Abstract
Background
The uric acid to high-density lipoprotein cholesterol ratio (UHR) is a novel inflammatory and metabolic biomarker. Studies have confirmed that it is closely associated with the onset and progression of various metabolic and cardiovascular diseases. However, the relationship between UHR and H-type hypertension (HTH) remains unclear. This study aims to investigate the association between the UHR and HTH.
Methods
This study was conducted as a cross-sectional analysis. The study population consisted of adults aged over 30 years from six randomly selected regions in Hunan Province, China, among whom 1,835 eligible participants were included in the analysis.Data were collected through questionnaire surveys, physical examinations, and laboratory tests. Logistic regression model was used to analyze the association between the UHR index and HTH. RCS were used to assess the potential nonlinear relationship between the two variables. Subgroup analyses were conducted to explore the interaction effects of different subgroups on the association between UHR and HTH. ROC curves were applied to evaluate the discriminatory ability of UHR for HTH.
Results
The analysis included 1835 participants, of whom 1026 had HTH, and 1,069 participants (58.3%) were female. Multivariate logistic regression analysis showed that, each one‑standard‑deviation increase in the UHR index was associated with a 40% increase in the risk of HTH (OR = 1.41, 95%CI: 1.24–1.60, P < 0.001). RCS analysis indicated a nonlinear dose–response relationship between the UHR index and the risk of HTH (P for nonlinear = 0.045). We further identified an inflection point of the threshold effect of UHR, above and below which the prevalence of HTH exhibited distinct increasing trends.Subgroup analysis showed that the association between UHR and HTH was more pronounced in females and the Han Chinese population. ROC curve analysis demonstrated that UHR had moderate discriminatory ability for HTH, with AUC of 0.618 (95%CI: 0.592–0.644, P < 0.001).
Conclusion
UHR is independently associated with HTH and exhibits a positive nonlinear association with the condition, with an inflection point at 0.09.
Keywords: Uric acid to high-density lipoprotein cholesterol ratio, H‑type hypertension, Cross-sectional study
Introduction
Hypertension is a leading cause of cardiovascular and cerebrovascular diseases, affecting the health of over 1 billion people worldwide [1]; currently, more than one-quarter of the global adult population has this condition [2]. The China Hypertension Survey (2012–2015) reported that 245 million adults aged ≥ 18 years had hypertension [3]. H-type hypertension (HTH) is a subtype of hypertension, defined as a blood homocysteine (Hcy) concentration exceeding 15 µmol/L in hypertensive patients. Among Chinese adults, the prevalence of hypertension is 23.2%, with approximately 75% of these patients having HTH [4]. HTH is not simply the coexistence of hypertension and hyperhomocysteinemia(HHcy), but rather a synergistic interaction between the two risk factors, producing an amplified effect of “1 + 1>2” on vascular injury [5]. Studies have shown that due to genetic factors, dietary structure, and environmental influences, HHcy remains prevalent in China, and its morbidity rate has been continuously rising over the past 20 years [6]. Therefore, developing and implementing early screening and precise intervention strategies is a key step in the prevention and control of HTH.
Uric acid (UA) is the end product of purine metabolism in the human body [7]. It exerts selective antioxidant and free radical scavenging effects under physiological conditions such as gout, hypertriglyceridemia, and diabetes [8, 9], and is closely associated with various metabolic diseases.High-density lipoprotein cholesterol (HDL-C) is a plasma lipoprotein with anti-inflammatory and antioxidant properties that plays a significant role in metabolic disorders [10]. Both have been confirmed to be closely associated with hypertension and cardiovascular disease [11]. The uric acid to high-density lipoprotein cholesterol ratio (UHR) integrates the pro-inflammatory and pro-oxidative effects of UA with the anti-inflammatory and antioxidant properties of HDL-C [12], serving as a novel indicator that reflects inflammatory and metabolic status [13]. A cross-sectional study based on the National Health and Nutrition Examination Survey found that UHR was significantly positively correlated with the prevalence of hypertension [14]. Other studies have shown that an elevated UHR is significantly associated with various diseases, including metabolic syndrome [15] and nonalcoholic fatty liver disease [16]. UHR can also serve as a novel composite lipid marker for predicting various diseases, including obesity, hypertension, and metabolic syndrome [17]. Although an association between UHR and hypertension has been established, studies directly examining the link between UHR and HTH are still scarce.
