Cardiovascular disease (CVD) is recognized as a significant public health concern due to its high prevalence, morbidity, and mortality rates globally (1, 2). The global burden of CVD is expected to increase, primarily driven by an aging population (3). A study examining the global burden of disease from 1990 to 2019 noted a rise in CVD cases from 271 million to 523 million, along with a marked increase in mortality rates (4). Cardiometabolic factors make one of the major contributors to the risk of CVD. Hypertension, hyperlipidemia, diabetes and obesity are modifiable metabolic risk factors for CVD (5, 6). Hence, it is important to identify and screen people at risk for CVD at an early stage, especially those with metabolic risk factors, and to implement timely interventions.

Cardiometabolic syndrome (CMS) serves as a significant risk factor for CVD. It contributes to heightened cardiovascular and all-cause mortality rates (7). CMS is defined by abdominal obesity, hypertension, increased triglycerides, reduced levels of high-density lipoprotein cholesterol (HDL-C), and glucose intolerance (8, 9). Typically, the diagnosis of CMS demands the existence of at least three of these risk factors. As the population ages, the global metabolic risk is rising, leading to an increased prevalence of CMS between 1999 and 2018 (10), the prevalence of CMS among American adults rose from 28.23% to 37.09%, suggesting a deterioration in cardiometabolic health status (11). Alarmingly, merely 6.8% of adults preserve optimal cardiometabolic well-being (12). It is widely recognized that excessive obesity, particularly centripetal obesity, independently intensifies a multitude of metabolic risk factors for CVD, including the induction of dyslipidemia, increased blood pressure, hyperglycemia, insulin resistance (IR), and systemic inflammation (5, 13). Obesity may also lead to an increased risk of cardio-metabolic disease in children and adolescents (14).

Obesity is identified as a primary contributor to the burden of CMS (11, 15, 16). Moreover, the incidence of CVD is significantly higher in obese people, and the excessive accumulation of body fat leads to the development of CVD by inducing an inflammatory response and increasing oxidative stress (17). Obesity is increasingly being used to identify people at risk for cardiovascular risk factors, and notably abdominal obesity is a strong risk factor for CVD. Thomas proposed a novel body roundness index (BRI) that estimates the rate of visceral fat to total body fat by combining waist circumference (WC) and height data (18). Traditional research methods frequently depend on indicators like body mass index (BMI) and WC for the identification and management of obesity (19, 20). However, they all have certain limitations. Compared with BMI and WC, BRI seems to reflect abdominal obesity more accurately and effectively predict obesity-related metabolic chronic diseases (21). The study by Lucas et al. confirmed that metabolically unhealthy/obese individuals present a higher risk of developing CVD compared to metabolically healthy/obese individuals (22). BRI can significantly identify the presence of CMS (23). At the same time, BRI is a valid predictor of the risk of CVD (24). A cohort study in China found that individuals with intermediate and high BRI levels had a 22% and 55% higher risk of experiencing cardiovascular events, respectively, compared to those with low BRI (25). However, the relationship between BRI and the risk of CVD in patients with CMS in the United States (U.S.) remains underexplored. The above suggests that it would be positive to further explore the relationship between BRI and the occurrence of CVD among CMS patients.

Therefore, this study intends to investigate the association of BRI with the risk of CVD and its components including congestive heart failure (CHF), coronary heart disease (CHD), angina, heart attack, and stroke in patients with CMS in the U.S. In addition, we assessed the ability of the BRI to identify CVD and its components in the U.S. population. We hypothesized that the BRI would identify CVD better than BMI or WC.

This study, which encompassed 6,640 participants, detected a positive correlation between a high BRI and an elevated risk of CVD among adults with CMS in the U.S.

Additionally, our results reveal a nonlinear relationship between BRI and CVD risk, whereas a u-shaped correlation was observed with the risk of CHF. When the BRI level was lower than 4.53, the risk of CHF decreased by 58% for every 1-unit decrease in BRI, but after this inflection point, the risk increased by 16% for every 1-unit increase in BRI. The association between BRI and CVD was consistent across subgroups and there was no significant interaction. However, BMI interacted with the association, with BRI being associated with decreased risk of CHF in a subgroup of normal weight subjects and increased risk of CHF in a subgroup of obese subjects. Multiple imputation suggests proof of the robustness of the findings. More importantly, BRI has similar ability to predict CVD and CHF as WC, and better than BMI.

Previous studies have focused on the potential role of BRI in cardiovascular disease morbidity and mortality or cardiometabolic risk factors (23, 25, 27). BRI has been shown to be a valid predictor of cardiometabolic risk and is significantly associated with cumulative cardiometabolic risk factors (23, 27, 28). Cohort studies in China have shown that a long-term increase in BRI is associated with an increased risk of CVD, stroke, or cardiac events (25, 29). Moreover, Li et al. demonstrated that the BRI was superior to other anthropometric measures, including waist circumference, in predicting the risk of cardiovascular disease in a Chinese population (24). However, Maessen et al. showed that the BRI was not superior as a novel body metric for identifying CVD compared to established anthropometric measures such as BMI and WC (30). Higher WC is associated with an increased risk of CVD, and the positive association between WC and new myocardial infarction and stroke has been shown to be higher in individuals <60 years of age (31). Our conclusions are consistent with previous studies that a higher BRI is associated with an increased risk of CVD. Also, BRI predicted CVD similarly to WC but better than BMI. Unusually, we also found a U-shaped association between BRI and CHF in participants evaluated for CMS, which has not been found in previous studies. This may be since previous studies focused on cardiovascular diseases such as stroke and myocardial infarction. However, the low prevalence of CHF in this study encourages future studies to further explore the potential nonlinear association between BRI and CHF. In addition, our study found that BRI was associated with a reduced risk of CHF in a subgroup of normal-weight subjects and an increased risk of CHF in a subgroup of obese subjects. There is a complex link between BMI and CHF that may involve multiple aspects. Higher BMI may increase the risk of CHF through melatonin secretion and metabolism, reduced sleep quality, and reduced activity (32). In the Belgian cohort, patients in the high inositol level group (highest tertile) also had a higher BMI, predicting poor clinical outcomes in heart failure patients with preserved ejection fraction (HFpEF) (33). This may be related to IR or other metabolic disorders due to obesity, which in turn affects inositol metabolism. This suggests that weight control may help reduce the risk of heart failure and improve prognosis. Another study suggested that machine learning algorithms may help to address the heterogeneity of HFpEF patients and provide new directions for precision therapy (34). This deserves further exploration.

Traditional indicators like BMI and WC have limitations in clinical scenarios. BMI does not distinguish between fat, muscle, and bone mass. Meanwhile, WC measures abdominal fat without taking height into consideration, resulting in potential misclassification of obesity in individuals with different heights (35). In contrast, the BRI uses an ellipsoid model to assess body size by integrating waist circumference and height independent of body weight, allowing for a more accurate representation of body fat as a proportion of total body weight (18). And it is critical for identifying health risks in individuals with an abnormal distribution of body fat (36). BRI effectively quantifies central obesity and is significantly associated with metabolic dysregulation, inflammatory response, vascular dysfunction, and oxidative stress, all of which can accelerate the progression of CVD (37). A study showed that the BRI performed similarly or better than BMI and WC in predicting CMS and CMS components in Peruvian adults, and showed the best ability to identify IR in obese and overweight populations (38). The mechanisms linking elevated BRI levels to increased CVD risk are not fully understood in patients with CMS. We will elaborate on the following aspects. First, previous cross-sectional studies have demonstrated a role for branched-chain amino acids (BCAAs) and lipid metabolism in the pathogenesis of type 2 diabetes mellitus (T2D) and coronary atherosclerotic heart disease (CAD), suggesting that obesity has an important role in the development of CVD (39). Secondly, the accumulation of excess body fat contributes to an inflammatory response in the blood and leads to oxidative stress, which increases the risk of CVD (17). This suggests that by calculating the BRI, we can identify the risk of metabolism in overweight and obese people at an early stage and reduce the risk of CVD by controlling weight and reducing fat accumulation. In recent years, an increasing number of studies have focused on the association between gut fungi and metabolism (40). This suggests that the relationship between gut fungi and other metabolic diseases, such as diabetes and cardiovascular disease, deserves further investigation. Kun et al. showed that gut fungi may affect CAD through mechanisms such as influencing immunity, metabolic processes, or systemic inflammation (41). Therefore, it is promising to develop interventional strategies based on gut fungi in the future, e.g., by modulating the intestinal fungal community for the prevention and treatment of metabolic diseases.

There are several limitations of our study. Firstly, being a cross-sectional study, it cannot establish a causal connection between BRI and the risk of CVD in patients with CMS. This design is unable to track changes in variables over time or recognize trends and dynamic patterns in time-series data, thus limiting its ability to assess long-term impacts and changes in time dynamics. In future studies, prospective cohort studies could be used to validate our findings. Secondly, diagnosis of CVD relied on participant self-reporting, but participants’ lack of knowledge about cardiovascular disease could affect the accuracy of subsequent analyses. Thus, the BRI’s ability to recognize CVD may be somewhat higher. In addition, the research sample is largely composed of subjects originating from the US., which limits the applicability of the findings to a worldwide context. Although we have strived to control for potential complicating variables, the likelihood of remaining confounding factors cannot be entirely ruled out (such as dietary habits and physical activity levels). Undocumented eating habits may affect body weight and fat distribution, which in turn affects BRI. Physical activity not only affects body weight and shape, but also directly affects cardiovascular health. Physical inactivity may lead to a higher risk of BRI and CVD, but this confounder may not be adequately captured in the NHANES data. Future studies should consider a more comprehensive assessment and adjustment of these potential confounders. Furthermore, we did not account for the influence of pharmacological treatments. Patients with CMS often experience multiple coexisting chronic diseases that necessitate antihypertensive, hypoglycemic, and lipid-lowering therapies. This could lead to a decrease in the number of patients we have diagnosed with CMS. Despite these limitations, this study highlights the association between BRI and the risk of cardiovascular disease in patients with CMS and paves the way for future longitudinal studies.

This study demonstrated the positive relationship between the BRI and CVD in individuals diagnosed with CMS. Notably, a U-shaped correlation was observed between BRI and the risk of CHF. Increased assessment of BRI would facilitate easier and more effective screening of individuals at risk for CHF. In addition, this threshold could be used as a target for interventions to reduce CHF risk. Future studies should further explore whether interventions targeting the BRI may improve the clinical prognosis of CMS patients with CVD.