. While sleep duration averaged around 6.3 hours, over two-thirds of the sample exhibited insufficient physical activity levels. Regarding clinical history, hypertension was the most prevalent self-reported condition compared to diabetes and kidney disease. Clinical profiles, including mean blood pressure, serum lipids, fasting glucose, and UA levels, are detailed in Table 2.
Table 2.
Baseline characteristics of the study population.
| Characteristics | Total (8238) | Female (4342) | Male (3896) |
|---|---|---|---|
| Age (yr) (%) | 59.4 (9.4) | 58.7 (9.4) | 60.3 (9.3) |
| Education (%) | |||
| Illiterate | 2394 (29.1) | 1871 (43.1) | 523 (13.4) |
| Primary | 3434 (41.7) | 1546 (35.6) | 1888 (48.5) |
| Second/High school | 2315 (28.1) | 895 (20.6) | 1420 (36.4) |
| College/Uni+ | 95 (1.2) | 30 (0.7) | 65 (1.7) |
| Smoking (%) | |||
| No | 4947 (60.1) | 3996 (92.0) | 951 (24.4) |
| Yes | 3291 (39.9) | 346 (8.0) | 2945 (75.6) |
| Drinking (%) | |||
| No | 5503 (66.8) | 3790 (87.3) | 1713 (44.0) |
| Yes | 2735 (33.2) | 552 (12.7) | 2183 (56.0) |
| Marital (%) | |||
| Married | 6902 (83.8) | 3492 (80.4) | 3410 (87.5) |
| Unmarried | 1336 (16.2) | 850 (19.6) | 486 (12.5) |
| Residential address (%) | |||
| City | 575 (7.0) | 289 (6.7) | 286 (7.3) |
| Rural | 7663 (93.0) | 4053 (93.3) | 3610 (92.7) |
| BMI (kg/m2) | 23.3 (3.7) | 23.7 (3.9) | 22.7 (3.4) |
| Sleep (hr) | 6.3 (1.9) | 6.3 (2.0) | 6.4 (1.8) |
| SBP (mm Hg) | 130.1 (21.5) | 130.2 (22.4) | 130.1 (20.5) |
| DBP (mm Hg) | 75.5 (12.1) | 75.1 (11.8) | 75.9 (12.4) |
| Hypertension history (%) | |||
| No | 6467 (78.5) | 3343 (77.0) | 3124 (80.2) |
| Yes | 1771 (21.5) | 999 (23.0) | 772 (19.8) |
| Diabetes history (%) | |||
| No | 7879 (95.6) | 4128 (95.1) | 3751 (96.3) |
| Yes | 359 (4.4) | 214 (4.9) | 145 (3.7) |
| Kidney disease history (%) | |||
| No | 7716 (93.7) | 4102 (94.5) | 3614 (92.8) |
| Yes | 522 (6.3) | 240 (5.5) | 282 (7.2) |
| PAV (%) | |||
| < 600 | 5693 (69.1) | 3011 (69.3) | 2682 (68.8) |
| ≥ 600 | 2545 (30.9) | 1331 (30.7) | 1214 (31.2) |
| TC (mmol/L) | 5.0 (1.0) | 5.1 (1.0) | 4.8 (1.0) |
| TG (mmol/L) | 1.4 (1.0) | 1.5 (1.0) | 1.4 (1.1) |
| HDLC (mmol/L) | 1.3 (0.4) | 1.4 (0.4) | 1.3 (0.4) |
| LDLC (mmol/L) | 3.0 (0.9) | 3.1 (0.9) | 2.9 (0.9) |
| Fasting glucose (mmol/L) | 6.1 (2.0) | 6.1 (2.0) | 6.1 (1.9) |
| UA (µmol/L) | 264.2 (74.3) | 237.2 (62.2) | 294.2 (75.3) |
BMI = body mass index, DBP = diastolic blood pressure, HDLC = high-density lipoprotein cholesterol, LDLC = low-density lipoprotein cholesterol, PAV = physical activity volume, SBP = systolic blood pressure, TC = total cholesterol, TG = triglycerides, UA = uric acid.
3.2. Correlation between key variables
Spearman correlation analysis (Table 3) revealed significant associations between BMI, UA, and serum lipid components. BMI was positively correlated with TG, TC, and UA, while exhibiting a significant inverse relationship with HDLC. Regarding the lipid mediators, both TG and TC showed positive correlations with UA levels. In contrast, HDLC was negatively associated with UA, whereas LDLC did not show a statistically significant correlation with UA.
Table 3.