This study aims to investigate the association between UHR and HTH, with the goal of providing preliminary evidence for its potential utility in risk stratification, generating hypotheses for future prospective studies, and offering a reference for clinical decision-making.
Methods
Study population
This cross-sectional study employed a multi-stage random cluster sampling method to collect data from urban communities, rural communities, and hospitals in six randomly selected cities of Hunan Province—Changsha, Yueyang, Zhuzhou, Hengyang, Yongzhou, and Xiangxi—between 2013 and 2014. A total of 5,258 adults were initially enrolled, of whom 1,835 participants were ultimately included in the final analysis (see Fig. 1 for the flow chart). Exclusion criteria were: recent major surgery; use of lipid-lowering agents, antidiabetic drugs, vitamin B, or folic acid; secondary hypertension; history of diabetes mellitus; history of stroke or heart disease; and malignancy. This study was approved by the Ethics Committee of Hunan Provincial People’s Hospital, and written informed consent was obtained from all participants.
Fig. 1.
Flow chart of the study participants
Data collection and definitions
In this study, face-to-face questionnaire surveys were used to collect participant data, and the investigators received specialized and standardized training. The questionnaire covered general demographic characteristics and behavioral habits, including age, sex, marital status, smoking, and alcohol consumption. Physical examinations were conducted by professionally trained examiners, and the measurements included height, weight, and blood pressure. For laboratory examinations, fasting blood samples were collected from participants after at least 8 h of fasting, and the measured indicators included homocysteine (Hcy), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), and others.
Hypertension was defined as a systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg during measurement [18], or a previous diagnosis of hypertension [19]. Educational level was categorized into five groups: illiterate, primary school, junior high school, senior high school, and junior college or above. Income level (in 10,000 yuan) was classified as ≤ 1, 1–4, 4–6, 6–8, and ≥ 8. Marital status was divided into four groups: unmarried, married, divorced, and separated or widowed. Smoking was defined as currently smoking or having smoked continuously for at least 6 months [20]. Alcohol consumption was defined as drinking at least three times per week for 6 months or more [20]. BMI was calculated as body weight (kg) divided by height squared (m²). UHR was calculated using the formula: UHR = UA (mg/dL) / HDL-C (mg/dL) [21].
Covariates
Potential confounding factors were selected based on prior evidence, clinical relevance, and univariate analysis. Multicollinearity among the selected variables was assessed using the variance inflation factor (VIF), with values < 5 indicating no significant collinearity [22]. The variables included were: age, gender, marital status, education, race, income, smoking, drinking, TC, TG, ALT.
Statistical analysis
Categorical variables were expressed as counts (percentages). Normally distributed continuous data were expressed as mean±standard deviation, while non-normally distributed continuous data were expressed as median and interquartile range. Differences in continuous variables between groups were compared using the independent samples t-test or non-parametric rank-sum test. Differences in categorical variables between groups were compared using the chi-square test.
The UHR index was analyzed by quartiles (Q1–Q4), with the lowest quartile (Q1) serving as the reference category. Q1: UHR ≤ 0.08, Q2: 0.08–0.10, Q3: 0.10–0.14, Q4: ≥ 0.14.
Binary logistic regression analysis was used to explore the association between UHR and the risk of HTH, and model adjustment was performed to control for potential confounding factors. The models were adjusted as follows: Model 1 included no covariates; Model 2 adjusted for general demographic characteristics (including age, sex, marital status, education level, ethnicity, and income); Model 3 was further adjusted for smoking, alcohol consumption, TC, TG, and ALT based on Model 2.
