Association of body roundness index and age acceleration with type 2 diabetes: Evident from the UK Biobank

Association of body roundness index and age acceleration with type 2 diabetes: Evident from the UK Biobank | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of body roundness index and age acceleration with type 2 diabetes: Evident from the UK Biobank Si Ding, Qingqing Jia, Shanshan Xu, Qiuling Xie, Yunjuan He, Liya Zhang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6175507/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Obesity and aging are regarded as significant risk factors for type 2 diabetes(T2D). However, joint effect of body roundness index (BRI) and age acceleration (ACC), novel predictors of visceral and the rate of aging, with incident T2D remains unclear. Objective: To examine the associations of BRI and ACC with incident T2D. Methods: This prospective cohort study used data from the UK Biobank, and participants with pre-existing diabetes and missing data were excluded in the analysis. The outcome of interest was incident T2D. Joint effect of BRI and ACC were assessed through eight paired quartile combinations. Kaplan-Meier curves were used to estimate cumulative incidence, while Cox proportional-hazards regression was used to analyze the independent and joint effect of BRI and ACC by gradually adjusting covariates. Results: Among 380,146 participants from the UK Biobank over 14.6 years of follow-up, 15,262 developed T2D. Kaplan-Meier survival curves indicated that participants with a higher level of BRI or ACC had a higher risk of T2D. Both BRI and ACC levels were positively associated with incident T2D (BRI:HR: 1.30, 95% CI: 1.28-1.32, ACC: HR: HR: 1.03, 95% CI: 1.03-1.03). When BRI and ACC were categorized into quartiles, those in the top quartile demonstrated a significantly increased T2D risk (BRI-Q4:HR:3.68, 95%CI: 3.35-4.04; ACC-Q4:HR:1.59, 95%CI:1.50-1.68; BRI-Q4 and ACC-Q4: HR: 6.76, 95% CI: 5.65-8.09). Conclusion: BRI and ACC were independently associated with increased risk of T2D, with BRI showing a stronger predictive capability. Their combined effects underscore their utility as non-invasive screening tools for T2D risk. Age acceleration Body roundness index Type 2 diabetes Figures Figure 1 Figure 2 Figure 3 Introduction Type 2 diabetes (T2D) is one of the most common chronic diseases in the worldwide, with increasing incidence and prevalence, especially in population with obesity and aging[1, 2]. Obesity has become a global epidemic with approximately 40% of adults being obese[3]. The rising prevalence of obesity has paralleled a surge in diabetes cases, emphasizing the urgent need for early identification and effective management of high-risk populations. Aging has emerged as another significant risk factor for T2D. Physiological changes may occur with age in the human body, such as decreased muscle mass, increased fat accumulation and glucose metabolic abnormalities, increasing the risk of insulin resistance and impaired glucose homeostasis[4]. Thus, understanding the combined effects of obesity and aging on T2D is vital for developing effective prevention strategies. Although body mass index (BMI) remains to be a long-standing accepted tool for obesity evaluation, it performs poor in capturing the complicated relationship between body fat and metabolic health. Without considering fat distribution, BMI fails to distinguish between lean mass and fat mass, which are critical to understanding the health risks associated with obesity[5]. To address this limitation, Thomas et al. [6] introduced the body roundness index (BRI), an anthropometric measure derived from waist circumference and height. This novel index is considered a more effective and sensitive predictor of obesity-related risks than BMI. Aging is a multifaceted process marked by progressive physiological dysregulation and heightened vulnerability to chronic diseases. Although chronological age is commonly used to define aging, its progression differs among individuals. Consequently, the concepts of “biological age” and “accelerated aging” have emerged to capture the pace of individual aging. Accelerated aging is shaped by genetic, environmental, and lifestyle factors and is linked to age-related diseases such as diabetes[7]. Evidence shows that individuals with accelerated aging are prone to metabolic dysfunctions, including insulin resistance, which heightens the risk of early T2D onset[8]. Accelerated aging could be quantified by different biomarkers, such as DNA methylation and telomere length, which could be used as the predictor of biological age[9, 10]. Individuals experiencing accelerated aging may exhibit a heightened susceptibility to adiposity-related metabolic dysfunctions [11]. Accordingly, it is essential to explore the role of aging and fat distribution in the development of diabetes. Although obesity and aging are recognized risk factors for T2D, most research has centered on BMI when evaluating T2D risk, paying less attention to BRI and ACC. Understanding how BRI and ACC work together to affect the susceptibility of T2D will provide valuable insights into the complex mechanisms of the disease and help identify individuals at higher risks before developing symptoms. In this study, BRI was calculated based on height, weight, and waist circumference while ACC was determined using phenotypic age (PhenoAge), in order to explore the association of BRI and ACC with diabetes events and determine whether individuals with higher BRI and ACC are at a higher risk of diabetes independent of BMI using UK Biobank. Methods Study Participants UK Biobank is a large-scale prospective study project with approximately 500,000 participants aged 40-69 years old recruited between 2006 and 2010 during the baseline survey, in order to support extensive health and medical researches.The UK Biobank study was approved by the North West MultiCentre Research Ethics Committee and written informed consent was provided by each participant before the study. Multiple follow-up visits were conducted to update participants’ health and track morbidity, medical information and mortality. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. We used data based on the UK Biobank with application number 99001, the approval date from 31 January 2023 to 31 January 2026. Of the initial 502,367 adults recruited for the study, participants with diabetes at baseline (n=33,772) and those with missing information for baseline characteristics and incomplete diagnostic records (n=88,449) were excluded. Since the sample data estimate is large enough, the actual value is not interpolated in this study in order to avoid that the data after multiple interpolations are not completely consistent with the data of real participants. Finally, a total of 380,146 participants were eligible for this study ( eFigure 1 in Supplement) . Survival Outcome According to the UK Biobank, the basic characteristics of each participant were mainly determined by registration records. The major outcomes of interest in this study were all diabetes, mainly including T2D. Incident diabetes data were obtained through baseline study questionnaires, biochemical indicators and electronic health records (EHR). Participants were recognized to possess diabetes as long as individual up to grade one of the following conditions: fasting blood glucose level≥7.0 mmol/L, hemoglobinA1c (HbA1c) level≥6.5%, the blood glucose content of random blood glucose or 2-hour oral glucose tolerance test of ≥11.1 mmol/L. The EHR was obtained from Health Episode Statistics (England and Wales) and Scottish Morbidity Records (Scotland). Classification was determined by international Classification of Diseases version 10 code (ICD-10). The follow-up time was calculated from the date of recruitment to the date that diabetes was diagnosed or end of the follow-up period (May 1, 2022), whichever occurred first. Body roundness index (BRI) definition BRI was calculated according to the formula developed by Thomas et al[6], and presented as follows: Height, weight and waist circumference were measured through standardized body measurement procedures. BRI was categorized into 4 groups according to the 25th, 50th and 75th quantiles, labeled as BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4, to explore the association with incident T2D. Age acceleration (ACC) PhenoAge was calculated using the formula proposed by Levine et al.