Correlations between BMI, UA, and serum lipid parameters.
| BMI | TG | UA | TC | HDLC | LDLC | |
|---|---|---|---|---|---|---|
| BMI | 1.000 | |||||
| TG | 0.290*** | 1.000 | ||||
| UA | 0.081*** | 0.132*** | 1.000 | |||
| TC | 0.099*** | 0.274*** | 0.066*** | 1.000 | ||
| HDLC | −0.312*** | −0.521*** | −0.120*** | 0.206*** | 1.000 | |
| LDLC | 0.115*** | 0.078*** | 0.020 | 0.839*** | 0.100*** | 1.000 |
BMI = body mass index, HDLC = high-density lipoprotein cholesterol, LDLC = low-density lipoprotein cholesterol, TC = total cholesterol, TG = triglycerides, LDLCUA = uric acid.
P < .001.
3.3. Mediation analysis
In multivariable linear regression (Table 4), BMI was positively associated with UA (β = 0.12, P < .001, adjusted R2 = 0.19) in Model 1. After adjusting for lipid mediators in Model 2, the association remained significant but attenuated (β = 0.09, P < .001, adjusted R2 = 0.23), suggesting partial mediation. In the final model, TG and TC remained positively associated with UA, while HDLC and LDLC showed inverse relationships.
Table 4.
Multivariable linear regression analysis of the association between BMI, lipids and UA.
| Model 1 | Model 2 (with mediator) | |||||||
|---|---|---|---|---|---|---|---|---|
| B | SE | β | P value | B | SE | β | P value | |
| BMI | 2.51 | 0.22 | 0.13 | < 0.001 | 1.79 | 0.22 | 0.09 | <0.001 |
| Age (yr) | 0.99 | 0.10 | 0.13 | < 0.001 | 1.00 | 0.10 | 0.13 | <0.001 |
| Gender | 54.01 | 2.23 | 0.36 | < 0.001 | 57.38 | 2.21 | 0.39 | <0.001 |
| Education (reference = College/Uni+) | ||||||||
| Illiterate | −8.38 | 7.20 | −0.051 | 0.24 | −11.04 | 7.05 | –0.067 | 0.12 |
| Primary | −3.39 | 7.07 | −0.023 | 0.63 | −6.45 | 6.93 | –0.043 | 0.35 |
| Secondary/High school | −3.87 | 7.06 | −0.023 | 0.58 | −6.70 | 6.92 | –0.041 | 0.33 |
| Marital (reference = Married) | −2.40 | 2.07 | −0.012 | 0.24 | −1.82 | 2.02 | –0.009 | 0.37 |
| Smoking (reference = No) | −1.00 | 2.10 | −0.007 | 0.63 | −2.28 | 2.06 | –0.015 | 0.27 |
| Drinking (reference = No) | 7.13 | 1.78 | 0.05 | < 0.001 | 6.73 | 1.77 | 0.04 | <0.001 |
| SBP | 0.11 | 0.06 | 0.03 | 0.06 | 0.10 | 0.06 | 0.03 | 0.08 |
| DBP | 0.16 | 0.10 | 0.03 | 0.10 | 0.07 | 0.09 | 0.01 | 0.48 |
| Sleep | −0.366 | 0.39 | −0.009 | 0.35 | −0.297 | 0.38 | −0.008 | 0.44 |
| Residential address (reference = city) | −14.77 | 3.01 | −0.051 | < 0.001 | −13.99 | 2.95 | −0.048 | <0.001 |
| Diabetes history (reference = No) | −12.99 | 3.84 | −0.036 | < 0.001 | −9.05 | 3.77 | –0.025 | 0.02 |
| Hypertension history (reference = No) | 12.98 | 1.98 | 0.07 | < 0.001 | 12.35 | 1.94 | 0.07 | <0.001 |
| Kidney disease history (reference = No) | 2.28 | 3.03 | 0.01 | 0.45 | 1.89 | 2.97 | 0.01 | 0.52 |
| Physical activity (reference = inactivity) | −1.66 | 1.60 | −0.010 | 0.30 | −0.96 | 1.57 | −0.006 | 0.54 |
| Blood glucose | −0.397 | 0.40 | −0.010 | 0.32 | −2.155 | 0.40 | −0.057 | <0.001 |
| TC | 12.36 | 2.91 | 0.16 | <0.001 | ||||
| HDLC | −9.03 | 3.43 | −0.048 | 0.01 | ||||
| LDLC | −5.82 | 2.87 | −0.070 | 0.04 | ||||
| TG | 7.44 | 1.73 | 0.10 | <0.001 | ||||
B = unstandardized coefficient, BMI = body mass index, DBP = diastolic blood pressure, HDLC = high-density lipoprotein cholesterol, LDLC = low-density lipoprotein cholesterol, SBP = systolic blood pressure, SE = standard error, TC = total cholesterol, TG = triglycerides, β = standardized coefficient.