Restricted cubic splines(RCS) were used to evaluate the association between UHR and HTH, to determine the threshold effect of the nonlinear model and identify significant inflection points. Threshold effect analysis was performed using two competing models: Model 1 was a standard logistic regression model assuming a linear relationship between UHR and the risk of HTH; Model 2 was a piecewise logistic regression model fitted with a potential inflection point (threshold). Finally, the likelihood ratio test was used to determine which model (linear Model 1 or piecewise Model 2) provided a statistically significant better fit to the data.
Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of UHR for HTH. The area under the ROC curve (AUC) of UHR was quantified and compared with that of UA and HDL-C, and the optimal cutoff value was determined.Meanwhile, a basic multivariable model (Model), adjusting for age, sex, marital status, income, education level, smoking, alcohol consumption, triglycerides, total cholesterol, alanine aminotransferase, and an extended model (Model + UHR), which added the uric acid to high-density lipoprotein cholesterol ratio to the basic model, were constructed. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative ability of the two models, and the DeLong test was employed to compare the difference in AUC.
All statistical analyses were performed using SPSS 25 and R 4.5.0 software. Two-sided tests were used, and a P value < 0.05 was considered statistically significant.
Subgroup analysis and sensitivity analysis
Subgroup analyses were conducted to test the moderating effects of age (< 60 vs. ≥60 years), sex, and race. To assess between‑subgroup heterogeneity, interaction terms were added to the multivariable models, and the likelihood ratio test was used to evaluate the interaction effects.
To assess the robustness of our findings, we conducted a series of sensitivity analyses. First, we calculated the E‑value [23] to quantify the minimum strength of association that an unmeasured confounder would need to have with both UHR and HTH to fully explain away the observed effect. Second, we recalculated UHR using different unit definitions (uric acid in µmol/L and mg/dL; HDL‑C in mmol/L and mg/dL) to examine whether our results depended on the specific formula for UHR. Third, we constructed multiple regression models with different sets of covariate adjustments to evaluate the sensitivity of the effect estimates to changes in model specification.
Results
Characteristics of the study participants
A total of 1835 participants were included in this study, of whom 1069 (58.3%) were Female. As shown in Table 1, and 1026 participants (53.96%) had HTH.Comparative analysis between the two groups showed that there were statistically significant differences (P < 0.05) in age, sex, marital status, education level, smoking, alcohol consumption, income, UHR, TG, TC, ALT, BUN, Cr, UA, HDL-C, and VLDL-C between the HTH group and the control group; whereas ethnicity, BMI, LDL-C, and CRP showed no statistically significant differences between the two groups (P > 0.05).
Table 1.
Baseline characteristics of the study participants
| Characteristics | Total (n = 1 835) |
Non-HTH (n = 809) |
HTH (n = 1 026) |
P |
|---|---|---|---|---|
| Age(years) | 60.00(51.00,69.00) | 59.00(50.00,65.50) | 62.00(51.00,72.00) | <0.001 |
| Gender(n, %) | 0.001 | |||
| Male | 766(41.70) | 303(37.50) | 463(45.10) | |
| Female | 1069(58.30) | 506(62.50) | 563(54.90) | |
| Race(n, %) | 0.642 | |||
| Han Chinese | 1502(81.90) | 666(82.30) | 836(81.50) | |
| Other ethnic | 333(18.10) | 143(17.70) | 190(18.50) | |
| Married(n, %) | 0.013 | |||
| Single | 32(1.70) | 14(1.70) | 18(1.80) | |
| Married | 1689(92.00) | 758(93.70) | 931(90.70) | |
| Divorced | 21(1.10) | 11(1.40) | 10(1.00) | |
| Separated or widowed | 93(5.10) | 26(3.20) | 67(6.50) | |
| Education(n, %) | <0.001 | |||
| Illiterate | 566(30.80) | 192(23.70) | 374(36.50) | |
| Primary school | 376(20.50) | 168(20.80) | 208(20.30) | |
| Junior high school | 532(29.00) | 259(32.00) | 273(26.60) | |
| Senior high school | 335(18.30) | 164(20.30) | 171(16.70) | |
| Associate degree or above | 26(1.40) | 26(3.20) | 0(0.00) | |
| Income(n, %) | 0.004 | |||
| ≤ 1 | 253(13.80) | 98(12.10) | 155(15.10) | |
| 1 ~ 4 | 1042(56.80) | 493(60.90) | 549(53.50) | |
| 4 ~ 6 | 370(20.20) | 160(19.80) | 210(20.50) | |
| 6 ~ 8 | 64(3.50) | 25(3.10) | 39(3.80) | |
| ≥ 8 | 106(5.80) | 33(4.10) | 73(7.10) | |
| Smoking(n, %) | <0.001 | |||
| Yes | 503(27.40) | 170(21.00) | 333(32.50) | |
| No | 1332(72.60) | 639(79.0) | 693(67.50) | |
| Drinking(n, %) | 0.007 | |||
| Yes | 410(22.30) | 157(19.40) | 253(24.70) | |
| No | 1424(77.70) | 652(80.60) | 773(75.30) | |
| BMI(n, %) | 0.066 | |||
| <18.5 | 66(3.60) | 35(4.30) | 31(3.00) | |
| 18.5 ~ 24 | 828(45.10) | 381(47.10) | 447(43.60) | |
| ≥ 24 | 941(51.30) | 393(48.60) | 548(53.40) | |
| UHR(mg/dL) | 0.10(0.08–1.14) | 0.09(0.07–1.13) | 0.11(0.08–1.15) | <0.001 |
| TG(mmol/L) | 1.67(1.14–2.64) | 1.43(1.02–2.11) | 1.95(1.26–2.87) | <0.001 |
| TC(mmol/L) | 4.75(4.13–5.47) | 4.65(4.11–5.27) | 4.80(4.18–5.58) | <0.001 |
| ALT(U/L) | 18.00(13.00–27.00) | 18.00(13.00–25.00) | 19.00(13.00–29.00) | 0.003 |
| Bun(mmol/L) | 5.29(4.23–6.44) | 5.18(4.14–6.28) | 5.41(4.30–6.51) | 0.019 |
| Cr(u mol/L) | 86.00(73.00−100.00) | 84.00(71.00–99.00) | 88.00(75.00−102.00) | <0.001 |
| UA(u mol/L) | 319.00(257.00−378.00) | 292.00(241.00−353.00) | 336.00(271.00−400.00) | <0.001 |
| HDL-C(mmol/L) | 1.21(1.03–1.45) | 1.26(1.07–1.50) | 1.17(1.01–1.42) | <0.001 |
| LDL-C(mmol/L) | 2.62(2.11–3.22) | 2.57(2.09–3.19) | 2.67(2.11–3.24) | 0.321 |
| VLDL-C(mmol/L) | 0.72(0.49–1.16) | 0.62(0.43–0.91) | 0.79(0.53–1.28) | <0.001 |
| CRP(mg/L) | 5.05(3.98–6.18) | 5.05(4.04–6.03) | 5.05(3.86–6.32) | 0.987 |
Association between UHR and HTH
To evaluate the association between UHR and the risk of HTH, multivariate logistic regression models were used for analysis, as shown in Table 2. In the unadjusted Model 1, UHR was significantly positively associated with an increased risk of HTH(OR = 1.57, 95%CI: 1.41–1.74, P < 0.001). This association remained significant after adjusting for confounding factors in Model 2 (OR = 1.52, 95%CI: 1.36–1.70, P < 0.001) and Model 3 (OR = 1.41, 95%CI: 1.24–1.60, P < 0.001).
Table 2.
Association between UHR and HTH
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | OR (95%CI) | P-value | |
| UHR | ||||||
| 1.57(1.41–1.74) | <0.001 | 1.52(1.36–1.70) | <0.001 | 1.41(1.24–1.60) | <0.001 | |
|
UHR (quartiles) | ||||||
| Q1 | 1.00 | 1.00 | 1.00 | |||
| Q2 | 1.76(1.35–2.28) | <0.001 | 1.73(1.32–2.28) | <0.001 | 1.76(1.33–2.33) | <0.001 |
| Q3 | 1.99(1.53–2.59) | <0.001 | 1.69(1.28–2.24) | <0.001 | 1.65(1.24–2.20) | 0.001 |
| Q4 | 3.21(2.45–4.22) | <0.001 | 2.98(2.23–3.99) | <0.001 | 2.55(1.85–3.52) | <0.001 |
| P for trend | <0.001 | <0.001 | <0.001 | |||
*Model 1: Unadjusted
Model 2: Sex, age, marital status, education level, ethnicity, and income
Model 3: Further adjusted for smoking, drinking, total cholesterol, triglycerides, alanine aminotransferase based on Model 2. UHR: Uric Acid to High-Density Lipoprotein Cholesterol Ratio; HTH: H-type Hypertension; OR: Odds Ratio; CI: Confidence Interval. Bold values indicate statistical significance
When UHR was divided into quartiles, all models showed that higher UHR quartiles were significantly and statistically associated with an increased risk of HTH. Compared with the lowest quartile (Q1), the adjusted OR for the highest quartile (Q4) was 2.55 (95%CI: 1.85–3.52, P < 0.001). This trend indicates that UHR is independently associated with HTH, and its risk effect remains significant after comprehensive adjustment for potential confounding variables.
Nonlinear association and threshold effect between UHR and HTH
RCS analysis revealed a significant non‑linear relationship between UHR and H‑type hypertension risk after adjusting for potential confounders (P for nonlinear = 0.045; Fig. 2). The RCS curve illustrated the overall smooth trend of the dose–response relationship; piecewise regression analysis further quantified the effect estimates across different threshold intervals and detected a significant change in slope at UHR = 0.09 (Table 3).
Fig. 2.
Restricted cubic spline model. A restricted cubic spline model with multivariable-adjusted association analysis was used to demonstrate the dose-response relationship between UHR and HTH. UHR: Uric Acid to High-Density Lipoprotein Cholesterol Ratio; HTH: H-type Hypertension; OR: Odds Ratio; CI: Confidence Interval
Table 3.
Threshold effect analysis of UHR for HTH
| Outcome | Effect | P-value |
|---|---|---|
| Model1 Fitting model by standard linear regression | 1.41(1.24–1.60) | <0.001 |
| Model 2 Fitting model by two-piecewise linear regression | ||
| Inflection point | 0.09 | |
| <0.09 | 3.07(1.42–6.62) | 0.004 |
| ≥ 0.09 | 1.24(1.04–1.47) | 0.014 |
| P for likelihood test | 0.028 | |
*UHR, Uric Acid to High-Density Lipoprotein Cholesterol Ratio; HTH, H-type Hypertension; OR, Odds Ratio; CI, Confidence Interval. Bold values indicate statistical significance
Subgroup analysis and interaction analysis
To elucidate the heterogeneity of the association between UHR and HTH risk across different subgroups, we performed subgroup analyses and presented the results as a forest plot (Fig. 3). After adjusting for various covariates, a stronger association between UHR and HTH risk was observed in female patients and in Han Chinese individuals, with significant interaction effects for sex and race (P for interaction = 0.003 and 0.011, respectively).
Fig. 3.
Subgroup analysis of the association between UHR and HTH
ROC curve assessment
To evaluate the discriminatory performance of UHR for HTH, we performed ROC analyses for UA, HDL-C, and UHR, respectively. As shown in Fig. 4; Table 4,,AUC for UHR was 0.618 (95% CI: 0.592–0.644, P < 0.001), for UA was 0.614 (95% CI: 0.589–0.640, P < 0.001), and for HDL-C was 0.424 (95% CI: 0.398–0.450, P < 0.001), with UHR demonstrating the highest AUC.The ROC analysis results of the two models are shown in Table 4; Fig. 5. The AUC of the baseline model was 0.699 (95% CI: 0.675–0.723), while that of the combined model was 0.712 (95% CI: 0.689–0.735). The DeLong test indicated a statistically significant difference (difference = 0.013, 95% CI: 0.0036–0.0216, Z = 2.743, P = 0.006).
Fig. 4.
ROC curves for predicting HTH. Using Multiple Continuous Variables. UHR: Uric Acid to High-Density Lipoprotein Cholesterol Ratio; UA: Uric Acid; HDL-C: High-Density Lipoprotein Cholesterol
Table 4.
Comparison of discriminative performance for HTH among UHR, UA, and HDL-C by ROC analysis
| Categories | AUC | 95%CI | Sensitivity | Specificity | Cut-off | P-value |
|---|---|---|---|---|---|---|
| UA | 0.614 | 0.589–0.640 | 0.653 | 0.548 | 0.201 | <0.001 |
| HDL-C | 0.424 | 0.398–0.450 | 0.017 | 0.988 | 0.005 | <0.001 |
| UHR | 0.618 | 0.592–0.644 | 0.712 | 0.472 | 0.184 | <0.001 |
| Model | 0.699 | 0.675–0.723 | 0.669 | 0.586 | 0.285 | <0.001 |
| Model + UHR | 0.712 | 0.689–0.735 | 0.636 | 0.663 | 0.299 | <0.001 |
Fig. 5.
ROC curves for predicting HTH model: Multivariable model (adjusted for age, gender, marital status, income, education level, smoking status, drinking, TG, TC, and ALT); Model + UHR: Based on model, with the addition of the uric acid to high-density lipoprotein cholesterol ratio
To assess the incremental predictive value of UHR, we calculated the NRI and IDI. In the single-marker comparison with UA, the NRI- was 0.0346 (95% CI: 0.0037–0.0641), suggesting that UHR effectively reduced the false-positive rate. In the multivariable model comparison, the IDI was 1.52% (95% CI: 0.0098–0.0206, P < 0.001), indicating a statistically significant improvement in overall discrimination after adding UHR.
Sensitivity analyses
In the comparison between the highest and lowest quartiles of UHR, the E‑value for the point estimate was 2.57, and the E-value for the confidence interval limit was 2.06, indicating that the observed association was moderately robust to unmeasured confounding. Furthermore, both using different definitions of UHR and employing different sets of covariate adjustments consistently demonstrated a significant positive association between UHR and HTH (all P < 0.001).
Discussion
Based on 1,835 Chinese adults, this study systematically investigated the association between UHR and HTH. We found that UHR was independently and positively associated with HTH, with a nonlinear dose–response relationship exhibiting a threshold effect at an inflection point of 0.09. Notably, this association was more pronounced in female and Han Chinese participants; moreover, adding UHR to the conventional risk factor model significantly improved the discriminatory ability for HTH. These findings suggest that UHR could serve as a simple, cost‑effective, and readily available biomarker for risk stratification of HTH, with particular potential for application in primary care settings.
The independent association between UHR and HTH observed in this study is consistent with the established roles of UA and HDL‑C in the pathophysiology of hypertension. Hyperuricemia may promote hypertension by inducing oxidative stress, activating the renin–angiotensin system, and inhibiting endothelial nitric oxide synthase [24]; in contrast, HDL‑C exerts vascular protective effects through its anti‑inflammatory and antioxidant properties [25]. As a novel biomarker, UHR integrates these two opposing metabolic signals [26], thereby reflecting the net effect of elevated uric acid and attenuated lipid protection [13, 27], which may synergistically contribute to the development of HTH. Previous studies have confirmed UHR as a predictor of hypertension [27]; the present study further extends this evidence to the HTH subtype, suggesting that UHR can serve as a composite marker simultaneously reflecting inflammation and metabolic disturbance. Indeed, multiple studies have indicated that UHR has emerged as a novel biomarker for predicting the risk of various diseases—including insulin resistance, metabolic syndrome, and cardiovascular disease—as well as all‑cause mortality [28].
Homocysteine (Hcy) is a sulfur-containing amino acid intermediate produced during the metabolic conversion of methionine (Met) to cysteine (Cys); defects in methionine metabolism can lead to elevated Hcy levels [29]. Hyperhomocysteinemia (HHcy) refers to a pathological state characterized by abnormally elevated blood Hcy levels [30]. It is an independent risk factor for various diseases [31] and can induce inflammatory responses by upregulating pro‑inflammatory cytokines and downregulating anti‑inflammatory cytokines, thereby leading to apoptosis.When hypertension is combined with HHcy, it is defined as HTH, and HTH has been recognized as a major public health issue [5].
Notably, our study revealed a nonlinear relationship with a threshold at UHR = 0.09, which is consistent with the nonlinear association between UHR and cardiovascular disease risk reported by Yi et al. [32]. This nonlinear pattern may reflect a biological threshold or an adaptive regulatory mechanism, the underlying basis of which likely involves the concentration‑dependent dual effects of uric acid: when its pro‑oxidant action outweighs its antioxidant properties, it can induce endothelial dysfunction [33], increased oxidative stress [34], and systemic inflammation [35], thereby promoting cardiovascular disease; conversely, when uric acid acts as an endogenous antioxidant, it may exert protective effects by reducing vascular damage [36]. Furthermore, the relationship between HDL‑C and cardiovascular risk is not a simple linear trend [37, 38], and its function may undergo qualitative changes under metabolic stress. Saturation of oxidative stress responses and complex interactions among multiple metabolic pathways may jointly shape this threshold effect. This quantitative reference value may help identify high‑risk individuals and provide clinicians with practical criteria for risk stratification.
Subgroup analyses revealed significant heterogeneity in the association between UHR and HTH across sex and ethnic groups, consistent with a large Iranian cross‑sectional study that also identified an independent association only in women [39]. This discrepancy likely reflects the protective effects of estrogen through modulation of the renin–angiotensin system, improvement of endothelial function [40], promotion of uric acid excretion [41], and elevation of high‑density lipoprotein cholesterol levels [42]. When estrogen levels decline or its function is compromised, the pathogenic effect of elevated UHR becomes more pronounced, particularly in postmenopausal women [43]. A stronger association was observed in the Han population compared with other ethnic groups, which may be attributable to differences in lifestyle, dietary patterns, and genetic backgrounds affecting uric acid and lipid metabolism [44]. Although the interaction term did not reach statistical significance in the age subgroup, a higher effect size was observed in individuals aged ≥ 60 years, indicating that elevated UHR poses a greater risk in the elderly. This may be explained by age‑related decline in renal function, which reduces uric acid excretion and is accompanied by aggravated lipid metabolic disorders [45], potentially further strengthening the UHR–HTH association. These findings suggest that sex and ethnicity should be carefully considered when developing screening strategies for HTH.
The ROC analysis indicates only moderate discriminatory ability, highlighting that UHR alone is insufficient as a standalone screening tool. Instead, the incremental value of UHR is better reflected in its addition to a multivariable model.Given its simplicity and cost‑effectiveness, UHR can serve as an auxiliary screening indicator for populations at risk of HTH in primary healthcare settings or large‑scale epidemiological surveys.