[12]: PhenoAge acceleration was calculated as a residual of phenoAge adjusted for chronological age by linear regression. ACC was divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: ACC-Q1, ACC-Q2, ACC-Q3, and ACC-Q4, to explore the association with incident T2D. The combined effect of BRI and ACC The four groups of BRI and the four groups of ACC are paired in all possible combinations, resulting in 8 pairs: BRI-Q1 and ACC-Q2, BRI-Q1 and ACC-Q3, BRI-Q1 and ACC-Q4, BRI-Q2 and ACC-Q1, BRI-Q2 and ACC-Q2, BRI-Q2 and ACC-Q3, BRI-Q2 and ACC-Q4, BRI-Q3 and ACC-Q1, BRI-Q3 and ACC-Q2, BRI-Q3 and ACC-Q3, BRI-Q3 and ACC-Q4, BRI-Q4 and ACC-Q1, BRI-Q4 and ACC-Q2, BRI-Q4 and ACC-Q3, BRI-Q4 and ACC-Q4, with BRI-Q1 and ACC-Q1 as the reference group, to explore the association of BRI and ACC with T2D. C ovariates The following information was collected at baseline through standardized questionnaires, interviews, and physical examinations: sociodemographic variables (age, sex, education level), lifestyle variables (smoking status, drinking status, BMI[Weight(kg)/Height(m) 2 ] and medical history (including hypertension and total cholesterol levels). Hypertension was identified if participants met any of following conditions: systolic blood pressure (SBP)≥140 mmHg, diastolic blood pressure (DBP)≥90mmHg, the use of antihypertension medication or reports of an ICD10-code(I10-I15); Hyperlipidemia was defined as low density lipoprotein cholesterol (LDL-C)≥5.72mmol/L or triglyceride≥1.70mmol/L. Other covariates were categorized as following standards. Smoking status and drinking status were divided into four categories: Never, Previous, Current and Prefer not to answer. Race were classified as Asian or Asian British, Black or Black British, White and Mixed. Education was grouped as college or higher, high school and middle school or lower. Statistical analysis Basic characteristics of participants were presented as continuous variables and categorical variables according to the quartiles of BRI and ACC. Continuous variables were assessed using analysis of variance, with results shown as mean accompanied by standard deviations (SD). Categorical variables were presented as percentages of specific groups and compared by χ2 tests. The cumulative incidence of diabetes was estimated using Kaplan-Meier curve and log-rank test. We investigated the association of BRI and ACC with the incident T2D in four different models using the Cox proportional hazards model, with baseline BRI and ACC fitted as continuous variables (per 1-SD increment) or categorical variables (with BRI-Q1 or ACC-Q1 as the reference group). Model 1 was not adjusted. Model 2 was adjusted for age, sex, BMI, smoking status, drinking status, education and race. Model 3 was further adjusted for high density lipoprotein (HDL), low density lipoprotein (LDL), glucose, hypertension and dyslipidemia on model 2. To assess the independent association of BRI or ACC with T2D events, model 4 was additionally adjusted for ACC or BRI, respectively on model 3. The proportional hazards assumptions for the Cox model were tested using the Schoenfeld residual method, showing no evidence of violation of the assumptions. Due to the relatively low missing rate of qualified covariates (<1%), missing values were not processed. Finally, Model 3 was used to explore the joint effects of BRI and ACC with T2D events. To assess the robustness of the findings, sensitivity analyses were performed by excluding adults who developed diabetes within two years after participation, mitigating potential reverse causation. Results Baseline Characteristics Of the 380,146 eligible participants in this study, 15,262 developed T2D over 14.6 years of follow-up. The demographic and clinical characteristics of the populations at baseline were shown in eTable 1 in the Supplement , and differences were found among groups BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4. Accompanied by higher quartiles of BRI, the proportion of males is significantly higher than that of females (p<0.001) and BMI also exhibited an increasing tendency. Overweight and obesity were more prevalent in participants with higher quartiles of BRI (p<0.001). ACC also increased significantly with BRI (p<0.001). The proportion of current smokers and drinkers is higher in participants with higher quartiles of BRI (p < 0.001). In terms of metabolic and clinical indices, glucose, LDL and CRP increased significantly with higher quartiles of BRI, showing worse metabolic and inflammatory conditions (p<0.001). In addition, the proportion of participants with hypertensive and dyslipidemia is significantly decreased with higher quartiles of BRI (p<0.001). These trends indicate that higher BRI values have significant correlations with adverse health parameters. In the meanwhile, comparison of demographic and clinical characteristics of the populations at baseline among groups ACC-Q1, ACC-Q2, ACC-Q3 and ACC-Q4 were depicted in eTable 2 in the Supplement and similar differences were found among different groups. Individuals with higher ACC values were more likely to be male, smokers, alcohol users, and to have obesity, a high level of glucose, LDL and CRP (all p<0.001), suggesting that participants with higher ACC tend to exhibit worse metabolic parameters. BRI changes with age Baseline BRI trends were summarized in age stratification by 5 intervals, with the mean values increased with age (Figure1) . Generally, BRI was higher in male than in female and exhibited an increasing tendency, with the difference between sexes remained stable. And the difference achieved highest among participants aged 50-55 years, showing a value of 0.6, with 3.54 in women and 4.14 in men. The mean ACC among groups BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4 was -5.53±5.25, -4.61±5.10, -3.78±5.14 and -2.20±5.60, respectively, exhibiting an increasing tendency, with the highest value in BRI-Q4 group ( eTable 1 in the Supplement) . Cumulative probability curve of T2D with different BRI and ACC As shown in Figure 2 , Kaplan-Meier survival curves showed a significant difference in the cumulative incidence of T2D among different BRI groups (p<0.001). Overall, the cumulative probability was highest in the top group (BRI-Q4, which was marked in purple), following by BRI-Q3 and BRI-Q2, indicating that compared to the lowest quartile in the BRI group (BRI-Q1, which was marked in red), participants with higher quartiles had a higher risk of diabetes, showing significant differences. The same results were also shown in different ACC groups (Figure 2) . Association of BRI and ACC with T2D In the multiple models, the relationship between BRI and the risk of T2D was very robust. A 1 U/L increase in BRI level is accompanied by a 53 percent increase risk of T2D (HR: 1.53, 95% CI: 1.52 -1.53) in the unadjusted model (model 1). A lower but still significant risk was observed in model 2 (HR: 1.32, 95% CI: 1.30-1.34) and model 3(HR:1.30, 95% CI: 1.28-1.32) (Table 1) . The positive relationship between ACC and T2D was less strong than that of BRI. In model 1, each SD change in ACC was only associated with a 6 percent increased risk of T2D (HR: 1.06, 95% CI: 1.06-1.06). This association was weaker but still significant in model 2(HR: 1.03, 95% CI: 1.03-1.04) and model 3(HR: 1.03, 95% CI: 1.03-1.03) (Table 2) . The participants were stratified by the quartiles of BRI and ACC. With BRI-Q1(lowest) as the reference group, participants with higher quartiles had a higher risk of T2D. The HRs and 95%CIs were 14.72(13.59-15.94), 4.66(4.25-5.11) and 3.68(3.35-4.04) for BRI-Q4 in model 1-3, respectively when compared to that of BRI-Q1(lowest). Moreover, with ACC-Q1(lowest) as the reference group, participants with higher quartiles also showed a higher risk of T2D. The HRs and 95%CIs were 3.62(3.44-3.81), 1.82(1.72-1.92) and 1.59(1.50-1.68) for ACC-Q4 in model 1-3, respectively when compared