Hypertension (HTN) is a major risk factor for stroke, cardiovascular disease, and kidney failure, and is a leading cause of death globally (1, 2). It is estimated that by 2025, the prevalence of HTN will have increased by 60%, affecting 1.56 billion people (3). In the US, it is estimated that over 100 million people suffer from this common chronic condition (4, 5). Uric acid is the end product of purine metabolism in humans, and any disruption of purine metabolism can lead to increased uric acid levels and hyperuricemia (HUA). According to recent statistics, the incidence of HUA in the US stands at 21.2% among males and slightly higher at 21.6% among females (6). Research has demonstrated that 25-40% of people with high uric acid have untreated HTN (7). The meta-analysis showed a substantial association between serum uric acid levels and HTN, even when traditional risk factors were taken into account (8–10). HTN and HUA are major features of the metabolic syndrome, and they are important risk factors for cardiovascular disease. When HTN is combined with HUA, the damage to organs is usually more extreme than that caused by HTN alone (11–13).

Obesity is a medical condition in which the body accumulates too much fat, resulting in a disrupted metabolism and physiology (14). The figures from 2017-2018 show that the rate of this disorder in the US is increasing, as 42% of the population have a body mass index (BMI) of 30 or higher, and 9.2% have a BMI of 40 or more (15, 16). It is known that obesity can lead to HTN and HUA (17, 18). Adipose tissue inflammation and immune responses caused by obesity can lead to metabolic issues and insulin resistance, both locally and systemically (19). BMI is a widely accepted measure of obesity, yet it is not sufficient to determine the amount of visceral fat, dyslipidemia, and insulin resistance linked to obesity. Therefore, researchers have proposed new anthropometric tools that better reflect these characteristics, such as atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI), triglyceride-glucose index (TyG), body roundness index (BRI), a body shape index (ABSI), and cardiometabolic index (CMI) (20–26).

While numerous studies have explored the correlation between various anthropometric indexes and either HTN or HUA (27–29), few have compared the predictive power of these indexes in patients with HTN-HUA. This research gap is particularly pronounced given the multitude of proposed anthropometric indexes. Moreover, to affirm the link between various anthropometric indexes and HTN-HUA, a large population sample is essential for validating extrapolated conclusions. Hence, this study aims to discern the predictive power of anthropometric indexes - AIP, LAP, VAI, TyG, BRI, ABSI, and CMI - in patients with HTN-HUA, with a view to identifying the most accurate predictors.

In this cross-sectional study, utilizing data from NHANES, we discovered that the prevalence of HTN was at 39.92%, with patients with HTN-HUA constituting 21.50% of the hypertensive population. Numerous studies have indicated that elevated blood uric acid levels increase the risk of cardiovascular events in hypertensive patients (11–13). As such, the early detection and management of HTN-HUA through anthropometric indexes, prior to the onset of clinical symptoms, could be crucial in managing HTN-HUA and preventing associated cardiovascular events.

In light of recent findings from the Uric Acid Right for Heart Health (URRAH) study, particularly those published in Maloberti et al. (33), reconsideration of the established diagnostic criteria for HUA in the context of cardiovascular risk is warranted. While our study adheres to the conventional threshold of 7.0 mg/dL in males and 6.0 mg/dL in females (30), primarily associated with gout implications, the URRAH research suggests a significantly lower cut-off of 6.5 mg/dL for both sexes concerning cardiovascular mortality. This insight is crucial for our study’s scope, which focuses on anthropometric indexes in predicting HTN-HUA among U.S. adults. Integrating this nuanced understanding of uric acid levels in relation to cardiovascular risk, possibly as sensitivity analyses, would enhance the depth of our analysis. Another study from Italy revealed that traditional HUA cut-offs are associated with higher ORs for obesity indices compared to the URRAH thresholds, with the LAP demonstrating the most significant association with HUA (34). These observations align with our findings, suggesting a multifaceted interplay between uric acid, lipids, and obesity in the general population. It appears that lower serum uric acid levels primarily impact cardiovascular events through lipid modifications, whereas higher serum uric acid levels may further precipitate metabolic and obesity-related abnormalities. Future studies are essential to further analyze and validate these complex relationships.