This study has several notable strengths. First, we confirmed an independent association between UHR and HTH. Second, we demonstrated a non‑linear dose–response relationship between UHR and HTH, with a clear threshold effect (cut‑off value = 0.09), which provides the first quantitative reference for risk stratification in this subtype. Additionally, we identified effect modification by sex and ethnicity, with stronger associations observed in females and Han Chinese individuals, offering population‑specific evidence for precision prevention. Moreover, we evaluated the incremental predictive value of UHR for HTH and confirmed its added utility as a composite biomarker. Finally, the UHR index is readily available, cost‑effective, and easy to implement, making it a practical screening tool to identify individuals at high risk of HTH. Collectively, these findings advance the clinical translation of UHR from a general risk marker to a subtype‑specific stratification tool for HTH, and provide new perspectives for the assessment of patients who may be at risk for this condition in future practice.
Several limitations of this study should be acknowledged. First, the cross‑sectional design precludes the establishment of a causal relationship between UHR and HTH; future prospective cohort studies and larger multicenter investigations are warranted to confirm our findings. Second, all participants were recruited from the Chinese population, which may introduce selection bias and limit the generalizability of our results to other populations. Third, the relatively small sample sizes in certain subgroups (e.g., other ethnic groups) may reduce the statistical power of the subgroup analyses. Fourth, although we adjusted for a wide range of confounding factors, residual or unmeasured confounding (e.g., dietary patterns, physical activity, and use of urate‑lowering or antihypertensive medications) cannot be completely excluded. Nonetheless, E‑value sensitivity analysis suggested that the observed association was reasonably robust to unmeasured confounding; however, future prospective studies with more comprehensive covariate measurements are still warranted to further validate these findings. Fifth, the moderate AUC value of UHR indicates that it should be used in combination with other clinical indicators rather than in isolation in practical applications.
Conclusion
In conclusion, this study confirms that UHR is independently associated with HTH and that a nonlinear relationship exists between the two, with an inflection point at a threshold of 0.09. UHR is a simple and cost‑effective composite biomarker that facilitates early identification and risk stratification of HTH, with particular potential value in women and the Han Chinese population.
Acknowledgements
We acknowledge the support provided by the funding project.
Abbreviations
- UHR
Uric acid to high-density lipoprotein cholesterol ratio
- HTH
H-type hypertension
- Hcy
Homocysteine
- HHcy
Hyperhomocysteinemia
- UA
Uric acid
- HDL-C
High-density lipoprotein cholesterol
- TG
Triglyceride
- TC
Total cholesterol
- LDL-C
Low-density lipoprotein cholesterol
- VLDL-C
Very low-density lipoprotein cholesterol
- BMI
Body-mass index
- Q1
First quartile
- Q2
Second quartile
- Q3
Third quartile
- Q4
Fourth quartile
- ALT
Alanine Aminotransferase
- CRP
C-reactive protein
- RCS
Restricted cubic splines
- ROC
Receiver operating characteristic
- OR
Odds ratio
- CI
Confidence interval
Authors’ contributions
NJ、LL and XH conceived and designed the study. NJ wrote the first draft. LZ、ML、 YP and XT collected and analyzed data, and interpreted the results. LL and XH critically revised the manuscript. All authors have read and approved the final manuscript.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by the Hunan Provincial Health Commission High-Level Talent Major Research Project (R2024161), the Hunan Provincial Natural Science Foundation Youth Project (2025JJ60519), the Hunan Provincial Department of Education Excellent Youth Project (25B0112), the Hunan Provincial Health Commission Project (20254401), and the Hunan Provincial Natural Science Foundation Key Project (2026JJ30163).
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The studies involving humans were approved by Ethics Committee of Hunan Normal University School of Medicine.The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Ling Li, Email: phdliling@163.com.
Xiuqin Hong, Email: xiuqinhong0528@hunnu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.