As a major chronic noncommunicable disease, diabetes can lead to substantial morbidity, mortality, and healthcare expenditures and has been a huge public health burden worldwide [1]. According to the 2021 International Diabetes Federation Diabetes Atlas, an estimated 537 million adults worldwide have diabetes, and direct health expenditures due to diabetes are already close to one trillion USD. China, the world’s largest developing country, has the largest number of people with diabetes (140 million in 2021), and it is estimated that this number will reach 175 million by 2045 [2]. Type 2 diabetes mellitus (T2DM) is the main type of diabetes accounting for 90%–95% of diabetes and is a preventable and controllable disease [3]. However, a previous study showed that about approximately half of people with T2DM do not suspect they have the disease until they are diagnosed [4]. Therefore, it is very important to identify people at risk for T2DM early and to reduce the global diabetes epidemic through a valid and reproducible diagnostic indicator [5].

Among several modifiable risk factors for diabetes, obesity is considered a major risk factor [6, 7]. In the general population, the most widely used measures to define excess body fatness (BF) are body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR). Previous studies have shown that higher BMI, WC, and WHtR are associated with an increased risk of type 2 diabetes mellitus (T2DM) [8, 9]. However, these anthropometric indicators have been criticized because they do not consider important factors related to adiposity, especially age, sex, and race [10, 11]. Therefore, a new practical adiposity index, the Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), was proposed and is considered to be a more accurate indicator of body fat estimation [12]. This method of estimating body fat percentage is based on BMI, sex, and age of Caucasian subjects and has the highest correlation with body fat percent as measured by air displacement volume tracing compared to other anthropometric methods [12]. Previous findings have shown that CUN-BAE is more strongly associated with T2DM and fat-related cardiovascular risk factors than BMI or WC [13, 14]. However, previous CUN-BAE assessment studies have been limited to white populations and need to be extended to other populations to determine its applicability.

To date, no studies have explored the applicability of CUN-BAE in the Chinese population. Therefore, the purpose of this study was to investigate the association of CUN-BAE with the risk of T2DM in the Chinese middle-aged and elderly population and to compare the strength of the association between CUN-BAE, BMI, WC and WHtR and T2DM.

In this retrospective cohort study, we investigated the association between CUN-BAE and the risk of T2DM in a Chinese middle-aged and elderly population and compared the strength of the association between CUN-BAE and BMI, WC, and WHtR with T2DM. We found that increased CUN-BAE was associated with an increased risk of T2DM and that CUN-BAE was more strongly associated with the risk of T2DM than BMI, WC, or WHtR. The same results were found in the age subgroup analysis. We found that the association between BMI and T2DM was weaker than CUN-BAE but stronger than WC and WHtR in the middle-aged population, whereas the association between BMI and T2DM was found to be weaker than WC and WHtR in the young elderly and elderly individuals. Dose‒response relationships by restricted cubic spline analysis revealed a nonlinear relationship between CUN-BAE, WC, WHtR, and T2DM in the total population and in the younger elderly.

To our knowledge, this is the first large population-based cohort study to examine the relationship between CUN-BAE, a new practical adiposity index, and the risk of T2DM in the Chinese population. Due to the infeasibility of using expensive techniques to measure body composition at a large-scale population level, prior research has relied heavily on the use of anthropometric methods to examine the relationship between obesity and T2DM [19–21]. Traditionally, BMI has been the most commonly used measure of overall obesity, as its calculation requires only simple height and weight information. In addition, WC and WHtR, which are used to measure abdominal obesity, have also increasingly attracted the interest of researchers [22–24]. In a meta-analysis of 21 prospective studies and a meta-analysis of 216 cohort studies, BMI, WC, and WHtR showed a strong positive association with the risk of T2DM [9, 25]. Consistent with these findings, a strong positive association of BMI, WC, and WHtR with T2DM was also found in our study analysis. Several mechanisms can be used to explain this positive association. First, individuals with a genetic susceptibility to T2DM are more likely to be obese because the inherent insulin resistance in the muscle and islet α-cells of these individuals leads to increased glucose and insulin release in the liver, which results in obesity [26]. Second, chronic inflammation associated with obesity and its pro-inflammatory cytokines produced by macrophages in adipose tissue can affect insulin-dependent tissues and beta cells [27]. Third, a series of secretory products released from the stress adipocytes of obese individuals can lead to a loss of insulin sensitivity and beta-cell capacity of the pancreas [28]. Fourth, obese people tend to consume high-calorie foods and high-fat diets, which have been shown to lead to hypothalamic mitochondrial dysfunction and endoplasmic reticulum contingencies, thereby promoting leptin and insulin resistance, which results in T2DM [29]. In addition, BMI and WC or WHtR are considered to be fairly good measures of overall obesity and abdominal obesity, respectively, yet neither can distinguish well between fat mass and lean body mass. A recent study suggested that it is crucial to understand the independent roles of fat mass and lean body mass in mortality [30]. In this study, we were able to distinguish more precisely between fat mass and lean body mass using validated CUN-BAE metrics. We found that CUN-BAE was more associated with the risk of T2DM than BMI, WC and WHtR in middle-aged and older populations. This finding suggests that BMI, WC or WHtR do not fully reflect the extent to which fat mass is detrimental to the risk of type T2DM [31] and that CUN-BAE metrics may address this limitation.

BMI, WC, and WHtR represent various aspects of body composition, yet the consensus on which metric is most closely associated with diabetes risk is unclear. A meta-analysis that included 31 studies found that WHtR was more strongly associated with T2DM than BMI or WC, while another meta-analysis that included 21 cohort studies showed no significant difference in identifying diabetes risk between BMI, WC, and WHtR [25, 32]. In recent years, there has been increasing evidence that abdominal fat, rather than overall fat, might be a more associated risk factor for the development of diabetes [33, 34]. Our study found a stronger association of abdominal fat metrics, specifically WC, with the risk of T2DM compared to BMI. However, it is noteworthy that subgroup analysis stratified by age showed that a strong association of abdominal fat indicators was only observed in the older age group of 60 years and older, whereas WC and WHtR were found to have weaker associations with T2DM risk than BMI in the middle-aged population aged 45–59 years. This suggests that the effectiveness of overall obesity indicators and abdominal obesity indicators in screening people at risk for T2DM may vary across age groups. Previous studies have found that body composition changes with age, including increased fat mass, decreased muscle mass, redistribution of adipose tissue, and shrinkage in height [35], which may be used to explain our findings. In addition, our findings indicate that both WC and WHtR are strongly associated with the risk of developing T2DM, but WHtR was not materially superior to WC in the older age group of 60 years and older, suggesting that there is no additional benefit to measuring height other than WC in the older population.

When we compared CUN-BAE with BMI, WC, and WHtR, CUN-BAE showed the strongest association with T2DM risk, consistent with the results of studies in European populations [12, 13, 36]. In a cross-sectional study of Spanish adults aged 18–96 years, Veronica [36] found that the CUN-BAE index was more strongly associated with cardiometabolic conditions, including diabetes, arterial hypertension and metabolic syndrome (Mets), than BMI and WC, suggesting that CUN-BAE may better identify people at risk for cardiometabolic disease than BMI. Vicente [13] found that CUN-BAE was more strongly associated with diabetes than BMI in adult men. In addition, [37] showed that CUN-BAE had the strongest association with metabolic syndrome compared to BMI, WHtR and other indicators, suggesting that CUN-BAE can be used as an alternative to BMI for the initial screening of people at high risk of metabolic syndrome. Multivariable adjusted restricted cubic spline analysis showed that CUN-BAE, WC, WHtR, and T2DM were nonlinearly associated, but BMI and T2DM were approximately log-linear. The reason for this result may be their different ways of assessing body fat and fat distribution, with CUN-BAE, WC, and WHtR reflecting body fat distribution, whereas BMI mainly reflects overall body fat mass. In the subgroup analysis stratified by age, we found a stronger association between CUN-BAE, BMI, WC, WHtR, and T2DM in the older group than in the middle-aged group, suggesting that different indicators of obesity may perform better in different age groups. However, in the subgroup analysis stratified by sex, we found that the association between CUN-BAE and T2DM was attenuated and approached that of BMI and T2DM. A sex-stratified analysis of 9,555 Iranian subjects indicated similar modest associations between BMI and CUN-BAE with the risk of developing cardiovascular disease risk factors, including metabolic syndrome, hypercholesterolemia, and hypertension [38]. Paradoxically, a prospective cohort study of 6,796 individuals in Norway showed that CUN-BAE was more associated with the risk of CVD events and diabetes than BMI at the time of sex-stratified analysis [14]. The differences in our findings may reflect differences in study design (cross-sectional study vs. cohort study). This may also be related to differences in body composition between Asian and European populations. Overall, our study supports for the first time a strong correlation between the CUN-BAE index and the incidence of T2DM in the Chinese population. More studies are needed to further support our findings.

Our study has several strengths. First, the large sample size, the long follow-up period, the standardized measures used, and the use of an annual health examination dataset in this study avoided recall bias to some extent. Second, we compared the differences in the association between obesity indicators and T2DM risk in different subgroups by age and sex subgroup analysis. Finally, a sensitivity analysis was performed to assess the robustness of the association between obesity indicators and the risk of developing T2DM. However, there are some limitations of this study that should be noted. First, this study focused on the middle-aged and elderly population, and it was therefore not possible to compare the relationship between obesity indicators and T2DM risk in other age groups, which limited the generalizability of this study. Second, although many confounding factors were adjusted for in the analysis of this study, there were still some potential confounding factors present that were not adjusted for, such as literacy and dietary habits. Finally, the dose‒response correlations should be considered with caution because our study sample limited the generalizability of our results.