To investigate the relationship between obesity and HTN-HUA, we utilized anthropometric indexes, which are measured by simple variables such as sex, TG, HDL-C, WC, BMI, and FPG. In this study, seven such anthropometric indexes were found to have significant associations with HTN-HUA. The odds ratios were especially high for LAP and BRI. LAP, calculated mainly based on sex, TG, and WC, had been previously employed to gauge the extent of lipid accumulation in the body (21). The VAI focuses more on the extent of visceral fat accumulation (22), as compared to the LAP with a higher overlap of calculated variables. Liu et al. found that high VAI is a measure of visceral fat and metabolic dysfunction, and is an independent risk factor for HUA in hypertensive people (35). However, Li et al. found that LAP was a better predictor of metabolic syndrome than VAI in both genders (36). This indicates that the overall lipid accumulation in the body, rather than solely visceral fat accumulation, may better predict HTN-HUA and other metabolic disorders. For example, Neeland et al. discovered a strong association between ectopic fat and the onset of clinical syndromes characterized by atherosclerotic dyslipidemia, hyperinsulinemia/glucose intolerance, HTN, atherosclerosis, and adverse cardiac remodeling/heart failure (37). Concurrently, research has shown that despite visceral fat being more closely linked to poor metabolic risk status, subcutaneous fat still contributes to unfavorable metabolic outcomes (38).

The BRI calculation primarily involves WC and height, and is predominantly used to evaluate obesity distribution in humans (24). The ABSI calculation incorporates several BRI variables as well as BMI, another measure used to assess human obesity distribution (25). However, there’s a distinction between the two: BRI is more commonly used to evaluate an individual’s overall physical fitness, while ABSI is more targeted toward reflecting the health implications of abdominal obesity. A study by Anto et al. revealed that after adjusting for all variables, the odds ratio of ABSI on the risk of metabolic syndrome was not statistically significant (p > 0.05), while BRI remained significant (p< 0.05) (39). Similarly, when identifying metabolic disorders in both adult and pediatric populations in China, BRI was found to possess superior predictive power compared to ABSI (40, 41). All of this suggests a higher predictive value of BRI than ABSI in forecasting metabolic disorders. Nevertheless, a study from China reported a significant non-linear positive dose-response relationship between all anthropometric measures, except ABSI, and HTN across sexes (p-nonlinearity< 0.05), including BRI (42). This study, however, was limited to a target population aged over 65 years. It is well-documented that age is a significant risk factor for HTN, with its prevalence increasing as people age (43–45). Consequently, the outcomes of the non-linear analysis may not be generalizable to the adult population in the U.S.

The TyG, a simple surrogate marker of insulin resistance, is calculated using TG and FPG (23). It’s notable that obese individuals often exhibit insulin resistance and lipoprotein metabolism disorders, such as heightened plasma concentrations of triglyceride-rich lipoprotein residues, residue-like particulate cholesterol, and apolipoprotein B, all of which are more pronounced in obese individuals with hypertriglyceridemia (46). Furthermore, elevated TG levels in obese individuals are reported to be linked to insulin resistance, underscoring the significance of TG in the pathogenesis of insulin resistance (47). However, some studies suggest that due to the crucial role of obesity in the pathophysiology of insulin resistance, integrating obesity markers with TyG for predicting metabolic disorders in humans could yield superior results (48–50). Therefore, relying solely on fasting TG and FPG may not be sufficient, and a better strategy might be to combine these with indexes that directly measure obesity in humans.