to that of ACC-Q1(lowest). These results emphasized that a higher level of BRI or ACC was associated with T2D, indicating that the risk of T2D in participants with the highest quartile of BRI and ACC was higher than those with the lowest quartile of BRI and ACC, showing significant differences. Joint effect of BRI and ACC on diabetes risks Subsequently, joint effect of BRI and ACC on T2D risks was investigated (Figure 3) . From ACC-Q1 group to AAC-Q4 group, the risk of T2D presented a gradient increase as BRI increased. Moreover, the risk of T2D was greatest in the BRI-Q4 group of each group of ACC. Overall, compared with the reference group, the lowest quartile of BRI and ACC (BRI-Q1 and ACC-Q1), the risk of T2D increased with higher quartiles of BRI and ACC, with the highest risk in BRI-Q4 and ACC-Q4 group (HR:6.76, 95%CI:5.56-8.09). These results suggest that BRI and ACC serve as useful indicators for predicting T2D. Discussion This large cohort study aimed to assess the independent and joint associations of BRI and ACC in the prediction of T2D risk. The results revealed a significant positive association of high BRI and ACC levels with incident T2D. Additionally, the predictive efficacy of the BRI in predicting T2D was found to be superior to that of ACC in this study. A more pronounced risk for T2D was observed when these BRI and ACC cluster together. BRI, a novel obesity index based on waist circumference and height, is commonly considered as a better predictor of visceral and total body fat. In this study, a robust and stable positive correlation between BRI and the risk of T2D was observed and the association still remained consistent after adjusting confounding factors. These findings were similar to previous studies[13, 14], which highly suggested that BRI could serve as an effective predictor of T2D. The potential mechanisms of obesity increase the risk of T2D are diverse and complex, mainly including insulin resistance[15], inflammation[16] and β-cell dysfunction[17]. Chronological age above 45 is considered a risk factor for T2D[18]. However, persons with the same chronological age may differ in their rates of aging. Positive ACC, in which individuals’ epigenetic age is older than their chronological age, has been linked to increase disease risk and mortality. In this study, our findings demonstrated that ACC could be used as an indicator to evaluate the risk for T2D. The consistent positive association between ACC and T2D persist across all subgroup analyses, underscoring the robustness of these results. Aging-related disruptions in cellular homeostasis result in the responsiveness to physiological stress, including oxidative stress and inflammation, which are implicated in the pathogenesis of insulin resistance and T2D. Additionally, aging is also related to dysfunction of the mitochondria, leading to the impairment of metabolic homeostasis and oxidative stress and contributing to the progression of insulin resistance and T2D. It is noteworthy that oxidative stress, inflammation, and mitochondrial dysfunction are interconnected in the mechanisms of both aging and insulin resistance, creating a vicious cycle [19]. In this study, ACC provided a significant but lower predictive capability of T2D compared to BRI. It has been reported that a healthy diet and regular physical activity was independently associated with decreased biological aging[20, 21], which unfortunately was not incorporated as confounding factor in our study. Caloric restriction results in a higher insulin sensitivity and lower but functional insulin and insulin-like growth factor-I(IGF-I) levels, and this may slow down age-related physiological decline and the incidence of age-related diseases[22, 23]. Lower insulin and IGF-I levels will limit oxidative stress and cellular damage, while enough resources will be available for preserving and maintaining normal functions of the body[22]. Thus, ACC was relatively weak in predicting T2D in this study. Most studies examined the independent effect of related indicators on the risk of T2D. In this study, we further investigated the joint contribution of BRI and ACC to the risk of T2D. The results showed that individuals in the combination group of highest BRI and ACC were at the highest risk of T2D compared to the reference group (BRI-Q1 and ACC-Q1group), illustrating that BRI and ACC contribute to an independent effect as well as joint cumulative effect on the risk of T2D. Interestingly, obese and elderly individuals exhibit strikingly similar immunological profiles in adipose tissues[24]. The adipose tissue of obese individuals exhibits several of the hallmarks of aging, including mitochondrial dysfunction, cellular senescence, and chronic inflammation[25]. Additionally, advanced age is accompanied by fat redistribution from subcutaneous to abdominal visceral depots[26, 27], resulting in increasing proinflammatory cytokines and chemokines, which are known to interfere with insulin action[28]. Thus, obesity and aging are active participants in a vicious cycle that can accelerate the onset of T2D. However, several limitations also exist. Firstly, this retrospective observational study provides the association of BRI and ACC with incident diabetes, which needs to be further verified by prospective studies. Secondly, body indices and biochemical indicators were measured at baseline, thus subtle changes fail to be captured during the follow-up. Thirdly, other confounding factors cannot be completely rule out, such as dietary habits, physical activity intensity and genetic predisposition. In summary, participants with higher BRI and ACC were associated with an increased risk of T2D, and BRI shows stronger predictive capability. Moreover, the combination of BRI and ACC increases the risk of T2D. Our findings will help clinicians to further identify the high-risk population of T2D, contributing to early management. Abbreviations ACC: Age acceleration; BMI: body mass index; BRI: Body Roundness Index; CRP, C-reactive protein; DBP: diastolic blood pressure; EHR: electronic health records; IGF-I: insulin-like growth factor-I; LDL: low density lipoprotein; LDL-C: low density lipoprotein cholesterol; SBP: systolic blood pressure; SD: standard deviations; T2D: Type 2 diabetes; WHO: World Health Organization. Declarations Acknowledgements We thank the UK Biobank participants. Funding This work was supported by grants from the Natural Science Foundation of China (82273647) and Noncommunicable Chronic Diseases-National Science and Technology Major Project(2023ZD0508700). Conflict of interest The authors have declared that no conflict of interest exists. Author contributions Si Ding: Conceptualization, Data Curation, Writing-original draft, Visualization. Qingqing Jia: Conceptualization, Writing-original draft, Data Curation, Methodology, Formal analysis. Shanshan Xu: Investigation, Methodology, ‌ Validation . Qiuling Xie: Investigation, Methodology, ‌ Validation . Yunjuan He: Investigation, Methodology, ‌ Validation . Liya Zhang: Investigation, Methodology, ‌ Validation . Huajun Li: Investigation, Methodology, ‌ Validation . Xinyi Zha: Investigation, Methodology, ‌ Validation . Yushu Qiu: Investigation, Methodology, ‌ Validation . Kang Cai: Investigation, Methodology, ‌ Validation . JinjunRan: Validation, Project administration . Xihao Du: Supervision, Validation, Project administration, Writing-review &editing. Xianting Jiao: Conceptualization, Supervision, Validation, Writing-review &editing, Project administration, Funding acquisition. Xianting Jiao and Xihao Du are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. Data availability The data supporting this study’s findings are available at UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Ethics approval and consent to participate UK Biobank received ethical approval from the Northwest Multi-center Research Ethics Committee (MREC reference:21/NW/0157). All participants gave written informed consent before enrolment in the study, which was conducted following the principles of the Declaration of Helsinki.  References International Diabetes Federation: IDF Diabetes Atlas 9th edition. Available from: https://diabetesatlas.org/idf-guide-for-epidemiology-studies/ Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, Shi B, Sun H, Ba J, Chen B et al : Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. Bmj 2020, 369:m997. World Health Organization (WHO). (2020). Obesity and overweight. 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Tables Table 1 Hazard ratios for T2D by continue BRI and BRI quartiles  Variable Model1 Model2 Model3 Model4 Continue BRI 1.53(1.52-1.53) 1.32(1.30-1.34) 1.30(1.28-1.32) 1.02(0.84-1.25) BRI quartiles BRI-Q1(lowest) Ref. Ref. Ref. Ref. BRI-Q2 2.44(2.23-2.68) 1.67(1.52-1.83) 1.47(1.34-1.62) 1.16(0.71-1.89) BRI-Q3 5.54(5.09-6.02) 2.83(2.59-3.09) 2.32(2.13-2.54) 1.04(0.63-1.72) BRI-Q4(highest) 14.72(13.59-15.94) 4.66(4.25-5.11) 3.68(3.35-4.04) 1.15(0.6-2.21) Abbreviations: BRI, body roundness index Model 1: unadjusted Model 2: Model 1+baseline age, sex, BMI, smoking status, drinking status, education and race Model 3: Model 2+ HDL, LDL, glucose, hypertension and dyslipidemia Model 4: Model 3+ACC BRI is divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: BRI-Q1, BRI-Q2, BRI-Q3, and BRI-Q4，BRI-Q1 as the reference group. Table 2 Hazard ratios for T2D by continue ACC and ACC quartiles Variable Model1 Model2 Model3 Model4 Continue ACC 1.06(1.06-1.06) 1.03(1.03-1.04) 1.03(1.03-1.03) 1.01(1.00-1.03) ACC quartiles ACC-Q1(lowest) ACC-Q2 1.52(1.44-1.62) 1.22(1.15-1.29) 1.14(1.07-1.21) 1.01(0.65-1.56) ACC-Q3 2.11(2.00-2.23) 1.40(1.32-1.48) 1.26(1.19-1.33) 1.16(0.75-1.78) ACC-Q4(highest) 3.62(3.44-3.81) 1.82(1.72-1.92) 1.59(1.50-1.68) 1.51(1.00-2.26) Abbreviations: ACC, age acceleration Model 1: unadjusted Model 2: Model 1+baseline age, sex, BMI, smoking status, drinking status, education and race Model 3: Model 2+ HDL, LDL, glucose, hypertension and dyslipidemia Model 4: Model 3+BRI ACC is divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: ACC-Q1, ACC-Q2, ACC-Q3, and ACC-Q4, ACC-Q1 as the reference group. Additional Declarations No competing interests reported. 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06:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6175507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6175507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78225801,"identity":"6129a136-44dd-40a7-a115-b8f997f80974","added_by":"auto","created_at":"2025-03-11 06:55:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends of mean BRI values by age (per 5 years) in UK adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Difference = mean of male BRI - mean of female BRI\u003c/p\u003e\n\u003cp\u003eAbbreviations: BRI, body roundness index;\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6175507/v1/fff3017be7d53e2f97917a7d.png"},{"id":78226789,"identity":"8f96d459-2aa0-46a9-94b1-c4ea87f6709c","added_by":"auto","created_at":"2025-03-11 07:03:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Kaplan-Meier curves for patients with T2D in different BRI or ACC groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBRI and ACC are divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: BRI-Q1, BRI-Q2, BRI-Q3, and BRI-Q4 as well as ACC-Q1, ACC-Q2, ACC-Q3, and ACC-Q4.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BRI, body roundness index; ACC, age acceleration\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6175507/v1/fb4d0efe0be101ce6b39d1b0.png"},{"id":78225993,"identity":"bf5402a3-7816-491b-b575-97983d380866","added_by":"auto","created_at":"2025-03-11 06:56:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe joint effect of BRI and ACC on patients with T2D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model was adjusted for age, sex, BMI, smoking status, drinking status, education, race, HDL, LDL, glucose, hypertensive and dyslipidemia.\u003c/p\u003e\n\u003cp\u003eBRI and ACC are divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: BRI-Q1, BRI-Q2, BRI-Q3, BRI-Q4 and ACC-Q1, ACC-Q2, ACC-Q3, ACC-Q4, respectively; The four groups of BRI and the four groups of ACC are paired in all possible combinations, resulting in 8 pairs: BRI-Q1 and ACC-Q2, BRI-Q1 and ACC-Q3, BRI-Q1 and ACC-Q4, BRI-Q2 and ACC-Q1, BRI-Q2 and ACC-Q2, BRI-Q2 and ACC-Q3, BRI-Q2 and ACC-Q4, BRI-Q3 and ACC-Q1, BRI-Q3 and ACC-Q2, BRI-Q3 and ACC-Q3, BRI-Q3 and ACC-Q4, BRI-Q4 and ACC-Q1, BRI-Q4 and ACC-Q2, BRI-Q4 and ACC-Q3, BRI-Q4 and ACC-Q4, with BRI-Q1 and ACC-Q1 as the reference group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BRI, body roundness index; ACC, age acceleration\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6175507/v1/789a7283b659dececbb84887.png"},{"id":78230729,"identity":"be5f2003-a62c-4632-bb9e-be55863a5ad1","added_by":"auto","created_at":"2025-03-11 07:27:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6175507/v1/12320b36-dfca-4133-9827-fff5c82dfc07.pdf"},{"id":78225814,"identity":"0ad27a79-5fd0-4ace-86f1-8a1e3cbfb60e","added_by":"auto","created_at":"2025-03-11 06:55:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5831433,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6175507/v1/2f641ac192fb13e9a3f26bf6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of body roundness index and age acceleration with type 2 diabetes: Evident from the UK Biobank\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes (T2D) is one of the most common chronic diseases in the worldwide, with increasing\u0026nbsp; incidence and prevalence, especially in population with obesity and aging[1, 2]. Obesity has become a global\u0026nbsp; epidemic with approximately 40% of adults being obese[3]. The rising prevalence of obesity has paralleled a surge in diabetes cases, emphasizing the urgent need for early identification and effective management of high-risk populations. Aging has emerged as another significant risk factor for T2D. Physiological changes may occur with age in the human body, such as decreased muscle mass, increased fat accumulation and glucose metabolic abnormalities, increasing the risk of insulin resistance and impaired glucose homeostasis[4]. Thus, understanding the combined effects of obesity and aging on T2D is vital for developing effective prevention strategies.\u003c/p\u003e\n\u003cp\u003eAlthough body mass index (BMI) remains to be a long-standing accepted tool for obesity evaluation, it performs poor in capturing the complicated relationship between body fat and metabolic health. Without considering fat distribution, BMI fails to distinguish between lean mass and fat mass, which are critical to understanding the health risks associated with obesity[5]. To address this limitation,\u0026nbsp; Thomas et al.\u0026nbsp;[6]\u0026nbsp;introduced the body roundness index (BRI), an anthropometric measure derived from waist circumference and height. This novel index is considered a more effective and sensitive predictor of obesity-related risks than BMI. Aging is a multifaceted process marked by progressive physiological dysregulation and heightened vulnerability to chronic diseases. Although chronological age is commonly used to define aging, its progression differs among individuals. Consequently, the concepts of \u0026ldquo;biological age\u0026rdquo; and \u0026ldquo;accelerated aging\u0026rdquo; have emerged to capture the pace of individual aging. Accelerated aging is shaped by genetic, environmental, and lifestyle factors and is linked to age-related diseases such as diabetes[7]. Evidence shows that individuals with accelerated aging are prone to metabolic dysfunctions, including insulin resistance, which heightens the risk of early T2D onset[8]. Accelerated aging could be quantified by different biomarkers, such as DNA methylation and telomere length, which could be used as the predictor of biological age[9, 10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividuals experiencing accelerated aging may exhibit a heightened susceptibility to adiposity-related metabolic dysfunctions [11]. Accordingly, it is essential to explore the role of aging and fat distribution in the development of diabetes. Although obesity and aging are recognized risk factors for T2D, most research has centered on BMI when evaluating T2D risk, paying less attention to BRI and ACC. Understanding how BRI and ACC work together to affect the susceptibility of T2D will provide valuable insights into the complex mechanisms of the disease and help identify individuals at higher risks before developing symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, BRI was calculated based on height, weight, and waist circumference while ACC was determined using phenotypic age (PhenoAge), in order to explore the association of BRI and ACC with diabetes events and determine whether individuals with higher BRI and ACC are at a higher risk of diabetes independent of BMI using UK Biobank.