The parallel mediation analysis (Fig. 1 and Table 5) quantified the contribution of each lipid component to the BMI–UA relationship. The total indirect effect via lipid mediators was 0.714 (95% CI: 0.557–0.881), explaining 28.4% of the total effect. Specifically, TG was the strongest mediator, accounting for 14.3% of the total effect, followed by HDLC (10.7%) and TC (8.1%), all of which reached statistical significance. In contrast, LDLC showed no significant mediating role (95% CI:−0.267–0.021). Collectively, these findings indicate that nearly one-third of the impact of BMI on UA is explained by its influence on specific lipid components, primarily TG, HDLC, and TC.
Figure 1.
Path diagram of the parallel mediation model examining the effect of BMI on serum UA via TG, TC, HDLC, and LDLC. BMI = body mass index, HDLC = high‐density lipoprotein cholesterol, LDLC = high‐density lipoprotein cholesterol, TC = total cholesterol, TG = triglycerides, UA = uric acie. * P < .05, ** P < .01, *** P < .001.
Table 5.
Direct and Indirect Effects of BMI on UA via TG, TC, HDLC, and LDLC.
| Pathway | Effect | Boot SE | 95% CI Lower | 95% CI Upper | Effect (%) |
|---|---|---|---|---|---|
| Direct effect | 1.800*** | 0.228 | 1.346 | 2.238 | 71.6 |
| BMI → TG → UA | 0.360*** | 0.107 | 0.152 | 0.574 | 14.3 |
| BMI → TC → UA | 0.203** | 0.073 | 0.075 | 0.359 | 8.1 |
| BMI → HDLC → UA | 0.268* | 0.125 | 0.02 | 0.513 | 10.7 |
| BMI → LDLC → UA | −0.118 | 0.074 | −0.267 | 0.021 | −4.7 |
| Total mediation effect | 0.714*** | 0.082 | 0.557 | 0.881 | 28.4 |
| Total effect | 2.514*** | 0.226 | 2.071 | 2.944 | 100.0 |
BMI = body mass index, Boot SE = bootstrapped standard error, CI = confidence interval, HDLC = high-density lipoprotein cholesterol, LDLC = low-density lipoprotein cholesterol, TC = total cholesterol, TG = triglycerides, UA = uric acid.
P < .001.
P < .01.
P < .05.
4. Discussion
This study comprehensively examined the mediating role of lipid components in the association between BMI and serum UA levels among middle-aged and older Chinese adults. We found that BMI was significantly associated with higher levels of TG and TC, and lower levels of HDLC, all of which, in turn, were associated with serum UA concentrations. Among them, TG had the strongest mediating effect (14.3%), followed by HDLC (10.7%) and TC (8.1%). Conversely,LDLC showed a weak, inverse, and statistically nonsignificant mediating effect. These findings highlight metabolic pathways that may partially explain obesity-related hyperuricemia.
Our findings are consistent with previous research demonstrating a strong positive correlation between BMI and UA levels.[8,9,20] For instance, a cross-sectional study involving 2482 subjects revealed statistically significant positive correlations between UA and serum TC, LDLC, as well as TG, while HDLC was negatively correlated with UA.[21] Another cross-sectional study in young Bangladeshi adults (n = 458) reported significant positive correlations between UA and TG, TC, and LDLC (all P < .001), with a significant negative correlation with HDLC (P < .001).[22] These findings imply that the complex interplay of various lipid components may contribute to the BMI-UA relationship. Future studies could further explore the relative contributions and potential interactive effects of these lipid mediators in the context of BMI and UA, which may provide a more comprehensive understanding of the underlying mechanisms of obesity-related hyperuricemia and offer additional therapeutic targets.
The mechanisms underlying the association between obesity and hyperuricemia are multifactorial, involving metabolic, inflammatory, and genetic pathways. Dysfunctional adipose tissue in obese individuals secretes adipokines such as leptin and adiponectin,[23] which disrupt UA metabolism by enhancing xanthine oxidase (XO) activity.[24] Insulin resistance, a key characteristic of obesity, worsens hyperuricemia by impairing the kidney’s ability to excrete urate.[25] Additionally, Duan et al demonstrated that the association between BMI and UA levels is partially mediated by the gut microbiota: particularly by Proteobacteria and bacterial genera such as Ralstonia, Oscillospira, and Faecalibacterium.[26] Experimental studies in animal models provide direct evidence for the interplay between lipids, UA, and obesity/BMI. In high-fat diet-fed mice, obesity increases hepatic XO activity and UA production, which promotes lipid accumulation in the liver (hepatic steatosis) and dyslipidemia through enhanced intestinal fat absorption; pharmacological XO inhibition (e.g., with febuxostat) reduces hepatic lipid accumulation, improves steatohepatitis, and ameliorates dyslipidemia.[27,28] These findings indicate a bidirectional relationship where obesity-driven XO activation elevates UA, and hyperuricemia in turn exacerbates lipid dysregulation and fat deposition, with lipids (especially TG) acting as mediators in the pathway from elevated BMI/obesity to hyperuricemia-related metabolic disturbances.[29,30] These mechanisms collectively underscore the multifaceted nature of obesity-related hyperuricemia and provide a broader context for understanding the role of lipids as a mediator in this pathway.