The AIP, derived from TG and HDL-C, has been correlated with insulin resistance and abnormalities in lipid metabolism (51, 52). Tan et al. discovered that an elevated AIP is significantly and positively associated with the risk of developing prehypertension or HTN in normoglycemic individuals, particularly in women aged 40 to 60 (53). Conversely, Li et al. found a stronger correlation between AIP and HTN risk in men (54). This observation might stem from the fact that individuals with prehypertension or HTN often exhibit chronic abnormalities in serum concentrations of TG, cholesterol, or both, as well as in associated lipoproteins (55). However, in this study, AIP demonstrated the least efficacy in discriminating HTN-HUA among the seven anthropometric measures assessed, and a multivariate-adjusted logistic regression with HTN alone did not yield any statistical significance. This discrepancy could be attributed to the interplay of regional and ethnic differences, lifestyle habits, and other variables (56), resulting in variations between the findings of the current study and previous research. Concurrently, several studies have illustrated that HUA can modulate molecular signals such as insulin resistance, inflammatory response, oxidative stress, endoplasmic reticulum stress, and endothelial dysfunction (57, 58). This modulation might explain the absence of observed true associations in cases of HTN alone.

The CMI is a relatively new index associated with lipid and obesity (26). Differing from AIP, CMI is calculated not only based on TG and HDL-C but also incorporates WC and height. Numerous studies have attested to the positive correlation between CMI and various metabolic disorders (26, 59, 60). In this study, CMI demonstrated moderate predictive power for HTN-HUA but did not exhibit stronger predictive power. From the perspective of anthropometric index components, the calculation of CMI encompasses the components of both AIP and BRI. The findings of this study could possibly suggest some degree of collinearity between the calculated components of the CMI. Firstly, a significant correlation between WC and lipids is well established (61). Secondly, TG and HDL-C, which are crucial components of lipids, may not enhance the predictive power for HTN-HUA when combined. This was also supported by the prediction of HTN-HUA by AIP in this study.

This study offers both strengths and limitations. Being the first large-scale study to examine the relationship between anthropometric indexes and HTN-HUA in an adult population using a nationally representative sample, it adds statistical strength and verifies the reliability of the reported results. However, several limitations warrant attention. Firstly, the study does not adequately establish the causal relationship between these anthropometric indexes and HTN-HUA, and future longitudinal studies are needed to verify this causal relationship. Secondly, the use of retrospective data in our study may introduce recall bias. Thirdly, there may be probability bias in this study, as the study population consists solely of individuals from the United States, the conclusions drawn may not be universally applicable. Fourthly, the lack of data on hypouricemic drugs, diuretics, and Sodium-Glucose Co-Transporter 2 (SGLT2) inhibitors in the current survey may influence uric acid levels (62, 63), which may have affected the results of the analysis in this study. Lastly, the significant absence of inflammatory markers like high-sensitivity C-reactive protein in our data restricts their inclusion in the regression model as adjusting variables. This omission affects the interpretation of the internal health dynamics in individuals with HTN-HUA (64), an aspect that warrants attention in future research endeavors.

In conclusion, this study underscores that various indexes, including AIP, LAP, VAI, TyG, BRI, ABSI, and CMI, are closely associated with HTN-HUA risk, often more so than HTN or HUA alone. Among these, LAP and BRI emerge as particularly noteworthy due to their pronounced ability to discriminate HTN-HUA risk. However, it is important to highlight that while both LAP and BRI are statistically robust indexes for predicting HTN-HUA, BRI stands out as more effective. The primary advantage of BRI lies in its non-invasive nature, eliminating the need for invasive testing procedures. This makes BRI not only a powerful tool in risk assessment but also a more practical and patient-friendly option in clinical settings. Consequently, BRI’s accessibility and efficacy position it as a superior choice for early warning indexes in managing HTN-HUA. Additionally, its non-invasive character enhances its suitability for use in obesity-based prevention and intervention strategies for HTN-HUA, broadening its applicability in public health initiatives.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

All data came from NHANES, which was approved by National Centre for Health Statistics Institutional Ethics Review Board, and all the subjects agreed on the survey and signed written consent. 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.

YL: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. LZ: Conceptualization, Funding acquisition, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing.