This study is the first to merge and analyze data from the 2007–2018 NHANES for exploring the association between BRI values and the presence of diabetes and prediabetes within individuals in USA. Our statistical analysis shows a strong and stable positive association between BRI and the incidence of developing diabetes and prediabetes. This association remained consistent even after adjusting for confounding variables, suggesting that diabetes and prediabetes could be impacted by BRI. The likelihood of diabetes and prediabetes increased by 17% with each one-unit rise in BRI. Individuals with high BRI scores (Q4) exhibited greater odds of diabetes and prediabetes compared to those in the Q1 category. We further fund a non-linear relationship among BRI with diabetes and prediabetes. Furthermore, subgroup analysis and interaction assessments revealed that these consistent relationships were upheld across diverse population groups. Moreover, BRI exhibited the highest predictive value for diabetes and prediabetes compared to BMI and WC, as indicated by the largest AUC. Therefore, these results highly implied the latent availability of BRI acting to be the predictor within diabetes and prediabetes.
It is indisputable that obesity plays a significant role in the development of diabetes. Specifically, deposition of adipose tissue in the abdominal visceral organs (abdominal obesity) exhibited a stronger correlation among insulin resistance and the risk of diabetes compared to subcutaneous fat [22, 23]. BMI is a commonly used indicator in diagnosing obesity, but it cannot fully show the actual body fat content [24], nor can it evaluate the risk of diseases associated with obesity in individuals possessing high body fat and low muscle mass [25]. Although WC is used as a diagnostic measure for identifying increased abdominal fat, it cannot distinguish between visceral fat and subcutaneous fat. Existing techniques for assessing abdominal fat through MRI or CT are expensive and require considerable time to perform, limiting their use in research and clinical settings. Consequently, new anthropometric indices have been continuously developed and applied. Consequently, BRI is developed and it has ability to reflect body roundness related to height, and compared to traditional anthropometric indices, it can more accurately approximate visceral fat levels [13].
The correlation between BRI level and diabetes is better than BMI, WC, and ABSI confirmed by a longitudinal study in Japan [16]. A cross-sectional study conducted in the Northeast region of China also yielded similar conclusions [26]. Another retrospective cohort study found that BRI is more accurate than BMI in predicting diabetes [17]. A cohort study of 7,902 participants in China showed that BRI was significantly associated with diabetes among both sexes [14]. Similarly, Zhang et al. [27] also indicated a positive correlation between diabetes and BRI levels in middle-aged and elderly Chinese. In another study, Zhao et al. [15]suggested that BRI displayed a positive correlation with diabetes risk in women and a curvilinear association in men. In both sexes, BRI is better than BMI and WC in predicting diabetes [15]. Nevertheless, the aforementioned studies had limited focus on the pre-disease stage of diabetes. A 4-year follow-up study conducted in Chinese adults aged 45 years and above revealed that a decrease in initial BRI levels was found to be correlated with a positive outcome of prediabetes regression, and its attributable fractions were around 20% [28]. However, among the population of Montenegro, there is no association between BRI and prediabetes [29]. For these inconsistent results, we think that differences in race, subject characteristics, region, sample size, and study design may account for the differences between these studies. Additionally, it has not explored the connection among BRI with diabetes as well as prediabetes in USA; therefore, in present study, we validated the relationship among BRI to diabetes and prediabetes within American adults. Our study suggests that the BRI can be used as an indicator to assess risk factors for diabetes and prediabetes, which is consistent with previous findings in other populations. Moreover, the consistent positive association between BRI and diabetes and prediabetes persisted across all subgroup analyses, underscoring the robustness of our results.
The following are a few plausible relationships may explain the connection among BRI and diabetes and prediabetes. Initially, dysregulated lipid accumulation triggers endoplasmic reticulum stress and oxidative stress responses, leading to reduced gene expression, secretion, and increased apoptosis of pancreatic β-cells, culminating in β-cell dysfunction, causing decreased insulin secretion and decreased insulin sensitivity, leading to chronic hyperglycemia [30]. Additionally, elevated visceral fat levels reduce lipocalin (a protective adipokine) levels, leading to an increase in inflammatory cytokines, such as interleukin 6 and tumor necrosis factor, potentially worsening insulin resistance [31]. Furthermore, compromised inhibition of lipolysis in adipocytes of individuals with obesity results in elevated levels of liver fatty acids, contributing to the accumulation of liver lipids. This process reduces liver insulin sensitivity, enhances hepatic gluconeogenesis, and ultimately results in dysglycemia [32].
Our study had two important advantages. Initially, the data was reliably analyze to provide a good representation of the current state of the U.S. population. Secondly, the correlation between BRI and diabetes and prediabetes remained consistent across various subgroup analyses, indicating the robustness of the findings. In spite of these important advantage, it also existed certain limitations that should not be overlooked. At First, based on a cross-sectional design, the study could not establish a causal connection among BRI with the development about diabetes and prediabetes. Furthermore, while we accounted for potential confounders in our data analysis, residual confounding stemming from undisclosed or unmeasured variables may remain. Finally, our study population consisted of only American participants, so the generalizability in this sudy to other national populations is uncertain.