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank is a large-scale prospective study project with approximately 500,000 participants aged 40-69 years old recruited between 2006 and 2010 during the baseline survey, in order to support extensive health and medical researches.The UK Biobank study was approved by the North West MultiCentre Research Ethics Committee and written informed consent was provided by each participant before the study. Multiple follow-up visits were conducted to update participants\u0026rsquo; health and track morbidity, medical information and mortality. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. We used data based on the UK Biobank with application number 99001, the approval date from 31 January 2023 to 31 January 2026.\u003c/p\u003e\n\u003cp\u003eOf the initial 502,367 adults recruited for the study, participants with diabetes at baseline (n=33,772) and those with missing information for baseline characteristics and incomplete diagnostic records\u0026nbsp;(n=88,449) were excluded. Since the sample data estimate is large enough, the actual value is not interpolated in this study in order to avoid that\u0026nbsp;the data after multiple interpolations are not completely consistent with the data of real participants. Finally, a total of 380,146 participants were eligible for this study \u003cstrong\u003e(\u003c/strong\u003eeFigure 1 in\u003cstrong\u003e\u0026nbsp;Supplement)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the\u0026nbsp;UK Biobank, the basic characteristics of each participant were mainly determined by registration records. The major outcomes of interest in this study were all diabetes, mainly including T2D. Incident diabetes data were obtained through baseline study questionnaires, biochemical indicators and electronic health records (EHR).\u0026nbsp; Participants were recognized to possess diabetes as long as individual up to grade one of the following conditions: fasting blood glucose level\u0026ge;7.0 mmol/L,\u0026nbsp;hemoglobinA1c (HbA1c) level\u0026ge;6.5%, the blood glucose content of random blood glucose or 2-hour\u0026nbsp;oral glucose tolerance test of\u0026nbsp;\u0026ge;11.1 mmol/L.\u0026nbsp;The EHR was obtained from Health Episode Statistics (England and Wales) and Scottish Morbidity Records (Scotland).\u0026nbsp;Classification was determined by international Classification of Diseases version 10 code (ICD-10). The follow-up time was calculated from the date of recruitment to the date that diabetes was diagnosed or end of the follow-up period (May 1, 2022), whichever occurred first.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody roundness index (BRI) definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBRI was calculated according to the formula developed by Thomas et al[6], and presented as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"562\" height=\"75\"\u003e\u003c/p\u003e\n\u003cp\u003eHeight, weight and waist circumference were measured through standardized body measurement procedures.\u0026nbsp; BRI was categorized into 4 groups according to the 25th, 50th and 75th quantiles, labeled as BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4, to explore the association with incident T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge acceleration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(ACC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenoAge was calculated using the formula proposed by Levine et al.[12]:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"938\" height=\"613\"\u003e\u003c/p\u003e\n\u003cp\u003ePhenoAge acceleration was calculated as a residual of phenoAge adjusted for chronological age by linear regression.\u0026nbsp;ACC was divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: ACC-Q1, ACC-Q2, ACC-Q3, and ACC-Q4, to explore the association with incident T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe combined effect of BRI and ACC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe four groups of BRI and the four groups of ACC are paired in all possible combinations, resulting in 8 pairs: BRI-Q1 and ACC-Q2, BRI-Q1 and ACC-Q3, BRI-Q1 and ACC-Q4, BRI-Q2 and ACC-Q1, BRI-Q2 and ACC-Q2, BRI-Q2 and ACC-Q3, BRI-Q2 and ACC-Q4, BRI-Q3 and ACC-Q1, BRI-Q3 and ACC-Q2, BRI-Q3 and ACC-Q3, BRI-Q3 and ACC-Q4, BRI-Q4 and ACC-Q1, BRI-Q4 and ACC-Q2, BRI-Q4 and ACC-Q3, BRI-Q4 and ACC-Q4, with BRI-Q1 and ACC-Q1 as the reference group, to explore the association of BRI and ACC with T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;following information was collected at baseline through standardized questionnaires, interviews, and physical examinations: sociodemographic variables (age, sex, education level), lifestyle variables (smoking status, drinking status, BMI[Weight(kg)/Height(m)\u003csup\u003e2\u003c/sup\u003e] and medical history (including hypertension and total cholesterol levels). Hypertension was identified if participants met any of following conditions: systolic blood pressure (SBP)\u0026ge;140 mmHg,\u0026nbsp; diastolic blood pressure (DBP)\u0026ge;90mmHg, the use of antihypertension medication or reports of an\u0026nbsp;ICD10-code(I10-I15); Hyperlipidemia was defined as low density lipoprotein cholesterol (LDL-C)\u0026ge;5.72mmol/L or triglyceride\u0026ge;1.70mmol/L. Other covariates were categorized as following standards. Smoking status and drinking status were divided into four categories: Never, Previous, Current and\u0026nbsp;Prefer not to answer. Race were classified as Asian or Asian British, Black or Black British, White and Mixed. Education was grouped as college or higher, high school and middle school or lower.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasic characteristics of participants were presented as continuous variables and categorical variables according to the quartiles of BRI and ACC. Continuous variables were assessed using analysis of variance, with results shown as mean accompanied by standard deviations (SD). Categorical variables were presented as percentages of specific groups and compared by \u0026chi;2 tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cumulative incidence of diabetes was estimated using Kaplan-Meier curve and log-rank test. We investigated the association of BRI and ACC with the incident T2D in four different models using the Cox proportional hazards model, with baseline BRI and ACC fitted as continuous variables (per 1-SD increment) or categorical variables (with BRI-Q1 or ACC-Q1 as the reference group). Model 1 was not adjusted. Model 2 was adjusted for age, sex, BMI, smoking status, drinking status, education and race. Model 3 was further adjusted for high density lipoprotein (HDL), low density lipoprotein (LDL), glucose, hypertension and dyslipidemia on model 2. To assess the independent association of BRI or ACC with T2D events, model 4 was additionally adjusted for ACC or BRI, respectively on model 3. The proportional hazards assumptions for the Cox model were tested using the Schoenfeld residual method, showing no evidence of violation of the assumptions. Due to the relatively low missing rate of qualified covariates (\u0026lt;1%), missing values were not processed. Finally, Model 3 was used to explore the joint effects of BRI and ACC with T2D events. To assess the robustness of the findings, sensitivity analyses were performed by excluding adults who developed diabetes within two years after participation, mitigating potential reverse causation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 380,146 eligible participants in this study, 15,262 developed T2D over 14.6 years of follow-up. The demographic and clinical characteristics of the populations at baseline were shown in eTable 1 in the \u003cstrong\u003eSupplement\u003c/strong\u003e, and differences were found among groups BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4. Accompanied by higher quartiles of BRI, the proportion of males is significantly higher than that of females (p\u0026lt;0.001) and BMI also exhibited an increasing tendency.\u0026nbsp;Overweight and obesity were more prevalent in\u0026nbsp;participants with higher quartiles of BRI\u0026nbsp;(p\u0026lt;0.001). ACC also increased significantly with BRI (p\u0026lt;0.001). The proportion of current smokers and drinkers is higher in participants with higher quartiles of BRI (p \u0026lt; 0.001). In terms of metabolic and clinical indices, glucose, LDL and CRP increased significantly with higher quartiles of BRI, showing worse metabolic and\u0026nbsp;inflammatory conditions\u0026nbsp;(p\u0026lt;0.001).\u0026nbsp;In addition, the proportion of participants with hypertensive and dyslipidemia is significantly decreased with higher quartiles of BRI (p\u0026lt;0.001). These trends indicate that higher BRI values have significant correlations with\u0026nbsp;adverse health parameters.\u003c/p\u003e\n\u003cp\u003eIn the meanwhile, comparison of demographic and clinical characteristics of the populations at baseline among groups ACC-Q1, ACC-Q2, ACC-Q3 and ACC-Q4 were depicted in eTable 2 in the \u003cstrong\u003eSupplement\u003c/strong\u003e and similar differences were found among different groups. Individuals with higher ACC values were more likely to be male, smokers, alcohol users, and to have obesity, a high level of glucose, LDL and CRP (all p\u0026lt;0.001), suggesting that participants with higher ACC tend to exhibit\u0026nbsp; worse metabolic parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBRI changes with age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline BRI trends were summarized in age stratification by 5 intervals, with the mean values increased with age \u003cstrong\u003e(Figure1)\u003c/strong\u003e. Generally, BRI was higher in male than in female and exhibited an increasing tendency,\u0026nbsp; with the difference between sexes remained stable. And the difference achieved highest among participants aged 50-55 years, showing a value of 0.6, with 3.54 in women and 4.14 in men.\u003c/p\u003e\n\u003cp\u003eThe mean ACC among groups BRI-Q1, BRI-Q2, BRI-Q3 and BRI-Q4 was \u0026nbsp; \u0026nbsp; \u0026nbsp; -5.53\u0026plusmn;5.25, -4.61\u0026plusmn;5.10, -3.78\u0026plusmn;5.14 and -2.20\u0026plusmn;5.60, respectively,\u0026nbsp; exhibiting an increasing tendency, with the highest value in BRI-Q4 group\u0026nbsp;\u003cstrong\u003e(\u003c/strong\u003eeTable 1 in the \u003cstrong\u003eSupplement)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprobability curve of T2D with different BRI and ACC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e, Kaplan-Meier survival curves showed a significant difference in the cumulative incidence of T2D among different BRI groups (p\u0026lt;0.001). Overall, the\u0026nbsp;cumulative probability\u0026nbsp;was highest in the top group (BRI-Q4, which was marked in purple), following by BRI-Q3 and BRI-Q2, indicating that compared to the lowest quartile in the BRI group (BRI-Q1, which was marked in red), participants with higher quartiles had a higher risk of diabetes, showing significant differences. The same results were also shown in different ACC groups\u003cstrong\u003e\u0026nbsp;(Figure 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of BRI and ACC with T2D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the multiple models, the relationship between BRI and the risk of T2D was very robust. A 1\u0026nbsp; U/L increase in BRI level is accompanied by a 53 percent increase risk of T2D (HR: 1.53, 95% CI: 1.52 -1.53) in the unadjusted model (model 1). A lower but still significant risk was observed in model 2 (HR: 1.32, 95% CI: 1.30-1.34) and model 3(HR:1.30, 95% CI: 1.28-1.32) \u003cstrong\u003e(Table 1)\u003c/strong\u003e.\u0026nbsp; The positive relationship between ACC and T2D was less strong than that of BRI. In model 1,\u0026nbsp; each SD change in ACC was only associated with a 6 percent increased risk of T2D (HR: 1.06, 95% CI: 1.06-1.06).\u0026nbsp; This association was weaker but still significant in model 2(HR: 1.03, 95% CI: 1.03-1.04) and model 3(HR: 1.03, 95% CI: 1.03-1.03) \u003cstrong\u003e(Table 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe participants were stratified by the quartiles of BRI and ACC. With BRI-Q1(lowest) as the reference group, participants with higher quartiles had a higher risk of T2D. The HRs and 95%CIs were 14.72(13.59-15.94), 4.66(4.25-5.11) and 3.68(3.35-4.04) for BRI-Q4 in model 1-3, respectively when compared to that of BRI-Q1(lowest). Moreover, with ACC-Q1(lowest) as the reference group, participants with higher quartiles also showed a higher risk of T2D. The HRs and 95%CIs were 3.62(3.44-3.81), 1.82(1.72-1.92) and 1.59(1.50-1.68) for ACC-Q4 in model 1-3, respectively when compared to that of ACC-Q1(lowest). These results emphasized that a higher level of BRI or ACC was associated with T2D, indicating that the risk of T2D in participants with the highest quartile of BRI and ACC was higher than those with the lowest quartile of BRI and ACC, showing significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoint effect of BRI and ACC on diabetes risks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, joint effect of BRI and ACC on T2D risks was investigated \u003cstrong\u003e(Figure 3)\u003c/strong\u003e. From ACC-Q1 group to AAC-Q4 group, the risk of T2D presented a gradient increase as BRI increased. Moreover, the risk of T2D was greatest in the BRI-Q4 group of each group of ACC. Overall, compared with the reference group, the lowest quartile of BRI and ACC (BRI-Q1 and ACC-Q1), the risk of T2D increased with higher quartiles of BRI and ACC, with the highest risk in BRI-Q4 and ACC-Q4 group (HR:6.76, 95%CI:5.56-8.09). These results suggest that BRI and ACC serve as useful indicators for predicting T2D.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large cohort study aimed to assess the independent and joint associations of BRI and ACC in the prediction of T2D risk.\u0026nbsp;The results revealed a significant positive association of high BRI and ACC levels with incident T2D. Additionally, the predictive efficacy of the BRI in predicting T2D was found to be superior to that of ACC in this study. A more pronounced risk for T2D was observed when these BRI and ACC cluster together.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBRI, a novel obesity index based on waist circumference and height, is commonly considered as a better predictor of visceral and total body fat. In this study, a robust and stable positive correlation between BRI and the risk of T2D was observed and the association still remained consistent after adjusting\u0026nbsp;confounding factors. These findings were similar to previous studies[13, 14], which highly suggested that BRI could serve as an effective predictor of\u0026nbsp;T2D. The potential mechanisms of obesity increase the risk of T2D are diverse and complex, mainly including insulin resistance[15], inflammation[16]\u0026nbsp;and \u0026beta;-cell dysfunction[17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChronological age above 45 is considered a risk factor for T2D[18]. However,\u0026nbsp; persons with the same chronological age may differ in their rates of aging.\u0026nbsp;Positive ACC, in which individuals\u0026rsquo; epigenetic age is older than their chronological age, has been linked to increase disease risk and mortality. In this study, our findings demonstrated that ACC could be used as an indicator to evaluate the risk for T2D. The consistent positive association between ACC and T2D persist across all subgroup analyses, underscoring the robustness of these results.\u0026nbsp;Aging-related disruptions in cellular homeostasis result in the responsiveness to physiological stress, including oxidative stress and inflammation, which are implicated in the pathogenesis of insulin resistance and T2D. Additionally, aging is also related to\u0026nbsp; dysfunction of the mitochondria, leading to the impairment of metabolic homeostasis and oxidative stress and contributing to the progression of insulin resistance and T2D. It is noteworthy that oxidative stress, inflammation, and mitochondrial dysfunction are interconnected in the mechanisms of both aging and insulin resistance, creating a vicious cycle\u0026nbsp;[19].\u003c/p\u003e\n\u003cp\u003eIn this study, ACC\u0026nbsp; provided a significant but lower predictive capability of T2D compared to BRI. It has been reported that a healthy diet and regular physical activity\u0026nbsp;was independently associated with decreased biological aging[20, 21], which unfortunately was not incorporated as confounding factor in our study.\u0026nbsp;Caloric restriction results in a higher insulin sensitivity and lower but functional insulin and\u0026nbsp; insulin-like growth factor-I(IGF-I) levels, and this may slow down age-related physiological decline and the incidence of age-related diseases[22, 23]. Lower insulin and IGF-I levels will limit oxidative stress and cellular damage, while enough resources will be available for preserving and maintaining normal functions of the body[22]. Thus, ACC\u0026nbsp;was\u0026nbsp;relatively weak in predicting\u0026nbsp;T2D\u0026nbsp;in this study.\u003c/p\u003e\n\u003cp\u003eMost studies examined the independent effect of related indicators on the risk of T2D. In this study, we further investigated the joint contribution of BRI and ACC to the risk of T2D. The results showed that individuals in the combination group of highest BRI and ACC were at the highest risk of T2D compared to the reference group (BRI-Q1 and ACC-Q1group), illustrating that BRI and ACC contribute to an independent effect as well as joint cumulative effect\u0026nbsp;on the risk of T2D. Interestingly, obese and elderly individuals exhibit strikingly similar immunological profiles in adipose tissues[24]. The adipose tissue of obese individuals exhibits several of the hallmarks of aging, including mitochondrial dysfunction, cellular\u0026nbsp; senescence, and chronic inflammation[25]. Additionally, advanced age is accompanied by fat redistribution from subcutaneous to abdominal visceral depots[26, 27], resulting in\u0026nbsp; increasing proinflammatory cytokines and chemokines, which are known to interfere with insulin action[28]. Thus, obesity and aging are active participants in a vicious cycle that can accelerate the onset of T2D.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;However, several limitations also exist. Firstly, this retrospective observational study provides the association of BRI and ACC with incident diabetes, which needs to be further verified by prospective studies. Secondly, body indices and biochemical indicators were measured at baseline, thus subtle changes fail to be captured during the follow-up. Thirdly,\u0026nbsp;other confounding factors cannot be completely rule out, such as dietary habits, physical activity intensity and genetic predisposition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, participants with higher BRI and ACC were associated with an increased risk of T2D, and BRI shows stronger predictive capability. Moreover, the combination of BRI and ACC increases the risk of T2D. Our findings will help clinicians to further identify the high-risk population of T2D, contributing to early management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC: Age acceleration; BMI: body mass index; BRI: Body Roundness Index; CRP, C-reactive protein; DBP: diastolic blood pressure; EHR: electronic health records; IGF-I: insulin-like growth factor-I; LDL: low density lipoprotein; LDL-C: low density lipoprotein cholesterol; SBP: systolic blood pressure; SD: standard deviations; T2D: Type 2 diabetes; WHO: World Health Organization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the UK Biobank participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Natural Science Foundation of China (82273647) and Noncommunicable Chronic Diseases-National Science and Technology Major Project(2023ZD0508700).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no conflict of interest exists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSi Ding:\u0026nbsp;\u003c/strong\u003eConceptualization, Data Curation, Writing-original draft, Visualization. \u003cstrong\u003eQingqing Jia:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing-original draft, Data Curation, Methodology, Formal analysis. \u003cstrong\u003eShanshan Xu:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e. \u003cstrong\u003eQiuling Xie:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;Yunjuan He:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e. \u003cstrong\u003eLiya Zhang:\u003c/strong\u003e Investigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;Huajun Li:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u0026nbsp;\u003cstrong\u003e\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e. \u003cstrong\u003eXinyi Zha:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;Yushu Qiu:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e. \u003cstrong\u003eKang Cai:\u003c/strong\u003e Investigation, Methodology,\u003cstrong\u003e\u0026nbsp;\u0026zwnj;\u003c/strong\u003e\u003cstrong\u003eValidation\u003c/strong\u003e.\u0026nbsp;\u003cstrong\u003eJinjunRan:\u003c/strong\u003eValidation,\u0026nbsp;\u003cstrong\u003eProject administration\u003c/strong\u003e. \u003cstrong\u003eXihao Du:\u003c/strong\u003e Supervision, Validation,\u0026nbsp;\u003cstrong\u003eProject administration,\u0026nbsp;\u003c/strong\u003eWriting-review \u0026amp;editing. \u003cstrong\u003eXianting Jiao:\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Validation, Writing-review \u0026amp;editing,\u0026nbsp;\u003cstrong\u003eProject administration,\u0026nbsp;\u003c/strong\u003eFunding acquisition.\u0026nbsp;\u003cstrong\u003eXianting Jiao and Xihao Du\u003c/strong\u003e are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026rsquo;s findings are available at UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank received ethical approval from the Northwest Multi-center Research Ethics Committee (MREC reference:21/NW/0157). All participants gave written informed consent before enrolment in the study, which was conducted following the principles of the Declaration of Helsinki. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Diabetes Federation: IDF Diabetes Atlas 9th edition. Available from: https://diabetesatlas.org/idf-guide-for-epidemiology-studies/\u003c/li\u003e\n\u003cli\u003eLi Y, Teng D, Shi X, Qin G, Qin Y, Quan H, Shi B, Sun H, Ba J, Chen B\u003cem\u003e et al\u003c/em\u003e: Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. \u003cem\u003eBmj \u003c/em\u003e2020, 369:m997.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2020). Obesity and overweight. 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1):S13-s28.\u003c/li\u003e\n\u003cli\u003eKitada M, Ogura Y, Monno I, Koya D: Sirtuins and Type 2 Diabetes: Role in Inflammation, Oxidative Stress, and Mitochondrial Function. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2019, 10:187.\u003c/li\u003e\n\u003cli\u003eThomas A, Belsky DW, Gu Y: Healthy Lifestyle Behaviors and Biological Aging in the U.S. National Health and Nutrition Examination Surveys 1999-2018. \u003cem\u003eJ Gerontol A Biol Sci Med Sci \u003c/em\u003e2023, 78(9):1535-1542.\u003c/li\u003e\n\u003cli\u003eYang G, Cao X, Li X, Zhang J, Ma C, Zhang N, Lu Q, Crimmins EM, Gill TM, Chen X\u003cem\u003e et al\u003c/em\u003e: Association of Unhealthy Lifestyle and Childhood Adversity With Acceleration of Aging Among UK Biobank Participants. \u003cem\u003eJAMA Netw Open \u003c/em\u003e2022, 5(9):e2230690.\u003c/li\u003e\n\u003cli\u003eParr T: Insulin exposure controls the rate of mammalian aging. \u003cem\u003eMech Ageing Dev \u003c/em\u003e1996, 88(1-2):75-82.\u003c/li\u003e\n\u003cli\u003eJanssen J: Hyperinsulinemia and Its Pivotal Role in Aging, Obesity, Type 2 Diabetes, Cardiovascular Disease and Cancer. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2021, 22(15).\u003c/li\u003e\n\u003cli\u003eTrim W, Turner JE, Thompson D: Parallels in Immunometabolic Adipose Tissue Dysfunction with Ageing and Obesity. \u003cem\u003eFront Immunol \u003c/em\u003e2018, 9:169.\u003c/li\u003e\n\u003cli\u003eSantos AL, Sinha S: Obesity and aging: Molecular mechanisms and therapeutic approaches. \u003cem\u003eAgeing Res Rev \u003c/em\u003e2021, 67:101268.\u003c/li\u003e\n\u003cli\u003eSepe A, Tchkonia T, Thomou T, Zamboni M, Kirkland JL: Aging and regional differences in fat cell progenitors - a mini-review. \u003cem\u003eGerontology \u003c/em\u003e2011, 57(1):66-75.\u003c/li\u003e\n\u003cli\u003eHughes VA, Roubenoff R, Wood M, Frontera WR, Evans WJ, Fiatarone Singh MA: Anthropometric assessment of 10-y changes in body composition in the elderly. \u003cem\u003eAm J Clin Nutr \u003c/em\u003e2004, 80(2):475-482.\u003c/li\u003e\n\u003cli\u003eBarzilai N, Huffman DM, Muzumdar RH, Bartke A: The critical role of metabolic pathways in aging. \u003cem\u003eDiabetes \u003c/em\u003e2012, 61(6):1315-1322.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Hazard ratios for\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;T2D\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;by\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtinue BRI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBRI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Continue BRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53(1.52-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32(1.30-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30(1.28-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02(0.84-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; BRI quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRI-Q1(lowest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRI-Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.44(2.23-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67(1.52-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47(1.34-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16(0.71-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRI-Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.54(5.09-6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.83(2.59-3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32(2.13-2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04(0.63-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRI-Q4(highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.72(13.59-15.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.66(4.25-5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.68(3.35-4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15(0.6-2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: BRI, body roundness index\u003c/p\u003e\n\u003cp\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003eModel 2: Model 1+baseline age, sex, BMI, smoking status, drinking status, education and race\u003c/p\u003e\n\u003cp\u003eModel 3: Model 2+ HDL, LDL, glucose, hypertension and dyslipidemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 4: Model 3+ACC\u003c/p\u003e\n\u003cp\u003eBRI is divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: BRI-Q1, BRI-Q2, BRI-Q3, and BRI-Q4，BRI-Q1 as the reference group.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Hazard ratios for\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;T2D\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;by\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtinue ACC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eACC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Continue ACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06(1.06-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03(1.03-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03(1.03-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01(1.00-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; ACC quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACC-Q1(lowest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACC-Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52(1.44-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22(1.15-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14(1.07-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01(0.65-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACC-Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.11(2.00-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40(1.32-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26(1.19-1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16(0.75-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACC-Q4(highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.62(3.44-3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82(1.72-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59(1.50-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51(1.00-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: ACC, age acceleration\u003c/p\u003e\n\u003cp\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003eModel 2: Model 1+baseline age, sex, BMI, smoking status, drinking status, education and race\u003c/p\u003e\n\u003cp\u003eModel 3: Model 2+ HDL, LDL, glucose, hypertension and dyslipidemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 4: Model 3+BRI\u003c/p\u003e\n\u003cp\u003eACC is divided into four groups based on the 25th, 50th, and 75th percentiles, labeled as: ACC-Q1, ACC-Q2, ACC-Q3, and ACC-Q4, ACC-Q1 as the reference group.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Age acceleration, Body roundness index, Type 2 diabetes","lastPublishedDoi":"10.21203/rs.3.rs-6175507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6175507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eObesity and aging are regarded as significant risk factors for type 2 diabetes(T2D). However, joint effect of body roundness index (BRI) and age acceleration (ACC), novel predictors of visceral and the rate of aging, with incident T2D remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo \u0026nbsp;examine the associations of BRI and ACC with incident T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis prospective cohort study used data from the UK Biobank, and participants with pre-existing diabetes and missing data were excluded in the analysis. The outcome of interest was incident T2D. Joint effect of BRI and ACC were assessed through eight paired quartile combinations. Kaplan-Meier curves were used to estimate cumulative incidence, while \u0026nbsp;Cox proportional-hazards regression was used to analyze the independent and joint effect of BRI and ACC by gradually adjusting covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 380,146 participants from the UK Biobank over 14.6 years of follow-up, 15,262 developed T2D. Kaplan-Meier survival curves indicated that participants with a higher level of BRI or ACC had a higher risk of T2D. Both BRI and ACC levels were positively associated with incident T2D (BRI:HR: 1.30, 95% CI: 1.28-1.32, ACC: HR: HR: 1.03, 95% CI: 1.03-1.03). \u0026nbsp;When BRI and ACC were categorized into quartiles, those in the top quartile demonstrated a significantly increased T2D risk (BRI-Q4:HR:3.68, 95%CI: 3.35-4.04; ACC-Q4:HR:1.59, 95%CI:1.50-1.68; BRI-Q4 and ACC-Q4: HR: 6.76, 95% CI: 5.65-8.09).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBRI and ACC were independently associated with increased risk of T2D, with BRI showing a stronger predictive capability. Their combined effects underscore their utility as non-invasive screening tools for T2D risk.\u003c/p\u003e","manuscriptTitle":"Association of body roundness index and age acceleration with type 2 diabetes: Evident from the UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 06:55:00","doi":"10.21203/rs.3.rs-6175507/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9737156c-bccc-4581-8f21-752157ea3eba","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T06:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 06:55:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6175507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6175507","identity":"rs-6175507","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}