Despite the strengths of our study, several limitations should be acknowledged. The cross‐sectional design precludes causal inference, as mediation analysis cannot establish temporality or rule out reverse causation. Reliance on self‐reported physical activity and comorbidity status may have introduced recall bias and misclassification. Although a comprehensive set of sociodemographic, lifestyle, and clinical covariates was included, residual confounding by unmeasured factors (such as dietary intake or genetic polymorphisms) remains possible. Lipid and UA levels were measured only once, which may not capture long‐term fluctuations. Moreover, while the structural equation modeling framework allowed simultaneous estimation of multiple lipid mediators, it did not account for potential nonlinear or interactive effects among lipids, an area that future studies should explore using longitudinal designs and more detailed metabolic profiling.
Given that TG, HDLC, and TC mediated modest but measurable proportions of the BMI–UA association in our model, existing evidence on lifestyle and pharmacological influences on lipid profiles offers useful context for interpreting these metabolic pathways. Dietary patterns such as the Mediterranean diet have been shown to reduce TG levels[31] and are inversely associated with serum UA,[32] while regular aerobic exercise modestly increases HDLC[33] and has also been linked to lower UA levels.[34] Lipid‐lowering medications, including statins, can reduce TC and LDL‐C and have been observed to accompany small reductions in serum urate.[35] Moreover, weight‐loss interventions likewise improve adiposity and lipid profiles and are associated with declines in UA.[36] Taken together, these observations highlight the potential involvement of lipid-related metabolic pathways in the broader network connecting BMI with UA.
5. Conclusion
In summary, our study demonstrates that serum lipid fractions (particularly TG, HDLC, and TC) explain approximately 28.4% of the association between BMI and UA levels in middle-aged and older Chinese adults. TG emerged as the strongest mediator (14.3%), followed by HDLC (10.7%) and TC (8.1%), whereas LDLC did not exert a significant indirect effect.These findings refine our understanding of the metabolic pathways linking adiposity to hyperuricemia and indicate that lipid metabolism represents 1 component of the broader network through which BMI may influence urate homeostasis. By clarifying these lipid-related pathways, our findings highlight the importance of considering metabolic profiles alongside BMI when evaluating hyperuricemia risk. Future work should include larger and more diverse cohort studies to validate these associations, as well as mechanistic research to elucidate the biological pathways through which lipid metabolism may influence urate regulation.
Acknowledgments
We extend our sincere gratitude to the National School of Development at Peking University and the China Social Science Survey Center at Peking University for providing the CHARLS data. This research was supported by the Rising Star Talent Program of Beijing Geriatric Hospital. The authors declare no conflicts of interest.
Author contributions
Conceptualization: Chaoxi Zhou.
Data curation: Jianhua Ma, Chuanyi Zang.
Formal analysis: Chaoxi Zhou.
Funding acquisition: Chaoxi Zhou.
Investigation: Zhilin Liang.
Software: Chuanyi Zang, Jie Tang.
Validation: Zhilin Liang.
Visualization: Jie Tang.
Writing – original draft: Chaoxi Zhou, Jianhua Ma.
Writing – review & editing: Chaoxi Zhou, Jie Tang, Zhilin Liang.
Abbreviations:
- BMI
- body mass index
- CHARLS
- China Health and Retirement Longitudinal Study
- CI
- confidence interval
- DBP
- diastolic blood pressure
- HDLC
- high-density lipoprotein cholesterol
- LDLC
- low-density lipoprotein cholesterol
- MET-min/wk
- metabolic equivalent minute per week
- PAV
- physical activity volume
- SBP
- systolic blood pressure
- TC
- total cholesterol
- TG
- triglycerides
- UA
- uric acid
- XO
- xanthine oxidase
This research was funded by Rising Star Talent Program of Beijing Geriatric Hospital.
This study is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which obtained ethical approval from the Institutional Review Board of Peking University (approval number: IRB00001052-11015). Written informed consent was obtained from all participants prior to their enrollment in the study. All procedures were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Zhou C, Ma J, Zang C, Tang J, Liang Z. Mediating effect of serum lipids on the BMI–uric acid association: A cross-sectional study. Medicine 2026;105:16(e48325).
CZ and JM contributed to this article equally.
Contributor Information
Jianhua Ma, Email: mjh7712@163.com.
Chuanyi Zang, Email: chuanyizang121@163.com.
Jie Tang, Email: tangjie10095@tom.com.
Zhilin Liang, Email: liangzhilin565@163.com.
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